DocChecker: Bootstrapping Code Large Language Model for Detecting and Resolving Code-Comment Inconsistencies

Comments within source code are essential for developers to comprehend the code's purpose and ensure its correct usage. However, as codebases evolve, maintaining an accurate alignment between the comments and the code becomes increasingly challenging. Recognizing the growing interest in automated solutions for detecting and correcting differences between code and its accompanying comments, current methods rely primarily on heuristic rules. In contrast, this paper presents DocChecker, a tool powered by deep learning. DocChecker is adept at identifying inconsistencies between code and comments, and it can also generate synthetic comments. This capability enables the tool to detect and correct instances where comments do not accurately reflect their corresponding code segments. We demonstrate the effectiveness of DocChecker using the Just-In-Time and CodeXGlue datasets in different settings. Particularly, DocChecker achieves a new State-of-the-art result of 72.3% accuracy on the Inconsistency Code-Comment Detection (ICCD) task and 33.64 BLEU-4 on the code summarization task against other Large Language Models (LLMs), even surpassing GPT 3.5 and CodeLlama. DocChecker is accessible for use and evaluation. It can be found on our GitHub https://github.com/FSoft-AI4Code/DocChecker and as an Online Tool http://4.193.50.237:5000/. For a more comprehensive understanding of its functionality, a demonstration video is available on YouTube https://youtu.be/FqnPmd531xw

Effect of arbuscular mycorrhizal fungus on the growth and polyphenol production of medicinal plants: Ehretia asperula and Solanum procumben

The study was conducted to evaluate the influence of arbuscular mycorrhizal fungus (Rhizophagus intradices) on growth and polyphenol production of the two important and popular medicinal plants in Vietnam: Ehretia asperula Zoll. & Mor. and Solanum procumbens Lour. The results showed a significant effect of the fungus on the growth of these two species with the growth indices such as height, weight and P content that were all higher than those of non-AM plants; although the indices of AM symbiosis in the plant roots were not as high as other plants in previous studies. The effect of AM fungus on polyphenol production was different between the two species. In E. asperula, the effect of AM fungi on polyphenol production was not significant; whereas in S. procumbens, AM symbiosis significantly increased polyphenol production in plant biomass, especially in roots. The different growth times of the two species might cause the different effects of AM fungus on polyphenol production

UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering

In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relevant information from medical image based on a given question and providing precise diagnostic answers. The ImageCLEFmed-MEDVQA-GI-2023 challenge carried out visual question answering task in the gastrointestinal domain, which includes gastroscopy and colonoscopy images. Our team approached Task 1 of the challenge by proposing a multimodal learning method with image enhancement to improve the VQA performance on gastrointestinal images. The multimodal architecture is set up with BERT encoder and different pre-trained vision models based on convolutional neural network (CNN) and Transformer architecture for features extraction from question and endoscopy image. The result of this study highlights the dominance of Transformer-based vision models over the CNNs and demonstrates the effectiveness of the image enhancement process, with six out of the eight vision models achieving better F1-Score. Our best method, which takes advantages of BERT+BEiT fusion and image enhancement, achieves up to 87.25% accuracy and 91.85% F1-Score on the development test set, while also producing good result on the private test set with accuracy of 82.01%.Comment: ImageCLEF2023 published version: https://ceur-ws.org/Vol-3497/paper-129.pd

Structure-Function Relationships Affecting the Sensing Mechanism of Monolayer-Protected Cluster Doped Xerogel Amperometric Glucose Biosensors

A systematic study of the structure–function relationships critical to understanding the sensing mechanism of 1st generation amperometric glucose biosensors with an embedded nanoparticle (NP) network is presented. Xerogel-based films featuring embedded glucose oxidase enzyme and doped with alkanethiolate-protected gold NPs, known as monolayer protected clusters (MPCs), exhibit significantly enhanced performance compared to analogous systems without NPs including higher sensitivity, faster response time, and extended linear/dynamic ranges. The proposed mechanism involves diffusion of the glucose to glucose oxidase within the xerogel, enzymatic reaction production of H2O2 with subsequent diffusion to the embedded network of MPCs where it is oxidized, an event immediately reported via fast electron transfer (ET) through the MPC system to the working electrode. Various aspects of the film construct and strategy are systematically probed using amperometry, voltammetry, and solid-state electronic conductivity measurements, including the effects of MPC peripheral chain length, MPC functionalization via place-exchange reaction, MPC core size, and the MPC density or concentration within the xerogel composite films. The collective results of these experiments support the proposed mechanism and identify interparticle spacing and the electronic communication through the MPC network is the most significant factor in the sensing scheme with the diffusional aspects of the mechanism that may be affected by film/MPC hydrophobicity and functionality (i.e., glucose and H2O2 diffusion) shown to be less substantial contributors to the overall enhanced performance. Understanding the structure–function relationships of effective sensing schemes allows for the employment of the strategy for future biosensor design toward clinically relevant targets

LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss

Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.Comment: Accepted to EASE'2

Pathogenicity of an H5N1 avian influenza virus isolated in Vietnam in 2012 and reliability of conjunctival samples for diagnosis of infection

The continued spread of highly pathogenic avian influenza virus (HPAIV) subtype H5N1 among poultry in Vietnam poses a potential threat to animals and public health. To evaluate the pathogenicity of a 2012 H5N1 HPAIV isolate and to assess the utility of conjunctival swabs for viral detection and isolation in surveillance, an experimental infection with HPAIV subtype H5N1 was carried out in domestic ducks. Ducks were infected with 10[superscript 7.2] TCID[subscript 50] of A/duck/Vietnam/QB1207/2012 (H5N1), which was isolated from a moribund domestic duck. In the infected ducks, clinical signs of disease, including neurological disorder, were observed. Ducks started to die at 3 days-post-infection (dpi), and the study mortality reached 67%. Viruses were recovered from oropharyngeal and conjunctival swabs until 7 dpi and from cloacal swabs until 4 dpi. In the ducks that died or were sacrificed on 3, 5, or 6 dpi, viruses were recovered from lung, brain, heart, pancreas and intestine, among which the highest virus titers were in the lung, brain or heart. Results of virus titration were confirmed by real-time RT-PCR. Genetic and phylogenetic analysis of the HA gene revealed that the isolate belongs to clade 2.3.2.1 similarly to the H5N1 viruses isolated in Vietnam in 2012. The present study demonstrated that this recent HPAI H5N1 virus of clade 2.3.2.1 could replicate efficiently in the systemic organs, including the brain, and cause severe disease with neurological symptoms in domestic ducks. Therefore, this HPAI H5N1 virus seems to retain the neurotrophic feature and has further developed properties of shedding virus from the oropharynx and conjunctiva in addition to the cloaca, potentially posing a higher risk of virus spread through cross-contact and/or environmental transmission. Continued surveillance and diagnostic programs using conjunctival swabs in the field would further verify the apparent reliability of conjunctival samples for the detection of AIV.Japan Society for the Promotion of Science (Grant-in-Aid for Bilateral Joint Projects)Heiwa Nakajima FoundationNational Institute of Allergy and Infectious Diseases (U.S.) (Contract HHSN2662007000010C

Retinal Alpha-Synuclein Accumulation Correlates with Retinal Dysfunction and Structural Thinning in the A53T Mouse Model of Parkinson’s Disease

Abnormal alpha-synuclein (α-SYN) protein deposition has long been recognized as one of the pathological hallmarks of Parkinson\u27s disease\u27s (PD). This study considers the potential utility of PD retinal biomarkers by investigating retinal changes in a well characterized PD model of α-SYN overexpression and how these correspond to the presence of retinal α-SYN. Transgenic A53T homozygous (HOM) mice overexpressing human α-SYN and wildtype (WT) control littermates were assessed at 4, 6, and 14  months of age (male and female

Clinically relevant preservation conditions for mesenchymal stem/stromal cells derived from perinatal and adult tissue sources

The interplay between mesenchymal stem/stromal cells (MSCs) and preservation conditions is critical to maintain the viability and functionality of these cells before administration. We observed that Ringer lactate (RL) maintained high viability of bone marrow–derived MSCs for up to 72 h at room temperature (18°C–22°C), whereas adipose-derived and umbilical cord-derived MSCs showed the highest viability for 72 h at a cold temperature (4°C–8°C). These cells maintained their adherence ability with an improved recovery rate and metabolic profiles (glycolysis and mitochondrial respiration) similar to those of freshly harvested cells. Growth factor and cytokine analyses revealed that the preserved cells released substantial amounts of leukaemia inhibitory factors (LIFs), hepatocyte growth factor (HGF) and vascular endothelial growth factor-A (VEGF-A), as well as multiple cytokines (eg IL-4, IL-6, IL-8, MPC-1 and TNF-α). Our data provide the simplest clinically relevant preservation conditions that maintain the viability, stemness and functionality of MSCs from perinatal and adult tissue sources

A New Approach and Tool in Verifying Asynchronous Circuits

Research in asynchronous circuit approach has been carried out recently when asynchronous circuits are presented more widely in electronic systems. As they are more important in human life, their correctness should be considered carefully. Although there are some EDA tools for design and synthesis of asynchronous circuits, they are lack of methods for verifying the correctness of the produced circuits. In this work, we are about to propose a verification method and apply it in making a new version of the PAiD tool that can enable engineers to design, synthesize and verify asynchronous circuits. Experiments in verifying circuits have been also provided in this work

Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202