I-AID categorizes disaster tweets into useful information types.
problem Filtering useful information from social media during disasters.
method Multimodel approach using BERT, GAT, and Relation Network.
result I-AID outperforms state-of-the-art approaches in F1 scores.
Detects and maps informal settlements using satellite data.
problem Mapping informal settlements for aid distribution.
method Combining satellite data and machine learning for roofing material detection.
result Effective aid distribution through accurate settlement mapping.
Detects and maps informal settlements in developing countries using satellite imagery.
problem Mapping informal settlements for aid delivery.
method Two methods: LR Sentinel-2 imagery and VHR satellite imagery.
result Successfully mapped informal settlements with LR satellite imagery.
Indirect competition emerged from the complex organization of human societies, and knowledge of the existing network topology may aid in developing effective strategies for success. Here, we propose an agent-based model of competition with systems co-existing in a `small-world' social network. We show that within the r…
Sentiment analysis from news and social media predicts forex market movements.
problem Forecasting forex market movements using sentiment analysis.
method Lexicon-based analysis and Naive Bayes machine learning.
result Sentiment analysis is effective in predicting forex market movements.
Study shows Lula's Zero Hunger program reduced income inequality in Brazil.
problem Income inequality in Brazil during Lula's administration.
method Breakpoint regression analysis using detailed descriptive statistics.
result The Zero Hunger program substantially reduced income inequality and provided income security for the poor.
Social media has recently emerged as a premier method to disseminate information online. Through these online networks, tens of millions of individuals communicate their thoughts, personal experiences, and social ideals. We therefore explore the potential of social media to predict, even prior to onset, Major Depressiv…
We look at a collection of conjectures with the unifying message that smaller social systems, tend to be less complex and can be aligned better, towards fulfilling their intended objectives. We touch upon a framework, referred to as the four pronged approach that can aid the analysis of social systems. The four prongs …
New estimator improves statistical validity of synthetic data integration.
problem Combining synthetic data generated by large language models with real data for valid inference.
method Generalized method of moments estimator with theoretical guarantees.
result Improves estimates of target parameter through interactions between synthetic and real data.
Study shows how leveraging hierarchical similarity graphs improves matrix completion in recommender systems.
problem Improving matrix completion in recommender systems using hierarchical similarity graphs.
method Characterizes the optimal sample complexity using hierarchical stochastic block models and low-rank rating matrices.
result Exploiting hierarchical structure of social graphs significantly reduces the number of observed entries needed for accurate matrix completion.
Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour. A typical work-flow includes the manual annotation (also called coding) of social signals in multi-modal corpora of considerable size. For the invo…
Paper proposes fairgroup construction to improve fairness in Medicaid eligibility decisions.
problem Improper decisions in Medicaid eligibility allocation due to ML/DL model limitations.
method Fairgroup construction based on legal doctrine of disparate impact.
result Demonstrates improved fairness in regressive classifiers for Medicaid eligibility decisions.
In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data an…
The paper develops a causal machine learning framework to optimize aid allocation.
problem Optimizing aid allocation to reduce new HIV infections in poor countries.
method The framework uses a balancing autoencoder, counterfactual generator, and inference model to predict heterogeneous treatment effects.
result The framework predicts a reduction of up to 3.3% in new HIV infections, saving 50,000 lives.
Bayesian networks learn sub-population differences from data.
problem Inference from a single network structure can be misleading when data populations are heterogeneous.
method A mixture of Bayesian networks where component probabilities depend on individual characteristics.
result Identifies both network structures and demographic predictors of sub-population membership.
UR-FUNNY dataset aids in understanding multimodal humor.
problem Understanding humor in a multimodal context is understudied.
method Developed a multimodal dataset (UR-FUNNY) for humor detection.
result UR-FUNNY opens the door to multimodal humor detection research.
New machine learning model detects informal settlements in developing countries using low-res satellite data.
problem Lack of detailed maps for vulnerable populations in developing countries.
method Developed a new machine learning data set and demonstrated effective classification schemes for low-resolution satellite imagery.
result It is possible to detect informal settlements using low-resolution satellite data, making it more accessible for NGOs.
Paper explores generalization of AID-based bi-level optimization methods.
problem Uncertainty in generalization properties of AID-based bi-level optimization methods.
method Uniform stability analysis and convergence study of AID-based methods.
result AID-based methods can achieve similar generalization as single-level nonconvex problems.
We develop a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions of random variables, and the Johnson-Lindenstrauss Lemma, a technique for dimension reduction. The resulting technique is a simple yet powerful tool that allows comparisons between data-…
Bayesian machine scientist uncovers accurate models from data.
problem Challenging scientific problems require interpretable mathematical models.
method Bayesian approach using Markov chain Monte Carlo to explore model space.
result Out-of-sample predictions are more accurate than existing methods.
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics. A key problem faced by practitioners is measuring the accuracy of an approximate infe…
CLCNet improves noise reduction in hearing aids with deep learning.
problem Noise reduction in hearing aids is challenging due to real-time and frequency resolution constraints.
method Proposes CLCNet, a deep learning framework based on complex linear coding.
result CLCNet outperforms traditional methods in noisy environments.
Deep learning aids ADMM-based decoding for binary linear codes.
problem Improving decoding efficiency for binary linear codes.
method Designing a decoding network based on ADMM and deep learning.
result Numerical results show improved performance compared to original ADMM.
New model predicts hate speech with uncertainty estimates.
problem Detect and explain hate speech without restricting free speech.
method Adapted deep neural networks with Monte Carlo dropout.
result Models can reliably assess when texts are borderline.
