2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)最新文献

{"title":"Human-Machine Collaboration for Smart Decision Making: Current Trends and Future Opportunities","authors":"Baocheng Geng, P. Varshney","doi":"10.1109/CIC56439.2022.00019","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00019","url":null,"abstract":"Recently, modeling of decision making and control systems that include heterogeneous smart sensing devices (machines) as well as human agents as participants is becoming an important research area due to the wide variety of applications including autonomous driving, smart manufacturing, internet of things, national security, and healthcare. To accomplish complex missions under uncertainty, it is imperative that we build novel human machine collaboration structures to integrate the cognitive strengths of humans with computational capabilities of machines in an intelligent manner. In this paper, we present an overview of the existing works on human decision making and human machine collaboration within the scope of signal processing and information fusion. We review several application areas and research domains relevant to human machine collaborative decision making. We also discuss current challenges and future directions in this problem domain.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116011889","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}

{"title":"Characterization of Emotional Contagion in Collaborative Decision Support Systems","authors":"Amab Sircar, M. Klein","doi":"10.1109/CIC56439.2022.00025","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00025","url":null,"abstract":"This research is focused on the analysis of how emotional aspects of people participating in decision-making through group discussions in online platforms manifest themselves. A particular emphasis is on the toxicity levels of discussions on various issues. Three platforms have been chosen for a comparative study: the first is a Reddit-like online forum, the second is Polis, where participants may vote on issues and is touted as a forum for computational democracy, and the third is called the Deliberatorium, developed at the Center for Collective Intelligence at the Massachusetts Institute of Technology. These platforms have their own specific structures that aid in group discussions on various issues. An important question that has been examined in this paper is how to characterize the contagion of toxicity in the three platforms and the extent to which their structures inhibit it. Our approach has been to first extract two sets of actual data from the three platforms: a control group and an experimental group where in the latter, an intervention is used by inserting an empathy statement to inhibit toxicity. Toxicity levels (determined using Google Perspective API) were compared using sample data from the three platforms. We use generative models that extend the sample datasets by adding synthetic nodes (artificial entries in group discussions) from the three platforms based on parameter values of popularity, novelty, root-bias, and reciprocity among participants. Two additional parameters of node type and toxicity score were added to these synthetic nodes. The generative models help in extending existing datasets to determine the contagion of toxicity in the platform. This is achieved by developing an index for toxicity contagion, ECtox expressed as a percentage of the total number of nodes. The idea of developing this index has been adapted from work in ecological studies where contagion of a certain type of land patch is examined in the presence of several different patch types. We use this metric to assess toxicity contagion in both the control and experimental groups of the three platforms. With the intervention of the empathy statement, Deliberatorium had an improvement in ECtox from 1.309% to 1.297%. In Forum, however, ECtox actually increased from 1.711% to 1.951%. Finally, in Polis, there was an improvement in ECtox from 1.750% to 1.663%. Thus, for Deliberatorium, the flow of toxicity is the least. We infer that there are inherent structural constructs in the design of the Deliberatorium, and that may naturally inhibit toxicity in group discussions.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126701961","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}

{"title":"A Survey of Mobile Edge Computing for the Metaverse: Architectures, Applications, and Challenges","authors":"Yitong Wang, Jun Zhao","doi":"10.1109/CIC56439.2022.00011","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00011","url":null,"abstract":"Metaverse is an emerging virtual universe where humans can have real-time interactions and solid social links like in the physical world, and it opens up a new era of Internet and interactions. In Metaverse, an immersive and photorealistic environment promotes social activities, including education, meetings, and shopping of digital avatars based on critical technologies, including 3D rendering, extended reality, digital twins, artificial intelligence, and Blockchain. However, the limitations of computation, storage, and energy resources restrict the development of Metaverse, and a series of system issues (e.g., latency, security, and battery-life) continue to arise. As a result, how to find corresponding measurements to mitigate unsatisfactory influences becomes the focus. Mobile edge computing (MEC) as a distributed computing paradigm offloads computation-intensive tasks to the edge of the network. It brings the resources as close as possible to the end devices, addressing the shortcomings mentioned above. In this paper, we propose a comprehensive survey of the MEC-based Metaverse. Particular emphasis is given to the technologies convergence, architectures, and application scenarios, e.g., BoundlessXR and CloudXR. Significantly, we introduce the potential future directions for developing Metaverse systems.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133704755","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}

{"title":"Cognitive Architectures and their Applications","authors":"C. Lebiere, Edward A. Cranford, Michael Martin, Don Morrison, Andrea Stocco","doi":"10.1109/CIC56439.2022.00018","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00018","url":null,"abstract":"Cognitive architectures are computational implementations of unified theories of cognition. The consensus of 50 years of research in cognitive architectures can be captured in the form of a Common Model of Cognition that can provide a guide for applications in neuroscience, artificial intelligence and robotics. Being able to represent human cognition in computational form enables a wide range of applications when humans and machines interact. Using cognitive models to represent common ground between deep learners and human users enables adaptive explanations. Cognitive models representing the behavior of cyber attackers can be used to optimize cyber defenses including techniques such as deceptive signaling. Cognitive models of human-automation interaction can improve robustness of human-machine teams by predicting disruptions to measures of trust under various adversarial situations.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125915087","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}

