Int. J. Networked Distributed Comput.最新文献

{"title":"Adaptive Parameter Tuning for Constructing Storage Tiers in an Autonomous Distributed Storage System","authors":"Atsushi Nunome, Hiroaki Hirata","doi":"10.1007/s44227-022-00004-3","DOIUrl":"https://doi.org/10.1007/s44227-022-00004-3","url":null,"abstract":"","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126626128","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}

{"title":"Application of 2‑gram and 3‑gram to Obtain Factor Scores of Statements Posted at Q&A Sites","authors":"Yuya Yokoyama, T. Hochin, Hiroki Nomiya","doi":"10.1007/s44227-022-00005-2","DOIUrl":"https://doi.org/10.1007/s44227-022-00005-2","url":null,"abstract":"","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125543849","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}

{"title":"Bountychain: Toward Decentralizing a Bug Bounty Program with Blockchain and IPFS","authors":"Alex A. J. Hoffman, Phillipe Austria, Chol Hyun Park, Yoohwan Kim","doi":"10.2991/IJNDC.K.210527.001","DOIUrl":"https://doi.org/10.2991/IJNDC.K.210527.001","url":null,"abstract":"The first Bug Bounty Program (BBP) was launched in 1995 by Netscape [2]. The company created the program to discover pre-release software defects in Netscape Navigator, which gave rise to a new paradigm of security research. Other companies and organizations were slow to adopt a similar program at first; however, adoption began to increase in 2002. Organizations either formed their own BBP or joined existing programs from thirdparty providers. Today, BBPs provide a platform for people of all skill-levels to ethically find, report, and get paid for discovering security vulnerabilities.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115496533","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}

{"title":"Secure Communications by Tit-for-Tat Strategy in Vehicular Networks","authors":"Fatima Zohra Mostefa, Zoulikha Mekkakia Maaza, Claude Duvallet","doi":"10.2991/ijndc.k.200925.001","DOIUrl":"https://doi.org/10.2991/ijndc.k.200925.001","url":null,"abstract":"The Intelligent Transport System (ITS) is an important component with a new form of mobile ad hoc network, Vehicular ad hoc Networks (VANET) generate a high interest from governments, universities and private sectors. The communications transiting by a vehicle network and the information on vehicles and their drivers have to be protected and secured in order to guarantee the correct functioning of a ITS. The sensitivity of data conveyed by a VANET network reveals a high need of security. Indeed, the importance of security in this context is crucial given the critical consequences of a violation, misbehavior or an attack. Furthermore, in a very dynamic environment characterized by a nearly instant arrivals and departures of vehicles, the deployment of a security solution has to face constraints and specific configurations. Game theory is a modern branch of intelligent optimization it has been widely applied to model the behavior in a variety of applications.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116555728","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}

{"title":"Vehicle Platooning Systems: Review, Classification and Validation Strategies","authors":"Faten Fakhfakh, M. Tounsi, M. Mosbah","doi":"10.2991/ijndc.k.200829.001","DOIUrl":"https://doi.org/10.2991/ijndc.k.200829.001","url":null,"abstract":"The increase in vehicle numbers has resulted in the growth of traffic jams in cities and highways, thereby raising various issues on fuel consumption, environmental pollution, and traffic safety [1]. Platooning is an Intelligent Transport System (ITS) [2] application which has emerged as a promising solution for the traffic management in highways. The main idea of vehicle platooning suggests that a set of vehicles travel together while maintaining a small distance between each other. This can lead to an increase in traffic capacity and then an improved traffic management and a reduced travel time. Moreover, the comfort and the safety of passengers are enhanced since the scenarios of extreme acceleration or deceleration are eliminated and the platoon vehicles are considered as a single unit. Furthermore, the emission performance and the fuel economy are significantly improved. A vehicle platoon (also called “convoy”) can be seen as a group of vehicles that travel in close coordination through a headway control mechanism. These vehicles maintain a short spacing between them and a relative velocity. The vehicle in the front position, called leader, represents the trajectory and velocity reference. It controls all the following vehicles in the platoon. Each vehicle of the platoon receives orders from the leader that may be communicated either directly or by the preceding vehicle.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121830763","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}

{"title":"Improved Version of Round Robin Scheduling Algorithm Based on Analytic Model","authors":"Al Fiad, Zoulikha Mekkakia Maaza, Hayat Bendoukha","doi":"10.2991/ijndc.k.200804.001","DOIUrl":"https://doi.org/10.2991/ijndc.k.200804.001","url":null,"abstract":"Nowadays, operating systems have become more complex and more efficient due to the multitasking functionality, which allows several processes to run simultaneously. The Operating System (OS) chooses a task from the Ready Queue (RQ) and allocates it to a processor, this process is known as scheduling. When several processes are in the RQ, the OS plays an important role in choosing the correct order of execution of the processes for achieving better Average Turnaround Time (ATAT) and Average Waiting Time (AWT). Task scheduling algorithms can also be applied in the cloud-computing environment; it is one of the most important activities in this environment.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115526480","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}

