System detects social interactions in crowds using mobile phone sensors.
problem Detecting social interactions in crowded settings.
method Multi-modal mobile sensing (BLE, accelerometer, gyroscope) and machine learning.
result 77.8% precision and 86.5% recall for predicting social interactions.
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can estab…
SafeRNet uses IoT and cloud computing to provide real-time safe routes.
problem High traffic fatality rates despite advanced technology.
method Bayesian network for safe route modeling.
result Demonstrated effectiveness with real traffic data.
MRCNet tackles crowd counting and density mapping in aerial imagery.
problem Accurate crowd counting and density estimation in aerial imagery.
method MRCNet is a novel encoder-decoder CNN that combines VGG-16 with FPN-inspired lateral connections.
result MRCNet outperforms state-of-the-art methods in aerial and CCTV-based crowd counting.
It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring immediate attention for a more effective and proactive surveil…
Max-MIG tackles crowdsourced label learning without knowing crowd information structure.
problem Learning from crowds without knowing the information structure among crowds.
method Max-MIG is an information theoretic approach that simultaneously aggregates crowdsourced labels and learns a data classifier.
result Max-MIG achieves state-of-the-art results in most settings, including real-world data.
We found that factors decay over time, with momentum fitting best.
problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.
Wisdom of the crowd, the collective intelligence derived from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual, and improve social decision-making and prediction accuracy. This can also integrate multiple programs or datasets, each as an individual, for…
Study shows how 'crowding' in equity trading affects performance and costs.
problem Deterioration of strategy performance, increased trading costs, and systemic risk due to equity factor crowding.
method Direct metrics of crowding based on imbalances of trades executed on the market, analyzing U.S. equity market data.
result Significant signs of crowding in well-known equity signals, especially Momentum, affecting order flow and portfolio rebalancing.
MTCNet uses MTL to estimate crowd density and count.
problem Crowd count estimation challenges due to scale variations and perspective.
method MTL deep neural network architecture with two tasks: density estimation and count classification.
result Achieves lower MAE than state-of-the-art methods on multiple datasets.
Crowd opinions in microblogs can predict event outcomes, matching with expert opinions.
problem Utilizing crowd wisdom for event outcome prediction in microblogs.
method Multi-label sentiment classification of tweets to gauge crowd opinion and compare with expert predictions.
result Crowd opinions in microblogs often match with expert opinions, especially in non-debate events.
ACFM predicts crowd flow adaptively integrating various factors.
problem Adaptive integration of factors affecting crowd flow changes.
method Unified neural network module with attention mechanism.
result Significant improvements over state-of-the-art methods.
New method improves crowd counting accuracy using inverse k-NN maps and multiscale upsampling.
problem Improving accuracy of crowd density maps for high-density gatherings.
method Developed MUD-ikNN architecture using inverse k-NN maps and multiscale upsampling. result New network architecture outperforms state-of-the-art crowd counting.
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
Machine learning outperforms crowd investors in predicting loan defaults and investment returns.
problem Determining if machine learning can outperform human decision-making in crowd lending.
method Using data from Prosper.com, a sophisticated ML algorithm was trained to predict loan defaults and investment returns.
result The ML algorithm outperforms crowd investors in predicting loan defaults and investment returns, especially for risky loans.
The paper interprets financial markets as crowds during booms and busts.
problem Understanding market irrationality during booms and busts.
method Integrates crowd psychology into behavioural finance.
result Markets behave like psychological crowds during booms and busts.
A model-free hedging method using stock crowding scores.
problem Designing costless portfolio strategies to hedge market risk.
method Network analysis of fund holdings to compute crowding scores, constructing long-short portfolios without numerical optimization.
result Long-short portfolios provide protection against both small and large market price fluctuations.
Crowdsourced wisdom improves causal learning.
problem Improving causal learning through collective intelligence.
method Crowdsourcing, expert knowledge elicitation, aggregation techniques, and LLMs.
result Collective contributions enhance global causal structure.
