OpenRaiser/Intern-Atlas huggingface dataset:
Forecasting of COVID-19 Cases, Using an Evolutionary Neural Architecture Search Approach
Venue: arXiv cs.NE 2021
Year: 2021

In late 2019, COVID-19, a severe respiratory disease, emerged, and since then, the world has been facing a deadly pandemic caused by it. This ongoing pandemic has had a significant effect on different aspects of societies. The uncertainty around the number of daily cases made it difficult for decision-makers to control the outbreak. Deep Learning models have proved that they can come in handy in many real-world problems such as healthcare ones. However, they require a lot of data to learn the features properly and output an acceptable solution. Since COVID-19 has been a lately emerged disease, there was not much data available, especially in the first stage of the pandemic, and this shortage of data makes it challenging to design an optimized model. To overcome these problems, we first introduce a new dataset with augmented features and then forecast COVID-19 cases with a new approach, using an evolutionary neural architecture search with Binary Bat Algorithm (BBA) to generate an optimized deep recurrent network. Finally, to show our approach's effectiveness, we conducted a comparative study on Iran's COVID-19 daily cases. The results prove our approach's capability to generate an accurate deep architecture to forecast the pandemic cases, even in the early stages with limited data.
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions
Venue: arXiv cs.CL 2021
Year: 2021

Existing text-to-SQL research only considers complete questions as the input, but lay-users might strive to formulate a complete question. To build a smarter natural language interface to database systems (NLIDB) that also processes incomplete questions, we propose a new task, prefix-to-SQL which takes question prefix from users as the input and predicts the intended SQL. We construct a new benchmark called PAGSAS that contains 124K user question prefixes and the intended SQL for 5 sub-tasks Advising, GeoQuery, Scholar, ATIS, and Spider. Additionally, we propose a new metric SAVE to measure how much effort can be saved by users. Experimental results show that PAGSAS is challenging even for strong baseline models such as T5. As we observe the difficulty of prefix-to-SQL is related to the number of omitted tokens, we incorporate curriculum learning of feeding examples with an increasing number of omitted tokens. This improves scores on various sub-tasks by as much as 9% recall scores on sub-task GeoQuery in PAGSAS.
Estimating Angle of Arrival (AoA) of multiple Echoes in a Steering Vector Space
Venue: arXiv cs.SD 2021
Year: 2021

Consider a microphone array, such as those present in Amazon Echos, conference phones, or self-driving cars. One of the goals of these arrays is to decode the angles in which acoustic signals arrive at them. This paper considers the problem of estimating K angle of arrivals (AoA), i.e., the direct path's AoA and the AoA of subsequent echoes. Significant progress has been made on this problem, however, solutions remain elusive when the source signal is unknown (such as human voice) and the channel is strongly correlated (such as in multipath settings). Today's algorithms reliably estimate the direct-path-AoA, but the subsequent AoAs diverge in noisy real-world conditions. We design SubAoA, an algorithm that improves on the current body of work. Our core idea models signal in a new AoA sub-space, and employs a cancellation approach that successively cancels each AoA to decode the next. We explain the behavior and complexity of the algorithm from the first principles, simulate the performance across a range of parameters, and present results from real-world experiments. Comparison against multiple existing algorithms like GCC-PHAT, MUSIC, and VoLoc shows increasing gains for the latter AoAs, while our computation complexity allows real-time operation. We believe progress in multi-AoA estimation is a fundamental building block to various acoustic and RF applications, including human or vehicle localization, multi-user separation, and even (blind) channel estimation.
Towards the Classification of Error-Related Potentials using Riemannian Geometry
Venue: arXiv cs.LG 2021
Year: 2021

