33 research outputs found

    CupCleaner: A Data Cleaning Approach for Comment Updating

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    Recently, deep learning-based techniques have shown promising performance on various tasks related to software engineering. For these learning-based approaches to perform well, obtaining high-quality data is one fundamental and crucial issue. The comment updating task is an emerging software engineering task aiming at automatically updating the corresponding comments based on changes in source code. However, datasets for the comment updating tasks are usually crawled from committed versions in open source software repositories such as GitHub, where there is lack of quality control of comments. In this paper, we focus on cleaning existing comment updating datasets with considering some properties of the comment updating process in software development. We propose a semantic and overlapping-aware approach named CupCleaner (Comment UPdating's CLEANER) to achieve this purpose. Specifically, we calculate a score based on semantics and overlapping information of the code and comments. Based on the distribution of the scores, we filter out the data with low scores in the tail of the distribution to get rid of possible unclean data. We first conducted a human evaluation on the noise data and high-quality data identified by CupCleaner. The results show that the human ratings of the noise data identified by CupCleaner are significantly lower. Then, we applied our data cleaning approach to the training and validation sets of three existing comment updating datasets while keeping the test set unchanged. Our experimental results show that even after filtering out over 30\% of the data using CupCleaner, there is still an improvement in all performance metrics. The experimental results on the cleaned test set also suggest that CupCleaner may provide help for constructing datasets for updating-related tasks

    WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

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    Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks

    A Bi2Te3-Filled Nickel Foam Film with Exceptional Flexibility and Thermoelectric Performance

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    The past decades have witnessed surging demand for wearable electronics, for which thermoelectrics (TEs) are considered a promising self-charging technology, as they are capable of converting skin heat into electricity directly. Bi2Te3 is the most-used TE material at room temperature, due to a high zT of ~1. However, it is different to integrate Bi2Te3 for wearable TEs owing to its intrinsic rigidity. Bi2Te3 could be flexible when made thin enough, but this implies a small electrical and thermal load, thus severely restricting the power output. Herein, we developed a Bi2Te3/nickel foam (NiFoam) composite film through solvothermal deposition of Bi2Te3 nanoplates into porous NiFoam. Due to the mesh structure and ductility of Ni Foam, the film, with a thickness of 160 μm, exhibited a high figure of merit for flexibility, 0.016, connoting higher output. Moreover, the film also revealed a high tensile strength of 12.7 ± 0.04 MPa and a maximum elongation rate of 28.8%. In addition, due to the film’s high electrical conductivity and enhanced Seebeck coefficient, an outstanding power factor of 850 μW m−1 K−2 was achieved, which is among the highest ever reported. A module fabricated with five such n-type legs integrated electrically in series and thermally in parallel showed an output power of 22.8 nW at a temperature gap of 30 K. This work offered a cost-effective avenue for making highly flexible TE films for power supply of wearable electronics by intercalating TE nanoplates into porous and meshed-structure materials

    Wideband Low RCS Antenna Based on Hybrid Absorptive-Diffusive Frequency Selective Reflector

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    A method for designing a wideband low radar cross section (RCS) antenna is proposed based on hybrid absorptive and diffusive frequency selective reflector (AD-FSR) in this paper. The low RCS antenna exhibits manifold frequency responses in three artificial bands, which are created by its different components. A simple U-slot patch antenna is borrowed, which is integrated with the diffusion metasurface, and placed under the absorptive sheet with about quarter wavelength. Then, the out-of-band incident wave is captured by the absorber in the lower frequency band, and diffused by the metasurface in the upper frequency band achieving the RCS reduction. The design strategies are explained and verified with the aid of the corresponding equivalent circuit model and current distributions. To illustrate the efficacy of the proposed approach, the low RCS antenna using the proposed hybrid AD-FSR structure is fabricated and tested, and the results demonstrate that the proposed structure is an attractive candidate for designing wideband low RCS antennas

    The Soft Coral <i>Sarcophyton trocheliophorum</i>: A Warehouse of Terpenoids with Structural and Pharmacological Diversity

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    The soft coral Sarcophyton trocheliophorum, which was frequently encountered on Indo-Pacific and Red Sea coral reefs, furnished a wealth of secondary metabolites. Notably, terpenoids dominated the chemical profile of this species. In this review, we summarized the discovery of 156 terpenoids from the soft coral S. trocheliophorum specimens in different geographical areas. The structures comprised 13 terpenoidal classes with various functionalities. We covered the era from the first report of S. trocheliophorum-derived metabolites in 1976 up to October 2022. The biological effects of these chemical compositions on a vast array of potential pharmacological activities such as protein tyrosine phosphatase 1B (PTP1B) inhibitory, neuroprotective, cytotoxic, anti-inflammatory, antibacterial, antivirus, and immunomodulatory activities were also presented. This review also revealed an immense demand to explore the terpene biosynthetic gene clusters of this species besides the chemo- and bio-investigations
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