OpenRaiser/Intern-Atlas huggingface dataset:
Training Deep Capsule Networks with Residual Connections
Venue: arXiv cs.CV 2021
Year: 2021

Capsule networks are a type of neural network that have recently gained increased popularity. They consist of groups of neurons, called capsules, which encode properties of objects or object parts. The connections between capsules encrypt part-whole relationships between objects through routing algorithms which route the output of capsules from lower level layers to upper level layers. Capsule networks can reach state-of-the-art results on many challenging computer vision tasks, such as MNIST, Fashion-MNIST, and Small-NORB. However, most capsule network implementations use two to three capsule layers, which limits their applicability as expressivity grows exponentially with depth. One approach to overcome such limitations would be to train deeper network architectures, as it has been done for convolutional neural networks with much increased success. In this paper, we propose a methodology to train deeper capsule networks using residual connections, which is evaluated on four datasets and three different routing algorithms. Our experimental results show that in fact, performance increases when training deeper capsule networks. The source code is available on https://github.com/moejoe95/res-capsnet.
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World
Venue: arXiv cs.CR 2021
Year: 2021

Deep neural networks (DNN) have been widely deployed in various applications. However, many researches indicated that DNN is vulnerable to backdoor attacks. The attacker can create a hidden backdoor in target DNN model, and trigger the malicious behaviors by submitting specific backdoor instance. However, almost all the existing backdoor works focused on the digital domain, while few studies investigate the backdoor attacks in real physical world. Restricted to a variety of physical constraints, the performance of backdoor attacks in the real physical world will be severely degraded. In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world. Specifically, in the training phase, we perform a series of physical transformations on these injected backdoor instances at each round of model training, so as to simulate various transformations that a backdoor may experience in real world, thus improves its physical robustness. Experimental results on the state-of-the-art face recognition model show that, compared with the backdoor methods that without PTB, the proposed attack method can significantly improve the performance of backdoor attacks in real physical world. Under various complex physical conditions, by injecting only a very small ratio (0.5%) of backdoor instances, the attack success rate of physical backdoor attacks with the PTB method on VGGFace is 82%, while the attack success rate of backdoor attacks without the proposed PTB method is lower than 11%. Meanwhile, the normal performance of the target DNN model has not been affected.
TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection
Venue: arXiv cs.CV 2021
Year: 2021

3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without relying on the mask appearance. However, the rPPG features for 3D mask PAD are still needed expert knowledge to design manually, which limits its further progress in the deep learning and big data era. In this letter, we propose a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently. At first, rPPG-based multi-scale spatial-temporal maps (MSTmap) are constructed from facial skin and background regions. Then the transformer fully mines the global relationship within MSTmaps for liveness representation, and gives a binary prediction for 3D mask detection. Comprehensive experiments are conducted on two benchmark datasets to demonstrate the efficacy of the TransRPPG on both intra- and cross-dataset testings. Our TransRPPG is lightweight and efficient (with only 547K parameters and 763M FLOPs), which is promising for mobile-level applications.
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Venue: arXiv cs.CL 2021
Year: 2021

Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization. While specialized model architectures and pre-training of seq2seq models have been proposed to address this issue, the former often comes at the cost of generality and the latter only shows limited success. In this paper, we study the impact of intermediate representations on compositional generalization in pre-trained seq2seq models, without changing the model architecture at all, and identify key aspects for designing effective representations. Instead of training to directly map natural language to an executable form, we map to a reversible or lossy intermediate representation that has stronger structural correspondence with natural language. The combination of our proposed intermediate representations and pre-trained models is surprisingly effective, where the best combinations obtain a new state-of-the-art on CFQ (+14.8 accuracy points) and on the template-splits of three text-to-SQL datasets (+15.0 to +19.4 accuracy points). This work highlights that intermediate representations provide an important and potentially overlooked degree of freedom for improving the compositional generalization abilities of pre-trained seq2seq models.
A Sample-Based Training Method for Distantly Supervised Relation Extraction with Pre-Trained Transformers
Venue: arXiv cs.CL 2021
Year: 2021

