While the COVID-19 pandemic encouraged higher education to be more flexible, there is now growing uncertainty regarding the usefulness of flexibility as students return to in-person classes, particularly in fast-paced, rigorous fields such as Computer Science (CS). A big part of providing flexibility in education is through assignment deadlines. On one hand, flexible deadline policies may reduce student stress, help students face unexpected circumstances, and improve student learning; on the other, too much flexibility may lead to procrastination and poor time management.

A key component to enabling the safe, secure, and efficient operations of Uncrewed Aerial Systems (UAS), or drones, is the ability to surveil such vehicles in flight. Remote identification systems (e.g., Remote ID) are designed to serve such purposes, and lay the foundations for denser, more advanced UAS operations. However, given the individualized nature of many commonly-envisioned UAS use cases (e.g., door-to-door package delivery), the design of remote identification systems must consider the trade-offs between surveillance versus consumer privacy. To this end, we present a comprehensive method for first characterizing the surveillance coverage of remote identification systems, then integrating in privacy from the perspective of plausible deniability: Surveillance gaps present opportunities for enhanced trajectory privacy, but too many gaps hinder the original purpose—surveillance and safety—of remote identification systems. We apply our method to realistic urban geographies (San Francisco, New York City, Los Angeles) where future UAS operations may perform door-to-door package deliveries between vendors and consumers. We provide ranges for the number of remote identification receivers needed to cover 50-95% of simulated UAS trajectories, then compute a privacy score based on the level of afforded plausible deniability. Our design and analysis framework contribute to the integration of UAS into the future intelligent transportation and smart city landscape, and will be useful for airspace designers, UAS fleet operators, air navigation service providers, and city planners interested in ensuring vehicle safety while considering data usage and an individual’s privacy.

Near Infrared (NIR) Spectral signal has been successfully applied to fabric component analysis (FCA), which is used to identify the category of the textile (defined as a classification task) and its corresponding content for that category (defined as a regression problem). Unlike conventional classification tasks, the prediction results usually contain more than two types of materials, i.e., classes. In addition, the spectral curves belonging to the same parent fiber are similar and thus lead to the problem that the intra-class variance is usually larger than the inter-class variance. The paper proposes a hierarchical architecture for Multi-Label Classification (MLC) and Multi-Output Regression (MOR) to simultaneously identify fabric classes and their contents, i.e., fabric component analysis. In addition, two constraints are applied to the loss function for final classification and regression results. Experimental results conducted on the FEAT-NIR dataset show that the proposed method successfully obtains the best performance compared to the baselines.

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health.Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients.In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals.Unfortunately, public resources for developing natural language models for this task are scant.We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date.We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects.Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area 1 .

Search algorithms such as LAO* and LRTDP coupled with admissible heuristics are widely used methods for optimal probabilistic planning. Their effectiveness depends on the degree to which heuristics are able to approximate the optimal cost of a state. Most common domain-independent heuristics, however, rely on determinization, and ignore the probabilities associated with different effects of actions. Here, we present a method for decomposing a probabilistic planning problem into subproblems by constraining possible action outcomes. Admissible heuristics evaluated for each subproblem can then be combined via a weighted sum to obtain an admissible heuristic for the original problem that takes into account a limited amount of probabilistic information. We use this approach to derive new admissible heuristics for probabilistic planning, and show that for some problems they are significantly more informative than existing heuristics, leading to up to an order of magnitude speedups in the time to converge to an optimal policy.

Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset - i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL - a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.

In my thesis, I develop computational models for an agent providing intelligent decision support (IDS) during human team planning sessions. My focus is on the development of an agent that help a team of human planners reach an agreement and produce high-quality plans prior to plan execution. I intend to develop novel techniques for an IDS agent that can 1) infer the team's intended plan from their planning conversation, 2) predict parts of the plan where the team's shared understanding is weak, and 3) suggest a resolution strategy when plan conflicts occur among teammates.

