A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
AppsPred predicts smartphone app usage based on context.
Smart phone apps that enable users to easily track their diets have become widespread in the last decade. This has created an opportunity to discover new insights into obesity and weight loss by analyzing the eating habits of the users of such apps. In this paper, we present diet2vec: an approach to modeling latent str…
The paper optimizes neural network inference on mobile GPUs.
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
Detects backdoors in trained classifiers without access to training data.
StrokeSave app diagnoses stroke via mobile sensors and AI.
Due to the popularity of the Internet and smart mobile devices, more and more financial transactions and activities have been digitalized. Compared to traditional financial fraud detection strategies using credit-related features, customers are generating a large amount of unstructured behavioral data every second. In …
Paper evaluates using app images for classification, improving accuracy.
Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis signifi…
Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to…
The paper uses action graphs to predict user engagement in Snapchat.
This study analyzes app reviews to understand students' behavior in the app market.
In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a …
AOBTM adapts online topic modeling for short app reviews, revealing coherent topics over time.
Paper presents AETN for efficient user modeling from mobile app usage.
DataLearner simplifies data mining on Android devices.
Graph machine learning and Super-App data improve credit risk prediction for financial inclusion.
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
Paper uses Super-App data to improve income estimation models.
The increasing popularity of cell phones has made them the most personal and ubiquitous communication devices nowadays. Typically, the ringing notifications of mobile phones are used to inform the users about the incoming calls. However, the notifications of inappropriate incoming calls sometimes cause interruptions no…
An app-based mHealth intervention uses reinforcement learning to send effective reminder notifications.
Relational Graph Neural Networks improve fraud detection in Super-Apps.
We propose a nonparametric model for time series with missing data based on low-rank matrix factorization. The model expresses each instance in a set of time series as a linear combination of a small number of shared basis functions. Constraining the functions and the corresponding coefficients to be nonnegative yields…
Alternative app data improves credit scoring for underserved borrowers.
FATE predicts user engagement on social apps with explainable explanations.
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…
Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In thi…
AppStreamer reduces mobile game storage by predicting needed files.
The paper tackles carousel personalization in music streaming apps using contextual bandits.
RL platform enhances user journeys in healthcare apps.
The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The classification accuracy may decrease and the complexity of the classifiers may increase due to the number of re…
In this paper we study the probabilistic properties of the posteriors in a speech recognition system that uses a deep neural network (DNN) for acoustic modeling. We do this by reducing Kaldi's DNN shared pdf-id posteriors to phone likelihoods, and using test set forced alignments to evaluate these using a calibration s…
Study user engagement in mobile health apps for health workers in resource-poor settings.
Mobile phones can record individual's daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time segments of individual's similar behavioral characteristics utilizing their mobile phone data. One of the determinants of an individual's beh…
Model predicts cognitive health risks based on smartphone usage patterns.
Improved contact tracing models outperform NIST challenge results.
A model for choosing crypto assets based on security and stability.
OCC system speeds up in-app communications for Uber drivers and riders.
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-l…
Smartphone app diagnoses pulmonary diseases from chest X-rays.
Predicts customer call intent for auto dealerships using CNN.
Smartphone app counts grapes for accurate yield estimation.
Study how predictions affect the data they're based on, improving generalization guarantees.
Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more…
Improved topic modeling captures temporal relationships in speech.
Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concen…
Recent work on end-to-end automatic speech recognition (ASR) has shown that the connectionist temporal classification (CTC) loss can be used to convert acoustics to phone or character sequences. Such systems are used with a dictionary and separately-trained Language Model (LM) to produce word sequences. However, they a…
Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit, and for potential borrowers to receive it. However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone…