On the Feature Discovery for App Usage Prediction in Smartphones

With the increasing number of mobile Apps developed, they are now closely integrated into daily life. In this paper, we develop a framework to predict mobile Apps that are most likely to be used regarding the current device status of a smartphone. Such an Apps usage prediction framework is a crucial prerequisite for fast App launching, intelligent user experience, and power management of smartphones. By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations. The IF feature is derived by constructing the proposed App Usage Graph (abbreviated as AUG) that models App usage transitions. In light of AUG, we are able to discover usage relations among Apps. Since users may have different usage behaviors on their smartphones, we further propose one personalized feature selection algorithm. We explore minimum description length (MDL) from the training data and select those features which need less length to describe the training data. The personalized feature selection can successfully reduce the log size and the prediction time. Finally, we adopt the kNN classification model to predict Apps usage. Note that through the features selected by the proposed personalized feature selection algorithm, we only need to keep these features, which in turn reduces the prediction time and avoids the curse of dimensionality when using the kNN classifier. We conduct a comprehensive experimental study based on a real mobile App usage dataset. The results demonstrate the effectiveness of the proposed framework and show the predictive capability for App usage prediction.Comment: 10 pages, 17 figures, ICDM 2013 short pape

Well-posedness of a class of perturbed optimization problems in Banach spaces

AbstractLet X be a Banach space and Z a nonempty subset of X. Let J:Z→R be a lower semicontinuous function bounded from below and p⩾1. This paper is concerned with the perturbed optimization problem of finding z0∈Z such that ‖x−z0‖p+J(z0)=infz∈Z{‖x−z‖p+J(z)}, which is denoted by minJ(x,Z). The notions of the J-strictly convex with respect to Z and of the Kadec with respect to Z are introduced and used in the present paper. It is proved that if X is a Kadec Banach space with respect to Z and Z is a closed relatively boundedly weakly compact subset, then the set of all x∈X for which every minimizing sequence of the problem minJ(x,Z) has a converging subsequence is a dense Gδ-subset of X∖Z0, where Z0 is the set of all points z∈Z such that z is a solution of the problem minJ(z,Z). If additionally p>1 and X is J-strictly convex with respect to Z, then the set of all x∈X for which the problem minJ(x,Z) is well-posed is a dense Gδ-subset of X∖Z0

Advances in Antenna Design and System Technologies for Next Generation Cellular Systems

10.1155/2013/610319International Journal of Antennas and Propagation201361031

Pseudomonas aeruginosa sepsis with ecthyma gangrenosum and pseudomembranous pharyngolaryngitis in a 5-month-old boy

Pseudomonas aeruginosa infection that induced pseudomembranous laryngopharyngitis and ecthyma gangrenosum simultaneously in a healthy infant is rare. We reported on a previously healthy 5-month-old boy with initial presentation of fever and diarrhea followed by stridor and progressive respiratory distress. P. aeruginosa sepsis was suspected because ecthyma gangrenosum over the right leg was found at the emergency department, and the diagnosis was confirmed by the blood culture. Fiberscope revealed bacterial pharyngolaryngitis without involvement of the trachea. Because of early recognition and adequate treatment, including antimicrobial therapy, noninvasive ventilation, incision, and drainage, he recovered completely without any complications

A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)

Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement. However, existing approaches are only able to evaluate offline performance since testing in real-time matches requires exhaustive costs and cannot be replicated. To test in a safe and reproducible simulator, we focus on turn-based sports and introduce a badminton environment by simulating rallies with different angles of view and designing the states, actions, and training procedures. This benefits not only coaches and players by simulating past matches for tactic investigation, but also researchers from rapidly evaluating their novel algorithms.Comment: Accepted by AAAI 2023 Student Abstract, code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environmen