Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581825
M. Hammad, W. Elmedany, Y. Ismail
Broadcasting applications such as video surveillance systems are using High Definition (HD) videos. The use of high-resolution videos increases significantly the data volume of video coding standards such as High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), which increases the challenge for storing, processing, encrypting, and transmitting these data over different communication channels. Video compression standards use state-of-the-art techniques to compress raw video sequences more efficiently, such techniques require high computational complexity and memory utilization. With the emergent of using HEVC and video surveillance systems, many security risks arise such as man-in-the-middle attacks, and unauthorized disclosure. Such risks can be mitigated by encrypting the traffic of HEVC. The most widely used encryption algorithm is the Advanced Encryption Standard (AES). Most of the computational complexity in AES hardware-implemented is due to S-box or sub-byte operation and that because it needs many resources and it is a non-linear structure. The proposed AES S-box ROM design considers the latest HEVC used for homeland security video surveillance systems. This paper presents different designs for VHDL efficient ROM implementation of AES S-box using IP core generator, ROM components, and using Functions, which are all supported by Xilinx. IP core generator has Block Memory Generator (BMG) component in its library. S-box IP core ROM is implemented using Single port block memory. The S-box lookup table has been used to fill the ROM using the .coe file format provided during the initialization of the IP core ROM. The width is set to 8-bit to address the 256 values while the depth is set to 8-bit which represents the data filed in the ROM. The whole design is synthesized using Xilinx ISE Design Suite 14.7 software, while Modelism (version10.4a) is used for the simulation process. The proposed IP core ROM design has shown better memory utilization compared to non-IP core ROM design, which is more suitable for memory-intensive applications. The proposed design is suitable for implementation using the FPGA ROM design. Hardware complexity, frequency, memory utilization, and delay are presented in this paper.
视频监控系统等广播应用正在使用高清(HD)视频。高分辨率视频的使用大大增加了视频编码标准的数据量，如高效视频编码(HEVC)和高级视频编码(AVC)，这增加了存储、处理、加密和在不同通信信道上传输这些数据的挑战。视频压缩标准使用最先进的技术来更有效地压缩原始视频序列，这些技术要求较高的计算复杂度和内存利用率。随着HEVC和视频监控系统的兴起，出现了中间人攻击、未经授权泄露等安全隐患。这种风险可以通过加密HEVC的流量来减轻。目前使用最广泛的加密算法是高级加密标准AES (Advanced encryption Standard)。AES硬件实现中的大部分计算复杂性是由于S-box或子字节操作，这是因为它需要很多资源，而且它是一个非线性结构。提出的AES S-box ROM设计考虑了用于国土安全视频监控系统的最新HEVC。本文介绍了Xilinx支持的基于IP核生成器、ROM组件和使用函数实现AES S-box的VHDL高效ROM实现的不同设计。IP核生成器在其库中具有块内存生成器(BMG)组件。S-box IP核ROM采用单端口块存储器实现。S-box查找表已用于使用IP核心ROM初始化期间提供的.coe文件格式填充ROM。宽度设置为8位以解决256个值，而深度设置为8位，表示ROM中提交的数据。整个设计使用Xilinx ISE design Suite 14.7软件合成，而Modelism (version10.4a)用于模拟过程。与非IP核ROM设计相比，所提出的IP核ROM设计显示出更好的内存利用率，更适合内存密集型应用。所提出的设计适合使用FPGA ROM设计实现。本文给出了硬件复杂度、频率、内存利用率和延迟。
{"title":"Design and Simulation of AES S-Box Towards Data Security in Video Surveillance Using IP Core Generator","authors":"M. Hammad, W. Elmedany, Y. Ismail","doi":"10.1109/3ICT53449.2021.9581825","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581825","url":null,"abstract":"Broadcasting applications such as video surveillance systems are using High Definition (HD) videos. The use of high-resolution videos increases significantly the data volume of video coding standards such as High-Efficiency Video Coding (HEVC) and Advanced Video Coding (AVC), which increases the challenge for storing, processing, encrypting, and transmitting these data over different communication channels. Video compression standards use state-of-the-art techniques to compress raw video sequences more efficiently, such techniques require high computational complexity and memory utilization. With the emergent of using HEVC and video surveillance systems, many security risks arise such as man-in-the-middle attacks, and unauthorized disclosure. Such risks can be mitigated by encrypting the traffic of HEVC. The most widely used encryption algorithm is the Advanced Encryption Standard (AES). Most of the computational complexity in AES hardware-implemented is due to S-box or sub-byte operation and that because it needs many resources and it is a non-linear structure. The proposed AES S-box ROM design considers the latest HEVC used for homeland security video surveillance systems. This paper presents different designs for VHDL efficient ROM implementation of AES S-box using IP core generator, ROM components, and using Functions, which are all supported by Xilinx. IP core generator has Block Memory Generator (BMG) component in its library. S-box IP core ROM is implemented using Single port block memory. The S-box lookup table has been used to fill the ROM using the .coe file format provided during the initialization of the IP core ROM. The width is set to 8-bit to address the 256 values while the depth is set to 8-bit which represents the data filed in the ROM. The whole design is synthesized using Xilinx ISE Design Suite 14.7 software, while Modelism (version10.4a) is used for the simulation process. The proposed IP core ROM design has shown better memory utilization compared to non-IP core ROM design, which is more suitable for memory-intensive applications. The proposed design is suitable for implementation using the FPGA ROM design. Hardware complexity, frequency, memory utilization, and delay are presented in this paper.