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ForestSemantic: a dataset for semantic learning of forest from close-range sensing
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Finding the Number of Latent Topics With Semantic Non-Negative Matrix Factorization
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Contrastive Meta-Learner for Automatic Text Labeling and Semantic Textual Similarity
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Rapid Constrained Object Motion Estimation based on Centroid Localization of Semantically Labeled Objects
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Investigating Resilience of Loops in HPC Programs: A Semantic Approach with LLMs
Soft errors have become one of the major concerns for the error resilience of the HPC applications as those errors may cause HPC applications to generate serious outcomes such as silent data corruptions (SDCs). Protecting the applications from soft errors is an essential while challenging task. Among different approaches, obtaining a profound understanding of the resilience proneness of an application is very important to devise efficient error detection and recovery strategies. Given the scale of the HPC applications both in the code size and execution time, there are often cases that the error propagation analysis on such applications would produce a massive volume of unstructured data, which requires a significant amount of efforts, to process and to obtain indicating actions towards error protection. In this paper, we present a control-flow based visual analysis framework to help the users conduct error propagation analysis and identify the critical sections of a program that may have a higher likelihood of leading to erroneous outcomes when affected by the control flow related errors. We also design and implement the scalable visualization framework - ResilienceVis that efficiently and effectively visualizes the affected program states under errors and the propagation traces for an application in a user-friendly manner, and eventually, we combine the analysis and visualization to exhibit the error-proneness of the different sections of applications.
AUTOMATIC IMAGE ANNOTATION USING INVERSE MAPS FROM SEMANTIC EMBEDDINGS
Abstract not provided
Semantic Framework for Spatial Query Reformulation for Disaster Monitoring Applications
In disasters, since time is of the essence, quick decision making based on actionable insights is desired. In our earlier work, we have demonstrated that the spatial relationships-based queries can play a vital role in the disaster response phase. However, we found that the utilization of spatial relationships rules (i.e. encoded spatial knowledge) via rule reasoning process do not scale well with the increased number of image regions. Most of the available Resource Description Framework (RDF) triplestores do not support rule reasoning due to the computational complexity and undecidable nature of the rule reasoning process. In this paper, we propose an alternative approach for utilizing spatial knowledge encoded in the form of spatial relationship rules. The proposed approach reformulates the spatial query by expanding it with the configuration encoded in the corresponding spatial relationship rule. The preliminary results are promising and show the applicability of the proposed approach during the time critical events such as flood disaster.
Enabling One-Sided Communication Semantics on ARM
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The Case for Explicit Reuse Semantics for RDMA Communication.
Abstract not provided.
Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid
Cyber-physical system (CPS) security for the smart grid enables secure communication for the SCADA and wide-area measurement system data. Power utilities world-wide use various SCADA protocols, namely DNP3, Modbus, and IEC 61850, for the data exchanges across substation field devices, remote terminal units (RTUs), and control center applications. Adversaries may exploit compromised SCADA protocols for the reconnaissance, data exfiltration, vulnerability assessment, and injection of stealthy cyberattacks to affect power system operation. In this paper, we propose an efficient algorithm to generate robust rule sets. We integrate the rule sets into an intrusion detection system (IDS), which continuously monitors the DNP3 data traffic at a substation network and detects intrusions and anomalies in real-time. To enable CPS-aware wide-area situational awareness, we integrated the methodology into an open-source distributed-IDS (D-IDS) framework. The D-IDS facilitates central monitoring of the detected anomalies from the geographically distributed substations and to the control center. The proposed algorithm provides an optimal solution to detect network intrusions and abnormal behavior. Different types of IDS rules based on packet payload, packet flow, and time threshold are generated. Further, IDS testing and evaluation is performed with a set of rules in different sequences. The detection time is measured for different IDS rules, and the results are plotted. All the experiments are conducted at Power Cyber Lab, Iowa State University, for multiple power grid models. After successful testing and evaluation, knowledge and implementation are transferred to field deployment.
A One-Stage Domain Adaptation Network With Image Alignment for Unsupervised Nighttime Semantic Segmentation
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Semantic segmentation of mouse jaws using convolutional neural networks
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SONET: a semantic ontological network graph for managing points of interest data heterogeneity
Scalability, standardization, and management are important issues when working with very large Volunteered Geographic Information (VGI). VGI is a rich and valuable source of Points of Interest (POI) information, but its inherent heterogeneity in content, structure, and scale across sources present major challenges for interlinking data sources for analysis. To be useful at scale, the raw information needs to be transformed into a standardized schema that can be easily and reliably used by data analysts. In this work, we tackle the problem of unifying POI categories (e.g. restaurants, temple, and hotel) across multiple data sources to aid in improving land use maps and population distribution estimation as well as support data analysts wishing to fuse multiple data sources with the OpenStreetMap (OSM) mapping platform or working with projects that are already configured in the OSM schema and wish to add additional sources of information. Graph theory and its implementation through the SONET graph database, provides a programmatic way to organize, store, and retrieve standardized POI categories at multiple levels of abstraction. Additionally, it addresses category heterogeneity across data sources by standardizing and managing categories in a way that makes cross-domain analysis possible.