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   An, Kijin; Tilevich, Eli (January 2021, Web Engineering. ICWE 2021. Lecture Notes in Computer Science, vol 12706. Springer, Cham.)

   null (Ed.)

   Best Paper Award 

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2. Locomotive: Optimizing mobile web traffic using selective compression

   https://doi.org/10.1109/WoWMoM.2017.7974350  

   Melissaris, Themis; Shaw, Kelly; Martonosi, Margaret (June 2017, 18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM))

   Mobile web traffic and application data demands are growing at a rapid rate and are at odds with resource-constrained, data-capped, wireless mobile devices. Data compression can be used to reduce web traffic, save energy, and make network transfers faster. Compression can, however, hurt performance if not used judiciously. We propose Locomotive, a library that improves the performance of network transfers in wireless mobile networks by employing selective compression based on data type and network conditions. We demonstrate that Locomotive improves performance of web transfers by roughly 12-24% while reducing data usage by 39%. 

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3. Rethinking Client-Side Caching for the Mobile Web

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   Mobile web browsing remains slow despite many efforts to accelerate page loads. Like others, we find that client-side computation (in particular, JavaScript execution) is a key culprit. Prior solutions to mitigate computation overheads, however, suffer from security, privacy, and deployability issues, hindering their adoption. To sidestep these issues, we propose a browser-based solution in which every client reuses identical computations from its prior page loads. Our analysis across roughly 230 pages reveals that, even on a modern smartphone, such an approach could reduce client-side computation by a median of 49% on pages which are most in need of such optimizations. 

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4. Demystifying Web-based Mobile Extended Reality Accelerated by WebAssembly

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5. A Comparative Study of Dark Patterns Across Web and Mobile Modalities

   https://doi.org/10.1145/3479521  

   Gunawan, Johanna; Pradeep, Amogh; Choffnes, David; Hartzog, Woodrow; Wilson, Christo (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Dark patterns are user interface elements that can influence a person's behavior against their intentions or best interests. Prior work identified these patterns in websites and mobile apps, but little is known about how the design of platforms might impact dark pattern manifestations and related human vulnerabilities. In this paper, we conduct a comparative study of mobile application, mobile browser, and web browser versions of 105 popular services to investigate variations in dark patterns across modalities. We perform manual tests, identify dark patterns in each service, and examine how they persist or differ by modality. Our findings show that while services can employ some dark patterns equally across modalities, many dark patterns vary between platforms, and that these differences saddle people with inconsistent experiences of autonomy, privacy, and control. We conclude by discussing broader implications for policymakers and practitioners, and provide suggestions for furthering dark patterns research. 

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