With the rapid development of computer network technology, the network structure has become increasingly complex, and the number and types of network devices have also shown explosive growth. In order to better understand, manage and optimize the network, visualization of network topology has become an important research field. However, the current visualization framework and tools have some problems in the practical process, such as complicated application process, single display effect and insufficient interaction ability, which can not meet the actual needs of users. In this regard, this paper will take network topology visualization as the research object, and build a network topology visualization system based on HTML5 through Web technology to realize the innovation of network topology management. Practice has proved that the whole system adopts B/S architecture design, and the front end is mainly based on HTML5, CSS and JavaScript, combined with BootStrap layout framework to complete the design and construction. At the same time, the Canvas is used to complete the drawing and rendering of visual graphics, which greatly improves the efficiency of network monitoring, conforms to the expected goal of network management system in displaying topological functions, and has certain practical application value.
{"title":"Research on network topology visualization under HTML5 technology","authors":"Jian Liu","doi":"10.1117/12.3032035","DOIUrl":"https://doi.org/10.1117/12.3032035","url":null,"abstract":"With the rapid development of computer network technology, the network structure has become increasingly complex, and the number and types of network devices have also shown explosive growth. In order to better understand, manage and optimize the network, visualization of network topology has become an important research field. However, the current visualization framework and tools have some problems in the practical process, such as complicated application process, single display effect and insufficient interaction ability, which can not meet the actual needs of users. In this regard, this paper will take network topology visualization as the research object, and build a network topology visualization system based on HTML5 through Web technology to realize the innovation of network topology management. Practice has proved that the whole system adopts B/S architecture design, and the front end is mainly based on HTML5, CSS and JavaScript, combined with BootStrap layout framework to complete the design and construction. At the same time, the Canvas is used to complete the drawing and rendering of visual graphics, which greatly improves the efficiency of network monitoring, conforms to the expected goal of network management system in displaying topological functions, and has certain practical application value.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376696","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}
Ranxin Gao, Sen Jing, Min Li, Yue Sun, Guanlin Si, Yue Zhang
The data center has complete equipment, professional management, and a comprehensive application service platform. A good data center security protection system can help prevent dangers and violations that have a serious impact on the business. However, the level of security protection in data centers varies greatly, and the effectiveness of protection lacks means of testing and verification. This article studies the health evaluation method of data center security protection system, proposes a security protection measure evaluation mechanism. The evaluation method provides strong technical support for the effectiveness verification and promotion of existing data center security protection measures.
{"title":"Research on health evaluation method of data center security protection system","authors":"Ranxin Gao, Sen Jing, Min Li, Yue Sun, Guanlin Si, Yue Zhang","doi":"10.1117/12.3032059","DOIUrl":"https://doi.org/10.1117/12.3032059","url":null,"abstract":"The data center has complete equipment, professional management, and a comprehensive application service platform. A good data center security protection system can help prevent dangers and violations that have a serious impact on the business. However, the level of security protection in data centers varies greatly, and the effectiveness of protection lacks means of testing and verification. This article studies the health evaluation method of data center security protection system, proposes a security protection measure evaluation mechanism. The evaluation method provides strong technical support for the effectiveness verification and promotion of existing data center security protection measures.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380414","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}
Static analysis tools are widely used to ensure code quality and security, especially in large software projects. Recently, the advent of Large Language Models (LLM), such as the Generative Pre-trained Transformer (GPT), seems to present a strong ability to handle tasks about static code analysis. This paper aims to answer the question, can large language model replace static analysis tools? We present an extensive evaluation of ChatGPT’s capabilities in identifying and analyzing issues detectable by three well-known Java static analysis tools: PMD, SpotBugs, and SonarQube. Through a series of experiments, we assess the performance of two versions of GPT, GPT-3.5 and GPT-4, across various categories of code issues. We conduct a detailed analysis of the experiment results and discuss the limitation of using ChatGPT to perform as a static analysis tool. The findings during our research suggest that while GPT, especially GPT-4 performs outstanding marks on the dataset we chose, it is improper to fully replace the static code analyzers at the time. Working as the supplementary of static code analyzers can be a nice way to enhance the code quality ensuring projects.
