386 research outputs found
Textual Manifold-based Defense Against Natural Language Adversarial Examples
Recent studies on adversarial images have shown that they tend to leave the
underlying low-dimensional data manifold, making them significantly more
challenging for current models to make correct predictions. This so-called
off-manifold conjecture has inspired a novel line of defenses against
adversarial attacks on images. In this study, we find a similar phenomenon
occurs in the contextualized embedding space induced by pretrained language
models, in which adversarial texts tend to have their embeddings diverge from
the manifold of natural ones. Based on this finding, we propose Textual
Manifold-based Defense (TMD), a defense mechanism that projects text embeddings
onto an approximated embedding manifold before classification. It reduces the
complexity of potential adversarial examples, which ultimately enhances the
robustness of the protected model. Through extensive experiments, our method
consistently and significantly outperforms previous defenses under various
attack settings without trading off clean accuracy. To the best of our
knowledge, this is the first NLP defense that leverages the manifold structure
against adversarial attacks. Our code is available at
\url{https://github.com/dangne/tmd}
Does hotter temperature increase poverty and inequality? Global evidence from subnational data analysis
Despite a vast literature documenting the harmful effects of climate change on various socioeconomic outcomes, little evidence exists on the global impacts of hotter temperature on poverty and inequality. Analysis of a new global panel dataset of subnational poverty in 134 countries finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty, using the US$1.90 daily poverty threshold. A similar increase in temperature causes a 1.4 percent increase in the Gini inequality index. The paper also finds negative effects of colder temperature on poverty and inequality. Yet, while poorer countries—particularly those in South Asia and Sub-Saharan Africa—are more affected by climate change, household adaptation could have mitigated some adverse effects in the long run. The findings provide relevant and timely inputs for the global fight against climate change as well as the current policy debate on the responsibilities of richer countries versus poorer countries
THE STATUS OF STUDENTS’ POSITIVE QUALITIES IN TABLE TENNIS 1 COURSE AT SAIGON UNIVERSITY, VIETNAM
The purpose of the article is to construct a scale to evaluate the positivity of students in the course Table Tennis 1 at Saigon University, Vietnam. Using common methods in the field of physical education (analysis and synthesis of documents, interviews, statistics). Through reference documents, references and interviews with experts, specialists and lecturers, the article has identified 32 criteria for evaluating the positivity of students in the course Table Tennis 1 at Saigon University. The results of applying 32 evaluation criteria show that the situation of students’ positivity in the course Table Tennis 1 at Saigon University is good in terms of awareness of learning table tennis and the need for positive learning table tennis; average in terms of motivation for positive learning table tennis and positive behavior when learning table tennis; about expressing interest when learning table tennis at a good level according to students and average according to lecturers. Article visualizations
A glucose biosensor based on modified-enzyme incorporated within electropolymerised poly(3,4-ethylenedioxythiophene) (PEDT) films
International audienceWe have constructed and characterised a glucose sensor using glucose oxidase (GOD) covalently attached to carboxylic acid polyethyleneglycol (PEG), called (PEG–GOD). This modified enzyme was entrapped afterwards within poly(3,4-ethylenedioxythiophene) (PEDT) films electrogenerated on glassy carbon (GC) electrodes. The composite (PEG–GOD/PEDT) film is more porous than the film without enzyme (PEDT+PEG). Data from electrochemical quartz microbalance (ECQM) and pH-stat experiments indicate a good relative activity of the modified enzyme, ca. 12–15%. Amperometric measurements, using ferrocenemethanol as the redox mediator, confirms that the modified enzyme is catalytically active. The effect of film thickness was also investigated. The sensitivities were quite similar for modified-GOD electrodes (ca. 3 mA cm−2 M−1) and unmodified-GOD electrodes (ca. 2.7 mA cm−2 M−1) but a better stability was obtained with modified PEG–GOD electrodes
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
Poly 3,4-ethylenedioxythiophene as an entrapment support for amperometric enzyme sensor
International audienceA conducting polymer of poly 3,4-ethylenedioxythiophene (PEDT) was used as a matrix for entrapment of enzymes onto a platinum electrode surface in order to construct amperometric biosensors. Glucose oxidase (GOD) was used as an example, and it was entrapped in the polymer during the electrochemical polymerization. Glucose in oxygenated solutions was tested by amperometric measurements at +650 mV (vs. SCE) in a batch system. The influence of several experimental parameters in the electropolymerization process was explored to optimize the analytical performance. The detection limit and sensitivity for this biosensor were 4×10−5 M and 15.2 mA M−1cm−2, respectively. A linear range of response was found from 0.2 to 8 mM of glucose. The response time was 2–5 s. The stability of the electropolymerized films was evaluated in operational conditions. The glucose probe, stored in buffer at 4 °C when not in use, showed a residual activity of 40% after about 1 month. Glucose in synthetic serum was determined under flow injection conditions using an amperometric flow cell
