OpenRaiser/Intern-Atlas huggingface dataset:
Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots
Venue: arXiv cs.NE 2021
Year: 2021

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in the performance of robotic systems. These mechanisms include the adaptability of their locomotion to any change registered in the environment through their biological sensors. In this regard, we aim to replicate such kind of adaptability in legged robots through a Spiking Central Pattern Generator. This Spiking Central Pattern Generator generates different locomotion (rhythmic) patterns which are driven by an external stimulus, that is, the output of a Force Sensitive Resistor connected to the robot to provide feedback. The Spiking Central Pattern Generator consists of a network of five populations of Leaky Integrate-and-Fire neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus. Therefore, the locomotion of the end robotic platform (any-legged robot) can be adapted to the terrain by using any sensor as input. The Spiking Central Pattern Generator with adaptive learning has been numerically validated at software and hardware level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for the latest. In particular, our experiments clearly show an adaptation in the oscillation frequencies between the spikes produced in the populations of the Spiking Central Pattern Generator while the input stimulus varies. To validate the robustness and adaptability of the Spiking Central Pattern Generator, we have performed several tests by variating the output of the sensor. These experiments were carried out in Brian 2 and SpiNNaker; both implementations showed a similar behavior with a Pearson correlation coefficient of 0.905.
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
Venue: arXiv cs.LG 2021
Year: 2021

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network. By using Natural Evolution Strategies and a population of SE parameter vectors, we train agents in the inner loop on evolving SEs while in the outer loop we use the performance on the target task as a score for meta-updating the SE population. We show empirically that our method is capable of learning SEs for two discrete-action-space tasks (CartPole-v0 and Acrobot-v1) that allow us to train agents more robustly and with up to 60% fewer steps. Not only do we show in experiments with 4000 evaluations that the SEs are robust against hyperparameter changes such as the learning rate, batch sizes and network sizes, we also show that SEs trained with DDQN agents transfer in limited ways to a discrete-action-space version of TD3 and very well to Dueling DDQN.
Classic versus deep learning approaches to address computer vision challenges
Venue: arXiv cs.CV 2021
Year: 2021

Computer vision and image processing address many challenging applications. While the last decade has seen deep neural network architectures revolutionizing those fields, early methods relied on 'classic', i.e., non-learned approaches. In this study, we explore the differences between classic and deep learning (DL) algorithms to gain new insight regarding which is more suitable for a given application. The focus is on two challenging ill-posed problems, namely faint edge detection and multispectral image registration, studying recent state-of-the-art DL and classic solutions. While those DL algorithms outperform classic methods in terms of accuracy and development time, they tend to have higher resource requirements and are unable to perform outside their training space. Moreover, classic algorithms are more transparent, which facilitates their adoption for real-life applications. As both classes of approaches have unique strengths and limitations, the choice of a solution is clearly application dependent.
Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks
Venue: arXiv cs.CL 2021
Year: 2021

In this work, we propose a novel goal-oriented dialog task, automatic symptom detection. We build a system that can interact with patients through dialog to detect and collect clinical symptoms automatically, which can save a doctor's time interviewing the patient. Given a set of explicit symptoms provided by the patient to initiate a dialog for diagnosing, the system is trained to collect implicit symptoms by asking questions, in order to collect more information for making an accurate diagnosis. After getting the reply from the patient for each question, the system also decides whether current information is enough for a human doctor to make a diagnosis. To achieve this goal, we propose two neural models and a training pipeline for the multi-step reasoning task. We also build a knowledge graph as additional inputs to further improve model performance. Experiments show that our model significantly outperforms the baseline by 4%, discovering 67% of implicit symptoms on average with a limited number of questions.
Context-Specific Likelihood Weighting
Venue: arXiv cs.AI 2021
Year: 2021

Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.
cGANs for Cartoon to Real-life Images
Venue: arXiv cs.CV 2021
Year: 2021

The image-to-image translation is a learning task to establish a visual mapping between an input and output image. The task has several variations differentiated based on the purpose of the translation, such as synthetic to real translation, photo to caricature translation, and many others. The problem has been tackled using different approaches, either through traditional computer vision methods, as well as deep learning approaches in recent trends. One approach currently deemed popular and effective is using the conditional generative adversarial network, also known shortly as cGAN. It is adapted to perform image-to-image translation tasks with typically two networks: a generator and a discriminator. This project aims to evaluate the robustness of the Pix2Pix model by applying the Pix2Pix model to datasets consisting of cartoonized images. Using the Pix2Pix model, it should be possible to train the network to generate real-life images from the cartoonized images.
Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext Tasks
Venue: arXiv cs.LG 2021
Year: 2021

Recent work on few-shot learning \cite{tian2020rethinking} showed that quality of learned representations plays an important role in few-shot classification performance. On the other hand, the goal of self-supervised learning is to recover useful semantic information of the data without the use of class labels. In this work, we exploit the complementarity of both paradigms via a multi-task framework where we leverage recent self-supervised methods as auxiliary tasks. We found that combining multiple tasks is often beneficial, and that solving them simultaneously can be done efficiently. Our results suggest that self-supervised auxiliary tasks are effective data-dependent regularizers for representation learning. Our code is available at: \url{https://github.com/nathanielsimard/improving-fs-ssl}.
TLRM: Task-level Relation Module for GNN-based Few-Shot Learning
Venue: arXiv cs.CV 2021
Year: 2021

