OpenRaiser/Intern-Atlas huggingface dataset:
Learning task-agnostic representation via toddler-inspired learning
Venue: arXiv cs.AI 2021
Year: 2021

One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e.g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own. To tackle this problem, we derive inspiration from a highly intentional learning system via action: the toddler. Inspired by the toddler's learning procedure, we design an interactive agent that can learn and store task-agnostic visual representation while exploring and interacting with objects in the virtual environment. Experimental results show that such obtained representation was expandable to various vision tasks such as image classification, object localization, and distance estimation tasks. In specific, the proposed model achieved 100%, 75.1% accuracy and 1.62% relative error, respectively, which is noticeably better than autoencoder-based model (99.7%, 66.1%, 1.95%), and also comparable with those of supervised models (100%, 87.3%, 0.71%).
GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette images to represent a person in each frame. However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes. Furthermore, silhouette images contain not only gait features but also other visual clues that can be recognized. Hence these approaches can not be considered as strict gait recognition. We leverage recent advances in human pose estimation to estimate robust skeleton poses directly from RGB images to bring back model-based gait recognition with a cleaner representation of gait. Thus, we propose GaitGraph that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a modern model-based approach for gait recognition. The main advantages are a cleaner, more elegant extraction of the gait features and the ability to incorporate powerful spatio-temporal modeling using GCN. Experiments on the popular CASIA-B gait dataset show that our method archives state-of-the-art performance in model-based gait recognition. The code and models are publicly available.
Efficient Video Summarization Framework using EEG and Eye-tracking Signals
Venue: arXiv cs.CV 2021
Year: 2021

This paper proposes an efficient video summarization framework that will give a gist of the entire video in a few key-frames or video skims. Existing video summarization frameworks are based on algorithms that utilize computer vision low-level feature extraction or high-level domain level extraction. However, being the ultimate user of the summarized video, humans remain the most neglected aspect. Therefore, the proposed paper considers human's role in summarization and introduces human visual attention-based summarization techniques. To understand human attention behavior, we have designed and performed experiments with human participants using electroencephalogram (EEG) and eye-tracking technology. The EEG and eye-tracking data obtained from the experimentation are processed simultaneously and used to segment frames containing useful information from a considerable video volume. Thus, the frame segmentation primarily relies on the cognitive judgments of human beings. Using our approach, a video is summarized by 96.5% while maintaining higher precision and high recall factors. The comparison with the state-of-the-art techniques demonstrates that the proposed approach yields ceiling-level performance with reduced computational cost in summarising the videos.
Partition of unity networks: deep hp-approximation
Venue: arXiv cs.LG 2021
Year: 2021

Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials. Motivated by this, we propose partition of unity networks (POUnets) which incorporate these elements directly into the architecture. Classification architectures of the type used to learn probability measures are used to build a meshfree partition of space, while polynomial spaces with learnable coefficients are associated to each partition. The resulting hp-element-like approximation allows use of a fast least-squares optimizer, and the resulting architecture size need not scale exponentially with spatial dimension, breaking the curse of dimensionality. An abstract approximation result establishes desirable properties to guide network design. Numerical results for two choices of architecture demonstrate that POUnets yield hp-convergence for smooth functions and consistently outperform MLPs for piecewise polynomial functions with large numbers of discontinuities.
Language Modelling as a Multi-Task Problem
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.
Tutorial implementation of Hoare logic in Haskell
Venue: arXiv cs.PL 2021
Year: 2021

Using the programming language Haskell, we introduce an implementation of propositional calculus, number theory, and a simple imperative language that can evaluate arithmetic and boolean expressions. Finally, we provide an implementation of Hoare's logic which will allow us to deduce facts about programs without the need for a full evaluation.
Launchers and Targets in Social Networks
Venue: arXiv cs.SI 2021
Year: 2021

