OpenRaiser/Intern-Atlas huggingface dataset:
Using Zigzag Persistent Homology to Detect Hopf Bifurcations in Dynamical Systems
Venue: arXiv cs.CG 2020
Year: 2020

Bifurcations in dynamical systems characterize qualitative changes in the system behavior. Therefore, their detection is important because they can signal the transition from normal system operation to imminent failure. While standard persistent homology has been used in this setting, it usually requires analyzing a collection of persistence diagrams, which in turn drives up the computational cost considerably. Using zigzag persistence, we can capture topological changes in the state space of the dynamical system in only one persistence diagram. Here we present Bifurcations using ZigZag (BuZZ), a one-step method to study and detect bifurcations using zigzag persistence. The BuZZ method is successfully able to detect this type of behavior in two synthetic examples as well as an example dynamical system.
Psoriasis Severity Assessment with a Similarity-Clustering Machine Learning Approach Reduces Intra- and Inter-observation variation
Venue: arXiv cs.CV 2020
Year: 2020

Psoriasis is a complex disease with many variations in genotype and phenotype. General advancements in medicine has further complicated both assessments and treatment for both physicians and dermatologist alike. Even with all of our technological progress we still primarily use the assessment tool Psoriasis Area and Severity Index (PASI) for severity assessments which was developed in the 1970s. In this study we evaluate a method involving digital images, a comparison web application and similarity clustering, developed to improve the assessment tool in terms of intra- and inter-observer variation. Images of patients was collected from a mobile device. Images were captured of the same lesion area taken approximately 1 week apart. Five dermatologists evaluated the severity of psoriasis by modified-PASI, absolute scoring and a relative pairwise PASI scoring using similarity-clustering and conducted using a web-program displaying two images at a time. mPASI scoring of single photos by the same or different dermatologist showed mPASI ratings of 50% to 80%, respectively. Repeated mPASI comparison using similarity clustering showed consistent mPASI ratings > 95%. Pearson correlation between absolute scoring and pairwise scoring progression was 0.72.
Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks
Venue: arXiv cs.AR 2020
Year: 2020

Computationally expensive temperature and power grid analyses are required during the design cycle to guide IC design. This paper employs encoder-decoder based generative (EDGe) networks to map these analyses to fast and accurate image-to-image and sequence-to-sequence translation tasks. The network takes a power map as input and outputs the corresponding temperature or IR drop map. We propose two networks: (i) ThermEDGe: a static and dynamic full-chip temperature estimator and (ii) IREDGe: a full-chip static IR drop predictor based on input power, power grid distribution, and power pad distribution patterns. The models are design-independent and must be trained just once for a particular technology and packaging solution. ThermEDGe and IREDGe are demonstrated to rapidly predict the on-chip temperature and IR drop contours in milliseconds (in contrast with commercial tools that require several hours or more) and provide an average error of 0.6% and 0.008% respectively.
Experimental Review of Neural-based approaches for Network Intrusion Management
Venue: arXiv cs.NI 2020
Year: 2020

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognising attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this paper we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.
COMET: A Neural Framework for MT Evaluation
Venue: arXiv cs.CL 2020
Year: 2020

We present COMET, a neural framework for training multilingual machine translation evaluation models which obtains new state-of-the-art levels of correlation with human judgements. Our framework leverages recent breakthroughs in cross-lingual pretrained language modeling resulting in highly multilingual and adaptable MT evaluation models that exploit information from both the source input and a target-language reference translation in order to more accurately predict MT quality. To showcase our framework, we train three models with different types of human judgements: Direct Assessments, Human-mediated Translation Edit Rate and Multidimensional Quality Metrics. Our models achieve new state-of-the-art performance on the WMT 2019 Metrics shared task and demonstrate robustness to high-performing systems.
Probabilistically Sampled and Spectrally Clustered Plant Genotypes using Phenotypic Characteristics
Venue: arXiv cs.LG 2020
Year: 2020

