Pub Date : 2023-05-18DOI: https://dl.acm.org/doi/10.1145/3543854
Yi-Bing Lin, Yuan-Fu Liao, Sin-Horng Chen, Shaw-Hwa Hwang, Yih-Ru Wang
The voice-based Internet of Multimedia Things (IoMT) is the combination of IoT interfaces and protocols with associated voice-related information, which enables advanced applications based on human-to-device interactions. An example is Automatic Speech Recognition (ASR) for live captioning and voice translation. Three major issues of ASR for IoMT are IoT development cost, speech recognition accuracy, and execution time complexity. For the first issue, most non-voice IoT applications are upgraded with the ASR feature through hard coding, which are error prone. For the second issue, recognition accuracy must be improved for ASR. For the third issue, many multimedia IoT services are real-time applications and, therefore, the ASR delay must be short.
This article elaborates on the above issues based on an IoT platform called VoiceTalk. We built the largest Taiwanese spoken corpus to train VoiceTalk ASR (VT-ASR) and show how the VT-ASR mechanism can be transparently integrated with existing IoT applications. We consider two performance measures for VoiceTalk: speech recognition accuracy and VT-ASR delay. For the acoustic tests of PAL-Labs, VT-ASR's accuracy is 96.47%, while Google's accuracy is 94.28%. We are the first to develop an analytic model to investigate the probability that the VT-ASR delay for the first speaker is complete before the second speaker starts talking. From the measurements and analytic modeling, we show that the VT-ASR delay is short enough to result in a very good user experience. Our solution has won several important government and commercial TV contracts in Taiwan. VT-ASR has demonstrated better Taiwanese Mandarin speech recognition accuracy than famous commercial products (including Google and Iflytek) in Formosa Speech Recognition Challenge 2018 (FSR-2018) and was the best among all participating ASR systems for Taiwanese recognition accuracy in FSR-2020.
基于语音的多媒体物联网(IoMT)是物联网接口和协议与相关语音相关信息的结合,它使基于人与设备交互的高级应用成为可能。一个例子是用于实时字幕和语音翻译的自动语音识别(ASR)。物联网ASR的三个主要问题是物联网开发成本、语音识别准确性和执行时间复杂性。对于第一个问题,大多数非语音物联网应用都是通过硬编码升级ASR功能的,这很容易出错。对于第二个问题,必须提高ASR的识别精度。对于第三个问题,许多多媒体物联网服务是实时应用,因此ASR延迟必须短。本文基于一个名为VoiceTalk的物联网平台详细阐述了上述问题。我们建立了最大的台湾口语语料库来训练VoiceTalk ASR (VT-ASR),并展示了VT-ASR机制如何与现有的物联网应用透明地集成。我们考虑了VoiceTalk的两个性能指标:语音识别精度和VT-ASR延迟。对于PAL-Labs的声学测试,VT-ASR的准确率为96.47%,而Google的准确率为94.28%。我们首先开发了一个分析模型来研究第一个说话者的VT-ASR延迟在第二个说话者开始说话之前完成的概率。从测量和分析建模中,我们表明VT-ASR延迟足够短,可以产生非常好的用户体验。我们的解决方案在台湾赢得了几个重要的政府和商业电视合同。在2018台塑语音识别挑战赛(FSR-2018)中,VT-ASR的台湾普通话识别准确率优于知名商用产品(包括Google和科大讯飞),在FSR-2020中,VT-ASR在所有参赛的ASR系统中台湾识别准确率最高。
{"title":"VoiceTalk: Multimedia-IoT Applications for Mixing Mandarin, Taiwanese, and English","authors":"Yi-Bing Lin, Yuan-Fu Liao, Sin-Horng Chen, Shaw-Hwa Hwang, Yih-Ru Wang","doi":"https://dl.acm.org/doi/10.1145/3543854","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3543854","url":null,"abstract":"<p>The voice-based Internet of Multimedia Things (IoMT) is the combination of IoT interfaces and protocols with associated voice-related information, which enables advanced applications based on human-to-device interactions. An example is Automatic Speech Recognition (ASR) for live captioning and voice translation. Three major issues of ASR for IoMT are IoT development cost, speech recognition accuracy, and execution time complexity. For the first issue, most non-voice IoT applications are upgraded with the ASR feature through hard coding, which are error prone. For the second issue, recognition accuracy must be improved for ASR. For the third issue, many multimedia IoT services are real-time applications and, therefore, the ASR delay must be short.</p><p>This article elaborates on the above issues based on an IoT platform called VoiceTalk. We built the largest Taiwanese spoken corpus to train <b>VoiceTalk ASR (VT-ASR)</b> and show how the VT-ASR mechanism can be transparently integrated with existing IoT applications. We consider two performance measures for VoiceTalk: speech recognition accuracy and VT-ASR delay. For the acoustic tests of PAL-Labs, VT-ASR's accuracy is 96.47%, while Google's accuracy is 94.28%. We are the first to develop an analytic model to investigate the probability that the VT-ASR delay for the first speaker is complete before the second speaker starts talking. From the measurements and analytic modeling, we show that the VT-ASR delay is short enough to result in a very good user experience. Our solution has won several important government and commercial TV contracts in Taiwan. VT-ASR has demonstrated better Taiwanese Mandarin speech recognition accuracy than famous commercial products (including Google and Iflytek) in Formosa Speech Recognition Challenge 2018 (FSR-2018) and was the best among all participating ASR systems for Taiwanese recognition accuracy in FSR-2020.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: https://dl.acm.org/doi/10.1145/3584020
