Pub Date : 2022-01-01DOI: 10.1016/j.is.2021.101874
C. Cappiello, M. Comuzzi, P. Plebani, Matheus Fim
{"title":"Assessing and improving measurability of process performance indicators based on quality of logs","authors":"C. Cappiello, M. Comuzzi, P. Plebani, Matheus Fim","doi":"10.1016/j.is.2021.101874","DOIUrl":"https://doi.org/10.1016/j.is.2021.101874","url":null,"abstract":"","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"12 1","pages":"101874"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87200324","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}
With the continuous development of society, the pressure of human beings is increasing, and the problem of psychological counseling for school students is getting more and more serious, which has brought great challenges to the education and management of school students. Universities should cultivate students’ professional ability on knowledge and practical skills; we should pay more attention to their psychological counseling and develop and design a simple and efficient learning environment. It is very necessary to establish a psychological evaluation system for college students. This article is based on the design and application of the mobile education information system for psychological education. It highlights that the current psychological counseling problems in school education are indispensable. To better manage the psychological counseling problems faced by many students, 67% of the students have many psychological problems, and the mental health of students in school education and psychological counseling have become the common concern and urgent problems of all college managers. This paper uses the methods of literature research, behavior interview, and experimental investigation to investigate the complementary relationship between the connotation of education and psychological counseling from the data of the fuzzy integral model of psychological counseling, so as to pay more attention to the students’ mental health problems and make the students grow up healthily. The experimental results of this article show that there is still a lot of room for improvement in the use of mobile education information systems to improve their ability to carry out psychological counseling work.
{"title":"Design and Application of Mobile Education Information System Based on Psychological Education","authors":"C-H Ko, Yucheng Shen","doi":"10.1155/2021/1789750","DOIUrl":"https://doi.org/10.1155/2021/1789750","url":null,"abstract":"With the continuous development of society, the pressure of human beings is increasing, and the problem of psychological counseling for school students is getting more and more serious, which has brought great challenges to the education and management of school students. Universities should cultivate students’ professional ability on knowledge and practical skills; we should pay more attention to their psychological counseling and develop and design a simple and efficient learning environment. It is very necessary to establish a psychological evaluation system for college students. This article is based on the design and application of the mobile education information system for psychological education. It highlights that the current psychological counseling problems in school education are indispensable. To better manage the psychological counseling problems faced by many students, 67% of the students have many psychological problems, and the mental health of students in school education and psychological counseling have become the common concern and urgent problems of all college managers. This paper uses the methods of literature research, behavior interview, and experimental investigation to investigate the complementary relationship between the connotation of education and psychological counseling from the data of the fuzzy integral model of psychological counseling, so as to pay more attention to the students’ mental health problems and make the students grow up healthily. The experimental results of this article show that there is still a lot of room for improvement in the use of mobile education information systems to improve their ability to carry out psychological counseling work.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"5 1","pages":"1789750:1-1789750:7"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79541234","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}
Education has improved the ideological style of society, improved people’s spiritual quality, and improved people’s lifestyle, provided sincere principles for people’s enthusiasm, and provided goals and political power for people’s ideals and contemporary thinking. Thinking diagrams are a teaching method that helps people sort out their ideas and solve difficult problems and can clearly connect the relationships between various concepts. This article is intended to explore the auxiliary effects of case analysis and thinking in psychological education in the context of mobile new systems. Through a series of research methods such as literature research method, action research method, mathematical statistics method, etc., finally 110 elementary and middle school students with psychological problems in a city conducted an experimental survey, and SPSS software was used to analyze and research the statistical results. The experimental results of this article show that psychological teachers use mental icons to provide psychological counseling to students with psychological problems. The success rate of counseling can be increased by 5–10%. It can be seen that an effective method of thinking diagram education can promote the better development of students, and it can also help more teenagers with psychological problems to get rid of psychological barriers.
