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}

DREQUS: an approach for the Discovery of REQuirements Using Scenarios

ABSTRACT: Requirements engineering is recognized as a complex cognitive problem-solving process that takes place in an unstructured and poorly-understood problem context. Requirements elicitation is the activity generally regarded as the most crucial step in the requirements engineering process. The term “elicitation” is preferred to “capture”, to avoid the suggestion that requirements are out there to be collected. Information gathered during requirements elicitation often has to be interpreted, analyzed, modeled, and validated before the requirements engineer can feel confident that a complete set of requirements of a system have been obtained. Requirements elicitation comprises the set of activities that enable discovering, understanding, and documenting the goals and motives for building a proposed software system. It also involves identifying the requirements that the resulting system must satisfy in to achieve these goals. The requirements to be elicited may range from modifications to well-understood problems and systems (i.e. software upgrades), to hazy understandings of new problems being automated, to relatively unconstrained requirements that are open to innovation (e.g. mass-market software). Requirements elicitation remains problematic; missing or mistaken requirements still delay projects and cause cost overruns. No firm definition has matured for requirements elicitation in comparison to other areas of requirements engineering. This research is aimed to improve the results of the requirements elicitation process directly impacting the quality of the software products derived from them

Learning from Dark : Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative samples for each node and incorporate the negative information contained in these samples into the representation updates. Moreover, we show that the process of selecting the negative samples is not trivial. Our theme therefore begins by describing the criteria for a good negative sample, followed by a determinantal point process algorithm for efficiently obtaining such samples. A GCN, boosted by diverse negative samples, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems

My House, My Rules: Learning Tidying Preferences with Graph Neural Networks

Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vector from a user by observing how they arrange scenes. Given any set of objects, this vector can then be used to generate an arrangement which is tailored to that user's spatial preferences, with word embeddings used for generalisation to new objects. We develop a tidying simulator to gather rearrangement examples from 75 users, and demonstrate empirically that our method consistently produces neat and personalised arrangements across a variety of rearrangement scenarios.Comment: Published at CoRL 2021. Webpage and video: https://www.robot-learning.uk/my-house-my-rule

Perceptions of ICT practitioners regarding software privacy

During software development activities, it is important for Information and Communication Technology (ICT) practitioners to know and understand practices and guidelines regarding information privacy, as software requirements must comply with data privacy laws and members of development teams should know current legislation related to the protection of personal data. In order to gain a better understanding on how industry ICT practitioners perceive the practical relevance of software privacy and privacy requirements and how these professionals are implementing data privacy concepts, we conducted a survey with ICT practitioners from software development organizations to get an overview of how these professionals are implementing data privacy concepts during software design. We performed a systematic literature review to identify related works with software privacy and privacy requirements and what methodologies and techniques are used to specify them. In addition, we conducted a survey with ICT practitioners from different organizations. Findings revealed that ICT practitioners lack a comprehensive knowledge of software privacy and privacy requirements and the Brazilian General Data Protection Law (Lei Geral de Proteção de Dados Pessoais, LGPD, in Portuguese), nor they are able to work with the laws and guidelines governing data privacy. Organizations are demanded to define an approach to contextualize ICT practitioners with the importance of knowledge of software privacy and privacy requirements, as well as to address them during software development, since LGPD must change the way teams work, as a number of features and controls regarding consent, documentation, and privacy accountability will be required