OpenRaiser/Intern-Atlas huggingface dataset:
Improving Productivity through Corporate Hackathons: A Multiple Case Study of Two Large-scale Agile Organizations
Venue: arXiv cs.SE 2021
Year: 2021

Software development companies organize hackathons to encourage innovation. Despite many benefits of hackathons, in large-scale agile organizations where many teams work together, stopping the ongoing work results in a significant decrease in the immediate output. Motivated by the need to understand whether and how to run hackathons, we investigated how the practice affects productivity on the individual and organizational levels. By mapping the benefits and challenges to an established productivity framework, we found that hackathons improve developers' satisfaction and well-being, strengthen the company culture, improve performance (as many ideas are tested), increase activity (as the ideas are developed quickly), and improve communication and collaboration (because the social network is strengthened). Addressing managerial concerns, we found that hackathons also increase efficiency and flow because people learn to complete work and make progress quickly, and they build new competence. Finally, with respect to virtual hackathons we found that developers work more in isolation because tasks are split between team members resulting in less collaboration. This means that some important, expected hackathon values in virtual contexts require extra effort and cannot be taken for granted.
Shennong: a Python toolbox for audio speech features extraction
Venue: arXiv cs.CL 2021
Year: 2021

We introduce Shennong, a Python toolbox and command-line utility for speech features extraction. It implements a wide range of well-established state of art algorithms including spectro-temporal filters such as Mel-Frequency Cepstral Filterbanks or Predictive Linear Filters, pre-trained neural networks, pitch estimators as well as speaker normalization methods and post-processing algorithms. Shennong is an open source, easy-to-use, reliable and extensible framework. The use of Python makes the integration to others speech modeling and machine learning tools easy. It aims to replace or complement several heterogeneous software, such as Kaldi or Praat. After describing the Shennong software architecture, its core components and implemented algorithms, this paper illustrates its use on three applications: a comparison of speech features performances on a phones discrimination task, an analysis of a Vocal Tract Length Normalization model as a function of the speech duration used for training and a comparison of pitch estimation algorithms under various noise conditions.
Collaborative Learning over Wireless Networks: An Introductory Overview
Venue: arXiv cs.LG 2021
Year: 2021

In this chapter, we will mainly focus on collaborative training across wireless devices. Training a ML model is equivalent to solving an optimization problem, and many distributed optimization algorithms have been developed over the last decades. These distributed ML algorithms provide data locality; that is, a joint model can be trained collaboratively while the data available at each participating device remains local. This addresses, to some extend, the privacy concern. They also provide computational scalability as they allow exploiting computational resources distributed across many edge devices. However, in practice, this does not directly lead to a linear gain in the overall learning speed with the number of devices. This is partly due to the communication bottleneck limiting the overall computation speed. Additionally, wireless devices are highly heterogeneous in their computational capabilities, and both their computation speed and communication rate can be highly time-varying due to physical factors. Therefore, distributed learning algorithms, particularly those to be implemented at the wireless network edge, must be carefully designed taking into account the impact of time-varying communication network as well as the heterogeneous and stochastic computation capabilities of devices.
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations
Venue: arXiv cs.CL 2021
Year: 2021

Evidence-based medicine, the practice in which healthcare professionals refer to the best available evidence when making decisions, forms the foundation of modern healthcare. However, it relies on labour-intensive systematic reviews, where domain specialists must aggregate and extract information from thousands of publications, primarily of randomised controlled trial (RCT) results, into evidence tables. This paper investigates automating evidence table generation by decomposing the problem across two language processing tasks: \textit{named entity recognition}, which identifies key entities within text, such as drug names, and \textit{relation extraction}, which maps their relationships for separating them into ordered tuples. We focus on the automatic tabulation of sentences from published RCT abstracts that report the results of the study outcomes. Two deep neural net models were developed as part of a joint extraction pipeline, using the principles of transfer learning and transformer-based language representations. To train and test these models, a new gold-standard corpus was developed, comprising almost 600 result sentences from six disease areas. This approach demonstrated significant advantages, with our system performing well across multiple natural language processing tasks and disease areas, as well as in generalising to disease domains unseen during training. Furthermore, we show these results were achievable through training our models on as few as 200 example sentences. The final system is a proof of concept that the generation of evidence tables can be semi-automated, representing a step towards fully automating systematic reviews.
Reward-Based Environment States for Robot Manipulation Policy Learning
Venue: arXiv cs.RO 2021
Year: 2021

