Detecting small objects in large-scale, high-resolution aerial images presents significant challenges. Most existing detectors focus primarily on the design of detection heads and fusion layers, often overlooking information loss in the backbone and the excessive computational resources required, which are particularly constrained in aerial image analysis. To address the aforementioned challenges, we propose the Resolution-Aware Criss-Cross Attention Detector (RACDet), which effectively leverages the contextual information embedded in an innovative backbone RACNet of aerial images. By decomposing the position information into orthogonal horizontal and vertical components, we achieve efficient modeling of spatial dependencies. For each pixel, RACNet gathers contextual information from all other pixels in the same position, establishing position relationships early, which can guide the subsequent processing in convolutional networks across different resolutions. The proposed method not only provides an adaptive representation of feature maps at multi-scale resolutions using normalized position encoding, but also enhances the detection accuracy of small objects by leveraging a regression loss function based on smooth Gaussian Wasserstein distance. We evaluate our method on two challenging aerial image datasets, including VisDrone2019 and UAVDT. Comprehensive experiments show that our approach achieves state-of-the-art performance while significantly decreasing the number of FLOPs.

We explored the effects of familiarity with mobile-assisted language learning environments (MALLE) on creativity. We administered English as a foreign language (EFL) course to university students. They learned EFL in class and then used new knowledge to apply it to the real world using mobile technology after class. All students were assigned to 3 groups: (1) students who learned in unfamiliar MALLE, (2) students who learned in familiar MALLE, and (3) students who learned in unfamiliar MALLE but with familiarization strategies. We evaluated and compared content of essays composed by students from 3 groups to explore the effects of familiarity with MALLE on students’ creativity. Our results evealed that students in the second and third groups (i.e. those who were already familiar with MALLE and those who become familiar with MALLE thanks to familiarization strategies) outperformed students in the first group (i.e. those who were not familiar with MALLE). This result may suggest that creativity of students was affected by their familiarity with MALLE. Based on this finding we suggest that instructors consider familiarization of students with MALLE when their language learning process takes place in the real world.

Blockchain is a kind of distributed accounting system with the characteristics of decentralization, openness, transparency, non-tampering and de-trust. Recently, blockchain-based applications appear in various fields such as IoT, supply-chain, etc. A practical requirement of applying the Blockchain technology to the conventional supply-chain system is the trans-action synchronization between databases and the blockchain systems. This paper proposes a middleware that supports non-intrusive, efficient, fault-tolerant transaction data synchronization. The middleware is designed under three principles. The first is to divide the process of transaction synchronization into several independent phases. The second is to convert the representation of database transaction into an optimal form for blockchain, and transactions are packed and written into blockchain in a batch mode. The third is to guarantee the consistency between database and blockchain through failure recovery mechanism. Utilities like pipeline processing and transaction mapping built on this middleware achieve significant performance improvement. The peak TPS of Hyperledger Fabric reaches above 16000, which is more than 32 times of the direct submission method.

With the highly demanded requirements for manipulating large scientific datasets, scientists are in need of flexible cluster-level software to execute fast scientific data analysis. In this paper, we discuss whether the Apache Spark framework is suitable for scientific data management. We present our system SparkArray, which extends Spark with a multidimensional array data model and a set of common used array operations (e.g., filter, subarray, smooth and join). We present analysis and performance evaluation results on different implementation methods for executing array operations on clusters. We also compared the performance of SparkArray to SciDB, a recent scientific database management system, using the workloads of the Standard Science DBMS Benchmark (SS-DB). The results show that SparkArray is feasible alternative solution for large-scale scientific data management, especially when scientists require fast data loading or one-time data analysis on large scientific datasets.

Current major big data analytical stacks often consist of a general purpose, multi-staged computation framework (e.g. Hadoop) and an SQL query system (e.g. Hive) on its top. A key factor of query performance is the efficiency of data shuffling between two execution stages (e.g. Map/Reduce). In current data shuffling, various useful information about the shuffled data and the query on the data is simply wasted. In this paper, we make a strong case of cross-layer optimizations for Hive/Hadoop stack: we have designed and implemented a novel data shuffling mechanism in Hadoop, called Structured Data Shuffling (S-Shuffle), which carefully leverages the rich information in data and queries to optimize the overall query processing. Our experimental results with industry-standard TPC-H benchmark show that, by using S-Shuffle, the performance of SQL query processing on Hadoop can be improved by up to 2.4x.

Game tree search is a classical problem in the field of game theory and artificial intelligence. Fast game tree search algorithm is critical for computer games asking for real-time responses. In this paper, we focus on how to leverage massive parallelism capabilities of GPU to accelerate the speed of game tree search algorithms and propose a concise and general parallel game tree search algorithm on GPU. The performance model of our algorithm is presented and analyzed theoretically. We implement the algorithm for two real computer games called Connect6 and Chess. We also use these two games to verify the effectiveness and efficiency of our algorithm. Experiments support our theoretical results and show good performance of our approach. Compared to classical CPU-based game tree search algorithms, our algorithm can achieve speedups of 89.95x for Connect6 and 11.43x for Chess, in case of no pruning. When pruning is considered, which means the practical performance of our algorithm, the speedup can reach about 10.58x for Connect6 and 7.26x for Chess. The insight of our work is that using GPU is a feasible way to improve the performance of game tree search algorithms.

Equi-join is heavily used in MapReduce-based log processing. With the rapid growth of dataset sizes, join methods on MapReduce are extensively studied recently. We find that existing join methods usually cannot get high query performance and affordable storage consumption at the same time when faced with a huge amount of log data. They either only optimize one aspect but significantly sacrifice the other or have limited applications. In this paper, after analyzing characteristics of the workloads and underlying MapReduce, we present a join method with specific optimizations for log processing called RHJoin (Repartition Hash Join) and its implementation on Hadoop. In RHJoin, reference tables are partitioned in the pre-processing step, the log table is partitioned on the map side and hash join is executed on the reduce side. The shuffle procedure of MapReduce is also optimized by removing the sort step and overlapping the execution of mappers and reducers. Comprehensive experiments show that RHJoin achieves high query performance with only a small extra storage cost, and has wide application circumstances for log processing.

