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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： U. Samson Ebenezar Jenila G. Vennila T. Suresh Balakrishnan Prabhakar Krishnan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of AI & ML School of Computing Mohan Babu University Tirupati Andhra Pradesh Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-campus India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing.

关键词： Support vector machines Measurement Accuracy Machine learning algorithms Computational modeling Neural networks Medical services

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

引用

International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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A HOLISTIC VIEW ON DATA PROTECTION FOR SHARING, COMMUNICATING, AND computing ENVIRONMENTS: TAXONOMY AND FUTURE DIRECTIONS

arXiv

引用

arXiv 2022年

作者： Gupta, Ishu Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The data is an important asset of an organization and it is essential to keep this asset secure. It requires security in whatever state is it i.e. data at rest, data in use, and data in transit. There is a need to pay more attention to it when the third party is included i.e. when the data is stored in the cloud then it requires more security. Since confidential data can reside on a variety of computing devices (physical servers, virtual servers, databases, file servers, PCs, point-of-sale devices, flash drives, and mobile devices) and move through a variety of network access points (wireline, wireless, VPNs, etc.), there is a need of solutions or mechanism that can tackle the problem of data loss, data recovery and data leaks. In this context, the paper presents a holistic view of data protection for sharing and communicating environments for any type of organization. A taxonomy of data leakage protection systems and major challenges faced while protecting confidential data are discussed. Data protection solutions, Data Leakage Protection System’s analysis techniques, and, a thorough analysis of existing state-of-the-art contributions empowering machine learning-based approaches are entailed. Finally, the paper explores and concludes various critical emerging challenges and future research directions concerning data protection. © 2022, CC BY.

关键词： Digital storage

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A Facial Recognition and Detection System using openVC

TechRxiv

引用

TechRxiv 2022年

作者： Goel, Yaman Gupta, Ishu Rani, Pooja Singh, Ashutosh Kumar Department of Computer Applications National Institute of Technology Kurukshetra136119 India Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan

Detection of the human face and matching it against user data, saved in our databases, with the intent of authenticating a person’s identity through the computer is an engaging and challenging task. With the development in the field of robotics and computer vision technologies, real-time human face recognition has also emerged as a key ongoing area. So, we proposed a novel scheme to process the real-time images according to requirements, captured in a computer web camera. By making use of open source computer vision library (OpenCV) and applying its in-built functions, we can easily detect human faces and then match them against the training sets (face images stored in the database) to recognize a human’s identity. Image handling and processing are done using basic library methods. These library functions are able to capture images from a webcam, create windows to hold the images at run time, save them in the database and process them to check exact likeness. CascadeClassifier is used for face detection, and HaarCascade is used for this purpose. FaceRecognizer class is used for face recognition. EigenFaces, FisherFaces and Local Binary Patterns are the basic methods that can be used for this. Images are stored in form of a CSV database. © 2022, CC BY.

关键词： Face recognition

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TIDF-DLPM: TERM AND INVERSE DOCUMENT FREQUENCY BASED DATA LEAKAGE PREVENTION MODEL A PREPRINT

arXiv

引用

arXiv 2022年

作者： Gupta, Ishu Mittal, Sloni Tiwari, Ankit Agarwal, Priya Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

Confidentiality of the data is being endangered as it has been categorized into false categories which might get leaked to an unauthorized party. For this reason, various organizations are mainly implementing data leakage prevention systems (DLPs). Firewalls and intrusion detection systems are being outdated versions of security mechanisms. The data which are being used, in sending state or are rest are being monitored by DLPs. The confidential data is prevented with the help of neighboring contexts and contents of DLPs. In this paper, a semantic-based approach is used to classify data based on the statistical data leakage prevention model. To detect involved private data, statistical analysis is being used to contribute secure mechanisms in the environment of data leakage. The favored Frequency-Inverse Document Frequency (TF-IDF) is the facts and details recapture function to arrange documents under particular topics. The results showcase that a similar statistical DLP approach could appropriately classify documents in case of extent alteration as well as interchanged documents. © 2022, CC BY.

关键词： Semantics

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PCA-RF: AN EFFICIENT PARKINSON'S DISEASE PREDICTION MODEL BASED ON RANDOM FOREST CLASSIFICATION

arXiv

引用

arXiv 2022年

作者： Gupta, Ishu Kaur, Sizman Sharma, Vartika Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has become quite easy. However, the complex and the selection of an optimal machine learning technique for the given dataset greatly affects the accuracy of the model. A large amount of datasets exists globally but there is no effective use of it due to its unstructured format. Hence, a lot of different techniques are available to extract something useful for the real world to implement. Therefore, accuracy becomes a major metric in evaluating the model. In this paper, a disease prediction approach is proposed that implements a random forest classifier on Parkinson's disease. We compared the accuracy of this model with the Principal Component Analysis (PCA) applied Artificial Neural Network (ANN) model and captured a visible difference. The model secured a significant accuracy of up to 90%. © 2022, CC BY.

