Pub Date : 2024-12-01DOI: 10.13052/jicts2245-800X.1243
Yongsen Cai;Danling Lin;Qing Lu
This paper assesses college students' mental health based on the symptom checklist 90 (SCL-90). In view of the assessment data processing and analysis, the performance of different machine learning algorithms, including random forest (RF), LightGBM3, extreme gradient boosting (XGBoost), in the classification of college students' mental health samples was compared. Moreover, the effect of different hyperparameter optimization methods (grid search, Bayesian optimization, and particle swarm optimization) was compared. The experiment on the SCL-90 assessment dataset found that the optimization effect of grid search was poor, and the highest F1 value and area under the curve (AUC) of the RF algorithm were 0.8914 and 0.9384, respectively, the highest F1 and AUC values of the XGBoost algorithm were 0.9166 and 0.9551, respectively. The LightGBM algorithm optimized by particle swarm optimization showed the best performance in the classification of mental health samples, with an F1 value of 0.9790 and an AUC of 0.9945. It also achieved optimal results when compared to machine learning algorithms such as naive Bayes and the support vector machines. The results prove the reliability and accuracy of the particle swarm optimization-improved Light-GBM algorithm in the analysis of college students' mental health assessment data. The algorithm can be applied in practice to provide an effective tool for the analysis of the mental health assessment data of college students.
{"title":"Comparison of Different Machine Learning Algorithms in the Mental Health Assessment of College Students","authors":"Yongsen Cai;Danling Lin;Qing Lu","doi":"10.13052/jicts2245-800X.1243","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1243","url":null,"abstract":"This paper assesses college students' mental health based on the symptom checklist 90 (SCL-90). In view of the assessment data processing and analysis, the performance of different machine learning algorithms, including random forest (RF), LightGBM3, extreme gradient boosting (XGBoost), in the classification of college students' mental health samples was compared. Moreover, the effect of different hyperparameter optimization methods (grid search, Bayesian optimization, and particle swarm optimization) was compared. The experiment on the SCL-90 assessment dataset found that the optimization effect of grid search was poor, and the highest F1 value and area under the curve (AUC) of the RF algorithm were 0.8914 and 0.9384, respectively, the highest F1 and AUC values of the XGBoost algorithm were 0.9166 and 0.9551, respectively. The LightGBM algorithm optimized by particle swarm optimization showed the best performance in the classification of mental health samples, with an F1 value of 0.9790 and an AUC of 0.9945. It also achieved optimal results when compared to machine learning algorithms such as naive Bayes and the support vector machines. The results prove the reliability and accuracy of the particle swarm optimization-improved Light-GBM algorithm in the analysis of college students' mental health assessment data. The algorithm can be applied in practice to provide an effective tool for the analysis of the mental health assessment data of college students.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"409-427"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.13052/jicts2245-800X.1244
Ying Zhang
Rapid translation of spoken English is conducive to international communication. This paper briefly introduces a convolutional neural network (CNN) algorithm for converting English speech to text and a long short-term memory (LSTM) algorithm for machine translation of English text. The two algorithms were combined for spoken English translation. Then, simulation experiments were performed by comparing the speech recognition performance among the CNN algorithm, the hidden Markov model, and the back-propagation neural network algorithm and comparing the machine translation performance with the LSTM algorithm and the recurrent neural network algorithm. Moreover, the performance of the spoken English translation algorithms combining different recognition algorithms was compared. The results showed that the CNN speech recognition algorithm, the LSTM machine translation algorithm and the combined spoken English translation algorithm had the best performance and sufficient anti-noise ability. In conclusion, utilizing a CNN for converting English speech to texts and LSTM for machine translation of the converted English text can effectively enhance the performance of translating spoken English.
