Real-time Short Video Recommendation on Mobile Devices

Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on mobile devices in a short period of time, and give explicit or implicit feedback very quickly to the short videos they watch. The recommender system needs to perceive users' preferences in real-time in order to satisfy their changing interests. Traditionally, recommender systems deployed at server side return a ranked list of videos for each request from client. Thus it cannot adjust the recommendation results according to the user's real-time feedback before the next request. Due to client-server transmitting latency, it is also unable to make immediate use of users' real-time feedback. However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. Specifically, we design and deploy a tiny on-device ranking model to enable real-time re-ranking of server-side recommendation results. We improve its prediction accuracy by exploiting users' real-time feedback of watched videos and client-specific real-time features. With more accurate predictions, we further consider interactions among candidate videos, and propose a context-aware re-ranking method based on adaptive beam search. The framework has been deployed on Kuaishou, a billion-user scale short video application, and improved effective view, like and follow by 1.28%, 8.22% and 13.6% respectively.Comment: Accepted by CIKM 2022, 10 page

On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models

Domain-Specific Modeling and Code Generation for Cross-Platform Multi-Device Mobile Apps

Nowadays, mobile devices constitute the most common computing device. This new computing model has brought intense competition among hardware and software providers who are continuously introducing increasingly powerful mobile devices and innovative OSs into the market. In consequence, cross-platform and multi-device development has become a priority for software companies that want to reach the widest possible audience. However, developing an application for several platforms implies high costs and technical complexity. Currently, there are several frameworks that allow cross-platform application development. However, these approaches still require manual programming. My research proposes to face the challenge of the mobile revolution by exploiting abstraction, modeling and code generation, in the spirit of the modern paradigm of Model Driven Engineering

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

AndroShield:automated Android applications vulnerability detection, a hybrid static and dynamic analysis approach

The security of mobile applications has become a major research field which is associated with a lot of challenges. The high rate of developing mobile applications has resulted in less secure applications. This is due to what is called the “rush to release” as defined by Ponemon Institute. Security testing—which is considered one of the main phases of the development life cycle—is either not performed or given minimal time; hence, there is a need for security testing automation. One of the techniques used is Automated Vulnerability Detection. Vulnerability detection is one of the security tests that aims at pinpointing potential security leaks. Fixing those leaks results in protecting smart-phones and tablet mobile device users against attacks. This paper focuses on building a hybrid approach of static and dynamic analysis for detecting the vulnerabilities of Android applications. This approach is capsuled in a usable platform (web application) to make it easy to use for both public users and professional developers. Static analysis, on one hand, performs code analysis. It does not require running the application to detect vulnerabilities. Dynamic analysis, on the other hand, detects the vulnerabilities that are dependent on the run-time behaviour of the application and cannot be detected using static analysis. The model is evaluated against different applications with different security vulnerabilities. Compared with other detection platforms, our model detects information leaks as well as insecure network requests alongside other commonly detected flaws that harm users’ privacy. The code is available through a GitHub repository for public contribution

Tragedy of the Regulatory Commons: LightSquared and the Missing Spectrum Rights

The endemic underuse of radio spectrum constitutes a tragedy of the regulatory commons. Like other common interest tragedies, the outcome results from a legal or market structure that prevents economic actors from executing socially efficient bargains. In wireless markets, innovative applications often provoke claims by incumbent radio users that the new traffic will interfere with existing services. Sometimes these concerns are mitigated via market transactions, a la “Coasian bargaining.” Other times, however, solutions cannot be found even when social gains dominate the cost of spillovers. In the recent “LightSquared debacle,” such spectrum allocation failure played out. GPS interests that access frequencies adjacent to the band hosting LightSquared’s new nationwide mobile network complained that the wireless entrant would harm the operation of locational devices. Based on these complaints, regulators then killed LightSquared’s planned 4G network. Conservative estimates placed the prospective 4G consumer gains at least an order of magnitude above GPS losses. “Win win” bargains were theoretically available, fixing GPS vulnerabilities while welcoming the highly valuable wireless innovation. Yet transaction costs—largely caused by policy choices to issue limited and highly fragmented spectrum usage rights (here in the GPS band)—proved prohibitive. This episode provides a template for understanding market and non-market failure in radio spectrum allocation