TripDecoder recovers metro routes and travel times from smart card data.
problem Recover unknown routes and travel times in metro systems.
method Decouples two inference tasks: travel time and route preference.
result TripDecoder improves accuracy and efficiency compared to competitors.
Model predicts missing boarding stops in smart card data.
problem Missing boarding stop information in public transport datasets.
method Supervised machine learning with ordinal classification.
result Proposed method significantly outperforms existing imputation methods.
Paper predicts in-situ metro passenger density using smart card data.
problem Crowd management in metro systems.
method Statistical models and EM algorithm for time-dependent OD matrix and travel time cost estimation.
result Accurate prediction of in-situ passenger density for future time points.
Bayesian model predicts public transport usage during events.
problem Difficulty in predicting transportation disruptions during special events.
method Bayesian additive model with Gaussian process components, using smart card records and web data.
result Model outperforms baselines by up to 26% in R2 and explains individual event components.
Investigates the cost-effectiveness of security features in smart card chips.
problem Costs of adding security features to smart card chips.
method Examines production phases, costs, and security features.
result Security features are worth the cost due to potential damages from attacks.
Wrist movements can reveal digits, posing security risks.
problem Security vulnerabilities in wrist wearable devices.
method Machine learning model trained on wrist movement data.
result 100% accuracy in predicting digits via wrist movement.
A tailored HTR system improves CER to 0.015 for medieval Latin.
problem Digitizing handwritten medieval Latin records for a low-resource language.
method End-to-end pipeline using image segmentation and transformer-based models with extensive data augmentation.
result Best-performing setup achieved CER of 0.015, superior to commercial models.
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
problem Detecting and preventing bank transaction fraud caused by data breaches.
method A distributed alternating algorithm that assigns probabilities to different locations being compromised.
result BreachRadar achieves over 90% precision and recall in detecting compromised cards.
Paper proposes sharing models instead of data for smart health predictions.
problem Sharing sensitive medical data is legally restricted and challenging.
method Train a teacher model on sensitive data, then transfer its knowledge to a student model without accessing original data.
result Student model mimics teacher model performance in making accurate predictions.
Hierarchical clustering models capture electricity load patterns from smart meters.
problem Improving efficiency and sustainability of electricity systems.
method Quantile autocovariances and autocorrelations for summarizing time series.
result Clusters identify relevant consumption behaviors and capture geo-demographic segmentation.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
problem Relapse prevention for alcohol and tobacco addiction users.
method Records user profiles, tracks relapse history, uses machine learning for prediction, and recommends activities.
result Predictive machine learning algorithms help in preventing relapse.
FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.
problem Manual rule setup for smart devices is inefficient and privacy-compromising.
method FedRule constructs user-specific graphs for rule recommendation, using federated learning to protect privacy.
result FedRule achieves comparable performance to centralized methods and outperforms others.
This paper tackles credit card fraud detection using graph-based learning methods.
problem Detecting credit card fraud to reduce financial losses.
method Graph p-Laplacian based semi-supervised learning combined with undersampling techniques.
result Graph p-Laplacian semi-supervised learning outperforms current methods.
Neural nets analyze Magic: the Gathering cards for text and images.
problem Classify Magic: the Gathering cards into multiple categories.
method Used Convolutional and Recurrent Neural Networks to analyze card text and images.
result Developed a method to generate card text matching an input image.
Study evaluates SHAP for credit card default model consistency.
problem Model transparency and fairness in credit card default prediction models.
method Evaluates SHAP stability in credit card default prediction models via a case study.
result SHAP consistency is related to variable importance level.
ETHGamDet detects crypto gambling contracts and addresses.
problem Identifying crypto gambling contracts and addresses.
method Multi-modal retrieval methods, LightGBM model with memory components.
result ETHGamDet achieves high F1-scores in address and contract classification.
