OpenRaiser/Intern-Atlas huggingface dataset:
Everybody Is Unique: Towards Unbiased Human Mesh Recovery
Venue: arXiv cs.CV 2021
Year: 2021

We consider the problem of obese human mesh recovery, i.e., fitting a parametric human mesh to images of obese people. Despite obese person mesh fitting being an important problem with numerous applications (e.g., healthcare), much recent progress in mesh recovery has been restricted to images of non-obese people. In this work, we identify this crucial gap in the current literature by presenting and discussing limitations of existing algorithms. Next, we present a simple baseline to address this problem that is scalable and can be easily used in conjunction with existing algorithms to improve their performance. Finally, we present a generalized human mesh optimization algorithm that substantially improves the performance of existing methods on both obese person images as well as community-standard benchmark datasets. A key innovation of this technique is that it does not rely on supervision from expensive-to-create mesh parameters. Instead, starting from widely and cheaply available 2D keypoints annotations, our method automatically generates mesh parameters that can in turn be used to re-train and fine-tune any existing mesh estimation algorithm. This way, we show our method acts as a drop-in to improve the performance of a wide variety of contemporary mesh estimation methods. We conduct extensive experiments on multiple datasets comprising both standard and obese person images and demonstrate the efficacy of our proposed techniques.
Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing
Venue: arXiv cs.SI 2021
Year: 2021

According to prior work, the type of relationship between the person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group, etc.) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in Switzerland, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards (a) supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully, and (b) reducing the need for user self-reports when creating drink logs.
On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization
Venue: arXiv cs.LG 2021
Year: 2021

Process mining algorithms discover a process model from an event log. The resulting process model is supposed to describe all possible event sequences of the underlying system. Generalization is a process model quality dimension of interest. A generalization metric should quantify the extent to which a process model represents the observed event sequences contained in the event log and the unobserved event sequences of the system. Most of the available metrics in the literature cannot properly quantify the generalization of a process model. A recently published method [1] called Adversarial System Variant Approximation leverages Generative Adversarial Networks to approximate the underlying event sequence distribution of a system from an event log. While this method demonstrated performance gains over existing methods in measuring the generalization of process models, its experimental evaluations have been performed under ideal conditions. This paper experimentally investigates the performance of Adversarial System Variant Approximation under non-ideal conditions such as biased and limited event logs. Moreover, experiments are performed to investigate the originally proposed sampling hyperparameter value of the method on its performance to measure the generalization. The results confirm the need to raise awareness about the working conditions of the Adversarial System Variant Approximation method. The outcomes of this paper also serve to initiate future research directions. [1] Theis, Julian, and Houshang Darabi. "Adversarial System Variant Approximation to Quantify Process Model Generalization." IEEE Access 8 (2020): 194410-194427.
Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field
Venue: arXiv cs.CV 2021
Year: 2021

The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most existing developmental stage classification methods conduct per-frame predictions using an image frame at each time step. However, classification using only images suffers from overlapping between cells and imbalance between stages. Temporal information can be valuable in addressing this problem by capturing movements between neighboring frames. In this work, we propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We develop a linear-chain conditional random field (CRF) on top of neural network features extracted from the temporal and image streams to make use of both modalities. The linear-chain CRF formulation enables tractable training of global sequential models over multiple frames while also making it possible to inject monotonic development order constraints into the learning process explicitly. We demonstrate our algorithm on two time-lapse embryo video datasets: i) mouse and ii) human embryo datasets. Our method achieves 98.1 % and 80.6 % for mouse and human embryo stage classification, respectively. Our approach will enable more profound clinical and biological studies and suggests a new direction for developmental stage classification by utilizing temporal information.
Stochastic Geometry based Interference Analysis of Multiuser mmWave Networks with RIS
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we utilize tools from stochastic geometry to estimate the interference propagation via reconfigurable intelligent surface (RIS) in the millimeter wave (mmWave, 30-300 GHz) band and specifically on the D band (110-170 GHz). The RISs have been of great interest lately to maximize the channel gains in non-line-of-sight (NLOS) communication situations. We derive expressions for stochastic interference level in RIS powered systems and validate those with simulations. It will be shown that the interference levels via RIS link are rather small compared to the designed RIS link or the LOS interference as the random interference loses significant part of the RIS gain. We also analyse the validity of far field channel and antenna gains in the near field of a large array. It is shown that, while the high frequency systems require large arrays that push the far field far away from the antenna, the far field equations are very accurate up to about half way of the near field.
Mining Idioms in the Wild
Venue: arXiv cs.SE 2021
Year: 2021

