Array最新文献

{"title":"Mechanisms and techniques to enhance the security of big data analytic framework with MongoDB and Linux Containers","authors":"Akalanka Mailewa ,&nbsp;Susan Mengel ,&nbsp;Lisa Gittner ,&nbsp;Hafiz Khan","doi":"10.1016/j.array.2022.100236","DOIUrl":"10.1016/j.array.2022.100236","url":null,"abstract":"<div><p>The frequency and scale of unauthorized access cases and misuses of data access privileges are a growing concern of many organizations. The protection of confidential data, such as social security numbers, financial information, etc., of the customers and/or employees is among the key responsibilities of any organization, and damage to such sensitive data can easily pose a threat to the future of a business and the security of the customers. Therefore, this paper proposes and implements some security mechanisms and techniques, such as secure authentication, secure authorization, and encryption, to assure the overall security of a big data analytic framework with MongoDB free community edition. This paper presents the fourth phase of our continuous research where in the first phase we proposed a data analytic framework with MongoDB and Linux Containers (LXCs) with basic security requirements. Next, in the second phase we proposed a vulnerability analysis testbed to find vulnerabilities associated with the system. Finally, in the third phase we discussed in detail root causes and some prevention techniques of vulnerabilities found in the system. In addition, this paper introduces a new security mechanism for privacy preserving data handling with MongoDB to ensure the privacy of the data before being processed. Our results show, with our initial model of the analytic framework, how well our newly introduced security mechanisms work and how these security mechanisms and techniques can be used to assure the confidentiality, integrity, and availability (CIA) of any data science project conducted on our proposed analytic framework. In addition, these security mechanisms and techniques help us to strengthen the current system against zero-day attacks where attacks on vulnerabilities that have not been patched or made public yet. Therefore, our vulnerability analysis testbed which is proposed in the second phase of this research will not be able to finds vulnerabilities related to zero-day attacks.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259000562200073X/pdfft?md5=9f51815b652b7bb9179c0c3a886fec36&pid=1-s2.0-S259000562200073X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45932587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How statistical modeling and machine learning could help in the calibration of numerical simulation and fluid mechanics models? Application to the calibration of models reproducing the vibratory behavior of an overhead line conductor","authors":"Hamdi Amroun ,&nbsp;Fikri Hafid ,&nbsp;Ammi Mehdi","doi":"10.1016/j.array.2022.100187","DOIUrl":"10.1016/j.array.2022.100187","url":null,"abstract":"<div><p>The world of fluid mechanics is increasingly generating a large amount of data, thanks to the use of numerical simulation techniques. This offers interesting opportunities for incorporating machine learning methods to solve data-related problems such as model calibration. One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate a phenomenon. Indeed, the computational cost generated by some models of fluid mechanics pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. In this paper, we propose a framework which is a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. an approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e−09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000418/pdfft?md5=a1d8ae6efd49cb986e23ea0e4664614d&pid=1-s2.0-S2590005622000418-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45989153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-quantum cryptography Algorithm's standardization and performance analysis","authors":"Manish Kumar","doi":"10.1016/j.array.2022.100242","DOIUrl":"https://doi.org/10.1016/j.array.2022.100242","url":null,"abstract":"<div><p>-Quantum computer is no longer a hypothetical idea. It is the world's most important technology and there is a race among countries to get supremacy in quantum technology. It is the technology that will reduce the computing time from years to hours or even minutes. The power of quantum computing will be a great support for the scientific community. However, it raises serious threats to cybersecurity. Theoretically, all the cryptography algorithms are vulnerable to attack. The practical quantum computers, when available with millions of qubits capacity, will be able to break nearly all modern public-key cryptographic systems. Before the quantum computers arrive with sufficient ‘qubit’ capacity, we must be ready with quantum-safe cryptographic algorithms, tools, techniques, and deployment strategies to protect the ICT infrastructure. This paper discusses in detail the global effort for the design, development, and standardization of various quantum-safe cryptography algorithms along with the performance analysis of some of the potential quantum-safe algorithms. Most quantum-safe algorithms need more CPU cycles, higher runtime memory, and a large key size. The objective of the paper is to analyze the feasibility of the various quantum-safe cryptography algorithms.