It is very important to evaluate the MapReduce-based frameworks for scientific data processing applications. Scientists need a low-cost, scalable, easy-to-use and fault-tolerance platform for large volume data processing eagerly. This paper presents an implementation of a scientific data management benchmark, SSDB, on Hive, a MapReduce-based data warehouse. A complete strategy of migrating SSDB to Hive is described in detail including query HQL implementation, data partition schema and adjustments of underlying storage facilities. We have tuned the performance using several system parameters provided by Hive, Hadoop and HDFS. This paper provides preliminary results and analysis. Evaluation results indicate that Hive achieves acceptable performance for some data analysis tasks even compared with some high efficient distributed parallel databases, but it needs subtle adjustments of underlying storage facilities and indexing mechanism.

Game tree search is a classical problem in the field of game theory and artificial intelligence. Fast game tree algorithm is critical for computer games asking for real-time responses. In this paper, we focus on how to leverage massive parallelism capabilities of GPUs to accelerate the speed of game tree algorithms and propose a concise and general parallel game tree algorithm on GPUs. The performance model of the algorithm is presented and analyzed theoretically. We also implement the algorithm for a real computer game called Connect6 and use it to verify the effectiveness and efficiency of our algorithm. Experiments support our theoretical results and show good performance of our approach. Compared to classical CPU-based game tree algorithms, our algorithm can achieve speedup of 70.8 in case of no pruning. When pruning is considered (which means the practical performance of our algorithm), the speedup can reach about 7.0. The insight of our work is that using GPUs is a feasible way to improve the performance of game tree algorithms.

In this paper, we design and implement IRain, a prototype of storage cloud for computer scientists and graduate students to manage personal data that spreads over the web. IRain (1) integrates personal data with various metainfo structures that comes from different web sites and personal computers; (2) provides a global, unified environment to users and supports user-defined file views via flexible combination of tags; (3) offers an easy way to integrate new web services via a VFS-liked interface.

Web Services and workflow have been widely adopted in building complicated business systems. In these systems, high security concern is claimed. But frequent authentication and computing-intensive encoding and decoding operation bring extra workload to the systems and greatly impact the performance of Web Services and workflow engine, especially under heavy load. In this paper, we propose Requests Automated Binding Mechanism, RABM, to automatically pack the requests from multiple clients but to a common target service in a short time period into one SOAP message, then sent by the workflow engine at runtime. Extra security operation costs are reduced without modifying of application programs. We use the Travel Service provided by W3C to evaluate RABM. As the experiment result shows, we can achieve up to 130% improvement of throughput and lower the average response time to 41%1.

Though XML is applied intensively in a lot of applications, XML parsing is not practical in many fields because of its poor performance. Parallel XML parsing on multi-core is a promising choice. Previous methods all adopt data parallel approach on XML parsing. As the semi-structured nature of XML, they were obliged to divide the data into well-formed XML chunks and then parse these chunks parallel. The division process is named as preparsing. As the preparsing is serial, it becomes the bottleneck of parallel XML parsing. Related work Simultaneous Finite Transducer (SFTXP) parallelized the preparsing stage. It maintained multiple preparser results for each equal sized chunk according to enumerated all possible parsing states. In spite of finite states for each XML, the overhead by SFTXP is tremendous, including CPU time and memory for multiple results generating and storing, respectively. In this work, we address parallel XML parsing by Key Element Parse Tracing (KEPT) method which parallelizes the preparsing and parsing at element level. It remolds the preparsing as a key element extracting process and schedules the processing of key elements in the framework of KEPT. Then parsing process is parallelized as a whole. To demonstrate the effectiveness, we implement it on libxml2 and obtain good scalability on both an 8-core Linux machine and an 8-core 24 SMT Sun machine running Solaris.

How to share the data in databases of autonomous organizations quickly and flexibly is an important problem. Traditional data integration systems are supposed to be a solution to the problem. Unfortunately, they require a global schema before they can be used to share data and they are centralized system. They can not support data sharing without a common schema and on-demand data sharing in applications. In addition, security is also the important requirements. This paper presents a community-based system for distributed data integration and sharing, Honeycomb. Honeycomb supports many virtual data integration and sharing systems on a single machine, and the system utilizes Web services to access data on other nodes to avoid changing security configuration of autonomous organizations. In addition, the system adopts new cache techniques to improve performance greatly.

As XQuery rapidly emerges as the standard for querying XML documents, it is very important to understand the architectural characteristics and behaviors of such workloads. A lot of efforts are focused on the implementation, optimization, and evaluation of XQuery tools. However, few or no prior work studies the architectural and memory system behaviors of XQuery workloads on modern hardware platforms. This makes it unclear whether modern CPU techniques, such as the multi-level caches and hardware branch predictors, can support such workloads well enough.

SOAP protocol has emerged as the Web service communication standard. Because of the relatively poor performance, many researchers focus on improving the speed of processing SOAP message. In this paper, we propose SPI, which introduces client usage pattern to the low level SOAP process infrastructure, in order to improve the performance of some kind of Web services applications with specific communication patterns. The pack interface of SPI is an approach to reduce the number of the SOAP messages to improve the latency on client side. This SOAP optimization technique packs concurrent service requests into one SOAP message on client side, and unpacks and executes them on server side. Our implementation and performance study demonstrates the usefulness and effectiveness of the technique