{"title":"A Study on the Translation of Spoken English from Speech to Text","authors":"Ying Zhang","doi":"10.13052/jicts2245-800X.1244","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1244","url":null,"abstract":"Rapid translation of spoken English is conducive to international communication. This paper briefly introduces a convolutional neural network (CNN) algorithm for converting English speech to text and a long short-term memory (LSTM) algorithm for machine translation of English text. The two algorithms were combined for spoken English translation. Then, simulation experiments were performed by comparing the speech recognition performance among the CNN algorithm, the hidden Markov model, and the back-propagation neural network algorithm and comparing the machine translation performance with the LSTM algorithm and the recurrent neural network algorithm. Moreover, the performance of the spoken English translation algorithms combining different recognition algorithms was compared. The results showed that the CNN speech recognition algorithm, the LSTM machine translation algorithm and the combined spoken English translation algorithm had the best performance and sufficient anti-noise ability. In conclusion, utilizing a CNN for converting English speech to texts and LSTM for machine translation of the converted English text can effectively enhance the performance of translating spoken English.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"429-441"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.13052/jicts2245-800X.1245
Guo Li;MingHua Wang
As cyberattacks become more frequent and sophisticated, network intrusion detection systems (IDS) play a critical role in safeguarding networks. However, traditional IDS models face challenges in detecting new, unseen attacks and typically require large volumes of labeled data for effective training. To address these issues, we propose a novel intrusion detection model based on meta-learning, integrating depthwise separable convolution (DSC). This model leverages few-shot learning to detect rare and emerging attack types with minimal labeled data. By using meta-learning, our model can rapidly adapt to new tasks, offering greater flexibility and scalability in various network scenarios. Experimental results on the CIC-DDoS2019 and CIC-IDS2017 datasets demonstrate that our model achieves competitive accuracy compared to state-of-the-art methods, even with fewer training samples. It also shows superior performance in terms of both detection accuracy and training efficiency, while being more resource-efficient, making it suitable for deployment in resource-constrained environments. In conclusion, our model offers a promising solution for network intrusion detection, enhancing the ability to detect new and emerging threats while ensuring computational efficiency for real-world applications.
{"title":"A Meta-Learning Approach for Few-Shot Network Intrusion Detection Using Depthwise Separable Convolution","authors":"Guo Li;MingHua Wang","doi":"10.13052/jicts2245-800X.1245","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1245","url":null,"abstract":"As cyberattacks become more frequent and sophisticated, network intrusion detection systems (IDS) play a critical role in safeguarding networks. However, traditional IDS models face challenges in detecting new, unseen attacks and typically require large volumes of labeled data for effective training. To address these issues, we propose a novel intrusion detection model based on meta-learning, integrating depthwise separable convolution (DSC). This model leverages few-shot learning to detect rare and emerging attack types with minimal labeled data. By using meta-learning, our model can rapidly adapt to new tasks, offering greater flexibility and scalability in various network scenarios. Experimental results on the CIC-DDoS2019 and CIC-IDS2017 datasets demonstrate that our model achieves competitive accuracy compared to state-of-the-art methods, even with fewer training samples. It also shows superior performance in terms of both detection accuracy and training efficiency, while being more resource-efficient, making it suitable for deployment in resource-constrained environments. In conclusion, our model offers a promising solution for network intrusion detection, enhancing the ability to detect new and emerging threats while ensuring computational efficiency for real-world applications.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"443-470"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.13052/jicts2245-800X.1242
Md Sameeruddin Khan;Thomas M. Chen;Mithileysh Sathiyanarayanan;Mohammed Mujeerulla;S. Pravinth Raja
The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.