Training robot manipulation policies is a challenging and open problem in robotics and artificial intelligence. In this paper we propose a novel and compact state representation based on the rewards predicted from an image-based task success classifier. Our experiments, using the Pepper robot in simulation with two deep reinforcement learning algorithms on a grab-and-lift task, reveal that our proposed state representation can achieve up to 97% task success using our best policies.
DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm
Venue: arXiv cs.AR 2021
Year: 2021

Computation in several real-world applications like probabilistic machine learning, sparse linear algebra, and robotic navigation, can be modeled as irregular directed acyclic graphs (DAGs). The irregular data dependencies in DAGs pose challenges to parallel execution on general-purpose CPUs and GPUs, resulting in severe under-utilization of the hardware. This paper proposes DPU, a specialized processor designed for the efficient execution of irregular DAGs. The DPU is equipped with parallel compute units that execute different subgraphs of a DAG independently. The compute units can synchronize within a cycle using a hardware-supported synchronization primitive, and communicate via an efficient interconnect to a global banked scratchpad. Furthermore, a precision-scalable posit arithmetic unit is developed to enable application-dependent precision. The DPU is taped-out in 28nm CMOS, achieving a speedup of 5.1$\times$ and 20.6$\times$ over state-of-the-art CPU and GPU implementations on DAGs of sparse linear algebra and probabilistic machine learning workloads. This performance is achieved while operating at a power budget of 0.23W, as opposed to 55W and 98W of the CPU and GPU, resulting in a peak efficiency of 538 GOPS/W with DPU, which is 1350$\times$ and 9000$\times$ higher than the CPU and GPU, respectively. Thus, with specialized architecture, DPU enables low-power execution of irregular DAG workloads.
Match Your Words! A Study of Lexical Matching in Neural Information Retrieval
Venue: arXiv cs.IR 2021
Year: 2021

Neural Information Retrieval models hold the promise to replace lexical matching models, e.g. BM25, in modern search engines. While their capabilities have fully shone on in-domain datasets like MS MARCO, they have recently been challenged on out-of-domain zero-shot settings (BEIR benchmark), questioning their actual generalization capabilities compared to bag-of-words approaches. Particularly, we wonder if these shortcomings could (partly) be the consequence of the inability of neural IR models to perform lexical matching off-the-shelf. In this work, we propose a measure of discrepancy between the lexical matching performed by any (neural) model and an 'ideal' one. Based on this, we study the behavior of different state-of-the-art neural IR models, focusing on whether they are able to perform lexical matching when it's actually useful, i.e. for important terms. Overall, we show that neural IR models fail to properly generalize term importance on out-of-domain collections or terms almost unseen during training
A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions
Venue: arXiv cs.AI 2021
Year: 2021

In a world of daily emerging scientific inquisition and discovery, the prolific launch of machine learning across industries comes to little surprise for those familiar with the potential of ML. Neither so should the congruent expansion of ethics-focused research that emerged as a response to issues of bias and unfairness that stemmed from those very same applications. Fairness research, which focuses on techniques to combat algorithmic bias, is now more supported than ever before. A large portion of fairness research has gone to producing tools that machine learning practitioners can use to audit for bias while designing their algorithms. Nonetheless, there is a lack of application of these fairness solutions in practice. This systematic review provides an in-depth summary of the algorithmic bias issues that have been defined and the fairness solution space that has been proposed. Moreover, this review provides an in-depth breakdown of the caveats to the solution space that have arisen since their release and a taxonomy of needs that have been proposed by machine learning practitioners, fairness researchers, and institutional stakeholders. These needs have been organized and addressed to the parties most influential to their implementation, which includes fairness researchers, organizations that produce ML algorithms, and the machine learning practitioners themselves. These findings can be used in the future to bridge the gap between practitioners and fairness experts and inform the creation of usable fair ML toolkits.
On Causally Disentangled Representations
Venue: arXiv cs.LG 2021
Year: 2021

Representation learners that disentangle factors of variation have already proven to be important in addressing various real world concerns such as fairness and interpretability. Initially consisting of unsupervised models with independence assumptions, more recently, weak supervision and correlated features have been explored, but without a causal view of the generative process. In contrast, we work under the regime of a causal generative process where generative factors are either independent or can be potentially confounded by a set of observed or unobserved confounders. We present an analysis of disentangled representations through the notion of disentangled causal process. We motivate the need for new metrics and datasets to study causal disentanglement and propose two evaluation metrics and a dataset. We show that our metrics capture the desiderata of disentangled causal process. Finally, we perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective.
Using Machine Learning to Predict Air Quality Index in New Delhi
Venue: arXiv cs.LG 2021
Year: 2021

