OpenRaiser/Intern-Atlas huggingface dataset:
Fast active learning for pure exploration in reinforcement learning
Venue: arXiv cs.LG 2020
Year: 2020

Realistic environments often provide agents with very limited feedback. When the environment is initially unknown, the feedback, in the beginning, can be completely absent, and the agents may first choose to devote all their effort on exploring efficiently. The exploration remains a challenge while it has been addressed with many hand-tuned heuristics with different levels of generality on one side, and a few theoretically-backed exploration strategies on the other. Many of them are incarnated by intrinsic motivation and in particular explorations bonuses. A common rule of thumb for exploration bonuses is to use $1/\sqrt{n}$ bonus that is added to the empirical estimates of the reward, where $n$ is a number of times this particular state (or a state-action pair) was visited. We show that, surprisingly, for a pure-exploration objective of reward-free exploration, bonuses that scale with $1/n$ bring faster learning rates, improving the known upper bounds with respect to the dependence on the horizon $H$. Furthermore, we show that with an improved analysis of the stopping time, we can improve by a factor $H$ the sample complexity in the best-policy identification setting, which is another pure-exploration objective, where the environment provides rewards but the agent is not penalized for its behavior during the exploration phase.
Improved 3-pass Algorithm for Counting 4-cycles in Arbitrary Order Streaming
Venue: arXiv cs.DS 2020
Year: 2020

The problem of counting small subgraphs, and specifically cycles, in the streaming model received a lot of attention over the past few years. In this paper, we consider arbitrary order insertion-only streams, improving over the state-of-the-art result on counting 4-cycles. Our algorithm computes a $(1+ε)$-approximation by taking three passes over the stream and using space $O(\frac{m \log n}{ε^2 T^{1/3}})$, where $m$ is the number of edges in the graph and $T$ is the number of 4-cycles.
Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation
Venue: arXiv cs.AR 2020
Year: 2020

Deep neural networks yield the state of the art results in many computer vision and human machine interface tasks such as object recognition, speech recognition etc. Since, these networks are computationally expensive, customized accelerators are designed for achieving the required performance at lower cost and power. One of the key building blocks of these neural networks is non-linear activation function such as sigmoid, hyperbolic tangent (tanh), and ReLU. A low complexity accurate hardware implementation of the activation function is required to meet the performance and area targets of the neural network accelerators. This paper presents an implementation of tanh function using the Catmull-Rom spline interpolation. State of the art results are achieved using this method with comparatively smaller logic area.
The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study
Venue: arXiv cs.CV 2020
Year: 2020

Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.
Detecting Transaction-based Tax Evasion Activities on Social Media Platforms Using Multi-modal Deep Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Social media platforms now serve billions of users by providing convenient means of communication, content sharing and even payment between different users. Due to such convenient and anarchic nature, they have also been used rampantly to promote and conduct business activities between unregistered market participants without paying taxes. Tax authorities worldwide face difficulties in regulating these hidden economy activities by traditional regulatory means. This paper presents a machine learning based Regtech tool for international tax authorities to detect transaction-based tax evasion activities on social media platforms. To build such a tool, we collected a dataset of 58,660 Instagram posts and manually labelled 2,081 sampled posts with multiple properties related to transaction-based tax evasion activities. Based on the dataset, we developed a multi-modal deep neural network to automatically detect suspicious posts. The proposed model combines comments, hashtags and image modalities to produce the final output. As shown by our experiments, the combined model achieved an AUC of 0.808 and F1 score of 0.762, outperforming any single modality models. This tool could help tax authorities to identify audit targets in an efficient and effective manner, and combat social e-commerce tax evasion in scale.
Coupling Learning of Complex Interactions
Venue: arXiv cs.LG 2020
Year: 2020

