The marginalization of autistic people in our society today is multi-faceted as it includes violence that is both physical and ideological in nature. It is rooted in the dehumanization, infantilization, and masculinization of autistic people and pervasive even in contemporary research studies that continue to echo ableist ideologies from the past. In this work, we identify how HRI research reproduces systemic social inequalities and explain how they align with historical misrepresentations, and other systemic barriers. We analyzed 142 papers focusing on HRI and autism published between 2016 and 2022. We critique these studies through a mixed-methods analysis of their definition of autism, study designs, participant recruitment, and results. Our findings indicate that HRI research stigmatizes autism in three dimensions - 1) the pathologization of autism, 2) gender and age-based essentialism, and 3) power imbalances. Our work uncovered that about 90% of HRI research during the timeline explored excluded the perspectives of autistic people, particularly those from understudied groups. We recommend broadening the inclusion of autistic people, considering research objectives beyond clinical use, and diversifying collaborations, foundational works considered, & participant demographics for more inclusive future work.

The availability of mobile money in Kenya has positively impacted commerce, financial transaction efforts, and the ability of individuals to receive and save their money. The addition of mobile loan applications provides access to loans without the hassle of going to a physical bank and, in some cases, completing paperwork. While it has its benefits, limited protection of user data has been a cause for concern. User complaints prompted a change in the mobile loan industry, requiring applications to be licensed and banning the use of specific permissions for Android versions of the apps placed in the Google Play Store. We investigate the impact of this change and explore ways to improve regulation by reviewing 30 licensed (n=15) and unlicensed (n=15) Kenyan-targeted digital lender apps. The results suggest that regulation has not yet had a significant impact on digital lender app development and thus encourages government-supported development guidelines and audits.

The use of mutual information as a tool in private data sharing has remained an open challenge due to the difficulty of its estimation in practice. In this paper, we propose InfoShape, a task-based encoder that aims to remove unnecessary sensitive information from training data while maintaining enough relevant information for a particular ML training task. We achieve this goal by utilizing mutual information estimators that are based on neural networks, in order to measure two performance metrics, privacy and utility. Using these together in a Lagrangian optimization, we train a separate neural network as a lossy encoder. We empirically show that InfoShape is capable of shaping the encoded samples to be informative for a specific downstream task while eliminating unnecessary sensitive information. Moreover, we demonstrate that the classification accuracy of downstream models has a meaningful connection with our utility and privacy measures.

We study the problem of finding unitary submatrices of the$N \times N$discrete Fourier transform matrix, in the context of interpolating a discrete bandlimited signal using an orthogonal basis. This problem is related to a diverse set of questions on idempotents on$\mathbb {Z}_{N}$and tiling$\mathbb {Z}_{N}$. In this work, we establish a graph-theoretic approach and connections to the problem of finding maximum cliques. We identify the key properties of these graphs that make the interpolation problem tractable when$N$is a prime power, and we identify the challenges in generalizing to arbitrary$N$. Finally, we investigate some connections between graph properties and the spectral-tile direction of the Fuglede conjecture.

In this paper, we propose a framework for online quadrotor motion planning for autonomous navigation in unknown environments. Based on the onboard state estimation and environment perception, we adopt a fast marching-based path searching method to find a path on a velocity field induced by the Euclidean signed distance field (ESDF) of the map, to achieve better time allocation. We generate a flight corridor for the quadrotor to travel through by inflating the path against the environment. We represent the trajectory as piecewise Bézier curves by using Bernstein polynomial basis and formulate the trajectory generation problem as typical convex programs. By using Bézier curves, we are able to bound positions and higher order dynamics of the trajectory entirely within safe regions. The proposed motion planning method is integrated into a customized light-weight quadrotor platform and is validated by presenting fully autonomous navigation in unknown cluttered indoor and outdoor environments. We also release our code for trajectory generation as an open-source package.

In this paper, we present a framework to do optimal time allocation for quadrotor trajectory generation. Using this method, we can generate minimum-time piecewise polynomial trajectories for quadrotor flights. We decouple the quadrotor trajectory generation problem into two folds. Firstly we generate a smooth and safe curve which is parameterized by a virtual variable. This curve named spatial trajectory is independent of time and has fixed spatial properties. Then a mapping function which decides how the quadrotor moves along the spatial trajectory respecting kinodynamic limits is found by minimizing total trajectory time. The mapping function maps the virtual variable to time is named temporal trajectory. We formulate the minimum-time temporal trajectory generation problem as a convex program which can be efficiently solved. We show that the proposed method can corporate with various types of previous trajectory generation method to obtain the optimal time allocation. The proposed method is integrated into a customized light-weight quadrotor platform and is validated by presenting autonomous flights in indoor and outdoor environments. We release our code for time optimization as an open-source ros-package.

An efficient peer grading mechanism is proposed for grading the multitude of assignments in online courses. This novel approach is based on game theory and mechanism design. A set of assumptions and a mathematical model is ratified to simulate the dominant strategy behavior of students in a given mechanism. A benchmark function accounting for grade accuracy and workload is established to quantitatively compare effectiveness and scalability of various mechanisms. After multiple iterations of mechanisms under increasingly realistic assumptions, three are proposed: Calibration, Improved Calibration, and Deduction. The Calibration mechanism performs as predicted by game theory when tested in an online crowd-sourced experiment, but fails when students are assumed to communicate. The Improved Calibration mechanism addresses this assumption, but at the cost of more effort spent grading. The Deduction mechanism performs relatively well in the benchmark, outperforming the Calibration, Improved Calibration, traditional automated, and traditional peer grading systems. The mathematical model and benchmark opens the way for future derivative works to be performed and compared.

We study the problem of interpolating all values of a discrete signal f of length N when dJ. The sampling pattern for f is specified by an index set I, and is said to be a universal sampling set if samples in the locations I can be used to interpolate signals from BJfor any J. When N is a prime power we give several characterizations of universal sampling sets, some structure theorems for such sets, an algorithm for their construction, and a formula that counts them. There are also natural applications to additive uncertainty principles.

Wireless frequency resources are often the limiting factor for WLAN throughput. Hence, wireless channel management is needed to mitigate co-channel interference and improve channel reuse efficiency in WLANs, particularly in those with high-density access point deployment. The key challenge is to accurately predict interference and its effect on network performance. This paper presents a new interference prediction model that incorporates realistic signal and traffic measurements, and accurately estimates network performance under alternative channel assignments. Using this model, we develop a channel management algorithm that can adapt to dynamic network situations. Experiments on an indoor testbed of 35 wireless nodes demonstrate that our algorithm significantly outperforms existing channel management techniques, with an average throughput increase of about 30%.