With the rapid advancement of AI generative image technologies, the quality of fabricated images has significantly improved, posing a serious challenge to research integrity. Western blot (WB) images, frequently used in scientific publications, have become a primary target for forgery, leading to serious academic misconduct. However, detecting AI-generated WB images remains particularly challenging due to the low texture complexity and structural simplicity of WB images. To address this issue, we propose a novel detection framework for generative WB forgeries by leveraging large-scale pretrained models with targeted fine-tuning. This approach retains the generalization capabilities of the backbone model while adapting it to the unique characteristics of WB images. Moreover, we introduce an adversarial feature alignment module with a direction-aware domain classifier designed according to the banded and structural nature of WB images, which enhances robustness under limited data and across unseen generative styles. Extensive experiments demonstrate that our method consistently achieves superior performance compared with existing approaches across multiple generative models, low-resource target domains, and cross-domain settings, showing higher detection accuracy, stronger generalization, and robustness to common post-processing operations. These results highlight the practical applicability of the proposed framework in real-world scientific integrity monitoring.

MapReduce is a popular distributed framework for big data analysis. However, the current MapReduce framework is insufficiently efficient in handling intermediate data, which may cause bottlenecks in I/O operations, computation, and network bandwidth. Previous work addresses the I/O problem by aggregating map task outputs (i.e. intermediate data) for each single reduce task on one machine. Unfortunately, when there are a large number of reduce tasks, their concurrent requests for intermediate data generate a large amount of I/O operations. In this paper, we present APA (Aggregation, Partition, and Allocation), a new intermediate data management system for the MapReduce framework. APA aggregates the intermediate data from the map tasks in each rack to one file, and the file host pushes the needed intermediate data to each reduce task. Thus, it reduces the number of disk seeks involved in handling intermediate data within one job. Rather than evenly distributing the intermediate data among reduce tasks based on the keys as in current MapReduce, APA partitions the intermediate data to balance the execution latency of different reduce tasks. APA further decides where to allocate each reduce task to minimize the intermediate data transmission time between map tasks and reduce tasks. Through experiments on a real MapReduce Hadoop cluster using the HiBench benchmark suite, we show that APA improves the performance of the current Hadoop by 40%-50%.

Search engine switching is the voluntary transition between Web search engines. Engine switching can occur for a number of reasons, including user dissatisfaction with search results, a desire for broader topic coverage or verification, user preferences, or even unintentionally. An improved understanding of switching rationales allows search providers to tailor the search experience according to the different causes. In this paper we study the reasons behind search engine switching within a session. We address the challenge of identifying switching rationales by designing and implementing client-side instrumentation to acquire in-situ feedbacks from users. Using this feedback, we investigate in detail the reasons that users switch engines within a session. We also study the relationship between implicit behavioral signals and the switching causes, and develop and evaluate models to predict the reasons for switching. In addition, we collect editorial judgments of switching rationales by third-party judges and show that we can recover switching causes a posteriori. Our findings provide valuable insights into why users switch search engines in a session and demonstrate the relationship between search behavior and switching motivations. The findings also reveal sufficient behavioral consistency to afford accurate prediction of switching rationale, which can be used to dynamically adapt the search experience and derive more accurate competitive metrics.