This study examines the design and implementation of the D3 (Decode, Discover, Design) curriculum, a culturally relevant AI and data literacy program for middle school students. Sixty students from a low-SES, predominantly Hispanic school in the southern United States participated in this project. The curriculum integrated three complementary perspectives: AI literacy, AI for social good, and culturally relevant computing. During the Decode and Discover phases, students learned foundational AI and NLP concepts while analyzing real-world community data. In the Design phase, students created digital artifacts that addressed issues. Mixed methods results revealed significant gains in AI knowledge and more positive attitudes toward AI. Students also demonstrated agency, civic engagement, and awareness of algorithmic bias. The study demonstrates the potential of culturally relevant computing and AI for social good, offering a transferable model for community-anchored, equity-focused AI education.

With the increasing prevalence of large language models (LLMs) such as ChatGPT, there is a growing need to integrate natural language processing (NLP) into K-12 education to better prepare young learners for the future AI landscape. NLP, a sub-field of AI that serves as the foundation of LLMs and many advanced AI applications, holds the potential to enrich learning in core subjects in K-12 classrooms. In this experience report, we present our efforts to integrate NLP into science classrooms with 98 middle school students across two US states, aiming to increase students' experience and engagement with NLP models through textual data analyses and visualizations. We designed learning activities, developed an NLP-based interactive visualization platform, and facilitated classroom learning in close collaboration with middle school science teachers. This experience report aims to contribute to the growing body of work on integrating NLP into K-12 education by providing insights and practical guidelines for practitioners, researchers, and curriculum designers.

Database replication is widely known and used for high availability or load balancing in many practical database systems. In this paper, we show how a replication engine can be used for three important practical cases that have not previously been studied very well. The three practical use cases include: 1) scaling out OLTP/OLAP-mixed workloads with partitioned replicas, 2) efficiently maintaining a distributed secondary index for a partitioned table, and 3) efficiently implementing an online re-partitioning operation. All three use cases are crucial for enabling a high-performance shared-nothing distributed database system. To support the three use cases more efficiently, we propose the concept of asymmetric-partition replication , so that replicas of a table can be independently partitioned regardless of whether or how its primary copy is partitioned. In addition, we propose the optimistic synchronous commit protocol which avoids the expensive two-phase commit without sacrificing transactional consistency. The proposed asymmetric-partition replication and its optimized commit protocol are incorporated in the production versions of the SAP HANA in-memory database system. Through extensive experiments, we demonstrate the significant benefits that the proposed replication engine brings to the three use cases.

NAND flash memory is widely used as secondary storage in mobile embedded systems such as cellular phones and digital cameras. These systems usually employ a compressed file system (CFS) to store system files which are fixed during the design phase, in combination with a normal file system to store data files. Since retrieving pages from a CFS requires additional decompression time, it is reasonable to grant them higher priorities when making a page replacement decision. In this paper, we present a new page replacement algorithm for NAND flash memory based embedded systems that considers asymmetric operation cost of each page. The proposed algorithm considers the decompression cost of a page from CFS as well as the asymmetric I/O costs of reads and writes in flash memory. To do this, the algorithm partitions the memory space into a read area, a write area, and a compressed area depending on different operation costs. The size of each area is then dynamically adjusted based on the change of access patterns and the contribution to reducing the I/O costs. Trace-driven simulations show that the proposed algorithm improves the I/O performance of mobile embedded systems significantly. Specifically, it reduces I/O time by 4.8-53.3% compared to widely acknowledged algorithms such as CLOCK, CAR, and CFLRU.

Recently, NAND flash memory is being used as the swap space of virtual memory as well as the file storage of embedded systems. Since temporal locality is dominant in page references of virtual memory, LRU and its approximated algorithms are widely used. However, we show that this is not true for write references. We analyze the characteristics of virtual memory read and write references separately, and find that the temporal locality of write references is weak and irregular. Based on this observation, we present a new page replacement algorithm that uses different strategies for read and write operations in predicting the re-reference likelihood of pages. For read operations, temporal locality alone is used, but for write operations, write frequency as well as temporal locality is used. The algorithm partitions the memory space into a read area and a write area to keep track of their reference patterns precisely, and then adjusts their sizes dynamically based on their reference patterns and I/O costs. Though the algorithm has no external parameter to tune, it performs better than CLOCK, CAR, and CFLRU by 20-66%. It also supports optimized implementations for virtual memory systems.

The reverse link traffic in the cdma2000 ixEV-DO standard is controlled by a distributed rate control scheme in a probabilistic way. In this paper, a new reverse-link rate control scheme that controls the maximum possible data rate by considering traffic load and cell region is proposed. The simulation results show that the proposed cell region based rate control scheme significantly improves the reverse link capacity in multi-cell environments.

When the cells in a location area are paged sequentially based on the specific information such as last registered area or mobile speed, paging load may be nonuniformly distributed among the cells. This nonuniform paging traffic causes additional paging delay due to the increase of waiting time in the cell with high paging load. In this paper, we introduce a new paging strategy in which the paging sequence in a location area is optimized based on both the location probability of a mobile terminal and paging load distribution among the cells. From numerical results, we show that the paging cost of our proposed scheme is very close to the optimal one. In addition, we observe the effects of the paging load distribution and the location probability distribution on the paging cost.