Air quality has a significant impact on human health. Degradation in air quality leads to a wide range of health issues, especially in children. The ability to predict air quality enables the government and other concerned organizations to take necessary steps to shield the most vulnerable, from being exposed to the air with hazardous quality. Traditional approaches to this task have very limited success because of a lack of access of such methods to sufficient longitudinal data. In this paper, we use a Support Vector Regression (SVR) model to forecast the levels of various pollutants and the air quality index, using archive pollution data made publicly available by Central Pollution Control Board and the US Embassy in New Delhi. Among the tested methods, a Radial Basis Function (RBF) kernel produced the best results with SVR. According to our experiments, using the whole range of available variables produced better results than using features selected by principal component analysis. The model predicts levels of various pollutants, like, sulfur dioxide, carbon monoxide, nitrogen dioxide, particulate matter 2.5, and ground-level ozone, as well as the Air Quality Index (AQI), at an accuracy of 93.4 percent.
The 3D Motorcycle Complex for Structured Volume Decomposition
Venue: arXiv cs.GR 2021
Year: 2021

The so-called motorcycle graph has been employed in recent years for various purposes in the context of structured and aligned block decomposition of 2D shapes and 2-manifold surfaces. Applications are in the fields of surface parametrization, spline space construction, semi-structured quad mesh generation, or geometry data compression. We describe a generalization of this motorcycle graph concept to the three-dimensional volumetric setting. Through careful extensions aware of topological intricacies of this higher-dimensional setting, we are able to guarantee important block decomposition properties also in this case. We describe algorithms for the construction of this 3D motorcycle complex on the basis of either hexahedral meshes or seamless volumetric parametrizations. Its utility is illustrated on examples in hexahedral mesh generation and volumetric T-spline construction.
Benchmarking human visual search computational models in natural scenes: models comparison and reference datasets
Venue: arXiv cs.CV 2021
Year: 2021

Visual search is an essential part of almost any everyday human goal-directed interaction with the environment. Nowadays, several algorithms are able to predict gaze positions during simple observation, but few models attempt to simulate human behavior during visual search in natural scenes. Furthermore, these models vary widely in their design and exhibit differences in the datasets and metrics with which they were evaluated. Thus, there is a need for a reference point, on which each model can be tested and from where potential improvements can be derived. In the present work, we select publicly available state-of-the-art visual search models in natural scenes and evaluate them on different datasets, employing the same metrics to estimate their efficiency and similarity with human subjects. In particular, we propose an improvement to the Ideal Bayesian Searcher through a combination with a neural network-based visual search model, enabling it to generalize to other datasets. The present work sheds light on the limitations of current models and how potential improvements can be accomplished by combining approaches. Moreover, it moves forward on providing a solution for the urgent need for benchmarking data and metrics to support the development of more general human visual search computational models.
Collecting Coupons is Faster with Friends
Venue: arXiv cs.DC 2021
Year: 2021

In this note, we introduce a distributed twist on the classic coupon collector problem: a set of $m$ collectors wish to each obtain a set of $n$ coupons; for this, they can each sample coupons uniformly at random, but can also meet in pairwise interactions, during which they can exchange coupons. By doing so, they hope to reduce the number of coupons that must be sampled by each collector in order to obtain a full set. This extension is natural when considering real-world manifestations of the coupon collector phenomenon, and has been remarked upon and studied empirically [Hayes and Hannigan 2006, Ahmad et al. 2014, Delmarcelle 2019]. We provide the first theoretical analysis for such a scenario. We find that "coupon collecting with friends" can indeed significantly reduce the number of coupons each collector must sample, and raises interesting connections to the more traditional variants of the problem. While our analysis is in most cases asymptotically tight, there are several open questions raised, regarding finer-grained analysis of both "coupon collecting with friends", and of a long-studied variant of the original problem in which a collector requires multiple full sets of coupons.
Improved Deterministic $(Δ+1)$-Coloring in Low-Space MPC
Venue: arXiv cs.DS 2021
Year: 2021

