Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort

Host-guest Interaction at Molecular Interfaces: Binding of Cucurbit[7]uril on Ferrocenyl Self-assembled Monolayers on Gold

Ferrocene (Fc) encapsulated cucurbit[7]uril (CB[7]) supramolecular host-guest complex  (Fc@CB[7]) as a synthetic recognition pair has been widely adapted for coupling biomolecules and nanomaterials due to its ultra-high binding affinity. In this paper, we have explored the binding of CB[7] on binary ferrocenylundecanethiolate/octanethiolate self-assembled monolayer on gold  (FcC11S-/C8S-Au), a model system to deepen our understanding of host-guest chemistry at molecular interfaces. It has been shown that upon incubation with CB[7] solution, the redox behavior FcC11S-/C8S-Au changes remarkably, i.e., a new pair of peaks appeared at more positive potential with narrowed widths. The ease of quantitation of surface bound-redox species (Fc+/Fc and  Fc+@CB[7]/ Fc@CB[7]) enabled us to determine the thermodynamic formation constant of  Fc@CB[7] at FcC11S-/C8S-Au (7.3±1.8 × 104 M-1). With time-dependent redox responses, we were able to, for the first time, deduce both the binding and dissociation rate constants, 2.8±0.3 × 103  M-1s-1 and 0.08±0.01 s-1, respectively. These results showed substantial differences both thermodynamically and kinetically for the formation of host-guest inclusion complex at molecular interfaces with respect to solution-diffused, homogenous environments

Representation Separation for Semantic Segmentation with Vision Transformers

Vision transformers (ViTs) encoding an image as a sequence of patches bring new paradigms for semantic segmentation.We present an efficient framework of representation separation in local-patch level and global-region level for semantic segmentation with ViTs. It is targeted for the peculiar over-smoothness of ViTs in semantic segmentation, and therefore differs from current popular paradigms of context modeling and most existing related methods reinforcing the advantage of attention. We first deliver the decoupled two-pathway network in which another pathway enhances and passes down local-patch discrepancy complementary to global representations of transformers. We then propose the spatially adaptive separation module to obtain more separate deep representations and the discriminative cross-attention which yields more discriminative region representations through novel auxiliary supervisions. The proposed methods achieve some impressive results: 1) incorporated with large-scale plain ViTs, our methods achieve new state-of-the-art performances on five widely used benchmarks; 2) using masked pre-trained plain ViTs, we achieve 68.9% mIoU on Pascal Context, setting a new record; 3) pyramid ViTs integrated with the decoupled two-pathway network even surpass the well-designed high-resolution ViTs on Cityscapes; 4) the improved representations by our framework have favorable transferability in images with natural corruptions. The codes will be released publicly.Comment: 17 pages, 13 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Genomic survey, characterization and expression profile analysis of the peptide transporter family in rice (Oryza sativa L.)

<p>Abstract</p> <p>Background</p> <p>Peptide transporter (PTR) family whose member can transport di-/tripeptides and nitrate is important for plant growth and development. Although the rice (<it>Oryza sativa </it>L.) genome has been sequenced for a few years, a genomic survey, characterization and expression profile analysis of the PTR family in this species has not been reported.</p> <p>Results</p> <p>In this study, we report a comprehensive identification, characterization, phylogenetic and evolutionary analysis of 84 PTR family members in rice (OsPTR) as well as their whole-life expression patterns. Chromosomal distribution and sequence analysis indicate that nearly 70% of OsPTR members are involved in the tandem and segmental duplication events. It suggests that genome duplication might be a major mechanism for expansion of this family. Highly conserved motifs were identified in most of the OsPTR members. Meanwhile, expression profile of <it>OsPTR </it>genes has been analyzed by using Affymetrix rice microarray and real-time PCR in two elite hybrid rice parents, Minghui 63 and Zhenshan 97. Seven genes are found to exhibit either preferential or tissue-specific expression during different development stages of rice. Under phytohormone (NAA, GA3 and KT) and light/dark treatments, 14 and 17 <it>OsPTR </it>genes are differentially expressed respectively. <it>Ka/Ks </it>analysis of the paralogous <it>OsPTR </it>genes indicates that purifying selection plays an important role in function maintenance of this family.</p> <p>Conclusion</p> <p>These investigations add to our understanding of the importance of OsPTR family members and provide useful reference for selecting candidate genes for functional validation studies of this family in rice.</p

Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system.Comment: Accepted by Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23

A Generic Framework for Constraint-Driven Data Selection in Mobile Crowd Photographing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios