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IEEE International Conference on Data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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Design of an energy-efficient IOT device-assisted wearable sensor platform for healthcare data management

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Measurement: Sensors 2023年 30卷

作者： Ahamed, Bazeer Sellamuthu, Suseela Karri, Purushotam Naidu Srinivas, Inumarthi V. Mohammed Zabeeulla, A.N. Ashok Kumar, M. Department of IT University of Technology And Applied Sciences Al Musanna Oman Senior Grade School of Computing Sciences and Engineering Vellore Institute of Technology TamilNadu Chennai India Department of CSE Gayatri Vidya Parishad College of Engineering for Women Andhra Pradesh Visakhapatnam535546 India Mumbai400077 India Bangalore India Department of Computer Science and Software Engineering Skyline University Nigeria.Kano Nigeria

Advances in health-related behaviors, technical breakthroughs, and the spread of healthy living activities have expanded personal health evaluation on a broad scale. In medical applications, networks, data, and communication systems are extensively utilized and further expanding. Real-time applications for remote health monitoring generate massive amounts of data in a short period. Internet of Things (IoT) and cloud systems, resulting in unprocessed and inadequate data for end-users. Since cloud technology is a dispersed intermediary layer between the edge network and the cloud infrastructure, the delay is significantly decreased, and reliable communications can be established using cloud computing on an IoT-enabled healthcare device. This study presents an Energy Efficient Healthcare Data Management Method (EE-HDMM), which enhances health monitoring using an IoT-assisted wearable sensor infrastructure. The difficulty of constrained sources and the energy consumption is minimized by proposing the optimum energy resource allocation method. Wearable sensor technology supported by cloud computing is the ideal and dependable platform for powerful life-critical systems that are unlikely to have communication delays. Compared to conventional techniques, the suggested EE-HDMM has a high precision rate of 97%, a sensitivity rate of 94%, and a system efficiency of 96%. © 2023

关键词： Internet of things

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An Intelligent System for Cucumber Leaf Disease Diagnosis Based on the Tuned Convolutional Neural Network Algorithm

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Mobile Information Systems 2022年第1期2022卷

作者： Omer, Saman M. Ghafoor, Kayhan Z. Askar, Shavan K. Department of Technical Information System Engineering Erbil Technical Engineering College Erbil Polytechnic University Kurdistan Region Erbil Iraq Department of Computer Science College of Basic Education University of Raparin Kurdistan Region Ranya Iraq Department of Computer Science Knowledge University Kurdistan Region Erbil44001 Iraq Department of Software and Informatics Engineering Salahaddin University-Erbil Kurdistan Region Erbil44001 Iraq

In agriculture farming, pests and other plant diseases are the most imperative factor that causes significant hindrance to cucumber production and its quality. Farmers around the globe are currently facing difficulty in recognizing various cucumber leaf diseases, which is imperative to preventing leaf diseases effectively. Manual techniques to diagnose cucumber diseases are often time-consuming, subjective, and laborious. To address this issue, this paper proposes a tuned convolutional neural network (CNN) algorithm to recognise five cucumber diseases and healthy leaves that comprises image enhancement, feature extraction, and classification. Data augmentation methods were utilized as a preprocessing step to enlarge the datasets, and it was also to decrease the chance of overfitting. Automatically features are extracted by using CNN layers. Finally, five cucumber leaf diseases and one healthy leaf are classified. Furthermore, to overcome the lack of a public dataset, a new dataset of cucumber leaf diseases has been constructed that includes spider, leaf miner, downy mildew, powdery mildew, one viral disease, and healthy class leaves. The dataset has a total of 4868 cucumber leaf images. In order to prove the authenticity of the proposed CNN, comparative experiments were conducted using pretrained models (AlexNet, Inception-V3, and ResNet-50). The proposed CNN achieves a recognition accuracy of 98.19% with the augmented dataset and 100% with the publicly plant disease dataset. The experimental results confirm that the proposed CNN algorithm was efficient for recognizing the cucumber leaf diseases compared with other algorithms. © 2022 Saman M. Omer et al.

