OpenRaiser/Intern-Atlas huggingface dataset:
Contrastive Neural Processes for Self-Supervised Learning
Venue: arXiv cs.LG 2021
Year: 2021

Recent contrastive methods show significant improvement in self-supervised learning in several domains. In particular, contrastive methods are most effective where data augmentation can be easily constructed e.g. in computer vision. However, they are less successful in domains without established data transformations such as time series data. In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes. It relies on recent advances in neural processes to perform time series forecasting. This allows to generate augmented versions of data by employing a set of various sampling functions and, hence, avoid manually designed augmentations. We extend conventional neural processes and propose a new contrastive loss to learn times series representations in a self-supervised setup. Therefore, unlike previous self-supervised methods, our augmentation pipeline is task-agnostic, enabling our method to perform well across various applications. In particular, a ResNet with a linear classifier trained using our approach is able to outperform state-of-the-art techniques across industrial, medical and audio datasets improving accuracy over 10% in ECG periodic data. We further demonstrate that our self-supervised representations are more efficient in the latent space, improving multiple clustering indexes and that fine-tuning our method on 10% of labels achieves results competitive to fully-supervised learning.
Bayesian Optimization and Deep Learning forsteering wheel angle prediction
Venue: arXiv cs.LG 2021
Year: 2021

Automated driving systems (ADS) have undergone a significant improvement in the last years. ADS and more precisely self-driving cars technologies will change the way we perceive and know the world of transportation systems in terms of user experience, mode choices and business models. The emerging field of Deep Learning (DL) has been successfully applied for the development of innovative ADS solutions. However, the attempt to single out the best deep neural network architecture and tuning its hyperparameters are all expensive processes, both in terms of time and computational resources. In this work, Bayesian Optimization (BO) is used to optimize the hyperparameters of a Spatiotemporal-Long Short Term Memory (ST-LSTM) network with the aim to obtain an accurate model for the prediction of the steering angle in a ADS. BO was able to identify, within a limited number of trials, a model -- namely BOST-LSTM -- which resulted, on a public dataset, the most accurate when compared to classical end-to-end driving models.
GANash -- A GAN approach to steganography
Venue: arXiv cs.CR 2021
Year: 2021

Data security is of the utmost concern of a communication system. Since the early days, many developments have been made to improve the performance of the system. PSNR of the received signal, secure transmission channel, quality of encoding used, etc. are some of the key attributes of a good system. To ensure security, the most commonly used technique is cryptography in which the message is altered with respect to a key and using the same, the encoded message is decoded at the receiver side. A complementary technique that is popularly used to insure security is steganography. The advancements in Artificial Intelligence(AI) have paved way for performing steganography in an intelligent, tamper-proof manner. The recent discovery by researchers in the field of Deep Learning(DL), an unsupervised learning network known as the Generative Adversarial Networks(GAN) has improved the performance of this technique exponentially. It has been demonstrated that deep neural networks are highly sensitive to tiny perturbations of input data, giving rise to adversarial examples. Though this property is usually considered a weakness of learned models, it could be beneficial if used appropriately. The work that has been accomplished by MIT for this purpose, a deep-neural model by the name of SteganoGAN, has shown obligation for using this technique for steganography. In this work, we have proposed a novel approach to improve the performance of the existing system using latent space compression on the encoded data. This theoretically would improve the performance exponentially. Thus, the algorithms used to improve the system's performance and the results obtained have been enunciated in this work. The results indicate the level of dominance this system could achieve to be able to diminish the difficulties in solving real-time problems in terms of security, deployment and database management.
MEKF Ignoring Initial Conditions for Attitude Estimation Using Vector Observations
Venue: arXiv cs.RO 2021
Year: 2021

