arXiv subjects

Devorah Kletenik

Publications and source records attributed to Devorah Kletenik.

15 recordsLinked to original sources

arXiv2025

Creating Opportunities: Co-designing an mHealth App with Older Adults

We conducted a qualitative co-design study with four adults aged 60+ to gather design insights on a Figma prototype and a generative AI (GenAI) chatbot for an app aimed at providing an AI coach to support older adults' physical activity. The initial design for both incorporates several novel aspects: a curated health knowledge base, personalised responses based on goals and health history, privacy considerations, integration with wearables for physical activity context, as well as dynamic context injection. The study yielded feedback on improving both the proposed user experience in the app and the conversation flow with the chatbot, and it will aid future work aimed at implementing a GenAI-powered health coach for older adults.

cs.HC↗

arXiv2025

Skill, Will, or Both? Understanding Digital Inaccessibility from Accessibility Professionals' Viewpoint

Digital inaccessibility continues to be a significant barrier to true inclusion and equality. WebAIM's 2024 report reveals that only 4.1% of the world's top one million website homepages are fully accessible. Furthermore, the percentage of web pages with detectable Web Content Accessibility Guidelines (WCAG) failures has only decreased by 1.9\% over the past five years, from 97.8%. To gain deeper insights into the persistent challenges of digital accessibility, we conducted a comprehensive survey with 160 accessibility professionals. Unlike previous studies, which often focused on technology professionals, our research examines inaccessibility through the lens of dedicated accessibility professionals, offering a more detailed analysis of the barriers they face. Our investigation explores (a) organizations' willingness to prioritize accessibility, (b) the challenges in ensuring accessibility, and (c) the current accessibility training practices in technology workspaces. This study aims to provide an updated perspective on the state of digital accessibility from the point of view of accessibility professionals.

cs.CY↗

arXiv2024

Toward Designing Accessible and Meaningful Software for Cancer Survivors

Cancer survivors experience a wide range of impairments arising from cancer or its treatment, such as chemo brain, visual impairments, and physical impairments. These impairments degrade their quality of life and potentially make software use more challenging for them. However, there has been limited research on designing accessible software for cancer survivors. To bridge this research gap, we conducted a formative study including a survey (n=46), semi-structured interviews (n=20), and a diary study (n=10) with cancer survivors. Our results revealed a wide range of impairments experienced by cancer survivors, including chemo brain, neuropathy, and visual impairments. Cancer survivors heavily relied on software for socialization, health purposes, and cancer advocacy, but their impairments made software use more challenging for them. Based on the results, we offer a set of accessibility guidelines that software designers can utilize when creating applications for cancer survivors. Further, we suggest design features for inclusion, such as health resources, socialization tools, and games, tailored to the needs of cancer survivors. This research aims to spotlight cancer survivors' software accessibility challenges and software needs and invite more research in this important yet under-investigated domain.

cs.HC↗

arXiv2024

Accessible Adventures: Teaching Accessibility to High School Students Through Games

Accessibility education has been rarely incorporated into the high school curricula. This is a missed opportunity to equip next-generation software designers and decision-makers with knowledge, awareness, and empathy regarding accessibility and disabilities. We taught accessibility to students (N=93) in a midwestern high school through empathy-driven games and interviewed three Computer Science high school teachers and one librarian who taught programming. Accessibility education is currently insufficient in high school, facing challenges such as teachers' knowledge and conflicted curriculum goals. The students exhibited increased knowledge and awareness of accessibility and empathy for people with disabilities after playing the games. With this education outreach, we aim to provide insights into teaching next-generation software designers about accessibility by leveraging games.

cs.HC↗

arXiv2022

Adaptivity Gaps for the Stochastic Boolean Function Evaluation Problem

We consider the Stochastic Boolean Function Evaluation (SBFE) problem where the task is to efficiently evaluate a known Boolean function $f$ on an unknown bit string $x$ of length $n$. We determine $f(x)$ by sequentially testing the variables of $x$, each of which is associated with a cost of testing and an independent probability of being true. If a strategy for solving the problem is adaptive in the sense that its next test can depend on the outcomes of previous tests, it has lower expected cost but may take up to exponential space to store. In contrast, a non-adaptive strategy may have higher expected cost but can be stored in linear space and benefit from parallel resources. The adaptivity gap, the ratio between the expected cost of the optimal non-adaptive and adaptive strategies, is a measure of the benefit of adaptivity. We present lower bounds on the adaptivity gap for the SBFE problem for popular classes of Boolean functions, including read-once DNF formulas, read-once formulas, and general DNFs. Our bounds range from $Ω(\log n)$ to $Ω(n/\log n)$, contrasting with recent $O(1)$ gaps shown for symmetric functions and linear threshold functions.

cs.DS↗

arXiv2022

The Stochastic Boolean Function Evaluation Problem for Symmetric Boolean Functions

