In developing and underdeveloped regions, many “Blind Colleges” exclusively enroll individuals with Blindness or Low Vision (BLV) for higher education. While advancements in accessible technologies have facilitated BLV student integration into “Integrated Colleges,” their implementation in “Blind Colleges” remains uneven due to complex economic, social, and policy challenges. This study investigates the practices, perceptions, and challenges of BLV students using accessible technologies in a Chinese “Blind College” through a two-part empirical approach. Our participants reported that tactile and digital technologies support their access to education, yet facing integration barriers. We also found BLV students’ aspirations for more inclusive educational environments, and the systemic obstacles within existing frameworks. We advocate for leveraging accessible technologies to transition “Blind Colleges” into “Integrated Colleges,” offering actionable insights for policymakers, designers, and educators. Finally, we suggest future research directions on accessible technology innovation and its implications for BLV education in resource-constrained settings.

Speech Emotion Recognition (SER) involves analyzing vocal expressions to determine the emotional state of speakers, where the comprehensive and thorough utilization of audio information is paramount. Therefore, we propose a novel approach on self-supervised learning (SSL) models that employs all available auxiliary information— specifically metadata—to enhance performance. Through a two-stage fine-tuning method in multi-task learning, we introduce the Augmented Residual Integration (ARI) module, which enhances transformer layers in encoder of SSL models. The module efficiently preserves acoustic features across all different levels, thereby significantly improving the performance of metadata-related auxiliary tasks that require various levels of features. Moreover, the Co-attention module is incorporated due to its complementary nature with ARI, enabling the model to effectively utilize multidimensional information and contextual relationships from metadata-related auxiliary tasks. Under pre-trained base models and speaker-independent setup, our approach consistently surpasses state-of-the-art (SOTA) models on multiple SSL encoders for the IEMOCAP dataset.

External knowledge provides critical clues for knowledge-based visual question answering (KB-VQA), while the implicit knowledge in images is difficult to capture in order to construct effective queries for knowledge bases. To this end, we propose a visual entity-centric prompting for knowledge retrieval (VEPR) to bridge the gap between the implicit and explicit knowledge driven by visual entities via large language models. More specifically, a visual entity question answering (EQ) module is devised to localize the critical entities from the given images and questions to generate entity-centric questions via a large language model. In particular, EQ obtains several entity-centric question-answer pairs via a visual language model. Furthermore, a question-answer-enhanced retrieval (ER) module is devised to construct a query by summarizing the text including question-answer pairs, captions and questions, in order to require explicit knowledge items. Finally, a multi-branch reader (MR) module is designed to encode the given questions, visual content and retrieved knowledge items, which are decoded to make answer predictions. Extensive experiments conducted on two public datasets demonstrate the effectiveness of the VEPR.

The deployment of large-scale data analytics between on-premise and cloud sites, i.e., hybrid clouds, requires careful partitioning of both data and computation to avoid massive networking costs. We present Moirai, a cost-optimization framework that analyzes job accesses and data dependencies and optimizes the placement of both in hybrid clouds. Moirai informs the job scheduler of data location and access predictions, so it can determine where jobs should be executed to minimize data transfer costs. Our optimizer achieves scalability and cost efficiency by exploiting recurring jobs to identify data dependencies and job access characteristics and reduces the search space by excluding data not accessed recently.

An increasing demand for cross-cloud and cross-region data access is bringing forth challenges related to high data transfer costs and latency. In response, we introduce Macaron, an auto-configuring cache system designed to minimize cost for remote data access. A key insight behind Macaron is that cloud cache size is tied to cost, not hardware limits, shifting the way we think about cache design and eviction policies. Macaron dynamically configures cache size and utilizes a mix of cloud storage types to adapt to workload changes and reduce costs. We demonstrate that Macaron reduces cross-cloud workload costs by 65% and cross-region costs by 67%, mainly by reducing outgoing data transfer and by leveraging object storage alongside DRAM to reduce capacity cost.

Caching is crucial for accelerating data access, employed as a ubiquitous design in modern systems at many parts of computer systems. With increasing core count, and shrinking latency gap between cache and modern storage devices, hit-path scalability becomes increasingly critical. However, existing production in-memory caches often use list-based management with promotion on each cache hit, which requires extensive locking and poses a significant overhead for scaling beyond a few cores. Moreover, existing techniques for improving scalability either (1) only focus on the indexing structure and do not improve cache management scalability, or (2) sacrifice efficiency or miss-path scalability.

LRU has been the basis of cache eviction algorithms for decades, with a plethora of innovations on improving LRU's miss ratio and throughput. While it is well-known that FIFO-based eviction algorithms provide significantly better throughput and scalability, they lag behind LRU on miss ratio, thus, cache efficiency.

As a cache eviction algorithm, FIFO has a lot of attractive properties, such as simplicity, speed, scalability, and flash-friendliness. The most prominent criticism of FIFO is its low efficiency (high miss ratio).