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Tangles in IOTA to make crypto currency transactions free and secure

International Journal of Cloud Computing 2022年第1期11卷 1-13页

作者： Anakath, A.S. Kannadasan, R. Prabakaran, N. Basha, M.S. Saleem Department of MCA EGS Pillai Engineering College Nagapattinam 611002 India School Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computing and Informatics Mazoon College Muscat133 Oman

Blockchain introduction has made a revolutionary change in the crypto currency around the world but it has not delivered on its promises of free and faster transaction confirmation. Serguei Popov's proposal of using tangles, a directed acyclic graph, which essentially is considered to be the successor of blockchains and offers the required features like machine to machine micro payment and fee-less transaction. It requires the user to approve the previous two transactions in the web to participate in the network. This essentially eliminates the miners and the mining part form the currency exchange and provides the user or participants to do their transactions fee-less. Since the participant verifies the previous two transactions it also contributes to the security of the tangle. In this paper, features of internet of things application (IOTA) and all the improvements in it using tangles are discussed along with how it contributed to the security and how it enables the participants to have fee-less transactions is also discussed. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Distributed ledger

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Classifying Upper Extremity Motor Function Using Deep Learning and Third-Person Video Data

Classifying Upper Extremity Motor Function Using Deep Learni...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tran, Tan Chang, Lin-Ching Lum, Peter The Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States The Catholic University of America Department of Biomedical Engineering WashingtonDC United States

ISBN: (纸本)9798350362480

Post-stroke rehabilitation of upper extremity (UE) motor function is essential. Despite the widespread use of UE rehabilitation in clinical settings, assessing the success of these treatments is challenging. Methods for evaluating UE motor function include task performance under clinical supervision, patient self-reports, and data analysis from wearable devices equipped with accelerometers and gyroscopes. In prior research, we demonstrated that machine learning and deep learning models using data from a single wrist-worn accelerometer sensor could accurately differentiate between functional and non-functional UE movements in stroke patients. To overcome the limitations of single wrist-worn accelerometer sensors - challenges in capturing the full context of functional movements - this study presents a new deep learning (DL) based framework designed to classify functional and non-functional arm movements in videos captured from a conventional camera. The system is entirely automated and comprises two DL networks. The first network performs human pose estimation, extracting 2D pose key points of the paretic arm(s) and torso on 2-second sequences of the frames. The second network then uses these 2D pose landmarks to classify the movement sequence as functional or non-functional. This system offers two key benefits for rehabilitation. First, it can automatically generate initial annotations for video frames, significantly reducing the time needed for manual labeling. Second, analyzing the effectiveness of UE rehabilitation through third-person videos allows for objective outcome measurement of UE treatments in stroke survivors' home environments. © 2024 IEEE.

关键词： Accelerometers

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A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

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computers, Materials & Continua 2023年第6期75卷 5697-5715页

作者： Seema Rani Anju Anupma Sangwan Krishna Kumar Kashif Nisar Tariq Rahim Soomro Ag.Asri Ag.Ibrahim Manoj Gupta Laxmi Chandand Sadiq Ali Khan Department of CSE Guru Jambheshwar University of Science&TechnologyHisar125001India Department of Hydro and Renewable Energy Indian Institute of Technology RoorkeeIndia College of Computer Science and Information Systems Institute of Business ManagementKorangi Creek RoadKarachi75190SindhPakistan Faculty of Computing and Informatics Universiti Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Computer Science Department UBIT Karachi UniversityKarachi75270Pakistan

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale *** of the major issues in such a network is the localization of underwater *** is required for tracking objects and detecting the *** is also considered tagging of data where sensed contents are not found of any use without *** is useless for application until the position of sensed content is *** article’s major goal is to review and analyze underwater node localization to solve the localization issues in *** present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes ***,a detailed subdivision of these localization schemes is ***,these localization schemes are compared from different *** detailed analysis of these schemes in terms of certain performance metrics has been discussed in this *** the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

关键词： Underwater wireless sensor networks localization schemes node localization ranging algorithms estimation based prediction based

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Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data Science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec

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computer Systems Science & engineering 2022年第6期41卷 1207-1222页

作者： Jin Wang Changqing Zhao Shiming He Yu Gu Osama Alfarraj Ahed Abugabah School of Computer and Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Chemistry Institute of Inorganic and Analytical ChemistryGoethe-UniversityFrankfurt60438Germany Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Zayed University CTIAbu Dhabi144534United Arab Emirates

System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.

关键词： Log anomaly detection log instability word2Vec feature extraction

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CervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification 4th

CervixNet: A Reward-Based Weighted Ensemble Framework for Ce...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 2nd

New Cloud Computing Authentication Based on Secure Hash Algo...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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