Recently, graph neural networks (GNNs) have shown powerful ability to handle few-shot classification problem, which aims at classifying unseen samples when trained with limited labeled samples per class. GNN-based few-shot learning architectures mostly replace traditional metric with a learnable GNN. In the GNN, the nodes are set as the samples embedding, and the relationship between two connected nodes can be obtained by a network, the input of which is the difference of their embedding features. We consider this method of measuring relation of samples only models the sample-to-sample relation, while neglects the specificity of different tasks. That is, this method of measuring relation does not take the task-level information into account. To this end, we propose a new relation measure method, namely the task-level relation module (TLRM), to explicitly model the task-level relation of one sample to all the others. The proposed module captures the relation representations between nodes by considering the sample-to-task instead of sample-to-sample embedding features. We conducted extensive experiments on four benchmark datasets: mini-ImageNet, tiered-ImageNet, CUB-$200$-$2011$, and CIFAR-FS. Experimental results demonstrate that the proposed module is effective for GNN-based few-shot learning.
Using Angle of Arrival for Improving Indoor Localization
Venue: arXiv cs.SD 2021
Year: 2021

In this paper, we primarily explore the improvement of single stream audio systems using Angle of Arrival calculations in both simulation and real life gathered data. We wanted to learn how to discern the direction of an audio source from gathered signal data to ultimately incorporate into a multi modal security system. We focused on the MUSIC algorithm for the estimation of the angle of arrival but briefly experimented with other techniques such as Bartlett and Capo. We were able to implement our own MUSIC algorithm on stimulated data from Cornell. In addition, we demonstrated how we are able to calculate the angle of arrival over time in a real life scene. Finally, we are able to detect the direction of arrival for two separate and simultaneous audio sources in a real life scene. Eventually, we could incorporate this tracking into a multi modal system combined with video. Overall, we are able to produce compelling results for angle of arrival calculations that could be the stepping stones for a better system to detect events in a scene.
Set Reconciliation for Blockchains with Slepian-Wolf Coding: Deletion Polar Codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a polar coding based scheme for set reconciliation between two network nodes. The system is modeled as a well-known Slepian-Wolf setting induced by a fixed number of deletions. The set reconciliation process is divided into two phases: 1) a deletion polar code is employed to help one node to identify the possible deletion indices, which may be larger than the number of genuine deletions; 2) a lossless compression polar code is then designed to feedback those indices with minimum overhead. Our scheme can be viewed as a generalization of polar codes to some emerging network-based applications such as the package synchronization in blockchains. Some connections with the existing schemes based on the invertible Bloom lookup tables (IBLTs) and network coding are also observed and briefly discussed.
Freezer: A Specialized NVM Backup Controller for Intermittently-Powered Systems
Venue: arXiv cs.AR 2021
Year: 2021

The explosion of IoT and wearable devices determined a rising attention towards energy harvesting as source for powering these systems. In this context, many applications cannot afford the presence of a battery because of size, weight and cost issues. Therefore, due to the intermittent nature of ambient energy sources, these systems must be able to save and restore their state, in order to guarantee progress across power interruptions. In this work, we propose a specialized backup/restore controller that dynamically tracks the memory accesses during the execution of the program. The controller then commits the changes to a snapshot in a Non-Volatile Memory (NVM) when a power failure is detected. Our approach does not require complex hybrid memories and can be implemented with standard components. % and integrated in any MCU with Results on a set of benchmarks show an average $8\times$ reduction in backup size. Thanks to our dedicated controller, the backup time is further reduced by more than $100\times$, with an area and power overhead of only 0.4\% and 0.8\%, respectively, w.r.t. a low-end IoT node.
A Unified Joint Maximum Mean Discrepancy for Domain Adaptation
Venue: arXiv cs.LG 2021
Year: 2021

Domain adaptation has received a lot of attention in recent years, and many algorithms have been proposed with impressive progress. However, it is still not fully explored concerning the joint probability distribution (P(X, Y)) distance for this problem, since its empirical estimation derived from the maximum mean discrepancy (joint maximum mean discrepancy, JMMD) will involve complex tensor-product operator that is hard to manipulate. To solve this issue, this paper theoretically derives a unified form of JMMD that is easy to optimize, and proves that the marginal, class conditional and weighted class conditional probability distribution distances are our special cases with different label kernels, among which the weighted class conditional one not only can realize feature alignment across domains in the category level, but also deal with imbalance dataset using the class prior probabilities. From the revealed unified JMMD, we illustrate that JMMD degrades the feature-label dependence (discriminability) that benefits to classification, and it is sensitive to the label distribution shift when the label kernel is the weighted class conditional one. Therefore, we leverage Hilbert Schmidt independence criterion and propose a novel MMD matrix to promote the dependence, and devise a novel label kernel that is robust to label distribution shift. Finally, we conduct extensive experiments on several cross-domain datasets to demonstrate the validity and effectiveness of the revealed theoretical results.
On maximum-likelihood estimation in the all-or-nothing regime
Venue: arXiv cs.IT 2021
Year: 2021

