Predicting movie box office success using historical data and modern computing.
problem Manual prediction of movie revenue is difficult due to many exogenous variables.
method Use modern computing power and historical data to model movie revenue.
result Predicted movie revenues can be used for planning production and distribution stages.
The Financial Crisis of 2008 is a worldwide financial crisis causing a worldwide economic decline that is the most severe since the 1930s. According to the International Monetary Fund (IMF), the global financial crisis gave impact on USD 3.4 trillion losses from financial institutions around the world between 2007 and …
This paper analyzes sound event detection in synthetic office audio, comparing different systems.
problem Comparing sound event detection systems in synthetic office audio.
method Analysis of systems submitted to DCASE 2016 task, using synthetic office sounds.
result Statistical analysis of results, highlighting system performance under controlled conditions.
New ML approach predicts glare in open-plan offices with high accuracy.
problem Challenging to predict discomfort glare in open-plan offices.
method Used Machine Learning algorithms to predict glare.
result ML model achieved 83.8% accuracy in predicting glare.
The study uses machine learning to analyze office floor plans and predict function based on geometry.
problem Lack of formalisms to describe spatial affordance in automated floor-plan generation tools.
method Supervised and unsupervised data mining techniques, including J48 algorithm, were used to analyze office floor plans.
result J48 algorithm can predict class performance on unseen examples up to 79.5% for office dataset.
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exp…
Post-pandemic, work patterns shifted with fewer days in offices and a new midweek mountain.
problem Shift in work patterns and integration of personal and professional life.
method Behavioral analysis using mobile geolocation records.
result Significant decline in office-based workdays and emergence of a new midweek mountain.
New technique reduces FRTB IMA capital calculation burden by over 90%.
problem Reduction of computational burden in FRTB IMA capital calculation.
method Orthogonal Chebyshev Sliding Technique based on high-dimensional Chebyshev Tensors.
result Reduction of computational burden by more than 90%.
Machine learning predicts TV show success based on factors like characters and direction.
problem Predicting the success of TV shows in a competitive market.
method Descriptive and predictive modeling using machine learning.
result Shows with strong characters and good direction tend to be more successful.
System tracks regulatory changes for compliance officers.
problem Struggling to keep up with regulatory changes.
method Fetch announcements, classify importance and applicability.
result Simple hierarchical classification works best.
Deep learning model predicts window openings for commercial buildings.
problem Window openings are often biased and require tuning for each occupant.
method Deep learning model trained on occupant data from multiple buildings.
result Model achieves high evaluation accuracy and F1 scores.
Mathematical framework for transfer learning feasibility and transfer risk.
problem Theoretical analysis of transfer learning.
method Reformulated transfer learning as an optimization problem, introduced transfer risk concept.
result Demonstrated the potential and benefits of incorporating transfer risk in transfer learning evaluation.
Spacematch matches office workers with suitable workspaces based on their preferences.
problem Matching workers with suitable workspaces in ABW environments.
method Developed a web-based mobile app to collect occupant preferences and IoT sensors for real-time environmental feedback.
result Occupant preferences can be segmented and matched to build a recommendation platform.
Study finds strict collection policies improve portfolio quality of microfinance banks.
problem Improving portfolio quality of microfinance banks through better credit collection policies.
method Multi-stage sampling, regression analysis, descriptive statistics.
result Collection policy has a higher effect on portfolio quality.
Language models learn automotive complaints, improving defect detection.
problem Detecting technical quality issues in customer feedback.
method Continual pre-training on ODI Complaints data set.
result Models achieve high precision in identifying technical terms.
Study models parking duration using machine learning and interpretable methods.
problem Parking issues in developing countries like India.
method Artificial neural networks (ANNs) for capturing relationships; Garson algorithm and LIME for model interpretation.
result LIME shows higher prediction accuracy and can be universally adopted.
Runaway feedback loops in predictive policing cause crime rate disparities.
problem Runaway feedback loops in predictive policing systems exacerbate crime rate disparities.
method Developed a mathematical model to explain and demonstrate interventions to prevent runaway feedback loops.
result Interventions can prevent runaway feedback loops and allow true crime rates to be learned.