{"title":"Entropy-Based Characterization of Influence Pathways in Traditional and Social Media","authors":"O. Garibay, Niloofar Yousefi, Kevin Aslett, J. Baggio, Erik Hemberg, Chathura Jayalath, Alexander V. Mantzaris, Bruce Miller, Una-May O’Reilly, W. Rand, Chathurani Senevirathna, I. Garibay","doi":"10.1109/CIC56439.2022.00016","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00016","url":null,"abstract":"Despite much work on social media, analysis of individual influence campaigns, messages, and platforms, we lack the tools and techniques and fundamental research to effectively understand the information flows and their effects on the dynamics of the entire information ecosystem. For example, how information is amplified or dampened as it moves from one online community to another, how information is spinned or framed into narratives that favor or malign viewpoints organically or by foreign actors, how disinformation flows from fringe to mainstream communities, etc. We postulate that the information ecosystem is an attention economy, and that influence-the ability to gather attention towards a particular message or messages- is its currency. As a result, we model the information ecosystem as a complex network of influences flowing between actors, communities and platforms. This paper advocate for the use of information-theoretic entropic methods to model and characterize this complex network of influences over time: Influence Cascades Ecosystem (ICE). We envision leveraging the concept of influence cascades in conjunction with a novel geopolitical news-centric model of the information ecosystem in order to better understand the influence pathways by which various types of information (new articles from trusted, untrusted, fringe or mainstream sources) propagate across the social and traditional hybrid media environment.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116732323","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}

{"title":"DP-ADA: Differentially Private Adversarial Domain Adaptation for Training Deep Learning based Network Intrusion Detection Systems","authors":"Ankush Singla, E. Bertino","doi":"10.1109/CIC56439.2022.00023","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00023","url":null,"abstract":"Recent work has shown that deep learning (DL) techniques are highly effective for assisting network intrusion detection systems (NIDS) in identifying attacks on networks. Training DL classification models, however, requires vast amounts of labeled data which is often expensive and time-consuming to collect. Also, DL models trained using data from one type of network may not be able to identify attacks on other types of network or identify new families of attacks discovered over time. In this paper, we introduce a differentially private adversarial DA (DP-ADA) workflow which allows organizations to share their labeled data with other organizations in a privacy preserving way. This workflow allows for more collaboration and sharing, so that more effective DL based NIDS models can be created for deployment on different types of networks and can detect newer attack families with very little effort. Our solution provides a much better performance than fine-tuning based transfer learning mechanism and almost matches the performance of adversarial DA when the actual source dataset is used, while at the same time reducing the size of data shared between the two parties. Our solution also provides privacy protection for heterogeneous DA, where source and target datasets have different feature dimensions.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172375","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}

{"title":"SciLedger: A Blockchain-based Scientific Workflow Provenance and Data Sharing Platform","authors":"Reagan Hoopes, Hamilton Hardy, Min Long, Gaby G. Dagher","doi":"10.1109/CIC56439.2022.00027","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00027","url":null,"abstract":"Researchers collaborating from different locations need a method to capture and store scientific workflow provenance that guarantees provenance integrity and reproducibility. As modern science is moving towards greater data accessibility, researchers also need a platform for open access data sharing. We propose SciLedger, a blockchain-based platform that provides secure, trustworthy storage for scientific workflow provenance to reduce research fabrication and falsification. SciLedger utilizes a novel invalidation mechanism that only invalidates necessary provenance records. SciLedger also allows for workflows with complex structures to be stored on a single blockchain so that researchers can utilize existing data in their scientific workflows by branching from and merging existing workflows. Our experimental results show that SciLedger provides an able solution for maintaining academic integrity and research flexibility within scientific workflows.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126893678","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}

{"title":"Steering Committee","authors":"J. Pokorný, Karel Richta","doi":"10.1109/CCGRID.2006.99","DOIUrl":"https://doi.org/10.1109/CCGRID.2006.99","url":null,"abstract":"The Adaptive Application Structure (AAS) software design resolves the problem with different users needs. It changes its structure based on the current context. Every user has different needs so it is quite challenging problem because the structure of application can be created with quite large number of options. The AAS-approach advantages are clear in form of flexible software design. Developer does not have to create different types of application based on users needs. This approach removes the problem of user modeling (e.g. Personas) for UI developers. As a resource of information about user we can use not only filled information or information about his behavior. We can use also context that will focus on his emotions while he uses the application. This paper considers emotions as a valid and important part of context-aware design.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128366703","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}

{"title":"A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels","authors":"Subash Neupane, Ivan A. Fernandez, Wilson Patterson, Sudip Mittal, Shahram Rahimi","doi":"10.1109/CIC56439.2022.00024","DOIUrl":"https://doi.org/10.1109/CIC56439.2022.00024","url":null,"abstract":"A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.","PeriodicalId":170721,"journal":{"name":"2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966857","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}