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Partially Hidden Policy Attribute-Based Multi-Keyword Searchable Encryption with Verification and Revocation

IEEE Transactions on Mobile Computing 2025年

作者： Chen, Liqing Xu, Shiguo Jin, Chunhua Zhang, Hao Weng, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Jiangsu Huai'an223003 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Jinan University College of Information Science and Technology Guangdong Guangzhou510632 China

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis. © 2002-2012 IEEE.

关键词： Differential privacy

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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer

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International Journal of Thermofluids 2025年 27卷

作者： Al-Betar, Mohammed Azmi Muskens, Otto L. Hammoodi, Karrar A. Abd Elaziz, Mohamed Shambour, Qusai Yousef Fraihat, Salam Elsheikh, Ammar H. College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ United Kingdom College of Engineering University of Al Maarif Al Anbar31001 Iraq Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Software Engineering Department Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Production Engineering and Mechanical Design Faculty of Engineering Tanta University Tanta31527 Egypt Faculty of Engineering Pharos University in Alexandria Egypt

Yield augmentation of solar distiller (SD) using low-cost modifications with minimum environmental impacts is highly desirable. In this study, we developed an eco-friendly steam generation device made of carbonized Ficus sycomorus wood to boost the yield of the conventional single-basin solar distiller. Moreover, a hybrid model composed of a long short-term memory (LSTM) network and a Starling murmuration optimizer (SMO) was developed to predict the yield of the established SD. To verify the accuracy of the model, three other models were employed and the distillate yield was predicted. The predicted data of all models was evaluated using different error measures. These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). Real field data were employed during the training and testing of all models. The thermal performance of the distiller with the steam generation device was compared with that of a traditional distiller based on exergy output, exergy efficiency, and energy efficiency. The distillate yield, exergy efficiency, and energy efficiency of the modified SD were enhanced by 34 %, 49 %, and 40 %, respectively, compared with that of the traditional distiller. Moreover, the total amount of produced drinkable water using the modified SD could reach a high value of 6.1 L/day with a low cost of 0.014 $/L. © 2025

关键词： Exergy

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Identification of the Diabetic Retinopathy Using ResNet-18

Identification of the Diabetic Retinopathy Using ResNet-18

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Cyber Resilience (ICCR), International Conference on

作者： Fahad Ahmed Areej Fatima Muhammed Mamoon Shan Khan Department of Computer Science National College of Business Administration economics Lahore Pakistan Department of Computer Science Lahore Garrison University Lahore Pakistan Department of Software Engineering Islamia University Bhawalpur Bahawalpur Pakistan FOIT(Faculty of Information Technology) University of Central Punjab Lahore Pakistan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In the realm of healthcare, trauma, aging, and diseases like diabetes can cause a variety of abnormalities in human eyes. Diabetic retinopathy stands out as a prevalent global cause of blindness. Early identification and diagnosis of diabetic retinopathy is essential for prompt treatment and blindness prevention. The analysis of medical images can contribute to the identification of diabetic retinopathy, assisting in the diagnostic process. This study uses a deep learning (DL) model, ResNet-18, to identify diabetic retinopathy. The dataset used in this study acquired from Kaggle is divided into training and testing. ResNet-18 accomplished a training accuracy of 99.91% and a testing accuracy of 96.65%. The findings demonstrate the effectiveness of utilizing DL models like ResNet-18 for the early identification of diabetic retinopathy, potentially revolutionizing the screening process in clinical settings. By streamlining the diagnostic process, this proposed model has the potential to significantly enhance early intervention and treatment strategies, ultimately mitigating the risk of blindness in diabetic patients. Incorporating such technology into the healthcare system could improve patient outcomes and resource allocation.

关键词： Training Deep learning Diabetic retinopathy Blindness Medical services Resource management Medical diagnostic imaging

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Mimd Programs Execution Support on Simd Machines

SSRN

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SSRN 2023年

作者： Mustafa, Dheya Alkhasawneh, Ruba Obeidat, Fadi Shatnawi, Ahmed S. Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa13133 Jordan Department of Communication and Computer Engineering Faculty of Engineering Al-Ahliyya Amman University Amman19111 Jordan Synopsys Inc. AustinTX United States Department of Software Engineering Jordan University of Science and Technology Irbid21110 Jordan

Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power, and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (API) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of collaborative execution of GPUs with CPUs and XeonPhi is presented in this paper. It is believed that this study will be beneficial for developers and researchers in the field of computer architecture and parallel computing-intensive scientific applications, specifically for early-stage high-performance computing researchers to obtain a brief of performance optimization opportunities as well as challenges of existing SIMD platforms. © 2023, The Authors. All rights reserved.

