Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00030
Xuedong Ou, J. Liu
Log anomaly detection is a fairly indispensable log analysis task for reliability and maintainability in cloud data center. By performing tasks such as log parsing and feature extraction on logs, which are common and valid data, a model with self-judgment capability can be trained for log anomaly detection. Improving the model used for anomaly detection is the main line of research in the current anomaly detection field. However, the data set partitioning method during anomaly detection also has an important impact on the results of anomaly detection, which should be given more considerations. Most of the existing anomaly detection models are single-architecture models, which cannot make full use of the multiple forms of information that logs have. This paper proposes a hybrid anomaly detection method, named LogKT, which is divided into two parts. First, a new dataset partitioning method is constructed based on time-series, randomness and imbalances of logs. It is a dataset partitioning method that fits the characteristics of log anomaly detection from the aspects of time-series feature preservation, sampling range expansion and training method change. Then, we further propose a hybrid anomaly detection model based on a Transformer and Bi-LSTM models, which can extract features from multiple information of logs and can fit well with the dataset partitioning method. Finally, we perform validation experiments on two public datasets, and the experimental results show that our LogKT approach has superior anomaly detection accuracy compared with baseline methods.
{"title":"LogKT: Hybrid Log Anomaly Detection Method for Cloud Data Center","authors":"Xuedong Ou, J. Liu","doi":"10.1109/COMPSAC57700.2023.00030","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00030","url":null,"abstract":"Log anomaly detection is a fairly indispensable log analysis task for reliability and maintainability in cloud data center. By performing tasks such as log parsing and feature extraction on logs, which are common and valid data, a model with self-judgment capability can be trained for log anomaly detection. Improving the model used for anomaly detection is the main line of research in the current anomaly detection field. However, the data set partitioning method during anomaly detection also has an important impact on the results of anomaly detection, which should be given more considerations. Most of the existing anomaly detection models are single-architecture models, which cannot make full use of the multiple forms of information that logs have. This paper proposes a hybrid anomaly detection method, named LogKT, which is divided into two parts. First, a new dataset partitioning method is constructed based on time-series, randomness and imbalances of logs. It is a dataset partitioning method that fits the characteristics of log anomaly detection from the aspects of time-series feature preservation, sampling range expansion and training method change. Then, we further propose a hybrid anomaly detection model based on a Transformer and Bi-LSTM models, which can extract features from multiple information of logs and can fit well with the dataset partitioning method. Finally, we perform validation experiments on two public datasets, and the experimental results show that our LogKT approach has superior anomaly detection accuracy compared with baseline methods.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134517733","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00156
Samaneh Mohammadi, Sima Sinaei, A. Balador, Francesco Flammini
Context: Federated Learning is an approach to distributed machine learning that enables collaborative model training on end devices. FL enhances privacy as devices only share local model parameters instead of raw data with a central server. However, the central server or eavesdroppers could extract sensitive information from these shared parameters. This issue is crucial in applications like speech emotion recognition (SER) that deal with personal voice data. To address this, we propose Optimized Paillier Homomorphic Encryption (OPHE) for SER applications in FL. Paillier homomorphic encryption enables computations on ciphertext, preserving privacy but with high computation and communication overhead. The proposed OPHE method can reduce this overhead by combing Paillier homomorphic encryption with pruning. So, we employ OPHE in one of the use cases of a large research project (DAIS) funded by the European Commission using a public SER dataset.
{"title":"Optimized Paillier Homomorphic Encryption in Federated Learning for Speech Emotion Recognition","authors":"Samaneh Mohammadi, Sima Sinaei, A. Balador, Francesco Flammini","doi":"10.1109/COMPSAC57700.2023.00156","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00156","url":null,"abstract":"Context: Federated Learning is an approach to distributed machine learning that enables collaborative model training on end devices. FL enhances privacy as devices only share local model parameters instead of raw data with a central server. However, the central server or eavesdroppers could extract sensitive information from these shared parameters. This issue is crucial in applications like speech emotion recognition (SER) that deal with personal voice data. To address this, we propose Optimized Paillier Homomorphic Encryption (OPHE) for SER applications in FL. Paillier homomorphic encryption enables computations on ciphertext, preserving privacy but with high computation and communication overhead. The proposed OPHE method can reduce this overhead by combing Paillier homomorphic encryption with pruning. So, we employ OPHE in one of the use cases of a large research project (DAIS) funded by the European Commission using a public SER dataset.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877994","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00151
Mst. Shapna Akter, H. Shahriar, Dan C. Lo, Nazmus Sakib, Kai Qian, Michael E. Whitman, Fan Wu
The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.
