Machine learning and data-driven solutions for cost-efficient network automation
Sabidur Rahman
Department of Computer Science, Sonoma State University
10/21/2020
Machine Learning (ML) and data-driven solutions have revolutionized many areas of technologies. Communication technology is also increasingly benefiting from such solutions. Automated network resource management powered by ML and data-driven solutions can help to reduce the cost of connectivity, to free up more bandwidths, to foster innovation on the connected services etc., leading to more connected society and businesses. Many time-consuming and complex tasks of network resource management are being automated; thanks to virtualization of network components, advancements in artificial intelligence, and insights learned from data. Dr. Sabidur's research works with Networks Research Labs at UC Davis and AT&T Labs explore important problems in this area of research. This is an exciting new area of research with potential impact on Edge Computing, IoT, Machine Intelligence, Industry 4.0, Smart City, 6G and beyond.
Next-Generation of HPC Batch Scheduling
Tapasya Patki
Lawrence Livermore National Laboratories
10/28/2020
Resource management and batch job scheduling is a crucial part of the software stack of operating capable large-scale supercomputers, enabling multiple users to share the available resources fairly and efficiently. In this talk, I will discuss some of the key challenges in the next-generation of HPC scheduling, which include: managing resources such as power, supporting diverse and complex high-throughput workflows, and efficient utilization of heterogeneous components. I will discuss SLURM and Flux, which are both well-known resource management frameworks for HPC. Flux is a hierarchical next-generation resource management framework that is being actively developed at LLNL. I will also present my ongoing research in power-aware scheduling and variation-aware scheduling with Flux.
Artificial Phronēsis
John Sullins
Sonoma State University
11/18/2020
Artificial Phronēsis (AP) claims that phronēsis, or practical wisdom, plays a primary role in high level moral reasoning and further asks the question of whether or not a functional equivalent to phronēsis is something that can be programmed into machines. The theory is agnostic on the eventuality of machines ever achieving this ability but it does claim that achieving AP is necessary for machines to be human equivalent moral agents. AP is not an attempt to fully describe the phronēsis described in classical ethics. AP is not attempting to derive a full account of phronēsis in humans either at the theoretical or neurological level. AP is not a claim that machines can become perfect moral agents. Instead AP is an attempt to describe an intentionally designed computational system that interacts ethically with human and artificial agents even in novel situations that require creative solutions. AP is to be achieved across multiple modalities and most likely in an evolutionary fashion. AP acknowledges that machines may only be able to simulate ethical judgement for sometime and that the danger of creating a seemingly ethical simulacrum is ever present. This means that AP sets a very high bar to judge machine ethical reasoning and behavior against. It is an ultimate goal but real systems will fall far short of this goal for the foreseeable future.
John P. Sullins is a full professor of philosophy at Sonoma State University. He has numerous publications on the ethics of autonomous weapons systems, self-driving cars, personal robotics, affective robotics, malware ethics, and the philosophy and ethics of information technologies as well as the design of autonomous ethical agents.
Fall 2020 Short Presentations Of Student Research
STUDENT PRESENTATIONS
12/02/2020
Short presentations of research carried out by Sonoma State Computer Science Students.
- Emily Ashley, "Visual Testing Interface Development"
- Ari'El Encarnacion and Cinthya Rosales, "Building a Geological Cyber-infrastructure: Automatically detecting Clasts in Photomicrographs"
- Matt Riedel, "Algorithm to solve jigsaw puzzles"
Toxicity in Two On-line Platforms: Dissenter and GitHub
Robert Beverly and Erik Rye
Naval Postgraduate School
02/03/2021
Understanding efforts by community-driven on-line platforms to enforce legal and policy-based norms are especially timely as these services rise in prominence and importance. For instance, recent actions to curb toxicity in such platforms have driven debate on censorship, de-platforming, and the emergence of unrestricted alternatives.
In this talk, we present a data-driven analysis of toxicity in two on-line platforms: the fringe "dissenter" overlay and the widely used GitHub code collaboration service. Dissenter is a browser that provides a conversational overlay for any web page -- thereby removing the power of content creators to control conversation on their content. In our IMC 2020 work, we obtain the full history of Dissenter comments, users, and websites being discussed, and analyze more than 1.6M comments to characterize users, toxicity, and the conditional probability of hateful comments given website political bias.
Last, we describe our initial work in mining and characterizing non-inclusive language and toxicity in program code and commit messages as publicly available in GitHub. Rather than residing in a fringe community, toxicity in GitHub may have important implications to broadening participation in STEM.