关键词： Program processors

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An Image Classification Approach to Pre-Determine Extent of Development of Post-Operative Necrosis in Skin Flaps

An Image Classification Approach to Pre-Determine Extent of ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： G. Spacagna T. Fevens J. Barralet Department of Surgery McGill University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada Faculty of Dentistry McGill University Montreal Canada

Purpose - Clinicians and care workers currently cannot determine the final extent of necrosis once it begins. If the area is relatively small, it may heal like a normal wound, whereas re-operation may be the only option if it spreads. This paper represents work toward a predictive algorithm using full-thickness random rat skin flap photographs to determine whether the tissue will develop irretrievable *** - Using post-surgery images taken over a series of days of ischemic flaps, features were extracted, selected, and input into a classification algorithm to see if it could provide information on the future condition of the flaps. We split our data into two groups: flaps that underwent normal healing and slow healing. When consulting with a specialist, it was observed that the resulting dermal damage was not severe when a flap had ≤ 40% necrosis over its length on the final day. Three classifiers were implemented: K-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM).Results - The trained KNN was able to correctly determine whether a flap developed a necrotic area larger or less than 40% of its length with an accuracy of 91% using only images one day post-surgery. Under leave-one-out cross-validation, the Random Forest and SVM achieved accuracies of 82% and 79.5%, respectively, using images spanning ten days *** - We have shown that a classifier can accurately determine whether ischemic skin flaps will develop severe necrotic *** Relevance— An algorithm that assesses early timepoint images and predicts necrosis spread is not only a vital tool for patients and clinicians but is also an extremely important tool to accelerate research into necrosis reduction strategies that ultimately may find application for more life-threatening necrosis-related conditions.

关键词：

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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A neural networks approach for designing compact all-optical photonic crystal based and logic gate

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Journal of Optical Communications 2024年 45卷 s2627-s2636页

作者： Parandin, Fariborz Yahya, Salah I. Rezaeenia, Mehdi Askarian, Asghar Roshani, Saeed Roshani, Sobhan Ghadi, Yazeed Yasin Jamshidi, Mohammad Rezaee, Sahar Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University Erbil Iraq Department of Software Engineering Faculty of Engineering Koya University KoyaKOY45 Iraq Department of Electrical Engineering Arak Branch Islamic Azad University Arak Iran Software Engineering and Computer Science Department Al Ain University Al Ain United Arab Emirates Hong Kong Department of Physics Kermanshah Branch Islamic Azad University Kermanshah Iran

This paper introduces a new method for creating an all-optical AND gate by utilizing a two-dimensional photonic crystal configuration for the first time. This gate design is intended for applications in optical computing and all-optical logic, offering the potential for rapid computation and parallel processing. The described gate is characterized by its compact dimensions and comprises two inputs and a single output. The high and low logic states are defined based on power values, where logic 0 corresponds to low power and logic 1 corresponds to high power emitted from the light source. To enhance the design process, artificial neural networks (ANNs) are utilized. ANNs offer a powerful tool for optimizing and fine-tuning the photonic crystal structure parameters to achieve the desired logic functionality. With the help of the applied ANNs, the design process is eased and high performance is achieved for the proposed photonic crystal structure. By integrating ANNs into the design process, this research opens up new possibilities for advancing the field of photonic logic circuits. Combining photonic crystals and ANN optimization provides a powerful approach to designing complex and efficient optical computing systems. The results show that the obtained power values are high for 1 logic state and low for the 0 logic state, which verifies the AND gate accuracy table. The achieved accurate results verify the validity of the proposed approach for achieving precise and reliable all-optical logic operations. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Photonic crystals

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Algorithmic Cluster Expansions for Quantum Problems

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PRX Quantum 2024年第1期5卷 010305-010305页

作者： Ryan L. Mann Romy M. Minko Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney NSW 2007 Australia School of Mathematics University of Bristol Bristol BS8 1UG United Kingdom