{"title":"Authentic Learning Approach for Artificial Intelligence Systems Security and Privacy","authors":"Mst. Shapna Akter, H. Shahriar, Dan C. Lo, Nazmus Sakib, Kai Qian, Michael E. Whitman, Fan Wu","doi":"10.1109/COMPSAC57700.2023.00151","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00151","url":null,"abstract":"The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133910458","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00092
Gebrehiwet B. Welearegai, Chenpo Hu, Christian Hammer
As the ARM processor is receiving increased attention due to the fast growth of mobile technologies and the internet-of-things (IoT), it is simultaneously becoming the target of several control flow attacks such as return-oriented programming (ROP), which uses code present in the software system in order to exploit memory bugs. While some research can detect control flow attacks on architectures like x86, the ARM architecture has been neglected. In this paper, we investigate whether ROP attack detection and prevention based on hardware performance counters (HPC) and machine learning can be effectively transferred to the ARM architecture. Given the observation that ROP attacks exhibit different micro-architectural events compared to benign executions of a software, we evaluate whether and which HPCs, which track these hardware events, are indicative on ARM to detect control flow attacks. We collect data exploiting real-world vulnerable applications running on ARM-based Raspberry Pi machines. The collected data then serves as training data for different machine learning techniques. We also implement an online monitor consisting of a modified program loader, kernel module and a classifier, which labels a program’s execution as benign or under attack, and stops its execution once the latter is detected. An evaluation of our approach provides detection accuracy of 92% for the offline training and 75% for the online monitoring, which demonstrates that variations in the HPCs are indicative of attacks on ARM architectures. The performance overhead of online monitoring evaluated on 8 real-world vulnerable applications exhibits a moderate 6.2% slowdown on average. The result of our evaluation indicates that the behavioral changes in micro-architectural events of the ARM platform can play a vital role in detecting memory attacks.
{"title":"Detecting and Preventing ROP Attacks using Machine Learning on ARM","authors":"Gebrehiwet B. Welearegai, Chenpo Hu, Christian Hammer","doi":"10.1109/COMPSAC57700.2023.00092","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00092","url":null,"abstract":"As the ARM processor is receiving increased attention due to the fast growth of mobile technologies and the internet-of-things (IoT), it is simultaneously becoming the target of several control flow attacks such as return-oriented programming (ROP), which uses code present in the software system in order to exploit memory bugs. While some research can detect control flow attacks on architectures like x86, the ARM architecture has been neglected. In this paper, we investigate whether ROP attack detection and prevention based on hardware performance counters (HPC) and machine learning can be effectively transferred to the ARM architecture. Given the observation that ROP attacks exhibit different micro-architectural events compared to benign executions of a software, we evaluate whether and which HPCs, which track these hardware events, are indicative on ARM to detect control flow attacks. We collect data exploiting real-world vulnerable applications running on ARM-based Raspberry Pi machines. The collected data then serves as training data for different machine learning techniques. We also implement an online monitor consisting of a modified program loader, kernel module and a classifier, which labels a program’s execution as benign or under attack, and stops its execution once the latter is detected. An evaluation of our approach provides detection accuracy of 92% for the offline training and 75% for the online monitoring, which demonstrates that variations in the HPCs are indicative of attacks on ARM architectures. The performance overhead of online monitoring evaluated on 8 real-world vulnerable applications exhibits a moderate 6.2% slowdown on average. The result of our evaluation indicates that the behavioral changes in micro-architectural events of the ARM platform can play a vital role in detecting memory attacks.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133198293","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}
AI models deployed in real-world tasks (e.g., surveillance, implicit mapping, health care) typically need to be online trained for better modelling of the changing real-world environments and various online training methods (e.g., domain adaptation, few shot learning) are proposed for refining the AI models based on training input incrementally sampled from the real world. However, in the whole loop of AI model online training, there is a section rarely discussed: how to sample training input from the real world. In this paper, we show from the perspective of online training of AI models deployed on edge devices (e.g., robots) that several problems in sampling of training input on the device are affecting the time and energy consumption for the online training process to reach high performance. Notably, the online training relies on training input consecutively sampled from the real world and the consecutive samples from nearby states (e.g., position and orientation of a camera) are too similar and would limit the training accuracy gain per training iteration; on the other hand, while we can choose to sample more about the inaccurate samples to better final training accuracy, it is costly to obtain the accuracy statistics of samples via traditional ways such as validating, especially for AI models deployed on edge devices. These findings aim to raise research effort for practical online training of AI models, so that they can achieve resiliently and sustainably high performance in real-world tasks.
