A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
problem Understanding individual metro usage dynamics over multi-year horizons.
method A state-based lifecycle modeling framework integrating HSMM and discrete-time survival analysis.
result Identification of interpretable mobility states, transition dynamics, and state-dependent exit and re-entry processes.
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.
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.
Unified theory explains housing cycle across metros, showing credit expansion impacts.
problem Puzzling correlations between income and mortgage growth across ZIP codes and metros.
method Unified credit expansion theory, double differences, instrumental variables.
result Credit expansion drives housing cycle, affecting boom, bust, and recovery phases.
New method improves tensor completion for weakly-dependent spatiotemporal data.
problem Improving tensor completion for weakly-dependent data on graphs.
method Introducing L1-norm and Graph Laplacian penalties for low-rank tensor decomposition and completion. result Improved performance in metro passenger flow prediction.
Bayesian framework predicts post-disruption travel times in metro networks.
problem Uncertainty in post-disruption travel times in metro networks.
method Bayesian spatiotemporal modeling framework capturing train interactions and non-Gaussian distributional characteristics.
result The proposed models consistently outperform baseline specifications in point prediction and uncertainty quantification.
METRO predicts reactions using minimal templates, reducing computational overhead and achieving state-of-the-art results.
problem Predicting possible reaction substrates for complex molecules from simpler precursors.
method METRO (Molecule-Edit Templates for RetrOsynthesis) uses minimal templates to predict reactions efficiently and accurately.
result METRO achieves state-of-the-art results on standard benchmarks, reducing computational overhead.
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.
problem Accurate parking availability prediction to support urban planning and management.
method Proposes SST-iTransformer, a self-supervised learning enhanced spatio-temporal inverted transformer, integrating multi-source data.
result SST-iTransformer achieves state-of-the-art performance in parking availability prediction.
Risk, including economic risk, is increasingly a concern for public policy and management. The possibility of dealing effectively with risk is hampered, however, by lack of a sound empirical basis for risk assessment and management. The paper demonstrates the general point for cost and demand risks in urban rail projec…
Method learns software resource usage from snapshots.
problem Challenges in learning time-varying, correlated resource usage.
method Graph structured Schrödinger bridge problem for nonparametric learning.
result Predicts most-likely resource distributions.
This study analyzes how weather impacts bike sharing usage in Washington D.C.
problem Understanding how weather affects bike sharing usage patterns.
method Gathered bike usage and weather data, used k-means clustering algorithm to identify clusters.
result Weather significantly impacts bike usage, with temperature and precipitation being the most influential factors.
Paper presents AETN for efficient user modeling from mobile app usage.
problem Efficient user modeling from mobile app usage with reduced manual effort.
method AutoEncoder-coupled Transformer Network (AETN).
result AETN achieves effective user embeddings with reduced manual effort.
Paper proposes transparent reporting of algorithmic energy usage to promote environmental sustainability.
problem Need for transparent reporting of algorithmic energy usage for environmental sustainability.
method Developed a Python package to make analyses of energy usage accessible to individual researchers, localized to specific power grids, and compared with global benchmarks.
result Demonstrated the use of automatically-generated Energy Usage Reports in model-choice for machine learning.
Proposes AtCoR for predicting bike station usage, improving station network reconfiguration.
problem Challenges in predicting new bike stations due to lack of historical data.
method AtCoR algorithm that predicts both existing and new bike stations using station-centered heatmaps and historical correlations.
result AtCoR outperforms existing models in predicting bike station usage.
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data p…
This study uses LSTM and SARIMA models to forecast CPU usage in cloud computing.
problem Predicting future CPU usage in cloud computing centers.
method Used LSTM and SARIMA models to forecast CPU usage over short and long time scales.
result SARIMA model performed better for long-term predictions but LSTM was more robust.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
problem Predicting time-varying hardware resource availability in control software.
method Path structured multimarginal Schrödinger bridge (MSBP) for learning stochastic resource usage.
result Guaranteed linear convergence to accurate prediction of hardware resource utilization.
New model for network analysis using functional data.
problem Existing network models treat nodes as functions, but this paper introduces functional edges.
method Transform adjacency matrix into functional adjacency tensor, apply Tucker decomposition, regularize basis matrices, and solve tensor completion problem.
result The model effectively captures community structure and handles irregular functional edge data.
A new model improves homogeneity in burn patient reimbursement.
problem Incomplete homogeneity checks for burn patients using LOS as a proxy.
method Cost-sensitive decision tree model considering patient-level cost and severity of burn.
result Identified groups with increased homogeneity compared to current HRG groups.
