OpenRaiser/Intern-Atlas huggingface dataset:
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks
Venue: arXiv cs.LG 2021
Year: 2021

The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversarial networks. Generative non-invertible models are unable to accurately generate such distributions, require long training and often are subject to mode collapse. Variational autoencoders (VAEs), which are based on the idea of keeping the latent space to be Gaussian for the sake of a simple sampling, allow an accurate reconstruction, while they experience significant limitations at generation task. In this work, instead of trying to keep the latent space to be Gaussian, we use a pre-trained contrastive encoder to obtain a clustered latent space. Then, for each cluster, representing a unimodal submanifold, we train a dedicated low complexity network to generate this submanifold from the Gaussian distribution. The proposed framework is based on the information-theoretic formulation of mutual information maximization between the input data and latent space representation. We derive a link between the cost functions and the information-theoretic formulation. We apply our approach to synthetic 2D distributions to demonstrate both reconstruction and generation of discontinuous distributions using continuous stochastic networks.
AI-Assisted Verification of Biometric Data Collection
Venue: arXiv cs.CV 2021
Year: 2021

Recognizing actions from a video feed is a challenging task to automate, especially so on older hardware. There are two aims for this project: one is to recognize an action from the front-facing camera on an Android phone, the other is to support as many phones and Android versions as possible. This limits us to using models that are small enough to run on mobile phones with and without GPUs, and only using the camera feed to recognize the action. In this paper we compare performance of the YOLO architecture across devices (with and without dedicated GPUs) using models trained on a custom dataset. We also discuss limitations in recognizing faces and actions from video on limited hardware.
Assessing m-Learning Adoption in Higher Education
Venue: arXiv cs.CY 2021
Year: 2021

New mobile platforms, connected seamlessly to the Internet via wireless access have become increasingly more powerful and have found usage in a diverse set of application areas, including the education sector. The educational institutions are becoming more open to embracing new learning platforms, which in turn has sparked the interest in developing new assessment frameworks. This paper has used the framework of the Capability Maturity Model (CMM) to design a model for M-learning within educational institutions. The framework has been validated with studies cases from higher education institutions.
Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows
Venue: arXiv cs.LG 2021
Year: 2021

As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparency in clinical decisions. Existing explainers discover an explanatory subgraph relevant to the prediction. However, such a subgraph is insufficient to reveal all the critical biological substructures for the prediction because the prediction will remain unchanged after removing that subgraph. Hence, an explanatory subgraph should be not only necessary for prediction, but also sufficient to uncover the most predictive regions for the explanation. Such explanation requires a measurement of information transferred from different input subgraphs to the predictive output, which we define as information flow. In this work, we address these key challenges and propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs. To evaluate the information flow within GNN's prediction, we first propose a novel notion of predictiveness, named $f$-information, which is directional and incorporates the realistic capacity of the GNN model. Based on it, IFEXPLAINER generates the explanatory subgraph with maximal information flow to the prediction. Meanwhile, it minimizes the information flow from the input to the predictive result after removing the explanation. Thus, the produced explanation is necessarily important to the prediction and sufficient to reveal the most crucial substructures. We evaluate IFEXPLAINER to interpret GNN's predictions on breast cancer subtyping. Experimental results on the BRACS dataset show the superior performance of the proposed method.
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
Venue: arXiv cs.LG 2021
Year: 2021

Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientific community developed strategies to order the examples according to their estimated complexity, to distil knowledge from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process of a neural classifier. The transformation progressively fades-out as long as training proceeds, until it completely vanishes. In this work we revisit and extend this idea, introducing a radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make them easier to be handled by the classifier at the current stage of the training procedure. The auxiliary network is trained jointly with the neural classifier, thus intrinsically increasing the 'depth' of the classifier, and it is expected to spot general regularities in the data alteration process. The effect of the auxiliary network is progressively reduced up to the end of training, when it is fully dropped and the classifier is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure involving several datasets and different neural architectures shows that Neural Friendly Training overcomes the originally proposed Friendly Training technique, improving the generalization of the classifier, especially in the case of noisy data.
Beyond Single-Deletion Correcting Codes: Substitutions and Transpositions
Venue: arXiv cs.IT 2021
Year: 2021