Influence propagation in social networks is a subject of growing interest. A relevant issue in those networks involves the identification of key influencers. These players have an important role on viral marketing strategies and message propagation, including political propaganda and fake news. In effect, an important way to fight malicious usage on social networks is to understand their properties, their structure and the way messages propagate. This paper proposes two new indices for analysing message propagation in social networks, based on the network topological nature and the power of the message. The first index involves the strength of each node as a launcher of the message, dividing the nodes into launchers and non-launchers. The second index addresses the potential of each member as a receiver (target) of the message, dividing the nodes into targets and non-targets. Launcher individuals should indicate strong influencers and target individuals should identify the best target consumers. These indices can assist other known metrics when used to select efficient influencers in a social network. For instance, instead of choosing a strong and probably expensive member according to its degree in the network (number of followers), we may previously select those belonging to the launchers group and look for the lowest degree members, which are probably cheaper but still guarantying almost the same influence effectiveness as the largest degree members. On a different direction, using the second index, the strong target members should characterize relevant consumers of information in the network, which may include fake news' regular collectors. We discuss these indices using small-world randomly generated graphs and a number of real-world social networks available in known datasets repositories.
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding
Venue: arXiv cs.CL 2021
Year: 2021

A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages. Due to the lack of pretrained ALBERT models for Korean language, the best available practice is the multilingual model or resorting back to the any other BERT-based model. In this paper, we develop and pretrain KoreALBERT, a monolingual ALBERT model specifically for Korean language understanding. We introduce a new training objective, namely Word Order Prediction (WOP), and use alongside the existing MLM and SOP criteria to the same architecture and model parameters. Despite having significantly fewer model parameters (thus, quicker to train), our pretrained KoreALBERT outperforms its BERT counterpart on 6 different NLU tasks. Consistent with the empirical results in English by Lan et al., KoreALBERT seems to improve downstream task performance involving multi-sentence encoding for Korean language. The pretrained KoreALBERT is publicly available to encourage research and application development for Korean NLP.
A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis
Venue: arXiv cs.CL 2021
Year: 2021

We propose a new Named entity recognition (NER) method to effectively make use of the results of Part-of-speech (POS) tagging, Chinese word segmentation (CWS) and parsing while avoiding NER error caused by POS tagging error. This paper first uses Stanford natural language process (NLP) tool to annotate large-scale untagged data so as to reduce the dependence on the tagged data; then a new NLP model, g-BERT model, is designed to compress Bidirectional Encoder Representations from Transformers (BERT) model in order to reduce calculation quantity; finally, the model is evaluated based on Chinese NER dataset. The experimental results show that the calculation quantity in g-BERT model is reduced by 60% and performance improves by 2% with Test F1 to 96.5 compared with that in BERT model.
Analysis of Basic Emotions in Texts Based on BERT Vector Representation
Venue: arXiv cs.CL 2021
Year: 2021

In the following paper the authors present a GAN-type model and the most important stages of its development for the task of emotion recognition in text. In particular, we propose an approach for generating a synthetic dataset of all possible emotions combinations based on manually labelled incomplete data.
Machine learning with limited data
Venue: arXiv cs.CV 2021
Year: 2021

Thanks to the availability of powerful computing resources, big data and deep learning algorithms, we have made great progress on computer vision in the last few years. Computer vision systems begin to surpass humans in some tasks, such as object recognition, object detection, face recognition and pose estimation. Lots of computer vision algorithms have been deployed to real world applications and started to improve our life quality. However, big data and labels are not always available. Sometimes we only have very limited labeled data, such as medical images which requires experts to label them. In this paper, we study few shot image classification, in which we only have very few labeled data. Machine learning with little data is a big challenge. To tackle this challenge, we propose two methods and test their effectiveness thoroughly. One method is to augment image features by mixing the style of these images. The second method is applying spatial attention to explore the relations between patches of images. We also find that domain shift is a critical issue in few shot learning when the training domain and testing domain are different. So we propose a more realistic cross-domain few-shot learning with unlabeled data setting, in which some unlabeled data is available in the target domain. We propose two methods in this setting. Our first method transfers the style information of the unlabeled target dataset to the samples in the source dataset and trains a model with stylized images and original images. Our second method proposes a unified framework to fully utilize all the data. Both of our methods surpass the baseline method by a large margin.
Normalization for Cubical Type Theory
Venue: arXiv cs.LO 2021
Year: 2021

