Journal of Internet Technology最新文献_第3页

Gait Analysis Based Parkinson’s Disease Auxiliary Diagnosis System 基于步态分析的帕金森病辅助诊断系统

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205005

Fangzhe Chen, Xuwei Fan, Jianpeng Li, Min Zou, Lianfen Huang

{"title":"Gait Analysis Based Parkinson’s Disease Auxiliary Diagnosis System","authors":"Fangzhe Chen, Xuwei Fan, Jianpeng Li, Min Zou, Lianfen Huang","doi":"10.53106/160792642021092205005","DOIUrl":"https://doi.org/10.53106/160792642021092205005","url":null,"abstract":"Parkinson’s disease (PD) is a neurodegenerative disease that often occurs in elderly people. Its symptoms are static tremor and slow movement, which affect the life of the patient seriously. With the development of medical technology, the early diagnosis of PD has attracted widespread attention. Many studies have shown that abnormal gait characteristics are potential bases for judging whether suffering from Parkinson’s disease. If PD can be diagnosed in the early stage, it will benefit the control of the disease and subsequent treatment. However, the diagnosis of PD is a complex task which often relies on the doctor’s experience and subjective evaluation. In this stage, because of the lack of professional knowledge of doctors or errors in subjective judgment, it is easy to misdiagnose and miss the best treatment time. In response to this problem, this paper designs an auxiliary diagnosis system for PD based on abnormal gait, composed of embedded devices, mobile terminals and servers. The embedded device uses the accelerometer to collect the patient’s six-dimensional gait data, then the data are transmitted to the mobile phone via Bluetooth and sent to the server. The server analyzes the data by 1D convolutional neural network model and monitors the abnormality of the patient’s gait. Herein, we proved that the use of 1D convolutional neural network for analysis has better performance with five-fold cross-validation, and its recognition accuracy rate reaches 91.4%.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"989-997"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47291682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Modes Communication of Cat Swarm Optimization Based WSN Node Location Algorithm 一种基于猫群优化的无线传感器网络节点定位算法

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205001

Jeng-Shyang Pan, Jiao Wang, Jinfeng Lai, Hao Luo, S. Chu

引用次数: 6

Enhancing Machine Comprehension Using Multi-Knowledge Bases and Offline Answer Span Improving System 利用多知识库和离线回答广度改进系统提高机器理解能力

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205013

Feifei Xu, Wenkai Zhang, Haizhou Du, Shanlin Zhou

引用次数: 0

GRUIFI: A Group Recommendation Model Covering User Importance and Feature Interaction GRUIFI:一种涵盖用户重要性和特征交互的群组推荐模型

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205017

Jingwei Zhang, Chen Jing, Ya Zhou, Qing Yang

{"title":"GRUIFI: A Group Recommendation Model Covering User Importance and Feature Interaction","authors":"Jingwei Zhang, Chen Jing, Ya Zhou, Qing Yang","doi":"10.53106/160792642021092205017","DOIUrl":"https://doi.org/10.53106/160792642021092205017","url":null,"abstract":"Group recommendation derives from a phenomenon that a group with similar interests have formed various communities, which creates the requirements that a group of users in one community want to share personalized services. Different from traditional recommendations that focus on individuals, group recommendation needs to consider the differences in preference of group members. How to build a proper model for group members to aggregate different preferences is still a challenging problem: (1) the influence of group members is quite different; (2) a user decision is directly or indirectly influenced by other members in the same group. This paper proposed a Group Recommendation model covering User Importance and automatic Feature Interaction (GRUIFI), which can model interaction data of group member and learn group potential preference representation. Our model exploits an attention mechanism to obtain the weights of group members that represent user importance, and those dynamic user weights are integrated to learn a group representation. Then we design a neural network that combines the multi-head attention to automatically learn fine-grained interactions between groups and items, and further capture the interdependency between group members. Finally, the experiments on the two real-world datasets show that GRUIFI performs significantly better than baseline methods.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1141-1153"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42506455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

lwEPSep: A Lightweight End-to-end Privacy-preserving Security Protocol for CTI Sharing in IoT Environments lwepsp:面向物联网环境下CTI共享的轻量级端到端隐私保护安全协议

