OpenRaiser/Intern-Atlas huggingface dataset:
Uncovering the Corona Virus Map Using Deep Entities and Relationship Models
Venue: arXiv cs.CL 2020
Year: 2020

We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus by employing a novel entities and relationship model. The entity recognition and relationship discovery models are trained with a multi-task learning objective on a large annotated corpus. We employ a concept masking paradigm to prevent the evolution of neural networks functioning as an associative memory and induce right inductive bias guiding the network to make inference using only the context. We uncover several import subnetworks, highlight important terms and concepts and elucidate several treatment modalities employed in related ailments in the past.
Iterative Correction of Sensor Degradation and a Bayesian Multi-Sensor Data Fusion Method
Venue: arXiv cs.LG 2020
Year: 2020

We present a novel method for inferring ground-truth signal from multiple degraded signals, affected by different amounts of sensor exposure. The algorithm learns a multiplicative degradation effect by performing iterative corrections of two signals solely from the ratio between them. The degradation function d should be continuous, satisfy monotonicity, and d(0) = 1. We use smoothed monotonic regression method, where we easily incorporate the aforementioned criteria to the fitting part. We include theoretical analysis and prove convergence to the ground-truth signal for the noiseless measurement model. Lastly, we present an approach to fuse the noisy corrected signals using Gaussian processes. We use sparse Gaussian processes that can be utilized for a large number of measurements together with a specialized kernel that enables the estimation of noise values of all sensors. The data fusion framework naturally handles data gaps and provides a simple and powerful method for observing the signal trends on multiple timescales(long-term and short-term signal properties). The viability of correction method is evaluated on a synthetic dataset with known ground-truth signal.
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction
Venue: arXiv cs.SE 2020
Year: 2020

Goal: We consider the problem of automatically grouping logs of runs that failed for the same underlying reasons, so that they can be treated more effectively, and investigate the following questions: (1) Does an approach developed to identify problems in system logs generalize to identifying problems in continuous deployment logs? (2) How does dimensionality reduction affect the quality of automated log clustering? (3) How does the criterion used for merging clusters in the clustering algorithm affect clustering quality? Method: We replicate and extend earlier work on clustering system log files to assess its generalization to continuous deployment logs. We consider the optional inclusion of one of these dimensionality reduction techniques: Principal Component Analysis (PCA), Latent Semantic Indexing (LSI), and Non-negative Matrix Factorization (NMF). Moreover, we consider three alternative cluster merge criteria (Single Linkage, Average Linkage, and Weighted Linkage), in addition to the Complete Linkage criterion used in earlier work. We empirically evaluate the 16 resulting configurations on continuous deployment logs provided by our industrial collaborator. Results: Our study shows that (1) identifying problems in continuous deployment logs via clustering is feasible, (2) including NMF significantly improves overall accuracy and robustness, and (3) Complete Linkage performs best of all merge criteria analyzed. Conclusions: We conclude that problem identification via automated log clustering is improved by including dimensionality reduction, as it decreases the pipeline's sensitivity to parameter choice, thereby increasing its robustness for handling different inputs.
A Fast Randomized Algorithm for Finding the Maximal Common Subsequences
Venue: arXiv cs.DS 2020
Year: 2020

Finding the common subsequences of $L$ multiple strings has many applications in the area of bioinformatics, computational linguistics, and information retrieval. A well-known result states that finding a Longest Common Subsequence (LCS) for $L$ strings is NP-hard, e.g., the computational complexity is exponential in $L$. In this paper, we develop a randomized algorithm, referred to as {\em Random-MCS}, for finding a random instance of Maximal Common Subsequence ($MCS$) of multiple strings. A common subsequence is {\em maximal} if inserting any character into the subsequence no longer yields a common subsequence. A special case of MCS is LCS where the length is the longest. We show the complexity of our algorithm is linear in $L$, and therefore is suitable for large $L$. Furthermore, we study the occurrence probability for a single instance of MCS and demonstrate via both theoretical and experimental studies that the longest subsequence from multiple runs of {\em Random-MCS} often yields a solution to $LCS$.
Leam: An Interactive System for In-situ Visual Text Analysis
Venue: arXiv cs.DB 2020
Year: 2020

