Responsible Computer Science (RCS) requires that students experience aspects of RCS in just about every CS course they take. It must be embedded across the curriculum, and it is the responsibility of all CS faculty to be part of the delivery of RCS. The purpose of this panel is to expand the community of practice around delivering RCS, so that students better understand how the integration of computing-related technologies into our daily lives has shifted the kinds of power and reach they have, both as individuals and in collective groups. Thus, we welcome those educators who are hesitant or are uncertain about adopting RCS into their teaching. To ease this adoption, we understand responsible computer science as broadly as possible and use it as an umbrella term that includes computing ethics, professionalism, computing for good, inclusive computing, diversity and inclusion in CS, security, safety, and computing for justice, just to name a few. The panelists, who have been collaborating on a project as part of the Mozilla Foundation Responsible CS project, will address concerns faculty have when incorporating RCS in their teaching, challenges faced when encouraging involvement of more CS faculty in the teaching of RCS, the value and need for interdisciplinary collaboration, and the value of grounding (or not) the delivery of some modules in ethical theories. Panelists will offer suggestions for moving toward a CS curriculum that embeds RCS modules and address issues that audience members raise.

This special session will involve three related components. It will begin with a history of the ACM Code of Ethics and Professional Conduct (the Code), its evolving presence in the computing curriculum guidelines over time, and its documented use outside of academe. This will lead into an overview of the major changes to the Code that occurred in the most recent update. The third component and primary focus of the session will be to work with participants to identify ways that ACM and the ACM Committee on Professional Ethics (COPE) can help Computer Science educators integrate the Code as broadly as possible into diverse programs, ranging from Kindergarten to PhD-level. The outcome of the session would be a preliminary set of guidelines for programs and departments to adopt the Code, potential challenges to be addressed when formalizing those guidelines, and suggested approaches to resolve these difficulties. If attendance is sufficiently large, we would adopt a jigsaw model, breaking into smaller focus groups that are tasked with distinct portions of the Code. Each group reports back at the end, and members of COPE will collate the results into a document for future distribution and work.

We consider machines that have the ability to masquerade as human in the context of Floridi's information ethics and artificial evil. We analyse a variety of different robots and contexts and the ethical implications for the development of such robots. We demonstrate numerous concerns that arise due to the ambiguity introduced by masquerading machines, suggesting a need for careful consideration regarding the development of masquerading robots.

In this paper we use Levels of Abstraction and an object-oriented model of trust to analyze quantum computing and cloud computing to identify important ethical concerns. The method of Levels of Abstraction aids in identifying broad social concerns of both methods of computation. Both methods of computation have the potential to deeply impact societal trust of computing.

Heuristics for calculating phylogenetic trees for a large sets of aligned rRNA sequences based on the maximum likelihood method are computationally expensive. The core of most parallel algorithms, which accounts for the greatest part of computation time, is the tree evaluation function, that calculates the likelihood value for each tree topology. This paper describes and uses Subtree Equality Vectors (SEVs) to reduce the number of required floating point operations during topology evaluation. We integrated our optimizations into various sequential programs and into parallel fastDNAml, one of the most common and efficient parallel programs for calculating large phylogenetic trees. Experimental results for our parallel program, which renders exactly the same output as parallel fastDNAml show global runtime improvements of 26% to 65%. The optimization scales best on clusters of PCs, which also implies a substantial cost saving factor for the determination of large trees.

Open source software is making a large impact on many aspects of society including the business community, the computing industry, the entertainment industry and higher education. The computer science education community has been quiet about issues of open source versus closed source and the role of open source code in the advancement of information technology. A survey of recent issues of SIGCSE Bulletin and SIGCSE conference proceedings shows little attention to the role open source software should play in computer science education. We are here to raise the question: "What are the social and ethical responsibilities of computer science faculty regarding open source software?"One set of issues concerns the use of open source software in teaching and the use of open source development models in the teaching of software development. Some basic questions that arise include "Should analysis of open source (and possible contributions to it) be the subject of class assignments?" and "Should open source software development models be taught?"A second set of issues concerns the use of open source software in support of teaching (e.g., using Linux as your standard platform). Some basic questions that arise include "Should we use open source software to support teaching?" and "Are these faculty ethically obligated to make contributions to the open source software base?"In this panel we will identify many of the parties involved in the ethical and social issues surrounding the use of open source in teaching and in the support of teaching, and we will identify the rights and responsibilities we, as faculty, have to the various parties. This panel will initiate a discussion that will identify additional parties and our further professional obligations.

MOTIVATION: Maximum-likelihood analysis of nucleotide and amino acid sequences is a powerful approach for inferring phylogenetic relationships and for comparing evolutionary hypotheses. Because it is a computationally demanding and time-consuming process, most algorithms explore only a minute portion of tree-space, with the emphasis on finding the most likely tree while ignoring the less likely, but not significantly worse, trees. However, when such trees exist, it is equally important to identify them to give due consideration to the phylogenetic uncertainty. Consequently, it is necessary to change the focus of these algorithms such that near optimal trees are also identified. RESULTS: This paper presents the Advanced Stepwise Addition Algorithm for exploring tree-space and two algorithms for generating all binary trees on a set of sequences. The Advanced Stepwise Addition Algorithm has been implemented in TrExML, a phylogenetic program for maximum-likelihood analysis of nucleotide sequences. TrExML is shown to be more effective at finding near optimal trees than a similar program, fastDNAml, implying that TrExML offers a better approach to account for phylogenetic uncertainty than has previously been possible. A program, TreeGen, is also described; it generates binary trees on a set of sequences allowing for extensive exploration of tree-space using other programs. AVAILABILITY: TreeGen, TrExML, and the sequence data used to test the programs are available from the following two WWW sites: http://whitetail.bemidji.msus. edu/trexml/and http://jcsmr.anu.edu.au/dmm/humgen.+ ++html.

No abstract available.

A tournament is a complete directed graph. A convex subset is a vertex subset with the property that every two-path beginning and ending inside the convex subset is contained completely within the subset. This paper shows that every nontrivial convex subset is the closure of a subset of vertices of cardinality two. This result leads to algorithms that find all convex subsets in a tournament in $O(n^4 )$ serial time and in $O(\log ^2 n)$ parallel time using $O(n^4)$ processors. Several variations of the problem that are solvable with this new algorithm are also presented.