{"title":"Application of Lenstra-Lenstra-Lovasz on Elliptic Curve Cryptosystem Using IOT Sensor Nodes","authors":"Md Sameeruddin Khan;Thomas M. Chen;Mithileysh Sathiyanarayanan;Mohammed Mujeerulla;S. Pravinth Raja","doi":"10.13052/jicts2245-800X.1242","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1242","url":null,"abstract":"The Internet of Things (IoT) model is presented in this paper with multi-layer security based on the Lenstra-Lenstra-Lovasz (LLL) algorithm. End nodes for the Internet of Things include inexpensive gadgets like the Raspberry Pi and Arduino boards. It is not practical to run rigorous algorithms on them, as opposed to computer systems. Therefore, a cryptography procedure is required that could function on this IOT equipment. Bitcoins and Ethereum are examples of cryptocurrency and Ripple employs techniques such as elliptic curve digital signature, Elliptic-Curve Diffie-Hellman (ECDH), and algorithm to sign any cryptocurrency on SECP256k1 elliptic curves transactions. By using Lenstra-Lenstra-Lovasz on a real-world Bitcoin blockchain and applying it to multiple dimensions, such as nonce leakage and weak nonces across several elliptic curves with different bit sizes on a Raspberry Pi, we can demonstrate the security of elliptic curve cryptosystems. Public key encryption techniques are seriously threatened by the development of quantum computing. Therefore, employing lattice encryption with Nth Degree Truncated Polynomial Ring Units (NTRU-NTH) on the Bitcoin blockchain will increase the resistance of Bitcoin blocks to quantum computing assaults. The execution time taken on SECP256k1 is 131.7 Milli seconds comparatively faster than NIST-224P and NIST-384P.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"381-407"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.13052/jicts2245-800X.1241
Haixiang He;Shiqi Ma
With the continuous development of social networks, negative social network public opinion appears frequently, which is particularly important for its safety monitoring and early warning. Taking Sina microblog as an example, this paper crawled texts from the platform, used BERT to generate word vectors, combined the bidirectional gated recurrent unit (BiGRU) and attention mechanism to design an emotion tendency classification method, and realized the classification of positive and negative emotion texts. Then, TCN was used to predict the negative emotion text to realize public opinion safety monitoring and early warning. It was found that BERT had the best performance. Compared with other deep learning methods, BERT-BiGRUA had a P value of 0.9431, an R value of 0.9012, and an F1 value of 0.9217 in the classification of emotion tendency, which were all the best. In the prediction of negative emotion text, TCN obtained a smaller mean square error and a higher $R^{2}$ than long short-term memory and other methods, showing a better prediction effect. The results verify the usability of the approach designed in this paper for practical safety monitoring and early warning of public opinion.
{"title":"Security Monitoring and Early Warning of Negative Public Opinion on Social Networks Under Deep Learning","authors":"Haixiang He;Shiqi Ma","doi":"10.13052/jicts2245-800X.1241","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1241","url":null,"abstract":"With the continuous development of social networks, negative social network public opinion appears frequently, which is particularly important for its safety monitoring and early warning. Taking Sina microblog as an example, this paper crawled texts from the platform, used BERT to generate word vectors, combined the bidirectional gated recurrent unit (BiGRU) and attention mechanism to design an emotion tendency classification method, and realized the classification of positive and negative emotion texts. Then, TCN was used to predict the negative emotion text to realize public opinion safety monitoring and early warning. It was found that BERT had the best performance. Compared with other deep learning methods, BERT-BiGRUA had a P value of 0.9431, an R value of 0.9012, and an F1 value of 0.9217 in the classification of emotion tendency, which were all the best. In the prediction of negative emotion text, TCN obtained a smaller mean square error and a higher <tex>$R^{2}$</tex> than long short-term memory and other methods, showing a better prediction effect. The results verify the usability of the approach designed in this paper for practical safety monitoring and early warning of public opinion.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 4","pages":"365-380"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10916566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.
{"title":"Cross-Layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint","authors":"Wei Ao;Jian Wei;Yindong Li;Xiaolong Zhang;Bin Yu;Kaiwen Hou","doi":"10.13052/jicts2245-800X.1234","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1234","url":null,"abstract":"In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 3","pages":"311-336"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.13052/jicts2245-800X.1231
Fatima Ez Zahra El Arbaoui;Kaoutar El Hari;Rajaa Saidi
Autism is a neurodevelopmental condition characterized by difficulties with social skills and communication. Autistic individuals require different types of assistance to cope with these challenges. The Picture Exchange Communication System (PECS) is a commonly used program for teaching nonverbal and symbolic communication skills, particularly for children with limited or no communication abilities. However, despite the development of various technology-based PECS systems, there is a lack of features that can simplify their use by children with autism. In our work, we are developing a design of a personalized and context-aware PECS system. Our system not only presents pictures as content but also adapts and enhances the content using contextual information.
