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3rd International Conference on Codes, Cryptology and Information Security, C2SI 2019

作者： Wang, Yongge Malluhi, Qutaibah M. Department of Software and Information Systems UNC Charlotte 9201 University City Blvd. CharlotteNC28223 United States Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (纸本)9783030164577

Since Yao introduced the garbled circuit concept in 1980s, it has been an open problem to design efficient reusable garbled Turing machines/circuits. Recently, Goldwasser et al. and Garg et al. answered this question affirmatively by designing reusable garbled circuits and reusable garbled Turing machines. Both of these reusable garbling schemes use fully homomorphic encryption (FHE) schemes as required building components. Here, we use multilinear maps to design a reusable Turing machine garbling scheme that will not need any FHE schemes. Though it is not clear whether our multilinear map based garbling approach could be more efficient than FHE based garbling approach, the goal of this paper is to develop alternative techniques for resuable garbling schemes to stimulate further research in this direction. © Springer Nature Switzerland AG 2019.

关键词： Turing machines

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An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services

An Indoor Localization Approach Based on Deep Learning for I...

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2019 IEEE Jordan International Joint Conference on Electrical engineering and Information Technology, JEEIT 2019

作者： Elbes, Mohammed Almaita, Eyad Alrawashdeh, Thamer Kanan, Tarek Alzurbi, Shadi Hawashin, Bilal Computer Science Department Al-Zaytoonah University of Jordan Amman Jordan Electrical Engineering Department Altafilah Technical University tafila Jordan Software Engineering Department Al-Zaytoonah University of Jordan Amman Jordan Computer Information Systems Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9781538679425

The rapid increase in the demand of location based services (LBS) for indoor environments has attracted scholars to indoor localization based on fingerprinting due its high accuracy. In this paper, we propose our novel indoor localization approach based on fingerprints of Received Signal Strength Indicator (RSSI) measurements. We present our approach of fingerprint preparation and setup and how we utilized machine learning techniques using Long Short-Term Memory (LSTM) Neural Networks for location estimation. Our experimental results shows that our localization approach outperforms well-known existing approaches like the KNN and localization techniques. © 2019 IEEE.

关键词： Location based services

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Corrigendum to “LightSOD: Towards lightweight and efficient network for salient object detection” [J. Comput. Vis. Imag. Underst. 249 (2024) 104148]

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computer Vision and Image Understanding 2025年 252卷

作者： Thien-Thu Ngo Hoang Ngoc Tran Md. Delowar Hossain Eui-Nam Huh School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Kristian IV:s väg 3 Halmstad 30118 Halland Sweden Department Computer Science and Engineering KyungHee University Global Campus Deogyeong-daero Yongin-si 17104 Gyeonggi-do Republic of Korea Department of Software Engineering FPT University 600 Nguyen Van Cu Street Ninh Kieu 94000 Can Tho Viet Nam Department of Computer Science and Engineering Hajee Mohammad Danesh Science & Technology University Basherhat Dinajpur 5200 Rangpur Division Bangladesh

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Review of Machine Learning Applications for Predicting the Quality Biomass Briquettes for Sustainable and Low-Carbon Energy Solutions

Green Energy and Resources

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Green Energy and Resources 2025年

作者： Constance Nakato Nakimuli Fred Kaggwa Johan De Greef David Kilama Okot Julien Blondeau Simon Kawuma Department of Information Technology Mbarara University of Science and Technology Uganda Brussels Institute for Thermal-Fluid Systems and Clean Energy(BRITE) Vrije Universiteit Brussel Belgium Thermodynamics and Fluid Mechanics Group Faculty of Engineering Vrije Universiteit Brussel (VUB) Belgium Department of Computer Science Mbarara University of Science and Technology Uganda Department of Materials Engineering – Leuven Group T Campus KU Leuven Leuven Belgium Department of Physics Mbarara University of Science and Technology Uganda Department of Software Engineering Mbarara University of Science and Technology Uganda

This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper has reviewed literature covering various Machine Learning models applied for predicting and optimizing briquette quality parameters including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example Random Forest with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstock and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policy makers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

关键词： Machine learning briquettes sustainable energy low-carbon solutions agricultural waste municipal waste

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Diagnosing covid-19 lung inflammation using machine learning algorithms: A comparative study

