Reviewerly/exHarmony huggingface dataset:
HYDRA: Pruning adversarially robust neural networks

In safety-critical but computationally resource-constrained applications, deep learning faces two key challenges: lack of robustness against adversarial attacks and large neural network size (often millions of parameters). While the research community has extensively explored the use of robust training and network pruning independently to address one of these challenges, only a few recent works have studied them jointly. However, these works inherit a heuristic pruning strategy that was developed for benign training, which performs poorly when integrated with robust training techniques, including adversarial training and verifiable robust training. To overcome this challenge, we propose to make pruning techniques aware of the robust training objective and let the training objective guide the search for which connections to prune. We realize this insight by formulating the pruning objective as an empirical risk minimization problem which is solved efficiently using SGD. We demonstrate that our approach, titled HYDRA, achieves compressed networks with state-of-the-art benign and robust accuracy, simultaneously. We demonstrate the success of our approach across CIFAR-10, SVHN, and ImageNet dataset with four robust training techniques: iterative adversarial training, randomized smoothing, MixTrain, and CROWN-IBP. We also demonstrate the existence of highly robust sub-networks within non-robust networks. Our code and compressed networks are publicly available at \url{this https URL}.
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to distinguish the sentiment polarities over aspect terms in a sentence.Existing approaches mostly focus on modeling the relationship between the given aspect words and their contexts with attention, and ignore the use of more elaborate knowledge implicit in the context.In this paper, we exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.And the subwords of BERT are integrated into the dependency tree graphs, which can obtain more accurate representations of words by graph attention.Experiments demonstrate the effectiveness of our model.
On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study

This article brings two major contributions. First, we present the results of a critical analysis of recent scientific articles about neural and non-neural approaches and representations for automatic text classification (ATC). This analysis is focused on assessing the scientific rigor of such studies. It reveals a profusion of potential issues related to the experimental procedures including: (i) use of inadequate experimental protocols, including no repetitions for the sake of assessing variability and generalization; (ii) lack of statistical treatment of the results; (iii) lack of details on hyperparameter tuning, especially of the baselines; (iv) use of inadequate measures of classification effectiveness (e.g., accuracy with skewed distributions). Second, we provide some organization and ground to the field by performing a comprehensive and scientifically sound comparison of recent neural and non-neural ATC solutions. Our study provides a more complete picture by looking beyond classification effectiveness, taking the trade-off between model costs (i.e., training time) into account. Our evaluation is guided by scientific rigor, which, as our literature review shows, is missing in a large body of work. Our experimental results, based on more than 1500 measurements, reveal that in the smaller datasets, the simplest and cheaper non-neural methods are among the best performers. In the larger datasets, neural Transformers perform better in terms of classification effectiveness. However, when compared to the best (properly tuned) non-neural solutions, the gains in effectiveness are not very expressive, especially considering the much longer training times (up to 23x slower). Our findings call for a self-reflection of best practices in the field, from the way experiments are conducted and analyzed to the choice of proper baselines for each situation and scenario.
A privacy and security analysis of early-deployed COVID-19 contact tracing Android apps

Abstract As this article is being drafted, the SARS-CoV-2/COVID-19 pandemic is causing harm and disruption across the world. Many countries aimed at supporting their contact tracers with the use of digital contact tracing apps in order to manage and control the spread of the virus. Their idea is the automatic registration of meetings between smartphone owners for the quicker processing of infection chains. To date, there are many contact tracing apps that have already been launched and used in 2020. There has been a lot of speculations about the privacy and security aspects of these apps and their potential violation of data protection principles. Therefore, the developers of these apps are constantly criticized because of undermining users’ privacy, neglecting essential privacy and security requirements, and developing apps under time pressure without considering privacy- and security-by-design. In this study, we analyze the privacy and security performance of 28 contact tracing apps available on Android platform from various perspectives, including their code’s privileges, promises made in their privacy policies, and static and dynamic performances. Our methodology is based on the collection of various types of data concerning these 28 apps, namely permission requests, privacy policy texts, run-time resource accesses, and existing security vulnerabilities. Based on the analysis of these data, we quantify and assess the impact of these apps on users’ privacy. We aimed at providing a quick and systematic inspection of the earliest contact tracing apps that have been deployed on multiple continents. Our findings have revealed that the developers of these apps need to take more cautionary steps to ensure code quality and to address security and privacy vulnerabilities. They should more consciously follow legal requirements with respect to apps’ permission declarations, privacy principles, and privacy policy contents.
Self-training Improves Pre-training for Natural Language Understanding