ARIMA model detects credit card fraud in unbalanced datasets.
problem Unsupervised credit card fraud detection in unbalanced datasets.
method ARIMA model applied to customer spending patterns for anomaly detection.
result ARIMA model outperforms benchmark anomaly detection methods.
Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact…
Study optimizes classifiers for credit card mail campaigns and default prediction.
problem Optimizing classifiers for credit card mail campaigns and default prediction.
method Three distinct models: response, risk, and response-risk. Optimized various performance metrics.
result Random Forest classifier achieves highest accuracy (83.2%) in multi-class response-risk model.
Paper quantifies dataset shift for credit card fraud detection.
problem Change in purchase behavior over time affects fraud detection accuracy.
method Measures day-to-day dataset shift using classification efficiency and clustering.
result Improves credit card fraud detection by incorporating dataset shift knowledge.
Standard economic theory, starting with Adam Smith's invisible hand, holds that those who trade for their own selfish motives of maximizing their private preferences may contribute more to the public wealth than those who claim altruistic motives. Under restrictive conditions, this has been shown to result from a self-…
Transformer learns shipping costs more accurately than traditional methods.
problem Inaccurate shipping cost estimates lead to poor financial decisions.
method Proposes Rate Card Transformer (RCT) using self-attention to encode shipping information.
result Cost predictions made by RCT have 28.82% less error compared to GBDT models.
Big data from phone calls improves credit scoring models and profits.
problem Improving credit scoring models to enhance financial inclusion.
method Combining call-detail records and traditional data to build scorecards using social network analytics.
result Combining call-detail records with traditional data significantly increases model performance and profit.
Enhances fraud detection with multiple HMM perspectives.
problem Detecting credit card fraud from sequential transactions.
method Modeling credit card transactions from three perspectives (card-holder, terminal, amount/time) using HMMs and combining likelihoods as features.
result 15% increase in precision-recall AUC compared to state-of-the-art methods.
Smart bin monitors predict medication adherence with high accuracy.
problem Predicting chronic medication adherence to improve healthcare efficiency.
method Machine learning models trained on IoT device data.
result High predictive performance (ROC AUC 0.86).
A new approach models credit card transactions using HMMs to detect fraud.
problem Detecting credit card fraud using isolated event analysis.
method Model sequences from three perspectives using HMMs and combine likelihoods as features.
result Improved fraud detection effectiveness compared to state-of-the-art methods.
CARD detects treatment responders with machine learning and adjustment.
problem Identifying responders in non-random treatment settings.
method Conformal prediction, machine learning, propensity score adjustment.
result High power responder detection in various scenarios.
Generative Adversarial Networks improve credit card fraud detection.
problem Detecting fraudulent credit card transactions accurately.
method Using GANs to generate synthetic data for oversampling.
result Wasserstein-GAN produces more realistic fraudulent transactions.
Wearable smart suit tracks infant movements with high accuracy.
problem Early detection of atypical motor development in infants.
method Developed a multi-sensor smart suit for data collection, trained a deep CNN algorithm for automatic posture and movement classification.
result Setup achieves human equivalent accuracy in infant posture and movement classification.
Unified model of urban consumer behavior and mobility patterns.
problem Understanding the lifestyles and infrastructure of urban regions.
method Collective matrix factorization for dual view modeling of consumer behavior and mobility.
result Unified model reveals deeper insights into consumer behavior and mobility patterns.
Expert system predicts credit card charge-offs using macroeconomic indicators.
problem Managing charge-off rates in the credit card industry.
method Developed an expert system using machine learning and macroeconomic indicators.
result Achieved mean squared error values of 1.15E-03 and 1.04E-03.
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
We present a model of credit card profitability, assuming that the card-holder always pays the full outstanding balance. The motivation for the model is to calculate an optimal credit limit, which requires an expression for the expected outstanding balance. We derive its Laplace transform, assuming that purchases are m…
Semi-supervised GANs with log-signatures improve credit card fraud detection.
problem Detecting fraud in large, complex financial transaction data streams.
method Conditional GANs with Bayesian inference and log-signatures for robust feature encoding.
result Consistent improvements over benchmarks in global and domain-specific metrics.