{"title":"Case Analysis and Thinking of Scheme Analysis Based on Mobile New Systems in Psychological Education","authors":"C-H Ko, Shun-yin Tang, Yucheng Shen","doi":"10.1155/2021/1361071","DOIUrl":"https://doi.org/10.1155/2021/1361071","url":null,"abstract":"Education has improved the ideological style of society, improved people’s spiritual quality, and improved people’s lifestyle, provided sincere principles for people’s enthusiasm, and provided goals and political power for people’s ideals and contemporary thinking. Thinking diagrams are a teaching method that helps people sort out their ideas and solve difficult problems and can clearly connect the relationships between various concepts. This article is intended to explore the auxiliary effects of case analysis and thinking in psychological education in the context of mobile new systems. Through a series of research methods such as literature research method, action research method, mathematical statistics method, etc., finally 110 elementary and middle school students with psychological problems in a city conducted an experimental survey, and SPSS software was used to analyze and research the statistical results. The experimental results of this article show that psychological teachers use mental icons to provide psychological counseling to students with psychological problems. The success rate of counseling can be increased by 5–10%. It can be seen that an effective method of thinking diagram education can promote the better development of students, and it can also help more teenagers with psychological problems to get rid of psychological barriers.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"3 1","pages":"1361071:1-1361071:11"},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79496922","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}
Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.
{"title":"A Shadow Capture Deep Neural Network for Underwater Forward-Looking Sonar Image Detection","authors":"Taowen Xiao, Zijian Cai, Cong Lin, Qiong Chen","doi":"10.1155/2021/3168464","DOIUrl":"https://doi.org/10.1155/2021/3168464","url":null,"abstract":"Image sonar is a widely used wireless communication technology for detecting underwater objects, but the detection process often leads to increased difficulty in object identification due to the lack of equipment resolution. In view of the remarkable results achieved by artificial intelligence techniques in the field of underwater wireless communication research, we propose an object detection method based on convolutional neural network (CNN) and shadow information capture to improve the object recognition and localization effect of underwater sonar images by making full use of the shadow information of the object. We design a Shadow Capture Module (SCM) that can capture the shadow information in the feature map and utilize them. SCM is compatible with CNN models that have a small increase in parameters and a certain degree of portability, and it can effectively alleviate the recognition difficulties caused by the lack of device resolution through referencing shadow features. Through extensive experiments on the underwater sonar data set provided by Pengcheng Lab, the proposed method can effectively improve the feature representation of the CNN model and enhance the difference between class and class features. Under the main evaluation standard of PASCAL VOC 2012, the proposed method improved from an average accuracy (mAP) of 69.61% to 75.73% at an IOU threshold of 0.7, which exceeds many existing conventional deep learning models, while the lightweight design of our proposed module is more helpful for the implementation of artificial intelligence technology in the field of underwater wireless communication.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"130 1","pages":"3168464:1-3168464:10"},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86496034","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}
A prior-art search on patents ascertains the patentability constraints of the invention through an organized review of prior-art document sources. This search technique poses challenges because of the inherent vocabulary mismatch problem. Manual processing of every retrieved relevant patent in its entirety is a tedious and time-consuming job that demands automated patent summarization for ease of access. This paper employs deep learning models for summarization as they take advantage of the massive dataset present in the patents to improve the summary coherence. This work presents a novel approach of patent summarization named PQPS: prior-art query-based patent summarizer using restricted Boltzmann machine (RBM) and bidirectional long short-term memory (Bi-LSTM) models. The PQPS also addresses the vocabulary mismatch problem through query expansion with knowledge bases such as domain ontology and WordNet. It further enhances the retrieval rate through topic modeling and bibliographic coupling of citations. The experiments analyze various interlinked smart device patent sample sets. The proposed PQPS demonstrates that retrievability increases both in extractive and abstractive summaries.
