Scott N. Gerard

Formalizing and verifying protocol refinements

A (business) protocol describes, in high-level terms, a pattern of communication between two or more participants, specifically via the creation and manipulation of the commitments between them. In this manner, a protocol offers both flexibility and rigor: a participant may communicate in any way it chooses as long as it discharges all of its activated commitments. Protocols thus promise benefits in engineering cross-organizational business processes. However, software engineering using protocols presupposes a formalization of protocols and a notion of the refinement of one protocol by another. Refinement for protocols is both intuitively obvious (e.g., PayViaCheck is clearly a kind of Pay) and technically nontrivial (e.g., compared to Pay, PayViaCheck involves different participants exchanging different messages). This article formalizes protocols and their refinement. It develops Proton, an analysis tool for protocol specifications that overlays a model checker to compute whether one protocol refines another with respect to a stated mapping. Proton and its underlying theory are evaluated by formalizing several protocols from the literature and verifying all and only the expected refinements.

Reflections on the Effectiveness of a High Density Ambient Sensor Deployment for Monitoring Healthy Aging

The percentage of the world’s population aged over 65 is growing at unprecedented rates. Many countries face the challenge of supporting an aging population despite increasing healthcare costs, and an insufficient number of caregivers. Emerging technologies, like the Internet of Things (IoT) and Artificial Intelligence (AI), can help. Activities of daily living (ADLs) are a good indicator of healthy aging and provide a baseline for detecting an elder’s changes in physical and cognitive state. Monitoring and accurately classifying elders’ activities helps prevent and mitigate some common risks faced by elders when they age-in-place. In our study, we partnered with a senior care provider to add sensors to five apartments in an independent living facility and provided the requisite 24/7 monitoring. Ambient sensors were deployed in each of the apartments and collected high-density IoT sensor data for six months for each of the study participants, who were all over the age of 70. While we successfully created a model to classify ADLs through the recognition and observation of patterns based on high-density ambient sensor placement, sensor data and semantics, and characteristics of activities, we discovered challenges in capturing every ADL for each participant. Often, the ADLs captured for each participant offered unique and personalized indicators of healthy aging. This paper explores the challenges of deploying consumer-grade, IoT, sensors, and the application of AI technology to learn and model elders’ ADLs. We also share results of our exploration to classify ADLs in the data using manual crowd-sourcing, rule-based reasoning and machine learned analytics.

Positron: Composing Commitment-Based Protocols

We understand a sociotechnical system (STS) as a microsociety in which social entities interact about and via technical entities. A protocol specifies an STS by describing how its members collaborate by giving meaning to their interactions. We restrict ourselves to protocols that specify messages between roles in terms of how they create and affect commitments among the roles. A key idea of our approach, Positron, is that a protocol specifies the accountability of one role to another in addition to the requirements from each role. Specifically, Positron incorporates role accountability and role requirements as two integral aspects of protocol composition. In this way, it seeks to promote collaboration in STSs through natural requirements elicitation; flexibility enactment; and compliance and validation (ascribing accountability for each requirement to a specific role). Positron maps composite protocols to the representations of a well-known model checker as a way to verify protocols to assist in their correct formulation. We evaluate Positron by demonstrating it on real-life protocols.

Adding an authorization dimension to strong type checking

Strongly typed languages, like Modula2, ensure that memory locations are interpreted in a consistent manner. I call this the structural dimension of the type system. I propose that an authorization (or capability, or fights) dimension be added to control who can use those memory locations. The structural dimension of a variable is the same for all procedures throughout a program, but different procedures may be given different authority tbr the same variable. Authorizations are similar to the in, o u t and i n o u t parameter modes of ADA (tm), but are more comprehensive and apply to all identifiers.