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100242"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000777/pdfft?md5=75d79c7ee26fdefa0ae827069af84d57&pid=1-s2.0-S2590005622000777-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138135192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Terminal independent security token derivation scheme for ultra-dense IoT networks","authors":"Vincent Omollo Nyangaresi","doi":"10.1016/j.array.2022.100210","DOIUrl":"10.1016/j.array.2022.100210","url":null,"abstract":"<div><p>The Fifth Generation (5G) networks deploy base station ultra-densification to boost data rates, capacities, reliability, energy efficiency as well as the reduction of communication latencies. To increase quality of service as well as quality of experience, a large number of Internet of Things (IoT) communications are relayed over 5G networks. For enhanced pervasive computing, most of the devices in 5G-IoT networks are continuously connected to the network, exchanging massive and sensitive data. Therefore, there is need to protect these networks from both privacy and security attacks. As a result, many security protocols have been presented in literature. Unfortunately, IoT devices are heterogeneous in nature with diverse communication and security architectures. These issues render privacy and security protection extremely challenging. Consequently, majority of the conventional protocols fail to fully address privacy and security issues in 5G-IoT networks. Particularly, user collusion, de-synchronization and side-channeling attacks are ignored in most of the security protocols. On the other hand, some of the developed protocols achieve salient security but at extremely high computation, storage and communication complexities. In this paper, an elliptic curve and biometric based security token derivation scheme is presented. Formal security analysis using Burrows–Abadi–Needham (BAN) logic shows the negotiation of a session key between the communicating parties. On the other hand, informal security analysis shows that this scheme is secure under all the Canetti- Krawczyk (CK) threat model assumptions. In terms of efficiency, the comparative performance evaluation carried out shows that this protocol has the least communication and computation complexities among other related protocols.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259000562200056X/pdfft?md5=369e2e6968bbd7581e28ac2e4b87535e&pid=1-s2.0-S259000562200056X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46337397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of unlabeled source domains for domain adaptation in remote sensing","authors":"Christian Geiß,&nbsp;Alexander Rabuske,&nbsp;Patrick Aravena Pelizari,&nbsp;Stefan Bauer,&nbsp;Hannes Taubenböck","doi":"10.1016/j.array.2022.100233","DOIUrl":"10.1016/j.array.2022.100233","url":null,"abstract":"<div><p>—In the context of supervised learning techniques, it can be desirable to utilize existing prior knowledge from a source domain to estimate a target variable in a target domain by exploiting the concept of domain adaptation. This is done to alleviate the costly compilation of prior knowledge, i.e., training data. Here, our goal is to select a single source domain for domain adaptation from multiple potentially helpful but unlabeled source domains. The training data is solely obtained for a source domain if it was identified as being relevant for estimating the target variable in the corresponding target domain by a selection mechanism. From a methodological point of view, we propose unsupervised source selection by voting from (an ensemble of) similarity metrics that follow aligned marginal distributions regarding image features of source and target domains. Thereby, we also propose an unsupervised pruning heuristic to solely include robust similarity metrics in an ensemble voting scheme. We provide an evaluation of the methods by learning models from training data sets created with Level-of-Detail-1 building models and regress <em>built-up density</em> and <em>height</em> on Sentinel-2 satellite imagery. To evaluate the domain adaptation capability, we learn and apply models interchangeably for the four largest cities in Germany. Experimental results underline the capability of the methods to obtain more frequently higher accuracy levels with an improvement of up to 10% regarding the most robust selection mechanisms compared to random source-target domain selections.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000716/pdfft?md5=3372953e18dfc031361e3f6c2606cfc4&pid=1-s2.0-S2590005622000716-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43075433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stochastic recognition of human daily activities via hybrid descriptors and random forest using wearable sensors","authors":"Nurkholish Halim","doi":"10.1016/j.array.2022.100190","DOIUrl":"10.1016/j.array.2022.100190","url":null,"abstract":"<div><p>Human daily activity recognition (HDAR) using wearable sensors is an important task for researchers aiming to develop an effective and feasible model which is capable of accurately detecting human motion patterns. These applications provide elderly care, surveillance systems, and wellness tracking. Despite the pervasive use, recognition and monitoring of human physical activities remains inaccurate, which may contribute to negative reactions and feedback. This paper addresses a data-driven approach to recognizing human daily activities in an indoor-outdoor environment. To improve the classification and recognition of human life-log activities (for example, walking, drinking, and exercising), a model is introduced that incorporates pre-processing (such as denoising), hybrid features extraction from four domains, including time, frequency, wavelet, and time-frequency respectively. After that, stochastic gradient descent is exploited to optimize the selected features. The optimal extracted features are advanced to random forest classifiers in order to develop adaptive for human life-log activities. Additionally, the proposed HDAR model is experimentally evaluated on three benchmark datasets, namely, USC-HAD, which is comprised of 12 physical activities, IM-WSHA, which involves 11 life-log activities, and MOTIONSENSE which contains six static and dynamic activities, respectively. The experimental results show that the proposed HDAR method significantly achieves better results and outperforms others in terms of recognition rates of 91.08%, 91.45%, and 93.16% respectively, when the USC-HAD, IM-WSHA, and MOTIONSENE databases are applied.