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"348 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134118733","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581890
Mohamed Almansoor, Y. Harrath
Automated Teller Machines (ATMs) often lack the required funds or become malfunctioned, which affects the customer experience and the reputation of the bank. Banks try to quickly resolve the problem through cash-in-transit companies that handle the operations of ATM refilling and maintenance. However, one of the largest dilemmas is to determine the order of visiting the ATMs as well as to balance the workload among the workforces during the day. In addition, there is a need to handle real-time and urgent requests during the day. This problem was modelled as a realtime multiple Travelling Salesmen Problem (mTSP). New constrains including traffic data, ATM priorities, and safety measurements were considered. We used big data analytics to extract useful features related to the customer withdrawal trends and active locations from real data provided by a Bahraini bank. To solve this NP-hard problem, we proposed a brute force method that generates optimal routes for limited-sized problem instances, up to 35 ATMs. Moreover, a greedy technique was proposed to solve large-sized instances considering one salesman. The obtained TSP route is then cut into clusters using unsupervised machine learning models. A modified version of k-Means has been applied with constrains to control the size of each cluster.
{"title":"Big Data Analytics, Greedy Approach, and Clustering Algorithms for Real-Time Cash Management of Automated Teller Machines","authors":"Mohamed Almansoor, Y. Harrath","doi":"10.1109/3ICT53449.2021.9581890","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581890","url":null,"abstract":"Automated Teller Machines (ATMs) often lack the required funds or become malfunctioned, which affects the customer experience and the reputation of the bank. Banks try to quickly resolve the problem through cash-in-transit companies that handle the operations of ATM refilling and maintenance. However, one of the largest dilemmas is to determine the order of visiting the ATMs as well as to balance the workload among the workforces during the day. In addition, there is a need to handle real-time and urgent requests during the day. This problem was modelled as a realtime multiple Travelling Salesmen Problem (mTSP). New constrains including traffic data, ATM priorities, and safety measurements were considered. We used big data analytics to extract useful features related to the customer withdrawal trends and active locations from real data provided by a Bahraini bank. To solve this NP-hard problem, we proposed a brute force method that generates optimal routes for limited-sized problem instances, up to 35 ATMs. Moreover, a greedy technique was proposed to solve large-sized instances considering one salesman. The obtained TSP route is then cut into clusters using unsupervised machine learning models. A modified version of k-Means has been applied with constrains to control the size of each cluster.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"551 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134292141","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581512
Hanan Sawalmeh, Manar Malayshi, S. Ahmad, Ahmed Awad
Through the COVID-19 pandemic, the number of clients using Virtual Private Network (VPN) has dramatically increased. Consequently, VPN vulnerabilities have become target points to be exploited by attackers. However, studies have been released to defend against such attacks with the purpose of securing VPN. Nevertheless, attacks with high sophistication still target VPNs to comprise the critical data being communicated. VPN servers use protocols to secure connections with clients. However, these protocols are still targeted specifically with Denial-of-Service (DoS) attacks. This paper analyzes and treats the vulnerability of key negotiation process in the main mode as well as aggressive mode of Internet Key Exchange (IKE) protocol in IP Security (IPsec) VPN. We demonstrate experiments of a DoS attack based on Open Shortest Path First (OSPF) protocol adjacent route spoofing. Thereafter, we propose a method to tackle those attacks through exploiting the Suricata as an Intrusion Detection System (IDS) in defending the VPN against DoS attacks.
