OpenRaiser/Intern-Atlas huggingface dataset:
If You Like It, GAN It. Probabilistic Multivariate Times Series Forecast With GAN
Venue: arXiv cs.LG 2020
Year: 2020

The contribution of this paper is two-fold. First, we present ProbCast - a novel probabilistic model for multivariate time-series forecasting. We employ a conditional GAN framework to train our model with adversarial training. Second, we propose a framework that lets us transform a deterministic model into a probabilistic one with improved performance. The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently. We conduct experiments over two publicly available datasets namely electricity consumption dataset and exchange-rate dataset. The results of the experiments demonstrate the remarkable performance of our model as well as the successful application of our proposed framework.
Quantifying human mobility behavior changes in response to non-pharmaceutical interventions during the COVID-19 outbreak in the United States
Venue: arXiv cs.CY 2020
Year: 2020

Ever since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including the United States. It is one of the major community mitigation strategies, also known as non-pharmaceutical interventions. However, our understanding is remaining limited in how people practice social distancing. In this study, we construct a Social Distancing Index (SDI) to evaluate people's mobility pattern changes along with the spread of COVID-19. We utilize an integrated dataset of mobile device location data for the contiguous United States plus Alaska and Hawaii over a 100-day period from January 1, 2020 to April 9, 2020. The major findings are: 1) the declaration of the national emergency concerning the COVID-19 outbreak greatly encouraged social distancing and the mandatory stay-at-home orders in most states further strengthened the practice; 2) the states with more confirmed cases have taken more active and timely responses in practicing social distancing; 3) people in the states with fewer confirmed cases did not pay much attention to maintaining social distancing and some states, e.g., Wyoming, North Dakota, and Montana, already began to practice less social distancing despite the high increasing speed of confirmed cases; 4) some counties with the highest infection rates are not performing much social distancing, e.g., Randolph County and Dougherty County in Georgia, and some counties began to practice less social distancing right after the increasing speed of confirmed cases went down, e.g., in Blaine County, Idaho, which may be dangerous as well.
NTIRE 2020 Challenge on Image and Video Deblurring
Venue: arXiv cs.CV 2020
Year: 2020

Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results from 3 competition tracks as well as the proposed solutions. Track 1 aims to develop single-image deblurring methods focusing on restoration quality. On Track 2, the image deblurring methods are executed on a mobile platform to find the balance of the running speed and the restoration accuracy. Track 3 targets developing video deblurring methods that exploit the temporal relation between input frames. In each competition, there were 163, 135, and 102 registered participants and in the final testing phase, 9, 4, and 7 teams competed. The winning methods demonstrate the state-ofthe-art performance on image and video deblurring tasks.
On the Parameterized Complexity of Deletion to $\mathcal{H}$-free Strong Components
Venue: arXiv cs.DS 2020
Year: 2020

{\sc Directed Feedback Vertex Set (DFVS)} is a fundamental computational problem that has received extensive attention in parameterized complexity. In this paper, we initiate the study of a wide generalization, the {\sc ${\cal H}$-free SCC Deletion} problem. Here, one is given a digraph $D$, an integer $k$ and the objective is to decide whether there is a vertex set of size at most $k$ whose deletion leaves a digraph where every strong component excludes graphs in the fixed finite family ${\cal H}$ as (not necessarily induced) subgraphs. When ${\cal H}$ comprises only the digraph with a single arc, then this problem is precisely DFVS. Our main result is a proof that this problem is fixed-parameter tractable parameterized by the size of the deletion set if ${\cal H}$ only contains rooted graphs or if ${\cal H}$ contains at least one directed path. Along with generalizing the fixed-parameter tractability result for DFVS, our result also generalizes the recent results of Göke et al. [CIAC 2019] for the {\sc 1-Out-Regular Vertex Deletion} and {\sc Bounded Size Strong Component Vertex Deletion} problems. Moreover, we design algorithms for the two above mentioned problems, whose running times are better and match with the best bounds for {\sc DFVS}, without using the heavy machinery of shadow removal as is done by Göke et al. [CIAC 2019].
Synchronizing Deterministic Push-Down Automata Can Be Really Hard
Venue: arXiv cs.FL 2020
Year: 2020