In this paper, the well-known multiplicative extended Kalman filter (MEKF) is re-investigated for attitude estimation using vector observations. From the Lie group theory, it is shown that the attitude estimation model is group affine and its error state model should be trajectory-independent. Moreover, with such trajectory-independent error state model, the linear Kalman filter is still effective for large initialization errors. However, the measurement model of the traditional MEKF is dependent on the attitude prediction, which is therefore trajectory-dependent. This is also the main reason why the performance of traditional MEKF is degraded for large initialization errors. Through substitution of the attitude prediction related term with the vector observation in body frame, a trajectory-independent measurement model is derived for MEKF. Meanwhile, the MEKFs with reference attitude error definition and with global state formulating on special Euclidean group have also been studied, with main focus on derivation of the trajectory-independent measurement models. Extensive Monte Carlo simulations and field test of attitude estimation implementations demonstrate that the performance of MEKFs can be much improved with trajectory-independent measurement models.
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Venue: arXiv cs.CV 2021
Year: 2021

Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate species-specific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic process to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a
DPCOVID: Privacy-Preserving Federated Covid-19 Detection
Venue: arXiv cs.CR 2021
Year: 2021

Coronavirus (COVID-19) has shown an unprecedented global crisis by the detrimental effect on the global economy and health. The number of COVID-19 cases has been rapidly increasing, and there is no sign of stopping. It leads to a severe shortage of test kits and accurate detection models. A recent study demonstrated that the chest X-ray radiography outperformed laboratory testing in COVID-19 detection. Therefore, using chest X-ray radiography analysis can help to screen suspected COVID-19 cases at an early stage. Moreover, the patient data is sensitive, and it must be protected to avoid revealing through model updates and reconstruction from the malicious attacker. In this paper, we present a privacy-preserving Federated Learning system for COVID-19 detection based on chest X-ray images. First, a Federated Learning system is constructed from chest X-ray images. The main idea is to build a decentralized model across multiple hospitals without sharing data among hospitals. Second, we first show that the accuracy of Federated Learning for COVID-19 identification reduces significantly for Non-IID data. We then propose a strategy to improve model's accuracy on Non-IID COVID-19 data by increasing the total number of clients, parallelism (client fraction), and computation per client. Finally, we apply a Differential Privacy Stochastic Gradient Descent (DP-SGD) to enhance the preserving of patient data privacy for our Federated Learning model. A strategy is also proposed to keep the robustness of Federated Learning to ensure the security and accuracy of the model.
Semantic Segmentation for Urban-Scene Images
Venue: arXiv cs.CV 2021
Year: 2021

Urban-scene Image segmentation is an important and trending topic in computer vision with wide use cases like autonomous driving [1]. Starting with the breakthrough work of Long et al. [2] that introduces Fully Convolutional Networks (FCNs), the development of novel architectures and practical uses of neural networks in semantic segmentation has been expedited in the recent 5 years. Aside from seeking solutions in general model design for information shrinkage due to pooling, urban-scene image itself has intrinsic features like positional patterns [3]. Our project seeks an advanced and integrated solution that specifically targets urban-scene image semantic segmentation among the most novel approaches in the current field. We re-implement the cutting edge model DeepLabv3+ [4] with ResNet-101 [5] backbone as our strong baseline model. Based upon DeepLabv3+, we incorporate HANet [3] to account for the vertical spatial priors in urban-scene image tasks. To boost up model efficiency and performance, we further explore the Atrous Spatial Pooling (ASP) layer in DeepLabv3+ and infuse a computational efficient variation called "Waterfall" Atrous Spatial Pooling (WASP) [6] architecture in our model. We find that our two-step integrated model improves the mean Intersection-Over-Union (mIoU) score gradually from the baseline model. In particular, HANet successfully identifies height-driven patterns and improves per-class IoU of common class labels in urban scenario like fence and bus. We also demonstrate the improvement of model efficiency with help of WASP in terms of computational times during training and parameter reduction from the original ASPP module.
CloudFindr: A Deep Learning Cloud Artifact Masker for Satellite DEM Data
Venue: arXiv cs.CV 2021
Year: 2021