Investment herding can reduce household consumption, a phenomenon called crowding-out effect.
problem Investment herding's impact on household consumption.
method Optimal control theory to model and solve for household investment and consumption decisions.
result Existence of crowding-out effect due to investment herding.
The average portfolio structure of institutional investors is shown to have properties which account for transaction costs in an optimal way. This implies that financial institutions unknowingly display collective rationality, or Wisdom of the Crowd. Individual deviations from the rational benchmark are ample, which il…
This paper proposes a general model for synchronized crowding behavior. An order parameter is introduced to quantify the level of synchronization which is shown a function of percentage of agents in reactive state. Further, synchronization is shown to be driven by the most active agents with the highest volatility. A t…
New method addresses crowding in high-dimensional data visualization.
problem Crowding issue in visualizing high-dimensional data.
method Adjusting capacity of high-dimensional balls and estimating correlation dimension.
result Mitigates crowding in various distance metrics.
Crowdsourcing can improve scientific investigation by enabling reproducibility and transparency.
problem Current research methods lack reproducibility and transparency, leading to unreliable decisions.
method Next-generation investigative approach leveraging human diversity, micro-specialized crowds, and computer-assisted control methods.
result The Theory of Enablers provides specific cognitive and non-cognitive enablers for crowd-based scientific investigation.
Enhances crowd safety through AI and data-driven models.
problem Improving crowd safety during events.
method Innovative data collection, AI, and machine learning.
result Accurate multi-day forecasts for event planning.
Bayesian model improves truth inference from highly redundant crowd annotations.
problem Inferring true annotations from highly redundant crowd annotations.
method Bayesian graphical model with conjugate priors and iterative expectation-maximisation inference.
result Our technique significantly outperforms majority vote heuristic at one-sided level 0.025.
Low dimensional embeddings that capture the main variations of interest in collections of data are important for many applications. One way to construct these embeddings is to acquire estimates of similarity from the crowd. However, similarity is a multi-dimensional concept that varies from individual to individual. Ex…
Study improves forecasting of ED crowding using advanced ML models.
problem Improving forecasting of emergency department crowding.
method Advanced machine learning models (N-BEATS, LightGBM, DeepAR) using multivariable input data.
result N-BEATS and LightGBM outperform benchmarks in forecasting ED occupancy.
Cost forecasting helps crowdsourcers manage growing task sets efficiently.
problem Crowdsourcers face resource overwhelm with growing task sets.
method Cost forecasting to decide between new tasks or existing ones based on cost efficiency.
result Cost forecasting improves accuracy and efficiency in crowdsourcing.
Market crowd trading behavior and volume impact stock prices in China.
problem Little known about the role of trading volume in market behavior.
method Adaptive hypotheses tested on Chinese stock market data.
result Market crowd trades efficiently and achieves agreement on prices.
The Wisdom of Crowds is a phenomenon described in social science that suggests four criteria applicable to groups of people. It is claimed that, if these criteria are satisfied, then the aggregate decisions made by a group will often be better than those of its individual members. Inspired by this concept, we present a…
Bayesian method predicts individual and crowd preferences from small data.
problem Difficult to predict preferences from limited personal data and noisy labels.
method Combines matrix factorization with Gaussian processes for scalable inference.
result Method predicts preferences for new users and items not in training set.
New method accurately reconstructs Russell 3000 index, revealing crowded portfolios.
problem Crowding in index portfolios during reconstitution events.
method Developed a Python package for accurate index reconstruction using CRSP US Stock data.
result Annual Russell 3000 portfolios are more crowded than quarterly ones, suggesting lower transaction costs.
Reviews six finance topics, including 'radical complexity'.
problem None explicitly stated, focuses on research directions.
method Informal review and discussion of open questions.
result No specific key result mentioned, focuses on research directions.