{"title":"VPN Remote Access OSPF-based VPN Security Vulnerabilities and Counter Measurements","authors":"Hanan Sawalmeh, Manar Malayshi, S. Ahmad, Ahmed Awad","doi":"10.1109/3ICT53449.2021.9581512","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581512","url":null,"abstract":"Through the COVID-19 pandemic, the number of clients using Virtual Private Network (VPN) has dramatically increased. Consequently, VPN vulnerabilities have become target points to be exploited by attackers. However, studies have been released to defend against such attacks with the purpose of securing VPN. Nevertheless, attacks with high sophistication still target VPNs to comprise the critical data being communicated. VPN servers use protocols to secure connections with clients. However, these protocols are still targeted specifically with Denial-of-Service (DoS) attacks. This paper analyzes and treats the vulnerability of key negotiation process in the main mode as well as aggressive mode of Internet Key Exchange (IKE) protocol in IP Security (IPsec) VPN. We demonstrate experiments of a DoS attack based on Open Shortest Path First (OSPF) protocol adjacent route spoofing. Thereafter, we propose a method to tackle those attacks through exploiting the Suricata as an Intrusion Detection System (IDS) in defending the VPN against DoS attacks.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133131799","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581655
Gokul S R Nath, Jashaswimalya Acharjee, S. Deb
Due to the expeditious increase in the number of vehicles, there is an increase in the number of road casualties even in a highly sophisticated roadway. This depicts the natural limitation of a human in maintaining Traffic rules. To avoid any lethal circumstance assistive driving vehicles are introduced which consists of systems that guide drivers in different Traffic situations. Traffic sign recognition systems play a crucial role in assistive driving vehicles these systems have been based on characteristics of the sign and two-state detectors due to accuracy and real-time factors systems on these bases are not used for real-time application. In this paper, we present a system that can recognize Traffic signs and their distance from the vehicle in non-ideal lighting as well as in varying climatic conditions. Our work proceeds with the implementation of YOLOv3(deep convolutional network based on end-to-end detection algorithm) used for Traffic sign recognition and segmentation. Training of the model is done with GTSRB dataset and achieves an accuracy of about 98.5% for the recognition task in different real-time scenarios. Furthermore, an efficient Heuristic-based approach has been deployed for estimating the distance between the Traffic sign and the monocular camera(placed in the vehicle) at every instance.
{"title":"Traffic Sign Recognition and Distance Estimation with YOLOv3 model","authors":"Gokul S R Nath, Jashaswimalya Acharjee, S. Deb","doi":"10.1109/3ICT53449.2021.9581655","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581655","url":null,"abstract":"Due to the expeditious increase in the number of vehicles, there is an increase in the number of road casualties even in a highly sophisticated roadway. This depicts the natural limitation of a human in maintaining Traffic rules. To avoid any lethal circumstance assistive driving vehicles are introduced which consists of systems that guide drivers in different Traffic situations. Traffic sign recognition systems play a crucial role in assistive driving vehicles these systems have been based on characteristics of the sign and two-state detectors due to accuracy and real-time factors systems on these bases are not used for real-time application. In this paper, we present a system that can recognize Traffic signs and their distance from the vehicle in non-ideal lighting as well as in varying climatic conditions. Our work proceeds with the implementation of YOLOv3(deep convolutional network based on end-to-end detection algorithm) used for Traffic sign recognition and segmentation. Training of the model is done with GTSRB dataset and achieves an accuracy of about 98.5% for the recognition task in different real-time scenarios. Furthermore, an efficient Heuristic-based approach has been deployed for estimating the distance between the Traffic sign and the monocular camera(placed in the vehicle) at every instance.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584108","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581861
Jia Heng Ong, K. Chia
Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that is widely utilized in Brain Control Interface (BCI) applications. Feature extraction is crucial to remove unwanted signals and improve the accuracy of a machine learning algorithm in BCI. Despite principal component analysis (PCA) is a popular feature extraction method in near-infrared spectroscopy, PCA is rarely studied in fNIRS. Thus, this study compared fNIRS-based BCI models that used PCA and that used statistical features in BCI for four mental activities classification. First, PCA was applied to transform pre-processed fNIRS signals into few principal components that were the inputs of artificial neural network (ANN) to form PCs-ANN. Three different combinations of fNIRS signals were used to study the performance of PCs-ANN using 10-fold cross-validation. The best PCs-ANN was compared with ANN that used statistical-based features. The finding shows that PCs-ANN outperformed ANN that used statistical-based features in the BCI classification application.
{"title":"Principal Components-Artificial Neural Network in Functional Near-Infrared Spectroscopy (fNIRS) for Brain Control Interface","authors":"Jia Heng Ong, K. Chia","doi":"10.1109/3ICT53449.2021.9581861","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581861","url":null,"abstract":"Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that is widely utilized in Brain Control Interface (BCI) applications. Feature extraction is crucial to remove unwanted signals and improve the accuracy of a machine learning algorithm in BCI. Despite principal component analysis (PCA) is a popular feature extraction method in near-infrared spectroscopy, PCA is rarely studied in fNIRS. Thus, this study compared fNIRS-based BCI models that used PCA and that used statistical features in BCI for four mental activities classification. First, PCA was applied to transform pre-processed fNIRS signals into few principal components that were the inputs of artificial neural network (ANN) to form PCs-ANN. Three different combinations of fNIRS signals were used to study the performance of PCs-ANN using 10-fold cross-validation. The best PCs-ANN was compared with ANN that used statistical-based features. The finding shows that PCs-ANN outperformed ANN that used statistical-based features in the BCI classification application.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130894213","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581896
Muammer Catak, Sarah AlRasheedi, Norah AlAli, Ghadeer AlQallaf, Malak AlMeri, Bibi Ali
In this study, classical music has been investigated mainly based on pieces of well-known composers Mozart and Beethoven, then AI composer based on Markov chains and RNN has been proposed. AI is an efficient tool in science and technology for many specific applications including music field. The database has been collected based on 25 classical music sheets. The notes were separated in two groups where they are right hand and left hand. The database includes the notes and their frequencies and durations. The transition probability of each note were calculated. After the selection of the first note randomly, then the following notes were generated by means of the transition matrix. According to the results, both methods show an adequate level of quality considering the generation of notes by means of AI composer. The authors recommend to use Markov chains if a simple but efficient tool is appropriate considering the design criteria.
