Open Computer Science最新文献_第5页

3D chaotic map-cosine transformation based approach to video encryption and decryption 基于三维混沌映射余弦变换的视频加解密方法

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2020-0225

M. Dua, Drishti Makhija, Pilla Yamini Lakshmi Manasa, Prashant Mishra

{"title":"3D chaotic map-cosine transformation based approach to video encryption and decryption","authors":"M. Dua, Drishti Makhija, Pilla Yamini Lakshmi Manasa, Prashant Mishra","doi":"10.1515/comp-2020-0225","DOIUrl":"https://doi.org/10.1515/comp-2020-0225","url":null,"abstract":"Abstract Data security is vital for multimedia communication. A number of cryptographic algorithms have been developed for the secure transmission of text and image data. Very few contributions have been made in the area of video encryption because of the large input data size and time constraints. However, due to the massive increase in digital media transfer within networks, the security of video data has become one of the most important features of network reliability. Block encryption techniques and 1D-chaotic maps have been previously used for the process of video encryption. Although the results obtained by using 1D-chaotic maps were quite satisfactory, the approach had many limitations as these maps have less dynamic behavior. To overcome these drawbacks, this article proposes an Intertwining Logistic Map (ILM)-Cosine transformation-based video encryption technique. The first step involved segmenting the input video into multiple frames based on the frames per second (FPS) value and the length of the video. Next, each frame was selected, and the correlation among the pixels was reduced by a process called permutation/scrambling. In addition, each frame was rotated by 90° in the anticlockwise direction to induce more randomness into the encryption process. Furthermore, by using an approach called the random order substitution technique, changes were made in each of the images, row-wise and column-wise. Finally, all the encrypted frames were jumbled according to a frame selection key and were joined to generate an encrypted video, which was the output delivered to the user. The efficiency of this method was tested based on the state of various parameters like Entropy, Unified Average Change in Intensity (UACI), and correlation coefficient (CC). The presented approach also decrypts the encrypted video, and the decryption quality was checked using parameters such as mean square error (MSE) and peak signal-to-noise ratio (PSNR).","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44121357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

BiSHM: Evidence detection and preservation model for cloud forensics BiSHM：用于云取证的证据检测和保存模型

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0241

Prasad Purnaye, Vrushali Kulkarni

{"title":"BiSHM: Evidence detection and preservation model for cloud forensics","authors":"Prasad Purnaye, Vrushali Kulkarni","doi":"10.1515/comp-2022-0241","DOIUrl":"https://doi.org/10.1515/comp-2022-0241","url":null,"abstract":"Abstract The cloud market is growing every day. So are cloud crimes. To investigate crimes that happen in a cloud environment, an investigation is carried out adhering to the court of law. Forensics investigations require evidence from the cloud. Evidence acquisition in the cloud requires formidable efforts because of physical inaccessibility and the lack of cloud forensics tools. Time is very crucial in any forensic investigation. If the evidence is preserved before the cloud forensic investigation, it can give the investigators a head start. To identify and preserve such potential evidence in the cloud, we propose a system with an artificial intelligence (AI)-based agent, equipped for binary classification that monitors and profiles the virtual machine (VM) from hypervisor level activities. The proposed system classifies and preserves evidence data generated in the cloud. The evidence repository module of the system uses a novel blockchain model approach to maintain the data provenance. The proposed system works at the hypervisor level, which makes it robust for anti-forensics techniques in the cloud. The proposed system identifies potential evidence reducing the effective storage space requirement of the evidence repository. Data provenance incorporated in the proposed system reduces trust dependencies on the cloud service provider (CSP).","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48427821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0233