Unsupervised pre-training has led to much recent progress in natural language understanding.In this paper, we study self-training as another way to leverage unlabeled data through semi-supervised learning.To obtain additional data for a specific task, we introduce SentAugment, a data augmentation method which computes task-specific query embeddings from labeled data to retrieve sentences from a bank of billions of unlabeled sentences crawled from the web.Unlike previous semisupervised methods, our approach does not require in-domain unlabeled data and is therefore more generally applicable.Experiments show that self-training is complementary to strong RoBERTa baselines on a variety of tasks.Our augmentation approach leads to scalable and effective self-training with improvements of up to 2.6% on standard text classification benchmarks.Finally, we also show strong gains on knowledge-distillation and few-shot learning.
Defending against Backdoors in Federated Learning with Robust Learning Rate

Federated learning (FL) allows a set of agents to collaboratively train a model without sharing their potentially sensitive data. This makes FL suitable for privacy-preserving applications. At the same time, FL is susceptible to adversarial attacks due to decentralized and unvetted data. One important line of attacks against FL is the backdoor attacks. In a backdoor attack, an adversary tries to embed a backdoor functionality to the model during training that can later be activated to cause a desired misclassification. To prevent backdoor attacks, we propose a lightweight defense that requires minimal change to the FL protocol. At a high level, our defense is based on carefully adjusting the aggregation server's learning rate, per dimension and per round, based on the sign information of agents' updates. We first conjecture the necessary steps to carry a successful backdoor attack in FL setting, and then, explicitly formulate the defense based on our conjecture. Through experiments, we provide empirical evidence that supports our conjecture, and we test our defense against backdoor attacks under different settings. We observe that either backdoor is completely eliminated, or its accuracy is significantly reduced. Overall, our experiments suggest that our defense significantly outperforms some of the recently proposed defenses in the literature. We achieve this by having minimal influence over the accuracy of the trained models. In addition, we also provide convergence rate analysis for our proposed scheme.
Temporal Action Segmentation from Timestamp Supervision

Temporal action segmentation approaches have been very successful recently. However, annotating videos with frame-wise labels to train such models is very expensive and time consuming. While weakly supervised methods trained using only ordered action lists require less annotation effort, the performance is still worse than fully supervised approaches. In this paper, we propose to use timestamp supervision for the temporal action segmentation task. Timestamps require a comparable annotation effort to weakly supervised approaches, and yet provide a more supervisory signal. To demonstrate the effectiveness of timestamp supervision, we propose an approach to train a segmentation model using only timestamps annotations. Our approach uses the model output and the annotated timestamps to generate frame-wise labels by detecting the action changes. We further introduce a confidence loss that forces the predicted probabilities to monotonically decrease as the distance to the timestamps increases. This ensures that all and not only the most distinctive frames of an action are learned during training. The evaluation on four datasets shows that models trained with timestamps annotations achieve comparable performance to the fully supervised approaches.
Lifting 2D StyleGAN for 3D-Aware Face Generation

We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation. Our model is "3D-aware" in the sense that it is able to (1) disentangle the latent space of StyleGAN2 into texture, shape, viewpoint, lighting and (2) generate 3D components for rendering synthetic images. Unlike most previous methods, our method is completely self-supervised, i.e. it neither requires any manual annotation nor 3DMM model for training. Instead, it learns to generate images as well as their 3D components by distilling the prior knowledge in StyleGAN2 with a differentiable renderer. The proposed model is able to output both the 3D shape and texture, allowing explicit pose and lighting control over generated images. Qualitative and quantitative results show the superiority of our approach over existing methods on 3D-controllable GANs in content controllability while generating realistic high quality images.
SPT-code