We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of traffic flows and joint global optimization of IP and optical layers using colorles…
Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid constraints, yet few tools exist to aid the physical configuration design of new networks.…
Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occupies less space, but also makes the code extremely difficult to manually inspect and understand. This paper presents Context2Name, a deep le…
A generalized gamification framework is introduced as a form of smart infrastructure with potential to improve sustainability and energy efficiency by leveraging humans-in-the-loop strategy. The proposed framework enables a Human-Centric Cyber-Physical System using an interface to allow building managers to interact wi…
Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past interactions. In this paper, we propose a solution for successfully addressi…
Large speech dataset for commercial use with 9.98% word error rate.
problem Creating a diverse speech recognition dataset for commercial purposes.
method Internet search for licensed audio data with transcriptions, training model on the dataset.
result Model trained on dataset achieves 9.98% word error rate on Librispeech's test-clean test set.
New algorithms optimize metrics for binary classification with class imbalance.
problem Optimizing metrics like Fβ, AM, Jaccard for imbalanced classes.
method Reformulates metric optimization as cost-sensitive learning, using surrogate loss functions.
result METRO algorithms provide strong theoretical guarantees and outperform baselines.
Efficiently discovers Bayesian network structure with reduced memory usage.
problem Bayesian network structure discovery is NP-hard and memory-intensive.
method Progressively leveled scoring approach with hierarchical computation.
result Achieved processing of a 28-variable Bayesian network using only memory.
Efficiently trains GCNs with reduced time and memory usage.
problem Hard training of GCNs over large graph datasets.
method Layer-wise and learned efficient training framework (L2-GCN). result Significantly reduces training time and memory usage.
Proposes a game-theoretic framework to motivate energy-efficient behavior in smart buildings.
problem Improving energy efficiency in smart building infrastructure through occupant behavior.
method Introduces a novel game-theoretic framework with human interaction, incorporating utility learning and deep neural networks.
result Demonstrates highly accurate prediction of occupant energy resource usage and explainable decision-making.
The study examines dataset usage patterns in machine learning research.
problem Lack of attention to dataset dynamics in machine learning research.
method Analysis of dataset usage patterns across machine learning subcommunities and time periods (2015-2020).
result Increasing concentration on fewer and fewer datasets, significant adoption from other tasks, and concentration across the field on datasets introduced by elite institutions.
Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present De…
Due to the popularity of context-awareness in the Internet of Things (IoT) and the recent advanced features in the most popular IoT device, i.e., smartphone, modeling and predicting personalized usage behavior based on relevant contexts can be highly useful in assisting them to carry out daily routines and activities. …
OpTorch optimizes deep learning for resource-limited environments.
problem Resource constraints in deep learning training.
method Optimized deep learning pipelines in training time and memory.
result Achieved similar accuracy to existing libraries with reduced memory usage.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
This review explores methods to explain deep neural networks and their applications.
problem Understanding the decision-making process of deep neural networks.
method Overview of interpretability methods, theoretical foundations, and comparative evaluations.
result Demonstrates the effectiveness of explainable AI in various applications.
Detecting inaccurate smart meters and targeting them for replacement can save significant resources. For this purpose, a novel deep-learning method was developed based on long short-term memory (LSTM) and a modified convolutional neural network (CNN) to predict electricity usage trajectories based on historical data. F…
Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy con…
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different dom…
RIn-Close_CVC is an efficient (take polynomial time per bicluster), complete (find all maximal biclusters), correct (all biclusters attend the user-defined level of consistency) and non-redundant (all the obtained biclusters are maximal and the same bicluster is not enumerated more than once) enumerative algorithm for …
An empirical investigation of active/continuous authentication for smartphones is presented in this paper by exploiting users' unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Variations of Hidden Markov Models (HMMs) are evaluated for continuous user verification, and chal…
Study develops a new model for predicting individual mobility based on activity patterns.
problem Existing mobility prediction methods overlook underlying activity patterns.
method Activity-based hidden Markov model (IOHMM) using transit smart card data.
result Proposed model achieves similar prediction performance to LSTM but also provides behavioral insights.
RDL-Net improves speech enhancement with fewer parameters and better performance.
problem Improving speech enhancement with fewer parameters and better performance.
method Proposes RDL-Net, a CNN combining residual and dense aggregations without over-allocating parameters.
result RDL-Net achieves higher speech enhancement performance with fewer parameters and lower computational requirements.
We demonstrate the usage of explicit form of the Thom class found by Mathai and Quillen for the definition of generating functional of a simple supersymmetric quantum mechanical model.
Proposes a new model for clustering passenger trajectories with graphs.
problem Hierarchical trip structure, inaccurate clustering number, and lack of spatial semantic graphs.
method Tensor Dirichlet Process Multinomial Mixture model with graphs and a tensor version of Collapsed Gibbs Sampling.
result Automatic determination of the number of clusters and better cluster quality.
WTM reduces clause usage and computation time for pattern recognition.
problem High computation time and memory usage in Tsetlin Machine.
method Weighting clauses and using binomial sampling to reduce complexity.
result WTM achieves similar accuracy with fewer clauses and faster training.
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
problem Lack of interpretability in digital mobility data for understanding urban lifestyles.
method Privacy-enhanced dataset of mobility visitation patterns, latent activity behavior decomposition.
result Detected 12 latent activity behaviors that describe urban lifestyles, not single lifestyles.