The paper calibrates phone likelihoods in speech recognition systems.
problem Improving the calibration of phone likelihoods in speech recognition systems.
method Reduced Kaldi's DNN shared pdf-id posteriors to phone likelihoods, evaluated using a calibration sensitive metric, and improved calibration through averaging and scaling.
result Improvement in the calibration of phone likelihoods through averaging and scaling of log likelihoods.
SilentPhone identifies opportune times to silence phones to reduce interruptions.
problem Inappropriate phone notifications cause interruptions for users and others.
method Data-driven approach using past phone log data to infer unavailability.
result Identifies opportune moments for call interruptions and generates silent mode rules.
Paper presents a robust model to improve prediction accuracy for real-life mobile phone data.
problem Noisy instances in real-life mobile phone data affect model accuracy.
method Identify and eliminate noisy instances using naive Bayes and Laplace estimators, then build a decision tree model.
result The robust model improves prediction accuracy as shown by experimental results.
Improved Naive Bayes for better phone call behavior classification.
problem Noise in mobile phone data affects phone call behavior classification accuracy.
method Improved naive Bayes classifier with behavioral pattern analysis and dynamic noise threshold.
result Our technique improves classification accuracy by 15%.
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of d…
Survey of mobility studies using mobile phone data.
problem Understanding human mobility patterns.
method Data Science techniques applied to mobile phone datasets.
result Applications in urban planning, data traffic prediction, etc.
Paper presents a new time-series segmentation technique for mobile phone user behavior.
problem Current segmentation techniques do not accurately capture individual user behavior over time.
method Behavior-Oriented Time Segmentation (BOTS) technique that considers temporal coverage and number of incidences.
result BOTS technique better captures user behavior at various times of day and week.
Mobile phone data predicts users' income levels.
problem Predicting users' income levels from mobile phone data.
method Comparison of feature extraction methods and machine learning techniques.
result Bayesian method based on the communication graph outperforms other methods.
Mobile phone data predicts loan repayment risk.
problem Lack of formal financial histories hinders credit extension.
method Behavioral signatures in mobile phone usage data.
result Mobile phone data outperforms traditional credit scoring methods.
Phone sensors detect gait changes during drinking episodes.
problem Detecting gait changes in heavy drinkers during natural drinking occasions.
method Used phone sensors to collect gait data, computed features, and trained an artificial neural network model.
result Phone sensor features correlate highly with estimated blood alcohol concentration (eBAC).
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.
Study uses mobile phone data to map Chagas disease risk zones.
problem Identifying geographical spread of Chagas disease.
method Analyzing geolocalized call records and public health information.
result Generated risk maps for public health campaigns.
Improved contact tracing models outperform NIST challenge results.
problem Contact tracing using phone data and machine learning.
method Developed two machine learning models (GBM and MLP) from phone instrumental data features.
result Outperformed the leading NIST challenge result by HKUST.
Improved phone classification accuracy using graph-based regularization.
problem Phone classification with limited labeled data.
method Graph-based semi-supervised learning with stochastic entropic regularization.
result Significantly improved phone classification accuracy with low labeled data.
The EM training algorithm of the classical i-vector extractor is often incorrectly described as a maximum-likelihood method. The i-vector model is however intractable: the likelihood itself and the hidden-variable posteriors needed for the EM algorithm cannot be computed in closed form. We show here that the classical …
The study compares prepaid and postpaid mobile phone users and predicts their subscription type.
problem Predicting mobile phone subscription type based on usage and network connections.
method Graph labelling approach using max-flow min-cut algorithms and indirect inference methods.
result Graph labelling approach achieves 87% classification accuracy, outperforming supervised learning methods.
Improved phone classification using semi-supervised learning with autoencoders.
problem Phone classification accuracy with limited labeled data.
method Semi-supervised learning with sparse autoencoders, using both labeled and unlabelled data.
result The method outperforms standard supervised training and provides competitive error rates.
Paper proposes real-time health monitoring using wearable sensors.
problem Real-time health monitoring using wearable sensors.
method Learning shallow detection cascades for real-time wearable-phone or wearable-phone-cloud systems.
result Cigarette smoking detection from actigraphy and respiration data.
Predicts customer call intent for auto dealerships using CNN.
problem Understanding customer intent from phone calls for better service.
method Developed a CNN-based supervised learning model for multi-class classification.
result CNN model performs well on customer call intent classification.
Improved topic modeling captures temporal relationships in speech.
problem Lack of temporal information in LDA for speech analysis.
method Temporal Markov chain extension to LDA for acoustic unit discovery.
result Improved phone segmentation results compared to base LDA.
Direct acoustics-to-word models improve speech recognition without LMs.
problem Improving speech recognition without requiring a Language Model (LM).
method Direct acoustics-to-word CTC models trained on public benchmark tasks.
result CTC word model achieves 13.0%/18.8% word error rate compared to 9.6%/16.0% for phone-based CTC with a 4-gram LM.
Predicts gender and age from mobile phone data for marketing.
problem Enhance marketing offers by predicting customer demographics.
method Machine learning algorithms applied to CDRs, CRM, and billing info.
result 85.6% accuracy in gender prediction, 65.5% in age prediction.
