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1. Online Delivery of Social Media Posts to Appropriate First Responders for Disaster Response

   https://doi.org/10.1145/3427477.3429272  

   Mittal, Viyom ; Jahanian, Mohammad ; Ramakrishnan, K. K. ( December 2020 , In 3rd International Workshop on Emergency Response Technologies and Services in Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking (ICDCN ’21))

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   Delivering the right information to the right people in a timely manner can greatly improve outcomes and save lives in emergency response. A communication framework that flexibly and efficiently brings victims, volunteers, and first responders together for timely assistance can be very helpful. With the burden of more frequent and intense disaster situations and first responder resources stretched thin, people increasingly depend on social media for communicating vital information. This paper proposes ONSIDE, a framework for coordination of disaster response leveraging social media, integrating it with Information-Centric dissemination for timely and relevant dissemination. We use a graph-based pub/sub namespace that captures the complex hierarchy of the incident management roles. Regular citizens and volunteers using social media may not know of or have access to the full namespace. Thus, we utilize a social media engine (SME) to identify disaster-related social media posts and then automatically map them to the right name(s) in near-real-time. Using NLP and classification techniques, we direct the posts to appropriate first responder(s) that can help with the posted issue. A major challenge for classifying social media in real-time is the labeling effort for model training. Furthermore, as disasters hits, there may be not enough data points available for labeling, and there may be concept drift in the content of the posts over time. To address these issues, our SME employs stream-based active learning methods, adapting as social media posts come in. Preliminary evaluation results show the proposed solution can be effective. 

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2. FLARE: federated active learning assisted by naming for responding to emergencies

   https://doi.org/10.1145/3460417.3482978  

   Mittal, Viyom ; Jahanian, Mohammad ; Ramakrishnan, K. K. ( September 2021 , ICN '21: Proceedings of the 8th ACM Conference on Information-Centric Networking)

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   During disasters, it is critical to deliver emergency information to appropriate first responders. Name-based information delivery provides efficient, timely dissemination of relevant content to first responder teams assigned to different incident response roles. People increasingly depend on social media for communicating vital information, using free-form text. Thus, a method that delivers these social media posts to the right first responders can significantly improve outcomes. In this paper, we propose FLARE, a framework using 'Social Media Engines' (SMEs) to map social media posts (SMPs), such as tweets, to the right names. SMEs perform natural language processing-based classification and exploit several machine learning capabilities, in an online real-time manner. To reduce the manual labeling effort required for learning during the disaster, we leverage active learning, complemented by dispatchers with specific domain-knowledge performing limited labeling. We also leverage federated learning across various public-safety departments with specialized knowledge to handle notifications related to their roles in a cooperative manner. We implement three different classifiers: for incident relevance, organization, and fine-grained role prediction. Each class is associated with a specific subset of the namespace graph. The novelty of our system is the integration of the namespace with federated active learning and inference procedures to identify and deliver vital SMPs to the right first responders in a distributed multi-organization environment, in real-time. Our experiments using real-world data, including tweets generated by citizens during the wildfires in California in 2018, show our approach outperforming both a simple keyword-based classification and several existing NLP-based classification techniques. 

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3. Graph-Based Namespaces and Load Sharing for Efficient Information Dissemination

   https://doi.org/10.1109/TNET.2021.3094839  

   Jahanian, Mohammad ; Chen, Jiachen ; Ramakrishnan, K. K. ( July 2021 , IEEE/ACM Transactions on Networking)

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   Graph-based namespaces are being increasingly used to represent the organization of complex and ever-growing information eco-systems and individual user roles. Timely and accurate information dissemination requires an architecture with appropriate naming frameworks, adaptable to changing roles, focused on content rather than network addresses. Today's complex information organization structures make such dissemination very challenging. To address this, we propose POISE, a name-based publish/subscribe architecture for efficient topic-based and recipient-based content dissemination. POISE proposes an information layer, improving on state-of-the-art Information-Centric Networking solutions in two major ways: 1) support for complex graph-based namespaces, and 2) automatic name-based load-splitting. POISE supports in-network graph-based naming, leveraged in a dissemination protocol which exploits information layer rendezvous points (RPs) that perform name expansions. For improved robustness and scalability, POISE supports adaptive load-sharing via multiple RPs, each managing a dynamically chosen subset of the namespace graph. Excessive workload may cause one RP to turn into a ``hot spot'', impeding performance and reliability. To eliminate such traffic concentration, we propose an automated load-splitting mechanism, consisting of an enhanced, namespace graph partitioning complemented by a seamless, loss-less core migration procedure. Due to the nature of our graph partitioning and its complex objectives, off-the-shelf graph partitioning, e.g., METIS, is inadequate. We propose a hybrid, iterative bi-partitioning solution, consisting of an initial and a refinement phase. We also implemented POISE on a DPDK-based platform. Using the important application of emergency response, our experimental results show that POISE outperforms state-of-the-art solutions, demonstrating its effectiveness in timely delivery and load-sharing. 