We give two approximation algorithms solving the Stochastic Boolean Function Evaluation (SBFE) problem for symmetric Boolean functions. The first is an $O(\log n)$-approximation algorithm, based on the submodular goal-value approach of Deshpande, Hellerstein and Kletenik. Our second algorithm, which is simple, is based on the algorithm solving the SBFE problem for $k$-of-$n$ functions, due to Salloum, Breuer, and Ben-Dov. It achieves a $(B-1)$ approximation factor, where $B$ is the number of blocks of 0's and 1's in the standard vector representation of the symmetric Boolean function. As part of the design of the first algorithm, we prove that the goal value of any symmetric Boolean function is less than $n(n+1)/2$. Finally, we give an example showing that for symmetric Boolean functions, minimum expected verification cost and minimum expected evaluation cost are not necessarily equal. This contrasts with a previous result, given by Das, Jafarpour, Orlitsky, Pan and Suresh, which showed that equality holds in the unit-cost case.

cs.DS↗

arXiv2021

A Tight Bound for Stochastic Submodular Cover

We show that the Adaptive Greedy algorithm of Golovin and Krause (2011) achieves an approximation bound of $(\ln (Q/η)+1)$ for Stochastic Submodular Cover: here $Q$ is the "goal value" and $η$ is the smallest non-zero marginal increase in utility deliverable by an item. (For integer-valued utility functions, we show a bound of $H(Q)$, where $H(Q)$ is the $Q^{th}$ Harmonic number.) Although this bound was claimed by Golovin and Krause in the original version of their paper, the proof was later shown to be incorrect by Nan and Saligrama (2017). The subsequent corrected proof of Golovin and Krause (2017) gives a quadratic bound of $(\ln(Q/η) + 1)^2$. Other previous bounds for the problem are $56(\ln(Q/η) + 1)$, implied by work of Im et al. (2016) on a related problem, and $k(\ln (Q/η)+1)$, due to Deshpande et al. (2016) and Hellerstein and Kletenik (2018), where $k$ is the number of states. Our bound generalizes the well-known $(\ln~m + 1)$ approximation bound on the greedy algorithm for the classical Set Cover problem, where $m$ is the size of the ground set.

cs.DS↗

arXiv2018

The Stochastic Score Classification Problem

Consider the following Stochastic Score Classification Problem. A doctor is assessing a patient's risk of developing a certain disease, and can perform $n$ tests on the patient. Each test has a binary outcome, positive or negative. A positive test result is an indication of risk, and a patient's score is the total number of positive test results. The doctor needs to classify the patient into one of $B$ risk classes, depending on the score (e.g., LOW, MEDIUM, and HIGH risk). Each of these classes corresponds to a contiguous range of scores. Test $i$ has probability $p_i$ of being positive, and it costs $c_i$ to perform the test. To reduce costs, instead of performing all tests, the doctor will perform them sequentially and stop testing when it is possible to determine the risk category for the patient. The problem is to determine the order in which the doctor should perform the tests, so as to minimize the expected testing cost. We provide approximation algorithms for adaptive and non-adaptive versions of this problem, and pose a number of open questions.

cs.DS↗

arXiv2017

Submodular Goal Value of Boolean Functions

Recently, Deshpande et al. introduced a new measure of the complexity of a Boolean function. We call this measure the "goal value" of the function. The goal value of $f$ is defined in terms of a monotone, submodular utility function associated with $f$. As shown by Deshpande et al., proving that a Boolean function $f$ has small goal value can lead to a good approximation algorithm for the Stochastic Boolean Function Evaluation problem for $f$. Also, if $f$ has small goal value, it indicates a close relationship between two other measures of the complexity of $f$, its average-case decision tree complexity and its average-case certificate complexity. In this paper, we explore the goal value measure in detail. We present bounds on the goal values of arbitrary and specific Boolean functions, and present results on properties of the measure. We compare the goal value measure to other, previously studied, measures of the complexity of Boolean functions. Finally, we discuss a number of open questions provoked by our work.

cs.DM↗

arXiv2016

Scenario Submodular Cover

Many problems in Machine Learning can be modeled as submodular optimization problems. Recent work has focused on stochastic or adaptive versions of these problems. We consider the Scenario Submodular Cover problem, which is a counterpart to the Stochastic Submodular Cover problem studied by Golovin and Krause. In Scenario Submodular Cover, the goal is to produce a cover with minimum expected cost, where the expectation is with respect to an empirical joint distribution, given as input by a weighted sample of realizations. In contrast, in Stochastic Submodular Cover, the variables of the input distribution are assumed to be independent, and the distribution of each variable is given as input. Building on algorithms developed by Cicalese et al. and Golovin and Krause for related problems, we give two approximation algorithms for Scenario Submodular Cover over discrete distributions. The first achieves an approximation factor of O(log Qm), where m is the size of the sample and Q is the goal utility. The second, simpler algorithm achieves an approximation bound of O(log QW), where Q is the goal utility and W is the sum of the integer weights. (Both bounds assume an integer-valued utility function.) Our results yield approximation bounds for other problems involving non-independent distributions that are explicitly specified by their support.

cs.DS↗

arXiv2015

Discrete Stochastic Submodular Maximization: Adaptive vs. Non-Adaptive vs. Offline