{"title":"Can large language model replace static analysis tools","authors":"Han Cui","doi":"10.1117/12.3031920","DOIUrl":"https://doi.org/10.1117/12.3031920","url":null,"abstract":"Static analysis tools are widely used to ensure code quality and security, especially in large software projects. Recently, the advent of Large Language Models (LLM), such as the Generative Pre-trained Transformer (GPT), seems to present a strong ability to handle tasks about static code analysis. This paper aims to answer the question, can large language model replace static analysis tools? We present an extensive evaluation of ChatGPT’s capabilities in identifying and analyzing issues detectable by three well-known Java static analysis tools: PMD, SpotBugs, and SonarQube. Through a series of experiments, we assess the performance of two versions of GPT, GPT-3.5 and GPT-4, across various categories of code issues. We conduct a detailed analysis of the experiment results and discuss the limitation of using ChatGPT to perform as a static analysis tool. The findings during our research suggest that while GPT, especially GPT-4 performs outstanding marks on the dataset we chose, it is improper to fully replace the static code analyzers at the time. Working as the supplementary of static code analyzers can be a nice way to enhance the code quality ensuring projects.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377183","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}
Syslogs play a crucial role in maintenance and troubleshooting, as they document the operational status and key events within computer systems. However, traditional methods of anomaly detection in Syslog face challenges due to the sheer volume and diversity of logs, making cross-system anomaly detection difficult. To address those challenges, this paper introduces CATL, a pioneering Contrast Adaptive Transfer Learning with Bidirectional Long Short-Term Memory (BiLSTM), which can effectively extract contextual features of the log sequence from both directions. CATL overcomes the difficulties arising from massive, less-correlated logs between different systems by leveraging a combination of labeled data from source and target systems and optimizing the Contrastive Domain Discrepancy (CDD) metric. This allows CATL to accurately model discrepancies within and across log classes, minimizing intra-class domain discrepancy while maximizing inter-class domain discrepancy in log sequence features from different domains to match existing anomaly detection decision boundaries better. Our empirical studies, conducted on prominent benchmarks including HDFS, Hadoop, Thunderbird, BGL, and Spirit, demonstrate that CATL addresses the syntactic diversity of log systems and outperforms existing methods in cross-system anomaly detection.
{"title":"CATL: contrast adaptive transfer learning for cross-system log anomaly detection","authors":"Junwei Zhou, Yafei Li, Xiangtian Yu, Yuxuan Zhao","doi":"10.1117/12.3031960","DOIUrl":"https://doi.org/10.1117/12.3031960","url":null,"abstract":"Syslogs play a crucial role in maintenance and troubleshooting, as they document the operational status and key events within computer systems. However, traditional methods of anomaly detection in Syslog face challenges due to the sheer volume and diversity of logs, making cross-system anomaly detection difficult. To address those challenges, this paper introduces CATL, a pioneering Contrast Adaptive Transfer Learning with Bidirectional Long Short-Term Memory (BiLSTM), which can effectively extract contextual features of the log sequence from both directions. CATL overcomes the difficulties arising from massive, less-correlated logs between different systems by leveraging a combination of labeled data from source and target systems and optimizing the Contrastive Domain Discrepancy (CDD) metric. This allows CATL to accurately model discrepancies within and across log classes, minimizing intra-class domain discrepancy while maximizing inter-class domain discrepancy in log sequence features from different domains to match existing anomaly detection decision boundaries better. Our empirical studies, conducted on prominent benchmarks including HDFS, Hadoop, Thunderbird, BGL, and Spirit, demonstrate that CATL addresses the syntactic diversity of log systems and outperforms existing methods in cross-system anomaly detection.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376675","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}
Qiu Zhen, Fan Xu, Wenpu Li, Fan Yang, Hongyu Wu, Huanhuan Li
With the rapid development and application of deep learning, its dataset size and network model are becoming increasingly large, and distributed model training is becoming increasingly popular. This article proposes a distributed heterogeneous task scheduling and resource allocation algorithm based on deep learning to address issues such as heterogeneity in resource usage, inability to predict task convergence time, communication time bottlenecks, and resource waste caused by static resource allocation during distributed collaborative training. This algorithm achieves dynamic scheduling and resource allocation of heterogeneous tasks and reduces task completion time in clusters. The experiment shows that the algorithm proposed in this article has significant improvements in both task completion time and system duration.