Artifact removal is an integral component of cinematic scientific visualization, and is especially challenging with big datasets in which artifacts are difficult to define. In this paper, we describe a method for creating cloud artifact masks which can be used to remove artifacts from satellite imagery using a combination of traditional image processing together with deep learning based on U-Net. Compared to previous methods, our approach does not require multi-channel spectral imagery but performs successfully on single-channel Digital Elevation Models (DEMs). DEMs are a representation of the topography of the Earth and have a variety applications including planetary science, geology, flood modeling, and city planning.
LayerZero: Trustless Omnichain Interoperability Protocol
Venue: arXiv cs.NI 2021
Year: 2021

The proliferation of blockchains has given developers a variety of platforms on which to run their smart contracts based on application features and requirements for throughput, security, and cost. However, a consequence of this freedom is severe fragmentation; Each chain is isolated, forcing users to silo their liquidity and limiting options to move liquidity and state between walled ecosystems. This paper presents LayerZero, the first trustless omnichain interoperability protocol, which provides a powerful, low level communication primitive upon which a diverse set of cross-chain applications can be built. Using this new primitive, developers can implement seamless inter-chain applications like a cross-chain DEX or multi-chain yield aggregator without having to rely on a trusted custodian or intermediate transactions. Simply put, LayerZero is the first system to trustlessly enable direct transactions across all chains. Allowing transactions to flow freely between chains provides opportunities for users to consolidate fragmented pockets of liquidity while also making full use of applications on separate chains. With LayerZero, we provide the network fabric underlying the fully-connected omnichain ecosystem of the future.
Frequency Centric Defense Mechanisms against Adversarial Examples
Venue: arXiv cs.CV 2021
Year: 2021

Adversarial example (AE) aims at fooling a Convolution Neural Network by introducing small perturbations in the input image.The proposed work uses the magnitude and phase of the Fourier Spectrum and the entropy of the image to defend against AE. We demonstrate the defense in two ways: by training an adversarial detector and denoising the adversarial effect. Experiments were conducted on the low-resolution CIFAR-10 and high-resolution ImageNet datasets. The adversarial detector has 99% accuracy for FGSM and PGD attacks on the CIFAR-10 dataset. However, the detection accuracy falls to 50% for sophisticated DeepFool and Carlini & Wagner attacks on ImageNet. We overcome the limitation by using autoencoder and show that 70% of AEs are correctly classified after denoising.
Provably Robust Model-Centric Explanations for Critical Decision-Making
Venue: arXiv cs.LG 2021
Year: 2021

We recommend using a model-centric, Boolean Satisfiability (SAT) formalism to obtain useful explanations of trained model behavior, different and complementary to what can be gleaned from LIME and SHAP, popular data-centric explanation tools in Artificial Intelligence (AI). We compare and contrast these methods, and show that data-centric methods may yield brittle explanations of limited practical utility. The model-centric framework, however, can offer actionable insights into risks of using AI models in practice. For critical applications of AI, split-second decision making is best informed by robust explanations that are invariant to properties of data, the capability offered by model-centric frameworks.
Diachronic Text Mining Investigation of Therapeutic Candidates for COVID-19
Venue: arXiv cs.CL 2021
Year: 2021