{"title":"Enhancing Communication for People with Autism Through the Design of a Context-Aware Mobile Application for PECS","authors":"Fatima Ez Zahra El Arbaoui;Kaoutar El Hari;Rajaa Saidi","doi":"10.13052/jicts2245-800X.1231","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1231","url":null,"abstract":"Autism is a neurodevelopmental condition characterized by difficulties with social skills and communication. Autistic individuals require different types of assistance to cope with these challenges. The Picture Exchange Communication System (PECS) is a commonly used program for teaching nonverbal and symbolic communication skills, particularly for children with limited or no communication abilities. However, despite the development of various technology-based PECS systems, there is a lack of features that can simplify their use by children with autism. In our work, we are developing a design of a personalized and context-aware PECS system. Our system not only presents pictures as content but also adapts and enhances the content using contextual information.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 3","pages":"243-270"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770619","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.13052/jicts2245-800X.1232
Sha Chen
Accurate and rapid translation is conducive to the cultural communication of different languages. This paper briefly introduces the long short-term memory (LSTM) algorithm. To enhance the performance of the LSTM algorithm, semantic features were introduced, and semantic similarity was used to screen the translations that are more in line with the semantics of the source text. Then, simulation experiments were conducted. The experiments first examined the effects of the quantity of hidden layer nodes and the type of activation function in LSTM on the translation performance. Then, the LSTM algorithm was compared with the recurrent neural network (RNN) and traditional LSTM algorithms. The proposed translation algorithm showed the best performance when there were 512 hidden layer nodes and the activation function was sigmoid, it performed better than the other two translation algorithms, and the obtained result was consistent with the semantic meaning of the source text and smooth.
{"title":"An Intelligent Algorithm for Semantic Feature Analysis and Translation of English Texts","authors":"Sha Chen","doi":"10.13052/jicts2245-800X.1232","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1232","url":null,"abstract":"Accurate and rapid translation is conducive to the cultural communication of different languages. This paper briefly introduces the long short-term memory (LSTM) algorithm. To enhance the performance of the LSTM algorithm, semantic features were introduced, and semantic similarity was used to screen the translations that are more in line with the semantics of the source text. Then, simulation experiments were conducted. The experiments first examined the effects of the quantity of hidden layer nodes and the type of activation function in LSTM on the translation performance. Then, the LSTM algorithm was compared with the recurrent neural network (RNN) and traditional LSTM algorithms. The proposed translation algorithm showed the best performance when there were 512 hidden layer nodes and the activation function was sigmoid, it performed better than the other two translation algorithms, and the obtained result was consistent with the semantic meaning of the source text and smooth.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 3","pages":"271-282"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770620","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.13052/jicts2245-800X.1235
Asmawa;Abdul Hakim;Hermawan;Ainul Hayat
This article discusses the process of digitization in the implementation of public policy within developing countries, its critical success factors, challenges, and lessons learned from case studies. Public administration can improve efficiencies, transparency, and effectiveness through the infusion of digital technologies into processes implicated in the making, execution, and evaluation of public policies. Key success factors identified include robust digital infrastructure, clear strategic vision and leadership, effective stakeholder engagement, capacity building, and a supportive regulatory framework. In light of these potential benefits, developing countries experience enormous challenges such as inadequate infrastructure, political instability, socio-political resistance, and gaps in digital literacy. Through a review of empirical case studies from countries such as Estonia, South Korea, China, Bangladesh, and Rwanda, the article identifies key lessons that can inform the digital strategies of other developing nations. The discussion addresses the challenges of sustaining digital infrastructure, including ongoing maintenance costs, the need for continuous technology updates, and the risks of obsolescence. Furthermore, the article explores the significance of capacity building, public-private partnerships, and evolving regulatory frameworks in ensuring the long-term viability of digital policies. The future directions proposed underscore the necessity of adopting adaptable, resilient, and well-governed digital infrastructures to fully realize the benefits of digital public policy in the context of developing countries.