The error-related potential (ErrP) is an event-related potential (ERP) evoked by an experimental participant's recognition of an error during task performance. ErrPs, originally described by cognitive psychologists, have been adopted for use in brain-computer interfaces (BCIs) for the detection and correction of errors, and the online refinement of decoding algorithms. Riemannian geometry-based feature extraction and classification is a new approach to BCI which shows good performance in a range of experimental paradigms, but has yet to be applied to the classification of ErrPs. Here, we describe an experiment that elicited ErrPs in seven normal participants performing a visual discrimination task. Audio feedback was provided on each trial. We used multi-channel electroencephalogram (EEG) recordings to classify ErrPs (success/failure), comparing a Riemannian geometry-based method to a traditional approach that computes time-point features. Overall, the Riemannian approach outperformed the traditional approach (78.2% versus 75.9% accuracy, p < 0.05); this difference was statistically significant (p < 0.05) in three of seven participants. These results indicate that the Riemannian approach better captured the features from feedback-elicited ErrPs, and may have application in BCI for error detection and correction.
MFEViT: A Robust Lightweight Transformer-based Network for Multimodal 2D+3D Facial Expression Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Vision transformer (ViT) has been widely applied in many areas due to its self-attention mechanism that help obtain the global receptive field since the first layer. It even achieves surprising performance exceeding CNN in some vision tasks. However, there exists an issue when leveraging vision transformer into 2D+3D facial expression recognition (FER), i.e., ViT training needs mass data. Nonetheless, the number of samples in public 2D+3D FER datasets is far from sufficient for evaluation. How to utilize the ViT pre-trained on RGB images to handle 2D+3D data becomes a challenge. To solve this problem, we propose a robust lightweight pure transformer-based network for multimodal 2D+3D FER, namely MFEViT. For narrowing the gap between RGB and multimodal data, we devise an alternative fusion strategy, which replaces each of the three channels of an RGB image with the depth-map channel and fuses them before feeding them into the transformer encoder. Moreover, the designed sample filtering module adds several subclasses for each expression and move the noisy samples to their corresponding subclasses, thus eliminating their disturbance on the network during the training stage. Extensive experiments demonstrate that our MFEViT outperforms state-of-the-art approaches with an accuracy of 90.83% on BU-3DFE and 90.28% on Bosphorus. Specifically, the proposed MFEViT is a lightweight model, requiring much fewer parameters than multi-branch CNNs. To the best of our knowledge, this is the first work to introduce vision transformer into multimodal 2D+3D FER. The source code of our MFEViT will be publicly available online.
Automated Mining of Leaderboards for Empirical AI Research
Venue: arXiv cs.CL 2021
Year: 2021

With the rapid growth of research publications, empowering scientists to keep oversight over the scientific progress is of paramount importance. In this regard, the Leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PapersWithCode among others are devoted to the construction of Leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be directly useful for scientists to keep track of research progress. The construction of Leaderboards could be greatly expedited with automated text mining. This study presents a comprehensive approach for generating Leaderboards for knowledge-graph-based scholarly information organization. Specifically, we investigate the problem of automated Leaderboard construction using state-of-the-art transformer models, viz. Bert, SciBert, and XLNet. Our analysis reveals an optimal approach that significantly outperforms existing baselines for the task with evaluation scores above 90% in F1. This, in turn, offers new state-of-the-art results for Leaderboard extraction. As a result, a vast share of empirical AI research can be organized in the next-generation digital libraries as knowledge graphs.
Evolving Digital Circuits for the Knapsack Problem
Venue: arXiv cs.NE 2021
Year: 2021

Multi Expression Programming (MEP) is a Genetic Programming variant that uses linear chromosomes for solution encoding. A unique feature of MEP is its ability of encoding multiple solutions of a problem in a single chromosome. In this paper we use Multi Expression Programming for evolving digital circuits for a well-known NP-Complete problem: the knapsack (subset sum) problem. Numerical experiments show that Multi Expression Programming performs well on the considered test problems.
Dynamic Allocation of Visual Attention for Vision-based Autonomous Navigation under Data Rate Constraints
Venue: arXiv cs.RO 2021
Year: 2021

This paper considers the problem of task-dependent (top-down) attention allocation for vision-based autonomous navigation using known landmarks. Unlike the existing paradigm in which landmark selection is formulated as a combinatorial optimization problem, we model it as a resource allocation problem where the decision-maker (DM) is granted extra freedom to control the degree of attention to each landmark. The total resource available to DM is expressed in terms of the capacity limit of the in-take information flow, which is quantified by the directed information from the state of the environment to the DM's observation. We consider a receding horizon implementation of such a controlled sensing scheme in the Linear-Quadratic-Gaussian (LQG) regime. The convex-concave procedure is applied in each time step, whose time complexity is shown to be linear in the horizon length if the alternating direction method of multipliers (ADMM) is used. Numerical studies show that the proposed formulation is sparsity-promoting in the sense that it tends to allocate zero data rate to uninformative landmarks.
Path Based Hierarchical Clustering on Knowledge Graphs
Venue: arXiv cs.AI 2021
Year: 2021

Knowledge graphs have emerged as a widely adopted medium for storing relational data, making methods for automatically reasoning with them highly desirable. In this paper, we present a novel approach for inducing a hierarchy of subject clusters, building upon our earlier work done in taxonomy induction. Our method first constructs a tag hierarchy before assigning subjects to clusters on this hierarchy. We quantitatively demonstrate our method's ability to induce a coherent cluster hierarchy on three real-world datasets.
SAU: Smooth activation function using convolution with approximate identities
Venue: arXiv cs.LG 2021
Year: 2021