Studies in Big Data

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Studies in Big Data 2020年 80卷 91-105页

作者： Ali, Abbas M. Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Mulahuwaish, Aos Department of Software Engineering Salahaddin University-Erbil Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wulfruna Street WolverhamptonWV1 1LY United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Koysinjaq Iraq Department of Computer Science and Information Systems Saginaw Valley State University 7400 Bay Rd Science East 174 University Center Itta BenaMI48710 United States

In this paper, we performed a comparative analysis using machine learning algorithms named support vector machine (SVM), decision tree (DT), k-nearest neighbor (kNN), and convolution neural network (CNN) to classify pneumonia level (mild, progressive, and severe stage) of the COVID-19 confirmed patients. More precisely, the proposed model consists of two phases: first, the model computes the volume and density of lesions and opacities of the CT images using morphological approaches. In the second phase, we use machine learning algorithms to classify the pneumonia level of the confirmed COVID-19 patient. Extensive experiments have been carried out and the results show the accuracy of 91.304%, 91.4%, 87.5%, 95.622% for kNN, SVM, DT, and CNN, respectively. © Springer International Publishing AG 2018.

关键词： COVID-19

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The use of a genetic algorithm in the problem of distribution of information security organizational and financial resources 2

The use of a genetic algorithm in the problem of distributio...

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2nd IEEE International Conference on Advanced Trends in Information Theory, ATIT 2020

作者： Lakhno, Valeriy Akhmetov, Bakhytzhan Adilzhanova, Saltanat Blozva, Andrii Svitlana, Rzaieva Dmytro, Rzaiev National University of Life and Environmental Sciences of Ukraine Department of Computer Systems and Networks Kyiv Ukraine Abai Kazakh National Pedagogical University Department of Information Technology Almaty Kazakhstan Al-Farabi Kazakh National University Department of Information Technology Almaty Kazakhstan Kyiv National University of Trade and Economics Department. of Software Engineering and Cybersecurity Kyiv Ukraine Kyiv National Economics University after Vadym Hetman Department Of. Informatics and Systemology Kyiv Ukraine

ISBN: (纸本)9781728197999

A model of the application of the genetic algorithm (GA) for solving the problem of multicriteria optimization in the distribution of resources allocated for the cybersecurity of the protected object is proposed. In the presented model, in contrast to the existing solutions, the Bellman-Zade principle is involved, which makes it possible to optimize the distribution, for example, of the organizational and financial resources of the protected object. It is shown that the use of GAs in the tasks of multicriteria optimization of cybersecurity resources will make it possible, based on multivariate simulation modeling, to select the optimal variant of resource allocation, which is especially important when these resources are limited in volume, and the defense side does not have data on the attackers' resources. © 2020 IEEE.

关键词： Resource allocation

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Analysis of rogue access points using SDR 2

Analysis of rogue access points using SDR

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Mercenit, Charles Rios, Juan C. Liu, Yongxin Wang, Jian Yuan, Jiawei Song, Houbing Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA90095 United States Department of Math and Computer Science Stetson University DeLandFL32723 United States Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona BeachFL32114 United States

ISBN: (数字)9781728129778

ISBN: (纸本)9781728129778

When people connect to the Internet with their mobile devices, they do not often think about the security of their data;however, the prevalence of rogue access points has taken advantage of a false sense of safety in unsuspecting victims. This paper analyzes the methods an attacker would use to create rogue WiFi access points using software-defined radio (SDR). To construct a rogue access point, a few essential layers of WiFi need simulation: the physical layer, link layer, network layer, and transport layer. Radio waves carrying WiFi packets, transmitted between two Universal software Radio Peripherals (USRPs), emulate the physical layer. The link layer consists of the connection between those same USRPs communicating directly to each other, and the network layer expands on this communication by using the network tunneling/network tapping (TUN/TAP) interfaces to tunnel IP packets between the host and the access point. Finally, the establishment of the transport layer constitutes transceiving the packets that pass through the USRPs. In the end, we found that creating a rogue access point and capturing the stream of data from a fabricated 'victim' on the Internet was effective and cheap with SDRs as inexpensive as $20 USD. Our work aims to expose how a cybercriminal could carry out an attack like this in order to prevent and defend against them in the future. © 2019 IEEE.