The paper tackles MSDA by learning dictionary atoms in Wasserstein space.
problem Mitigating data distribution shifts across multiple source domains to target domain.
method Dictionary learning and optimal transport in Wasserstein space; DaDiL algorithm for learning.
result Improved classification performance by 3.15%, 2.29%, and 7.71% in benchmarks.
Using the empirical data from the Norwegian tax office, we analyse the wealth and income of the richest individuals in Norway during the period 2010--2013. We find that both annual income and wealth level of the richest individuals are describable using the Pareto law. We find that the robust mean Pareto exponent over …
Selective pseudo-labeling improves unsupervised domain adaptation.
problem Classifying unlabeled target domain samples with labeled source domain samples.
method Structured prediction for selective pseudo-labeling.
result Selective pseudo-labeling outperforms state-of-the-art methods.
Paper predicts Bitcoin price volatility using trading data.
problem Short-term prediction of Bitcoin price fluctuations.
method Statistical and machine learning approaches applied to trading data.
result Machine learning models outperform traditional methods in volatility prediction.
AutoTune learns wireless identifiers for facial recognition in real-world settings.
problem Facial recognition requires extensive user training, making it impractical for widespread deployment.
method Uses ambient wireless identifiers to train deep neural networks for facial recognition without user effort.
result Demonstrates a system that continuously refines facial recognition using wireless identifiers over time.
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
problem Mitigating energy consumption in commercial buildings through occupant plugload control.
method Field experiments with visual feedback and monetary incentives in government and university buildings.
result Mean energy reduction of ~9.52% in office environments and ~21.61% in university environments with visual feedback.
Study on excess mortality in Germany during 2020-21.
problem Analyzing excess mortality during the pandemic in Germany.
method Empirical study using official death counts.
result Provided conclusions for insurance businesses.
The learning of domain-invariant representations in the context of domain adaptation with neural networks is considered. We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Although some standard distributi…
Overlay framework simplifies exotic derivative pricing.
problem Inconsistent pricing models for exotics.
method Combines path reweighting and conic optimisation.
result Practically model-independent price bands for exotics.
Enhances xVA valuation with initial margin and CSA discounting.
problem Inconsistency in discount curves and aggregation of trades.
method Unified approach using BSDEs with initial margin, CSA discounting, and non-linear aggregation.
result Consistent xVA valuation with trade-specific discount curves.
Machine learning used in German official statistics.
problem Identifying areas and tasks where machine learning is applied in official statistics.
method Surveys conducted at statistical institutions and divisions of Destatis.
result Machine learning methods are used in various statistical areas and tasks.
Study compares distances for indoor WiFi mapping, finding Earth Mover's Distance effective.
problem Indoor localization and mapping using WiFi signals.
method Comparison of distance measures and kernel density estimation.
result Earth Mover's Distance is most beneficial for indoor localization.
Gamification enhances law enforcement training on terrorism financing.
problem Complex technologies limit engagement in fighting cyber-criminal activities.
method Combining learning and training methods with gamification.
result Participants show increased expertise and positive feedback.
Paper classifies pillow box isometric deformations preserving crease patterns.
problem Classifying pillow box isometric deformations preserving crease patterns.
method Continuous isometric deformations from pillow boxes to double rectangles, preserving crease patterns.
result Such deformations necessarily change pillow box topology.
New sEMG dataset for ADL activities recognized with high accuracy.
problem Developing a robust dataset for sEMG-based ADL activity recognition.
method Acquired sEMG data from 25 subjects performing 22 ADL activities. Used 4 classifiers with various feature sets.
result SVM classifier achieved 83.21% accuracy on 5 FAABOS categories.
CDSPP learns domain-specific projections for heterogeneous domain adaptation.
problem Heterogeneous domain adaptation problems where source and target domains have different modalities or feature dimensions.
method Cross-Domain Structure Preserving Projection (CDSPP) algorithm that learns domain-specific projections to map features into a common subspace.
result CDSPP achieves superior performance in both supervised and semi-supervised HDA compared to state-of-the-art methods.
