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.
Paper presents a new time-series segmentation technique for mobile phone user behavior.
Mobile phone data predicts users' income levels.
Paper presents a robust model to improve prediction accuracy for real-life mobile phone data.
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…
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…
Survey of mobility studies using mobile phone data.
Predicts gender and age from mobile phone data for marketing.
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…
Recently, mobile operators in many developing economies have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, a consistent difficulty has been identifying individuals most likely to benefit fr…
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…
Method discovers user habits from mobile data.
This paper discusses issues in mining user behavioral rules for context-aware mobile apps.
The paper optimizes neural network inference on mobile GPUs.
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyz…
Proposes E-MIIM for better mobile phone interruptions management.
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
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…
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…
Chagas disease is a neglected disease, and information about its geographical spread is very scarse. We analyze here mobility and calling patterns in order to identify potential risk zones for the disease, by using public health information and mobile phone records. Geolocalized call records are rich in social and mobi…
Unified model of urban consumer behavior and mobility patterns.
Bluetooth data predicts depression severity, showing 18.8% extra variance.
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…
Real-time drowsiness detection on mobile phones reduces road trauma.
We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent 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 …
Develops a graph-based convolutional network for multi-view networks to improve poverty research.
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 …
CalBehav models individual smartphone user behavior for calendar events.
In this paper we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections betwee…
Survey of knowledge distillation for resource-limited devices.
AMS improves video inference on edge devices by adapting a small model with online knowledge distillation.
We present and test a sequential learning algorithm for the short-term prediction of human mobility. This novel approach pairs the Exponential Weights forecaster with a very large ensemble of experts. The experts are individual sequence prediction algorithms constructed from the mobility traces of 10 million roaming mo…
Bayesian model uses mobile data to assess business resilience after hurricanes.
Study uses remotely sensed data to infer economic outcomes in experiments and quasi-experiments.
LEAF benchmarks federated learning challenges.
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbei…
Deep learning improves Android malware detection.
Tensor factorization uncovers hidden patterns in student behavior data.
Paper uses UKS to improve BLE RSSI for proximity inference in mobile phone apps.
(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…
Big data from phone calls improves credit scoring models and profits.
Paper develops fine-grain spatiotemporal risk scores using high-resolution mobility data.
City2City translates place representations across cities using language translation techniques.
DETECT clusters mobility behaviors from trajectories using deep learning.
Model captures sleep patterns in infant data.
A real-time federated neural architecture search approach reduces costs and improves performance.
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
Paper analyzes privacy-aware mobility behavior using entropy metrics.
Single-Path NAS designs efficient ConvNets for mobile devices in hours.