{"title":"Artificial Intelligence Composer","authors":"Muammer Catak, Sarah AlRasheedi, Norah AlAli, Ghadeer AlQallaf, Malak AlMeri, Bibi Ali","doi":"10.1109/3ICT53449.2021.9581896","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581896","url":null,"abstract":"In this study, classical music has been investigated mainly based on pieces of well-known composers Mozart and Beethoven, then AI composer based on Markov chains and RNN has been proposed. AI is an efficient tool in science and technology for many specific applications including music field. The database has been collected based on 25 classical music sheets. The notes were separated in two groups where they are right hand and left hand. The database includes the notes and their frequencies and durations. The transition probability of each note were calculated. After the selection of the first note randomly, then the following notes were generated by means of the transition matrix. According to the results, both methods show an adequate level of quality considering the generation of notes by means of AI composer. The authors recommend to use Markov chains if a simple but efficient tool is appropriate considering the design criteria.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556920","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9582144
Farheen Akram, Muhammad Abrar ul Haq, H. Malik, Nadeem Mahmood
While considering the challenges of online teaching in the current scenario of Covid-19 pandemic, the current study aimed to analyze the effect of student participation, teachers' skills and strategies, teacher training, teaching domain and teaching perception on effectiveness of online teaching. Primary data was used to test the proposed model of the study, data was collected through emails using convenience sampling from university teachers of Pakistan. Structural equation modelling technique was used through SmartPLS (v.3.3.3) to analyze the model of this research. Findings of the current study indicate that student participation, teachers' skills and strategies, teacher's training, teaching domain and teaching perception have a significant positive effect on effectiveness of online teaching. Hence, this study recommends that universities must have to focus on the individual teachers' need to make online teaching more effective.
{"title":"Effectiveness of Online Teaching during COVID-19","authors":"Farheen Akram, Muhammad Abrar ul Haq, H. Malik, Nadeem Mahmood","doi":"10.1109/3ICT53449.2021.9582144","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9582144","url":null,"abstract":"While considering the challenges of online teaching in the current scenario of Covid-19 pandemic, the current study aimed to analyze the effect of student participation, teachers' skills and strategies, teacher training, teaching domain and teaching perception on effectiveness of online teaching. Primary data was used to test the proposed model of the study, data was collected through emails using convenience sampling from university teachers of Pakistan. Structural equation modelling technique was used through SmartPLS (v.3.3.3) to analyze the model of this research. Findings of the current study indicate that student participation, teachers' skills and strategies, teacher's training, teaching domain and teaching perception have a significant positive effect on effectiveness of online teaching. Hence, this study recommends that universities must have to focus on the individual teachers' need to make online teaching more effective.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123619786","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581693
Anwaar Buzaboon, Hanan Alboflasa, W. Alnaser, S. Shatnawi, Khawla Albinali
To evaluate the ESHERSs and determine their efficiency to measure environmental sustainability, we tackle this problem as a classification assignment. This study benchmark three ESHERSs: UI GreenMetric, Times Higher Education Impact ranking, and STARS (Sustainability Tracking, Assessment Rating System) by AASHE (the association for the advancement of sustainability in higher education). Next, we recruited a group of experts who mapped the ESHERS indicators to the SDGs indicators. Then, we use NLP techniques to classify (map) the ESHERS indicators to the SDGs indicators. Since most of the ESHERS indicators and the SDGs indicators are in the form of short text, we use the query expansion technique to make the NLP techniques more effective. Each ESHERS indicator and its expanded text represents a document. And, each SDG indicator and its expanded text represents a document. We took the expanded text from the description of the ESHERS indicators and the description of SDG indicators, forming the corpus for our study. Then, we used document similarity to find the similarity between every pair of the corpus documents. We used different similarity measures to see the similarity between the forms. Then, we used a voting system to map the ESHERSs indicators to the SDGs indicators. The proposed system was able to automatically map the underlying ranking systems indicators to the UN SDGs with 99% accuracy compared to the experts mapping.