关键词： Neural networks

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HISA-SMFM: HISTORICAL AND SENTIMENT ANALYSIS BASED STOCK MARKET FORECASTING MODEL

arXiv

引用

arXiv 2022年

作者： Gupta, Ishu Madan, Tarun Kumar Singh, Sukhman Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-Sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machine learning, it is not an impossible task as the various machine learning techniques if modeled properly may be able to provide the best prediction values. This would enable the investors to decide whether to buy, sell or hold the share. The aim of this paper is to predict the future of the financial stocks of a company with improved accuracy. In this paper, we have proposed the use of historical as well as sentiment data to efficiently predict stock prices by applying LSTM. It has been found by analyzing the existing research in the area of sentiment analysis that there is a strong correlation between the movement of stock prices and the publication of news articles. Therefore, in this paper, we have integrated these factors to predict the stock prices more accurately. © 2022, CC BY.

关键词： Sentiment analysis

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Physics-inspired Machine Learning for Quantum Error Mitigation

arXiv

引用

arXiv 2025年

作者： Xu, Xiao-Yue Xue, Xin Chen, Tianyu Ding, Chen Li, Tian Zhou, Haoyi Huang, He-Liang Bao, Wan-Su Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan450000 China Department of Computer Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Department of Software Beihang University Beijing100191 China

Noise is a major obstacle in current quantum computing, and Machine Learning for Quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accuracy while reducing the sampling overheads of standard QEM methods. Yet, existing models lack physical interpretability and rely heavily on extensive datasets, hindering their scalability in large-scale quantum circuits. To tackle these issues, we introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for ML-QEM that incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. Experimental results demonstrate that NNAS outperforms current methods across a spectrum of metrics, including error mitigation capability, quantum resource consumption, and training dataset size. Notably, for deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors. NNAS also demands substantially fewer training data, reducing dataset reliance by at least an order of magnitude, due to its ability to rapidly capture noise accumulation patterns across circuit layers. This work pioneers the integration of quantum process-derived structural characteristics into neural network architectures, broadly enhancing QEM's performance and applicability, and establishes an integrative paradigm that extends to various quantum-inspired neural network architectures. © 2025, CC BY.

关键词： Quantum computers

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computing Augustin Information via Hybrid Geodesically Convex Optimization

Computing Augustin Information via Hybrid Geodesically Conve...

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IEEE International Symposium on Information Theory

作者： Guan-Ren Wang Chung-En Tsai Hao-Chung Cheng Yen-Huan Li Graduate Institute of Networking and Multimedia National Taiwan University Department of Computer Science and Information Engineering National Taiwan University Department of Electrical Engineering and Graduate Institute of Communication Enaineering National Taiwan University Department of Mathematics National Taiwan University Hon Hai (Foxconn) Quantum Computing Centre Center for Quantum Science and Engineering National Taiwan University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

We propose a Riemannian gradient descent with the Poincaré metric to compute the $\mathbf{order}-\alpha$ Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of $\mathcal{O}(1/T)$ . As far as we know, this is the first algorithm with a non-asymptotic optimization error guarantee for all positive orders. Numerical experimental results demonstrate the empirical efficiency of the algorithm. Our result is based on a novel hybrid analysis of Riemannian gradient descent for functions that are geodesically convex in a Riemannian metric and geodesically smooth in another.

关键词： Measurement Quantum computing Finance Linear programming Convex functions Optimization Ellipsoids

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Logarithmic-regret quantum learning algorithms for zero-sum games 23

Logarithmic-regret quantum learning algorithms for zero-sum ...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Minbo Gao Zhengfeng Ji Tongyang Li Qisheng Wang State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Center on Frontiers of Computing Studies and School of Computer Science Peking University Beijing China Graduate School of Mathematics Nagoya University Nagoya Japan

We propose the first online quantum algorithm for solving zero-sum games with Õ(1) regret under the game setting. Moreover, our quantum algorithm computes an ε-approximate Nash equilibrium of an m × n matrix zero-sum game in quantum time Õ(√m + n /ε2.5). Our algorithm uses standard quantum inputs and generates classical outputs with succinct descriptions, facilitating end-to-end applications. Technically, our online quantum algorithm "quantizes" classical algorithms based on the optimistic multiplicative weight update method. At the heart of our algorithm is a fast quantum multi-sampling procedure for the Gibbs sampling problem, which may be of independent interest.

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