{"title":"Research on distributed heterogeneous task scheduling and resource allocation algorithms based on deep learning","authors":"Qiu Zhen, Fan Xu, Wenpu Li, Fan Yang, Hongyu Wu, Huanhuan Li","doi":"10.1117/12.3032073","DOIUrl":"https://doi.org/10.1117/12.3032073","url":null,"abstract":"With the rapid development and application of deep learning, its dataset size and network model are becoming increasingly large, and distributed model training is becoming increasingly popular. This article proposes a distributed heterogeneous task scheduling and resource allocation algorithm based on deep learning to address issues such as heterogeneity in resource usage, inability to predict task convergence time, communication time bottlenecks, and resource waste caused by static resource allocation during distributed collaborative training. This algorithm achieves dynamic scheduling and resource allocation of heterogeneous tasks and reduces task completion time in clusters. The experiment shows that the algorithm proposed in this article has significant improvements in both task completion time and system duration.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376422","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}
Knowledge graphs are widely used in the field of natural language processing applications. In order to study how to use the structural and attribute information of entities for cross language entity alignment, we have successively borrowed the high-speed gate mechanism of the HGCN model and the relationship aware neighborhood matching model of the RNM model. Firstly, using Graph Convolutional Neural Network (GCN) for knowledge graph embedding learning, and then introducing the method of attribute information and highway gates mechanism to jointly embed the structure and attributes for learning. In entity alignment, relationship aware neighborhood matching is used to improve alignment performance. Therefore, this article proposes a research method for entity alignment based on graph convolutional neural networks and attribute information. Experiments were conducted on the publicly available dataset DBP15k, and from the results, it can be seen that Hits@1 The indicators reached 85.24%, 87.26%, and 94.76% respectively, achieving better experimental results.
{"title":"Cross-language entity alignment based on graph convolution neural network and attribute information","authors":"Xiaozhan Hu, Yuan Sun","doi":"10.1117/12.3031901","DOIUrl":"https://doi.org/10.1117/12.3031901","url":null,"abstract":"Knowledge graphs are widely used in the field of natural language processing applications. In order to study how to use the structural and attribute information of entities for cross language entity alignment, we have successively borrowed the high-speed gate mechanism of the HGCN model and the relationship aware neighborhood matching model of the RNM model. Firstly, using Graph Convolutional Neural Network (GCN) for knowledge graph embedding learning, and then introducing the method of attribute information and highway gates mechanism to jointly embed the structure and attributes for learning. In entity alignment, relationship aware neighborhood matching is used to improve alignment performance. Therefore, this article proposes a research method for entity alignment based on graph convolutional neural networks and attribute information. Experiments were conducted on the publicly available dataset DBP15k, and from the results, it can be seen that Hits@1 The indicators reached 85.24%, 87.26%, and 94.76% respectively, achieving better experimental results.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376529","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}
In order to study secure federated learning for resource-constrained devices such as drones to protect user privacy and data security in drone networks, a blockchain-based secure federated learning scheme for drones is proposed. Currently, researchers focus on transferring models for federated learning after local training using drones, but in reality, drones will be limited in accomplishing local training due to their own resource and arithmetic issues. In this paper, the scheme offloads the training task of the UAV to the local server, and the UAV is only responsible for performing model aggregation and delivery. At the same time, a new consensus algorithm PoE (Proof-of-Energy) is proposed to model the energy and evaluate the arithmetic power of drones, which assigns roles to each drone node within the blockchain network and ensures that the drones effectively participate in the federated learning process. Due to the open and transparent nature of the blockchain, ring signatures are used to replace the traditional signatures in order to protect the private information such as the behavior and identity of each node and the content of block transactions. The experimental results show that the proposed model can ensure that UAVs effectively participate in federated learning. In addition, when there is a poisoning sample to disrupt the training process, the accuracy of the global model can be effectively ensured compared to the traditional scheme.