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205011

Hoonyong Park, Jiyoon Kim, Sangmin Lee, Daniel Gerbi Duguma, I. You

{"title":"lwEPSep: A Lightweight End-to-end Privacy-preserving Security Protocol for CTI Sharing in IoT Environments","authors":"Hoonyong Park, Jiyoon Kim, Sangmin Lee, Daniel Gerbi Duguma, I. You","doi":"10.53106/160792642021092205011","DOIUrl":"https://doi.org/10.53106/160792642021092205011","url":null,"abstract":"The Internet of Things (IoT) is vulnerable to a wide range of security risks, which can be effectively mitigated by applying Cyber Threat Intelligence (CTI) sharing as a proactive mitigation approach. In realizing CTI sharing, it is of paramount importance to guarantee end-to-end protection of the shared information as unauthorized disclosure of CTI is disastrous for organizations using IoT. Furthermore, resource-constrained devices should be supported through lightweight operations. Unfortunately, the aforementioned are not satisfied by the Hypertext Transfer Protocol Secure (HTTPS), which state-of-the-art CTI sharing systems mainly depends on. As a promising alternative to HTTPS, Ephemeral Diffie-Hellman over COSE (EDHOC) can be considered because it meets the above requirements. However, EDHOC in its current version contains several security flaws, most notably due to the unprotected initial message. Consequently, we propose a lightweight end-to-end privacy-preserving security protocol that improves the existing draft EDHOC protocol by utilizing previously shared keys and keying materials while providing ticket-based optimized re-authentication. The proposed protocol is not only formally validated through BAN-logic and AVISPA, but also proved to fulfill essential security properties such as mutual authentication, secure key exchange, perfect forward secrecy, anonymity, confidentiality, and integrity. Also, comparing the protocol’s performance to that of the EDHOC protocol reveals a substantial improvement with a single roundtrip to allow frequent CTI sharing.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1067-1079"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44109261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Entropy and Structural-Hole Based Node Ranking Methods 基于熵和结构洞的节点排序方法

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205007

C. Ezeh, Tao Ren, Yan-Jie Xu, Shixuan Sun, Zhe Li

{"title":"Entropy and Structural-Hole Based Node Ranking Methods","authors":"C. Ezeh, Tao Ren, Yan-Jie Xu, Shixuan Sun, Zhe Li","doi":"10.53106/160792642021092205007","DOIUrl":"https://doi.org/10.53106/160792642021092205007","url":null,"abstract":"Several research works had been carried out to discover suitable algorithms to quantify node centralities. Among the many existing centrality metrics, only few consider centrality at the sub-graph level or deal with structural hole capabilities of pivot nodes. Research has proven the importance of sub-graph information in distinguishing influential nodes. In this work, two centrality metrics are proposed to distinguish and rank nodes in complex networks. The first metric called Sub-graph Degree Information centrality is based on entropy quantification of a node’s sub-graph degree distribution to determine its influence. The second metric called Sub-graph Degree and Structural Hole centrality considers a node’s sub-graph degree distribution and its structural hole property. The two metrics are designed to efficiently support weighted and unweighted networks. Performance evaluations were done on five real world datasets and one artificial network. The proposed metrics were equally compared against some classic centrality metrics. The results show that the proposed metrics can accurately distinguish and rank nodes distinctly on complex networks. They can equally discover highly influential and spreader nodes capable of causing epidemic spread and maximum network damage.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1009-1017"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46330114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Classification for Multi-modal Behavioral Authentication on Large-Scale Data 大规模数据多模态行为认证的有效分类

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205019

Shuji Yamaguchi Shuji Yamaguchi, Hidehito Gomi Shuji Yamaguchi, Ryosuke Kobayashi Hidehito Gomi, Rie Shigetomi Yamaguchi Ryosuke Kobayashi

引用次数: 1

Using Machine Learning Approaches to Improve Ultra-Wideband Positioning 利用机器学习方法改进超宽带定位

