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1. Automated Network Services for Exascale Data Movement

   https://doi.org/10.1051/epjconf/202429501009  

   Balcas, Justas; Newman, Harvey; Bhat, Preeti P; Würthwein, Frank; Guiang, Jonathan; Arora, Aashay; Davila, Diego; Graham, John; Hutton, Thomas; Lehman, Tom; et al (January 2024, EPJ Web of Conferences)

   De_Vita, R; Espinal, X; Laycock, P; Shadura, O (Ed.)

   The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment. 

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2. Dynamic Controller Deployment for Mixed-Grid Optical Networks

   https://doi.org/10.1109/ACP.2018.8596290  

   Rahman, Sabidur; Ahmed, Tanjila; Ferdousi, Sifat; Bhaumik, Partha; Chowdhury, Pulak; Tornatore, Massimo; Das, Goutam; Mukherjee, Biswanath (October 2018, 2018 Asia Communications and Photonics Conference (ACP))

   Co-existing fixed-grid and flex-grid (i.e., mixed- grid) optical networks introduce new challenges for network orchestration. Such mixed-grid networks are often controlled by hierarchical distributed architecture comprising of Optical Network Controllers and Software-Defined Network Controllers. Optimal deployment of these controllers is very important for efficient management of mixed-grid optical networks. 

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3. Effects of Situational Factors on Metaphor Detection in an Online Discussion Forum

   Jang, Hyeju; Wen, Miaomiao; Rose, Carolyn (June 2015, Third Workshop on Metaphor in NLP)

   Accurate metaphor detection remains an open challenge. In this paper, we explore a new type of clue for disambiguating terms that may be used metaphorically or literally in an online medical support community. In particular, we investigate the influence of situational factors on propensity to employ the metaphorical sense of words when they can be used to illustrate the emotion behind the experience of the event. Specifically we consider the experience of stressful illness-related events in a poster’s recent history as situational factors. We evaluate the positive impact of automatically extracted cancer events on a metaphor detection task using data from an online cancer forum. We also provide a discussion of specific associations between events and metaphors, such as journey with diagnosis or warrior with chemotherapy. 

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4. Provenance for Intent-Based Networking

   Ujcich, Benjamin E.; Bates, Adam; Sanders, William H. (January 2020, Proceedings of the IEEE Conference on Network Softwarization)

   Intent-based networking (IBN) promises to simplify the network management and automated orchestration of high-level policies in future networking architectures such as software-defined networking (SDN). However, such abstraction and automation creates new network visibility challenges. Existing SDN network forensics and diagnostics tools operate at a lower level of network abstraction, which makes intent-level reasoning difficult. We present PROVINTENT, a framework extension for SDN control plane tools that accounts for intent semantics. PROVINTENT records the provenance and evolution of intents as the network’s state and apps’ requests change over time and enables reasoning at multiple abstractions. We define an intent provenance model, we implement a proof-of-concept tool, and we evaluate the efficacy of PROVINTENT’s explanatory capabilities by using a representative intent-driven network application. 

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5. A General Context Learning and Reasoning Framework for Object Detection in Urban Scenes [A General Context Learning and Reasoning Framework for Object Detection in Urban Scenes]

   https://doi.org/10.5220/0011637600003417  

   Wang, Xuan; Tang, Hao; Zhu, Zhigang (January 2023, Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023))

   Contextual information has been widely used in many computer vision tasks. However, existing approaches design specific contextual information mechanisms for different tasks. In this work, we propose a general context learning and reasoning framework for object detection tasks with three components: local contextual labeling, contextual graph generation and spatial contextual reasoning. With simple user defined parameters, local contextual labeling automatically enlarge the small object labels to include more local contextual information. A Graph Convolutional Network learns over the generated contextual graph to build a semantic space. A general spatial relation is used in spatial contextual reasoning to optimize the detection results. All three components can be easily added and removed from a standard object detector. In addition, our approach also automates the training process to find the optimal combinations of user defined parameters. The general framework can be easily adapted to different tasks. In this paper we compare our framework with a previous multistage context learning framework specifically designed for storefront accessibility detection and a state of the art detector for pedestrian detection. Experimental results on two urban scene datasets demonstrate that our proposed general framework can achieve same performance as the specifically designed multistage framework on storefront accessibility detection, and with improved performance on pedestrian detection over the state of art detector. 

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