Computer science

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

-

B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Situational Awareness and Decision-Making Support for Crowd Management in Mina, A Grand Challenge Project Funded by the Ministry of Education

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KAUST

Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.

Choosing the right scheme for green communications

1 min read ·
Performance analysis reveals how different resource allocation schemes may impact the energy efficiency of a cognitive radio system.

The crest of waveforms for next-gen radar

1 min read ·
A new method for shaping the waveform generated by multi-antenna radar systems is inexpensive and practical.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Viruses as communication molecules

1 min read ·
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Divided attention could ease wireless congestion

1 min read ·
Predicting wireless traffic using artificial intelligence could improve the reliability of future wireless communications.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.