关键词： software radio

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IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

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Quantitative Biology 2020年第4期8卷 369-381页

作者： Jianyu Zhou Pan Li Wanwen Zeng Wenxiu Ma Zhipeng Lu Rui Jiang Qiangfeng Cliff Zhang Tao Jiang Bioinformatics Division BNRISTTsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural BiologyCenter for Synthetic and Systems BiologyTsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China College of Software Nankai UniversityTianjin 300071China Department of Statistics University of CaliforniaRiversideCA 92521USA Department of Pharmacology and Pharmaceutical Sciences University of Southern CaliforniaCA 90089USA Department of Automation Tsinghua UniversityBeijing 100084China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain ***(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in ***,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base ***:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS *** generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS ***:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural *** is implemented in Python and freely available at http://***:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure *** expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

关键词： RNA secondary structure PARIS data in vivo structure ensembles incompatible reads

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Influence of Network Speed on Structured Query Language (SQL) Database Data Transmission Performance in an Internet of Things (IoT) Sensing Device on Single Board computers (SBC)

Influence of Network Speed on Structured Query Language (SQL...

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International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Nor Azhar, A. Nur Ayuni Hasna, S. Roslina, A.H. Fauziah, Z. Rahmah, M. Department of Computer Science Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Computer Systems and Networking Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia Department of Graphic and Multimedia Technology Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Pahang Kuantan26300 Malaysia

One of the crucial elements in an Internet of Things (IoT) environment is a database. IoT performance will be at stake if the wrong database is adopted. In this research, three Structured Query Language (SQL) databases were tested against multiple network speeds in an IoT device. Single board computers (SBC) were used as a media of testing instead of an ordinary computer. A controlled wireless sensor network (WSN) was developed with several uniform constants and variable network speeds. A new data transmission rate equation also was proposed and tested. From the result, it was found that not all SQL databases can cater IoT devices. Some of them showed that performance decreased along with network speed. The correlation, between 0.90 and 0.96, proved the strong influence between the research subjects. In addition, file-type database is the best option available for SQL-wise IoT storage. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Integrating Privacy Enhancing Techniques into Blockchains Using Sidechains

Integrating Privacy Enhancing Techniques into Blockchains Us...

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2019 IEEE Canadian Conference of Electrical and computer engineering, CCECE 2019

作者： Parizi, Reza M. Homayoun, Sajad Yazdinejad, Abbas Dehghantanha, Ali Choo, Kim-Kwang Raymond Department of Software Engineering and Game Development Kennesaw State University GA United States Department of Computer Engineering and Information Technology Shiraz University of Technology Shiraz Iran Faculty of Computer Engineering University of Isfahan Iran School of Computer Science University of Guelph ON Canada Department of Information Systems and Cyber Security University of Texas at San Antonio TX United States

ISBN: (纸本)9781728103198

Blockchains are turning into decentralized computing platforms and are getting worldwide recognition for their unique advantages. There is an emerging trend beyond payments that blockchains could enable a new breed of decentralized applications, and serve as the foundation for Internet's security infrastructure. The immutable nature of the blockchain makes it a winner on security and transparency;it is nearly inconceivable for ledgers to be altered in a way not instantly clear to every single user involved. However, most blockchains fall short in privacy aspects, particularly in data protection. Garlic Routing and Onion Routing are two of major Privacy Enhancing Techniques (PETs) which are popular for anonymization and security. Garlic Routing is a methodology using by I2P Anonymous Network to hide the identity of sender and receiver of data packets by bundling multiple messages into a layered encryption structure. The Onion Routing attempts to provide low-latency Internet-based connections that resist traffic analysis, deanonymization attack, eavesdropping, and other attacks both by outsiders (e.g. Internet routers) and insiders (Onion Routing servers themselves). As there are a few controversies over the rate of resistance of these two techniques to privacy attacks, we propose a PET-Enabled Sidechain (PETES) as a new privacy enhancing technique by integrating Garlic Routing and Onion Routing into a Garlic Onion Routing (GOR) framework suitable to the structure of blockchains. The preliminary proposed GOR aims to improve the privacy of transactions in blockchains via PETES structure. © 2019 IEEE.

关键词： Blockchain

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