Clustering genotypes based upon their phenotypic characteristics is used to obtain diverse sets of parents that are useful in their breeding programs. The Hierarchical Clustering (HC) algorithm is the current standard in clustering of phenotypic data. This algorithm suffers from low accuracy and high computational complexity issues. To address the accuracy challenge, we propose the use of Spectral Clustering (SC) algorithm. To make the algorithm computationally cheap, we propose using sampling, specifically, Pivotal Sampling that is probability based. Since application of samplings to phenotypic data has not been explored much, for effective comparison, another sampling technique called Vector Quantization (VQ) is adapted for this data as well. VQ has recently given promising results for genome data. The novelty of our SC with Pivotal Sampling algorithm is in constructing the crucial similarity matrix for the clustering algorithm and defining probabilities for the sampling technique. Although our algorithm can be applied to any plant genotypes, we test it on the phenotypic data obtained from about 2400 Soybean genotypes. SC with Pivotal Sampling achieves substantially more accuracy (in terms of Silhouette Values) than all the other proposed competitive clustering with sampling algorithms (i.e. SC with VQ, HC with Pivotal Sampling, and HC with VQ). The complexities of our SC with Pivotal Sampling algorithm and these three variants are almost same because of the involved sampling. In addition to this, SC with Pivotal Sampling outperforms the standard HC algorithm in both accuracy and computational complexity. We experimentally show that we are up to 45% more accurate than HC in terms of clustering accuracy. The computational complexity of our algorithm is more than a magnitude lesser than HC.
Group Fairness by Probabilistic Modeling with Latent Fair Decisions
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning systems are increasingly being used to make impactful decisions such as loan applications and criminal justice risk assessments, and as such, ensuring fairness of these systems is critical. This is often challenging as the labels in the data are biased. This paper studies learning fair probability distributions from biased data by explicitly modeling a latent variable that represents a hidden, unbiased label. In particular, we aim to achieve demographic parity by enforcing certain independencies in the learned model. We also show that group fairness guarantees are meaningful only if the distribution used to provide those guarantees indeed captures the real-world data. In order to closely model the data distribution, we employ probabilistic circuits, an expressive and tractable probabilistic model, and propose an algorithm to learn them from incomplete data. We evaluate our approach on a synthetic dataset in which observed labels indeed come from fair labels but with added bias, and demonstrate that the fair labels are successfully retrieved. Moreover, we show on real-world datasets that our approach not only is a better model than existing methods of how the data was generated but also achieves competitive accuracy.
Running the COVID-19 marathon: the behavioral adaptations in mobility and facemask over 27 weeks of pandemic in Seoul, South Korea
Venue: arXiv cs.CY 2020
Year: 2020

Battle with COVID-19 turned out to be a marathon, not a sprint, and behavioral adjustments have been unavoidable to stay viable. In this paper, we employ a data-centric approach to investigate individual mobility adaptations and mask-wearing in Seoul, South Korea. We first identify six epidemic phases and two waves based on COVID-19 case count and its geospatial dispersion. The phase-specific linear models reveal the strong, self-driven mobility reductions in the first escalation and peak with a common focus on public transit use and less-essential weekend/afternoon trips. However, comparable reduction was not present in the second wave, as the shifted focus from mobility to mask-wearing was evident. Although no lockdowns and gentle nudge to wear mask seemed counter-intuitive, simple and persistent communication on personal safety has been effective and sustainable to induce cooperative behavioral adaptations. Our phase-specific analyses and interpretation highlight the importance of targeted response consistent with the fluctuating epidemic risk.
COVID-19 Literature Topic-Based Search via Hierarchical NMF
Venue: arXiv cs.DL 2020
Year: 2020

A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.
A Survey on Smart Metering Systems using Blockchain for E-Mobility
Venue: arXiv cs.CY 2020
Year: 2020