{"title":"New Problems in Active Sampling for Mobile Robotic Online Learning","authors":"Xiuxian Guan, Junming Wang, Zekai Sun, Zongyuan Zhang, Tian-dong Duan, Shengliang Deng, Fangming Liu, Heming Cui","doi":"10.1109/COMPSAC57700.2023.00174","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00174","url":null,"abstract":"AI models deployed in real-world tasks (e.g., surveillance, implicit mapping, health care) typically need to be online trained for better modelling of the changing real-world environments and various online training methods (e.g., domain adaptation, few shot learning) are proposed for refining the AI models based on training input incrementally sampled from the real world. However, in the whole loop of AI model online training, there is a section rarely discussed: how to sample training input from the real world. In this paper, we show from the perspective of online training of AI models deployed on edge devices (e.g., robots) that several problems in sampling of training input on the device are affecting the time and energy consumption for the online training process to reach high performance. Notably, the online training relies on training input consecutively sampled from the real world and the consecutive samples from nearby states (e.g., position and orientation of a camera) are too similar and would limit the training accuracy gain per training iteration; on the other hand, while we can choose to sample more about the inaccurate samples to better final training accuracy, it is costly to obtain the accuracy statistics of samples via traditional ways such as validating, especially for AI models deployed on edge devices. These findings aim to raise research effort for practical online training of AI models, so that they can achieve resiliently and sustainably high performance in real-world tasks.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126578808","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00122
Lin Miao, D. Towey, Yingrui Ma, T. Chen, Z. Zhou
Concerns have been growing over fake news and its impact. Software that can automatically detect fake news is becoming more popular. However, the accuracy and reliability of such fake-news detection software remains questionable, partly due to a lack of testing and verification. Testing this kind of software may face the oracle problem, which refers to difficulty (or inability) of identifying the correctness of the software’s output in a reasonable amount of time. Metamorphic testing (MT) has a record of effectively alleviating the oracle problem, and has been successfully applied to testing fake-news detection software. This paper reports on a study, extending previous work, exploring the use of MT for fake-news detection software. The study includes new metamorphic relations and additional experimental results and analysis. Some alternative MR-generation approaches are also explored. The study targets software where the output is a real/fake news decision, enhancing the applicability of MT to current fake-news detection software. The paper also explores the impact of the prediction accuracy of the fake-news detection software on the MT process. The study demonstrates the validity and applicability of MT to fake-news detection software. The prediction accuracy of the software has a greater impact on MT experiments with greater changes between the source and follow-up inputs, and less dependence on prediction stability. Some possible factors affecting the experimental results are discussed, and directions for future work are provided.
{"title":"Exploring Metamorphic Testing for Fake-News Detection Software: A Case Study","authors":"Lin Miao, D. Towey, Yingrui Ma, T. Chen, Z. Zhou","doi":"10.1109/COMPSAC57700.2023.00122","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00122","url":null,"abstract":"Concerns have been growing over fake news and its impact. Software that can automatically detect fake news is becoming more popular. However, the accuracy and reliability of such fake-news detection software remains questionable, partly due to a lack of testing and verification. Testing this kind of software may face the oracle problem, which refers to difficulty (or inability) of identifying the correctness of the software’s output in a reasonable amount of time. Metamorphic testing (MT) has a record of effectively alleviating the oracle problem, and has been successfully applied to testing fake-news detection software. This paper reports on a study, extending previous work, exploring the use of MT for fake-news detection software. The study includes new metamorphic relations and additional experimental results and analysis. Some alternative MR-generation approaches are also explored. The study targets software where the output is a real/fake news decision, enhancing the applicability of MT to current fake-news detection software. The paper also explores the impact of the prediction accuracy of the fake-news detection software on the MT process. The study demonstrates the validity and applicability of MT to fake-news detection software. The prediction accuracy of the software has a greater impact on MT experiments with greater changes between the source and follow-up inputs, and less dependence on prediction stability. Some possible factors affecting the experimental results are discussed, and directions for future work are provided.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133764549","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00028
Yue Jiang, Hoi-yan Doris. Lin, Long Fai Cheung, Henry C. B. Chan, Ping Li
Hybrid/online teaching in general and HyFlex teaching in particular are now widely adopted among higher education institutions around the world due to their hybrid (physical/virtual) advantage and flexible arrangement. To gain a better understanding of hybrid/online teaching, we have conducted a survey at the International Conference on Teaching, Assessment and Learning for Engineering (TALE) 2022, one of the flagship conferences of the IEEE Education Society (i.e., for international conference participants from more than 16 countries/cities/regions). The aim is to evaluate hybrid/online teaching in general and the 4C elements (Content, Collaboration, Community and Communication) of a hybrid/online classroom model. Results from this international survey provide valuable insights, perspectives and good practices, and point to future research directions on this important topic.