Do Corporate Social Responsibilities Support Its Financial Performance? Evidence of the Listed Firms in Vietnam
In the latest decades, corporate social responsibilities (CSR) are incrementally noticed in emerging countries regarding to the surge of globalization and the advance of social concerns. However, there exist opposing arguments on how CSR practices influence a firm’s financial performance (CFP), either in theoretical or empirical perspectives, causing many managers’ hesitation in CSR engagement. This study, therefore, examines the impacts of CSR on CFP, considering the individual effect of each CSR endeavor by using the data from listed firms’ financial statements during the period 2015-2019 and panel regression analysis methods. The findings reveal inconstant effects of different CSR activities on CFP. Specifically, a firm’s fulfillment of shareholders’ and customers’ interests contributes to raise its profitability, while its engagement in the benefits of employees and creditors causes a reduction in financial returns. Interestingly, the correlations between firms’ responsibilies towards regulators and suppliers and CFP are statistically insignificant. Furthermore, we make pairwise marginal comparisons to identify the distinctions of CSR-CFP relations across industries. The results only indicate that listed firms in Utilities sector have the lower level of CSR intensity than those in Consumer Discretionary and Consumer Staples sectors. The implications and limitations are also discussed in this study
A Study on the Impact of the Usage of Cost Information on Business Performance of Enterprises in Vietnam
Purpose: The primary objective of the research was to investigate the correlation between the utilization of cost information and business performance of enterprises in Vietnam, particularly in the context of the Covid-19 pandemic.
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Design/methodology/approach: A survey was conducted, involving a sample of 380 participants consisting of CEOs, CFOs, managers, board of directors, and individuals in charge of accounting and finance departments. The study employed a structure equation model to analyze the relationship between the use of cost information and business performance.
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Findings: The study revealed a significant and positive impact of cost information utilization on business performance among the surveyed enterprises in Vietnam. Additionally, the study provided recommendations for enterprises in Vietnam on how to make use of cost information for various purposes, including planning, evaluating, decision-making, and cost control, to optimize their business performance.
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Research, Practical & Social implication: The study's results suggest that utilizing cost information effectively can lead to improved business performance and may provide valuable insights for Vietnamese enterprises on how to navigate the challenging and competitive post-Covid-19 economic environment.
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Originality/value: The research employed a structural equation modeling (SEM) approach, and a test for univariate normality demonstrated an outstanding critical skewness ratio value. Furthermore, the kurtosis indicated that the data followed a normal distribution, making it suitable for analysis. Since SEM employs rigorous criteria to evaluate model fit, it can be inferred that the measurement model exhibited a high level of fit. The findings affirm the beneficial impact of utilizing cost information on business performance and offer significant recommendations for Vietnamese businesses in the current post-COVID era
HyperCUT: Video Sequence from a Single Blurry Image using Unsupervised Ordering
We consider the challenging task of training models for image-to-video
deblurring, which aims to recover a sequence of sharp images corresponding to a
given blurry image input. A critical issue disturbing the training of an
image-to-video model is the ambiguity of the frame ordering since both the
forward and backward sequences are plausible solutions. This paper proposes an
effective self-supervised ordering scheme that allows training high-quality
image-to-video deblurring models. Unlike previous methods that rely on
order-invariant losses, we assign an explicit order for each video sequence,
thus avoiding the order-ambiguity issue. Specifically, we map each video
sequence to a vector in a latent high-dimensional space so that there exists a
hyperplane such that for every video sequence, the vectors extracted from it
and its reversed sequence are on different sides of the hyperplane. The side of
the vectors will be used to define the order of the corresponding sequence.
Last but not least, we propose a real-image dataset for the image-to-video
deblurring problem that covers a variety of popular domains, including face,
hand, and street. Extensive experimental results confirm the effectiveness of
our method. Code and data are available at
https://github.com/VinAIResearch/HyperCUT.gitComment: Accepted to CVPR 202
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