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

Impacts of Foreign Direct Investment on Economic Development: Does Institutional Quality Matter?

The linkage between foreign direct investment (FDI) and economic development has been demonstrated in economic literature. In this study, we analyze the impact of FDI on economic development, considering the role of institutional quality in 63 provinces/cities in Vietnam in the period 2005–2022. Applying various regression methods, such as Pooled OLS, FEM, REM, GMM, and PVAR, the results confirm that foreign direct investment and institutional quality have a positive impact on economic development. Findings also provide evidence that institutional quality is an important factor in attracting FDI, determining both the quality and quantity of inflows from other countries into Vietnam. Some policy implications are given to promote the role of institutions and attract foreign direct investment, thereby promoting the economic development of provinces and cities in Vietnam. Doi: 10.28991/ESJ-2023-07-06-05 Full Text: PD

Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis

Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in both tumor localization and cancer classification tasks. Even though previous single models achieved great performance in both tasks, these methods have some limitations in inference time, GPU requirement, and separate fine-tuning for each model. In this study, we aim to redesign and build end-to-end multi-task architecture to conduct both segmentation and classification. With our proposed approach, we achieved outstanding performance and time efficiency, with 79.8% and 86.4% in DeepLabV3+ architecture in the segmentation task.Comment: 7 pages, 3 figure

Investigation of the local environment of SnO2 in an applied magnetic field

This paper presents the results of time-differential perturbed gamma–gamma angular correlation measurements of SnO2 thin films carried out in an applied magnetic field. The measurements were performed upon the implantation of Fe at 80 keV and 111In (111Cd) at 160 keV. The samples were further characterized by energydispersive X-ray spectroscopy. The hyperfine parameters were studied at room temperature with and without an applied magnetic field. The results indicate the presence of two distinct local environments for the probe nuclei. Both occupy a paramagnetic state and correspond to a substitutional Sn site in the rutile phase of SnO2 with different numbers of electrons added to SnO2:Cd0. In addition, the crystal homogeneity of the site 1 increases upon applying the magnetic field

Investigation of sorption of Cu₂₊, Zn₂₊ and Cd₂₊ ions by a composite adsorbent obtained from bentonite-like clay and hydroxyapatite

The article analyses the results of the measuring of pHpzc (point zero charge) of the surface of adsorbents by potentiometric titration are presente

Medication Use and Adherence in Patients with Hypertension: A Prospective Study in Vietnam

Objective: to document patients’ antihypertensive agents, determine their medication adherence, and identify factors associated with the adherence.  Material and Methods: A prospective study was performed on a group of hypertensive outpatients, with social health insurance, in Can Tho, Vietnam. The study included 330 patients over 18 years old, who agreed to participate and could listen, speak and answer questions in Vietnamese. The data collection method was based on prescriptions and patient interviews. Data were analyzed using descriptive statistics, and Generalized Estimating Equations with Poisson-log linear distribution.  Results: Among the drug use characteristics, 76.1% were prescribed beta-blockers, 91.5% polytherapy, and 63.0% changed drugs at the third follow-up visit. The percentage of patients who adhered to medication ranged from 70.0% to 91.2%. Factors that improved drug adherence included: the academic level at high school or higher (39.0% increase), living in urban areas (15.0% increase), having a job related to social interaction (11.2%), and having a family history of hypertension (9.0% increase). Factors that reduced adherence included: advanced age (22.0% decrease), prolonged disease duration (16.0% decrease), prolonged treatment duration (11.0% decrease), and changes in at least one type of antihypertensive drug (8.0% decrease).  Conclusion: The highlight of this study is the demonstration of an inverse relationship between the adherence rate and the number of follow-up visits: the higher the number of visits, the lower the adherence rate. The 3rd follow-up adherence rate was 70.0%, and the decreased adherence rate is related to older age, higher education levels, and a longer duration of treatment

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