{"title":"Blockchain-based federal learning program for drone safety","authors":"Jingyuan Jing, Yanbo Yang, Mingchao Li, Baoshan Li, Jiawei Zhang, Jianfeng Ma","doi":"10.1117/12.3031895","DOIUrl":"https://doi.org/10.1117/12.3031895","url":null,"abstract":"In order to study secure federated learning for resource-constrained devices such as drones to protect user privacy and data security in drone networks, a blockchain-based secure federated learning scheme for drones is proposed. Currently, researchers focus on transferring models for federated learning after local training using drones, but in reality, drones will be limited in accomplishing local training due to their own resource and arithmetic issues. In this paper, the scheme offloads the training task of the UAV to the local server, and the UAV is only responsible for performing model aggregation and delivery. At the same time, a new consensus algorithm PoE (Proof-of-Energy) is proposed to model the energy and evaluate the arithmetic power of drones, which assigns roles to each drone node within the blockchain network and ensures that the drones effectively participate in the federated learning process. Due to the open and transparent nature of the blockchain, ring signatures are used to replace the traditional signatures in order to protect the private information such as the behavior and identity of each node and the content of block transactions. The experimental results show that the proposed model can ensure that UAVs effectively participate in federated learning. In addition, when there is a poisoning sample to disrupt the training process, the accuracy of the global model can be effectively ensured compared to the traditional scheme.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377786","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}
The ever-changing cyber threat landscape, characterized by increasingly sophisticated attacks and evolving malware, demands robust and layered security strategies. Traditional firewalls, while effective as a first line of defense, struggle to keep pace with the complexity of modern threats. This necessitates the adoption of Next-Generation Firewalls (NGFWs) as a vital component of a comprehensive defense-in-depth approach. This research delves into the limitations of traditional firewalls and explores the key capabilities offered by NGFWs. It examines how features like application awareness, deep packet inspection, and SSL/TLS decryption contribute to a more secure and resilient network infrastructure. Through in-depth analysis and relevant case studies, this paper aims to demonstrate the critical role of NGFWs in today's dynamic cybersecurity environment. It emphasizes the critical need for organizations to embrace a defense-in-depth approach, with NGFWs serving as the cornerstone for a robust and resilient network infrastructure. By deploying NGFWs and adopting a comprehensive layered security strategy, organizations can significantly improve their cybersecurity posture and effectively protect themselves against the ever-present and evolving threat actors.
{"title":"Synergizing next-generation firewalls and defense-in-depth strategies in a dynamic cybersecurity landscape","authors":"Sun Lei","doi":"10.1117/12.3031957","DOIUrl":"https://doi.org/10.1117/12.3031957","url":null,"abstract":"The ever-changing cyber threat landscape, characterized by increasingly sophisticated attacks and evolving malware, demands robust and layered security strategies. Traditional firewalls, while effective as a first line of defense, struggle to keep pace with the complexity of modern threats. This necessitates the adoption of Next-Generation Firewalls (NGFWs) as a vital component of a comprehensive defense-in-depth approach. This research delves into the limitations of traditional firewalls and explores the key capabilities offered by NGFWs. It examines how features like application awareness, deep packet inspection, and SSL/TLS decryption contribute to a more secure and resilient network infrastructure. Through in-depth analysis and relevant case studies, this paper aims to demonstrate the critical role of NGFWs in today's dynamic cybersecurity environment. It emphasizes the critical need for organizations to embrace a defense-in-depth approach, with NGFWs serving as the cornerstone for a robust and resilient network infrastructure. By deploying NGFWs and adopting a comprehensive layered security strategy, organizations can significantly improve their cybersecurity posture and effectively protect themselves against the ever-present and evolving threat actors.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141377491","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}
The increasingly severe situation of information security puts forward higher requirements for wireless communication transmission. Minimum shift keying (MSK) offers outstanding advantages, including the constant signal envelope, the narrow main lobe of the power spectrum, and strong channel adaptability. It can be used to increase the modulation map diversity and achieve the purpose of waveform encryption of the transmit signal. As a result, this paper proposes the MSK modulator structure hopping (MSKMSH) method for physical layer secure transmission. Firstly, a set of MSK modulator structures with a space size of 16 is constructed according to signal waveform characteristics. Secondly, the pattern set generated by the structure hopping law is analyzed to ensure traceless conversion between different MSK modulators. Finally, a data-assisted synchronization scheme utilizing unique characters is designed to synchronize the legitimate destination and the source with structure hopping. Through simulation experiments and theoretical analysis, it is demonstrated that the MSKMSH method can realize reliable and secure transmission of radio signals.