We envision an intelligent agent that analyzes conversations during human team meetings in order to infer the team’s plan, with the purpose of providing decision support to strengthen that plan. We present a novel learning technique to infer teams' final plans directly from a processed form of their planning conversation. Our method employs reinforcement learning to train a model that maps features of the discussed plan and patterns of dialogue exchange among participants to a final, agreed-upon plan. We employ planning domain models to efficiently search the large space of possible plans, and the costs of candidate plans serve as the reinforcement signal. We demonstrate that our technique successfully infers plans within a variety of challenging domains, with higher accuracy than prior art. With our domain-independent feature set, we empirically demonstrate that our model trained on one planning domain can be applied to successfully infer team plans within a novel planning domain.

The generation of near-optimal plans for multi-agent systems with numerical states and temporal actions is computationally challenging. Current off-the-shelf planners can take a very long time before generating a near-optimal solution. In an effort to reduce plan computation time, increase the quality of the resulting plans, and make them more interpretable by humans, we explore collaborative planning techniques that actively involve human users in plan generation. Specifically, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner. Through human subject experimentation, we empirically demonstrate that this approach results in statistically significant improvements to plan quality, without substantially increasing computation time. We also show that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions, as compared to plans that do not incorporate user-provided strategies.

We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns.A variation of our model is also discussed when discourse relations are treated as latent variables.Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrasebased content selection and discourse relation prediction tasks.We also evaluate our model on predicting the consistency among team members' understanding of their group decisions.Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.D: Three different types of batteries.Um can either use a hand dynamo, or the kinetic type ones, you know that they use in watches, or else uh a solar powered one.B: Um the bat uh the battery for a a watch wouldn't require a lot of power, would be my one query.Is a kinetic one going to be able to supply enough power?D: Yeah, I don't think it would.C: Yeah.D: We should probably just use conventional batteries.

Upon concluding a meeting, participants can occasionally leave with different understandings of what had been discussed. Detecting inconsistencies in understanding is a desired capability for an intelligent system designed to monitor meetings and provide feedback to spur stronger shared understanding. In this paper, we present a computational model for the automatic prediction of consistency among team members' understanding of their group's decisions. The model utilizes dialogue features focused on the dynamics of group decision-making. We trained a hidden Markov model using the AMI meeting corpus and achieved a prediction accuracy of 64.2%, as well as robustness across different meeting phases. We, then, implemented our model in an intelligent system that participated in human team planning about a hypothetical emergency response mission. The system suggested topics that the team would derive the most benefit from reviewing with one another. Through an experiment with 30 participants, we evaluated the utility of such a feedback system and observed a statistically significant increase of 17.5% in objective measures of the teams' understanding compared with that obtained using a baseline interactive system.

Occasionally, participants in a meeting can leave with different understandings of what had been discussed. For meetings that require immediate response (such as disaster response planning), the participants must share a common understanding of the decisions reached by the group to ensure successful execution of their mission. In such domains, inconsistency among individuals' understanding of the meeting results would be detrimental, as this can potentially degrade group performance. Thus, detecting the occurrence of inconsistencies in understanding among meeting participants is a desired capability for an intelligent system that would monitor meetings and provide feedback to spur stronger group understanding. In this paper, we seek to predict the consistency among team members' understanding of group decisions. We use self-reported summaries as a representative measure for team members' understanding following meetings, and present a computational model that uses a set of verbal and nonverbal features from natural dialogue. This model focuses on the conversational dynamics between the participants, rather than on what is being discussed. We apply our model to a real-world conversational dataset and show that its features can predict group consistency with greater accuracy than conventional dialogue features. We also show that the combination of verbal and nonverbal features in multimodal fusion improves several performance metrics, and that our results are consistent across different meeting phases.

We describe the design of an aerial robot inspired by birds and the underlying theoretical developments leading to novel control and closed-loop guidance algorithms for a perching maneuver. A unique feature of this robot is that it uses wing articulation to control the flight path angle as well as the heading angle. It lacks a vertical tail for improved agility, which results in unstable lateral-directional dynamics. New closed-loop motion planning algorithms with guaranteed stability are obtained by rewriting the flight dynamic equations in the spatial domain rather than as functions of time, after which dynamic inversion is employed. It is shown that nonlinear dynamic inversion naturally leads to proportional-integral-derivative controllers, thereby providing an exact method for tuning the gains. The capabilities of the proposed bioinspired robot design and its novel closed-loop perching controller have been successfully demonstrated with perched landings on a human hand.