关键词： Image enhancement

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Modular Verification of SPARCv8 Code

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Journal of computer science & Technology 2020年第6期35卷 1382-1405页

作者： Jun-Peng Zha Xin-Yu Feng Lei Qiao Department of Computer Science and Technology Nanjing UniversityNanjing 210023China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Beijing Institute of Control Engineering Beijing 100080China

Inline assembly code is common in system software to interact with the underlying hardware platforms. The safety and correctness of the assembly code is crucial to guarantee the safety of the whole system. In this paper, we propose a practical Hoare-style program logic for verifying SPARC (Scalable Processor Architecture) assembly code. The logic supports modular reasoning about the main features of SPARCv8 ISA (instruction set architecture), including delayed control transfers, delayed writes to special registers, and register windows. It also supports relational reasoning for refinement verification. We have applied it to verify that there is a contextual refinement between a context switch routine in SPARCv8 and a switch primitive. The program logic and its soundness proof have been mechanized in Coq.

关键词： Scalable Processor Architecture Version 8(SPARCv8) assembly code verification context switch Coq refinement verification

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Quantum reversible circuits for multiplicative inverse

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EPJ Quantum Technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

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Machine Learning Classifiers for Network Intrusion Detection System: Comparative Study

Machine Learning Classifiers for Network Intrusion Detection...

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2021 International Conference on Information Technology, ICIT 2021

作者： Almomani, Omar Almaiah, Mohammed Amin Alsaaidah, Adeeb Smadi, Sami Mohammad, Adel Hamdan Althunibat, Ahmad The World Islamic Sciences and Education University Department of Information System and Networks Jordan College of Computer Science and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia The World Islamic Sciences and Education University Department of Computer Science Amman Jordan Al-Zaytoonah University of Jordan Department of Software Engineering Amman11733 Jordan

ISBN: (纸本)9781665428705

Network security risks are increasing at an exponential rate as Internet technology advances. Keeping the network protected is one of the most challenging of network security. Many security mechanisms were implemented to detect and identify any malicious activity on the network. Intrusion Detection System (IDS) one of the most used mechanisms to reduce the effects of these risks. Machine Learning (ML) classifiers are widely begin used to classify the network traffic as normal or abnormal. In this paper a comparative evaluation of the following ML classifiers: LogisticRegression, Multinomial Naive Bayesian, Gaussian Naive Bayesian, Bernoulli Naive Bayesian, k-Nearest Neighbors, Decision Tree, Adaptive Boosting, Random Forest, Multilayer Perceptron, and GradientBoosting is performed to specify the best classifier in identifying intrusion detection. The used evaluation metrics are accuracy, precision, and F-measure. The UNSW-NB15 dataset is used to assess ML classifiers. The experimental results show that the RandomForest classifier outperforms the other classifiers in terms of accuracy at 87%, precision 98%, and F-measure 84%. © 2021 IEEE.

关键词： Decision trees

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Experimental Study on Surface Integrity of Ti6Al4V by Broaching

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International Journal of engineering, Transactions B: Applications 2022年第2期35卷 481-492页

作者： Khanjanzadeh, P. Amirabadi, H. Sadri, J. Department of Mechanical Engineering University of Birjand Birjand Iran Faculty of Engineering University of Neyshabur Neyshabur Iran Department of Computer Science and Software Engineering Concordia University Montreal Canada Camp in Labs Foundation 1 Westmount Square Bureau 1001 WestmountQC Canada

The performance of many parts in the airplane, aircraft engine and biomedical implants is highly related to their fatigue life, which is clearly depend on the condition of their surface integrity. The geometry parameters of broaching tools have an important influence on the surface integrity after broaching Ti6Al4V alloy. Therefore, this research studies the surface integrity of Ti6Al4V by broaching. The surface integrity is studied at different geometric parameters such as rake angles (α, and α 0), clearance angles (β, and β0), and radius of the cutting edge (r0) in two last teeth of the broaching tool that perform chipping. The broached surface integrity is assessed in cases of surface roughness, microstructural, residual stresses, and micro hardness. These results show that the sample broached by tool number 1 (α = 18.4, β = 3.1, α0 = 45, β0 = 9, r0 = 0.02) had higher surface integrity because it was the smoothest surface and the thinnest deformed layer among the other samples. Since the main criterions in selection of the optimal tool are to create the smoothest surface and the least deformed layer depth in the broached sample, tool number 1(α = 18.4, β = 3.1, α0 = 45, β0 = 9, r0 = 0.02) is suggest ed as the optimal tool. © 2022 Materials and Energy Research Center. All rights reserved.

