While visual augmentation dominates the augmented reality landscape, devices like Meta Ray-Ban audio smart glasses signal growing industry movement toward audio augmented reality (AAR). Hearing is a primary channel for sensing context, anticipating change, and navigating social space, yet AAR's everyday potential remains underexplored. We address this gap through a collaborative autoethnography (N=5, authoring) and an online survey (N=74). We identify ten roles for AAR, grouped into three categories: task- and utility-oriented, emotional and social, and perceptual collaborator. These roles are further layered with a rhythmic and embodied collaborator framing, mapping them onto micro-, meso-, and macro-rhythms of everyday life. Our analysis surfaces nuanced tensions, such as blocking distractions without erasing social presence, highlighting the need for context-aware design. This paper contributes a foundational and forward-looking framework for AAR in everyday life, providing design groundwork for systems attuned to daily routines, sensory engagement, and social expectations.

In the task of remote sensing ship image retrieval, due to the significant Angle changes and multi-scale characteristics of ships in the image, it is difficult to extract advanced features and the efficiency of feature descriptors is low. Therefore, this paper proposes a spatial invariant hash based on self-attention mechanism for remote sensing ship image retrieval (SIHS). The algorithm consists of two core modules: First, a module based on spatial invariance is designed, which uses deep convolutional neural network to extract continuous real-valued descriptors, and introduces the spatial transformation attention mechanism, and enhances the adaptation ability and learning efficiency of the model to the spatial invariance features through self-learning affine transformation and attention calculation; Secondly, a module based on self-attention hashing is proposed, which improves the efficiency of image representation by multi-scale image embedding, optimizes the attention regularization in the visual encoder, and effectively solves the problem of quantization loss in hash mapping. The experimental results show that the retrieval performance of SIHS algorithm on GGWS, DSCR, FGSC-23 and FGSCR-42 datasets is superior to the existing methods based on deep features.

Classical approaches to clean data have relied on using integrity constraints, statistics, or machine learning. These approaches are known to be limited in the cleaning accuracy, which can usually be improved by consulting master data and involving experts to resolve ambiguity. The advent of knowledge bases KBs both general-purpose and within enterprises, and crowdsourcing marketplaces are providing yet more opportunities to achieve higher accuracy at a larger scale. We propose KATARA, a knowledge base and crowd powered data cleaning system that, given a table, a KB, and a crowd, interprets table semantics to align it with the KB, identifies correct and incorrect data, and generates top-k possible repairs for incorrect data. Experiments show that KATARA can be applied to various datasets and KBs, and can efficiently annotate data and suggest possible repairs.

Data cleaning with guaranteed reliability is hard to achieve without accessing external sources, since the truth is not necessarily discoverable from the data at hand. Furthermore, even in the presence of external sources, mainly knowledge bases and humans, effectively leveraging them still faces many challenges, such as aligning heterogeneous data sources and decomposing a complex task into simpler units that can be consumed by humans. We present K atara , a novel end-to-end data cleaning system powered by knowledge bases and crowdsourcing. Given a table, a kb , and a crowd, K atara (i) interprets the table semantics w.r.t. the given kb ; (ii) identifies correct and wrong data; and (iii) generates top- k possible repairs for the wrong data. Users will have the opportunity to experience the following features of K atara : (1) Easy specification: Users can define a K atara job with a browser-based specification; (2) Pattern validation: Users can help the system to resolve the ambiguity of different table patterns ( i.e. , table semantics) discovered by K atara ; (3) Data annotation: Users can play the role of internal crowd workers, helping K atara annotate data. Moreover, K atara will visualize the annotated data as correct data validated by the kb , correct data jointly validated by the kb and the crowd, or erroneous tuples along with their possible repairs.

Integrity constraints (ICs) are valuables tools for enforcing correct application semantics. However, manually designing ICs require experts and time, hence the need for automatic discovery. Previous automatic ICs discovery suffer from (1) limited ICs language expressiveness; and (2) time-consuming manual verification of discovered ICs. We introduce RULEMINER, a system for discovering data quality rules that addresses the limitations of existing solutions.

To explore chip-level parallelism, the PSC (Parallel Shared Cache) model is provided in this paper to describe high performance shared cache of Chip Multi-Processors (CMP). Then for a specific application, parallel sorting, a cache-conscious parallel algorithm, PMCC (Partition-Merge based Cache-Conscious) is designed based on the PSC model. The PMCC algorithm consists of two steps: the partition-based in-cache sorting and merge-based k-way merge sorting. In the first stage, PMCC first divides the input dataset into multiple blocks so that each block can fit into the shared L2 cache, and then employs multiple cores to perform parallel cache sorting to generate sorted blocks. In the second stage, PMCC first selects an optimized parameter k which can not only improve the parallelism but also reduce the cache missing rate, then performs a k-way merge sorting to merge all the sorted blocks. The I/O complexity of the in-cache sorting step and k-way merge step are analyzed in detail. The simulation results show that the PSC based PMCC algorithm can out-performance the latest PEM based cache-conscious algorithm and the scalability of PMCC is also discussed. The low I/O complexity, high parallelism and the high scalability of PMCC can take advantage of CMP to improve its performance significantly and deal with large scale problem efficiently.

The task scheduling model and algorithm is very important to achieve high performance for message passing programs. The SPG (subtask precedence graph) model abstracts a task as a set of communication and computation sections so it can explore the dependence among subtasks precisely. The TSSF (task section based scheduling framework ) is designed to show how to generate subtasks and how to schedule subtasks on to different processors. Based on the SPG model and the TSSF Framework, two TSD(task section duplication based) algorithms, SMU(searching-marking-unmarking) and Scalable SMU are described in detail to show how to get multiple parallel executing paths based on task section duplication. Compared with four typical traditional task scheduling algorithms, the simulation results show that our algorithms outperform other algorithms significantly.