SURGNET: An Integrated Surgical Data Transmission System for Telesurgery

Remote surgery information requires quick and reliable transmission between the surgeon and the patient site. However, the networks that interconnect the surgeon and patient sites are usually time varying and lossy which can cause packet loss and delay jitter. In this paper we propose SURGNET, a telesurgery system for which we developed the architecture, algorithms and implemented it on a testbed. The algorithms include adaptive packet prediction and buffer time adjustment techniques which reduce the negative effects caused by the lossy and time varying networks. To evaluate the proposed SURGNET system, at the therapist site, we implemented a therapist panel which controls the force feedback device movements and provides image analysis functionality. At the patient site we controlled a virtual reality applet built in Matlab. The varying network conditions were emulated using NISTNet emulator. Our results show that even for severe packet loss and variable delay jitter, the proposed integrated synchronization techniques significantly improve SURGNET performance

Contextual Suggestions Based on Driving Stage and Context

A framework for providing suggestions based on drive context is described. Described techniques can be implemented in virtual assistant or other software accessed via a device directly installed in a vehicle or available via a user mobile device paired with a vehicle infotainment system. With user permission, a drive context and a drive stage (e.g., pre-drive, active drive, end of drive) is determined based on one or more user-permitted factors, and is utilized along with other permitted contextual information to generate a ranked list of suggestions for activities such as media playback, communication actions (calls, messages, etc.), etc. and of informational content. The top ranking suggestions are provided to the user via a user interface. User selection of the suggestions can trigger user-requested actions such as starting media playback, placing a call, etc

Essays in Market Design:

Thesis advisor: Utku M. UnverThe Market Design approach, which involves the creation of markets with desirable properties, has been successfully applied to study a wide range of real-world economic problems. The market design approach is helpful in scenarios where money can’t be used as a medium of exchange to facilitate transactions. The allocation of school/college seats to students, assigning residency positions to physicians, cadet-branch matching, and exchange of organs like kidneys and liver are some problems that have been successfully studied using the market design approach. Typically, the market design approach concerns with the setting up of two-sided markets with agents on each side of the market having preferences over each other or agents on one side and objects (school seats, military branches, public health goods like beds, ventilators, etc.) on the other side with agents having preferences over the objects and objects having priority over the agents. Priority ranking of agents can be considered an entitlement ranking where agents with higher priority have the right for the object compared to the agent with lower priority. The insights from the matching theory are then used to create a mechanism that matches agents with agents or objects for the given set of preferences/priority ranking satisfying desirable properties. Primary among these properties is stability, an equilibrium concept for matching. Stable matching ensures that matched agents/objects on the two sides of the market do not have an incentive to break up their respective matching and form a better matching for themselves. In the market design problem of matching agents to objects, stability ensures that the agent’s priority for objects is not violated. Other properties include strategy-proofness, where agents do not have an incentive to misreport their preferences. Strategy-proof mechanisms are simple and ensure that high-information agents cannot game the system at the expense of low-information agents. The priority ranking thus used in matching agents to objects has been subject to much criticism. The underlying process that generates the priority rankings can be inherently discriminatory. Exam scores are used to generate the priority ranking in allocating school seats to students. In the New York City school system, there has been a growing call for abolishing exams since it is considered to favor students with more resources. Similarly, the priority system used in the exchange of organs like kidneys and liver and triage allocation of scarce resources and services like hospital beds, vaccines, and ventilators has received much criticism. Triage protocols are developed with a utilitarian notion of maximum benefit given the constraints. This can result in people with better access to health care resources being better positioned under a triage protocol than those with lesser access. The dissertation comprises two essays, joint work with Kenzo Imamura where I study the pairwise kidney-exchange problem and a ventilator sharing problem where I study the triage allocation of ventilator slots under sharing. In the first essay, I consider the problem of allocating ventilator slots for sharing under a triage protocol that generates the priority order. The triage protocol is considered discriminatory since patients with better access to health care through their life cycle have a better chance to be placed ahead in the order when compared with patients with lesser access to healthcare services. I consider the allocation of ventilator slots under a system of reserves, where slots are set-aside for types of patients to address the shortcoming of the triage protocol. Sharing is possible between patients who are compatible. In addition to addressing the shortcomings of the generated priority order, I focus on the question of what respecting the generating priority order in a sharing environment means. In the second essay, we consider the pairwise-kidney exchange problem, where incompatible patient donor pairs are matched with each other subject to patient donor pairs being compatible with each other and acceptable to each other under a priority order. The priority order is generated using a composite score which includes variables like the area of patient donor location and post-transplant medical survivability, among other factors. In response to the concerns, two mechanisms have been developed in the literature for pairwise-kidney exchange, a mechanism that facilitates pairwise-kidney exchange under a strict priority order and an egalitarian mechanism that doesn’t have a priority ordering among compatible patients. Owing to the utilitarian nature of priority order ranking, the egalitarian mechanism has not been considered for adoption. We develop a compromise mechanism between the egalitarian mechanism and the mechanism which respects strict priority order. We show that the compromise mechanism carries forward nice properties like strategy-proofness, which incentivizes each patient-donor pair to reveal their complete set of compatible patient-donor pairs and bridges the concern of a need for priority order with egalitarianism. The predominant literature in Matching theory considers matching agents with agents/objects under a priority order considering all agents to be equal and the priority ordering to be the only difference in consideration among agents. My dissertation contributes to the matching literature where different agents can vary in ways other than the priority ordering, and we try to find solutions that strive to address the inequity. I thank my advisors for their generous advice and feedback in shaping my dissertation.Thesis (PhD) — Boston College, 2022.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Economics

