DRPN: Making CNN Dynamically Handle Scale Variation

Based on our observations of infrared targets, serious scale variation along within sequence frames has high-frequently occurred. In this paper, we propose a dynamic re-parameterization network (DRPN) to deal with the scale variation and balance the detection precision between small targets and large targets in infrared datasets. DRPN adopts the multiple branches with different sizes of convolution kernels and the dynamic convolution strategy. Multiple branches with different sizes of convolution kernels have different sizes of receptive fields. Dynamic convolution strategy makes DRPN adaptively weight multiple branches. DRPN can dynamically adjust the receptive field according to the scale variation of the target. Besides, in order to maintain effective inference in the test phase, the multi-branch structure is further converted to a single-branch structure via the re-parameterization technique after training. Extensive experiments on FLIR, KAIST, and InfraPlane datasets demonstrate the effectiveness of our proposed DRPN. The experimental results show that detectors using the proposed DRPN as the basic structure rather than SKNet or TridentNet obtained the best performances

Long-tail Augmented Graph Contrastive Learning for Recommendation

Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usually encounter data sparsity issue in real-world scenarios. To address this issue, GCN-based recommendation methods employ contrastive learning to introduce self-supervised signals. Despite their effectiveness, these methods lack consideration of the significant degree disparity between head and tail nodes. This can lead to non-uniform representation distribution, which is a crucial factor for the performance of contrastive learning methods. To tackle the above issue, we propose a novel Long-tail Augmented Graph Contrastive Learning (LAGCL) method for recommendation. Specifically, we introduce a learnable long-tail augmentation approach to enhance tail nodes by supplementing predicted neighbor information, and generate contrastive views based on the resulting augmented graph. To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes. Additionally, we employ generative adversarial networks to ensure that the distribution of the generated tail/head nodes matches that of the original tail/head nodes. Extensive experiments conducted on three benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the uniformity of learned representations and the superiority of LAGCL on long-tail performance. Code is publicly available at https://github.com/im0qianqian/LAGCLComment: 17 pages, 6 figures, accepted by ECML/PKDD 2023 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Antimikrobna rezistencija i svojstva virulencije bakterije Enterococcus faecium izolirane u goveda s kliničkim mastitisom iz pokrajine Ningxia, Kina

This study was conducted to determine the antimicrobial resistance and virulence traits of 32 Enterococcus faecium isolates from clinical bovine mastitis cases in Ningxia Province, China. In total, 32 E. faecium isolates were taken from subclinical bovine mastitis on the basis of morphological characterization and biochemical testing, and screened for antimicrobial susceptibility. The virulence genes of the isolates were studied using polymerase chain reaction (PCR). The disc diffusion assay revealed a high occurrence of resistance against tetracycline (78.1%) and erythromycin (68.8%) in the E. faecium isolates. However, all tested E. faecium were susceptible to linezolid and vancomycin. Moreover, all E. faecium isolates harbored the erythromycin-resistant genes ermA, ermB and ermC, as well as the tetracycline-resistant genes tetK, tetL and tetM. Furthermore, all E. faecium isolates carried more than 3 of the tested virulence genes. The presence of agg (100%), cpd (100%), efaA (100%), gelE (93.4%), and esp (75.0%) was found most frequently in all the tested isolates. These findings are useful for making appropriate antimicrobial choices and developing antivirulence therapies for subclinical bovine mastitis caused by E. faecium in Ningxia Province, China.Istraživanje je provedeno kako bi se odredila antimikrobina rezistencija i svojstva virulencije izolata bakterije Enterococcus faecium uzetih u goveda s kliničkim mastitisom. U ukupno 32 izolata goveda iz pokrajine Ningxia u Kini, procijenjena je antimikrobna osjetljivost na temelju morfološke karakterizacije i biokemijskih pretraga. Geni virulencije izolata istraženi su polimeraznom lančanom reakcijom (PCR). Disk-difuzijski test je u izolatu bakterije E. faecium pokazao visoku pojavnost rezistencije na tetraciklin (78,1 %) i eritromicin (68,8 %). Svi su pretraženi izolati bili osjetljivi na linezolid i vankomicin i imali gene rezisentne na eritromicin ermA, ermB i ermC, kao i na tetraciklin, tetK, tetL i tetM. Osim toga svi izolati E. faecium nosili su više od tri istraživana gena virulencije. Najčešći geni bili agg (100 %), cpd (100 %), efaA (100 %), gelE (93,4 %) i esp (75,0 %). Ovi rezultati mogu u pokrajini Ningxia u Kini pridonijeti pravilnom izboru antimikrobnog lijeka i razvoju uspješne terapije za supklinički goveđi mastitis uzrokovan bakterijom E. faecium

Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning

Android Apps are frequently updated to keep up with changing user, hardware, and business demands. Ensuring the correctness of App updates through extensive testing is crucial to avoid potential bugs reaching the end user. Existing Android testing tools generate GUI events focussing on improving the test coverage of the entire App rather than prioritising updates and its impacted elements. Recent research has proposed change-focused testing but relies on random exploration to exercise the updates and impacted GUI elements that is ineffective and slow for large complex Apps with a huge input exploration space. We propose directed testing of App updates with Hawkeye that is able to prioritise executing GUI actions associated with code changes based on deep reinforcement learning from historical exploration data. Our empirical evaluation compares Hawkeye with state-of-the-art model-based and reinforcement learning-based testing tools FastBot2 and ARES using 10 popular open-source and 1 commercial App. We find that Hawkeye is able to generate GUI event sequences targeting changed functions more reliably than FastBot2 and ARES for the open source Apps and the large commercial App. Hawkeye achieves comparable performance on smaller open source Apps with a more tractable exploration space. The industrial deployment of Hawkeye in the development pipeline also shows that Hawkeye is ideal to perform smoke testing for merge requests of a complicated commercial App

EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Events serve as fundamental units of occurrence within various contexts. The processing of event semantics in textual information forms the basis of numerous natural language processing (NLP) applications. Recent studies have begun leveraging large language models (LLMs) to address event semantic processing. However, the extent that LLMs can effectively tackle these challenges remains uncertain. Furthermore, the lack of a comprehensive evaluation framework for event semantic processing poses a significant challenge in evaluating these capabilities. In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects. To comprehensively evaluate the event semantic processing abilities of models, we introduce a novel benchmark called EVEVAL. We collect 8 datasets that cover all aspects of event semantic processing. Extensive experiments are conducted on EVEVAL, leading to several noteworthy findings based on the obtained results