Existing code repositories contain numerous instances of code patterns that are idiomatic ways of accomplishing a particular programming task. Sometimes, the programming language in use supports specific operators or APIs that can express the same idiomatic imperative code much more succinctly. However, those code patterns linger in repositories because the developers may be unaware of the new APIs or have not gotten around to them. Detection of idiomatic code can also point to the need for new APIs. We share our experiences in mine idiomatic patterns from the Hack repo at Facebook. We found that existing techniques either cannot identify meaningful patterns from syntax trees or require test-suite-based dynamic analysis to incorporate semantic properties to mine useful patterns. The key insight of the approach proposed in this paper -- \emph{Jezero} -- is that semantic idioms from a large codebase can be learned from \emph{canonicalized} dataflow trees. We propose a scalable, lightweight static analysis-based approach to construct such a tree that is well suited to mine semantic idioms using nonparametric Bayesian methods. Our experiments with Jezero on Hack code shows a clear advantage of adding canonicalized dataflow information to ASTs: \emph{Jezero} was significantly more effective than a baseline that did not have the dataflow augmentation in being able to effectively find refactoring opportunities from unannotated legacy code.
Complexity and Second Moment of the Mathematical Theory of Communication
Venue: arXiv cs.IT 2021
Year: 2021

The performance of an error correcting code is evaluated by its error probability, rate, and en/decoding complexity. The performance of a series of codes is evaluated by, as the block lengths approach infinity, whether their error probabilities decay to zero, whether their rates converge to capacity, and whether their growth in complexities stays under control. Over any discrete memoryless channel, I build codes such that: (1) their error probabilities and rates scale like random codes; and (2) their en/decoding complexities scale like polar codes. Quantitatively, for any constants $p,r>0$ s.t. $p+2r<1$, I construct a series of codes with block length $N$ approaching infinity, error probability $\exp(-N^p)$, rate $N^{-r}$ less than the capacity, and en/decoding complexity $O(N\log N)$ per block. Over any discrete memoryless channel, I also build codes such that: (1) they achieve capacity rapidly; and (2) their en/decoding complexities outperform all known codes over non-BEC channels. Quantitatively, for any constants $t,r>0$ s.t. $2r<1$, I construct a series of codes with block length $N$ approaching infinity, error probability $\exp(-(\log N)^t)$, rate $N^{-r}$ less than the capacity, and en/decoding complexity $O(N\log(\log N))$ per block. The two aforementioned results are built upon two pillars: a versatile framework that generates codes on the basis of channel polarization, and a calculus-probability machinery that evaluates the performances of codes. The framework that generates codes and the machinery that evaluates codes can be extended to many other scenarios in network information theory. To name a few: lossless compression, lossy compression, Slepian-Wolf, Wyner-Ziv, multiple access channel, wiretap channel, and broadcast channel. In each scenario, the adapted notions of error probability and rate approach their limits at the same paces as specified above.
Tourbillon: a Physically Plausible Neural Architecture
Venue: arXiv cs.LG 2021
Year: 2021

In a physical neural system, backpropagation is faced with a number of obstacles including: the need for labeled data, the violation of the locality learning principle, the need for symmetric connections, and the lack of modularity. Tourbillon is a new architecture that addresses all these limitations. At its core, it consists of a stack of circular autoencoders followed by an output layer. The circular autoencoders are trained in self-supervised mode by recirculation algorithms and the top layer in supervised mode by stochastic gradient descent, with the option of propagating error information through the entire stack using non-symmetric connections. While the Tourbillon architecture is meant primarily to address physical constraints, and not to improve current engineering applications of deep learning, we demonstrate its viability on standard benchmark datasets including MNIST, Fashion MNIST, and CIFAR10. We show that Tourbillon can achieve comparable performance to models trained with backpropagation and outperform models that are trained with other physically plausible algorithms, such as feedback alignment.
RIS-Enhanced Spectrum Sensing: How Many Reflecting Elements Are Required to Achieve a Detection Probability Close to 1?
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose an reconfigurable intelligent surface (RIS) enhanced spectrum sensing system, in which the primary transmitter is equipped with single antenna, the secondary transmitter is equipped with multiple antennas, and the RIS is employed to improve the detection performance. Without loss of generality, we adopt the maximum eigenvalue detection approach, and propose a corresponding analytical framework based on large dimensional random matrix theory, to evaluate the detection probability in the asymptotic regime. Besides, the phase shift matrix of the RIS is designed with only the statistical channel state information (CSI), which is shown to be quite effective when the RIS-related channels are of Rician fading or line-of-sight (LoS). With the designed phase shift matrix, the asymptotic distributions of the equivalent channel gains are derived. Then, we provide the theoretical predictions about the number of reflecting elements (REs) required to achieve a detection probability close to 1. Finally, we present the Monte-Carlo simulation results to evaluate the accuracy of the proposed asymptotic analytical framework for the detection probability and the validity of the theoretical predictions about the number of REs required to achieve a detection probability close to 1. Moreover, the simulation results show that the proposed RIS-enhanced spectrum sensing system can substantially improve the detection performance.
Polynomially tractable cases in the popular roommates problem
Venue: arXiv cs.DM 2021
Year: 2021