{"title":"Enhancing Case-based Reasoning Approach using Incremental Learning Model for Automatic Adaptation of Classifiers in Mobile Phishing Detection","authors":"San Kyaw Zaw, S. Vasupongayya","doi":"10.2991/ijndc.k.200515.001","DOIUrl":"https://doi.org/10.2991/ijndc.k.200515.001","url":null,"abstract":"Nowadays, millions of mobile phone users over the world are put at risk by phishing while more than 3.8 billion smartphones are estimated to be used in 2020 [1]. As a consequence, the security of these devices becomes a top priority. Moreover, mobile devices become the primary means of communication and information access [2]. Thus, in our prior work [3], some analyses on the literatures of phishing detection are performed and identified the important features for the mobile phishing detection. Then, the adaptive mobile phishing detection model is proposed in another prior work [4] by using a Case-based Reasoning (CBR) approach. In our previous work [4], the experiments were conducted to demonstrate the design decision of our proposed model and to verify the performance in handling the concept drift. However, the main challenge faced by the CBR approach is learning a new case in order to adapt the system to a new phishing pattern. The mismatching input features with the existing cases in the case-base was lacking in our prior work [4]. In this work, the incremental learning model for the adaptation to the new examples to the case-base is proposed.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132412124","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}

{"title":"On Learning Associative Relationship Memory among Knowledge Concepts","authors":"Zhenping Xie, Kun Wang, Yuan Liu","doi":"10.2991/ijndc.k.200515.005","DOIUrl":"https://doi.org/10.2991/ijndc.k.200515.005","url":null,"abstract":"Knowledge graph is firstly put forward by Google in 2012 [1], which uses graph structure to represent knowledge information on conceptual items. In knowledge graph, each graph node denotes a knowledge concept, and edges equipped with labels represent semantic relations among knowledge nodes. Knowledge graph is a very useful tool to represent and store the information in natural language text, and has been widely and successively applied to natural translation [2], question-answer system [3], and natural language understanding [4].","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132097586","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}

{"title":"Performance Analysis of Blockchain-based Access Control Model for Personal Health Record System with Architectural Modelling and Simulation","authors":"T. Thwin, S. Vasupongayya","doi":"10.2991/ijndc.k.200515.002","DOIUrl":"https://doi.org/10.2991/ijndc.k.200515.002","url":null,"abstract":"The privacy and trust of Personal Health Record (PHR) system [1,2] are improved by applying the blockchain technology, in our prior works [3]. The issues that affect the use of blockchain in the PHR development were introduced and addressed with an existing private blockchain and cryptographic mechanisms in our prior work [4]. However, the usability evaluation is still lacking in our previous work [4]. Only the achievement of the privacy and the security as well as the effectiveness of the proposed blockchainbased PHR system was shown. Since, the performance is a concern for the use of blockchain for the PHR development, the usability of the proposed blockchain-based PHR system must be addressed. For that reason, the performance analysis is conducted in this work.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123961782","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}

{"title":"Research on Intelligent Question and Answering Based on a Pet Knowledge Map","authors":"Yuan Liu, Wen Zhang, Qi Yuan, Jie Zhang","doi":"10.2991/ijndc.k.200515.004","DOIUrl":"https://doi.org/10.2991/ijndc.k.200515.004","url":null,"abstract":"question: What are the symptoms of nm getting nd? In the above example, the pet’s proper nouns, such as golden retriever, which are involved in the natural language question of the user, will be converted into the golden retriever’s part of speech nm after the entity similarity calculation, and the shift is transformed into the distemper word nf instead. The advantage of this is that it can reduce the selection workload of the naive Bayesian classifier feature. Additionally, because there is no special dataset in the pet field, the workload of building the dataset can be reduced, and the required training set can be reduced in size. The specific conversion is shown in Table 8. 3.8. Text Classification Based on Multiple Naive Bayes This article requires multiple classifications of pet text datasets. At present, there are many machine learning and deep learning Y. Liu et al. / International Journal of Networked and Distributed Computing 8(3) 162–170 169 Table 8 | Rule conversion table Conversion rule User problem Abstract problem Pet breed name — nm Golden retriever price Price of nm Pet disease name — nd What are the symptoms of golden retrievers? What is the symptom of nm? Pet food — nf Can golden retriever eat grapes? Can nm eat nf? algorithms that can perform multi-classification of texts. Multiple naive Bayes have stable classification efficiency and good performance for small-scale data and multi-classification. Because there are very few corpora in the pet field, the size of the corpus built in this paper is also very small, so this paper adopts a naive Bayesian text classifier based on polynomials. Based on the knowledge of pet knowledge maps, a total of 24 categories are constructed according to the pet breed, pet disease and pet food attributes. The user’s natural language question will match one of the 24 categories after multidirectional naive Bayes classification as the classification results. 3.9. Matching Word Sequence Diagram Through the classification result of the text quantifier based on multiple naive Bayes, the labels of the categories corresponding to the natural language problem of the user, such as weight, price, and main symptoms, are obtained, which are labels corresponding to the user problem and correspond to natural language questions. Then, the determined intention tag maps the corresponding question template, matching the word order graph in the template. The natural language question basically describes the relationship between the subject and the object, while the graph model can describe the relationship between the node and the node through the edge. The word map is a directed graph, the subject points to the object, and the predicate is used as an edge. In directed graphs, subjects and objects are entities, and predicates are relationships between entities, including attribute relationships. For example, what are the symptoms of a golden retriever with canine distemper? The conversion into a word sequence diagram","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134304981","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}