The question if a deterministic finite automaton admits a software reset in the form of a so-called synchronizing word can be answered in polynomial time. In this paper, we extend this algorithmic question to deterministic automata beyond finite automata. We prove that the question of synchronizability becomes undecidable even when looking at deterministic one-counter automata. This is also true for another classical mild extension of regularity, namely that of deterministic one-turn push-down automata. However, when we combine both restrictions, we arrive at scenarios with a PSPACE-complete (and hence decidable) synchronizability problem. Likewise, we arrive at a decidable synchronizability problem for (partially) blind deterministic counter automata. There are several interpretations of what synchronizability should mean for deterministic push-down automata. This is depending on the role of the stack: should it be empty on synchronization, should it be always the same or is it arbitrary? For the automata classes studied in this paper, the complexity or decidability status of the synchronizability problem is mostly independent of this technicality, but we also discuss one class of automata where this makes a difference.
Storing, preprocessing and analyzing Tweets: Finding the suitable NoSQL system
Venue: arXiv cs.SE 2020
Year: 2020

NoSQL systems are a new generation of databases that aim to handle a large volume of data. However there is a large set of NoSQL systems, each has its own characteristics. Consequently choosing the suitable NoSQL system to handle Tweets is challenging. Based on these motivations, this work is carried out to find the suitable NoSQL system to store, preprocess and analyze Tweets. This paper presents the requirements of managing Tweets and provides a detailed comparison of five of the most popular NoSQL systems namely, Redis, Cassandra, MongoDB, Couchbase and Neo4j regarding to these requirements. The results of this work show that for Tweets storing, preprocessing and analyzing, MongoDB and Couchbase are the most suitable NoSQL systems. Unlike related works, this work compares five NoSQL systems from different types in a real scenario which is Tweet storing, preprocessing and analyzing. The chosen scenario enables to evaluate not only the performance of read and write operations, but also other requirements related to Tweets management such as scalability, analysis tools support and analysis languages support.
Explaining AI-based Decision Support Systems using Concept Localization Maps
Venue: arXiv cs.LG 2020
Year: 2020

Human-centric explainability of AI-based Decision Support Systems (DSS) using visual input modalities is directly related to reliability and practicality of such algorithms. An otherwise accurate and robust DSS might not enjoy trust of experts in critical application areas if it is not able to provide reasonable justification of its predictions. This paper introduces Concept Localization Maps (CLMs), which is a novel approach towards explainable image classifiers employed as DSS. CLMs extend Concept Activation Vectors (CAVs) by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. They provide qualitative and quantitative assurance of a classifier's ability to learn and focus on similar concepts important for humans during image recognition. To better understand the effectiveness of the proposed method, we generated a new synthetic dataset called Simple Concept DataBase (SCDB) that includes annotations for 10 distinguishable concepts, and made it publicly available. We evaluated our proposed method on SCDB as well as a real-world dataset called CelebA. We achieved localization recall of above 80% for most relevant concepts and average recall above 60% for all concepts using SE-ResNeXt-50 on SCDB. Our results on both datasets show great promise of CLMs for easing acceptance of DSS in practice.
Interactive distributed cloud-based web-server systems for the smart healthcare industry
Venue: arXiv cs.OH 2020
Year: 2020

The work aims to investigate the possible contemporary interactive cloud based solutions in the fields of the applied medicine for the smart Healthcare as the data visualization open-source free system distributed under the MIT license. A comparative study of a number of the well-known implementations of the Ray Casting algorithms was studied. A new method of numerical calculus is proposed for calculating the volume -- the method of spheres, as well as a proposal for paralleling the algorithm on graphic accelerators in a linearly homogeneous computing environment using the block decomposition methods. For the artifacts control -- algorithm of the cubic interpolation was used. The cloud server architecture was proposed.
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
Venue: arXiv cs.LG 2020
Year: 2020

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, MeshfreeFlowNet accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder. We empirically study the performance of MeshfreeFlowNet on the task of super-resolution of turbulent flows in the Rayleigh-Benard convection problem. Across a diverse set of evaluation metrics, we show that MeshfreeFlowNet significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MeshfreeFlowNet and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes.
Research Challenges and Prospective Business Impacts of Cloud Computing: A Survey
Venue: arXiv cs.DC 2020
Year: 2020

In today's information technology (IT) era, a major part of the costs is being spent on computational needs. Enterprises are in efforts to increase their Return on Investment (ROI) and individuals are trying to reduce their costs. In this regard, cloud computing which emerges as a fifth utility can reduce costs and enhance performance of IT solutions. A large number of companies and institutions are dealing with cloud related issues as a provider or user. Due to the fact that cloud computing services have been proposed in recent years, organizations and individuals face with various challenges and problems such as how to migrate applications and software platforms into cloud and how to ensure security of migrated applications and etc. Given that many different definitions of cloud computing is presented in many publications and projects, a concrete and clear definition for cloud computing considering its characteristics, models and services is provided in this paper. In addition, current challenges and open issues in cloud computing is discussed in details and further recommendations and roadmaps for scientific activities and researches as well as potentials for improvements in this area from scientific and commercial points of view are given in this paper.
Towards Reconfigurable Intelligent Surfaces Powered Green Wireless Networks
Venue: arXiv cs.IT 2020
Year: 2020