{"title":"Hybrid/Online Teaching: A Survey and Key Issues","authors":"Yue Jiang, Hoi-yan Doris. Lin, Long Fai Cheung, Henry C. B. Chan, Ping Li","doi":"10.1109/COMPSAC57700.2023.00028","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00028","url":null,"abstract":"Hybrid/online teaching in general and HyFlex teaching in particular are now widely adopted among higher education institutions around the world due to their hybrid (physical/virtual) advantage and flexible arrangement. To gain a better understanding of hybrid/online teaching, we have conducted a survey at the International Conference on Teaching, Assessment and Learning for Engineering (TALE) 2022, one of the flagship conferences of the IEEE Education Society (i.e., for international conference participants from more than 16 countries/cities/regions). The aim is to evaluate hybrid/online teaching in general and the 4C elements (Content, Collaboration, Community and Communication) of a hybrid/online classroom model. Results from this international survey provide valuable insights, perspectives and good practices, and point to future research directions on this important topic.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124121249","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00073
Yongqiang Gao, Zhihan Li
In recent years, the advancement of 4G/5G network technologies and smart devices has led to an increasing demand for smooth, massively multiplayer online games on mobile terminals. These games necessitate high performance and heavy workloads, often consuming substantial amounts of computing and storage resources while imposing strict latency requirements. However, due to the limited resources of end devices, such tasks cannot be efficiently and independently executed. The traditional solution typically involves processing gaming tasks at centralized cloud servers. However, this approach introduces issues such as bandwidth pressure, high latency, load imbalance, and elevated costs. Recently, mobile edge computing (MEC) has gained popularity, and its low-latency capabilities can be integrated with cloud gaming to enhance the gaming performance experience. In this paper, we explore the offloading and placement of rendering services in a scenario that combines MEC with cloud gaming. We propose a model-free algorithm based on deep reinforcement learning to learn the optimal task offloading and placement policy, which optimizes a combination of four metrics: latency, cost, bandwidth, and load balancing. Additionally, the algorithm predicts future bandwidth using LSTM, significantly improving the player's gaming experience and fairness. Simulation results demonstrate that our proposed task placement strategy outperforms state-of-the-art methods applied to similar problems.
{"title":"Deep Reinforcement Learning Based Rendering Service Placement for Cloud Gaming in Mobile Edge Computing Systems","authors":"Yongqiang Gao, Zhihan Li","doi":"10.1109/COMPSAC57700.2023.00073","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00073","url":null,"abstract":"In recent years, the advancement of 4G/5G network technologies and smart devices has led to an increasing demand for smooth, massively multiplayer online games on mobile terminals. These games necessitate high performance and heavy workloads, often consuming substantial amounts of computing and storage resources while imposing strict latency requirements. However, due to the limited resources of end devices, such tasks cannot be efficiently and independently executed. The traditional solution typically involves processing gaming tasks at centralized cloud servers. However, this approach introduces issues such as bandwidth pressure, high latency, load imbalance, and elevated costs. Recently, mobile edge computing (MEC) has gained popularity, and its low-latency capabilities can be integrated with cloud gaming to enhance the gaming performance experience. In this paper, we explore the offloading and placement of rendering services in a scenario that combines MEC with cloud gaming. We propose a model-free algorithm based on deep reinforcement learning to learn the optimal task offloading and placement policy, which optimizes a combination of four metrics: latency, cost, bandwidth, and load balancing. Additionally, the algorithm predicts future bandwidth using LSTM, significantly improving the player's gaming experience and fairness. Simulation results demonstrate that our proposed task placement strategy outperforms state-of-the-art methods applied to similar problems.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463244","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00061
Yumi Fujita, Sho Tsugawa
The dissemination of messages countering misinformation is considered a promising approach for limiting the spread of misinformation. On social network, the approach can be posed as a problem, the influence limitation problem. Although most existing studies on the influence limitation problem assume a single-layer structure for social networks, in reality, each individual in society usually has multiple communication channels; moreover, the social network has a multilayer structure. Therefore, this study investigates the problems in limiting the spread of negative influences (i.e., misinformation) in multilayer networks by spreading positive influences (i.e., counter messages against misinformation). Furthermore, we formulate the problem on a two-layered multiplex network by extending the influence limitation problem on a single-layer network. By conducting simulation experiments using synthetic and real multiplex networks, we evaluated the effectiveness of the methods to select seed nodes that trigger the spread of positive influence. The results show that even in two-layered multiplex networks, the seed-node selection methods that use a single-layer structure achieve effectiveness comparable to that of the seed-node selection method that uses both layers of the two-layered network. A method that selects seed nodes from the community boundary nodes can effectively limit the spread of negative influence in most cases.