Yibin Xu, Jianhua Shao, Tijs Slaats, Boris Düdder
Blockchain sharding splits a blockchain into several shards where consensus is reached at the shard level rather than over the entire blockchain. It improves transaction throughput and reduces the computational resources required of individual nodes. But a derivation of trustworthy consensus within a shard becomes an issue as the longest chain based mechanisms used in conventional blockchains can no longer be used. Instead, a vote-based consensus mechanism must be employed. However, existing vote-based Byzantine fault tolerance consensus protocols do not offer sufficient security guarantees for sharded blockchains. First, when used to support consensus where only one block is allowed at a time (binary consensus), these protocols are susceptible to progress-hindering attacks (i.e., unable to reach a consensus). Second, when used to support a stronger type of consensus where multiple concurrent blocks are allowed (strong consensus), their tolerance of adversary nodes is low. This article proposes a new consensus protocol to address all these issues. We call the new protocol MWPoW+, as its basic framework is based on the existing Multiple Winners Proof of Work (MWPoW) protocol but includes new mechanisms to address the issues mentioned previously. MWPoW+ is a vote-based protocol for strong consensus, asynchronous in consensus derivation but synchronous in communication. We prove that it can tolerate up to f < n/2 adversary nodes in a n-node system as if using a binary consensus protocol and does not suffer from progress-hindering attacks.
{"title":"MWPoW+: A Strong Consensus Protocol for Intra-Shard Consensus in Blockchain Sharding","authors":"Yibin Xu, Jianhua Shao, Tijs Slaats, Boris Düdder","doi":"https://dl.acm.org/doi/10.1145/3584020","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3584020","url":null,"abstract":"<p>Blockchain sharding splits a blockchain into several shards where consensus is reached at the shard level rather than over the entire blockchain. It improves transaction throughput and reduces the computational resources required of individual nodes. But a derivation of trustworthy consensus within a shard becomes an issue as the longest chain based mechanisms used in conventional blockchains can no longer be used. Instead, a vote-based consensus mechanism must be employed. However, existing vote-based Byzantine fault tolerance consensus protocols do not offer sufficient security guarantees for sharded blockchains. First, when used to support consensus where only one block is allowed at a time (binary consensus), these protocols are susceptible to progress-hindering attacks (i.e., unable to reach a consensus). Second, when used to support a stronger type of consensus where multiple concurrent blocks are allowed (strong consensus), their tolerance of adversary nodes is low. This article proposes a new consensus protocol to address all these issues. We call the new protocol <i>MWPoW</i>+, as its basic framework is based on the existing Multiple Winners Proof of Work (MWPoW) protocol but includes new mechanisms to address the issues mentioned previously. MWPoW+ is a vote-based protocol for strong consensus, asynchronous in consensus derivation but synchronous in communication. We prove that it can tolerate up to <i>f</i> < <i>n</i>/2 adversary nodes in a n-node system as if using a binary consensus protocol and does not suffer from progress-hindering attacks.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: https://dl.acm.org/doi/10.1145/3533430
Yazhou Zhang, Dan Ma, Prayag Tiwari, Chen Zhang, Mehedi Masud, Mohammad Shorfuzzaman, Dawei Song
Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.