Multiple instance learning (MIL) has become the standard learning paradigm for distantly supervised relation extraction (DSRE). However, due to relation extraction being performed at bag level, MIL has significant hardware requirements for training when coupled with large sentence encoders such as deep transformer neural networks. In this paper, we propose a novel sampling method for DSRE that relaxes these hardware requirements. In the proposed method, we limit the number of sentences in a batch by randomly sampling sentences from the bags in the batch. However, this comes at the cost of losing valid sentences from bags. To alleviate the issues caused by random sampling, we use an ensemble of trained models for prediction. We demonstrate the effectiveness of our approach by using our proposed learning setting to fine-tuning BERT on the widely NYT dataset. Our approach significantly outperforms previous state-of-the-art methods in terms of AUC and P@N metrics.
Reward Optimization for Neural Machine Translation with Learned Metrics
Venue: arXiv cs.CL 2021
Year: 2021

Neural machine translation (NMT) models are conventionally trained with token-level negative log-likelihood (NLL), which does not guarantee that the generated translations will be optimized for a selected sequence-level evaluation metric. Multiple approaches are proposed to train NMT with BLEU as the reward, in order to directly improve the metric. However, it was reported that the gain in BLEU does not translate to real quality improvement, limiting the application in industry. Recently, it became clear to the community that BLEU has a low correlation with human judgment when dealing with state-of-the-art models. This leads to the emerging of model-based evaluation metrics. These new metrics are shown to have a much higher human correlation. In this paper, we investigate whether it is beneficial to optimize NMT models with the state-of-the-art model-based metric, BLEURT. We propose a contrastive-margin loss for fast and stable reward optimization suitable for large NMT models. In experiments, we perform automatic and human evaluations to compare models trained with smoothed BLEU and BLEURT to the baseline models. Results show that the reward optimization with BLEURT is able to increase the metric scores by a large margin, in contrast to limited gain when training with smoothed BLEU. The human evaluation shows that models trained with BLEURT improve adequacy and coverage of translations. Code is available via https://github.com/naver-ai/MetricMT.
Hierarchical Learning for Generation with Long Source Sequences
Venue: arXiv cs.CL 2021
Year: 2021

One of the challenges for current sequence to sequence (seq2seq) models is processing long sequences, such as those in summarization and document level machine translation tasks. These tasks require the model to reason at the token level as well as the sentence and paragraph level. We design and study a new Hierarchical Attention Transformer-based architecture (HAT) that outperforms standard Transformers on several sequence to sequence tasks. Furthermore, our model achieves state-of-the-art ROUGE scores on four summarization tasks, including PubMed, arXiv, CNN/DM, SAMSum, and AMI. Our model outperforms document-level machine translation baseline on the WMT20 English to German translation task. We investigate what the hierarchical layers learn by visualizing the hierarchical encoder-decoder attention. Finally, we study hierarchical learning on encoder-only pre-training and analyze its performance on classification tasks.
Who Needs Consensus? A Distributed Monetary System Between Rational Agents via Hearsay
Venue: arXiv cs.DC 2021
Year: 2021

We propose a novel distributed monetary system called Hearsay that tolerates both Byzantine and rational behavior without the need for agents to reach consensus on executed transactions. Recent work [5, 10, 15] has shown that distributed monetary systems do not require consensus and can operate using a broadcast primitive with weaker guarantees, such as reliable broadcast. However, these protocols assume that some number of agents may be Byzantine and the remaining agents are perfectly correct. For the application of a monetary system in which the agents are real people with economic interests, the assumption that agents are perfectly correct may be too strong. We expand upon this line of thought by weakening the assumption of correctness and instead adopting a fault tolerance model which allows up to $t < \frac{N}{3}$ agents to be Byzantine and the remaining agents to be rational. A rational agent is one which will deviate from the protocol if it is in their own best interest. Under this fault tolerance model, Hearsay implements a monetary system in which all rational agents achieve agreement on executed transactions. Moreover, Hearsay requires only a single broadcast per transaction. In order to incentivize rational agents to behave correctly in Hearsay, agents are rewarded with transaction fees for participation in the protocol and punished for noticeable deviations from the protocol. Additionally, Hearsay uses a novel broadcast primitive called Rational Reliable Broadcast to ensure that agents can broadcast messages under Hearsay's fault tolerance model. Rational Reliable Broadcast achieves equivalent guarantees to Byzantine Reliable Broadcast [7] but can tolerate the presence of rational agents. To show this, we prove that following the Rational Reliable Broadcast protocol constitutes a Nash equilibrium between rational agents and may therefore be of independent interest.
A proposal to systematize introducing DevOps into the software development process
Venue: arXiv cs.SE 2021
Year: 2021