为了评估eshers并确定其衡量环境可持续性的效率，我们将此问题作为分类分配来处理。本研究对三个eshers进行了基准测试:UI GreenMetric, Times Higher Education Impact排名，以及AASHE(高等教育可持续发展促进协会)的STARS(可持续性跟踪评估评级系统)。接下来，我们招募了一组专家，将ESHERS指标与可持续发展目标指标相对应。然后，我们使用自然语言处理技术将ESHERS指标分类(映射)到可持续发展目标指标。由于ESHERS指标和SDGs指标大多采用短文本的形式，我们使用查询扩展技术使NLP技术更加有效。每个ESHERS指标及其扩展文本代表一份文件。而且，每个可持续发展目标指标及其扩展文本代表一份文件。我们从ESHERS指标的描述和SDG指标的描述中提取了扩展文本，形成了我们研究的语料库。然后，我们使用文档相似度来寻找每对语料库文档之间的相似度。我们用不同的相似度来衡量表单之间的相似度。然后，我们使用投票系统将eshers指标映射到可持续发展目标指标。与专家绘制的地图相比，拟议的系统能够自动将基础排名系统指标映射到联合国可持续发展目标，准确率达到99%。
{"title":"Automated Mapping of Environmental Higher Education Ranking Systems Indicators to SDGs Indicators using Natural Language Processing and Document Similarity","authors":"Anwaar Buzaboon, Hanan Alboflasa, W. Alnaser, S. Shatnawi, Khawla Albinali","doi":"10.1109/3ICT53449.2021.9581693","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581693","url":null,"abstract":"To evaluate the ESHERSs and determine their efficiency to measure environmental sustainability, we tackle this problem as a classification assignment. This study benchmark three ESHERSs: UI GreenMetric, Times Higher Education Impact ranking, and STARS (Sustainability Tracking, Assessment Rating System) by AASHE (the association for the advancement of sustainability in higher education). Next, we recruited a group of experts who mapped the ESHERS indicators to the SDGs indicators. Then, we use NLP techniques to classify (map) the ESHERS indicators to the SDGs indicators. Since most of the ESHERS indicators and the SDGs indicators are in the form of short text, we use the query expansion technique to make the NLP techniques more effective. Each ESHERS indicator and its expanded text represents a document. And, each SDG indicator and its expanded text represents a document. We took the expanded text from the description of the ESHERS indicators and the description of SDG indicators, forming the corpus for our study. Then, we used document similarity to find the similarity between every pair of the corpus documents. We used different similarity measures to see the similarity between the forms. Then, we used a voting system to map the ESHERSs indicators to the SDGs indicators. The proposed system was able to automatically map the underlying ranking systems indicators to the UN SDGs with 99% accuracy compared to the experts mapping.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124585624","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}
Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581449
A. Mittal, H. Bhandari
There are very few studies that directly address the effects of technology adoption intention on the success of women entrepreneurs specifically in the Indian context. The current study addresses the linkage between technology adoption intention and its antecedents on the success of a very niche and unexplored segment of women entrepreneurs i.e., architects. Using a modified form of the unified theory of acceptance and use of technology (UTAUT) model, this study uses structural equation modeling to test the proposed model. The model consists of the following constructs: Mental Access towards technology, Technical Skills, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Technology Adoption Intention, and Women Entrepreneurial Success. The data has been collected from 188 respondents using the chain referral sampling method. The benefit of this study can be seen as a better understanding of technology adoption which will help to reduce barriers that women architects face in technology adoption and devise strategies promoting entrepreneurial success for women architects working all over India.
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Pub Date : 2021-09-29DOI: 10.1109/3ICT53449.2021.9581789
M. S. Sharif, Mina Moein
The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods.
{"title":"An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification","authors":"M. S. Sharif, Mina Moein","doi":"10.1109/3ICT53449.2021.9581789","DOIUrl":"https://doi.org/10.1109/3ICT53449.2021.9581789","url":null,"abstract":"The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods.","PeriodicalId":133021,"journal":{"name":"2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115202144","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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