{"title":"Stance-level Sarcasm Detection with BERT and Stance-centered Graph Attention Networks","authors":"Yazhou Zhang, Dan Ma, Prayag Tiwari, Chen Zhang, Mehedi Masud, Mohammad Shorfuzzaman, Dawei Song","doi":"https://dl.acm.org/doi/10.1145/3533430","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3533430","url":null,"abstract":"<p>Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT)-enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, home automation, and so on. Due to the emergence of machine translation, CL solutions have increased tremendously for different natural language processing (NLP) applications. Nowadays, NLP-enabled IoMT is essential for its success. Sarcasm detection, a recently emerging artificial intelligence (AI) and NLP task, aims at discovering sarcastic, ironic, and metaphoric information implied in texts that are generated in the IoMT. It has drawn much attention from the AI and IoMT research community. The advance of sarcasm detection and NLP techniques will provide a cost-effective, intelligent way to work together with machine devices and high-level human-to-device interactions. However, existing sarcasm detection approaches neglect the hidden stance behind texts, thus insufficient to exploit the full potential of the task. Indeed, the stance, i.e., whether the author of a text is in favor of, against, or neutral toward the proposition or target talked in the text, largely determines the text’s actual sarcasm orientation. To fill the gap, in this research, we propose a new task: stance-level sarcasm detection (SLSD), where the goal is to uncover the author’s latent stance and based on it to identify the sarcasm polarity expressed in the text. We then propose an integral framework, which consists of Bidirectional Encoder Representations from Transformers (BERT) and a novel stance-centered graph attention networks (SCGAT). Specifically, BERT is used to capture the sentence representation, and SCGAT is designed to capture the stance information on specific target. Extensive experiments are conducted on a Chinese sarcasm sentiment dataset we created and the SemEval-2018 Task 3 English sarcasm dataset. The experimental results prove the effectiveness of the SCGAT framework over state-of-the-art baselines by a large margin.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-18DOI: https://dl.acm.org/doi/10.1145/3533431
Mu-Yen Chen, Yi-Wei Lai, Jiunn-Woei Lian
The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of Coronavirus Disease (COVID-19), a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the World Health Organization (WHO), posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and compare them based on different text feature selections. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The long and short-term memory (LSTM) model, the gated recurrent unit (GRU) model, and the bidirectional long and short-term memory (BiLSTM) model were selected for fake news detection. BiLSTM produces the best detection result, with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82%.
{"title":"Using Deep Learning Models to Detect Fake News about COVID-19","authors":"Mu-Yen Chen, Yi-Wei Lai, Jiunn-Woei Lian","doi":"https://dl.acm.org/doi/10.1145/3533431","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3533431","url":null,"abstract":"<p>The proliferation of mobile networked devices has made it easier and faster than ever for people to obtain and share information. However, this occasionally results in the propagation of erroneous information, which may be difficult to distinguish from the truth. The widespread diffusion of such information can result in irrational and poor decision making on potentially important issues. In 2020, this coincided with the global outbreak of <b>Coronavirus Disease (COVID-19)</b>, a highly contagious and deadly virus. The proliferation of misinformation about COVID-19 on social media has already been identified as an “infodemic” by the <b>World Health Organization (WHO)</b>, posing significant challenges for global governments seeking to manage the pandemic. This has driven an urgent need for methods to automatically detect and identify such misinformation. The research uses multiple deep learning model frameworks to detect misinformation in Chinese and English, and <b>compare them based on different text feature selection</b>s. The model learns the textual characteristics of each type of true and misinformation for subsequent true/false prediction. The <b>long and short-term memory (LSTM)</b> model, the <b>gated recurrent unit (GRU)</b> model, and the <b>bidirectional long and short-term memory (BiLSTM)</b> model were selected for fake news detection. BiLSTM produces the best detection result, <b>with detection accuracy reaching 94% for short-sentence English texts, and 99% for long-sentence English texts, while the accuracy for Chinese texts was 82%</b>.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Usman Ahmed, Jerry Chun‐wei Lin, Gautam Srivastava
Cyber-manufacturing Systems (CMS) have been growing in popularity. Transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Thus, it is likely that this new manufacturing model will become increasingly popular shortly. By building new technologies on top of existing CMS, these systems ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim. There are still some challenges to overcome, such as ensuring data reliability, but these can be overcome with further research and development. In summary, the use of CMS can revolutionize the manufacturing industry. This paper comprehensively analyses these systems and their potential applications and implications. The article gives an overview of the field and then explores the various aspects of CMS in greater detail. A taxonomy of the most common and current approaches to cyber-manufacturing systems is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, the paper identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, the paper identifies several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. The paper provides a comprehensive overview of current technology and valuable insights into the potential impact of the technology on society and industry.