The software development industry has been evolving with new development standards and service delivery models. Agile methodologies have reached their completion with DevOps, thereby increasing the quality of the software and creating greater speed in delivery. However, a gap regarding the formalization of its adoption and implementation doubts became relevant. My hypothesis is that, by systematizing the introduction of DevOps into the software development process and defining the function of the members of the DevOps team members, may well make it quicker to implement this process, thus reducing conflicts between the teams. As part of the investigation of this hypothesis, the result of the research will be applied in practical development environments i.e. in a Technology Agency of the State of the Brazilian Government and also at the Brazilian Company Neurotech in order to evaluate its effectiveness from metrics appropriate for DevOps environments.
Extending two families of maximum rank distance codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper we provide a large family of rank-metric codes, which contains properly the codes recently found by Longobardi and Zanella (2021) and by Longobardi, Marino, Trombetti and Zhou (2021). These codes are $\mathbb{F}_{q^{2t}}$-linear of dimension $2$ in the space of linearized polynomials over $\mathbb{F}_{q^{2t}}$, where $t$ is any integer greater than $2$, and we prove that they are maximum rank distance codes. For $t\ge 5$, we determine their equivalence classes and these codes turn out to be inequivalent to any other construction known so far, and hence they are really new.
Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering
Venue: arXiv cs.CL 2021
Year: 2021

Recent approaches have exploited weaknesses in monolingual question answering (QA) models by adding adversarial statements to the passage. These attacks caused a reduction in state-of-the-art performance by almost 50%. In this paper, we are the first to explore and successfully attack a multilingual QA (MLQA) system pre-trained on multilingual BERT using several attack strategies for the adversarial statement reducing performance by as much as 85%. We show that the model gives priority to English and the language of the question regardless of the other languages in the QA pair. Further, we also show that adding our attack strategies during training helps alleviate the attacks.
Improving Gender Translation Accuracy with Filtered Self-Training
Venue: arXiv cs.CL 2021
Year: 2021

Targeted evaluations have found that machine translation systems often output incorrect gender, even when the gender is clear from context. Furthermore, these incorrectly gendered translations have the potential to reflect or amplify social biases. We propose a gender-filtered self-training technique to improve gender translation accuracy on unambiguously gendered inputs. This approach uses a source monolingual corpus and an initial model to generate gender-specific pseudo-parallel corpora which are then added to the training data. We filter the gender-specific corpora on the source and target sides to ensure that sentence pairs contain and correctly translate the specified gender. We evaluate our approach on translation from English into five languages, finding that our models improve gender translation accuracy without any cost to generic translation quality. In addition, we show the viability of our approach on several settings, including re-training from scratch, fine-tuning, controlling the balance of the training data, forward translation, and back-translation.
Survey about protection motivation on social networking sites: University of Maribor students, 2018
Venue: arXiv cs.CR 2021
Year: 2021

This paper reports on a study aiming to explore factors associated with protection motivation of users on social networking sites. The objectives of this study were to determine how trust in internet service provider, trust in social media provider, trust in government, privacy concerns, fear of government intrusions, locus of control, and perceived threats affect protection motivation of users on social networking sites. The study employed a cross-sectional research design. A survey was conducted among University of Maribor students between October 2018 and January 2019. A total of 289 respondents completed the survey providing for N=276 useful responses after excluding poorly completed responses (27.9 percent response rate). The survey questionnaire was developed in English. A Slovenian translation of the survey questionnaire is available.
Optimizing Multiple Multi-Way Stream Joins
Venue: arXiv cs.DB 2021
Year: 2021

