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Found 55 papers, 23 papers with code
"Actionable Help" in Crises: A Novel Dataset and Resource-Efficient Models for Identifying Request and Offer Social Media Posts
During crises, social media serves as a crucial coordination tool, but the vast influx of posts--from "actionable" requests and offers to generic content like emotional support, behavioural guidance, or outdated information--complicates effective classification.
Millimeter-Wave ISAC Testbed Using Programmable Digital Coding Dynamic Metasurface Antenna: Practical Design and Implementation
We envision the proposed mmWave testbed as a platform for enabling the seamless integration of sensing and communication by allowing video transmission to be replaced with sensing data or utilizing an auxiliary wireless channel to transmit sensing information to multiple receivers.
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods.
Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features
The widespread use of microblogging platforms like X (formerly Twitter) during disasters provides real-time information to governments and response authorities.
Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA
We intend our approaches to be the backbone of a working product of interest for SMEs to use in production.
A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis
SELSP is trained and evaluated on a ternary polarity classification task, demonstrating its faster performance and better accuracy in polarity prediction tasks compared to conventional parsers like Stanza and to heuristic approaches that use shallow syntactic rules for SA like VADER.
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
This is primarily due to the high computational cost associated with (1) the use of large number of time steps (e. g., 1, 000) in diffusion processes and (2) the increased dimensionality of medical images, which are often 3D or 4D.
CReMa: Crisis Response through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media
Therefore, we propose CReMa (Crisis Response Matcher), a systematic approach that integrates textual, temporal, and spatial features to address the challenges of effectively identifying and matching requests and offers on social media platforms during emergencies.
A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
This module uses the cross-slice attention mechanism to effectively capture 3D spatial information by learning long-range dependencies between the center slice (for segmentation) and its neighboring slices.
RetinaRegNet: A Zero-Shot Approach for Retinal Image Registration
We introduce RetinaRegNet, a zero-shot image registration model designed to register retinal images with minimal overlap, large deformations, and varying image quality.
Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models
Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies.
CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention
Advancements in medical imaging and endovascular grafting have facilitated minimally invasive treatments for aortic diseases.
CrisisViT: A Robust Vision Transformer for Crisis Image Classification
We leverage the new Incidents1M crisis image dataset to develop a range of new transformer-based image classification models.
RIS-Enhanced MIMO Channels in Urban Environments: Experimental Insights
Can the smart radio environment paradigm measurably enhance the performance of contemporary urban macrocells?
Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study
Our pipeline begins with the reconstruction of pseudo-whole-mount histopathology images and a 3-dimensional (3D) micro-US volume.
MicroSegNet: A Deep Learning Approach for Prostate Segmentation on Micro-Ultrasound Images
During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations.
A large-scale multimodal dataset of human speech recognition
The dataset has been validated and has potential for the research of lip reading and multimodal speech recognition.
RweetMiner: Automatic identification and categorization of help requests on twitter during disasters
Many people turn to social media during disasters for requesting help and/or providing relief to others.
CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks
A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored.
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals.
Bias-Aware Face Mask Detection Dataset
In this work, we present a novel face mask detection dataset that contains images posted on Twitter during the pandemic from around the world.
A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques.
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977, 088 images, with 43 incident and 49 place categories.
Fight Detection from Still Images in the Wild
We also propose a new dataset, named Social Media Fight Images (SMFI), comprising real-world images of fight actions.
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports.
Landslide Detection in Real-Time Social Media Image Streams
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly.
MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification
This is the first dataset of its kind: social media images, disaster response, and multi-task learning research.
Mapping Vulnerable Populations with AI
Building functions shall be retrieved by parsing social media data like for instance tweets, as well as ground-based imagery, to automatically identify different buildings functions and retrieve further information such as the number of building stories.
Robust Training of Social Media Image Classification Models for Rapid Disaster Response
Images shared on social media help crisis managers gain situational awareness and assess incurred damages, among other response tasks.
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters.
Implicit Feedback-based Group Recommender System for Internet of Thing Applications
With the prevalence of Internet of Things (IoT)-based social media applications, the distance among people has been greatly shortened.
Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets
For example, results show that, for training a location mention recognition model, Twitter-based data is preferred over general-purpose data; and crisis-related data is preferred over general-purpose Twitter data.
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response
In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment.
Word Representations Concentrate and This is Good News!
This article establishes that, unlike the legacy tf*idf representation, recent natural language representations (word embedding vectors) tend to exhibit a so-called \textit{concentration of measure phenomenon}, in the sense that, as the representation size $p$ and database size $n$ are both large, their behavior is similar to that of large dimensional Gaussian random vectors.
Detecting natural disasters, damage, and incidents in the wild
While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes.
Artificial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends
Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world.
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters.
CrisisBench: Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing
Time-critical analysis of social media streams is important for humanitarian organizations for planing rapid response during disasters.
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings.
Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
Multimedia content in social media platforms provides significant information during disaster events.
Ranked #1 on
Disaster Response
on CrisisMMD
Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies
The recent advances in information and communication technology (ICT) have promoted the evolution of conventional computer-aided manufacturing industry to smart data-driven manufacturing.
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
Emotion detection from the text is an important and challenging problem in text analytics.
Ghost-free multi exposure image fusion technique using dense SIFT descriptor and guided filter
A ghost-free multi-exposure image fusion technique using the dense SIFT descriptor and the guided filter is proposed in this paper.
Domain Adaptation with Adversarial Training and Graph Embeddings
In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains.
CrisisMMD: Multimodal Twitter Datasets from Natural Disasters
Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types.
Social and Information Networks Computers and Society
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts.
Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises
The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly.
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting
An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date.
Summarizing Situational and Topical Information During Crises
The use of microblogging platforms such as Twitter during crises has become widespread.
Applications of Online Deep Learning for Crisis Response Using Social Media Information
During natural or man-made disasters, humanitarian response organizations look for useful information to support their decision-making processes.
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results.
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons.
Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages
Scarcity of labeled data causes poor performance in machine training.
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream.