New method for calculating crosscap numbers of knots.
problem Calculating crosscap numbers of knots efficiently.
method Deformation of plumbing and computer aid.
result Efficient calculation of crosscap numbers.
A new DVAE architecture improves channel estimation by incorporating temporal correlations.
problem Improving the estimation of time-varying channels.
method Introducing k-MemoryMarkovVAE (k-MMVAE) architecture to learn temporal correlations.
result The k-MMVAE aided channel estimator outperforms other ML aided estimators.
Study analyzes global public sentiment on DeFi from 2012-2022.
problem Global public sentiment on DeFi is understudied.
method Sentiment analysis, spatial econometrics, clustering, topic modeling.
result Economic development significantly influences DeFi engagement, especially after 2015.
Machine learning method characterizes network interference in A/B tests.
problem Compromised A/B test reliability due to network interference.
method Causal network motifs and machine learning models.
result Outperforms conventional methods in characterizing network interference.
PHASE dataset simulates complex social interactions in physical environments.
problem Lack of datasets for evaluating physically grounded perception of complex social interactions.
method Created PHASE dataset of 2D animations with procedural generation and physics engine.
result SIMPLE model outperforms neural networks in recognizing complex social interactions.
Paper proposes a faster neural machine translation model using election methods and Q-learning.
problem Slower inference time in complex attention models.
method Modelled attention network using election methods and Q-learning.
result Inference time is less than a standard Bahdanau translator, results comparable.
New algorithms improve direction finding using prior signal knowledge.
problem Efficiently estimate signal direction from sensor data.
method Multi-step knowledge-aided iterative conjugate gradient algorithms.
result MS-KAI-CG algorithms outperform existing techniques in simulations.
Improved neural model for social recommendation by integrating social and interest networks.
problem Data sparsity and lack of higher-order relationships in social recommendation.
method DiffNet++ models neural influence diffusion and interest diffusion in a unified framework using a multi-level attention network.
result Extensive experiments on real-world datasets show the effectiveness of DiffNet++.
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
We propose a new image denoising algorithm, dubbed as Fully Convolutional Adaptive Image DEnoiser (FC-AIDE), that can learn from an offline supervised training set with a fully convolutional neural network as well as adaptively fine-tune the supervised model for each given noisy image. We significantly extend the frame…
Consumers adjust their spending based on firms' social stances, influencing firm profits.
problem How consumer spending responds to firms' social stances.
method Using payment card transactions to predict and measure consumer responses to firms' social stances.
result Consumers' spending increases by 19% and decreases by 12% in response to firms' social stances, with effects lasting up to a year.
Social learning can make financial markets inefficient, but individual learning can fix this.
problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.
SINN combines social science and deep learning for predicting opinion dynamics.
problem Predicting opinion dynamics in social networks using traditional models requires extensive calibration with real data.
method SINN integrates theoretical models and social media data using physics-informed neural networks (PINNs) and matrix factorization.
result SINN outperforms six baseline methods in predicting opinion dynamics on real-world and synthetic datasets.
Paper proposes a method to encrypt faces while maintaining visual similarity.
problem Protecting personal data from unauthorized face recognition.
method Targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
result TIP-IM provides 95%+ protection success rate against face recognition models.
Optimizes social interactions for profit, people, and planet using mathematical models.
problem Determining the most effective social configurations for mutual objectives.
method Formulated as a mathematical optimization problem, using (meta)relational models theory.
result Identifies the most suitable combination of sociality forms for mutual objectives.
Model predicts increased social unrest during COVID-19 using social media data.
problem Detecting rising conflict potential in societies during pandemics.
method Neural implicit motive pattern recognition from social media texts.
result Significant increase in conflict indicators during the pandemic.
Enhances traditional MV model for socially responsible investors.
problem Traditional MV models ignore ESG scores relevant to socially responsible investors.
method Implemented an amended MV model considering ESG scores.
result SR investors can achieve competitive SR portfolios with a trade-off between Sharpe Ratio and ESG scores.
The rise in online social networking has brought about a revolution in social relations. However, its effects on offline interactions and its implications for collective well-being are still not clear and are under-investigated. We study the ecology of online and offline interaction in an evolutionary game framework wh…
ViWi dataset framework tackles wireless communication problems with visual data.
problem Leveraging visual sensory information in wireless communications.
method Developed a parametric, systematic, and scalable data generation framework using 3D modeling and ray-tracing.
result Offers a way to generate training and testing datasets for assessing machine learning solutions.
Paper proposes PP-GCN for fine-grained social event categorization.
problem Challenges in mining social events due to heterogeneous event elements and social network structures.
method Design an event meta-schema, build an HIN, propose PP-GCN, and use KIES.
result PP-GCN outperforms other techniques in social event detection and clustering.
Reward shaping speeds up human learning through IRL.
problem Slow learning in humans, especially for challenging tasks.
method Extended IRL algorithm with kernel methods, conducted experiments with online game players.
result Players learn desired policies more quickly with reward shaping.
A new method for recommending groups and activities based on geo-social data.
problem Recommendation of groups and activities based on geo-social data with social constraints.
method Proposes an attentive geo-social group recommendation method with an attention mechanism and a spatial query algorithm.
result Significantly outperforms baseline methods in real-world datasets.
AI improves OFDM receivers for robust real-world communication.
problem Performance gap between simulation and real-world OFDM systems.
method Comparison of AI-aided OFDM receivers and development of SwitchNet receiver.
result SwitchNet receiver adapts to real channels online, improving robustness.
MGpi model predicts social actions in group interactions.
problem Social interaction among multiple agents and groups.
method Deep neural network with Kinesic-Proxemic-Message gate for social signal gating.
result Achieves state-of-the-art performance in group identification.