{"title":"Transforming Public Policy in Developing Countries: A Comprehensive Review of Digital Implementation","authors":"Asmawa;Abdul Hakim;Hermawan;Ainul Hayat","doi":"10.13052/jicts2245-800X.1235","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1235","url":null,"abstract":"This article discusses the process of digitization in the implementation of public policy within developing countries, its critical success factors, challenges, and lessons learned from case studies. Public administration can improve efficiencies, transparency, and effectiveness through the infusion of digital technologies into processes implicated in the making, execution, and evaluation of public policies. Key success factors identified include robust digital infrastructure, clear strategic vision and leadership, effective stakeholder engagement, capacity building, and a supportive regulatory framework. In light of these potential benefits, developing countries experience enormous challenges such as inadequate infrastructure, political instability, socio-political resistance, and gaps in digital literacy. Through a review of empirical case studies from countries such as Estonia, South Korea, China, Bangladesh, and Rwanda, the article identifies key lessons that can inform the digital strategies of other developing nations. The discussion addresses the challenges of sustaining digital infrastructure, including ongoing maintenance costs, the need for continuous technology updates, and the risks of obsolescence. Furthermore, the article explores the significance of capacity building, public-private partnerships, and evolving regulatory frameworks in ensuring the long-term viability of digital policies. The future directions proposed underscore the necessity of adopting adaptable, resilient, and well-governed digital infrastructures to fully realize the benefits of digital public policy in the context of developing countries.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 3","pages":"337-364"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01DOI: 10.13052/jicts2245-800X.1233
Lihong Ge;Pengyu Zhang;Kaiwen Hou;Wei Ao;Lin Zhou;Yindong Li
Large-scale machine communication scenarios are faced with the characteristics of a large number of terminal devices, small communication packets and frequent transmissions. Traditional multiple access technology can't fully apply to these characteristics, so it needs to be improved and designed. In order to further improve the effect of distributed access strategy formulation of large-scale multi-source terminal for 5G, this paper proposes a distributed access technology for large-scale multi-source terminals for 5G. For distributed access scenarios, antenna units in the distributed system are scattered in the system, and each antenna unit keeps communication with the surrounding terminal equipment, which can expand the number of terminal devices randomly accessed in the same time slot. Then, taking advantage of this inherent advantage and the advantage of time-shifted pilot, this paper proposes a distributed random access scheme based on time-shifted pilot. Finally, through analysis and simulation, this paper proves that this scheme can effectively reduce the pilot collision probability of each antenna element and the average access delay in the system, which provides a technical reference for the subsequent distributed access of large-scale multi-source terminals.
{"title":"Research on Distributed Access Simulation of Large-Scale Multi-Source Terminals for 5G","authors":"Lihong Ge;Pengyu Zhang;Kaiwen Hou;Wei Ao;Lin Zhou;Yindong Li","doi":"10.13052/jicts2245-800X.1233","DOIUrl":"https://doi.org/10.13052/jicts2245-800X.1233","url":null,"abstract":"Large-scale machine communication scenarios are faced with the characteristics of a large number of terminal devices, small communication packets and frequent transmissions. Traditional multiple access technology can't fully apply to these characteristics, so it needs to be improved and designed. In order to further improve the effect of distributed access strategy formulation of large-scale multi-source terminal for 5G, this paper proposes a distributed access technology for large-scale multi-source terminals for 5G. For distributed access scenarios, antenna units in the distributed system are scattered in the system, and each antenna unit keeps communication with the surrounding terminal equipment, which can expand the number of terminal devices randomly accessed in the same time slot. Then, taking advantage of this inherent advantage and the advantage of time-shifted pilot, this paper proposes a distributed random access scheme based on time-shifted pilot. Finally, through analysis and simulation, this paper proves that this scheme can effectively reduce the pilot collision probability of each antenna element and the average access delay in the system, which provides a technical reference for the subsequent distributed access of large-scale multi-source terminals.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"12 3","pages":"283-310"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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