Well-known activation functions like ReLU or Leaky ReLU are non-differentiable at the origin. Over the years, many smooth approximations of ReLU have been proposed using various smoothing techniques. We propose new smooth approximations of a non-differentiable activation function by convolving it with approximate identities. In particular, we present smooth approximations of Leaky ReLU and show that they outperform several well-known activation functions in various datasets and models. We call this function Smooth Activation Unit (SAU). Replacing ReLU by SAU, we get 5.12% improvement with ShuffleNet V2 (2.0x) model on CIFAR100 dataset.
Challenges and Opportunities of Speech Recognition for Bengali Language
Venue: arXiv cs.CL 2021
Year: 2021

Speech recognition is a fascinating process that offers the opportunity to interact and command the machine in the field of human-computer interactions. Speech recognition is a language-dependent system constructed directly based on the linguistic and textual properties of any language. Automatic Speech Recognition (ASR) systems are currently being used to translate speech to text flawlessly. Although ASR systems are being strongly executed in international languages, ASR systems' implementation in the Bengali language has not reached an acceptable state. In this research work, we sedulously disclose the current status of the Bengali ASR system's research endeavors. In what follows, we acquaint the challenges that are mostly encountered while constructing a Bengali ASR system. We split the challenges into language-dependent and language-independent challenges and guide how the particular complications may be overhauled. Following a rigorous investigation and highlighting the challenges, we conclude that Bengali ASR systems require specific construction of ASR architectures based on the Bengali language's grammatical and phonetic structure.
Using Pause Information for More Accurate Entity Recognition
Venue: arXiv cs.CL 2021
Year: 2021

Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants. However, current systems struggle to accurately parse spoken queries with the typical use of text input alone, and often fail to understand the user intent. Previous work in linguistics has identified a cross-language tendency for longer speech pauses surrounding nouns as compared to verbs. We demonstrate that the linguistic observation on pauses can be used to improve accuracy in machine-learnt language understanding tasks. Analysis of pauses in French and English utterances from a commercial voice assistant shows the statistically significant difference in pause duration around multi-token entity span boundaries compared to within entity spans. Additionally, in contrast to text-based NLU, we apply pause duration to enrich contextual embeddings to improve shallow parsing of entities. Results show that our proposed novel embeddings improve the relative error rate by up to 8% consistently across three domains for French, without any added annotation or alignment costs to the parser.
PASS: An ImageNet replacement for self-supervised pretraining without humans
Venue: arXiv cs.CV 2021
Year: 2021

Computer vision has long relied on ImageNet and other large datasets of images sampled from the Internet for pretraining models. However, these datasets have ethical and technical shortcomings, such as containing personal information taken without consent, unclear license usage, biases, and, in some cases, even problematic image content. On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining. We thus propose an unlabelled dataset PASS: Pictures without humAns for Self-Supervision. PASS only contains images with CC-BY license and complete attribution metadata, addressing the copyright issue. Most importantly, it contains no images of people at all, and also avoids other types of images that are problematic for data protection or ethics. We show that PASS can be used for pretraining with methods such as MoCo-v2, SwAV and DINO. In the transfer learning setting, it yields similar downstream performances to ImageNet pretraining even on tasks that involve humans, such as human pose estimation. PASS does not make existing datasets obsolete, as for instance it is insufficient for benchmarking. However, it shows that model pretraining is often possible while using safer data, and it also provides the basis for a more robust evaluation of pretraining methods.
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning
Venue: arXiv cs.LG 2021
Year: 2021

Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great interest in the field of artificial intelligence. Probabilistic graphical models (PGMs) have been recognized as a powerful tool for modeling complex systems with many advantages, e.g., the ability to handle uncertainty and possessing good interpretability. Considering the success of these two aforementioned research areas, it seems natural to apply PGMs to transfer learning. However, although there are already some excellent PGMs specific to transfer learning in the literature, the potential of PGMs for this problem is still grossly underestimated. This paper aims to boost the development of PGMs for transfer learning by 1) examining the pilot studies on PGMs specific to transfer learning, i.e., analyzing and summarizing the existing mechanisms particularly designed for knowledge transfer; 2) discussing examples of real-world transfer problems where existing PGMs have been successfully applied; and 3) exploring several potential research directions on transfer learning using PGM.
Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network
Venue: arXiv cs.LG 2021
Year: 2021