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000431/pdfft?md5=e18a5c4bd1b432d2b0fa6a36f03e450e&pid=1-s2.0-S2590005622000431-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43471794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved probability propagation algorithm for density peak clustering based on natural nearest neighborhood","authors":"Wendi Zuo ,&nbsp;Xinmin Hou","doi":"10.1016/j.array.2022.100232","DOIUrl":"https://doi.org/10.1016/j.array.2022.100232","url":null,"abstract":"<div><p>Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance (<span><math><msub><mrow><mi>d</mi></mrow><mrow><mi>c</mi></mrow></msub></math></span>), the cluster number (<span><math><mi>k</mi></math></span>) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000704/pdfft?md5=d384e72bc11472f329ffedfc0c3ee02c&pid=1-s2.0-S2590005622000704-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138135179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum advantage on proof of work","authors":"Dan A. Bard,&nbsp;Joseph J. Kearney,&nbsp;Carlos A. Perez-Delgado","doi":"10.1016/j.array.2022.100225","DOIUrl":"https://doi.org/10.1016/j.array.2022.100225","url":null,"abstract":"<div><p>Proof-of-Work (PoW) is a fundamental underlying technology behind most major blockchain cryptocurrencies. It has been previously pointed out that quantum devices provide a computational advantage in performing PoW in the context of Bitcoin. Here we make the case that this quantum advantage extends not only to all existing PoW mechanisms, but to any possible PoW as well. This has strong consequences regarding both quantum-based attacks on the integrity of the entirety of the blockchain, as well as more legitimate uses of quantum computation for the purpose of mining Bitcoin and other cryptocurrencies. For the first case, we estimate when these quantum attacks will become feasible, for various cryptocurrencies, and discuss the impact of such attacks. For the latter, we derive a precise formula to calculate the economic incentive for switching to quantum-based cryptocurrency miners. Using this formula, we analyze several test scenarios, and conclude that investing in quantum hardware for cryptocurrency mining has the potential to pay off immensely.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000650/pdfft?md5=d9c047725c4e24f0889feb0b9b60e0a8&pid=1-s2.0-S2590005622000650-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138135415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context","authors":"Md. Sabab Zulfiker ,&nbsp;Nasrin Kabir ,&nbsp;Al Amin Biswas ,&nbsp;Sunjare Zulfiker ,&nbsp;Mohammad Shorif Uddin","doi":"10.1016/j.array.2022.100204","DOIUrl":"10.1016/j.array.2022.100204","url":null,"abstract":"<div><p>Since December 2019, the world has been fighting against the COVID-19 pandemic. This epidemic has revealed a bitter truth that though humans have advanced to unprecedented heights in the last few decades in terms of technology, they are lagging far behind in the fields of medical science and health care. Several institutes and research organizations have stepped up to introduce different vaccines to combat the pandemic. Bangladesh government has also taken steps to provide widespread vaccinations from January 2021. The Bangladeshi netizens are frequently sharing their thoughts, emotions, and experiences about the COVID-19 vaccines and the vaccination process on different social media sites like Facebook, Twitter, etc. This study has analyzed the views and opinions that they have expressed on different social media platforms about the vaccines and the ongoing vaccination program. For performing this study, the reactions of the Bangladeshi netizens on social media have been collected. The Latent Dirichlet Allocation (LDA) model has been used to extract the most common topics expressed by the netizens regarding the vaccines and vaccination process in Bangladesh. Finally, this study has applied different deep learning as well as traditional machine learning algorithms to identify the sentiments and polarity of the opinions of the netizens. The performance of these models has been assessed using a variety of metrics such as accuracy, precision, sensitivity, specificity, and F1-score to identify the best one. Sentiment analysis lessons from these opinions can help the government to prepare itself for the future pandemic.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9600965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual SLAM algorithms and their application for AR, mapping, localization and wayfinding","authors":"Charalambos Theodorou ,&nbsp;Vladan Velisavljevic ,&nbsp;Vladimir Dyo ,&nbsp;Fredi Nonyelu","doi":"10.1016/j.array.2022.100222","DOIUrl":"https://doi.org/10.1016/j.array.2022.100222","url":null,"abstract":"<div><p>Visual simultaneous localization and mapping (vSLAM) algorithms use device camera to estimate agent's position and reconstruct structures in an unknown environment. As an essential part of augmented reality (AR) experience, vSLAM enhances the real-world environment through the addition of virtual objects, based on localization (location) and environment structure (mapping). From both technical and historical perspectives, this paper categorizes and summarizes some of the most recent visual SLAM algorithms proposed in research communities, while also discussing their applications in augmented reality, mapping, navigation, and localization.</p></div>","PeriodicalId":8417,"journal":{"name":"Array","volume":"15 ","pages":"Article 100222"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590005622000637/pdfft?md5=8e2566cf29ca9419d95297c820869bd1&pid=1-s2.0-S2590005622000637-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138135191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}