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Open Access Publications from the University of California


The Donald Bren School of Information and Computer Sciences aims for excellence in research and education. Our mission is to lead the innovation of new information and computing technology by fundamental research in the core areas of information and computer sciences and cultivating authentic, cutting-edge research collaborations across the broad range of computing and information application domains as well as studying their economic, commercial and social significance.

Department of Computer Science

There are 627 publications in this collection, published between 1996 and 2023.
Faculty Publications (3)

Teaching Computational Thinking to English Learners

Computational thinking is an essential skill for full participation in society in today’s world (Wing, 2006). Yet there has been little discussion about the teaching and learning of computational thinking to English learners. In this paper, we first review what computational thinking is, why it is important in education, and the particular challenges faced in teaching computational thinking to speakers of English as a second language. We then discuss some approaches for addressing these challenges, giving examples from two recent K–12 initiatives in which we have been involved.

Integration of computational thinking into English language arts

This paper describes the development and implementation of a yearlong integrated English Language Arts (ELA) and computational thinking (CT) curriculum that has been adapted to meet the needs of multilingual students. The integration of computational thinking into K-12 literacy instruction has only been examined in a handful of studies, and little is known about how such integration supports the development of CT for multilingual students. We conducted a qualitative case study on curricular implementation in a general education classroom with large numbers of students designated as English learners. Results from detailed field notes revealed that the strategic application of instructional practices was implemented in the service of building on students' existing literacy skills to teach CT concepts and dispositions. The CT and literacy framework put forth in this study can be used as an analytic framework to highlight how instructional strategies mobilize the existing literacy and CT resources of linguistically diverse students. Based on our findings, we discuss recommendations for future integrated ELA-CT curricula.

Open Access Policy Deposits (623)

Towards a systems view of IBS

© 2015 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. Despite an extensive body of reported information about peripheral and central mechanisms involved in the pathophysiology of IBS symptoms, no comprehensive disease model has emerged that would guide the development of novel, effective therapies. In this Review, we will first describe novel insights into some key components of brain–gut interactions, starting with the emerging findings of distinct functional and structural brain signatures of IBS. We will then point out emerging correlations between these brain networks and genomic, gastrointestinal, immune and gut-microbiome-related parameters. We will incorporate this new information, as well as the reported extensive literature on various peripheral mechanisms, into a systems-based disease model of IBS, and discuss the implications of such a model for improved understanding of the disorder, and for the development of more-effective treatment approaches in the future.

Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data Networking

Content-Centric Networking (CCN) is an emerging networking paradigm being considered as a possible replacement for the current IP-based host-centric Internet infrastructure. CCN focuses on content distribution, which is arguably not well served by IP. Named-Data Networking (NDN) is an example of CCN. NDN is also an active research project under the NSF Future Internet Architectures (FIA) program. FIA emphasizes security and privacy from the outset and by design. To be a viable Internet architecture, NDN must be resilient against current and emerging threats. This paper focuses on distributed denial-of-service (DDoS) attacks; in particular we address interest flooding, an attack that exploits key architectural features of NDN. We show that an adversary with limited resources can implement such attack, having a significant impact on network performance. We then introduce Poseidon: a framework for detecting and mitigating interest flooding attacks. Finally, we report on results of extensive simulations assessing proposed countermeasure. © 2013 IEEE.

Protein profiles: Biases and protocols.

The use of evolutionary profiles to predict protein secondary structure, as well as other protein structural features, has been standard practice since the 1990s. Using profiles in the input of such predictors, in place or in addition to the sequence itself, leads to significantly more accurate predictions. While profiles can enhance structural signals, their role remains somewhat surprising as proteins do not use profiles when folding in vivo. Furthermore, the same sequence-based redundancy reduction protocols initially derived to train and evaluate sequence-based predictors, have been applied to train and evaluate profile-based predictors. This can lead to unfair comparisons since profiles may facilitate the bleeding of information between training and test sets. Here we use the extensively studied problem of secondary structure prediction to better evaluate the role of profiles and show that: (1) high levels of profile similarity between training and test proteins are observed when using standard sequence-based redundancy protocols; (2) the gain in accuracy for profile-based predictors, over sequence-based predictors, strongly relies on these high levels of profile similarity between training and test proteins; and (3) the overall accuracy of a profile-based predictor on a given protein dataset provides a biased measure when trying to estimate the actual accuracy of the predictor, or when comparing it to other predictors. We show, however, that this bias can be mitigated by implementing a new protocol (EVALpro) which evaluates the accuracy of profile-based predictors as a function of the profile similarity between training and test proteins. Such a protocol not only allows for a fair comparison of the predictors on equally hard or easy examples, but also reduces the impact of choosing a given similarity cutoff when selecting test proteins. The EVALpro program is available in the SCRATCH suite ( and can be downloaded at:

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