CARD models predict the distribution of continuous or categorical responses.
problem Uncertainty in predicting continuous or categorical responses.
method Combines denoising diffusion and conditional mean estimation.
result Outperforms state-of-the-art methods in conditional distribution prediction.
The study reveals fundamental limits of fraud detection in card payment networks.
problem Fraud detection in card payment networks is challenging due to structural information impairments.
method Formalized card authorization as a sequential decision problem with delayed feedback, derived minimax regret lower bound.
result Improving issuer reporting quality or reducing censorship can yield larger reductions in the regret floor than increasing model complexity.
Research examines motivations and factors influencing retailers' payment method choices.
problem Understanding motivations and factors affecting retailers' payment method choices.
method Qualitative and quantitative analysis of various factors including regulatory constraints, merchant service providers, and demographic variables.
result Lower interchange fees and regulatory constraints make card payment adoption financially feasible for merchants.
This paper summarizes AI methods for detecting credit card fraud.
problem Detecting credit card fraud from millions of transactions.
method Rule-based and AI approaches, addressing imbalanced datasets, real-time scenarios, and feature engineering.
result Summarizes state-of-the-art AI methods for fraud detection.
Study evaluates AD methods for fraud detection in online credit card payments.
problem Fraud detection in online credit card payments using anomaly detection methods.
method Assessed several recent anomaly detection methods and compared them with standard supervised learning methods.
result LightGBM outperforms other methods but is more sensitive to distribution shifts.
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
problem Credit card fraud detection overlooks causal structure of transactions.
method CaT-GNN combines causal invariant learning and temporal graph neural networks.
result CaT-GNN outperforms existing methods on various datasets.
Money flow models are essential tools to understand different economical phenomena, like saving propensities and wealth distributions. In spite of their importance, most of them are based on synthetic transaction networks with simple topologies, e.g. random or scale-free ones, as the characterisation of real networks i…
We introduce a new virtual environment for simulating a card game known as "Big 2". This is a four-player game of imperfect information with a relatively complicated action space (being allowed to play 1,2,3,4 or 5 card combinations from an initial starting hand of 13 cards). As such it poses a challenge for many curre…
Proposes Textual Echo Cancellation to improve speech recognition.
problem Improving speech recognition performance and user experience for smart devices.
method A novel sequence-to-sequence model with multi-source attention that processes both the microphone mixture signal and source text of TTS playback.
result Demonstrates enhanced speech recognition performance and reduced latency.
CARDS improves decoding efficiency and alignment quality for LLMs.
problem Efficiency bottlenecks in decoding-time alignment for LLMs.
method Cascade Reward Sampling (CARDS) with segment-level rejection sampling and uncertainty-based segmentation.
result Significant improvement in decoding efficiency and alignment quality.
Detects non-technical electricity losses using image representations of time-series data.
problem Detecting anomalies in large-scale, unlabelled data for non-technical losses.
method Transformed time-series data into image representations, used semi-supervised deep learning.
result Significant improvement in detecting abnormal electricity consumption patterns.
Paper proposes a DAE algorithm to improve credit card fraud detection.
problem Imbalanced data classification problem in credit card fraud detection.
method Proposes a denoising autoencoder neural network (DAE) algorithm to oversample and denoise minority class samples.
result Improves classification accuracy of minority class samples in imbalanced datasets.
Study Figgie card game strategies using agent-based simulation.
problem Analyze strategies for Figgie card game and market behavior.
method Develop agent-based discrete-event market simulation to test strategies.
result Fundamentalist strategy is profit-maximizing in all tested combinations.
Study examines fraud detection methods for credit cards with limited data.
problem Data imbalance in credit card fraud detection.
method Assesses different sampling methods and machine learning algorithms.
result Monte Carlo analysis shows random undersampling outperforms SMOTE in fraud cost reduction.