We present a deterministic $O(\log \log \log n)$-round low-space Massively Parallel Computation (MPC) algorithm for the classical problem of $(Δ+1)$-coloring on $n$-vertex graphs. In this model, every machine has a sublinear local memory of size $n^ϕ$ for any arbitrary constant $ϕ\in (0,1)$. Our algorithm works under the relaxed setting where each machine is allowed to perform exponential (in $n^ϕ$) local computation, while respecting the $n^ϕ$ space and bandwidth limitations. Our key technical contribution is a novel derandomization of the ingenious $(Δ+1)$-coloring LOCAL algorithm by Chang-Li-Pettie (STOC 2018, SIAM J. Comput. 2020). The Chang-Li-Pettie algorithm runs in $T_{local}=poly(\log\log n)$ rounds, which sets the state-of-the-art randomized round complexity for the problem in the local model. Our derandomization employs a combination of tools, most notably pseudorandom generators (PRG) and bounded-independence hash functions. The achieved round complexity of $O(\log\log\log n)$ rounds matches the bound of $\log(T_{local})$, which currently serves an upper bound barrier for all known randomized algorithms for locally-checkable problems in this model. Furthermore, no deterministic sublogarithmic low-space MPC algorithms for the $(Δ+1)$-coloring problem were previously known.
A Novel Gaussian Process Based Ground Segmentation Algorithm with Local-Smoothness Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Autonomous Land Vehicles (ALV) shall efficiently recognize the ground in unknown environments. A novel $\mathcal{GP}$-based method is proposed for the ground segmentation task in rough driving scenarios. A non-stationary covariance function is utilized as the kernel for the $\mathcal{GP}$. The ground surface behavior is assumed to only demonstrate local-smoothness. Thus, point estimates of the kernel's length-scales are obtained. Thus, two Gaussian processes are introduced to separately model the observation and local characteristics of the data. While, the \textit{observation process} is used to model the ground, the \textit{latent process} is put on length-scale values to estimate point values of length-scales at each input location. Input locations for this latent process are chosen in a physically-motivated procedure to represent an intuition about ground condition. Furthermore, an intuitive guess of length-scale value is represented by assuming the existence of hypothetical surfaces in the environment that every bunch of data points may be assumed to be resulted from measurements from this surfaces. Bayesian inference is implemented using \textit{maximum a Posteriori} criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results shows the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process based ground segmentation methods. While adjacent segments do not have similar ground structure in an uneven scene, the proposed method gives an efficient ground estimation based on a whole-frame viewpoint instead of just estimating segment-wise probable ground surfaces.
Improved Approximation Algorithms for Dyck Edit Distance and RNA Folding
Venue: arXiv cs.DS 2021
Year: 2021

The Dyck language, which consists of well-balanced sequences of parentheses, is one of the most fundamental context-free languages. The Dyck edit distance quantifies the number of edits (character insertions, deletions, and substitutions) required to make a given parenthesis sequence well-balanced. RNA Folding involves a similar problem, where a closing parenthesis can match an opening parenthesis of the same type irrespective of their ordering. For example, in RNA Folding, both $\tt{()}$ and $\tt{)(}$ are valid matches, whereas the Dyck language only allows $\tt{()}$ as a match. Using fast matrix multiplication, it is possible to compute their exact solutions of both problems in time $O(n^{2.824})$. Whereas combinatorial algorithms would be more desirable, the two problems are known to be at least as hard as Boolean matrix multiplication. In terms of fast approximation algorithms that are combinatorial in nature, both problems admit an $εn$-additive approximation in $\tilde{O}(\frac{n^2}ε)$ time. Further, there is a $O(\log n)$-factor approximation algorithm for Dyck edit distance in near-linear time. In this paper, we design a constant-factor approximation algorithm for Dyck edit distance that runs in $O(n^{1.971})$ time. Moreover, we develop a $(1+ε)$-factor approximation algorithm running in $\tilde{O}(\frac{n^2}ε)$ time, which improves upon the earlier additive approximation. Finally, we design a $(3+ε)$-approximation that takes $\tilde{O}(\frac{nd}ε)$ time, where $d\ge 1$ is an upper bound on the sought distance. As for RNA folding, for any $s\ge1$, we design a factor-$s$ approximation algorithm that runs in $O(n+(\frac{n}{s})^3)$ time. To the best of our knowledge, this is the first nontrivial approximation algorithm for RNA Folding that can go below the $n^2$ barrier. All our algorithms are combinatorial.
Leaping Through Time with Gradient-based Adaptation for Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of the main challenges in recommender system modeling. Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies. LeapRec characterizes temporal dynamics by two complement components named global time leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term patterns by finding the shortest learning path across unordered temporal data. Cooperatively, OTL learns short-term patterns by considering the sequential nature of the temporal data. Our experimental results show that LeapRec consistently outperforms the state-of-the-art methods on several datasets and recommendation metrics. Furthermore, we provide an empirical study of the interaction between GTL and OTL, showing the effects of long- and short-term modeling.