Complex applications such as big data analytics involve different forms of coupling relationships that reflect interactions between factors related to technical, business (domain-specific) and environmental (including socio-cultural and economic) aspects. There are diverse forms of couplings embedded in poor-structured and ill-structured data. Such couplings are ubiquitous, implicit and/or explicit, objective and/or subjective, heterogeneous and/or homogeneous, presenting complexities to existing learning systems in statistics, mathematics and computer sciences, such as typical dependency, association and correlation relationships. Modeling and learning such couplings thus is fundamental but challenging. This paper discusses the concept of coupling learning, focusing on the involvement of coupling relationships in learning systems. Coupling learning has great potential for building a deep understanding of the essence of business problems and handling challenges that have not been addressed well by existing learning theories and tools. This argument is verified by several case studies on coupling learning, including handling coupling in recommender systems, incorporating couplings into coupled clustering, coupling document clustering, coupled recommender algorithms and coupled behavior analysis for groups.
HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics
Venue: arXiv cs.DC 2020
Year: 2020

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.
Oblique Predictive Clustering Trees
Venue: arXiv cs.LG 2020
Year: 2020

Predictive clustering trees (PCTs) are a well established generalization of standard decision trees, which can be used to solve a variety of predictive modeling tasks, including structured output prediction. Combining them into ensembles yields state-of-the-art performance. Furthermore, the ensembles of PCTs can be interpreted by calculating feature importance scores from the learned models. However, their learning time scales poorly with the dimensionality of the output space. This is often problematic, especially in (hierarchical) multi-label classification, where the output can consist of hundreds of potential labels. Also, learning of PCTs can not exploit the sparsity of data to improve the computational efficiency, which is common in both input (molecular fingerprints, bag of words representations) and output spaces (in multi-label classification, examples are often labeled with only a fraction of possible labels). In this paper, we propose oblique predictive clustering trees, capable of addressing these limitations. We design and implement two methods for learning oblique splits that contain linear combinations of features in the tests, hence a split corresponds to an arbitrary hyperplane in the input space. The methods are efficient for high dimensional data and capable of exploiting sparse data. We experimentally evaluate the proposed methods on 60 benchmark datasets for 6 predictive modeling tasks. The results of the experiments show that oblique predictive clustering trees achieve performance on-par with state-of-the-art methods and are orders of magnitude faster than standard PCTs. We also show that meaningful feature importance scores can be extracted from the models learned with the proposed methods.
Index Coding at the WiFi Edge: An Implementation Study for Video Delivery
Venue: arXiv cs.NI 2020
Year: 2020

HTTP based video streaming has become the de facto standard for video content delivery across different video streaming services. However, video content delivery continues to be challenged at the wireless edge by inadequate and highly variable bandwidth. In this paper, we describe WiCode, a platform that improves HTTP based video content delivery at the WiFi edge. WiCode uses coded delivery at the WiFi AP to reduce data transmissions in order to improve the perceived performance of video streaming at the users. WiCode performs index coding on video segments to reduce the number of bits transmitted. Further, it also performs index coding on UDP packets that are retransmitted to reduce the number of bits transmitted. This paper describes the design and implementation of WiCode, and the practical gains achievable due to employing coded delivery in a real system taking into account the overheads introduced by WiCode. The WiCode module at the client side is a browser plugin that does not require any client side device configuration changes. We also show the effect of variable and fixed length segment size on the perceived performance of WiCode.
On using Product-Specific Schema.org from Web Data Commons: An Empirical Set of Best Practices
Venue: arXiv cs.IR 2020
Year: 2020

Schema.org has experienced high growth in recent years. Structured descriptions of products embedded in HTML pages are now not uncommon, especially on e-commerce websites. The Web Data Commons (WDC) project has extracted schema.org data at scale from webpages in the Common Crawl and made it available as an RDF `knowledge graph' at scale. The portion of this data that specifically describes products offers a golden opportunity for researchers and small companies to leverage it for analytics and downstream applications. Yet, because of the broad and expansive scope of this data, it is not evident whether the data is usable in its raw form. In this paper, we do a detailed empirical study on the product-specific schema.org data made available by WDC. Rather than simple analysis, the goal of our study is to devise an empirically grounded set of best practices for using and consuming WDC product-specific schema.org data. Our studies reveal six best practices, each of which is justified by experimental data and analysis.
Resource-rational Task Decomposition to Minimize Planning Costs
Venue: arXiv cs.AI 2020
Year: 2020