We consider the problem of designing low-redundancy codes in settings where one must correct deletions in conjunction with substitutions or adjacent transpositions; a combination of errors that is usually observed in DNA-based data storage. One of the most basic versions of this problem was settled more than 50 years ago by Levenshtein, or one substitution, with nearly optimal redundancy. However, this approach fails to extend to many simple and natural variations of the binary single-edit error setting. In this work, we make progress on the code design problem above in three such variations: We construct linear-time encodable and decodable length-$n$ non-binary codes correcting a single edit error with nearly optimal redundancy $\log n+O(\log\log n)$, providing an alternative simpler proof of a result by Cai, Chee, Gabrys, Kiah, and Nguyen (IEEE Trans. Inf. Theory 2021). This is achieved by employing what we call weighted VT sketches, a notion that may be of independent interest. We construct linear-time encodable and list-decodable binary codes with list-size $2$ for one deletion and one substitution with redundancy $4\log n+O(\log\log n)$. This matches the existential bound up to an $O(\log\log n)$ additive term. We show the existence of a binary code correcting one deletion or one adjacent transposition with nearly optimal redundancy $\log n+O(\log\log n)$.
Wasserstein Generative Learning of Conditional Distribution
Venue: arXiv cs.LG 2021
Year: 2021

Conditional distribution is a fundamental quantity for describing the relationship between a response and a predictor. We propose a Wasserstein generative approach to learning a conditional distribution. The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution. The conditional generator is estimated by matching a joint distribution involving the conditional generator and the target joint distribution, using the Wasserstein distance as the discrepancy measure for these joint distributions. We establish non-asymptotic error bound of the conditional sampling distribution generated by the proposed method and show that it is able to mitigate the curse of dimensionality, assuming that the data distribution is supported on a lower-dimensional set. We conduct numerical experiments to validate proposed method and illustrate its applications to conditional sample generation, nonparametric conditional density estimation, prediction uncertainty quantification, bivariate response data, image reconstruction and image generation.
Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We?
Venue: arXiv cs.SE 2021
Year: 2021

Bug reports are common artefacts in software development. They serve as the main channel for users to communicate to developers information about the issues that they encounter when using released versions of software programs. In the descriptions of issues, however, a user may, intentionally or not, expose a vulnerability. In a typical maintenance scenario, such security-relevant bug reports are prioritised by the development team when preparing corrective patches. Nevertheless, when security relevance is not immediately expressed (e.g., via a tag) or rapidly identified by triaging teams, the open security-relevant bug report can become a critical leak of sensitive information that attackers can leverage to perform zero-day attacks. To support practitioners in triaging bug reports, the research community has proposed a number of approaches for the detection of security-relevant bug reports. In recent years, approaches in this respect based on machine learning have been reported with promising performance. Our work focuses on such approaches, and revisits their building blocks to provide a comprehensive view on the current achievements. To that end, we built a large experimental dataset and performed extensive experiments with variations in feature sets and learning algorithms. Eventually, our study highlights different approach configurations that yield best performing classifiers.
Masked Deep Q-Recommender for Effective Question Scheduling
Venue: arXiv cs.AI 2021
Year: 2021

Providing appropriate questions according to a student's knowledge level is imperative in personalized learning. However, It requires a lot of manual effort for teachers to understand students' knowledge status and provide optimal questions accordingly. To address this problem, we introduce a question scheduling model that can effectively boost student knowledge level using Reinforcement Learning (RL). Our proposed method first evaluates students' concept-level knowledge using knowledge tracing (KT) model. Given predicted student knowledge, RL-based recommender predicts the benefits of each question. With curriculum range restriction and duplicate penalty, the recommender selects questions sequentially until it reaches the predefined number of questions. In an experimental setting using a student simulator, which gives 20 questions per day for two weeks, questions recommended by the proposed method increased average student knowledge level by 21.3%, superior to an expert-designed schedule baseline with a 10% increase in student knowledge levels.
Proactive Autoscaling for Edge Computing Systems with Kubernetes
Venue: arXiv cs.DC 2021
Year: 2021

