HIGH SCHOOL STUDENTS’ PERCEPTIONS OF THE USE OF FACEBOOK-BASED E-PORTFOLIOS IN EFL WRITING: A CASE IN THE MEKONG DELTA, VIETNAM

Electronic portfolios (e-portfolios) have grown in popularity in ESL/EFL writing instruction for their contributions to students’ writing. However, selecting an electronic tool in e-portfolios is of concern to many researchers and teachers. The combination of Facebook and e-portfolios is a rather new implementation in educational research, especially in high school contexts. Therefore, the current research aimed at exploring (1) high school students’ perceptions of the use of Facebook-based e-portfolios in terms of their contributions in writing, and (2) problems in using Facebook-based e-portfolios in writing. Fifty grade 11 students at a high school in Soc Trang province, Vietnam participated in the research. Both quantitative and qualitative data were collected by using close-ended questionnaire and the interview. Prior to data collection, the students spent six weeks writing on Facebook close-typed groups. The results showed that the students highly appreciated the contributions of Facebook-based e-portfolios in terms of enhancing interaction, giving and receiving feedback, motivation and confidence in writing, writing skills, vocabulary, and grammar knowledge. No significant problems were found in using Facebook-based e-portfolios. The research is expected to shed light on implementing Facebook-based e-portfolios in improving the quality of teaching EFL/ESL writing in high school contexts.  Article visualizations

ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text

Lexical normalization, a fundamental task in Natural Language Processing (NLP), involves the transformation of words into their canonical forms. This process has been proven to benefit various downstream NLP tasks greatly. In this work, we introduce Vietnamese Lexical Normalization (ViLexNorm), the first-ever corpus developed for the Vietnamese lexical normalization task. The corpus comprises over 10,000 pairs of sentences meticulously annotated by human annotators, sourced from public comments on Vietnam's most popular social media platforms. Various methods were used to evaluate our corpus, and the best-performing system achieved a result of 57.74% using the Error Reduction Rate (ERR) metric (van der Goot, 2019a) with the Leave-As-Is (LAI) baseline. For extrinsic evaluation, employing the model trained on ViLexNorm demonstrates the positive impact of the Vietnamese lexical normalization task on other NLP tasks. Our corpus is publicly available exclusively for research purposes.Comment: Accepted at the EACL 2024 Main Conferenc

Assessing Risk in Women who have Sexually Offended: The Role of Psychopathy

The aim of this thesis was to examine whether current risk assessment tools predicted recidivism in a group of women who sexually offended. To date, psychopathy is one of the best predictors for aggression and antisocial behavior. Past research has examined the utility of The Psychopathy Checklist – Revised (PCL-R) in justice-involved men, including men who sexually offended, and has exhibited solid support. However, results for justice-involved women were mixed. This study aims to fill the gap in research by examining the utility of the PCL-R in a sample of 242 women incarcerated, and subsequently released, in Texas for an index sexual offense. Logistic regressions were used to examine whether PCL-R scores could predict overall recidivism, general recidivism, or violent recidivism. Results indicated the PCL-R was a significant predictor for overall and general recidivism, but not violent. Additionally, age and total prior arrests were considered significant control variables when predicting recidivism. Only one woman in the sample sexually recidivated. The findings provide modest support for the utility of the PCL-R in the risk assessment of females who have sexually offended. Because there are no validated risk assessments for females who have sexually offended, current results will help guide assessment of this group of offenders

The application of social network analysis to study supply chain resilience : a thesis presented in partial fulfilment of the requirement for the degree of Master of Supply Chain Management at Massey University, Auckland, New Zealand

The purpose of this research was to assess the applicability of social network analysis for studying supply chain resilience. Supply chain resilience contains various attributes related to the supply chain ability to prepare, react, recover and grow in the face of a disturbance. The study aimed at exploring which social network analysis tools and techniques can be appropriate to evaluate a range of supply chain resilience attributes. The thesis delivers an empirical study of agricultural supply chain network in a rural area in New Zealand. Thirty-nine businesses were interviewed regarding their supply chain relationships and their organizational attributes. In addition to these 39 central actors, 283 secondary nodes were identified as their suppliers and customers, forming a supply chain network of 322 members for the research analysis. UCINET software was then used to model the network characteristics from three levels; holistic network, group level cliques and individual nodes. Visualization via graph theory and simulations were also utilized to obtain meaningful findings. This study presents the findings of how to use social network analysis as a comprehensive approach to model supply chain resilience. Interconnectedness, network structure and actor criticality can be modelled for five resilience attributes: adaptation, robustness, agility, visibility and anticipation. For each association between network properties and resilience attributes, different analysis tools are proposed, included in three categories: graph theory, analytics and simulations. The thesis proposes a comprehensive framework of which social network analysis tools can be appropriate to analyze which network properties and to evaluate which attributes of supply chain resilience. The work has therefore extended the study of supply chain resilience and the contexts in which social network analysis is applicable. Practically, it contributes to building a resilient supply chain which can be initiated by evaluating the current status via social network analyses. Therefore, this research is useful to various stakeholders such as academic researchers, business managers and policymakers

Toward global fits using Higgs STXS data with Lilith

In this talk, we present the program Lilith, a python package for constraining new physics from Higgs measurements. We discuss the usage of signal strength results in the latest published version of Lilith, which allows for constraining deviations from SM Higgs couplings through coupling modifiers. Moreover, we discuss the on-going development to include Higgs STXS data and SMEFT parametrizations in Lilith with the aim of performing global fits of the ATLAS and CMS data. As we point out, detailed information on Standard Model uncertainties and their correlations is important to enable the proper reuse of the experimental results.Comment: content unchanged, citation and references made more explici

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

DISTRIBUTION OF USEFUL AND HARMFUL MICROORGANISMS IN SHRIMP AQUACULTURE WATER IN TIEN HAI COASTAL OF THAI BINH PROVINCE

Joint Research on Environmental Science and Technology for the Eart