Research finds investors may lose from more diverse workplaces.
problem Investors' returns may be lower in more diverse companies.
method Examined D&I scores by Refinitiv in US and European markets.
result Investors may suffer lower returns for investing in more diverse companies.
ODS improves adversarial attacks by maximizing output diversity.
problem Efficiency and effectiveness of adversarial attacks, especially black-box attacks.
method Output Diversified Sampling (ODS) that maximizes diversity in model outputs.
result ODS reduces the number of queries needed for black-box attacks on ImageNet by a factor of two.
New research shows many recent defenses against adversarial examples are ineffective against black-box attacks.
problem The robustness of recent defenses against adversarial examples is insufficient, especially against black-box attacks.
method Evaluation of nine defenses on two black-box adversarial models and six attacks on CIFAR-10 and Fashion-MNIST datasets.
result Most recent defenses provide only marginal improvements in security (<25%) compared to undefended networks. New method improves black-box attacks using pre-trained models.
problem Efficiently attacking black-box models with limited information.
method EigenBA algorithm leveraging pre-trained white-box model's Jacobian matrix.
result Optimal perturbations are related to right singular vectors of Jacobian matrix.
Methodology measures financial impacts using existing credit loss infrastructure.
problem Measuring the impact of financial scenarios on expected credit losses.
method Captures scenario effects through changes in default probabilities; uses existing provisioning infrastructure.
result Methodology validated through standardized climate scenario exercise in Canada and Quebec.
Avoid explaining black box models; use interpretable ones instead.
problem High-stakes decisions made by black box models cause societal harm.
method Design inherently interpretable models instead of trying to explain black box models.
result Inherently interpretable models are a better approach for high-stakes decisions.
Extracts weighted automata from black box models for sequential data.
problem Global interpretability of black box models for symbolic sequential data.
method Spectral algorithm for extracting weighted automata from black boxes without access to inner representation.
result Approximation of black box models using inferred weighted automata is of high quality.
NP-Attack reduces query counts for black-box adversarial attacks.
problem Efficiency of black-box adversarial attacks is low.
method Uses Neural Process to characterize image structure and find adversarial examples.
result NP-Attack significantly decreases query counts.
Paper improves ATN for generating adversarial examples.
problem Generating adversarial examples to fool models.
method Improves Adversarial Transformation Networks (ATN).
result Won 2nd place in CAAD 2018 non-target task.
Enhanced Elastic-Net with box-constraint improves support recovery in noisy measurements.
problem Support recovery of sparse signals from noisy measurements.
method Box-Elastic Net (Box-EN) method with mean squared error and probability of support recovery analysis.
result The Box-Elastic Net outperforms the standard Elastic-Net in support recovery.
Researchers organize and analyze a large public safety imagery dataset.
problem Efficiently organizing and analyzing a vast public safety imagery dataset.
method Hierarchical organization approach using Lincoln Laboratory Supercomputing Cluster for compute and storage.
result Successfully organized and evaluated the dataset with large-scale imagery inference across terabytes of data.
We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the d…
Spanning attack improves black-box attacks with unlabeled data.
problem Query inefficiency in black-box attacks due to high input space dimensionality.
method Proposes spanning attack by constraining adversarial perturbations in a low-dimensional subspace via an auxiliary unlabeled dataset.
result Significantly improves query efficiency of black-box attacks.
Distill-and-Compare audits black-box models by training transparent models to mimic them.
problem Auditing proprietary, opaque black-box risk scoring models.
method Model distillation and comparison of transparent student models to black-box models.
result Identifies missing features in black-box models, improving transparency.
A new method trains deep neural networks for open set domain adaptation without negative open set difference.
problem Training deep neural networks for open set domain adaptation without negative open set difference.
method Proposes a new upper bound of target-domain risk, including source-domain risk, ε-open set difference (Δε), distributional discrepancy, and constant. Uses gradient descent for source-domain risk and Δε, and adversarial training for distributional discrepancy. Trains DNNs via minimizing the new upper bound. result Shows state-of-the-art performance on benchmark datasets.