With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing applications do not utilize heterogeneous resources of edge systems efficiently, leaving scope for performance improvement. In this work, we propose a Proactive Pod Autoscaler (PPA) for edge computing applications on Kubernetes. The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications up and down correspondingly. The PPA is optimized and evaluated on an example CPU-intensive edge computing application further. It can be concluded that the proposed PPA outperforms the default pod autoscaler of Kubernetes on both efficiency of resource utilization and application performance. The article also highlights future possible improvements on the proposed PPA.
Denoised Labels for Financial Time-Series Data via Self-Supervised Learning
Venue: arXiv cs.LG 2021
Year: 2021

The introduction of electronic trading platforms effectively changed the organisation of traditional systemic trading from quote-driven markets into order-driven markets. Its convenience led to an exponentially increasing amount of financial data, which is however hard to use for the prediction of future prices, due to the low signal-to-noise ratio and the non-stationarity of financial time series. Simpler classification tasks -- where the goal is to predict the directions of future price movement -- via supervised learning algorithms, need sufficiently reliable labels to generalise well. Labelling financial data is however less well defined than other domains: did the price go up because of noise or because of signal? The existing labelling methods have limited countermeasures against noise and limited effects in improving learning algorithms. This work takes inspiration from image classification in trading and success in self-supervised learning. We investigate the idea of applying computer vision techniques to financial time-series to reduce the noise exposure and hence generate correct labels. We look at the label generation as the pretext task of a self-supervised learning approach and compare the naive (and noisy) labels, commonly used in the literature, with the labels generated by a denoising autoencoder for the same downstream classification task. Our results show that our denoised labels improve the performances of the downstream learning algorithm, for both small and large datasets. We further show that the signals we obtain can be used to effectively trade with binary strategies. We suggest that with proposed techniques, self-supervised learning constitutes a powerful framework for generating "better" financial labels that are useful for studying the underlying patterns of the market.
Active Weighted Aging Ensemble for Drifted Data Stream Classification
Venue: arXiv cs.LG 2021
Year: 2021

One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon is required to adapt the classifier to the changing probabilistic characteristics. One of the significant problems in implementing such a solution is the access to data labels. It is usually costly, so to minimize the expenses related to this process, learning strategies based on semi-supervised learning are proposed, e.g., employing active learning methods indicating which of the incoming objects are valuable to be labeled for improving the classifier's performance. This paper proposes a novel chunk-based method for non-stationary data streams based on classifier ensemble learning and an active learning strategy considering a limited budget that can be successfully applied to any data stream classification algorithm. The proposed method has been evaluated through computer experiments using both real and generated data streams. The results confirm the high quality of the proposed algorithm over state-of-the-art methods.
Demanding and Designing Aligned Cognitive Architectures
Venue: arXiv cs.AI 2021
Year: 2021

With AI systems becoming more powerful and pervasive, there is increasing debate about keeping their actions aligned with the broader goals and needs of humanity. This multi-disciplinary and multi-stakeholder debate must resolve many issues, here we examine three of them. The first issue is to clarify what demands stakeholders might usefully make on the designers of AI systems, useful because the technology exists to implement them. We make this technical topic more accessible by using the framing of cognitive architectures. The second issue is to move beyond an analytical framing that treats useful intelligence as being reward maximization only. To support this move, we define several AI cognitive architectures that combine reward maximization with other technical elements designed to improve alignment. The third issue is how stakeholders should calibrate their interactions with modern machine learning researchers. We consider how current fashions in machine learning create a narrative pull that participants in technical and policy discussions should be aware of, so that they can compensate for it. We identify several technically tractable but currently unfashionable options for improving AI alignment.
Parallel Algorithms for Adding a Collection of Sparse Matrices
Venue: arXiv cs.DC 2021
Year: 2021

We develop a family of parallel algorithms for the SpKAdd operation that adds a collection of k sparse matrices. SpKAdd is a much needed operation in many applications including distributed memory sparse matrix-matrix multiplication (SpGEMM), streaming accumulations of graphs, and algorithmic sparsification of the gradient updates in deep learning. While adding two sparse matrices is a common operation in Matlab, Python, Intel MKL, and various GraphBLAS libraries, these implementations do not perform well when adding a large collection of sparse matrices. We develop a series of algorithms using tree merging, heap, sparse accumulator, hash table, and sliding hash table data structures. Among them, hash-based algorithms attain the theoretical lower bounds both on the computational and I/O complexities and perform the best in practice. The newly-developed hash SpKAdd makes the computation of a distributed-memory SpGEMM algorithm at least 2x faster than that the previous state-of-the-art algorithms.
A Predictive Autonomous Decision Aid for Calibrating Human-Autonomy Reliance in Multi-Agent Task Assignment
Venue: arXiv cs.MA 2021
Year: 2021

