Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00053
Chi Mai Nguyen, Phat Thai, Duy Khang Lam, Van Tuan Nguyen
We live in an age of information overload. Manual information processing is increasingly overwhelmed with the enormous amount of information created by the explosive growth of news portals and online social networks. Such a situation calls for an automatic system that can support the process of handling, analyzing, and filtering information, especially information from online sources. In this work, we proposed a text analysis system that automatically collects, extracts, and analyses information from public-source-text documents such as news portals and social media networks. The proposed system can handle both long and short-text documents. It also has real-time features and is not restricted by any input data domain. The system can be used in different domains, such as scientific research, marketing, and security-related domains. Moreover, the system is engineered in modules and is flexible. Each module is an independent micro-service that can be used as a separate standalone application. The system is also extensible since new modules can be added easily. Index Terms—text analysis system, data mining, natural language processing
{"title":"A Real-Time Text Analysis System","authors":"Chi Mai Nguyen, Phat Thai, Duy Khang Lam, Van Tuan Nguyen","doi":"10.1109/COMPSAC57700.2023.00053","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00053","url":null,"abstract":"We live in an age of information overload. Manual information processing is increasingly overwhelmed with the enormous amount of information created by the explosive growth of news portals and online social networks. Such a situation calls for an automatic system that can support the process of handling, analyzing, and filtering information, especially information from online sources. In this work, we proposed a text analysis system that automatically collects, extracts, and analyses information from public-source-text documents such as news portals and social media networks. The proposed system can handle both long and short-text documents. It also has real-time features and is not restricted by any input data domain. The system can be used in different domains, such as scientific research, marketing, and security-related domains. Moreover, the system is engineered in modules and is flexible. Each module is an independent micro-service that can be used as a separate standalone application. The system is also extensible since new modules can be added easily. Index Terms—text analysis system, data mining, natural language processing","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"70 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":"130055860","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/COMPSAC57700.2023.00199
Bipin Chhetri, Saroj Gopali, Rukayat Olapojoye, Samin Dehbashi, A. Namin
Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the model’s transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.
{"title":"A Survey on Blockchain-Based Federated Learning and Data Privacy","authors":"Bipin Chhetri, Saroj Gopali, Rukayat Olapojoye, Samin Dehbashi, A. Namin","doi":"10.1109/COMPSAC57700.2023.00199","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00199","url":null,"abstract":"Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the model’s transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"30 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":"130212190","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/COMPSAC57700.2023.00018
Anoop Bhagyanath, K. Schneider
In traditional von Neumann processors, the central register file is an inherent limiting factor in exploiting the instruction-level parallelism (ILP) of programs. To alleviate this problem, many processors follow a hybrid von Neumann/dataflow computing model in which specific instruction sequences are executed in dataflow order by communicating intermediate values directly from producer processing units (PUs) to consumer PUs without using a central register file. However, the intermediate values often reside in local registers of the PUs, which requires a synchronization of the data transports that still limits the exploitation of the ILP.To avoid the use of a central register file and the need for any synchronization between PUs, some newer architectures suggest first-in-first-out (FIFO) buffers instead of local registers at the input and output ports of the PUs. Since values are produced and consumed, and are thus never overwritten (as in registers), the compiler must determine the required number of copies of each value. Furthermore, it is necessary to control the number of copies of values to develop buffer size aware compilation methods. However, the number of variable uses in a sequential program may depend on the future execution. This paper presents transformations for ‘balancing’ a given program, i.e., transforming the program so that for all points in the program, the number of future uses of all variables can be accurately determined in order to allocate the required buffer sizes in the later compilation phases. The classical space-time trade-off is demonstrated by the experimental results which show an improvement of the processor performance with increasing buffer sizes and vice versa. More importantly, the experimental results demonstrate the potential of buffered hybrid dataflow architectures for a scalable use of ILP.