Accurate lake temperature estimation is essential for numerous problems tackled in both hydrological and ecological domains. Nowadays physical models are developed to estimate lake dynamics; however, computations needed for accurate estimation of lake surface temperature can get prohibitively expensive. We propose to aggregate simulations of lake temperature at a certain depth together with a range of meteorological features to probabilistically estimate lake surface temperature. Accordingly, we introduce a spatio-temporal neural network that combines Bayesian recurrent neural networks and Bayesian graph convolutional neural networks. This work demonstrates that the proposed graphical model can deliver homogeneously good performance covering the whole lake surface despite having sparse training data available. Quantitative results are compared with a state-of-the-art Bayesian deep learning method. Code for the developed architectural layers, as well as demo scripts, are available on https://renkulab.io/projects/das/bstnn.
CS Education for the Socially-Just Worlds We Need: The Case for Justice-Centered Approaches to CS in Higher Education
Venue: arXiv cs.CY 2021
Year: 2021

Justice-centered approaches to equitable computer science (CS) education frame CS learning as a means for advancing peace, antiracism, and social justice rather than war, empire, and corporations. However, most research in justice-centered approaches in CS education focus on K-12 learning environments. In this position paper, we review justice-centered approaches to CS education, problematize the lack of justice-centered approaches to CS in higher education in particular, and describe a justice-centered approach for undergraduate Data Structures and Algorithms. Our approach emphasizes three components: (1) ethics: critiques the sociopolitical values of data structure and algorithm design as well as the underlying logics of dominant computing culture; (2) identity: draws on culturally responsive-sustaining pedagogies to emphasize student identity as rooted in resistance to the dominant computing culture; and (3) political vision: ensures the rightful presence of political struggles by reauthoring rights to frame CS learning as a force for social justice. Through a case study of this Critical Comparative Data Structures and Algorithms pedagogy, we argue that justice-centered approaches to higher CS education can help all computing students not only learn about the ethical implications of nominally technical concepts, but also develop greater respect for diverse epistemologies, cultures, and experiences surrounding computing that are essential to creating the socially-just worlds we need.
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
Venue: arXiv cs.LG 2021
Year: 2021

Optimization-based meta-learning typically assumes tasks are sampled from a single distribution - an assumption oversimplifies and limits the diversity of tasks that meta-learning can model. Handling tasks from multiple different distributions is challenging for meta-learning due to a so-called task ambiguity issue. This paper proposes a novel method, ST-MAML, that empowers model-agnostic meta-learning (MAML) to learn from multiple task distributions. ST-MAML encodes tasks using a stochastic neural network module, that summarizes every task with a stochastic representation. The proposed Stochastic Task (ST) strategy allows a meta-model to get tailored for the current task and enables us to learn a distribution of solutions for an ambiguous task. ST-MAML also propagates the task representation to revise the encoding of input variables. Empirically, we demonstrate that ST-MAML matches or outperforms the state-of-the-art on two few-shot image classification tasks, one curve regression benchmark, one image completion problem, and a real-world temperature prediction application. To the best of authors' knowledge, this is the first time optimization-based meta-learning method being applied on a large-scale real-world task.
Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients
Venue: arXiv cs.RO 2021
Year: 2021

In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations. This is achieved by building a differentiable data-driven simulator on top of perception outputs and high-fidelity HD maps of the area. It allows us to synthesize new driving experiences from existing demonstrations using mid-level representations. Using this simulator we then train a policy network in closed-loop employing policy gradients. We train our proposed method on 100 hours of expert demonstrations on urban roads and show that it learns complex driving policies that generalize well and can perform a variety of driving maneuvers. We demonstrate this in simulation as well as deploy our model to self-driving vehicles in the real-world. Our method outperforms previously demonstrated state-of-the-art for urban driving scenarios -- all this without the need for complex state perturbations or collecting additional on-policy data during training. We make code and data publicly available.
Evolving reversible circuits for the even-parity problem
Venue: arXiv cs.NE 2021
Year: 2021

Reversible computing basically means computation with less or not at all electrical power. Since the standard binary gates are not usually reversible we use the Fredkin gate in order to achieve reversibility. An algorithm for designing reversible digital circuits is described in this paper. The algorithm is based on Multi Expression Programming (MEP), a Genetic Programming variant with a linear representation of individuals. The case of digital circuits for the even-parity problem is investigated. Numerical experiments show that the MEP-based algorithm is able to easily design reversible digital circuits for up to the even-8-parity problem.