The adoption of reconfigurable intelligent surface (RIS) in wireless networks can enhance the spectrum- and energy-efficiency by controlling the propagation environment. Although the RIS does not consume any transmit power, the circuit power of the RIS cannot be ignored, especially when the number of reflecting elements is large. In this paper, we propose the joint design of beamforming vectors at the base station, active RIS set, and phase-shift matrices at the active RISs to minimize the network power consumption, including the RIS circuit power consumption, while taking into account each user's target data rate requirement and each reflecting element's constant modulus constraint. However, the formulated problem is a mixed-integer quadratic programming (MIQP) problem, which is NP-hard. To this end, we present an alternating optimization method, which alternately solves second order cone programming (SOCP) and MIQP problems to update the optimization variables. Specifically, the MIQP problem is further transformed into a semidefinite programming problem by applying binary relaxation and semidefinite relaxation. Finally, an efficient algorithm is developed to solve the problem. Simulation results show that the proposed algorithm significantly reduces the network power consumption and reveal the importance of taking into account the RIS circuit power consumption.
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
Venue: arXiv cs.CL 2020
Year: 2020

We address the task of explaining the effects of perturbations in procedural text, an important test of process comprehension. Consider a passage describing a rabbit's life-cycle: humans can easily explain the effect on the rabbit population if a female rabbit becomes ill -- i.e., the female rabbit would not become pregnant, and as a result not have babies leading to a decrease in rabbit population. We present QUARTET, a system that constructs such explanations from paragraphs, by modeling the explanation task as a multitask learning problem. QUARTET provides better explanations (based on the sentences in the procedural text) compared to several strong baselines on a recent process comprehension benchmark. We also present a surprising secondary effect: our model also achieves a new SOTA with a 7% absolute F1 improvement on a downstream QA task. This illustrates that good explanations do not have to come at the expense of end task performance.
"Can you do this?" Self-Assessment Dialogues with Autonomous Robots Before, During, and After a Mission
Venue: arXiv cs.RO 2020
Year: 2020

Autonomous robots with sophisticated capabilities can make it difficult for human instructors to assess its capabilities and proficiencies. Therefore, it is important future robots have the ability to: introspect on their capabilities and assess their task performance. Introspection allows the robot to determine what it can accomplish and self-assessment allows the robot estimate the likelihood it will accomplish at given task. We introduce a general framework for introspection and self-assessment that enables robots to have task and performance-based dialogues before, during, and after a mission. We then realize aspects of the framework in the cognitive robotic DIARC architecture, and finally show a proof-of-concept demonstration on a Nao robot showing its self-assessment capabilities before, during, and after an instructed task.
Trade-offs in the Design of Blockchain of Finite-Lifetime Blocks for Edge-IoT Applications
Venue: arXiv cs.CR 2020
Year: 2020

Unlike cryptocurrency transactions in bitcoin that are stored indefinitely, the data of certain applications like IoT have finite-lifetime. In this context, one of the recent research works proposed LiTiChain - a new architecture for Blockchain of finite-lifetime blocks with applications to Edge-IoT. The novelty of LiTiChain lies in ensuring the connectivity of the chain even after the expired blocks are deleted from the chain. To provide the same level of security as conventional blockchain, in LiTiChain, some blocks are stored longer than their lifetime, which incurs additional storage cost. This paper presents two new blockchain architectures i.e. p-LiTiChain and s-LiTiChain that are variants of LiTiChain. The proposed architectures offer a degree of freedom in the design of blockchain of finite-lifetime blocks in terms of a tradeoff between storage cost, security and computational cost. With extensive simulations and analysis, it is demonstrated that the proposed architectures have the potential to decrease the additional storage cost incurred by LiTiChain to zero and improve security at the expense of computational cost.
Mining Message Flows using Recurrent Neural Networks for System-on-Chip Designs
Venue: arXiv cs.DC 2020
Year: 2020