{"title":"Program Balancing in Compilation for Buffered Hybrid Dataflow Processors","authors":"Anoop Bhagyanath, K. Schneider","doi":"10.1109/COMPSAC57700.2023.00018","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00018","url":null,"abstract":"In traditional von Neumann processors, the central register file is an inherent limiting factor in exploiting the instruction-level parallelism (ILP) of programs. To alleviate this problem, many processors follow a hybrid von Neumann/dataflow computing model in which specific instruction sequences are executed in dataflow order by communicating intermediate values directly from producer processing units (PUs) to consumer PUs without using a central register file. However, the intermediate values often reside in local registers of the PUs, which requires a synchronization of the data transports that still limits the exploitation of the ILP.To avoid the use of a central register file and the need for any synchronization between PUs, some newer architectures suggest first-in-first-out (FIFO) buffers instead of local registers at the input and output ports of the PUs. Since values are produced and consumed, and are thus never overwritten (as in registers), the compiler must determine the required number of copies of each value. Furthermore, it is necessary to control the number of copies of values to develop buffer size aware compilation methods. However, the number of variable uses in a sequential program may depend on the future execution. This paper presents transformations for ‘balancing’ a given program, i.e., transforming the program so that for all points in the program, the number of future uses of all variables can be accurately determined in order to allocate the required buffer sizes in the later compilation phases. The classical space-time trade-off is demonstrated by the experimental results which show an improvement of the processor performance with increasing buffer sizes and vice versa. More importantly, the experimental results demonstrate the potential of buffered hybrid dataflow architectures for a scalable use of ILP.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"15 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":"126970702","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/COMPSAC57700.2023.00213
Martin Brown, Md Abdullah Khan, Dominic Thomas, Yong Pei, M. Nandan
Early detection of and intervention in behavioral health cases, including mental health, is crucial to prevent harm to one’s self and others. Police reports generated by officers on duty or in response to 911 calls remain an untapped resource for identifying such incidents. To expedite the detection process, we propose a workflow that involves collaboration between experts to manually annotate cases and correct model predictions. This approach can improve both initial annotation and model performance. Therefore, we advocate for the incorporation of manual annotations from experts, natural language processing (NLP), active learning, and advanced machine learning techniques to detect behavioral health cases within police reports. The experimentation suggests that a CNN-LSTM model achieves the best performance with an accuracy of 86.67% and an F1-score of 0.82 in detecting behavioral health issues.
{"title":"Detection of Behavioral Health Cases from Sensitive Police Officer Narratives","authors":"Martin Brown, Md Abdullah Khan, Dominic Thomas, Yong Pei, M. Nandan","doi":"10.1109/COMPSAC57700.2023.00213","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00213","url":null,"abstract":"Early detection of and intervention in behavioral health cases, including mental health, is crucial to prevent harm to one’s self and others. Police reports generated by officers on duty or in response to 911 calls remain an untapped resource for identifying such incidents. To expedite the detection process, we propose a workflow that involves collaboration between experts to manually annotate cases and correct model predictions. This approach can improve both initial annotation and model performance. Therefore, we advocate for the incorporation of manual annotations from experts, natural language processing (NLP), active learning, and advanced machine learning techniques to detect behavioral health cases within police reports. The experimentation suggests that a CNN-LSTM model achieves the best performance with an accuracy of 86.67% and an F1-score of 0.82 in detecting behavioral health issues.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"42 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":"122376623","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/COMPSAC57700.2023.00066
Hongyi Zhang, Jingya Li, Z. Qi, Anders Aronsson, Jan Bosch, H. H. Olsson
Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.
{"title":"Multi-Agent Reinforcement Learning in Dynamic Industrial Context","authors":"Hongyi Zhang, Jingya Li, Z. Qi, Anders Aronsson, Jan Bosch, H. H. Olsson","doi":"10.1109/COMPSAC57700.2023.00066","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00066","url":null,"abstract":"Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.","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":"125248751","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/COMPSAC57700.2023.00234
Manohar Murikipudi, ABM.Adnan Azmee, Md Abdullah Al Hafiz Khan, Yong Pei
Suicide has become a significant cause of concern worldwide over recent years. The early identification and providing treatment of individuals having suicidal tendencies are necessary for preventing suicides. Past suicidal behavior information of an individual is recorded in the electronic health records (EHR) reports which can help to understand a patient’s current mental health condition. In this paper, to identify the people who are ideating and are anticipating attempting suicide, we propose a novel model named CMTN, which utilizes the textual EHR data for the prediction of suicidal behaviors. The proposed framework employs convolutional and transformer layers to capture local and global relationships in the text and the attention mechanism to assess the significance of various input text components. Overall, the suggested model has achieved the highest precision for the SA class with a score of 0.97 and the highest recall and f1-score of 0.56 and 0.52, respectively, for the SI class, compared with all other state-of-the-art and baseline models. We have also employed different embeddings such as BERT, BioBERT, and PubMedBERT to our state-of-the-art model and illustrated the model’s improved performance. In addition, we have also shared the data alignment and annotation extraction algorithms in this paper, allowing other researchers to generate the dataset, thereby expediting development in the prevention of suicides.