Fariba Sarhangnia, Shima Mahjoobi, Samaneh Jamshidi

{"title":"A novel similarity measure of link prediction in bipartite social networks based on neighborhood structure","authors":"Fariba Sarhangnia, Shima Mahjoobi, Samaneh Jamshidi","doi":"10.1515/comp-2022-0233","DOIUrl":"https://doi.org/10.1515/comp-2022-0233","url":null,"abstract":"Abstract Link prediction is one of the methods of social network analysis. Bipartite networks are a type of complex network that can be used to model many natural events. In this study, a novel similarity measure for link prediction in bipartite networks is presented. Due to the fact that classical social network link prediction methods are less efficient and effective for use in bipartite network, it is necessary to use bipartite network-specific methods to solve this problem. The purpose of this study is to provide a centralized and comprehensive method based on the neighborhood structure that performs better than the existing classical methods. The proposed method consists of a combination of criteria based on the neighborhood structure. Here, the classical criteria for link prediction by modifying the bipartite network are defined. These modified criteria constitute the main component of the proposed similarity measure. In addition to low simplicity and complexity, this method has high efficiency. The simulation results show that the proposed method with a superiority of 0.5% over MetaPath, 1.32% over FriendLink, and 1.8% over Katz in the f-measure criterion shows the best performance.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41516809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Greatest-common-divisor dependency of juggling sequence rotation efficient performance 最大公约数依赖性杂耍序列旋转的高效性能

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0234

Joseph A. Erho, J. I. Consul, B. R. Japheth

{"title":"Greatest-common-divisor dependency of juggling sequence rotation efficient performance","authors":"Joseph A. Erho, J. I. Consul, B. R. Japheth","doi":"10.1515/comp-2022-0234","DOIUrl":"https://doi.org/10.1515/comp-2022-0234","url":null,"abstract":"Abstract In previous experimental study with three-way-reversal and juggling sequence rotation algorithms, using 20,000,000 elements for type LONG in Java, the average execution times have been shown to be 49.66761ms and 246.4394ms, respectively. These results have revealed appreciable low performance in the juggling algorithm despite its proven optimality. However, the juggling algorithm has also exhibited efficiency with some offset ranges. Due to this pattern of the juggling algorithm, the current study is focused on investigating source of the inefficiency on the average performance. Samples were extracted from the previous experimental data, presented differently and analyzed both graphically and in tabular form. Greatest common divisor values from the data that equal offsets were used. As emanating from the previous study, the Java language used for the rotation was to simulate ordering of tasks for safety and efficiency in the context of real-time task scheduling. Outcome of the investigation shows that juggling rotation performance competes favorably with three-way-reversal rotation (and even better in few cases) for certain offsets, but poorly with the rests. This study identifies the poorest performances around offsets in the neighborhood of square root of the sequence size. From the outcome, the study therefore strongly advises application developers (especially for real-time systems) to be mindful of where and how to in using juggling rotation.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46679192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Designing of fault-tolerant computer system structures using residue number systems 基于剩余数系统的容错计算机系统结构设计

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2020-0171

V. Krasnobayev, A. Kuznetsov, A. Kiian

引用次数: 0

Cross-modal biometric fusion intelligent traffic recognition system combined with real-time data operation 结合实时数据操作的跨模态生物特征融合智能交通识别系统

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0252

Wei Xu, Yujin Zhai

{"title":"Cross-modal biometric fusion intelligent traffic recognition system combined with real-time data operation","authors":"Wei Xu, Yujin Zhai","doi":"10.1515/comp-2022-0252","DOIUrl":"https://doi.org/10.1515/comp-2022-0252","url":null,"abstract":"Abstract Intelligent traffic recognition system is the development direction of the future traffic system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology into the entire ground traffic management system. It establishes a real-time, accurate, and efficient integrated transportation management system that plays a role in a wide range and all directions. The aim of this article is to integrate cross-modal biometrics into an intelligent traffic recognition system combined with real-time data operations. Based on the cross-modal recognition algorithm, it can better re-identify the vehicle cross-modally by building a model. First, this article first presents a general introduction to the cross-modal recognition method. Then, the experimental analysis is conducted on the classification of vehicle images recognized by the intelligent transportation system, the complexity of vehicle logo recognition, and the recognition of vehicle images with different lights. Finally, the cross-modal recognition algorithm is introduced into the dynamic analysis of the intelligent traffic recognition system. The cross-modal traffic recognition system experiment is carried out. The experimental results show that the intraclass distribution loss function can improve the Rank 1 recognition rate and mAP value by 6–7% points on the basis of the baseline method. This shows that improving the modal invariance feature by reducing the distribution difference between different modal images of the same vehicle can effectively deal with the feature information imbalance caused by modal changes.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41451055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks Word2Vec:最优超参数及其对自然语言处理下游任务的影响