With the increase in scale and availability of digital text generated on the web, enterprises such as online retailers and aggregators often use text analytics to mine and analyze the data to improve their services and products alike. Text data analysis is an iterative, non-linear process with diverse workflows spanning multiple stages, from data cleaning to visualization. Existing text analytics systems usually accommodate a subset of these stages and often fail to address challenges related to data heterogeneity, provenance, workflow reusability and reproducibility, and compatibility with established practices. Based on a set of design considerations we derive from these challenges, we propose Leam, a system that treats the text analysis process as a single continuum by combining advantages of computational notebooks, spreadsheets, and visualization tools. Leam features an interactive user interface for running text analysis workflows, a new data model for managing multiple atomic and composite data types, and an expressive algebra that captures diverse sets of operations representing various stages of text analysis and enables coordination among different components of the system, including data, code, and visualizations. We report our current progress in Leam development while demonstrating its usefulness with usage examples. Finally, we outline a number of enhancements to Leam and identify several research directions for developing an interactive visual text analysis system.
A Longitudinal Analysis of a Social Network of Intellectual History
Venue: arXiv cs.SI 2020
Year: 2020

The history of intellectuals consists of a complicated web of influences and interconnections of philosophers, scientists, writers, their work, and ideas. How did these influences evolve over time? Who were the most influential scholars in a period? To answer these questions, we mined a network of influence of over 12,500 intellectuals, extracted from the Linked Open Data provider YAGO. We enriched this network with a longitudinal perspective, and analysed time-sliced projections of the complete network differentiating between within-era, inter-era, and accumulated-era networks. We thus identified various patterns of intellectuals and eras, and studied their development in time. We show which scholars were most influential in different eras, and who took prominent knowledge broker roles. One essential finding is that the highest impact of an era's scholar was on their contemporaries, as well as the inter-era influence of each period was strongest to its consecutive one. Further, we see quantitative evidence that there was no re-discovery of Antiquity during the Renaissance, but a continuous reception since the Middle Ages.
Black Lives Matter discourse on US social media during COVID: polarised positions enacted in a new event
Venue: arXiv cs.CY 2020
Year: 2020

Black Lives Matter has been a major force for social change in the US since 2014, with social media playing a core role in the development and proliferation of the movement. The largest protests in US history occurred in late May and early June 2020, following the death of George Floyd at the hands of Minneapolis police. This incident reignited widespread support for the BLM movement. The protests were notable not only for their size but also that they occurred at a time the US was still struggling to control the spread of the COVID-19 pandemic, with more than 20,000 new cases per day. With protest conditions and police crowd control tactics exacerbating disease spread and with COVID disproportionately affecting minority populations, it was hypothesised that participation in and support for the protests would involve a balancing act between the risks of systemic racism and of disease spread. However, social media data suggest that this was not the case, with discussion of the BLM movement replacing discussion of COVID on US social media. Neither supporters or opposers of the BLM movement or protest action mentioned COVID as a factor. Framings of the movement by BLM supporters largely replicated those of earlier studies, with new frames emerging surrounding the opposition the movement has received from racism, police militarisation and President Donald Trump. Discourse evidenced worrying levels of polarisation, hate, incivility and conspiracy content and bore many similarities to previously studied COVID discourse. This suggests that George Floyd's death, as yet another example of an African American man killed by US police, was largely seen through established, polarised identity positions that made reactions to the incident and resulting protest largely a foregone conclusion, established and articulated without reference to the ongoing pandemic.
Deep Active Inference for Partially Observable MDPs
Venue: arXiv cs.LG 2020
Year: 2020

Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state representation by means of a variational autoencoder. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning, a state-of-the-art deep reinforcement learning algorithm.
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
Venue: arXiv cs.NE 2020
Year: 2020

Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur frequently in real life, for example when data acquisition or labeling is expensive. In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples. We argue that this problem is very likely to occur on the edge, when the embedded device directly acquires the data, and the expert needed to perform labeling cannot be prompted often. To address this problem, we consider an algorithm consisting of the concatenation of transfer learning with clustering using Self-Organizing Maps (SOMs). We introduce a TensorFlow-based implementation to speed-up the process in multi-core CPUs and GPUs. Finally, we demonstrate the effectiveness of the method using standard off-the-shelf few-shot classification benchmarks.
Fast and Reliable WiFi Fingerprint Collection for Indoor Localization
Venue: arXiv cs.NI 2020
Year: 2020