We study the problem of estimating a rank-1 additive deformation of a Gaussian tensor according to the \emph{maximum-likelihood estimator} (MLE). The analysis is carried out in the sparse setting, where the underlying signal has a support that scales sublinearly with the total number of dimensions. We show that for Bernoulli distributed signals, the MLE undergoes an \emph{all-or-nothing} (AoN) phase transition, already established for the minimum mean-square-error estimator (MMSE) in the same problem. The result follows from two main technical points: (i) the connection established between the MLE and the MMSE, using the first and second-moment methods in the constrained signal space, (ii) a recovery regime for the MMSE stricter than the simple error vanishing characterization given in the standard AoN, that is here proved as a general result.
Democratizing information visualization. A study to map the value of graphic design to easier knowledge transfer of scientific research
Venue: arXiv cs.HC 2021
Year: 2021

Visual representations are becoming important in science communication and education. This explorative study investigates the perception of STEM researchers, without any specific visual design background, and the value of visual representations as tools to support the communication of technical and scientific knowledge among academics and a wider non-technical community. Early findings show that visual representations can positively support scientists to share research outcomes in a more compelling, visually clear, and impactful manner, reaching a wider audience across different disciplines.
SEA-BREW: A Scalable Attribute-Based Encryption Scheme for Low-Bitrate IoT Wireless Networks
Venue: arXiv cs.CR 2021
Year: 2021

Attribute-Based Encryption (ABE) is an emerging cryptographic technique that allows one to embed a fine-grained access control mechanism into encrypted data. In this paper we propose a novel ABE scheme called SEA-BREW (Scalable and Efficient Abe with Broadcast REvocation for Wireless networks), which is suited for Internet of Things (IoT) and Industrial IoT (IIoT) applications. In contrast to state-of-the-art ABE schemes, ours is capable of securely performing key revocations with a single short broadcast message, instead of a number of unicast messages that is linear with the number of nodes. This is desirable for low-bitrate Wireless Sensor and Actuator Networks (WSANs) which often are the heart of (I)IoT systems. In SEA-BREW, sensors, actuators, and users can exchange encrypted data via a cloud server, or directly via wireless if they belong to the same WSAN. We formally prove that our scheme is secure also in case of an untrusted cloud server that colludes with a set of users, under the generic bilinear group model. We show by simulations that our scheme requires a constant computational overhead on the cloud server with respect to the complexity of the access control policies. This is in contrast to state-of-the-art solutions, which require instead a linear computational overhead.
A Review of Graph Neural Networks and Their Applications in Power Systems
Venue: arXiv cs.LG 2021
Year: 2021

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
Venue: arXiv cs.CL 2021
Year: 2021

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist. Such approaches overlook potential ambiguities, in which multiple emotions overlap. We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence. Furthermore, we introduce a loss function focused on modelling multiple co-existing emotions in the input sentence. Experiments performed on the SemEval2018 multi-label emotion data over three language sets (i.e., English, Arabic and Spanish) demonstrate our method's effectiveness. Finally, we present different analyses that illustrate the benefits of our method in terms of improving the model performance and learning meaningful associations between emotion classes and words in the sentence.
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
Venue: arXiv cs.CV 2021
Year: 2021

There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.
Unified Citizen Identity System Using Blockchain
Venue: arXiv cs.CY 2021
Year: 2021

The citizenship identities of a nation's occupants enable the state to identify and authenticate them unquestionably. These documents help individuals in recognizing themselves and to profit from the rights and advantages given to them by the legislature or the constitution of the land. There are problems in the traditional way of issuance f these identities and many hurdles that impede people from getting their benefits or exercising their rights. These paper-based identities can be forged easily and are hard to authenticate at various civil end points. There are reports of identity thefts. In this paper, we are discussing how Blockchain can be employed to overcome these problems and makes these identities confidential, immutable, and secured. Blockchain technology can help the governing bodies in maintaining and verifying these identities in a quick manner with less chance for human errors, meaning more reach for government plans and aid
Automating Program Structure Classification
Venue: arXiv cs.CY 2021
Year: 2021

When students write programs, their program structure provides insight into their learning process. However, analyzing program structure by hand is time-consuming, and teachers need better tools for computer-assisted exploration of student solutions. As a first step towards an education-oriented program analysis toolkit, we show how supervised machine learning methods can automatically classify student programs into a predetermined set of high-level structures. We evaluate two models on classifying student solutions to the Rainfall problem: a nearest-neighbors classifier using syntax tree edit distance and a recurrent neural network. We demonstrate that these models can achieve 91% classification accuracy when trained on 108 programs. We further explore the generality, trade-offs, and failure cases of each model.