We prove normalization for (univalent, Cartesian) cubical type theory, closing the last major open problem in the syntactic metatheory of cubical type theory. Our normalization result is reduction-free, in the sense of yielding a bijection between equivalence classes of terms in context and a tractable language of $β/η$-normal forms. As corollaries we obtain both decidability of judgmental equality and the injectivity of type constructors.
Evolution of artificial intelligence languages, a systematic literature review
Venue: arXiv cs.AI 2021
Year: 2021

The field of Artificial Intelligence (AI) has undoubtedly received significant attention in recent years. AI is being adopted to provide solutions to problems in fields such as medicine, engineering, education, government and several other domains. In order to analyze the state of the art of research in the field of AI, we present a systematic literature review focusing on the Evolution of AI programming languages. We followed the systematic literature review method by searching relevant databases like SCOPUS, IEEE Xplore and Google Scholar. EndNote reference manager was used to catalog the relevant extracted papers. Our search returned a total of 6565 documents, whereof 69 studies were retained. Of the 69 retained studies, 15 documents discussed LISP programming language, another 34 discussed PROLOG programming language, the remaining 20 documents were spread between Logic and Object Oriented Programming (LOOP), ARCHLOG, Epistemic Ontology Language with Constraints (EOLC), Python, C++, ADA and JAVA programming languages. This review provides information on the year of implementation, development team, capabilities, limitations and applications of each of the AI programming languages discussed. The information in this review could guide practitioners and researchers in AI to make the right choice of languages to implement their novel AI methods.
Polynomial Time $k$-Shortest Multi-Criteria Prioritized and All-Criteria-Disjoint Paths
Venue: arXiv cs.DS 2021
Year: 2021

The shortest secure path (routing) problem in communication networks has to deal with multiple attack layers e.g., man-in-the-middle, eavesdropping, packet injection, packet insertion, etc. Consider different probabilities for each such attack over an edge, probabilities that can differ across edges. Furthermore, usage of a single shortest path (for routing) implies possible traffic bottleneck, which should be avoided if possible, which we term pathneck security avoidance. Finding all Pareto-optimal solutions for the multi-criteria single-source single-destination shortest secure path problem with non-negative edge lengths might yield a solution with an exponential number of paths. In the first part of this paper, we study specific settings of the multi-criteria shortest secure path problem, which are based on prioritized multi-criteria and on $k$-shortest secure paths. In the second part, we show a polynomial-time algorithm that, given an undirected graph $G$ and a pair of vertices $(s,t)$, finds prioritized multi-criteria $2$-disjoint (vertex/edge) shortest secure paths between $s$ and $t$. In the third part of the paper, we introduce the $k$-disjoint all-criteria-shortest secure paths problem, which is solved in time $O(\min(k|E|, |E|^{3/2}))$.
Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
Venue: arXiv cs.CV 2021
Year: 2021

We introduce SynSE, a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence embedding and embeddings of Parts of Speech (PoS) tagged words in the corresponding action description. We deploy SynSE for the task of skeleton-based action sequence recognition. Our design choices enable SynSE to generalize compositionally, i.e., recognize sequences whose action descriptions contain words not encountered during training. We also extend our approach to the more challenging Generalized Zero-Shot Learning (GZSL) problem via a confidence-based gating mechanism. We are the first to present zero-shot skeleton action recognition results on the large-scale NTU-60 and NTU-120 skeleton action datasets with multiple splits. Our results demonstrate SynSE's state of the art performance in both ZSL and GZSL settings compared to strong baselines on the NTU-60 and NTU-120 datasets. The code and pretrained models are available at https://github.com/skelemoa/synse-zsl
Efficient Graph Deep Learning in TensorFlow with tf_geometric
Venue: arXiv cs.LG 2021
Year: 2021