{"title":"Exploring the Potential of Cyber Manufacturing Systems in the Digital Age","authors":"Usman Ahmed, Jerry Chun‐wei Lin, Gautam Srivastava","doi":"10.1145/3596602","DOIUrl":"https://doi.org/10.1145/3596602","url":null,"abstract":"Cyber-manufacturing Systems (CMS) have been growing in popularity. Transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Thus, it is likely that this new manufacturing model will become increasingly popular shortly. By building new technologies on top of existing CMS, these systems ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim. There are still some challenges to overcome, such as ensuring data reliability, but these can be overcome with further research and development. In summary, the use of CMS can revolutionize the manufacturing industry. This paper comprehensively analyses these systems and their potential applications and implications. The article gives an overview of the field and then explores the various aspects of CMS in greater detail. A taxonomy of the most common and current approaches to cyber-manufacturing systems is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, the paper identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, the paper identifies several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. The paper provides a comprehensive overview of current technology and valuable insights into the potential impact of the technology on society and industry.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44081378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-08DOI: https://dl.acm.org/doi/10.1145/3596602
Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava
Cyber-manufacturing Systems (CMS) have been growing in popularity. Transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Thus, it is likely that this new manufacturing model will become increasingly popular shortly. By building new technologies on top of existing CMS, these systems ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim. There are still some challenges to overcome, such as ensuring data reliability, but these can be overcome with further research and development. In summary, the use of CMS can revolutionize the manufacturing industry. This paper comprehensively analyses these systems and their potential applications and implications. The article gives an overview of the field and then explores the various aspects of CMS in greater detail. A taxonomy of the most common and current approaches to cyber-manufacturing systems is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, the paper identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, the paper identifies several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. The paper provides a comprehensive overview of current technology and valuable insights into the potential impact of the technology on society and industry.
{"title":"Exploring the Potential of Cyber Manufacturing Systems in the Digital Age","authors":"Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava","doi":"https://dl.acm.org/doi/10.1145/3596602","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3596602","url":null,"abstract":"<p>Cyber-manufacturing Systems (CMS) have been growing in popularity. Transitioning from conventional manufacturing to an innovative paradigm that emphasizes innovation, automation, better customer service, and intelligent systems. A new manufacturing model can improve efficiency and productivity, and provide better customer service and response times. In addition, it may revolutionize the way products are produced, from design to completion. Thus, it is likely that this new manufacturing model will become increasingly popular shortly. By building new technologies on top of existing CMS, these systems ensure that data exchange and integration between decentralized systems are reliable and secure. Recently published case studies from industry and the literature support this claim. There are still some challenges to overcome, such as ensuring data reliability, but these can be overcome with further research and development. In summary, the use of CMS can revolutionize the manufacturing industry. This paper comprehensively analyses these systems and their potential applications and implications. The article gives an overview of the field and then explores the various aspects of CMS in greater detail. A taxonomy of the most common and current approaches to cyber-manufacturing systems is presented, including networked cyber-manufacturing systems, distributed cyber-manufacturing systems, cloud-based cyber-manufacturing systems, and cyber-physical systems (CPS). Furthermore, the paper identifies several popular open-source software and datasets and discusses how these resources can reduce barriers to CMS research. In addition, the paper identifies several important issues and research opportunities associated with CMS, including better integration between hardware and software, improved security and privacy protocols, communication protocols, and improved data management systems. The paper provides a comprehensive overview of current technology and valuable insights into the potential impact of the technology on society and industry.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138533486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Gioacchini, L. Vassio, M. Mellia, I. Drago, Z. B. Houidi, Dario Rossi
Darknets are probes listening to traffic reaching IP addresses that host no services. Traffic reaching a darknet results from the actions of internet scanners, botnets, and possibly misconfigured hosts. Such peculiar nature of the darknet traffic makes darknets a valuable instrument to discover malicious online activities, e.g., identifying coordinated actions performed by bots or scanners. However, the massive amount of packets and sources that darknets observe makes it hard to extract meaningful insights, calling for scalable tools to automatically identify and group sources that share similar behaviour. We here present i-DarkVec, a methodology to learn meaningful representations of Darknet traffic. i-DarkVec leverages Natural Language Processing techniques (e.g., Word2Vec) to capture the co-occurrence patterns that emerge when scanners or bots launch coordinated actions. As in NLP problems, the embeddings learned with i-DarkVec enable several new machine learning tasks on the darknet traffic, such as identifying clusters of senders engaged in similar activities. We extensively test i-DarkVec and explore its design space in a case study using real darknets. We show that with a proper definition of services, the learned embeddings can be used to (i) solve the classification problem to associate unknown sources’ IP addresses to the correct classes of coordinated actors and (ii) automatically identify clusters of previously unknown sources performing similar attacks and scans, easing the security analyst’s job. i-DarkVec leverages a novel incremental embedding learning approach that is scalable and robust to traffic changes, making it applicable to dynamic and large-scale scenarios.