{"title":"CMTN: A Convolutional Multi-Level Transformer to Identify Suicidal Behaviors Using Clinical Notes","authors":"Manohar Murikipudi, ABM.Adnan Azmee, Md Abdullah Al Hafiz Khan, Yong Pei","doi":"10.1109/COMPSAC57700.2023.00234","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00234","url":null,"abstract":"Suicide has become a significant cause of concern worldwide over recent years. The early identification and providing treatment of individuals having suicidal tendencies are necessary for preventing suicides. Past suicidal behavior information of an individual is recorded in the electronic health records (EHR) reports which can help to understand a patient’s current mental health condition. In this paper, to identify the people who are ideating and are anticipating attempting suicide, we propose a novel model named CMTN, which utilizes the textual EHR data for the prediction of suicidal behaviors. The proposed framework employs convolutional and transformer layers to capture local and global relationships in the text and the attention mechanism to assess the significance of various input text components. Overall, the suggested model has achieved the highest precision for the SA class with a score of 0.97 and the highest recall and f1-score of 0.56 and 0.52, respectively, for the SI class, compared with all other state-of-the-art and baseline models. We have also employed different embeddings such as BERT, BioBERT, and PubMedBERT to our state-of-the-art model and illustrated the model’s improved performance. In addition, we have also shared the data alignment and annotation extraction algorithms in this paper, allowing other researchers to generate the dataset, thereby expediting development in the prevention of suicides.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"3 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":"121503010","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/COMPSAC57700.2023.00214
A. Hossain, Md. Aminul Islam, A. Chowdhury, S. Rahman, Alounoud Salman, J. Dias, M. Subu, Mohammad Yousef Alkhawaldeh, Amina Al-Marzouqi, Heba H Hijazi, Mohamad Qasim Alshabi, Nabeel Al-Yateem
Cyberchondria is a distinct behavioral syndrome that is closely related to health anxiety/hypochondria and excessive online searching for health information and/or digital self-tracking. Despite the reported prevalence of self-medication, cyberchondria research is still in its infancy in Bangladesh. We investigated the relationship between Cyberchondria and self-medication among adults. This was a cross-sectional study conducted with 480 individuals who had internet access and who can read both Bangla and English. The Cyberchondria Severity Scale and the self-medication perception Questionnaire were applied to the participants. Univariate and hierarchical multiple linear regression analyses were used to analyze the data. Of the study group 283 (59%) were male, and 197 (41%), were female. Their ages ranged from 18 to 40 years, with an average of 25.1 (± 5.97) years. The positive perception of self-medication was prevalent in 279 (58.1%) adults. Cyberchondria and perception of self-medication were positively related and in the final model self-medication, age and residence were found to be the significant determinants of cyberchondria. Positive perception of self-medication practice may be a potential risk factor for Cyberchondria. People's health-related actions can be influenced by their cyberchondria behavior, so it's crucial that online health resources are safe. Cyberchondria is a mental health disorder, and this study's findings could inform future research into the causes of this condition.
{"title":"Positive Perception of Self-Medication Practice and Cyberchondria Behavior Among Adults in Bangladesh","authors":"A. Hossain, Md. Aminul Islam, A. Chowdhury, S. Rahman, Alounoud Salman, J. Dias, M. Subu, Mohammad Yousef Alkhawaldeh, Amina Al-Marzouqi, Heba H Hijazi, Mohamad Qasim Alshabi, Nabeel Al-Yateem","doi":"10.1109/COMPSAC57700.2023.00214","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00214","url":null,"abstract":"Cyberchondria is a distinct behavioral syndrome that is closely related to health anxiety/hypochondria and excessive online searching for health information and/or digital self-tracking. Despite the reported prevalence of self-medication, cyberchondria research is still in its infancy in Bangladesh. We investigated the relationship between Cyberchondria and self-medication among adults. This was a cross-sectional study conducted with 480 individuals who had internet access and who can read both Bangla and English. The Cyberchondria Severity Scale and the self-medication perception Questionnaire were applied to the participants. Univariate and hierarchical multiple linear regression analyses were used to analyze the data. Of the study group 283 (59%) were male, and 197 (41%), were female. Their ages ranged from 18 to 40 years, with an average of 25.1 (± 5.97) years. The positive perception of self-medication was prevalent in 279 (58.1%) adults. Cyberchondria and perception of self-medication were positively related and in the final model self-medication, age and residence were found to be the significant determinants of cyberchondria. Positive perception of self-medication practice may be a potential risk factor for Cyberchondria. People's health-related actions can be influenced by their cyberchondria behavior, so it's crucial that online health resources are safe. Cyberchondria is a mental health disorder, and this study's findings could inform future research into the causes of this condition.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 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":"124405229","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/COMPSAC57700.2023.00284
Md Mostafizur Rahman, Aiasha Siddika Arshi, Md. Golam Moula Mehedi Hasan, Sumayia Farzana Mishu, H. Shahriar, Fan Wu
This survey paper provides an overview of the current state of AI attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques.
