Photo filter apps: understanding analogue nostalgia in the new media ecology

As digital media have become more pervasive and entrenched in our daily routines, a nostalgic countertrend has increasingly valued the physical and tactile nature of the analogue image. In the past few years, technologically obsolete devices, such as lo-fi cameras and vinyl records, have not faded out of sight completely but are instead experiencing a comeback. At the same time, digital media capitalise on the nostalgia for the analogue and fetishise the retro aesthetics of old technologies. This article explores the emergence of photo filter and effect applications which allow users to modify digital photos, adding signifiers of age such as washed-out colours, scratches and torn borders. It is argued that these new technologies, with programs such as Instagram, Hipstamatic and Camera 360, bring back the illusory physicality of picture-taking through digital skeuomorphism. Drawing on media archaeology practice, this article interrogates the limits of the retro sensibility and the fetishisation of the past in the context of digital media, in particular by focusing on the case study of the start-up Instagram. This photo filter application neither merely stresses the twilight nature of photography nor represents the straightforward digital evolution of previous analogue features. Rather, it responds to the necessity to feel connected to the past by clear and valued signs of age, mimicking a perceived sense of loss. Faced with the persistent hipster culture and the newness of digital media, photo filter apps create comfortable memories, ageing pictures and adding personal value. As such, it will be argued that this phenomenon of nostalgia for analogue photography can be linked to the concepts of ritual and totem. By providing a critical history of Instagram as a photo-sharing social network, this article aims to explain new directions in the rapidly changing system of connective media

adPerf: Characterizing the Performance of Third-party Ads

Monetizing websites and web apps through online advertising is widespread in the web ecosystem. The online advertising ecosystem nowadays forces publishers to integrate ads from these third-party domains. On the one hand, this raises several privacy and security concerns that are actively studied in recent years. On the other hand, given the ability of today's browsers to load dynamic web pages with complex animations and Javascript, online advertising has also transformed and can have a significant impact on webpage performance. The performance cost of online ads is critical since it eventually impacts user satisfaction as well as their Internet bill and device energy consumption. In this paper, we apply an in-depth and first-of-a-kind performance evaluation of web ads. Unlike prior efforts that rely primarily on adblockers, we perform a fine-grained analysis on the web browser's page loading process to demystify the performance cost of web ads. We aim to characterize the cost by every component of an ad, so the publisher, ad syndicate, and advertiser can improve the ad's performance with detailed guidance. For this purpose, we develop an infrastructure, adPerf, for the Chrome browser that classifies page loading workloads into ad-related and main-content at the granularity of browser activities (such as Javascript and Layout). Our evaluations show that online advertising entails more than 15% of browser page loading workload and approximately 88% of that is spent on JavaScript. We also track the sources and delivery chain of web ads and analyze performance considering the origin of the ad contents. We observe that 2 of the well-known third-party ad domains contribute to 35% of the ads performance cost and surprisingly, top news websites implicitly include unknown third-party ads which in some cases build up to more than 37% of the ads performance cost

Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation

We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the "maliciousness" of a behavior. To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Auto- mated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a ver- sion of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.Comment: Appears in 3rd Annual ACM CCS workshop on Security and Privacy in SmartPhones and Mobile Devices (SPSM'13), Berlin, Germany, 201

Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools

Many people struggle to control their use of digital devices. However, our understanding of the design mechanisms that support user self-control remains limited. In this paper, we make two contributions to HCI research in this space: first, we analyse 367 apps and browser extensions from the Google Play, Chrome Web, and Apple App stores to identify common core design features and intervention strategies afforded by current tools for digital self-control. Second, we adapt and apply an integrative dual systems model of self-regulation as a framework for organising and evaluating the design features found. Our analysis aims to help the design of better tools in two ways: (i) by identifying how, through a well-established model of self-regulation, current tools overlap and differ in how they support self-control; and (ii) by using the model to reveal underexplored cognitive mechanisms that could aid the design of new tools.Comment: 11.5 pages (excl. references), 6 figures, 1 tabl