Fingerprinting is a popular indoor localization technique since it can utilize existing infrastructures (e.g., access points). However, its site survey process is a labor-intensive and time-consuming task, which limits the application of such systems in practice. In this paper, motivated by the availability of advanced sensing capabilities in smartphones, we propose a fast and reliable fingerprint collection method to reduce the time and labor required for site survey. The proposed method uses a landmark graph-based method to automatically associate the collected fingerprints, which does not require active user participation. We will show that besides fast fingerprint data collection, the proposed method results in accurate location estimate compared to the state-of-the-art methods. Experimental results show that the proposed method is an order of magnitude faster than the manual fingerprint collection method, and using the radio map generated by our method achieves a much better accuracy compared to the existing methods.
Wireless Networks with Cache-Enabled and Backhaul-Limited Aerial Base Stations
Venue: arXiv cs.NI 2020
Year: 2020

Use of aerial base stations (ABSs) is a promising approach to enhance the agility and flexibility of future wireless networks. ABSs can improve the coverage and/or capacity of a network by moving supply towards demand. Deploying ABSs in a network presents several challenges such as finding an efficient 3D-placement of ABSs that takes network objectives into account. Another challenge is the limited wireless backhaul capacity of ABSs and consequently, potentially higher latency incurred. Content caching is proposed to alleviate the backhaul congestion and decrease the latency. We consider a limited backhaul capacity for ABSs due to varying position-dependent path loss values and define two groups of users (delay-tolerant and delay-sensitive) with different data rate requirements. We study the problem of jointly determining backhaul-aware 3D placement for ABSs, user-BS associations and corresponding bandwidth allocations while minimizing total downlink transmit power. Proposed iterative algorithm applies a decomposition method. First, the 3D locations of ABSs are found using semi-definite relaxation and coordinate descent methods, and then user-BS associations and bandwidth allocations are optimized. The simulation results demonstrate the effectiveness of the proposed algorithm and provide insights about the impact of traffic distribution and content caching on transmit power and backhaul usage of ABSs.
Tight List-Sizes for Oblivious AVCs under Constraints
Venue: arXiv cs.IT 2020
Year: 2020

We study list-decoding over adversarial channels governed by oblivious adversaries (a.k.a. oblivious Arbitrarily Varying Channels (AVCs)). This type of adversaries aims to maliciously corrupt the communication without knowing the actual transmission from the sender. For any oblivious AVCs potentially with constraints on the sender's transmitted sequence and the adversary's noise sequence, we determine the exact value of the minimum list-size that can support a reliable communication at positive rate. This generalizes a classical result by Hughes (IEEE Transactions on Information Theory, 1997) and answers an open question posed by Sarwate and Gastpar (IEEE Transactions on Information Theory, 2012). A lower bound on the list-decoding capacity (whenever positive) is presented. Under a certain combinatorial conjecture, we also prove a matching upper bound. En route to a tight characterization of the list-decoding capacity, we propose a method for subcode construction towards the resolution of the combinatorial conjecture.
A First Look at Zoombombing
Venue: arXiv cs.CY 2020
Year: 2020

Online meeting tools like Zoom and Google Meet have become central to our professional, educational, and personal lives. This has opened up new opportunities for large scale harassment. In particular, a phenomenon known as zoombombing has emerged, in which aggressors join online meetings with the goal of disrupting them and harassing their participants. In this paper, we conduct the first data-driven analysis of calls for zoombombing attacks on social media. We identify ten popular online meeting tools and extract posts containing meeting invitations to these platforms on a mainstream social network, Twitter, and on a fringe community known for organizing coordinated attacks against online users, 4chan. We then perform manual annotation to identify posts that are calling for zoombombing attacks, and apply thematic analysis to develop a codebook to better characterize the discussion surrounding calls for zoombombing. During the first seven months of 2020, we identify over 200 calls for zoombombing between Twitter and 4chan, and analyze these calls both quantitatively and qualitatively. Our findings indicate that the vast majority of calls for zoombombing are not made by attackers stumbling upon meeting invitations or bruteforcing their meeting ID, but rather by insiders who have legitimate access to these meetings, particularly students in high school and college classes. This has important security implications, because it makes common protections against zoombombing, such as password protection, ineffective. We also find instances of insiders instructing attackers to adopt the names of legitimate participants in the class to avoid detection, making countermeasures like setting up a waiting room and vetting participants less effective. Based on these observations, we argue that the only effective defense against zoombombing is creating unique join links for each participant.
LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets
Venue: arXiv cs.CL 2020
Year: 2020

We describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets. Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets. We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic. Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set.
Asymmetric Aging Effect on Modern Microprocessors
Venue: arXiv cs.AR 2020
Year: 2020

Reliability is a crucial requirement in any modern microprocessor to assure correct execution over its lifetime. As mission critical components are becoming common in commodity systems; e.g., control of autonomous cars, the demand for reliable processing has even further heightened. Latest process technologies even worsened the situation; thus, microprocessors design has become highly susceptible to reliability concerns. This paper examines asymmetric aging phenomenon, which is a major reliability concern in advanced process nodes. In this phenomenon, logical elements and memory cells suffer from unequal timing degradation over time and consequently introduce reliability concerns. So far, most studies approached asymmetric aging from circuit or physical design viewpoint, but these solutions were quite limited and suboptimal. In this paper we introduce an asymmetric aging aware micro-architecture that aims at reducing its impact. The study is mainly focused on the following subsystems: execution units, register files and the memory hierarchy. Our experiments indicate that the proposed solutions incur minimal overhead while significantly mitigating the asymmetric aging stress.
Modeling Wildfire Perimeter Evolution using Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

With the increased size and frequency of wildfire eventsworldwide, accurate real-time prediction of evolving wildfirefronts is a crucial component of firefighting efforts and for-est management practices. We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in24 hour periods. The fire spreading simulation is based ona deep convolutional neural network (CNN) that is trainedon remotely sensed atmospheric and environmental time se-ries data. We show that the model is able to learn wildfirespreading dynamics from real historic data sets from a seriesof wildfires in the Western Sierra Nevada Mountains in Cal-ifornia. We validate the model on a previously unseen wild-fire and produce realistic results that significantly outperformhistoric alternatives with validation accuracies ranging from78% - 98%
Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression
Venue: arXiv cs.LG 2020
Year: 2020

Given $m$ $d$-dimensional responsors and $n$ $d$-dimensional predictors, sparse regression finds at most $k$ predictors for each responsor for linear approximation, $1\leq k \leq d-1$. The key problem in sparse regression is subset selection, which usually suffers from high computational cost. Recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid on the non-approximate method of subset selection, which is very necessary for many questions in data analysis. Here we consider sparse regression from the view of correlation, and propose the formula of conditional uncorrelation. Then an efficient non-approximate method of subset selection is proposed in which we do not need to calculate any coefficients in regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from $O(\frac{1}{6}{k^3}+mk^2+mkd)$ to $O(\frac{1}{6}{k^3}+\frac{1}{2}mk^2)$ for each candidate subset in sparse regression. Because the dimension $d$ is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of non-approximate subset selection.
A new architecture for hand-worn Sign language to Speech translator
Venue: arXiv cs.HC 2020
Year: 2020

People with speech and hearing impairments often rely on sign language to communicate with others but most of the general population cannot understand sign language and sign language itself is a difficult language to learn, so there is a definite need for technologies to translate sign language to speech. In this paper, we describe the design and implementation of Smart glove, a hand-worn hardware device capable of translating American Sign Language gestures into English speech by tracking the finger's orientation, gestures and hand motion. It uses hardware sensors like Flex, Accelerometer and gyroscope and intelligent software to capture and translate the gestures into speech. This paper explains the translation of both Alphabet and Word gestures. New approaches and algorithms are proposed and implemented to address hardware-dependent issues in existing glove based designs. The whole device is designed to be modular with distributed processing units to encourage modular enhancement, reducing complexity, and interrelation between subsystems.Decision Trees are used in gesture recognition and error correction. We hope that the henceforth mentioned design and architecture would be the basis for the advancement in research related to sensor-based sign language translation along with research for smart glove and cybernetic accessories.
Quantifying the Effects of COVID-19 on Mental Health Support Forums
Venue: arXiv cs.CL 2020
Year: 2020

The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health. Understanding its impact can inform strategies for mitigating negative consequences. In this work, we seek to better understand the effects of COVID-19 on mental health by examining discussions within mental health support communities on Reddit. First, we quantify the rate at which COVID-19 is discussed in each community, or subreddit, in order to understand levels of preoccupation with the pandemic. Next, we examine the volume of activity in order to determine whether the quantity of people seeking online mental health support has risen. Finally, we analyze how COVID-19 has influenced language use and topics of discussion within each subreddit.