{"title":"PQPS: Prior-Art Query-Based Patent Summarizer Using RBM and Bi-LSTM","authors":"G. Kumaravel, Swamynathan Sankaranarayanan","doi":"10.1155/2021/2497770","DOIUrl":"https://doi.org/10.1155/2021/2497770","url":null,"abstract":"A prior-art search on patents ascertains the patentability constraints of the invention through an organized review of prior-art document sources. This search technique poses challenges because of the inherent vocabulary mismatch problem. Manual processing of every retrieved relevant patent in its entirety is a tedious and time-consuming job that demands automated patent summarization for ease of access. This paper employs deep learning models for summarization as they take advantage of the massive dataset present in the patents to improve the summary coherence. This work presents a novel approach of patent summarization named PQPS: prior-art query-based patent summarizer using restricted Boltzmann machine (RBM) and bidirectional long short-term memory (Bi-LSTM) models. The PQPS also addresses the vocabulary mismatch problem through query expansion with knowledge bases such as domain ontology and WordNet. It further enhances the retrieval rate through topic modeling and bibliographic coupling of citations. The experiments analyze various interlinked smart device patent sample sets. The proposed PQPS demonstrates that retrievability increases both in extractive and abstractive summaries.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"51 1","pages":"2497770:1-2497770:19"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82257284","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}
Tarun Dhar Diwan, Siddartha Choubey, H. Hota, S. B. Goyal, Sajjad Shaukat Jamal, P. Shukla, B. Tiwari
Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for IoT security. Tracking and blocking unwanted traffic flows in the IoT network is required to design a framework for the identification of attacks more accurately, quickly, and with less complexity. Many machine learning (ML) algorithms proved their efficiency to detect intrusion in IoT networks. But this ML algorithm suffers many misclassification problems due to inappropriate and irrelevant feature size. In this paper, an in-depth study is presented to address such issues. We have presented lightweight low-cost feature selection IoT intrusion detection techniques with low complexity and high accuracy due to their low computational time. A novel feature selection technique was proposed with the integration of rank-based chi-square, Pearson correlation, and score correlation to extract relevant features out of all available features from the dataset. Then, feature entropy estimation was applied to validate the relationship among all extracted features to identify malicious traffic in IoT networks. Finally, an extreme gradient ensemble boosting approach was used to classify the features in relevant attack types. The simulation is performed on three datasets, i.e., NSL-KDD, USNW-NB15, and CCIDS2017, and results are presented on different test sets. It was observed that on the NSL-KDD dataset, accuracy was approx. 97.48%. Similarly, the accuracy of USNW-NB15 and CCIDS2017 was approx. 99.96% and 99.93%, respectively. Along with that, state-of-the-art comparison is also presented with existing techniques.
{"title":"Feature Entropy Estimation (FEE) for Malicious IoT Traffic and Detection Using Machine Learning","authors":"Tarun Dhar Diwan, Siddartha Choubey, H. Hota, S. B. Goyal, Sajjad Shaukat Jamal, P. Shukla, B. Tiwari","doi":"10.1155/2021/8091363","DOIUrl":"https://doi.org/10.1155/2021/8091363","url":null,"abstract":"Identification of anomaly and malicious traffic in the Internet of things (IoT) network is essential for IoT security. Tracking and blocking unwanted traffic flows in the IoT network is required to design a framework for the identification of attacks more accurately, quickly, and with less complexity. Many machine learning (ML) algorithms proved their efficiency to detect intrusion in IoT networks. But this ML algorithm suffers many misclassification problems due to inappropriate and irrelevant feature size. In this paper, an in-depth study is presented to address such issues. We have presented lightweight low-cost feature selection IoT intrusion detection techniques with low complexity and high accuracy due to their low computational time. A novel feature selection technique was proposed with the integration of rank-based chi-square, Pearson correlation, and score correlation to extract relevant features out of all available features from the dataset. Then, feature entropy estimation was applied to validate the relationship among all extracted features to identify malicious traffic in IoT networks. Finally, an extreme gradient ensemble boosting approach was used to classify the features in relevant attack types. The simulation is performed on three datasets, i.e., NSL-KDD, USNW-NB15, and CCIDS2017, and results are presented on different test sets. It was observed that on the NSL-KDD dataset, accuracy was approx. 97.48%. Similarly, the accuracy of USNW-NB15 and CCIDS2017 was approx. 99.96% and 99.93%, respectively. Along with that, state-of-the-art comparison is also presented with existing techniques.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"54 1","pages":"8091363:1-8091363:13"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82721617","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}
S. Shokat, R. Riaz, S. S. Rizvi, Inayat Khan, Anand Paul
Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.