{"title":"i-DarkVec: Incremental Embeddings for Darknet Traffic Analysis","authors":"L. Gioacchini, L. Vassio, M. Mellia, I. Drago, Z. B. Houidi, Dario Rossi","doi":"10.1145/3595378","DOIUrl":"https://doi.org/10.1145/3595378","url":null,"abstract":"Darknets are probes listening to traffic reaching IP addresses that host no services. Traffic reaching a darknet results from the actions of internet scanners, botnets, and possibly misconfigured hosts. Such peculiar nature of the darknet traffic makes darknets a valuable instrument to discover malicious online activities, e.g., identifying coordinated actions performed by bots or scanners. However, the massive amount of packets and sources that darknets observe makes it hard to extract meaningful insights, calling for scalable tools to automatically identify and group sources that share similar behaviour. We here present i-DarkVec, a methodology to learn meaningful representations of Darknet traffic. i-DarkVec leverages Natural Language Processing techniques (e.g., Word2Vec) to capture the co-occurrence patterns that emerge when scanners or bots launch coordinated actions. As in NLP problems, the embeddings learned with i-DarkVec enable several new machine learning tasks on the darknet traffic, such as identifying clusters of senders engaged in similar activities. We extensively test i-DarkVec and explore its design space in a case study using real darknets. We show that with a proper definition of services, the learned embeddings can be used to (i) solve the classification problem to associate unknown sources’ IP addresses to the correct classes of coordinated actors and (ii) automatically identify clusters of previously unknown sources performing similar attacks and scans, easing the security analyst’s job. i-DarkVec leverages a novel incremental embedding learning approach that is scalable and robust to traffic changes, making it applicable to dynamic and large-scale scenarios.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49505233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-03DOI: https://dl.acm.org/doi/10.1145/3595378
Luca Gioacchini, Luca Vassio, Marco Mellia, Idilio Drago, Zied Ben Houidi
Darknets are probes listening to traffic reaching IP addresses that host no services. Traffic reaching a darknet results from the actions of internet scanners, botnets and possibly misconfigured hosts. Such peculiar nature of the darknet traffic makes darknets a valuable instrument to discover malicious online activities, e.g., identifying coordinated actions performed by bots or scanners. However, the massive amount of packets and sources that darknets observe makes it hard to extract meaningful insights, calling for scalable tools to automatically identify and group sources that share similar behaviour.
We here present i-DarkVec, a methodology to learn meaningful representations of Darknet traffic. i-DarkVec leverages Natural Language Processing techniques (e.g., Word2Vec) to capture the co-occurrence patterns that emerge when scanners or bots launch coordinated actions. As in NLP problems, the embeddings learned with i-DarkVec enable several new machine learning tasks on the darknet traffic, such as identifying clusters of senders engaged in similar activities.
We extensively test i-DarkVec and explore its design space in a case study using real darknets. We show that with a proper definition of services, the learned embeddings can be used to (i) solve the classification problem to associate unknown sources’ IP addresses to the correct classes of coordinated actors, and (ii) automatically identify clusters of previously unknown sources performing similar attacks and scans, easing the security analyst’s job. i-DarkVec leverages a novel incremental embedding learning approach that is scalable and robust to traffic changes, making it applicable to dynamic and large-scale scenarios.