关键词： Titanium alloys

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Economic and environmental analysis of a solar-powered ev charging system in india—a case study 2nd

Economic and environmental analysis of a solar-powered ev ch...

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2nd International Conference on Electrical and Electronics engineering, ICEEE 2021

作者： Shrivastava, Prashant Soon, Teykok Mekhilef, Saad Ahmad, Furkan Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Power Electronics and Renewable Energy Laboratory Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia School of Software and Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Indian Institute of Technology New Delhi Delhi110016 India

ISBN: (纸本)9789811607486

With the growing environmental concern, the demand for electric vehicle is increasing in India. However, the limited existing charging facilities slow down the rate of adoption in the market. Presently, the solar-based charging system is gaining popularity, due to its low greenhouse gas emission. In this study, a case study for different solar charging systems suited in Delhi is presented. The economic and environmental analysis of different solar-based EV charging systems utilized for charging three types of commercial EVs such as 2, 3, and 4-W is explained. The cost of energy required for 100 km drive is lower with vehicle integrating PV charging system;however, it can act as a supportive system for driving range improvement. The analysis shows that the economically solar rooftop charging system is suitable for all types of EVs. The CO2 emission produced by all types of solar-based EV charging systems is lower than coal-based charging systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Solar energy

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VML-MOC: Segmenting a multiply oriented and curved handwritten text lines dataset

arXiv

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arXiv 2021年

作者： Barakat, Berat Kurar Cohen, Rafi El-Sana, Jihad Rabaev, Irina Department of Computer Science Ben-Gurion University Israel Software Engineering Department Shamoon College of Engineering Israel

This paper publishes a natural and very complicated dataset of handwritten documents with multiply oriented and curved text lines, namely VML-MOC dataset. These text lines were written as remarks on the page margins by different writers over the years. They appear at different locations within the orientations that range between 0◦ and 180◦ or as curvilinear forms. We evaluate a multi-oriented Gaussian based method to segment these handwritten text lines that are skewed or curved in any orientation. It achieves a mean pixel Intersection over Union score of 80.96% on the test documents. The results are compared with the results of a single-oriented Gaussian based text line segmentation method. © 2021, CC BY.

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Comparison of Persian Handwritten Digit Recognition in Three Color Modalities Using Deep Neural Networks 2nd

Comparison of Persian Handwritten Digit Recognition in Three...

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2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： Zohrevand, Abbas Sattari, Mahdi Sadri, Javad Imani, Zahra Suen, Ching Y. Djeddi, Chawki Department of Computer Engineering Kosar University of Bojnord Bojnord Iran Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Department of Electrical and Computer Engineering Babol Noshirvani University of Technology Babol Iran Department of Mathematics and Computer Science Larbi Tebessi University Tebessa Algeria

ISBN: (纸本)9783030598297

Most of the methods on handwritten digit recognition in the literature are focused and evaluated on black and white image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), we investigate to see whether color modalities of handwritten digits affect their recognition rate? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten digit databases that have all three color modalities of handwritten digits. To answer this question, we select 13,330 isolated digits from novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected dataset are divided into training, validation, and testing sets. Afterward, similar conventional CNN models are trained with the samples of our training set. While the experimental results on the testing set show that CNN on the black and white digit images has a better performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modality. Also, comparisons of training times in three color modalities show that recognition of handwritten digits in black and white using CNN is much more efficient. © 2020, Springer Nature Switzerland AG.

关键词： Color

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