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4. Graph-based Namespaces and Load Sharing for Efficient Information Dissemination in Disasters

   https://doi.org/10.1109/ICNP.2019.8888047  

   Jahanian, Mohammad ; Chen, Jiachen ; Ramakrishnan, K. K. ( October 2019 , 2019 IEEE 27th International Conference on Network Protocols (ICNP))

   Timely, flexible and accurate information dissemination can make a life-and-death difference in managing disasters. Complex command structures and information organization make such dissemination challenging. Thus, it is vital to have an architecture with appropriate naming frameworks, adaptable to the changing roles of participants, focused on content rather than network addresses. To address this, we propose POISE, a name-based and recipient-based publish/subscribe architecture for efficient content dissemination in disaster management. POISE proposes an information layer, improving on state-of-the-art Information-Centric Networking (ICN) solutions such as Named Data Networking (NDN) in two major ways: 1) support for complex graph-based namespaces, and 2) automatic name-based load-splitting. To capture the complexity and dynamicity of disaster response command chains and information flows, POISE proposes a graph-based naming framework, leveraged in a dissemination protocol which exploits information layer rendezvous points (RPs) that perform name expansions. For improved robustness and scalability, POISE allows load-sharing via multiple RPs each managing a subset of the namespace graph. However, excessive workload on one RP may turn it into a “hot spot”, thus impeding performance and reliability. To eliminate such traffic concentration, we propose an automatic load-splitting mechanism, consisting of a namespace graph partitioning complemented by a seamless, loss-less core migration procedure. Due to the nature of our graph partitioning and its complex objectives, off-the-shelf graph partitioning, e.g., METIS, is inadequate. We propose a hybrid partitioning solution, consisting of an initial and a refinement phase. Our simulation results show that POISE outperforms state-of-the-art solutions, demonstrating its effectiveness in timely delivery and load-sharing. 

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5. Layer-based, depth-resolved computation of attenuation coefficients and backscattering fractions in tissue using optical coherence tomography

   https://doi.org/10.1364/BOE.427833  

   Cannon, Taylor M. ; Bouma, Brett E. ; Uribe-Patarroyo, Néstor ( July 2021 , Biomedical Optics Express)

   Structural optical coherence tomography (OCT) images of tissue stand to benefit from greater functionalization and quantitative interpretation. The OCT attenuation coefficientµ, an analogue of the imaged sample’s scattering coefficient, offers potential functional contrast based on the relationship ofµto sub-resolution physical properties of the sample. Attenuation coefficients are computed either by fitting a representativeµover several depth-wise pixels of a sample’s intensity decay, or by using previously-developed depth-resolved attenuation algorithms by Girardet al.[Invest. Ophthalmol. Vis. Sci.52,7738(2011).10.1167/iovs.10-6925] and Vermeeret al.[Biomed. Opt. Express5,322(2014).10.1364/BOE.5.000322]. However, the former method sacrifices axial information in the tomogram, while the latter relies on the stringent assumption that the sample’s backscattering fraction, another optical property, does not vary along depth. This assumption may be violated by layered tissues commonly observed in clinical imaging applications. Our approach preserves the full depth resolution of the attenuation map but removes its dependence on backscattering fraction by performing signal analysis inside individual discrete layers over which the scattering properties (e.g., attenuation and backscattering fraction) vary minimally. Although this approach necessitates the detection of these layers, it removes the constant-backscattering-fraction assumption that has constrained quantitative attenuation coefficient analysis in the past, and additionally yields a layer-resolved backscattering fraction, providing complementary scattering information to the attenuation coefficient. We validate our approach using automated layer detection in layered phantoms, for which the measured optical properties were in good agreement with theoretical values calculated with Mie theory, and show preliminary results in tissue alongside corresponding histological analysis. Together, accurate backscattering fraction and attenuation coefficient measurements enable the estimation of both particle density and size, which is not possible from attenuation measurements alone. We hope that this improvement to depth-resolved attenuation coefficient measurement, augmented by a layer-resolved backscattering fraction, will increase the diagnostic power of quantitative OCT imaging.

    

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