We establish a general framework for developing approximation algorithms for a class of counting problems. Our framework is based on the cluster expansion of the abstract polymer model formalism of Kotecký and Preiss. We apply our framework to obtain efficient algorithms for (1) approximating probability amplitudes of a class of quantum circuits close to the identity, (2) approximating expectation values of a class of quantum circuits with operators close to the identity, (3) approximating partition functions of a class of quantum spin systems at high temperature, and (4) approximating thermal expectation values of a class of quantum spin systems at high temperature with positive-semidefinite operators. Further, we obtain hardness of approximation results for approximating probability amplitudes of quantum circuits and partition functions of quantum spin systems. This establishes a computational complexity transition for these problems and shows that our algorithmic conditions are optimal under complexity-theoretic assumptions. Finally, we show that our algorithmic condition is almost optimal for expectation values and optimal for thermal expectation values in the sense of zero freeness.

关键词： Phase transitions Quantum computation Quantum-to-classical transition Approximation methods for many-body systems Combinatorics Computational complexity Ising model Lee-Yang & Fisher zeroes Series expansions & exact enumeration

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A public data-set for synchronous motor electrical faults diagnosis with CNN and LSTM reference classifiers

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Energy and AI 2023年第4期14卷 221-237页

作者： Zhenglong Sun Ram Machlev Qianchao Wang Juri Belikov Yoash Levron Dmitry Baimel Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of Education(Northeast Electric Power University)JilinChina The Andrew and Erna Viterbi Faculty of Electrical&Computer Engineering Technion-Israel Institute of TechnologyHaifa 3200003Israel Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing210096China Department of Software Science Tallinn University of TechnologyAkadeemia tee 15a12618 TallinnEstonia Department of Electrical and Electronics Engineering Sami Shamoon College of EngineeringBeer-Sheva84100Israel

In the last two decades,motor operation monitoring tools have become a necessity,and many studies focus on the detection and diagnosis of motor electrical ***,at present,a core obstacle that prevents the direct comparison of such classification techniques is the lack of a standard database that can be used as a *** view of this,we offer here a public experimental data-set that has beendesigned specifically for the comparison of synchronous motor electrical fault *** data-set comprises five types of motor electrical faults:open phase between inverter and motor;short circuit/leakage current between two phases;short circuit/leakage current in phase-to-neutral;rotor excitation voltage disconnection;and variation of rotor excitation *** addition,each fault has been recorded as a four-dimensional signal:three phase voltages;three phase currents;motor speed;and motor *** package includes two deep-learning reference classifiers that are based on a convolutional neural network(CNN)and long short term memory(LSTM).Due to the good performance of these classifiers,we suggest that they can be used by the community as benchmarks for the development of new and better motor electrical fault classification *** database and the reference classifiers are examined and insights regarding different combinations of features and lengths of recording points are *** developed code is available online,and is free to use.

关键词： Motor faults Synchronous motors Public data-set Classification Deep-learning CNN LSTM

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Analysis of Web Monitoring Servers and Tools for Cloud Computing Services 1st

Analysis of Web Monitoring Servers and Tools for Cloud Compu...

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1st International Conference on engineering software for Modern Challenges, ESMoC 2021

作者： Sarwar, Nadeem Qureshi, Junaid Nasir Iqbal, Tahir Waheed, Haroon Abdul Yasin, Muhammad Hammad Irshad, Asma Computer Science Department Bahria University Lahore Campus Lahore Pakistan Department of Software Engineering University of Central Punjab Lahore Pakistan Faculty of Computing Riphah International University Islamabad Pakistan School of Biochemistry & Biotechnology University of the Punjab Lahore Pakistan

ISBN: (纸本)9783031199677

This research aims to find best cloud service monitoring tools and techniques according to trending parameters. The services of cloud computing are most popular services now a days. Many organizations are migrating to cloud technology in order to reduce the management’s complications of resources. The effective monitoring methodologies should be implemented, to certify the Quality of Experience (QoE) and Quality of Services (QoS) of cloud computing services for end users. For this purpose, cloud services should be monitor and control using different tools and techniques. There are so many server-based or web-based monitoring tools are available. In this paper, analysis of different tools is based on their pricing, working efficiency, key features, platforms and environments in which they could be used. This paper also analyzes the cloud computing structure and investigates that which type of monitoring services are required at which level. This research paper shows that no monitoring tool is mature enough to fulfil all requirements and desires of clients. This research also interprets that which tool is recommended for which kind of platform and environment. © 2022, Springer Nature Switzerland AG.

关键词： Quality of service

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