{"title":"Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques","authors":"S. Shokat, R. Riaz, S. S. Rizvi, Inayat Khan, Anand Paul","doi":"10.1155/2021/7211419","DOIUrl":"https://doi.org/10.1155/2021/7211419","url":null,"abstract":"Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"8 1","pages":"7211419:1-7211419:16"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83850021","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}
This paper takes the secondary low-carbon supply chain composed of a manufacturer and a retailer as the research object and takes the government subsidy strategy of low-carbon supply chain as the research theme, the government invests in the construction of blockchain technology application platform, and supply chain enterprises use blockchain technology for compensation. Under this background, considering consumers’ low-carbon preference and green trust, a three-stage game model dominated by the government and followed by manufacturers and retailers under the four scenarios of emission reduction technology input subsidy and low-carbon product output subsidy before and after the application of blockchain technology is established. By comparing the optimal social welfare, carbon emission reduction rate, and low-carbon product output under the four scenarios, the optimal problem of government low-carbon subsidy strategy is discussed. It is found that when the product of consumers’ low-carbon preference coefficient and green trust coefficient is greater than a certain fixed value, the government can obtain higher social welfare and promote enterprises’ emission reduction through output subsidies. The government can always promote the demand for low-carbon products through output subsidies. When the emission reduction cost coefficient approaches infinity, the demand for low-carbon products when the government implements output subsidies are four times that when the government implements technology subsidies. When the single cycle shared cost of the blockchain platform and the unit cost of enterprise application of blockchain technology are less than a certain threshold, the government’s construction of the blockchain platform can obtain higher social welfare, promote enterprise emission reduction, and improve consumers’ demand for low-carbon products.
{"title":"Research on Resource Carbon Emission Allocation Efficiency Based on Blockchain Consensus Algorithm","authors":"M. Yin","doi":"10.1155/2021/9565379","DOIUrl":"https://doi.org/10.1155/2021/9565379","url":null,"abstract":"This paper takes the secondary low-carbon supply chain composed of a manufacturer and a retailer as the research object and takes the government subsidy strategy of low-carbon supply chain as the research theme, the government invests in the construction of blockchain technology application platform, and supply chain enterprises use blockchain technology for compensation. Under this background, considering consumers’ low-carbon preference and green trust, a three-stage game model dominated by the government and followed by manufacturers and retailers under the four scenarios of emission reduction technology input subsidy and low-carbon product output subsidy before and after the application of blockchain technology is established. By comparing the optimal social welfare, carbon emission reduction rate, and low-carbon product output under the four scenarios, the optimal problem of government low-carbon subsidy strategy is discussed. It is found that when the product of consumers’ low-carbon preference coefficient and green trust coefficient is greater than a certain fixed value, the government can obtain higher social welfare and promote enterprises’ emission reduction through output subsidies. The government can always promote the demand for low-carbon products through output subsidies. When the emission reduction cost coefficient approaches infinity, the demand for low-carbon products when the government implements output subsidies are four times that when the government implements technology subsidies. When the single cycle shared cost of the blockchain platform and the unit cost of enterprise application of blockchain technology are less than a certain threshold, the government’s construction of the blockchain platform can obtain higher social welfare, promote enterprise emission reduction, and improve consumers’ demand for low-carbon products.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"12 1","pages":"9565379:1-9565379:8"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88700639","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}
Cloud computing offers significant impacts on organization by changing how information systems are developed, deployed, operated, maintained, and paid for. Therefore, the adoption of cloud computing becomes the focus of relevant research; however, previous studies have mostly studied the factors affecting cloud computing adoption from the perspective of adopters, ignoring the influence of the vendors. This study defines cloud service capability and develops scale to measure it from the perspective of cloud computing vendors to empirically examine the impact of the supply-side of cloud computing. The initial scale of cloud service capability is constructed using qualitative research, and the formal scale is obtained after two rounds of pretest. The statistical results of matched data collected from 132 cloud computing vendors and their users show that cloud service capability significantly affects cloud computing adoption. This study shifts the research perspective on cloud adoption to make theoretical contributions and management insights from the perspective of cloud computing vendors.