{"title":"Physical layer secure transmission method based on MSK modulator structure hopping","authors":"Xiaoyu Wang, Yawei Yu, Long Yu, Yuanyuan Gao","doi":"10.1117/12.3032071","DOIUrl":"https://doi.org/10.1117/12.3032071","url":null,"abstract":"The increasingly severe situation of information security puts forward higher requirements for wireless communication transmission. Minimum shift keying (MSK) offers outstanding advantages, including the constant signal envelope, the narrow main lobe of the power spectrum, and strong channel adaptability. It can be used to increase the modulation map diversity and achieve the purpose of waveform encryption of the transmit signal. As a result, this paper proposes the MSK modulator structure hopping (MSKMSH) method for physical layer secure transmission. Firstly, a set of MSK modulator structures with a space size of 16 is constructed according to signal waveform characteristics. Secondly, the pattern set generated by the structure hopping law is analyzed to ensure traceless conversion between different MSK modulators. Finally, a data-assisted synchronization scheme utilizing unique characters is designed to synchronize the legitimate destination and the source with structure hopping. Through simulation experiments and theoretical analysis, it is demonstrated that the MSKMSH method can realize reliable and secure transmission of radio signals.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381240","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}
Macro work is a significant Online Labor Platforms (OLPs) operation characterized by higher professionalism for service providers. Therefore, the professionalism assessment for providers of macro work is vital for OLPs. However, due to the high ambiguity of textual data, OLPs often overlook them when evaluating the Service Provider Professionalism (SPP) of macro work. Within OLPs, there is a large amount of textual data, which contains information reflecting their professionalism. Hence, this study proposes a method for evaluating the SPP of macro work on OLPs based on text sentiment analysis: (1) Select professional vocabulary related to a specific type of macro work as sentiment words; (2) Collect texts and score their professionalism values; (3) Calculate the sentiment word professionalism value based on the NBSP algorithm - an algorithm that combines the Naive Bayes and Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithms; (4) Calculate the text professionalism value, namely the SPP value. Algorithm validation results show that compared to baseline algorithms, the NBSP algorithm achieves an increase in the accuracy of calculating text professionalism values by 4.45 - 27.75 percent points. To validate this method's effectiveness, this study conducted a comparative experiment on predicting the annual transaction amounts of IT service providers on a certain Chinese OLP under eight main-stream predictive models, incorporating the feature of SPP reduced MSE by 6% - 12%. This study contributes to expanding research in structuring textual data and text sentiment analysis in OLPs and enhances professionalism assessment for service providers of macro work on OLPs.
{"title":"Text-based sentiment analysis for evaluating the service provider professionalism (SPP) of macro work on online labor platforms (OLPs)","authors":"Hongbin Zhang, Jiajun Xu","doi":"10.1117/12.3031905","DOIUrl":"https://doi.org/10.1117/12.3031905","url":null,"abstract":"Macro work is a significant Online Labor Platforms (OLPs) operation characterized by higher professionalism for service providers. Therefore, the professionalism assessment for providers of macro work is vital for OLPs. However, due to the high ambiguity of textual data, OLPs often overlook them when evaluating the Service Provider Professionalism (SPP) of macro work. Within OLPs, there is a large amount of textual data, which contains information reflecting their professionalism. Hence, this study proposes a method for evaluating the SPP of macro work on OLPs based on text sentiment analysis: (1) Select professional vocabulary related to a specific type of macro work as sentiment words; (2) Collect texts and score their professionalism values; (3) Calculate the sentiment word professionalism value based on the NBSP algorithm - an algorithm that combines the Naive Bayes and Semantic Orientation Pointwise Mutual Information (SO-PMI) algorithms; (4) Calculate the text professionalism value, namely the SPP value. Algorithm validation results show that compared to baseline algorithms, the NBSP algorithm achieves an increase in the accuracy of calculating text professionalism values by 4.45 - 27.75 percent points. To validate this method's effectiveness, this study conducted a comparative experiment on predicting the annual transaction amounts of IT service providers on a certain Chinese OLP under eight main-stream predictive models, incorporating the feature of SPP reduced MSE by 6% - 12%. This study contributes to expanding research in structuring textual data and text sentiment analysis in OLPs and enhances professionalism assessment for service providers of macro work on OLPs.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380788","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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