On-device Ranking for Displaying Suggestions

An important capability of mobile device operating systems and/or virtual assistants is to provide contextually appropriate suggestions that enable users to perform quick actions such as obtain navigation guidance, start media playback, listen and respond to messages, etc. Devices such as vehicle infotainment consoles include additional contextual information, e.g., fuel gauge level, driving stage, etc. that can be important when determining suggestions that are provided via such consoles. This disclosure describes on-device ranking of candidate suggestions to deliver the top ranking suggestions via the vehicle infotainment console. With user permission, the ranking can be based on relevant contextual factors, e.g., driving stage, available locally to the infotainment console. Selective display of suggestions improves the user experience of interacting with suggestions provided via such consoles

VOICE USER INTERFACE BASED PERMISSION GRANT SYSTEM FOR VEHICLES

A vehicle (e.g., automobile, motorcycle, a bus, a recreational vehicle (RV), a semi-trailer truck, a tractor or other type of farm equipment, a train, a plane, a helicopter, etc.) may include a so-called “head unit” that provides a voice user interface (VUI) by which to enable spoken human interaction with the head unit to respond to requests for permission (e.g., to access user personal data, to enable the usage of third-party services, etc.). For example, responsive to detecting that an action to be performed has not been granted permission, the head unit may produce (e.g., via one or more speakers) an audio prompt requesting the required permission. A user may answer the audio prompt with an audio input in the form of human speech, which the head unit may receive (e.g., via one or more microphones). The head unit may parse the audio input using speech recognition (e.g., a natural language understanding module) to identify a valid input (e.g., grant or deny permission to perform an action, request additional information, etc.) to which the audio input corresponds and, responsive to identifying a valid input, the head unit may perform the action (e.g., granting or denying permission to perform the action, providing additional information, etc.) associated with the valid input. In this way, the head unit may enable the user to control the granting of permissions via the VUI, which may be particularly beneficial in vehicle settings in which the user is operating the vehicle, as the hands-free, eyes-free user experience may reduce distractions to the user while operating the vehicle and thereby promote safety