{"title":"Security Risk and Attacks in AI: A Survey of Security and Privacy","authors":"Md Mostafizur Rahman, Aiasha Siddika Arshi, Md. Golam Moula Mehedi Hasan, Sumayia Farzana Mishu, H. Shahriar, Fan Wu","doi":"10.1109/COMPSAC57700.2023.00284","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00284","url":null,"abstract":"This survey paper provides an overview of the current state of AI attacks and risks for AI security and privacy as artificial intelligence becomes more prevalent in various applications and services. The risks associated with AI attacks and security breaches are becoming increasingly apparent and cause many financial and social losses. This paper will categorize the different types of attacks on AI models, including adversarial attacks, model inversion attacks, poisoning attacks, data poisoning attacks, data extraction attacks, and membership inference attacks. The paper also emphasizes the importance of developing secure and robust AI models to ensure the privacy and security of sensitive data. Through a systematic literature review, this survey paper comprehensively analyzes the current state of AI attacks and risks for AI security and privacy and detection techniques.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 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":"131039387","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/COMPSAC57700.2023.00204
Joylal Das, Sulalitha Bowala, R. Thulasiram, A. Thavaneswaran
Constructing resilient portfolios is of crucial and utmost importance to investment management. This study compares traditional and data-driven models for building resilient portfolios and analyzes their performance for stocks (S&P 500) and highly volatile cryptocurrency markets. The study investigates the performance of traditional models, such as mean-variance and constrained optimization, and a recently proposed data-driven resilient portfolio optimization model for stocks. Moreover, the study analyzes these methods with evolving S&P CME bitcoin futures index and the Crypto20 index. These analyses highlight the need for further investigation into traditional and data-driven approaches for resilient portfolio optimization, including higher-order moments, particularly under varying market conditions. This study provides valuable insights for investors and portfolio managers aiming to build resilient portfolios that could be used in different market environments.
{"title":"Resilient Portfolio Optimization using Traditional and Data-Driven Models for Cryptocurrencies and Stocks","authors":"Joylal Das, Sulalitha Bowala, R. Thulasiram, A. Thavaneswaran","doi":"10.1109/COMPSAC57700.2023.00204","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00204","url":null,"abstract":"Constructing resilient portfolios is of crucial and utmost importance to investment management. This study compares traditional and data-driven models for building resilient portfolios and analyzes their performance for stocks (S&P 500) and highly volatile cryptocurrency markets. The study investigates the performance of traditional models, such as mean-variance and constrained optimization, and a recently proposed data-driven resilient portfolio optimization model for stocks. Moreover, the study analyzes these methods with evolving S&P CME bitcoin futures index and the Crypto20 index. These analyses highlight the need for further investigation into traditional and data-driven approaches for resilient portfolio optimization, including higher-order moments, particularly under varying market conditions. This study provides valuable insights for investors and portfolio managers aiming to build resilient portfolios that could be used in different market environments.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 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":"128612196","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/COMPSAC57700.2023.00155
Yongju Lee, Hongzhou Duan, Yuxian Sun
The growing number of large scale RDF Big Data raises a challenging data management problem; how should RDF Big Data be stored, queried and integrated. We propose a novel semantic-based content convergence system which consists of acquisition, RDF storage, ontology learning and mashup subsystems. This system serves as a basis for implementing other more sophisticated applications required in the area of Linked Big Data.
{"title":"Semantically Enabled Content Convergence System for Large Scale RDF Big Data","authors":"Yongju Lee, Hongzhou Duan, Yuxian Sun","doi":"10.1109/COMPSAC57700.2023.00155","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00155","url":null,"abstract":"The growing number of large scale RDF Big Data raises a challenging data management problem; how should RDF Big Data be stored, queried and integrated. We propose a novel semantic-based content convergence system which consists of acquisition, RDF storage, ontology learning and mashup subsystems. This system serves as a basis for implementing other more sophisticated applications required in the area of Linked Big Data.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"227 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":"133231441","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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