Recent years have seen the successful application of large pre-trained models to code representation learning, resulting in substantial improvements on many code-related downstream tasks. But there are issues surrounding their application to SE tasks. First, the majority of the pre-trained models focus on pre-training only the encoder of the Transformer. For generation tasks that are addressed using models with the encoder-decoder architecture, however, there is no reason why the decoder should be left out during pre-training. Second, many existing pre-trained models, including state-of-the-art models such as T5-learning, simply reuse the pretraining tasks designed for natural languages. Moreover, to learn the natural language description of source code needed eventually for code-related tasks such as code summarization, existing pretraining tasks require a bilingual corpus composed of source code and the associated natural language description, which severely limits the amount of data for pre-training. To this end, we propose SPT-Code, a sequence-to-sequence pre-trained model for source code. In order to pre-train SPT-Code in a sequence-to-sequence manner and address the aforementioned weaknesses associated with existing pre-training tasks, we introduce three pre-training tasks that are specifically designed to enable SPT-Code to learn knowledge of source code, the corresponding code structure, as well as a natural language description of the code without relying on any bilingual corpus, and eventually exploit these three sources of information when it is applied to downstream tasks. Experimental results demonstrate that SPT-Code achieves state-of-the-art performance on five code-related downstream tasks after fine-tuning.
Non-Local Robust Quaternion Matrix Completion for Large-Scale Color Image and Video Inpainting

The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in many recently proposed machining learning algorithms for image processing. However, there is no theoretical analysis on its working principle in the literature. In this paper, we discover a potential causality between NSS and low-rank property of color images, which is also available to grey images. A new patch group based NSS prior scheme is proposed to learn explicit NSS models of natural color images. The numerical low-rank property of patched matrices is also rigorously proved. The NSS-based QMC algorithm computes an optimal low-rank approximation to the high-rank color image, resulting in high PSNR and SSIM measures and particularly the better visual quality. A new tensor NSS-based QMC method is also presented to solve the color video inpainting problem based on quaternion tensor representation. The numerical experiments on color images and videos indicate the advantages of NSS-based QMC over the state-of-the-art methods.
The Many Shades of Anonymity: Characterizing Anonymous Social Media Content

Recently, there has been a significant increase in the popularity of anonymous social media sites like Whisper and Secret. Unlike traditional social media sites like Facebook and Twitter, posts on anonymous social media sites are not associated with well defined user identities or profiles. In this study, our goals are two-fold: (i) to understand the nature (sensitivity, types) of content posted on anonymous social media sites and (ii) to investigate the differences between content posted on anonymous and non-anonymous social me- dia sites like Twitter. To this end, we gather and analyze ex- tensive content traces from Whisper (anonymous) and Twitter (non-anonymous) social media sites. We introduce the notion of anonymity sensitivity of a social media post, which captures the extent to which users think the post should be anonymous. We also propose a human annotator based methodology to measure the same for Whisper and Twitter posts. Our analysis reveals that anonymity sensitivity of most whispers (unlike tweets) is not binary. Instead, most whispers exhibit many shades or different levels of anonymity. We also find that the linguistic differences between whispers and tweets are so significant that we could train automated classifiers to distinguish between them with reasonable accuracy. Our findings shed light on human behavior in anonymous media systems that lack the notion of an identity and they have important implications for the future designs of such systems.
CREANDO: Tool for creating pervasive games to increase the learning motivation in higher education students

The pervasive games (PG) have had a great reception in recent years. This kind of games combines the virtual world with the real world to generate extensions of reality in terms of time, space and social interaction. These extensions have involved users in experiences as varied as those exposed by augmented reality and the location of the player or mixed reality scenarios. In the educational context, games have been integrated little by little into learning processes. Specifically, PG have shown to have a higher level of immersion in people's daily activities. Therefore, a greater impact is generated when integrating the technologies of the PG and the narrative of a story that is told through the gaming experience. In order to explore some fields, we focus the study on the narrative and geolocation applied to close spaces, it is a way of giving an enriched experience to the player through a digital transformation perspective. For this reason, it is necessary to have a platform that allows the creation and edition of PG based on narrative to increase the motivation of higher education students. This article describes the creation of a platform that integrates tools for creating stories and editing narrative content in the game experiences. The main objective is focused on increasing the motivation of students in educational processes. Specifically, the platform allows the creation of pervasive gaming experiences in closed spaces on university campuses. In addition, the results of a test case developed with the platform and its validation through statistical tools and gameplay metrics are exposed, thus demonstrating the increase in students' motivation through the use of PG.
The use of digital-image correlation to investigate the cohesive zone in a double-cantilever beam, with comparisons to numerical and analytical models