Foundational Models for Fault Diagnosis of Electrical Motors

A majority of recent advancements related to the fault diagnosis of electrical motors are based on the assumption that training and testing data are drawn from the same distribution. However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors. Consequently, this assumption limits the practical implementation of existing studies for fault diagnosis, as they rely on fully labelled training data spanning all operating conditions and assume a consistent distribution. This is because obtaining a large number of labelled samples for several machines across different fault cases and operating scenarios may be unfeasible. In order to overcome the aforementioned limitations, this work proposes a framework to develop a foundational model for fault diagnosis of electrical motors. It involves building a neural network-based backbone to learn high-level features using self-supervised learning, and then fine-tuning the backbone to achieve specific objectives. The primary advantage of such an approach is that the backbone can be fine-tuned to achieve a wide variety of target tasks using very less amount of training data as compared to traditional supervised learning methodologies. The empirical evaluation demonstrates the effectiveness of the proposed approach by obtaining more than 90\% classification accuracy by fine-tuning the backbone not only across different types of fault scenarios or operating conditions, but also across different machines. This illustrates the promising potential of the proposed approach for cross-machine fault diagnosis tasks in real-world applications.Comment: 7 pages, 1 figure, 5 tables, submitted to IEEE PESGRE 202

Active Foundational Models for Fault Diagnosis of Electrical Motors

Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the safe and reliable operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be taken in order to reduce the severity of faults. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples, where annotations are expensive and time-consuming. However, a major portion of unlabeled condition monitoring data is not exploited in the training process. To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques. It consists of a transformer network-based backbone model trained using an advanced nearest-neighbor contrastive self-supervised learning method. This approach empowers the backbone to learn improved representations of samples derived from raw, unlabeled vibration data. Subsequently, the backbone can undergo fine-tuning to address a range of downstream tasks, both within the same machines and across different machines. The effectiveness of the proposed methodology has been assessed through the fine-tuning of the backbone for multiple target tasks using three distinct machine-bearing fault datasets. The experimental evaluation demonstrates a superior performance as compared to existing state-of-the-art fault diagnosis methods with less amount of labeled data.Comment: 30 pages, 2 figures, 7 table

StaticFixer: From Static Analysis to Static Repair

Static analysis tools are traditionally used to detect and flag programs that violate properties. We show that static analysis tools can also be used to perturb programs that satisfy a property to construct variants that violate the property. Using this insight we can construct paired data sets of unsafe-safe program pairs, and learn strategies to automatically repair property violations. We present a system called \sysname, which automatically repairs information flow vulnerabilities using this approach. Since information flow properties are non-local (both to check and repair), \sysname also introduces a novel domain specific language (DSL) and strategy learning algorithms for synthesizing non-local repairs. We use \sysname to synthesize strategies for repairing two types of information flow vulnerabilities, unvalidated dynamic calls and cross-site scripting, and show that \sysname successfully repairs several hundred vulnerabilities from open source {\sc JavaScript} repositories, outperforming neural baselines built using {\sc CodeT5} and {\sc Codex}. Our datasets can be downloaded from \url{http://aka.ms/StaticFixer}

Frustrated with Code Quality Issues? LLMs can Help!

As software projects progress, quality of code assumes paramount importance as it affects reliability, maintainability and security of software. For this reason, static analysis tools are used in developer workflows to flag code quality issues. However, developers need to spend extra efforts to revise their code to improve code quality based on the tool findings. In this work, we investigate the use of (instruction-following) large language models (LLMs) to assist developers in revising code to resolve code quality issues. We present a tool, CORE (short for COde REvisions), architected using a pair of LLMs organized as a duo comprised of a proposer and a ranker. Providers of static analysis tools recommend ways to mitigate the tool warnings and developers follow them to revise their code. The \emph{proposer LLM} of CORE takes the same set of recommendations and applies them to generate candidate code revisions. The candidates which pass the static quality checks are retained. However, the LLM may introduce subtle, unintended functionality changes which may go un-detected by the static analysis. The \emph{ranker LLM} evaluates the changes made by the proposer using a rubric that closely follows the acceptance criteria that a developer would enforce. CORE uses the scores assigned by the ranker LLM to rank the candidate revisions before presenting them to the developer. CORE could revise 59.2% Python files (across 52 quality checks) so that they pass scrutiny by both a tool and a human reviewer. The ranker LLM is able to reduce false positives by 25.8% in these cases. CORE produced revisions that passed the static analysis tool in 76.8% Java files (across 10 quality checks) comparable to 78.3% of a specialized program repair tool, with significantly much less engineering efforts