Bayesian deep learning counts crowds robustly despite occlusions and scale variations.
problem Accurately counting individuals in crowded scenes with occlusions and varying sizes.
method Proposes a Bayesian multi-scale neural network with a ResNet feature extractor, dilated convolutions, and a Perspective-aware Aggregation Module.
result Achieves superior performance on crowd counting benchmarks with uncertainty estimates.
Lower bounds show OLS outperforms basis pursuit in overparameterized linear regression.
problem Excess risk of sparse interpolating procedures in overparameterized linear regression.
method Proved lower bounds on excess risk for OLS and basis pursuit.
result Excess risk of basis pursuit can converge at an exponentially slower rate than OLS.
Proposes methods to improve wisdom of crowds by considering worker diversity and correlations.
problem Improving wisdom of crowds by considering worker diversity and correlations.
method Proposes inference, learning, and teaching methods considering worker diversity and correlations.
result Proposes methods to improve wisdom of crowds by considering worker diversity and correlations.
The study evaluates Bregman divergences for learning crowd probabilities.
problem Learning crowd probabilities from global perspectives.
method Adapting machine learning models to target probability distributions using Bregman divergences.
result Special attention is needed when constructing objective functions for neural network optimization.
Study predicts Euroleague basketball games with simple models, less accurate than crowds.
problem Predicting Euroleague basketball games with machine learning.
method Extracted features from Euroleague data, applied supervised machine learning techniques.
result Simple machine learning models achieve accuracy of less than 67% on test sets, outperformed by crowds.
Model learns metrics and preferences from user comparisons.
problem Simultaneous metric and preference learning from user comparisons.
method Jointly learns a metric and latent ideal points for each user.
result Model captures individual preferences and learns metrics efficiently.
Deep learning model detects and corrects outliers in crowd-sourced weather data.
problem Data quality issues in crowd-sourced weather data.
method Bayesian deep learning approach with Gaussian-uniform mixture density network.
result Automated outlier detection in spatio-temporal environmental modeling.
Trans-Sense uses smartphones to predict public transit wait times and schedules.
problem Traffic congestion and lack of public transportation in developing countries.
method Crowdsourced mobile phones to estimate waiting times and transit schedules.
result Achieves high accuracy in predicting passenger arrival times and station dimensions.
Proposes a method to find traffic regulations efficiently.
problem Finding appropriate traffic regulations in congested events.
method Graph Convolutional Networks for modeling regulation effects.
result The method can find a road to close that reduces travel time.
The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation-based model for crowd labeled data that is a significant generalization of the classical Dawid-Skene model, and introduce a new error metri…
Hybrid model assesses flash crash contagion and systemic risk.
problem Understanding conditions for flash crash contagion and systemic risk.
method Micro-macro agent-based model with endogenous price impact.
result Systemic risk depends on algorithmic trader behavior, leverage, and network topology.
Proposes supervised topic models for classification and regression from crowds.
problem Ambiguity and noise in annotation tasks, especially with large volumes of documents.
method Develops two supervised topic models and an efficient stochastic variational inference algorithm.
result Empirically demonstrates superior performance over state-of-the-art approaches.
Proposes candidate labeling for more efficient use of crowdsourced labels.
problem Inefficiency of standard crowd learning where annotators provide single labels.
method Allows annotators to provide multiple labels per instance.
result Candidate labeling extracts more knowledge from labelers than standard approach.
A new network predicts object/non-object masks to improve crowd counting.
problem Counting objects in images or videos with varying density.
method Proposes a network with a branch for object/non-object mask prediction, integrating it into the density map estimation.
result Demonstrates superior performance compared to baseline methods on five public datasets.
Smartphone app counts grapes for accurate yield estimation.
problem Accurate yield estimation at minimal cost.
method Adapted Deep Learning algorithms for crowd counting to fruit counting.
result Demonstrated smartphone app for grape yield estimation.