Management of narrow directional beams is critical for mm-wave communication systems. Translational or rotational motion of the user can cause misalignment of transmit and receive beams with the base station losing track of the mobile. Reacquiring the user can take about one second in 5G New Radio systems and significantly impair performance of applications, besides being energy intensive. It is therefore important to manage beams to continually maintain high received signal strength and prevent outages. It is also important to be able to recover from sudden but transient blockage caused by a hand or face interposed in the Line-of-Sight (LoS) path. This work presents a beam management protocol called BeamSurfer that is targeted to the use case of users walking indoors near a base station. It is designed to be minimalistic, employing only in-band information, and not requiring knowledge such as location or orientation of the mobile device or any additional sensors. Evaluations under pedestrian mobility show that 96% of the time it employs beams that are within 3 dB of what an omniscient Oracle would have chosen as the pair of optimal transmit-receive LoS beams. It also recovers from transient LoS blockage by continuing to maintain control packet communication over a pre-selected reflected path that preserves time-synchronization with the base station, which allows it to rapidly recover to the LoS link as soon as it is unblocked.

Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that have clearly written text and carefully segmented labels. In this paper, we instead focus on learning handwritten characters from maintenance logs, a constrained setting where data is very limited and noisy. We break the problem into two consecutive stages of word segmentation and word recognition respectively, and utilize data augmentation techniques to train both stages. Extensive comparisons with popular baselines for scene-text detection and word recognition show that our system achieves a lower error rate and is more suited to handle noisy and difficult documents.

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions are often ambiguous and contain high-level abstractions which require complex editing steps. Motivated by this inexperience aspect, we aim to smooth the learning curve by teaching the novices to edit images using low-level command terminologies. Towards this end, we develop a task-oriented dialogue system to investigate low-level instructions for NLIE. Our system grounds language on the level of edit operations, and suggests options for users to choose from. Though compelled to express in low-level terms, user evaluation shows that 25% of users found our system easy-to-use, resonating with our motivation. Analysis shows that users generally adapt to utilizing the proposed low-level language interface. We also identified object segmentation as the key factor to user satisfaction. Our work demonstrates advantages of low-level, direct language-action mapping approach that can be applied to other problem domains beyond image editing such as audio editing or industrial design.

For text content retrieval, the user can easily scan through and select from a list of retrieved items. This is impossible for spoken content retrieval, because the retrieved items are not easily displayed on-screen. In addition, due to the high degree of uncertainty for speech recognition, retrieval results can be very noisy. One way to counter such difficulties is through user-machine interaction. The machine can take different actions to interact with the user to obtain better retrieval results before showing them to the user. For example, the machine can request extra information from the user, return a list of topics for the user to select from, and so on. In this paper, we propose using deep-Q-network (DQN) to determine the machine actions for interactive spoken content retrieval. DQN bypasses the need to estimate hand-crafted states, and directly determines the best action based on the present retrieval results even without any human knowledge. It is shown to achieve significantly better performance as compared with the previous hand-crafted states. We further find that double DQN and dueling DQN improve the naive version.

User-machine interaction is important for spoken content retrieval.For text content retrieval, the user can easily scan through and select on a list of retrieved item.This is impossible for spoken content retrieval, because the retrieved items are difficult to show on screen.Besides, due to the high degree of uncertainty for speech recognition, the retrieval results can be very noisy.One way to counter such difficulties is through user-machine interaction.The machine can take different actions to interact with the user to obtain better retrieval results before showing to the user.The suitable actions depend on the retrieval status, for example requesting for extra information from the user, returning a list of topics for user to select, etc.In our previous work, some hand-crafted states estimated from the present retrieval results are used to determine the proper actions.In this paper, we propose to use Deep-Q-Learning techniques instead to determine the machine actions for interactive spoken content retrieval.Deep-Q-Learning bypasses the need for estimation of the hand-crafted states, and directly determine the best action base on the present retrieval status even without any human knowledge.It is shown to achieve significantly better performance compared with the previous hand-crafted states.