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0236

Tosin P. Adewumi, F. Liwicki, M. Liwicki

{"title":"Word2Vec: Optimal hyperparameters and their impact on natural language processing downstream tasks","authors":"Tosin P. Adewumi, F. Liwicki, M. Liwicki","doi":"10.1515/comp-2022-0236","DOIUrl":"https://doi.org/10.1515/comp-2022-0236","url":null,"abstract":"Abstract Word2Vec is a prominent model for natural language processing tasks. Similar inspiration is found in distributed embeddings (word-vectors) in recent state-of-the-art deep neural networks. However, wrong combination of hyperparameters can produce embeddings with poor quality. The objective of this work is to empirically show that Word2Vec optimal combination of hyper-parameters exists and evaluate various combinations. We compare them with the publicly released, original Word2Vec embedding. Both intrinsic and extrinsic (downstream) evaluations are carried out, including named entity recognition and sentiment analysis. Our main contributions include showing that the best model is usually task-specific, high analogy scores do not necessarily correlate positively with F1 scores, and performance is not dependent on data size alone. If ethical considerations to save time, energy, and the environment are made, then relatively smaller corpora may do just as well or even better in some cases. Increasing the dimension size of embeddings after a point leads to poor quality or performance. In addition, using a relatively small corpus, we obtain better WordSim scores, corresponding Spearman correlation, and better downstream performances (with significance tests) compared to the original model, which is trained on a 100 billion-word corpus.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42899205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Security and privacy issues in federated healthcare – An overview 联邦医疗保健中的安全和隐私问题-概述

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0230

Jansi Rani Amalraj, Robert Lourdusamy

引用次数: 1

A student-based central exam scheduling model using A* algorithm 基于A*算法的学生中心考试调度模型

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0237

M. S. Başar, Sinan Kul

{"title":"A student-based central exam scheduling model using A* algorithm","authors":"M. S. Başar, Sinan Kul","doi":"10.1515/comp-2022-0237","DOIUrl":"https://doi.org/10.1515/comp-2022-0237","url":null,"abstract":"Abstract In this study, a student-based placement model using the A* algorithm is proposed and applied to solve the problem of placing the courses in exam sessions. The application area of the model is midterm and final exams, conducted by the Open Education Faculty. The reason for choosing open education exams for the practice is that the exams are applied across the country and more than 100,000 students participate. The main problem is to obtain a suitable distribution that can satisfy many constraints simultaneously. In the current system, the lessons in the sessions were placed once using the curriculum knowledge. This placement plan is applied in all exams. When the placement is done according to the curriculum information, the courses in the sessions cannot be placed effectively and efficiently due to a large number of common courses and the large number of students taking the exam. This makes the booklets more expensive and the organization more prone to errors. Both the opening of new programs and the increase in the number of students regularly lead to the necessity of placing the classes in sessions dynamically each semester. In addition, to prevent conflicts with the calendars of other central exams, it is necessary to conduct all exams in three sessions. A better solution was obtained by using a different model than the currently used model in the study. With this solution, distribution of the courses of successful students with few courses to all sessions is provided, and difficult courses of unsuccessful students who have a large number of courses were gathered in the same session. This study can support future studies on two issues: the first issue is the approach of using the course that will be taken by most students instead of the courses taught in most departments in the selection of the course to be placed in the booklet. The second issue is to try to find the most suitable solution by performing performance tests on many algorithms whose performance has been determined by many academic studies.","PeriodicalId":43014,"journal":{"name":"Open Computer Science","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48049071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data sharing platform and security mechanism based on cloud computing under the Internet of Things 物联网下基于云计算的数据共享平台及安全机制

IF 1.5

Open Computer Science Pub Date : 2022-01-01 DOI: 10.1515/comp-2022-0256

Jiejian Cai, J. Wang

引用次数: 0