We address the joint optimization of multiple stream joins in a scale-out architecture by tailoring prior work on multi-way stream joins to predicate-driven data partitioning schemes. We present an integer linear programming (ILP) formulation for selecting the partitioning and tuple routing with minimal probe load and describe how routing and operator placement can be rewired dynamically at changing data characteristics and arrival or expiration of queries. The presented algorithms and optimization schemes are implemented in CLASH, a data stream processor developed in our group that translates queries to deployable Apache Storm topologies after optimization. The experiments conducted over real-world data exhibit the potential of multi-query optimization of multi-way stream joins and the effectiveness and feasibility of the ILP optimization problem.
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
Venue: arXiv cs.CL 2021
Year: 2021

Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT. While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar). Would it be safe to release the weights of such a model if they did? In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT. Specifically, we attempt to recover patient names and conditions with which they are associated. We find that simple probing methods are not able to meaningfully extract sensitive information from BERT trained over the MIMIC-III corpus of EHR. However, more sophisticated "attacks" may succeed in doing so: To facilitate such research, we make our experimental setup and baseline probing models available at https://github.com/elehman16/exposing_patient_data_release
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks
Venue: arXiv cs.CV 2021
Year: 2021

Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficult due to the limited memory and computation resources. Recently, the inverted residual block becomes the dominating solution for the architecture design of compact CNNs. In this work, we comprehensively investigated the existing design concepts, rethink the functional characteristics of two pointwise convolutions in the inverted residuals. We propose a novel design, called asymmetrical bottlenecks. Precisely, we adjust the first pointwise convolution dimension, enrich the information flow by feature reuse, and migrate saved computations to the second pointwise convolution. By doing so we can further improve the accuracy without increasing the computation overhead. The asymmetrical bottlenecks can be adopted as a drop-in replacement for the existing CNN blocks. We can thus create AsymmNet by easily stack those blocks according to proper depth and width conditions. Extensive experiments demonstrate that our proposed block design is more beneficial than the original inverted residual bottlenecks for mobile networks, especially useful for those ultralight CNNs within the regime of <220M MAdds. Code is available at https://github.com/Spark001/AsymmNet
Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data
Venue: arXiv cs.CV 2021
Year: 2021

The success of supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model. When training samples are collected from an image (or a spatial region) different from the one used for mapping, spectral shifts between the two distributions are likely to make the model fail. Such shifts are generally due to differences in acquisition and atmospheric conditions or to changes in the nature of the object observed. In order to design classification methods that are robust to data-set shifts, recent remote sensing literature has considered solutions based on domain adaptation (DA) approaches. Inspired by machine learning literature, several DA methods have been proposed to solve specific problems in remote sensing data classification. This paper provides a critical review of the recent advances in DA for remote sensing and presents an overview of methods divided into four categories: i) invariant feature selection; ii) representation matching; iii) adaptation of classifiers and iv) selective sampling. We provide an overview of recent methodologies, as well as examples of application of the considered techniques to real remote sensing images characterized by very high spatial and spectral resolution. Finally, we propose guidelines to the selection of the method to use in real application scenarios.
Robot to support older people to live independently
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents an overview on how the PAL Robotics ARI robot is participating in the European SHAPES project to promote healthy and active living among older people, by integrating digital solutions from project partners and adapting the system in order to improve human-robot interaction and user acceptability in a wide range of tasks.
Out-of-Distribution Detection for Dermoscopic Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

Medical image diagnosis can be achieved by deep neural networks, provided there is enough varied training data for each disease class. However, a hitherto unknown disease class not encountered during training will inevitably be misclassified, even if predicted with low probability. This problem is especially important for medical image diagnosis, when an image of a hitherto unknown disease is presented for diagnosis, especially when the images come from the same image domain, such as dermoscopic skin images. Current out-of-distribution detection algorithms act unfairly when the in-distribution classes are imbalanced, by favouring the most numerous disease in the training sets. This could lead to false diagnoses for rare cases which are often medically important. We developed a novel yet simple method to train neural networks, which enables them to classify in-distribution dermoscopic skin disease images and also detect novel diseases from dermoscopic images at test time. We show that our BinaryHeads model not only does not hurt classification balanced accuracy when the data is imbalanced, but also consistently improves the balanced accuracy. We also introduce an important method to investigate the effectiveness of out-of-distribution detection methods based on presence of varying amounts of out-of-distribution data, which may arise in real-world settings.