TTC: A Tensor Transposition Compiler for Multiple Architectures

We consider the problem of transposing tensors of arbitrary dimension and describe TTC, an open source domain-specific parallel compiler. TTC generates optimized parallel C++/CUDA C code that achieves a significant fraction of the system's peak memory bandwidth. TTC exhibits high performance across multiple architectures, including modern AVX-based systems (e.g.,~Intel Haswell, AMD Steamroller), Intel's Knights Corner as well as different CUDA-based GPUs such as NVIDIA's Kepler and Maxwell architectures. We report speedups of TTC over a meaningful baseline implementation generated by external C++ compilers; the results suggest that a domain-specific compiler can outperform its general purpose counterpart significantly: For instance, comparing with Intel's latest C++ compiler on the Haswell and Knights Corner architecture, TTC yields speedups of up to $8\times$ and $32\times$, respectively. We also showcase TTC's support for multiple leading dimensions, making it a suitable candidate for the generation of performance-critical packing functions that are at the core of the ubiquitous BLAS 3 routines

The Persuasive Effect of Privacy Recommendations

Several researchers have recently suggested that in order to avoid privacy problems, location-sharing services should provide finer-grained methods of location-sharing. This may however turn each “check-in” into a rather complex decision that puts an unnecessary burden on the user. We present two studies that explore ways to help users with such location-sharing decisions. Study 1 shows that users’ evaluation of their activity is a good predictor of the sharing action they choose. Study 2 develops several “privacy recommenders” that tailor the list of sharing actions to this activity evaluation. We find that these recommenders have a strong persuasive effect, and that users find short lists of recommended actions helpful. We also find, however, that users ultimately find it more satisfying if we do not ask them to evaluate the activity

Inferring Capabilities of Intelligent Agents

We investigate the usability of human-like agent-based interfaces. In an experiment we manipulate the capabili­ties and the “human-likeness” of a travel advisory agent. We show that users of the more human-like agent form an anthropomorphic use image of the system: they act as if the system is human, and try to exploit typical human-like capabilities. Unfortu­nately, this severely reduces the usa­bility of the agent that looks human but lacks human-like capabilities (overestima­tion effect). We also show that the use image users form of agent-based systems is inherently integrated (as opposed to the compositional use image they form of conventional GUIs): cues provided by the system do not instill user responses in a one-to-one manner, but are instead integrated into a single use image. Consequently, users try to exploit capabilities that were not signaled by the system to begin with, thereby further exacerbating the overestimation effect

Exacerbating Mindless Compliance: The Danger of Justifications during Privacy Decision Making in the Context of Facebook Applications

Online companies exploit mindless compliance during users’ privacy decision making to avoid liability while not impairing users’ willingness to use their services. These manipulations can play against users since they subversively influence their decisions by nudging them to mindlessly comply with disclosure requests rather than enabling them to make deliberate choices. In this paper, we demonstrate the compliance-inducing effects of defaults and framing in the context of a Facebook application that nudges people to be automatically publicly tagged in their friends’ photos and/or to tag their friends in their own photos. By studying these effects in a Facebook application, we overcome a common criticism of privacy research, which often relies on hypothetical scenarios. Our results concur with previous findings on framing and default effects. Specifically, we found a reduction in privacy-preserving behaviors (i.e., a higher tagging rate in our case) in positively framed and accept-by-default decision scenarios. Moreover, we tested the effect that two types of justifications—information that implies what other people do (normative) or what the user ought to do (rationale based)— have on framing- and default-induced compliance. Existing work suggests that justifications may increase compliance in a positive (agree-by-) default scenario even when the justification does not relate to the decision. In this study, we expand this finding and show that even a justification that is opposite to the default action (e.g., a justification suggesting that one should not use the application) can increase mindless compliance with the default. Thus, when companies abide by policy makers’ requirements to obtain informed user consent through explaining the privacy settings, they will paradoxically induce mindless compliance and further threaten user privacy

Counteracting the Negative Effect of Form Auto-completion on the Privacy Calculus

When filling out web forms, people typically do not want to submit every piece of requested information to every website. Instead, they selectively disclose information after weighing the potential benefits and risks of disclosure: a process called “privacy calculus”. Giving users control over what to enter is a prerequisite for this selective disclosure behavior. Exercising this control by manually filling out a form is a burden though. Modern browsers therefore offer an auto-completion feature that automatically fills out forms with previously stored values. This feature is convenient, but it makes it so easy to submit a fully completed form that users seem to skip the privacy calculus altogether. In an experiment we compare this traditional auto-completion tool with two alternative tools that give users more control than the traditional tool. While users of the traditional tool indeed forego their selective disclosure behavior, the alternative tools effectively reinstate the privacy calculus

Advanced genome-wide screening in human genomic disorders

UBL - phd migration 201