We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs) as well as implementations of popular GNNs. The kernel libraries consist of infrastructures for building efficient GNNs, including graph data structures, graph map-reduce framework, graph mini-batch strategy, etc. These infrastructures enable tf_geometric to support single-graph computation, multi-graph computation, graph mini-batch, distributed training, etc.; therefore, tf_geometric can be used for a variety of graph deep learning tasks, such as transductive node classification, inductive node classification, link prediction, and graph classification. Based on the kernel libraries, tf_geometric implements a variety of popular GNN models for different tasks. To facilitate the implementation of GNNs, tf_geometric also provides some other libraries for dataset management, graph sampling, etc. Different from existing popular GNN libraries, tf_geometric provides not only Object-Oriented Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to handle advanced graph deep learning tasks such as graph meta-learning. The APIs of tf_geometric are friendly, and they are suitable for both beginners and experts. In this paper, we first present an overview of tf_geometric's framework. Then, we conduct experiments on some benchmark datasets and report the performance of several popular GNN models implemented by tf_geometric.
Closed-form Quadrangulation of N-Sided Patches
Venue: arXiv cs.GR 2021
Year: 2021

We analyze the problem of quadrangulating a $n$-sided patch, each side at its boundary subdivided into a given number of edges, using a single irregular vertex (or none, when $n = 4$) that breaks the otherwise fully regular lattice. We derive, in an analytical closed-form, (1) the necessary and sufficient conditions that a patch must meet to admit this quadrangulation, and (2) a full description of the resulting tessellation(s).
Dexterous Manipulation Primitives for the Real Robot Challenge
Venue: arXiv cs.RO 2021
Year: 2021

This report describes our approach for Phase 3 of the Real Robot Challenge. To solve cuboid manipulation tasks of varying difficulty, we decompose each task into the following primitives: moving the fingers to the cuboid to grasp it, turning it on the table to minimize orientation error, and re-positioning it to the goal position. We use model-based trajectory optimization and control to plan and execute these primitives. These grasping, turning, and re-positioning primitives are sequenced with a state-machine that determines which primitive to execute given the current object state and goal. Our method shows robust performance over multiple runs with randomized initial and goal positions. With this approach, our team placed second in the challenge, under the anonymous name "sombertortoise" on the leaderboard. Example runs of our method solving each of the four levels can be seen in this video (https://www.youtube.com/watch?v=I65Kwu9PGmg&list=PLt9QxrtaftrHGXcp4Oh8-s_OnQnBnLtei&index=1).
LS-HDIB: A Large Scale Handwritten Document Image Binarization Dataset
Venue: arXiv cs.CV 2021
Year: 2021

Handwritten document image binarization is challenging due to high variability in the written content and complex background attributes such as page style, paper quality, stains, shadow gradients, and non-uniform illumination. While the traditional thresholding methods do not effectively generalize on such challenging real-world scenarios, deep learning-based methods have performed relatively well when provided with sufficient training data. However, the existing datasets are limited in size and diversity. This work proposes LS-HDIB - a large-scale handwritten document image binarization dataset containing over a million document images that span numerous real-world scenarios. Additionally, we introduce a novel technique that uses a combination of adaptive thresholding and seamless cloning methods to create the dataset with accurate ground truths. Through an extensive quantitative and qualitative evaluation over eight different deep learning based models, we demonstrate the enhancement in the performance of these models when trained on the LS-HDIB dataset and tested on unseen images.
Hadamard Extensions and the Identification of Mixtures of Product Distributions
Venue: arXiv cs.LG 2021
Year: 2021

The Hadamard Extension of a matrix is the matrix consisting of all Hadamard products of subsets of its rows. This construction arises in the context of identifying a mixture of product distributions on binary random variables: full column rank of such extensions is a necessary ingredient of identification algorithms. We provide several results concerning when a Hadamard Extension has full column rank.
Art and Science Interaction Lab -- A highly flexible and modular interaction science research facility
Venue: arXiv cs.HC 2021
Year: 2021

The Art and Science Interaction Lab (ASIL) is a unique, highly flexible and modular interaction science research facility to effectively bring, analyse and test experiences and interactions in mixed virtual/augmented contexts as well as to conduct research on next-gen immersive technologies. It brings together the expertise and creativity of engineers, performers, designers and scientists creating solutions and experiences shaping the lives of people. The lab is equipped with state-of-the-art visual, auditory and user-tracking equipment, fully synchronized and connected to a central backend. This synchronization allows for highly accurate multi-sensor measurements and analysis.