Electricity is an essential comfort to support our daily activities. With the competitive increase and energy costs by the industry, new values and opportunities for delivering electricity to customers are produced. One of these new opportunities is electric vehicles. With the arrival of electric vehicles, various challenges and opportunities are being presented in the electric power system worldwide. For example, under the traditional electric power billing scheme, electric power has to be consumed where it is needed so that end-users could not charge their electric vehicles at different points (e.g. a relative's house) if this the correct user is not billed (this due to the high consumption of electrical energy that makes it expensive). To achieve electric mobility, they must solve new challenges, such as the smart metering of energy consumption and the cybersecurity of these measurements. The present work shows a study of the different smart metering technologies that use blockchain and other security mechanisms to achieve e-mobility.
Open-Source Synthesizable Analog Blocks for High-Speed Link Designs: 20-GS/s 5b ENOB Analog-to-Digital Converter and 5-GHz Phase Interpolator
Venue: arXiv cs.AR 2020
Year: 2020

Using digital standard cells and digital place-and-route (PnR) tools, we created a 20 GS/s, 8-bit analog-to-digital converter (ADC) for use in high-speed serial link applications with an ENOB of 5.6, a DNL of 0.96 LSB, and an INL of 2.39 LSB, which dissipated 175 mW in 0.102 mm2 in a 16nm technology. The design is entirely described by HDL so that it can be ported to other processes with minimal effort and shared as open source.
C-SAW: A Framework for Graph Sampling and Random Walk on GPUs
Venue: arXiv cs.DC 2020
Year: 2020

Many applications require to learn, mine, analyze and visualize large-scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies. Many applications have requirements to analyze, transform, visualize and learn large scale graphs. These graphs are often too large to be addressed efficiently using conventional graph processing technologies. Recent literatures convey that graph sampling/random walk could be an efficient solution. In this paper, we propose, to the best of our knowledge, the first GPU-based framework for graph sampling/random walk. First, our framework provides a generic API which allows users to implement a wide range of sampling and random walk algorithms with ease. Second, offloading this framework on GPU, we introduce warp-centric parallel selection, and two optimizations for collision migration. Third, towards supporting graphs that exceed GPU memory capacity, we introduce efficient data transfer optimizations for out-of-memory sampling, such as workload-aware scheduling and batched multi-instance sampling. In its entirety, our framework constantly outperforms the state-of-the-art projects. First, our framework provides a generic API which allows users to implement a wide range of sampling and random walk algorithms with ease. Second, offloading this framework on GPU, we introduce warp-centric parallel selection, and two novel optimizations for collision migration. Third, towards supporting graphs that exceed the GPU memory capacity, we introduce efficient data transfer optimizations for out-of-memory and multi-GPU sampling, such as workload-aware scheduling and batched multi-instance sampling. Taken together, our framework constantly outperforms the state of the art projects in addition to the capability of supporting a wide range of sampling and random walk algorithms.
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing
Venue: arXiv cs.CL 2020
Year: 2020

With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules -- quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English--German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.
Design and Development of a Gecko-Adhesive Gripper for the Astrobee Free-Flying Robot
Venue: arXiv cs.RO 2020
Year: 2020

Assistive free-flying robots are a promising platform for supporting and working alongside astronauts in carrying out tasks that require interaction with the environment. However, current free-flying robot platforms are limited by existing manipulation technologies in being able to grasp and manipulate surrounding objects. Instead, gecko-inspired adhesives offer many advantages for an alternate grasping and manipulation paradigm for use in assistive free-flyer applications. In this work, we present the design of a gecko-inspired adhesive gripper for performing perching and grasping maneuvers for the Astrobee robot, a free-flying robot currently operating on-board the International Space Station. We present software and hardware integration details for the gripper units that were launched to the International Space Station in 2019 for in-flight experiments with Astrobee. Finally, we present preliminary results for on-ground experiments conducted with the gripper and Astrobee on a free-floating spacecraft test bed.
Shimon the Rapper: A Real-Time System for Human-Robot Interactive Rap Battles
Venue: arXiv cs.AI 2020
Year: 2020