People often plan hierarchically. That is, rather than planning over a monolithic representation of a task, they decompose the task into simpler subtasks and then plan to accomplish those. Although much work explores how people decompose tasks, there is less analysis of why people decompose tasks in the way they do. Here, we address this question by formalizing task decomposition as a resource-rational representation problem. Specifically, we propose that people decompose tasks in a manner that facilitates efficient use of limited cognitive resources given the structure of the environment and their own planning algorithms. Using this model, we replicate several existing findings. Our account provides a normative explanation for how people identify subtasks as well as a framework for studying how people reason, plan, and act using resource-rational representations.
Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence Sensing
Venue: arXiv cs.LG 2020
Year: 2020

The classical approach to non-linear regression in physics, is to take a mathematical model describing the functional dependence of the dependent variable from a set of independent variables, and then, using non-linear fitting algorithms, extract the parameters used in the modeling. Particularly challenging are real systems, characterized by several additional influencing factors related to specific components, like electronics or optical parts. In such cases, to make the model reproduce the data, empirically determined terms are built-in the models to compensate for the impossibility of modeling things that are, by construction, impossible to model. A new approach to solve this issue is to use neural networks, particularly feed-forward architectures with a sufficient number of hidden layers and an appropriate number of output neurons, each responsible for predicting the desired variables. Unfortunately, feed-forward neural networks (FFNNs) usually perform less efficiently when applied to multi-dimensional regression problems, that is when they are required to predict simultaneously multiple variables that depend from the input dataset in fundamentally different ways. To address this problem, we propose multi-task learning (MTL) architectures. These are characterized by multiple branches of task-specific layers, which have as input the output of a common set of layers. To demonstrate the power of this approach for multi-dimensional regression, the method is applied to luminescence sensing. Here the MTL architecture allows predicting multiple parameters, the oxygen concentration and the temperature, from a single set of measurements.
Work Practices and Perceptions from Women Core Developers in OSS Communities
Venue: arXiv cs.SE 2020
Year: 2020

The effect of gender diversity in open source communities has gained increasing attention from practitioners and researchers. For instance, organizations such as the Python Software Foundation and the OpenStack Foundation started actions to increase gender diversity and promote women to top positions in the communities. Although the general underrepresentation of women (a.k.a. horizontal segregation) in open source communities has been explored in a number of research studies, little is known about the vertical segregation in open source communities -- which occurs when there are fewer women in high-level positions. To address this research gap, in this paper we present the results of a mixed-methods study on gender diversity and work practices of core developers contributing to open-source communities. In the first study, we used mining-software repositories procedures to identify the core developers of 711 open source projects, in order to understand how common are women core developers in open source communities and characterize their work practices. In the second study, we surveyed the women core developers we identified in the first study to collect their perceptions of gender diversity and gender bias they might have observed while contributing to open source systems. Our findings show that open source communities present both horizontal and vertical segregation (only 2.3% of the core developers are women). Nevertheless, differently from previous studies, most of the women core developers (65.7%) report never having experienced gender discrimination when contributing to an open source project. Finally, we did not note substantial differences between the work practices among women and men core developers. We reflect on these findings and present some ideas that might increase the participation of women in open source communities.
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Venue: arXiv cs.LG 2020
Year: 2020

Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings where experimentation is limited, such as education and healthcare. But, in these very same settings, observed actions are often confounded by unobserved variables making OPE even more difficult. We study an OPE problem in an infinite-horizon, ergodic Markov decision process with unobserved confounders, where states and actions can act as proxies for the unobserved confounders. We show how, given only a latent variable model for states and actions, policy value can be identified from off-policy data. Our method involves two stages. In the first, we show how to use proxies to estimate stationary distribution ratios, extending recent work on breaking the curse of horizon to the confounded setting. In the second, we show optimal balancing can be combined with such learned ratios to obtain policy value while avoiding direct modeling of reward functions. We establish theoretical guarantees of consistency, and benchmark our method empirically.
Leveraging the Power of Place: A Data-Driven Decision Helper to Improve the Location Decisions of Economic Immigrants
Venue: arXiv cs.CY 2020
Year: 2020

A growing number of countries have established programs to attract immigrants who can contribute to their economy. Research suggests that an immigrant's initial arrival location plays a key role in shaping their economic success. Yet immigrants currently lack access to personalized information that would help them identify optimal destinations. Instead, they often rely on availability heuristics, which can lead to the selection of sub-optimal landing locations, lower earnings, elevated outmigration rates, and concentration in the most well-known locations. To address this issue and counteract the effects of cognitive biases and limited information, we propose a data-driven decision helper that draws on behavioral insights, administrative data, and machine learning methods to inform immigrants' location decisions. The decision helper provides personalized location recommendations that reflect immigrants' preferences as well as data-driven predictions of the locations where they maximize their expected earnings given their profile. We illustrate the potential impact of our approach using backtests conducted with administrative data that links landing data of recent economic immigrants from Canada's Express Entry system with their earnings retrieved from tax records. Simulations across various scenarios suggest that providing location recommendations to incoming economic immigrants can increase their initial earnings and lead to a mild shift away from the most populous landing destinations. Our approach can be implemented within existing institutional structures at minimal cost, and offers governments an opportunity to harness their administrative data to improve outcomes for economic immigrants.
La-MAML: Look-ahead Meta Learning for Continual Learning
Venue: arXiv cs.LG 2020
Year: 2020

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning shows great potential for reducing interference between old and new tasks, the current training procedures tend to be either slow or offline, and sensitive to many hyper-parameters. In this work, we propose Look-ahead MAML (La-MAML), a fast optimisation-based meta-learning algorithm for online-continual learning, aided by a small episodic memory. Our proposed modulation of per-parameter learning rates in our meta-learning update allows us to draw connections to prior work on hypergradients and meta-descent. This provides a more flexible and efficient way to mitigate catastrophic forgetting compared to conventional prior-based methods. La-MAML achieves performance superior to other replay-based, prior-based and meta-learning based approaches for continual learning on real-world visual classification benchmarks. Source code can be found here: https://github.com/montrealrobotics/La-MAML
SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses on applying multiple deep learning models and conducting in depth error analysis of results to provide system implications for future development considerations. To pursue our goal, a Recurrent Neural Network (RNN), a Gated Recurrent Unit (GRU), and Long-Short Term Memory (LSTM) models with different design architectures have been developed and evaluated. The SalamNET, a Bi-directional Gated Recurrent Unit (Bi-GRU) based model, reports a macro-F1 score of 0.83.
Automated Unit Test Generation for Python
Venue: arXiv cs.SE 2020
Year: 2020

Automated unit test generation is an established research field, and mature test generation tools exist for statically typed programming languages such as Java. It is, however, substantially more difficult to automatically generate supportive tests for dynamically typed programming languages such as Python, due to the lack of type information and the dynamic nature of the language. In this paper, we describe a foray into the problem of unit test generation for dynamically typed languages. We introduce Pynguin, an automated unit test generation framework for Python. Using Pynguin, we aim to empirically shed light on two central questions: (1) Do well-established search-based test generation methods, previously evaluated only on statically typed languages, generalise to dynamically typed languages? (2) What is the influence of incomplete type information and dynamic typing on the problem of automated test generation? Our experiments confirm that evolutionary algorithms can outperform random test generation also in the context of Python, and can even alleviate the problem of absent type information to some degree. However, our results demonstrate that dynamic typing nevertheless poses a fundamental issue for test generation, suggesting future work on integrating type inference.