Wireless Global Positioning System Fleet Tracking System at the University at Albany

This report provides an overview of the project undertaken at the University at Albany to make alternative transportation a more viable option by implementing a GPS Tracking System on the University bus fleet and broadcasting the bus locations to commuters via the internet and a “smart phone” application. According to a survey administered by the University, students and faculty identified convenience as the number one barrier to taking the bus. In line with its commitment to environmental sustainability, University at Albany wished to increase mass transit ridership by making it more convenient and predictable, thus favorably impacting commuting patterns. This report details the successes and challenges of the project, focusing on lessons learned and suggestions for futureprojects of a similar nature

Commuting in Portland, Oregon: the advantages of living within a transit oriented development community compared to traditional suburban development by comparing environmental, economic and health factors

Transportation is a function that affects nearly all life decisions, but is often not given much thought by the average individual throughout their daily routines. Most of this complacency streams from the mainstream development patterns in the United States that have changed little from the end of World War II. During the immediate post-war years a perfect mix for suburban living came together: the mass production of automobiles, guaranteed mortgages from the federal government through the G.I. Bill, and in 1956 the passage of the National Interstate and Defense Highways Act. These factors, along with the dominate social paradigm that the American Dream was to have a personal front and back yard, helped profoundly transform development in the country. Over half a century later, the United States is now experiencing the consequences of this sprawled, auto-dependent development pattern. Energy prices have increased substantially over the past decade, which were only contained momentarily by a worldwide recession that was arguably caused by the same development patterns. Environmental consequences are becoming increasingly evident, ranging from contaminated storm-water runoff, to global climate change. Similarly, mental and physical health has degraded rapidly, with a soaring depression and obesity rates. The United States can, and should do better than this. Transit Oriented Development (TOD) offers a solution to help alleviate many of the complex issues that many communities must address. While there is no perfect template, TOD is an important step forward for the overall quality of life for individuals throughout the nation. This report will look at the steps that have been taken in the Portland Oregon Metropolitan Area to discourage sprawl development, measuring the effects of their actions on environmental, economic and health factors

3R- Reach, Recruit, Reform: Working with the Grand Rapids Community to Meet the Volunteer Needs of the Heartside Gleaning Initiative

The purpose of this project is to address the volunteer needs of the Heartside Gleaning Initiative, a nonprofit organization founded by Grand Valley State University professor Lisa Sisson. The mission of the Heartside Gleaning Initiative is to “empower the Heartside community to become healthier through nutrition education and improving accessibility of healthy foods” (Heartside, 2014). Members of the Heartside Gleaning Initiative are currently working to give people living in the Heartside community of Grand Rapids access to fresh produce. Volunteers glean the produce from local farmers at the Fulton Street Farmers Market and then deliver it to shelters in the Heartside neighborhood. This work is also a part of a larger goal to fight the national issue of food insecurity, which affects millions of people living in the United States. For this project, our group chose to focus on volunteer recruitment. Volunteers are the backbone of the Heartside Gleaning Initiative and a necessary component for the work being done. We have begun to work with several Grand Rapids schools and local churches in the Heartside neighborhood to generate awareness about the initiative and to try and fill this need for volunteers. The organization specifically needs a core group of four to five volunteers who can consistently work with the initiative. Though we have generated interest among community members to volunteer for a weekend, we struggled to establish this core group of leaders. This proved to be our biggest challenge with the project, and finding a group of leaders will continue to be a task for the initiative in the future, though we have several suggestions that may help their efforts. The final goal for this project was to provide the Heartside Gleaning Initiative with recruitment materials. We recreated a pamphlet for the organization to give to potential volunteers. It includes information about the goals and the mission of the organization and contact information. It can be used to generate awareness and knowledge about the Heartside Gleaning Initiative. We also provided the organization with a list of the local churches and schools with interested members. We hope the initiative will be able to use these materials to continue to recruit a stable group of volunteers

Per-host DDoS mitigation by direct-control reinforcement learning

DDoS attacks plague the availability of online services today, yet like many cybersecurity problems are evolving and non-stationary. Normal and attack patterns shift as new protocols and applications are introduced, further compounded by burstiness and seasonal variation. Accordingly, it is difficult to apply machine learning-based techniques and defences in practice. Reinforcement learning (RL) may overcome this detection problem for DDoS attacks by managing and monitoring consequences; an agent’s role is to learn to optimise performance criteria (which are always available) in an online manner. We advance the state-of-the-art in RL-based DDoS mitigation by introducing two agent classes designed to act on a per-flow basis, in a protocol-agnostic manner for any network topology. This is supported by an in-depth investigation of feature suitability and empirical evaluation. Our results show the existence of flow features with high predictive power for different traffic classes, when used as a basis for feedback-loop-like control. We show that the new RL agent models can offer a significant increase in goodput of legitimate TCP traffic for many choices of host density

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

Revisiting the Classics: Online RL in the Programmable Dataplane

Data-driven networking is becoming more capable and widely researched, partly driven by the efficacy of Deep Reinforcement Learning (DRL) algorithms. Yet the complexity of both DRL inference and learning force these tasks to be pushed away from the dataplane to hosts, harming latency-sensitive applications. Online learning of such policies cannot occur in the dataplane, despite being useful techniques when problems evolve or are hard to model.We present OPaL—On Path Learning—the first work to bring online reinforcement learning to the dataplane. OPaL makes online learning possible in constrained SmartNIC hardware by returning to classical RL techniques—avoiding neural networks. Our design allows weak yet highly parallel SmartNIC NPUs to be competitive against commodity x86 hosts, despite having fewer features and slower cores. Compared to hosts, we achieve a 21 × reduction in 99.99th tail inference times to 34 µs, and 9.9 × improvement in online throughput for real-world policy designs. In-NIC execution eliminates PCIe transfers, and our asynchronous compute model ensures minimal impact on traffic carried by a co-hosted P4 dataplane. OPaL’s design scales with additional resources at compile-time to improve upon both decision latency and throughput, and is quickly reconfigurable at runtime compared to reinstalling device firmware

Substrate-guided optimization of the syringolins yields potent proteasome inhibitors with activity against leukemia cell lines

Natural products that inhibit the proteasome have been fruitful starting points for the development of drug candidates. Those of the syringolin family have been underexploited in this context. Using the published model for substrate mimicry by the syringolins and knowledge about the substrate preferences of the proteolytic subunits of the human proteasome, we have designed, synthesized, and evaluated syringolin analogs. As some of our analogs inhibit the activity of the proteasome with second-order rate constants 5-fold greater than that of the methyl ester of syringolin B, we conclude that the substrate mimicry model for the syringolins is valid. The improvements in in vitro potency and the activities of particular analogs against leukemia cell lines are strong bases for further development of the syringolins as anti-cancer drugs.National Institutes of Health (U.S.) (Grant AI-16892