In this work, we develop a game-theoretic modeling of the interaction between a human operator and an autonomous decision aid when they collaborate in a multi-agent task allocation setting. In this setting, we propose a decision aid that is designed to calibrate the operator's reliance on the aid through a sequence of interactions to improve overall human-autonomy team performance. The autonomous decision aid employs a long short-term memory (LSTM) neural network for human action prediction and a Bayesian parameter filtering method to improve future interactions, resulting in an aid that can adapt to the dynamics of human reliance. The proposed method is then tested against a large set of simulated human operators from the choice prediction competition (CPC18) data set, and shown to significantly improve human-autonomy interactions when compared to a myopic decision aid that only suggests predicted human actions without an understanding of reliance.
Parallel Multi-Scale Networks with Deep Supervision for Hand Keypoint Detection
Venue: arXiv cs.CV 2021
Year: 2021

Keypoint detection plays an important role in a wide range of applications. However, predicting keypoints of small objects such as human hands is a challenging problem. Recent works fuse feature maps of deep Convolutional Neural Networks (CNNs), either via multi-level feature integration or multi-resolution aggregation. Despite achieving some success, the feature fusion approaches increase the complexity and the opacity of CNNs. To address this issue, we propose a novel CNN model named Multi-Scale Deep Supervision Network (P-MSDSNet) that learns feature maps at different scales with deep supervisions to produce attention maps for adaptive feature propagation from layers to layers. P-MSDSNet has a multi-stage architecture which makes it scalable while its deep supervision with spatial attention improves transparency to the feature learning at each stage. We show that P-MSDSNet outperforms the state-of-the-art approaches on benchmark datasets while requiring fewer number of parameters. We also show the application of P-MSDSNet to quantify finger tapping hand movements in a neuroscience study.
Model-based gait recognition using graph network on very large population database
Venue: arXiv cs.CV 2021
Year: 2021

At present, the existing gait recognition systems are focusing on developing methods to extract robust gait feature from silhouette images and they indeed achieved great success. However, gait can be sensitive to appearance features such as clothing and carried items. Compared with appearance-based method, model-based gait recognition is promising due to the robustness against these variations. In recent years, with the development of human pose estimation, the difficulty of model-based gait recognition methods has been mitigated. In this paper, to resist the increase of subjects and views variation, local features are built and a siamese network is proposed to maximize the distance of samples from the same subject. We leverage recent advances in action recognition to embed human pose sequence to a vector and introduce Spatial-Temporal Graph Convolution Blocks (STGCB) which has been commonly used in action recognition for gait recognition. Experiments on the very large population dataset named OUMVLP-Pose and the popular dataset, CASIA-B, show that our method archives some state-of-the-art (SOTA) performances in model-based gait recognition. The code and models of our method are available at https://github.com/timelessnaive/Gait-for-Large-Dataset after being accepted.
Contrastive Attention Network with Dense Field Estimation for Face Completion
Venue: arXiv cs.CV 2021
Year: 2021

Most modern face completion approaches adopt an autoencoder or its variants to restore missing regions in face images. Encoders are often utilized to learn powerful representations that play an important role in meeting the challenges of sophisticated learning tasks. Specifically, various kinds of masks are often presented in face images in the wild, forming complex patterns, especially in this hard period of COVID-19. It's difficult for encoders to capture such powerful representations under this complex situation. To address this challenge, we propose a self-supervised Siamese inference network to improve the generalization and robustness of encoders. It can encode contextual semantics from full-resolution images and obtain more discriminative representations. To deal with geometric variations of face images, a dense correspondence field is integrated into the network. We further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine the restored and known regions in an adaptive manner. This multi-scale architecture is beneficial for the decoder to utilize discriminative representations learned from encoders into images. Extensive experiments clearly demonstrate that the proposed approach not only achieves more appealing results compared with state-of-the-art methods but also improves the performance of masked face recognition dramatically.