{"title":"i-DarkVec: Incremental Embeddings for Darknet Traffic Analysis","authors":"Luca Gioacchini, Luca Vassio, Marco Mellia, Idilio Drago, Zied Ben Houidi","doi":"https://dl.acm.org/doi/10.1145/3595378","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3595378","url":null,"abstract":"<p>Darknets are probes listening to traffic reaching IP addresses that host no services. Traffic reaching a darknet results from the actions of internet scanners, botnets and possibly misconfigured hosts. Such peculiar nature of the darknet traffic makes darknets a valuable instrument to discover malicious online activities, e.g., identifying coordinated actions performed by bots or scanners. However, the massive amount of packets and sources that darknets observe makes it hard to extract meaningful insights, calling for scalable tools to automatically identify and group sources that share similar behaviour. </p><p>We here present i-DarkVec, a methodology to learn meaningful representations of Darknet traffic. i-DarkVec leverages Natural Language Processing techniques (e.g., Word2Vec) to capture the co-occurrence patterns that emerge when scanners or bots launch coordinated actions. As in NLP problems, the embeddings learned with i-DarkVec enable several new machine learning tasks on the darknet traffic, such as identifying clusters of senders engaged in similar activities. </p><p>We extensively test i-DarkVec and explore its design space in a case study using real darknets. We show that with a proper definition of <i>services</i>, the learned embeddings can be used to (i) solve the classification problem to associate unknown sources’ IP addresses to the correct classes of coordinated actors, and (ii) automatically identify clusters of previously unknown sources performing similar attacks and scans, easing the security analyst’s job. i-DarkVec leverages a novel incremental embedding learning approach that is scalable and robust to traffic changes, making it applicable to dynamic and large-scale scenarios.</p>","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138541989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lizhen Deng, Guoxia Xu, Jiaqi Pi, Hu Zhu, Xiaokang Zhou
Cyber-Manufacturing combines industrial big data with intelligent analysis to find and understand the intangible problems in decision-making, which requires a systematic method to deal with rich signal data. With the development of spectral detection and photoelectric imaging technology, spectral blind deconvolution has achieved remarkable results. However, spectral processing is limited by one-dimensional signal, and there is no available structural information with few training samples. Moreover, in the majority of practical applications, it is entirely feasible to gather unpaired spectrum dataset for training. This training method of unpaired learning is practical and valuable. Therefore, a two-stage deconvolution scheme combining self supervised learning and feature extraction is proposed in this paper, which generates two complementary paired sets through self supervised learning to extract the final deconvolution network. In addition, a new deconvolution network is designed for feature extraction. The spectrum is pre-trained through spectral feature extraction and noise estimation network to improve the training efficiency and meet the assumed noise characteristics. Experimental results show that this method is effective in dealing with different types of synthetic noise.
{"title":"Unpaired Self-supervised Learning for Industrial Cyber-Manufacturing Spectrum Blind Deconvolution","authors":"Lizhen Deng, Guoxia Xu, Jiaqi Pi, Hu Zhu, Xiaokang Zhou","doi":"10.1145/3590963","DOIUrl":"https://doi.org/10.1145/3590963","url":null,"abstract":"Cyber-Manufacturing combines industrial big data with intelligent analysis to find and understand the intangible problems in decision-making, which requires a systematic method to deal with rich signal data. With the development of spectral detection and photoelectric imaging technology, spectral blind deconvolution has achieved remarkable results. However, spectral processing is limited by one-dimensional signal, and there is no available structural information with few training samples. Moreover, in the majority of practical applications, it is entirely feasible to gather unpaired spectrum dataset for training. This training method of unpaired learning is practical and valuable. Therefore, a two-stage deconvolution scheme combining self supervised learning and feature extraction is proposed in this paper, which generates two complementary paired sets through self supervised learning to extract the final deconvolution network. In addition, a new deconvolution network is designed for feature extraction. The spectrum is pre-trained through spectral feature extraction and noise estimation network to improve the training efficiency and meet the assumed noise characteristics. Experimental results show that this method is effective in dealing with different types of synthetic noise.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48915509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-03DOI: https://dl.acm.org/doi/10.1145/3590963
Lizhen Deng, Guoxia Xu, Jiaqi Pi, Hu Zhu, Xiaokang Zhou
Cyber-Manufacturing combines industrial big data with intelligent analysis to find and understand the intangible problems in decision-making, which requires a systematic method to deal with rich signal data. With the development of spectral detection and photoelectric imaging technology, spectral blind deconvolution has achieved remarkable results. However, spectral processing is limited by one-dimensional signal, there is no available structural information with little training samples. Moreover, in most practical applications, it is feasible to collect unpaired noise and clean spectrum. This training method of unpaired learning is practical and valuable. Therefore, a two-stage deconvolution scheme combining self supervised learning and feature extraction is proposed in this paper, which generates two complementary paired sets through self supervised learning to extract the final deconvolution network. In addition, a new deconvolution network is designed for feature extraction. The spectrum is pre-trained through spectral feature extraction and noise estimation network to improve the training efficiency and meet the assumed noise characteristics. Experimental results show that this method is effective in dealing with different types of synthetic noise.
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