{"title":"Limiting the Spread of Misinformation on Multiplex Social Networks","authors":"Yumi Fujita, Sho Tsugawa","doi":"10.1109/COMPSAC57700.2023.00061","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00061","url":null,"abstract":"The dissemination of messages countering misinformation is considered a promising approach for limiting the spread of misinformation. On social network, the approach can be posed as a problem, the influence limitation problem. Although most existing studies on the influence limitation problem assume a single-layer structure for social networks, in reality, each individual in society usually has multiple communication channels; moreover, the social network has a multilayer structure. Therefore, this study investigates the problems in limiting the spread of negative influences (i.e., misinformation) in multilayer networks by spreading positive influences (i.e., counter messages against misinformation). Furthermore, we formulate the problem on a two-layered multiplex network by extending the influence limitation problem on a single-layer network. By conducting simulation experiments using synthetic and real multiplex networks, we evaluated the effectiveness of the methods to select seed nodes that trigger the spread of positive influence. The results show that even in two-layered multiplex networks, the seed-node selection methods that use a single-layer structure achieve effectiveness comparable to that of the seed-node selection method that uses both layers of the two-layered network. A method that selects seed nodes from the community boundary nodes can effectively limit the spread of negative influence in most cases.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114661083","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 : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00159
Mst. Shapna Akter, Nova Ahmed, H. Shahriar
This paper represents the experience of women from STEM and Non-STEM fields from the perspective of the rural area of Bangladesh. In Bangladesh, the social and cultural perspectives differ from one area to another. Previously no work has been done in Bangladesh from the perspective of the rural areas. We have come up with the motivation to work on this particular area to find out what barriers and challenges women face when they move to urban areas to study in the STEM field. From our findings, we have found some barriers from the Non-STEM field and opportunities from the STEM field. We have studied n=8 participants (5 Non-STEM, 3 STEM). Through their shared experience, they face barriers such as early marriage, excessive usage of social media, lack of support from teachers, peers, institutions, and family, challenge of Accommodation and security system outside of the hometown, and parents’ mentality towards children’s career. We have found opportunities such as parents’ educational background, female role models, and family support from the shared experience of STEM field participants. This research uncovered the barriers and opportunities that rural women face for entering the STEM field, which is very important to mitigate the barriers to entering the STEM field for rural women, which will help to create a new dimension in the HCI field. We believe that by following our work, future researchers might get motivated to contribute in this area, which will help the area to be considered as a big and an important part of the future investigation.
{"title":"Understanding Rural women’s Experience in STEM and Non-STEM field in Bangladesh","authors":"Mst. Shapna Akter, Nova Ahmed, H. Shahriar","doi":"10.1109/COMPSAC57700.2023.00159","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00159","url":null,"abstract":"This paper represents the experience of women from STEM and Non-STEM fields from the perspective of the rural area of Bangladesh. In Bangladesh, the social and cultural perspectives differ from one area to another. Previously no work has been done in Bangladesh from the perspective of the rural areas. We have come up with the motivation to work on this particular area to find out what barriers and challenges women face when they move to urban areas to study in the STEM field. From our findings, we have found some barriers from the Non-STEM field and opportunities from the STEM field. We have studied n=8 participants (5 Non-STEM, 3 STEM). Through their shared experience, they face barriers such as early marriage, excessive usage of social media, lack of support from teachers, peers, institutions, and family, challenge of Accommodation and security system outside of the hometown, and parents’ mentality towards children’s career. We have found opportunities such as parents’ educational background, female role models, and family support from the shared experience of STEM field participants. This research uncovered the barriers and opportunities that rural women face for entering the STEM field, which is very important to mitigate the barriers to entering the STEM field for rural women, which will help to create a new dimension in the HCI field. We believe that by following our work, future researchers might get motivated to contribute in this area, which will help the area to be considered as a big and an important part of the future investigation.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"458 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116182345","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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