Governing Privatisation (Equitisation) in Vietnam: an Inquiry within an Institutionalist Perspective

This dissertation presents the findings of an investigation into the governance of privatisation (equitisation) in Vietnam from an institutionalist perspective. It examines the political economy of privatisation policy, in terms of both theory and practice, to answer the following research question: What constitutes and influences the privatisation policy discourse and the implementation process, and in particular how is the governance of the latter to be understood? Privatisation developed under the influence of neoliberalism as a policy in developed economies, where its success has been uneven, conditional and contested. It was introduced into developing countries as a one-size-fits-all solution, regardless of the embryonic status of their institutional development. Practical experiences of transition economies in the CEE and the former CIS and the developmental states in East Asia reveal alternative approaches to privatisation, with contrasting outcomes. Because the transfer of assets from public to private ownership involves ideological arbitrariness and contestation between private interests, privatisation is a politically constructed project – a political construction. Broad economic and social objectives can only be achieved if the process is properly governed, and productive efficiency improvement will only be realised if it is based on the development of an institutional framework. In its approach to privatisation, the Vietnamese party-state has vacillated between neoliberalism and developmental states, with neither philosophy being pursued completely or successfully. This dissertation argues that, as Vietnam faces the challenge of sustaining economic growth, it should pursue the philosophy of the developmental state, and that a broad range of economic and social development stakeholder objectives – including the state’s need for capacity to coordinate investments and achieve social equity – should be taken into account in privatisation, rather than the sole objective of supporting narrowly-defined shareholder values

SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring

Deep learning has demonstrated tremendous potential for Automatic Text Scoring (ATS) tasks. In this paper, we describe a new neural architecture that enhances vanilla neural network models with auxiliary neural coherence features. Our new method proposes a new \textsc{SkipFlow} mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads. Subsequently, the semantic relationships between multiple snapshots are used as auxiliary features for prediction. This has two main benefits. Firstly, essays are typically long sequences and therefore the memorization capability of the LSTM network may be insufficient. Implicit access to multiple snapshots can alleviate this problem by acting as a protection against vanishing gradients. The parameters of the \textsc{SkipFlow} mechanism also acts as an auxiliary memory. Secondly, modeling relationships between multiple positions allows our model to learn features that represent and approximate textual coherence. In our model, we call this \textit{neural coherence} features. Overall, we present a unified deep learning architecture that generates neural coherence features as it reads in an end-to-end fashion. Our approach demonstrates state-of-the-art performance on the benchmark ASAP dataset, outperforming not only feature engineering baselines but also other deep learning models.Comment: Accepted to AAAI 201

Color dipole cross section and inelastic structure function

Instead of starting from a theoretically motivated form of the color dipole cross section in the dipole picture of deep inelastic scattering, we start with a parametrization of the deep inelastic structure function for electromagnetic scattering with protons, and then extract the color dipole cross section. Using the parametrizations of $F_2(\xi=x \ {\rm or}\ W^2,Q^2)$ by Donnachie-Landshoff and Block et al., we find the dipole cross section from an approximate form of the presumed dipole cross section convoluted with the perturbative photon wave function for virtual photon splitting into a color dipole with massless quarks. The color dipole cross section determined this way reproduces the original structure function within about 10\% for $0.1$ GeV$^2\leq Q^2\leq 10$ GeV$^2$. We discuss the large and small form of the dipole cross section and compare with other parameterizations.Comment: 11 pages, 12 figure

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}

Integrating Image Features with Convolutional Sequence-to-sequence Network for Multilingual Visual Question Answering

Visual Question Answering (VQA) is a task that requires computers to give correct answers for the input questions based on the images. This task can be solved by humans with ease but is a challenge for computers. The VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the multilingual domain on a newly released dataset: UIT-EVJVQA, in which the questions and answers are written in three different languages: English, Vietnamese and Japanese. We approached the challenge as a sequence-to-sequence learning task, in which we integrated hints from pre-trained state-of-the-art VQA models and image features with Convolutional Sequence-to-Sequence network to generate the desired answers. Our results obtained up to 0.3442 by F1 score on the public test set, 0.4210 on the private test set, and placed 3rd in the competition.Comment: VLSP2022-EVJVQ