We present a system for real-time lyrical improvisation between a human and a robot in the style of hip hop. Our system takes vocal input from a human rapper, analyzes the semantic meaning, and generates a response that is rapped back by a robot over a musical groove. Previous work with real-time interactive music systems has largely focused on instrumental output, and vocal interactions with robots have been explored, but not in a musical context. Our generative system includes custom methods for censorship, voice, rhythm, rhyming and a novel deep learning pipeline based on phoneme embeddings. The rap performances are accompanied by synchronized robotic gestures and mouth movements. Key technical challenges that were overcome in the system are developing rhymes, performing with low-latency and dataset censorship. We evaluated several aspects of the system through a survey of videos and sample text output. Analysis of comments showed that the overall perception of the system was positive. The model trained on our hip hop dataset was rated significantly higher than our metal dataset in coherence, rhyme quality, and enjoyment. Participants preferred outputs generated by a given input phrase over outputs generated from unknown keywords, indicating that the system successfully relates its output to its input.
Humans learn too: Better Human-AI Interaction using Optimized Human Inputs
Venue: arXiv cs.HC 2020
Year: 2020

Humans rely more and more on systems with AI components. The AI community typically treats human inputs as a given and optimizes AI models only. This thinking is one-sided and it neglects the fact that humans can learn, too. In this work, human inputs are optimized for better interaction with an AI model while keeping the model fixed. The optimized inputs are accompanied by instructions on how to create them. They allow humans to save time and cut on errors, while keeping required changes to original inputs limited. We propose continuous and discrete optimization methods modifying samples in an iterative fashion. Our quantitative and qualitative evaluation including a human study on different hand-generated inputs shows that the generated proposals lead to lower error rates, require less effort to create and differ only modestly from the original samples.
Sparse Communication for Training Deep Networks
Venue: arXiv cs.LG 2020
Year: 2020

Synchronous stochastic gradient descent (SGD) is the most common method used for distributed training of deep learning models. In this algorithm, each worker shares its local gradients with others and updates the parameters using the average gradients of all workers. Although distributed training reduces the computation time, the communication overhead associated with the gradient exchange forms a scalability bottleneck for the algorithm. There are many compression techniques proposed to reduce the number of gradients that needs to be communicated. However, compressing the gradients introduces yet another overhead to the problem. In this work, we study several compression schemes and identify how three key parameters affect the performance. We also provide a set of insights on how to increase performance and introduce a simple sparsification scheme, random-block sparsification, that reduces communication while keeping the performance close to standard SGD.
On combinatorial optimization for dominating sets (literature survey, new models)
Venue: arXiv cs.DS 2020
Year: 2020

The paper focuses on some versions of connected dominating set problems: basic problems and multicriteria problems. A literature survey on basic problem formulations and solving approaches is presented. The basic connected dominating set problems are illustrated by simplifyed numerical examples. New integer programming formulations of dominating set problems (with multiset estimates) are suggested.
A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions
Venue: arXiv cs.SE 2020
Year: 2020

Automated filtering of toxic conversations may help an Open-source software (OSS) community to maintain healthy interactions among the project participants. Although, several general purpose tools exist to identify toxic contents, those may incorrectly flag some words commonly used in the Software Engineering (SE) context as toxic (e.g., 'junk', 'kill', and 'dump') and vice versa. To encounter this challenge, an SE specific tool has been proposed by the CMU Strudel Lab (referred as the `STRUDEL' hereinafter) by combining the output of the Perspective API with the output from a customized version of the Stanford's Politeness detector tool. However, since STRUDEL's evaluation was very limited with only 654 SE text, its practical applicability is unclear. Therefore, this study aims to empirically evaluate the Strudel tool as well as four state-of-the-art general purpose toxicity detectors on a large scale SE dataset. On this goal, we empirically developed a rubric to manually label toxic SE interactions. Using this rubric, we manually labeled a dataset of 6,533 code review comments and 4,140 Gitter messages. The results of our analyses suggest significant degradation of all tools' performances on our datasets. Those degradations were significantly higher on our dataset of formal SE communication such as code review than on our dataset of informal communication such as Gitter messages. Two of the models from our study showed significant performance improvements during 10-fold cross validations after we retrained those on our SE datasets. Based on our manual investigations of the incorrectly classified text, we have identified several recommendations for developing an SE specific toxicity detector.
Biomedical Event Extraction with Hierarchical Knowledge Graphs
Venue: arXiv cs.CL 2020
Year: 2020

Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.