Comprehensive specifications are essential for various activities across the entire validation continuum for system-on-chip (SoC) designs. However, specifications are often ambiguous, incomplete, or even contain inconsistencies or errors. This paper addresses this problem by developing a specification mining approach that automatically extracts sequential patterns from SoC transaction-level traces such that the mined patterns collectively characterize system-level specifications for SoC designs. This approach exploits long short-term memory (LSTM) networks trained with the collected SoC execution traces to capture sequential dependencies among various communication events. Then, a novel algorithm is developed to efficiently extract sequential patterns on system-level communications from the trained LSTM models. Several trace processing techniques are also proposed to enhance the mining performance. We evaluate the proposed approach on simulation traces of a non-trivial multi-core SoC prototype. Initial results show that the proposed approach is capable of extracting various patterns on system-level specifications from the highly concurrent SoC execution traces.
The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America
Venue: arXiv cs.IR 2020
Year: 2020

Chronicling America is a product of the National Digital Newspaper Program, a partnership between the Library of Congress and the National Endowment for the Humanities to digitize historic newspapers. Over 16 million pages of historic American newspapers have been digitized for Chronicling America to date, complete with high-resolution images and machine-readable METS/ALTO OCR. Of considerable interest to Chronicling America users is a semantified corpus, complete with extracted visual content and headlines. To accomplish this, we introduce a visual content recognition model trained on bounding box annotations of photographs, illustrations, maps, comics, and editorial cartoons collected as part of the Library of Congress's Beyond Words crowdsourcing initiative and augmented with additional annotations including those of headlines and advertisements. We describe our pipeline that utilizes this deep learning model to extract 7 classes of visual content: headlines, photographs, illustrations, maps, comics, editorial cartoons, and advertisements, complete with textual content such as captions derived from the METS/ALTO OCR, as well as image embeddings for fast image similarity querying. We report the results of running the pipeline on 16.3 million pages from the Chronicling America corpus and describe the resulting Newspaper Navigator dataset, the largest dataset of extracted visual content from historic newspapers ever produced. The Newspaper Navigator dataset, finetuned visual content recognition model, and all source code are placed in the public domain for unrestricted re-use.
A Framework for Plant Topology Extraction Using Process Mining and Alarm Data
Venue: arXiv cs.DB 2020
Year: 2020

Industrial plants are prone to faults. To notify the operator of a fault occurrence, alarms are utilized as a basic part of modern computer-controlled plants. However, due to the interconnections of different parts of a plant, a single fault often propagates through the plant and triggers a (sometimes large) number of alarms. A graphical plant topology can help operators, process engineers and maintenance experts find the root cause of a plant upset or discover the propagation path of a fault. In this paper, a method is developed to extract plant topology form alarm data. The method is based on process mining, a collection of concepts and algorithms that model a process (not necessarily an engineering one) based on recorded events. The event based nature of alarm data as well as the chronological order of recorded alarms make them suitable for process mining. The methodology developed in this paper is based on preparing alarm data for process mining and then using the suitable process mining algorithms to extract plant topology. The extracted topology is represented by the familiar Petri net which can be used for root cause analysis and discovering fault propagation paths. The methods to evaluate the extracted topology are also discussed. A case study on the well-known Tennessee Eastman process demonstrates the utility of the proposed method.
Reward Constrained Interactive Recommendation with Natural Language Feedback
Venue: arXiv cs.CL 2020
Year: 2020

Text-based interactive recommendation provides richer user feedback and has demonstrated advantages over traditional interactive recommender systems. However, recommendations can easily violate preferences of users from their past natural-language feedback, since the recommender needs to explore new items for further improvement. To alleviate this issue, we propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference, which is incorporated into the standard RL objective of maximizing expected cumulative future rewards. Our proposed framework is general and is further extended to the task of constrained text generation. Empirical results show that the proposed method yields consistent improvement relative to standard RL methods.
Application of accelerated fixed-point algorithms to hydrodynamic well-fracture coupling
Venue: arXiv cs.CE 2020
Year: 2020

The coupled simulations of dynamic interactions between the well, hydraulic fractures and reservoir have significant importance in some areas of petroleum reservoir engineering. Several approaches to the problem of coupling between the numerical models of these parts of the full system have been developed in the industry in past years. One of the possible approaches allowing formulation of the problem as a fixed-point problem is studied in the present work. Accelerated Anderson's and Aitken's fixed-point algorithms are applied to the coupling problem. Accelerated algorithms are compared with traditional Picard iterations on the representative set of test cases including ones remarkably problematic for coupling. Relative performance is measured, and the robustness of the algorithms is tested. Accelerated algorithms enable a significant (up to two orders of magnitude) performance boost in some cases and convergent solutions in the cases where simple Picard iterations fail. Based on the analysis, we provide recommendations for the choice of the particular algorithm and tunable relaxation parameter depending on anticipated complexity of the problem.