{"title":"Investigating the Impact of Cloud Computing Vendor on the Adoption of Cloud Computing","authors":"Ge Zhang, Lu Liu, Haolei Guo","doi":"10.1155/2021/6557937","DOIUrl":"https://doi.org/10.1155/2021/6557937","url":null,"abstract":"Cloud computing offers significant impacts on organization by changing how information systems are developed, deployed, operated, maintained, and paid for. Therefore, the adoption of cloud computing becomes the focus of relevant research; however, previous studies have mostly studied the factors affecting cloud computing adoption from the perspective of adopters, ignoring the influence of the vendors. This study defines cloud service capability and develops scale to measure it from the perspective of cloud computing vendors to empirically examine the impact of the supply-side of cloud computing. The initial scale of cloud service capability is constructed using qualitative research, and the formal scale is obtained after two rounds of pretest. The statistical results of matched data collected from 132 cloud computing vendors and their users show that cloud service capability significantly affects cloud computing adoption. This study shifts the research perspective on cloud adoption to make theoretical contributions and management insights from the perspective of cloud computing vendors.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"54 1","pages":"6557937:1-6557937:18"},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79381067","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}
M. Wan, Xinlu Xu, Yan Song, Quanliang Li, Jiawei Li
Due to its openness and simplicity, Modbus TCP has wide applications to facilitate the actual management and control in industrial wireless fields. However, its potential security vulnerabilities can also create lots of complicated information security challenges, which are increasingly threatening the availability of industrial real-time traffic delivery. Although anomaly detection has been recognized as a workable security measure to identify attacks, the critical step to successfully extract data characteristics is an extremely difficult task. In this paper, we focus on the continuous control mode in industrial processes and propose a control tracing feature algorithm to extract the function-driven tracing characteristics from Modbus TCP data traffic. Furthermore, this algorithm can flexibly integrate the time factor with critical functional operations and adequately describe the dynamic control change of technological processes. To closely cooperate with this algorithm, one optimized SVM (support vector machine) classifier is introduced as the practicable decision engine. By designing one applicable attack mode, we develop an in-depth and meticulous analysis on the decision accuracy, and all experimental results clearly explain that the extracted features can strongly reflect the changing pattern of continuous functional operations, and the proposed algorithm can effectively cooperate with the optimized SVM classifier to distinguish abnormal Modbus TCP data traffic.
{"title":"Extracting Function-Driven Tracing Characteristics for Optimized SVM Classification","authors":"M. Wan, Xinlu Xu, Yan Song, Quanliang Li, Jiawei Li","doi":"10.1155/2021/6839179","DOIUrl":"https://doi.org/10.1155/2021/6839179","url":null,"abstract":"Due to its openness and simplicity, Modbus TCP has wide applications to facilitate the actual management and control in industrial wireless fields. However, its potential security vulnerabilities can also create lots of complicated information security challenges, which are increasingly threatening the availability of industrial real-time traffic delivery. Although anomaly detection has been recognized as a workable security measure to identify attacks, the critical step to successfully extract data characteristics is an extremely difficult task. In this paper, we focus on the continuous control mode in industrial processes and propose a control tracing feature algorithm to extract the function-driven tracing characteristics from Modbus TCP data traffic. Furthermore, this algorithm can flexibly integrate the time factor with critical functional operations and adequately describe the dynamic control change of technological processes. To closely cooperate with this algorithm, one optimized SVM (support vector machine) classifier is introduced as the practicable decision engine. By designing one applicable attack mode, we develop an in-depth and meticulous analysis on the decision accuracy, and all experimental results clearly explain that the extracted features can strongly reflect the changing pattern of continuous functional operations, and the proposed algorithm can effectively cooperate with the optimized SVM classifier to distinguish abnormal Modbus TCP data traffic.","PeriodicalId":18790,"journal":{"name":"Mob. Inf. Syst.","volume":"27 1","pages":"6839179:1-6839179:12"},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78329657","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}
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