The paper optimizes neural network inference on mobile GPUs.
problem Limited computing power and thermal constraints on mobile CPUs.
method Leverage mobile GPUs for neural network inference.
result Real-time inference of deep neural networks on Android and iOS devices.
The paper analyzes how speech enhancement and recognition can be improved in noisy environments.
problem Improving speech recognition in multi-talker scenarios with limited resources.
method Developed and trained two LSTM-based models for speech enhancement and phone recognition, then studied their joint optimization.
result Joint optimization of speech enhancement and recognition leads to a significant reduction in Phone Error Rate (PER).
Develops a graph-based convolutional network for multi-view networks to improve poverty research.
problem Binary treatment of social network relations in graph learning models.
method Multi-GCN: Graph Convolutional Networks for Multi-View Networks.
result Multi-GCN outperforms state-of-the-art algorithms on poverty prediction tasks and broader multi-view network tasks.
Proposes E-MIIM for better mobile phone interruptions management.
problem Improper phone interruptions in daily life activities.
method Ensemble learning based on context-aware mobile telephony model.
result E-MIIM outperforms existing MIIM models in prediction accuracy.
Real-time drowsiness detection on mobile phones reduces road trauma.
problem Driver drowsiness increases crash risk and road trauma.
method Depthwise separable 3D convolutions combined with early fusion of spatial and temporal information.
result Real-time drowsiness detection on mobile phones reduces road trauma.
VINE reconstructs networks from diffusion data efficiently.
problem Reconstructing networks from limited diffusion data.
method Variational Inference for Network Reconstruction (VINE).
result VINE accurately recovers connected graphs from diffusion data.
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic m…
Survey of knowledge distillation for resource-limited devices.
problem Deploying large deep learning models on resource-limited devices.
method Knowledge distillation using a smaller model trained with information from a larger model.
result A new metric (distillation metric) for comparing different knowledge distillation algorithms.
AMS improves video inference on edge devices by adapting a small model with online knowledge distillation.
problem High computation cost of Deep Neural Networks for real-time video inference on edge devices.
method AMS uses a remote server to continually train and adapt a small model on edge devices, using online knowledge distillation from a large model.
result 0.4--17.8 percent mean Intersection-over-Union improvement in video semantic segmentation.
End-to-end model detects articulatory features from speech data.
problem Detecting articulatory features from speech data for various applications.
method Apply Listen, Attend and Spell (LAS) architecture and attention models.
result End-to-end training of manners and places of articulation detectors.
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
problem Imperfect measurement of economic outcomes by remotely sensed variables.
method Combines experimental and observational data to identify causal parameters, using satellite imagery and mobile phone activity.
result Developed a robust method for n^{-1/2} inference that does not restrict remotely sensed variable processing algorithms.
LEAF benchmarks federated learning challenges.
problem Federated learning challenges with scale and heterogeneity.
method Modular benchmarking framework with open-source datasets and implementations.
result Captures obstacles and intricacies of practical federated environments.
Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative a…
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.
(ABRIDGED) In previous work, two platforms have been developed for testing computer-vision algorithms for robotic planetary exploration (McGuire et al. 2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de S…
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.
Algorithm estimates COVID-19 cases from phone calls.
problem Delay in confirming COVID-19 cases.
method Modeling calls as background plus signal, fitting data with high R2. result Algorithm estimates cases days before lab results.
Study analyzes mobile money adoption in 3 cultures, finds context-dependent models.
problem Identifying individuals most likely to benefit from mobile money adoption.
method Combining terabyte-scale data from Ghana, Pakistan, and Zambia to develop supervised learning models.
result Models fit on one population do not generalize to another, highlighting context-dependency.
We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent activity levels that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone …
We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is est…
Deep learning improves Android malware detection.
problem Detecting and preventing Android malware.
method Review of static, dynamic, and hybrid deep learning approaches.
result Identifies strengths and weaknesses of deep learning methods.
Framework transfers inertial motion across domains without labeled data.
problem Inertial measurements are sensitive to sensor placement and motion dynamics.
method Extracts domain-invariant features and transfers them to new domains.
result Framework converts raw IMU sequences into accurate inertial trajectories.
Research uses private information from borrower networks to predict P2P lending profitability.
problem Improving credit scoring in P2P lending networks.
method Applied machine learning algorithms to graph and location metrics of borrower networks.
result Graph topology and borrower location information predict loan profitability, reducing error by 4%.
Bluetooth data predicts depression severity, showing 18.8% extra variance.
problem Predicting depressive symptom severity using Bluetooth data.
method Extracted 49 Bluetooth features from NBDC data, used linear mixed-effect and hierarchical Bayesian linear regression models.
result Hierarchical Bayesian model achieved best prediction metrics (R2=0.526, RMSE=3.891).
Sum-product networks enhance sequence modeling with higher-order factors.
problem Modeling complex relations in sequence data with first-order models.
method Combining sum-product networks with higher-order linear-chain conditional random fields.
result Improved performance in sequence labeling tasks compared to state-of-the-art methods.
The paper explores modifications to filter banks for speech recognition.
problem Improving speech recognition accuracy using modified filter banks.
method The authors investigate replacing triangular filters with Gabor or Gammatone filters, and rearranging filter bank computations to integrate features over smaller time scales.
result No significant improvements in phone error rate were observed with the modifications.