Digital-image correlation (DIC) is used to analyze an adhesively-bonded double-cantilever beam (DCB), and to determine the traction-separation law for a cohesive-zone model. The issues involved with how to extract useful information from the digital data of DIC are addressed. In addition, DIC is used to explore how the cohesive zone evolves, and to determine how the elastic arms deform in response to the loading and to the adhesive. The results of these observations are compared to numerical and analytical models for the DCB geometry. In particular, the well-known concept of root rotation is demonstrated. It is shown that, by combining the effects of shear into an effective root rotation, it is possible to use a simple Euler-beam approximation to describe the compliance of the DCB. The experiments and analysis also illustrate the lesser-known concept that significant compression can occur beyond the tensile region in the cohesive zone ahead of a DCB crack tip. Therefore, for accurate numerical predictions, a cohesive-zone model must incorporate compressive deformation. The DIC results are further used to illustrate the concept of a cohesive-length scale. This is defined in terms of the work done against crack-tip tractions, the opening displacement, the stiffness of the arms, and a characteristic geometrical length. The cohesive-length scale is measured experimentally in this paper, and its magnitude is shown to indicate when linear elasticity can be used to describe the deformation of a DCB geometry. The cohesive-length scale is shown to correlate with both the root rotation and the length of a cohesive zone in a fashion that is very similar to what is predicted analytically by elastic-foundation models. Finally, it is demonstrated that, when used in a cohesive-zone model of the geometry, the experimentally determined traction-separation law gives excellent predictions for the evolution of the cohesive zone and for the deformation of the elastic beams. A very minor discrepancy is associated with in-plane tensile stresses that must develop within an adhesive layer in response to the deformation of the beams.
LAMANCO: A Lightweight Anonymous Mutual Authentication Scheme for $N$ -Times Computing Offloading in IoT

Nowadays in many application scenarios of Internet of Things (IoT), low latency is achieved at the cost of computing-complexity which is beyond the capabilities of IoT devices. Offloading the computing intensive tasks to more powerful edge devices is expected to provide new generation computing-intensive and delay-sensitive services. In the three hierarchy architecture user/IoT-edge-cloud, private and secure mutual authentication are necessary between user, IoT device, and edge device. However, in the emerging computing paradigms, such as mobile transparent computing, edge computing, fog computing, and several threats, such as edge device compromise, privacy leaking, and denial of service (DoS) might crash the security of the system. Here, we propose a lightweight anonymous mutual authentication scheme for ${n}$ -times computing offloading (CO) in IoT. In our novel scheme, through a smartcard as token and an edge device as a security proxy, a user is able to subscribe or renew ${n}$ -times CO service and consume it securely in daily use. Moreover, both IoT and edge devices authenticate each other anonymously without leaking user's sensitive information, which will preserve the privacy even when an edge device is comprised. Finally, our scheme is based on lightweight one-way hash function and MAC function, therefore the adversary is not able to perform a DoS attack. To evaluate the solution, a security analysis and a performance analysis are presented. Compared with similar schemes, our approach achieves all designed security features and achieves a $1.66\boldsymbol {\times }$ and $2.87\boldsymbol {\times }$ of computing speed on IoT and edge devices, respectably.
IDeRs: Iterative dehazing method for single remote sensing image

Remote sensing images (RSIs) taken in hazy conditions, such as haze, fog, thin could, snow, silt, dust, offgas, etc., suffer from sever color and contrast degradations. Dehazing algorithm is therefore highly demanded to restore hazed RSIs from their degradations. In the literatures, most dehazing algorithms were originally designed for natural images dehazing (NID). For our analysis, the physical model of NID is different from that of RSI dehazing (RSID), which was not clearly addressed yet. In this paper, a new concept of "virtual depth" concerning physical model of RSI is firstly raised. Virtual depth is different from real depth of nature image in that the former gives the distance of an object departing from the foreground, while the later measures the coverings of the earth's surface, such as snow, dust, cloud and haze/fog. These coverings act as the hazes in a natural image, providing the hint of foreground and background. Secondly, an Iterative Dehazing for Remote Sensing image (IDeRS) is proposed, in which dehazing operator is implemented iteratively to remove haze progressively until arriving at a satisfied result. In IDeRS, we also raise a fusion model for combining patch-wise and pixel-wise dehazing operators to overcome both halos and over-saturation caused by them respectively. Extensive experimental results tested on publicly available databases demonstrate that the proposed IDeRS outperforms most state-of-the-arts in RSID.