The input of the popular roommates problem consists of a graph $G = (V, E)$ and for each vertex $v\in V$, strict preferences over the neighbors of $v$. Matching $M$ is more popular than $M'$ if the number of vertices preferring $M$ to $M'$ is larger than the number of vertices preferring $M'$ to $M$. A matching $M$ is called popular if there is no matching $M'$ that is more popular than $M$. Only recently Faenza et al. and Gupta et al. resolved the long-standing open question on the complexity of deciding whether a popular matching exists in a popular roommates instance and showed that the problem is NP-complete. In this paper we identify a class of instances that admit a polynomial-time algorithm for the problem. We also test these theoretical findings on randomly generated instances to determine the existence probability of a popular matching in them.
DVMN: Dense Validity Mask Network for Depth Completion
Venue: arXiv cs.CV 2021
Year: 2021

LiDAR depth maps provide environmental guidance in a variety of applications. However, such depth maps are typically sparse and insufficient for complex tasks such as autonomous navigation. State of the art methods use image guided neural networks for dense depth completion. We develop a guided convolutional neural network focusing on gathering dense and valid information from sparse depth maps. To this end, we introduce a novel layer with spatially variant and content-depended dilation to include additional data from sparse input. Furthermore, we propose a sparsity invariant residual bottleneck block. We evaluate our Dense Validity Mask Network (DVMN) on the KITTI depth completion benchmark and achieve state of the art results. At the time of submission, our network is the leading method using sparsity invariant convolution.
Federated Mixture of Experts
Venue: arXiv cs.LG 2021
Year: 2021

Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a central location. One of the important challenges in this setting is data heterogeneity, i.e. different users have different data characteristics. For this reason, training and using a single global model might be suboptimal when considering the performance of each of the individual user's data. In this work, we tackle this problem via Federated Mixture of Experts, FedMix, a framework that allows us to train an ensemble of specialized models. FedMix adaptively selects and trains a user-specific selection of the ensemble members. We show that users with similar data characteristics select the same members and therefore share statistical strength while mitigating the effect of non-i.i.d data. Empirically, we show through an extensive experimental evaluation that FedMix improves performance compared to using a single global model across a variety of different sources of non-i.i.d.-ness.
Fast and Slow Enigmas and Parental Guidance
Venue: arXiv cs.AI 2021
Year: 2021

We describe several additions to the ENIGMA system that guides clause selection in the E automated theorem prover. First, we significantly speed up its neural guidance by adding server-based GPU evaluation. The second addition is motivated by fast weight-based rejection filters that are currently used in systems like E and Prover9. Such systems can be made more intelligent by instead training fast versions of ENIGMA that implement more intelligent pre-filtering. This results in combinations of trainable fast and slow thinking that improves over both the fast-only and slow-only methods. The third addition is based on "judging the children by their parents", i.e., possibly rejecting an inference before it produces a clause. This is motivated by standard evolutionary mechanisms, where there is always a cost to producing all possible offsprings in the current population. This saves time by not evaluating all clauses by more expensive methods and provides a complementary view of the generated clauses. The methods are evaluated on a large benchmark coming from the Mizar Mathematical Library, showing good improvements over the state of the art.
Simulation of Dissemination Strategies on Temporal Networks
Venue: arXiv cs.DC 2021
Year: 2021

