Spanning Trees With Edge Conflicts and Wireless Connectivity

We introduce the problem of finding a spanning tree along with a partition of the tree edges into fewest number of feasible sets, where constraints on the edges define feasibility. The motivation comes from wireless networking, where we seek to model the irregularities seen in actual wireless environments. Not all node pairs may be able to communicate, even if geographically close - thus, the available pairs are specified with a link graph {L}=(V,E). Also, signal attenuation need not follow a nice geometric formula - hence, interference is modeled by a conflict (hyper)graph {C}=(E,F) on the links. The objective is to maximize the efficiency of the communication, or equivalently, to minimize the length of a schedule of the tree edges in the form of a coloring. We find that in spite of all this generality, the problem can be approximated linearly in terms of a versatile parameter, the inductive independence of the interference graph. Specifically, we give a simple algorithm that attains a O(rho log n)-approximation, where n is the number of nodes and rho is the inductive independence, and show that near-linear dependence on rho is also necessary. We also treat an extension to Steiner trees, modeling multicasting, and obtain a comparable result. Our results suggest that several canonical assumptions of geometry, regularity and "niceness" in wireless settings can sometimes be relaxed without a significant hit in algorithm performance

Towards improving emotion self-report collection using self-reflection

In an Experience Sampling Method (ESM) based emotion self-report collection study, engaging participants for a long period is challenging due to the repetitiveness of answering self-report probes. This often impacts the self-report collection as participants dropout in between or respond with arbitrary responses. Self-reflection (or commonly known as analyzing past activities to operate more efficiently in the future) has been effectively used to engage participants in logging physical, behavioral, or psychological data for Quantified Self (QS) studies. This motivates us to apply self-reflection to improve the emotion self-report collection procedure. We design, develop, and deploy a self-reflection interface and augment it with a smartphone keyboard-based emotion self-report collection application. The interface

Designing an experience sampling method for smartphone based emotion detection

Smartphones provide the capability to perform in-situ sampling of human behavior using Experience Sampling Method (ESM). Designing an ESM schedule involves probing the user repeatedly at suitable moments to collect self-reports. Timely probe generation to collect high fidelity user responses while keeping probing rate low is challenging. In mobile-based ESM, timeliness of the probe is also impacted by user's availability to respond to self-report request. Thus,

Emotion detection from touch interactions during text entry on smartphones

There are different modes of interaction with a software keyboard on a smartphone, such as typing and swyping. Patterns of such touch interactions on a keyboard may reflect emotions of a user. Since users may switch between different touch modalities while using a keyboard, therefore, automatic detection of emotion from touch patterns must consider both modalities in combination to detect the pattern. In this paper, we focus on identifying different features of touch interactions with a smartphone keyboard that lead to a personalized model for inferring user emotion. Since distinguishing typing and swyping activity is important to record the correct features, we designed a technique to correctly identify the modality. The ground truth labels for user emotion are collected directly from the user by periodically collecting self-reports. We jointly model typing and swyping features and correlate them with user provided self-reports to build a personalized machine learning model, which detects four emotion states (happy, sad, stressed, relaxed). We combine these design choices into an Android application TouchSense and evaluate the same in a 3-week in-the-wild study involving 22 participants. Our key evaluation results and post-study participant assessment demonstrate that it is possible to predict these emotion states with an average accuracy (AUCROC) of 73% (std dev. 6%, maximum 87%) combining these two touch interactions only

Impact of experience sampling methods on tap pattern based emotion recognition

Smartphone based emotion recognition uses predictive modeling to recognize user's mental states. In predictive modeling, determining ground truth plays a crucial role in labeling and training the model. Experience Sampling Method (ESM) is widely used in behavioral science to gather user responses about mental states. Smartphones equipped with sensors provide new avenues to design Experience Sampling Methods. Sensors provide multiple contexts that can be used to trigger collection of user responses. However, subsampling of sensor data can bias the inference drawn from trigger based ESM. We investigate whether continuous sensor data simplify the design of ESM. We use the typing pattern of users on smartphone as the context that can trigger response collection. We compare the context based and time based ESM designs to determine the impact of ESM strategies on emotion modeling. The results indicate how different ESM designs compare against each other

Does emotion influence the use of auto-suggest during smartphone typing?

Typing based interfaces are common across many mobile applications, especially messaging apps. To reduce the difficulty of typing using keyboard applications on smartphones, smartwatches with restricted space, several techniques, such as auto-complete, auto-suggest, are implemented. Although helpful, these techniques do add more cognitive load on the user. Hence beyond the importance to improve the word recommendations, it is useful to understand the pattern of use of auto-suggestions during typing. Among several factors that may influence use of auto-suggest, the role of emotion has been mostly overlooked, often due to the difficulty of unobtrusively inferring emotion. With advances in affective computing, and ability to infer user's emotional states accurately, it is imperative to investigate how auto-suggest can be guided by emotion aware decisions. In this work, we investigate correlations between user emotion and usage of auto-suggest i.e. whether users prefer to use auto-suggest in specific emotion states. We developed an Android keyboard application, which records auto-suggest usage and collects emotion self-reports from users in a 3-week in-the-wild study. Analysis of the dataset reveals relationship between user reported emotion state and use of auto-suggest. We used the data to train personalized models for predicting use of auto-suggest in specific emotion state. The model can predict use of auto-suggest with an average accuracy (AUCROC) of 82% showing the feasibility of emotion-aware auto-suggestion