We consider the problem of stochastic monotone submodular function maximization, subject to constraints. We give results on adaptivity gaps, and on the gap between the optimal offline and online solutions. We present a procedure that transforms a decision tree (adaptive algorithm) into a non-adaptive chain. We prove that this chain achieves at least $τ$ times the utility of the decision tree, over a product distribution and binary state space, where $τ = \min_{i,j} \Pr[x_i=j]$. This proves an adaptivity gap of $1/τ$ (which is $2$ in the case of a uniform distribution) for the problem of stochastic monotone submodular maximization subject to state-independent constraints. For a cardinality constraint, we prove that a simple adaptive greedy algorithm achieves an approximation factor of $(1-1/e^τ)$ with respect to the optimal offline solution; previously, it has been proven that the algorithm achieves an approximation factor of $(1-1/e)$ with respect to the optimal adaptive online solution. Finally, we show that there exists a non-adaptive solution for the stochastic max coverage problem that is within a factor $(1-1/e)$ of the optimal adaptive solution and within a factor of $τ(1-1/e)$ of the optimal offline solution.

cs.DS↗

arXiv2014

Evaluation of DNF Formulas

Stochastic Boolean Function Evaluation (SBFE) is the problem of determining the value of a given Boolean function $f$ on an unknown input $x$, when each bit of $x_i$ of $x$ can only be determined by paying a given associated cost $c_i$. Further, $x$ is drawn from a given product distribution: for each $x_i$, $Prob[x_i=1] = p_i$, and the bits are independent. The goal is to minimize the expected cost of evaluation. Stochastic Boolean Function Evaluation (SBFE) is the problem of determining the value of a given Boolean function $f$ on an unknown input $x$, when each bit of $x_i$ of $x$ can only be determined by paying a given associated cost $c_i$. Further, $x$ is drawn from a given product distribution: for each $x_i$, $Prob[x_i=1] = p_i$, and the bits are independent. The goal is to minimize the expected cost of evaluation. In this paper, we study the complexity of the SBFE problem for classes of DNF formulas. We consider both exact and approximate versions of the problem for subclasses of DNF, for arbitrary costs and product distributions, and for unit costs and/or the uniform distribution.

cs.CC↗

arXiv2013

Approximation Algorithms for Stochastic Boolean Function Evaluation and Stochastic Submodular Set Cover

Stochastic Boolean Function Evaluation is the problem of determining the value of a given Boolean function f on an unknown input x, when each bit of x_i of x can only be determined by paying an associated cost c_i. The assumption is that x is drawn from a given product distribution, and the goal is to minimize the expected cost. This problem has been studied in Operations Research, where it is known as "sequential testing" of Boolean functions. It has also been studied in learning theory in the context of learning with attribute costs. We consider the general problem of developing approximation algorithms for Stochastic Boolean Function Evaluation. We give a 3-approximation algorithm for evaluating Boolean linear threshold formulas. We also present an approximation algorithm for evaluating CDNF formulas (and decision trees) achieving a factor of O(log kd), where k is the number of terms in the DNF formula, and d is the number of clauses in the CNF formula. In addition, we present approximation algorithms for simultaneous evaluation of linear threshold functions, and for ranking of linear functions. Our function evaluation algorithms are based on reductions to the Stochastic Submodular Set Cover (SSSC) problem. This problem was introduced by Golovin and Krause. They presented an approximation algorithm for the problem, called Adaptive Greedy. Our main technical contribution is a new approximation algorithm for the SSSC problem, which we call Adaptive Dual Greedy. It is an extension of the Dual Greedy algorithm for Submodular Set Cover due to Fujito, which is a generalization of Hochbaum's algorithm for the classical Set Cover Problem. We also give a new bound on the approximation achieved by the Adaptive Greedy algorithm of Golovin and Krause.

cs.DS↗

arXiv2011

Tight Bounds on Proper Equivalence Query Learning of DNF

We prove a new structural lemma for partial Boolean functions $f$, which we call the seed lemma for DNF. Using the lemma, we give the first subexponential algorithm for proper learning of DNF in Angluin's Equivalence Query (EQ) model. The algorithm has time and query complexity $2^{(\tilde{O}{\sqrt{n}})}$, which is optimal. We also give a new result on certificates for DNF-size, a simple algorithm for properly PAC-learning DNF, and new results on EQ-learning $\log n$-term DNF and decision trees.

cs.LG↗

arXiv2011

On the gap between ess(f) and cnf_size(f)

Given a Boolean function f, the quantity ess(f) denotes the largest set of assignments that falsify f, no two of which falsify a common implicate of f. Although ess(f)$ is clearly a lower bound on cnf_size(f) (the minimum number of clauses in a CNF formula for f), Cepek et al. showed that it is not, in general, a tight lower bound. They gave examples of functions f for which there is a small gap between ess(f) and cnf_size(f). We demonstrate significantly larger gaps. We show that the gap can be exponential in n for arbitrary Boolean functions, and Theta(sqrt{n}) for Horn functions, where n is the number of variables of f. We also introduce a natural extension of the quantity ess(f), which we call ess_k(f), which is the largest set of assignments, no k of which falsify a common implicate of f.

cs.DM↗