In distributed environments, such as distributed ledgers technologies and other peer-to-peer architectures, communication represents a crucial topic. The ability to efficiently disseminate contents is strongly influenced by the type of system architecture, the protocol used to spread such contents over the network and the actual dynamicity of the communication links (i.e. static vs. temporal nets). In particular, the dissemination strategies either focus on achieving an optimal coverage, minimizing the network traffic or providing assurances on anonymity (that is a fundamental requirement of many cryptocurrencies). In this work, the behaviour of multiple dissemination protocols is discussed and studied through simulation. The performance evaluation has been carried out on temporal networks with the help of LUNES-temporal, a discrete event simulator that allows to test algorithms running on a distributed environment. The experiments show that some gossip protocols allow to either save a considerable number of messages or to provide better anonymity guarantees, at the cost of a little lower coverage achieved and/or a little increase of the delivery time.
Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud
Venue: arXiv cs.CV 2021
Year: 2021

In this preliminary work we attempt to apply submanifold sparse convolution to the task of 3D person detection. In particular, we present Person-MinkUNet, a single-stage 3D person detection network based on Minkowski Engine with U-Net architecture. The network achieves a 76.4% average precision (AP) on the JRDB 3D detection benchmark.
Governing Decentralized Complex Queries Through a DAO
Venue: arXiv cs.DC 2021
Year: 2021

Recently, a new generation of P2P systems capable of addressing data integrity and authenticity has emerged for the development of new applications for a "more" decentralized Internet, i.e., Distributed Ledger Technologies (DLT) and Decentralized File Systems (DFS). However, these technologies still have some unanswered issues, mostly related to data lookup and discovery. In this paper, first, we propose a Distributed Hash Table (DHT) system that efficiently manages decentralized keyword-based queries executed on data stored in DFS. Through a hypercube logical layout, queries are efficiently routed among the network, where each node is responsible for a specific keywords set and the related contents. Second, we provide a framework for the governance of the above network, based on a Decentralized Autonomous Organization (DAO) implementation. We show how the use of smart contracts enables organizational decision making and rewards for nodes that have actively contributed to the DHT. Finally, we provide experimental validation of an implementation of our proposal, where the execution of the same protocol for different logical nodes of the hypercube allows us to evaluate the efficiency of communication within the network.
Indonesia's Fake News Detection using Transformer Network
Venue: arXiv cs.CL 2021
Year: 2021

Fake news is a problem faced by society in this era. It is not rare for fake news to cause provocation and problem for the people. Indonesia, as a country with the 4th largest population, has a problem in dealing with fake news. More than 30% of rural and urban population are deceived by this fake news problem. As we have been studying, there is only few literatures on preventing the spread of fake news in Bahasa Indonesia. So, this research is conducted to prevent these problems. The dataset used in this research was obtained from a news portal that identifies fake news, turnbackhoax.id. Using Web Scrapping on this page, we got 1116 data consisting of valid news and fake news. The dataset can be accessed at https://github.com/JibranFawaid/turnbackhoax-dataset. This dataset will be combined with other available datasets. The methods used are CNN, BiLSTM, Hybrid CNN-BiLSTM, and BERT with Transformer Network. This research shows that the BERT method with Transformer Network has the best results with an accuracy of up to 90%.
WAccess -- A Web Accessibility Tool based on WCAG 2.2, 2.1 and 2.0 Guidelines
Venue: arXiv cs.HC 2021
Year: 2021

The vision of providing access to all web content equally for all users makes web accessibility a fundamental goal of today's internet. Web accessibility is the practice of removing barriers from websites that could hinder functionality for users with various disabilities. Web accessibility is measured against the accessibility guidelines such as WCAG, GIGW, and so on. WCAG 2.2 is the latest set of guidelines for web accessibility that helps in making websites accessible. The web accessibility tools available in the World Wide Web Consortium (W3C), only conform up to WCAG 2.1 guidelines, while no tools exist for the latest set of guidelines. Despite the availability of several tools to check the conformity of websites with WCAG 2.1 guidelines, there is a scarcity of tools that are both open source and scalable. To support automated accessibility evaluation of numerous websites against WCAG 2.2, 2.1, and 2.0 we present a tool, WAccess. WAccess highlights violations of 13 guidelines from WCAG 2.0, 9 guidelines from WCAG 2.1, and 7 guidelines from WCAG 2.2 of a specific web page on the web console and suggests the fix for violations while specifying violating code snippet simultaneously. We evaluated WAccess against 2227 government websites of India and observed a total of about 6.1 million violations.