Short video streaming systems such as TikTok, YouTube Shorts, Instagram Reels, etc., have reached billions of active users worldwide. At the core of such systems are (proprietary) recommendation algorithms which recommend a sequence of videos to each user, in a personalized way. We aim to understand the temporal evolution of recommendations made by such algorithms, as well as the interplay between the recommendations and user experience. While past work has studied recommendation algorithms using textual data (e.g., titles, hashtags, etc.) as well as user studies and interviews, we add a third modality of analysis—we perform automated analysis of the videos themselves. To perform such multimodal analysis, we develop a new HCI measurement approach that starts with our new tool called VCA (Video Content Analysis) that leverages recent advances in Vision Language Models (VLMs). We apply VCA on a trifecta of HCI methodologies—real user studies, interviews, and data donation. This allows us to understand temporal aspects of how well TikTok’s recommendation algorithm is perceived by users, is affected by user interactions, and aligns with user history; how users are sensitive to the order of videos recommended; and how the algorithm’s effectiveness itself may be predictable in the future. While it is not our goal to reverse-engineer TikTok’s recommendation algorithm, our new findings indicate behavioral aspects that the TikTok user community can benefit from.

Unmanned aerial vehicles (UAVs) rely on optical sensors such as cameras and lidar for autonomous operation. However, such optical sensors are error-prone in bad lighting, inclement weather conditions including fog and smoke, and around textureless or transparent surfaces. In this paper, we ask: is it possible to fly UAVs without relying on optical sensors, i.e., can UAVs fly without seeing? We present BatMobility, a lightweight mmWave radar-only perception system for UAVs that eliminates the need for optical sensors. BatMobility enables two core functionalities for UAVs - radio flow estimation (a novel FMCW radar-based alternative for optical flow based on surface-parallel doppler shift) and radar-based collision avoidance. We build BatMobility using commodity sensors and deploy it as a real-time system on a small off-the-shelf quadcopter running an unmodified flight controller. Our evaluation1 shows that BatMobility achieves comparable or better performance than commercial-grade optical sensors across a wide range of scenarios.

The advent of radio sensing that works through walls & obstacles challenges the notion of indoor privacy. An eavesdropper can deploy such sensing to snoop on their neighbors and a smart sensor embedded with such sensing capabilities can perform large scale behavioral and health data mining. We present RF-Protect, a new framework that enables privacy by injecting fake humans in the sensed data. RF-Protect consists of a novel hardware reflector design that modifies radio waves to create reflections at arbitrary locations in the environment and a new generative mechanism to create realistic human trajectories. RF-Protect's design doesn't require any high bandwidth hardware or physical motion. We implement RF-Protect using commodity hardware and validate its ability to generate fake human trajectories.

Massive MIMO forms a crucial component for 5G because of its ability to improve quality of service and support multiple streams simultaneously. However, for real-world MIMO deployments, estimating the downlink wireless channel from each antenna on the base station to every client device is a critical bottleneck, especially for the widely used frequency duplexed designs that cannot utilize reciprocity. Typically, this channel estimation requires explicit feedback from client devices and is prohibitive for large antenna deployments. In this paper, we present FIRE, a system that uses an end-to-end machine learning approach to enable accurate channel estimation without requiring any feedback from client devices. FIRE is interpretable, accurate, and has low compute overhead. We show that FIRE can successfully support MIMO transmissions in a real-world testbed and achieves SNR improvement over 10 dB in MIMO transmissions compared to the current state-of-the-art.

Convolutional Neural Networks(CNNs) are widely used in many fields, including artificial intelligence, computer vision and video coding. However, CNNs are typically over-parameterized and contain significant redundancy. Traditional model acceleration methods mainly rely on specific manual rules. This usually leads to sub-optimal results with relatively limited compression ratio. Recent works have deployed the self-learning agent on the layer-level acceleration but still combined with human-designed criterias. In this paper, we proposed a filter-based model acceleration method to directly and automatically decide which filters should be pruned with the reinforcement learning method DDPG. We designed a novel reward function with the reward shaping technique for the training process. Our method is utilized on the models trained on MNIST and CIFAR-10 datasets and achieves both higher acceleration ratio and less accuracy loss than the conventional methods simultaneously.