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205008

Che-Cheng Chang, Hong-Wen Wang, Yu-Xiang Zeng, Jin-Da Huang

引用次数: 0

A Deep Learning Based Equalization Scheme for Bandwidthcompressed Non-orthogonal Multicarrier Communication 一种基于深度学习的带宽压缩非正交多载波通信均衡方案

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205006

Qiang Chen, Linzhou Li

{"title":"A Deep Learning Based Equalization Scheme for Bandwidthcompressed Non-orthogonal Multicarrier Communication","authors":"Qiang Chen, Linzhou Li","doi":"10.53106/160792642021092205006","DOIUrl":"https://doi.org/10.53106/160792642021092205006","url":null,"abstract":"Spectrally efficient frequency division multiplexing (SEFDM) is a bandwidth-compressed non-orthogonal multicarrier communication scheme, which provides improved spectral efﬁciency compared to orthogonal frequency division multiplexing (OFDM) system. The loss of orthogonality yields the self-introduced inter-carrier interference (ICI) complicating the equalizer design. In this work, a deep learning (DL) -based SEFDM equalization scheme is proposed to characterize the ICI and to detect the transmitted information bits. The DL-based equalization scheme is trained ofﬂine using randomly-generated data and then deployed online. The performance of the equalization scheme is tested by extensive numerical simulations. The results show that the proposed equalization scheme outperforms the linear equalization based equalization scheme, such as zero forcing (ZF), minimum mean squared error (MMSE) and truncated singular value decomposition (TSVD), under additive white Gaussian noise (AWGN) channel in terms of the bit-error rate (BER). Especially for BPSK, the uncoded BER performance approaches the traditional OFDM even for the compression ratio of 0.7, which saves the bandwidth by 30%.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"1001-1009"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42780889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Vehicles Positioning in Tunnel: A Real-Time Localization System Using DL-TDOA Technology 隧道车辆定位:基于DL-TDOA技术的实时定位系统

IF 1.6 4区计算机科学

Journal of Internet Technology Pub Date : 2021-09-01 DOI: 10.53106/160792642021092205003

Xin You, Daxin Tian, Chen Liu, Xiao-Xuan Yu, Liangliang Song

{"title":"Vehicles Positioning in Tunnel: A Real-Time Localization System Using DL-TDOA Technology","authors":"Xin You, Daxin Tian, Chen Liu, Xiao-Xuan Yu, Liangliang Song","doi":"10.53106/160792642021092205003","DOIUrl":"https://doi.org/10.53106/160792642021092205003","url":null,"abstract":"Due to the dim light in the tunnel and the characteristics of natural electromagnetic shielding, drivers are prone to accidents in the tunnel and are unable to inform the navigation system in time. Therefore, it is still a bottleneck in the field of intelligent transportation how to obtain real-time vehicle position information and vehicle state information when the vehicle is running at high speeds in the tunnel. In this paper, a new technology is proposed to achieve accurate real-time positioning in the tunnel scene by combining the downlink time difference of arrival (DL-TDOA) with UWB technology. The DL-TDOA technology is based on Ultra Wide Band (UWB) data transmission technology, which can effectively reduce the interference of other electromagnetic waves and reduce the data transmission time. By calculating the transmission time of the wireless electromagnetic wave between the vehicle and the fixed base station, the technology can determine the real-time position of the vehicle and greatly reduce the time loss of data in transmission. DL-TDOA based on UWB technology has high precision, while DL-TDOA based on UWB technology has many advantages, such as high precision, strong anti-jamming ability, low power consumption and a high transmission rate which are suitable for accurate positioning and navigation in tunnel scenarios. In the final tunnel experiment, several tests were carried out at speeds of 30km/h, 60km/h and 80km/h respectively. By comparing the coordinate position after the conversion with the satellite coordinate, the real-time kinematics (RTK), it was concluded that the position error of vehicles in the tunnel is less than 1m, and the real-time positioning of vehicles in the tunnel is realized.","PeriodicalId":50172,"journal":{"name":"Journal of Internet Technology","volume":"22 1","pages":"965-976"},"PeriodicalIF":1.6,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46466583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0