Diachronic text mining has frequently been applied to long-term linguistic surveys of word meaning and usage shifts over time. In this paper we apply short-term diachronic text mining to a rapidly growing corpus of scientific publications on COVID-19 captured in the CORD-19 dataset in order to identify co-occurrences and analyze the behavior of potential candidate treatments. We used a data set associated with a COVID-19 drug re-purposing study from Oak Ridge National Laboratory. This study identified existing candidate coronavirus treatments, including drugs and approved compounds, which had been analyzed and ranked according to their potential for blocking the ability of the SARS-COV-2 virus to invade human cells. We investigated the occurrence of these candidates in temporal instances of the CORD-19 corpus. We found that at least 25% of the identified terms occurred in temporal instances of the corpus to the extent that their frequency and contextual dynamics could be evaluated. We identified three classes of behaviors: those where frequency and contextual shifts were small and positively correlated; those where there was no correlation between frequency and contextual changes; and those where there was a negative correlation between frequency and contextual shift. We speculate that the latter two patterns are indicative that a target candidate therapeutics is undergoing active evaluation. The patterns we detected demonstrate the potential benefits of using diachronic text mining techniques with a large dynamic text corpus to track drug-repurposing activities across international clinical and laboratory settings.
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
Venue: arXiv cs.CL 2021
Year: 2021

The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing(NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these MLand NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications, namely (i) rumors detection, (ii) satires detection, (iii) clickbait & spams identification, (iv) hate speech detection, (v)misinformation detection, and (vi) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.
BioGrad: Biologically Plausible Gradient-Based Learning for Spiking Neural Networks
Venue: arXiv cs.NE 2021
Year: 2021

Spiking neural networks (SNN) are delivering energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic chips. To harness these computational benefits, SNN need to be trained by learning algorithms that adhere to brain-inspired neuromorphic principles, namely event-based, local, and online computations. Yet, the state-of-the-art SNN training algorithms are based on backprop that does not follow the above principles. Due to its limited biological plausibility, the application of backprop to SNN requires non-local feedback pathways for transmitting continuous-valued errors, and relies on gradients from future timesteps. The introduction of biologically plausible modifications to backprop has helped overcome several of its limitations, but limits the degree to which backprop is approximated, which hinders its performance. We propose a biologically plausible gradient-based learning algorithm for SNN that is functionally equivalent to backprop, while adhering to all three neuromorphic principles. We introduced multi-compartment spiking neurons with local eligibility traces to compute the gradients required for learning, and a periodic "sleep" phase to further improve the approximation to backprop during which a local Hebbian rule aligns the feedback and feedforward weights. Our method achieved the same level of performance as backprop with multi-layer fully connected SNN on MNIST (98.13%) and the event-based N-MNIST (97.59%) datasets. We deployed our learning algorithm on Intel's Loihi to train a 1-hidden-layer network for MNIST, and obtained 93.32% test accuracy while consuming 400 times less energy per training sample than BioGrad on GPU. Our work shows that optimal learning is feasible in neuromorphic computing, and further pursuing its biological plausibility can better capture the benefits of this emerging computing paradigm.
Automated Evaluation of Web Site Accessibility Using A Dynamic Accessibility Measurement Crawler
Venue: arXiv cs.IR 2021
Year: 2021

Achieving accessibility compliance is extremely important for many government agencies and businesses who wish to improve services for their consumers. With the growing reliance on dynamic web applications many organizations are finding it difficult to implement accessibility standards, often due to the inability of current automated testing tools to test the stateful environments created by dynamic web applications. In this paper, we present mathematical foundations and theory for the Demodocus framework and prototype, and outline its approach to using web science, web crawling,and accessibility testing to automatically navigate and test interactive content for accessibility. Our approach simulates the page interactions of users with and without disabilities, and compares graphs of reachable states from these simulations to determine both the accessibility and the difficulty of content access for these different users.
A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection
Venue: arXiv cs.NE 2021
Year: 2021

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE.
From Image to Imuge: Immunized Image Generation
Venue: arXiv cs.CV 2021
Year: 2021

We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and fragile to diverse digital attack, i.e. image cropping and JPEG compression. To address this issue, we jointly train a U-Net backboned encoder, a tamper localization network and a decoder for image recovery. Given an original image, the encoder produces a visually indistinguishable immunized image. At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks. Several strategies are proposed to boost the training efficiency. We demonstrate that our method can recover the details of the tampered regions with a high quality despite the presence of various kinds of attacks. Comprehensive ablation studies are conducted to validate our network designs.