OpenRaiser/Intern-Atlas huggingface dataset:
Torrit: A GUI-Based Power System Simulation Platform
Venue: arXiv cs.HC 2020
Year: 2020

An adequate education on power system operations and controls requires a hands-on experience on a graphical user interface (GUI) based software. At present, most commercial software do not have free editions with high flexibility and most freeware do not have good interfaces. This paper introduces a GUI-based application called "Torrit" for executing operations of power systems, especially for transmission systems. It is written in Python for it's rapid development ability. Torrit's main window includes a single canvas with some standard graphical interactions like create, delete, copy, move, double click etc. The beta version of this application is the focus of this paper that allows executing, saving and re-opening a project in three different modes: per unit computations, power flow, and state estimation. However, it is still in a rudimentary stage and many extensions are planned for future to match the needs of both academia and industry.
Microsoft Recommenders: Tools to Accelerate Developing Recommender Systems
Venue: arXiv cs.IR 2020
Year: 2020

The purpose of this work is to highlight the content of the Microsoft Recommenders repository and show how it can be used to reduce the time involved in developing recommender systems. The open source repository provides python utilities to simplify common recommender-related data science work as well as example Jupyter notebooks that demonstrate use of the algorithms and tools under various environments.
Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study
Venue: arXiv cs.CL 2020
Year: 2020

Large generative language models such as GPT-2 are well-known for their ability to generate text as well as their utility in supervised downstream tasks via fine-tuning. Our work is twofold: firstly we demonstrate via human evaluation that classifiers trained to discriminate between human and machine-generated text emerge as unsupervised predictors of "page quality", able to detect low quality content without any training. This enables fast bootstrapping of quality indicators in a low-resource setting. Secondly, curious to understand the prevalence and nature of low quality pages in the wild, we conduct extensive qualitative and quantitative analysis over 500 million web articles, making this the largest-scale study ever conducted on the topic.
COVID-19 Kaggle Literature Organization
Venue: arXiv cs.IR 2020
Year: 2020

The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature, there arose a need for organizing those documents. We describe an approach to organize and visualize the scientific literature on or related to COVID-19 using machine learning techniques so that papers on similar topics are grouped together. By doing so, the navigation of topics and related papers is simplified. We implemented this approach using the widely recognized CORD-19 dataset to present a publicly available proof of concept.
PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records
Venue: arXiv cs.IR 2020
Year: 2020

The broad adoption of Electronic Health Records (EHR) has led to vast amounts of data being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. In this work, we design a two-stage attention-based personalized medication recommender system called PREMIER which incorporates information from the EHR to suggest a set of medications. Our system takes into account the interactions among drugs in order to minimize the adverse effects for the patient. We utilize the various attention weights in the system to compute the contributions from the information sources for the recommended medications. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Two case studies are also presented demonstrating that the justifications provided by PREMIER are appropriate and aligned to clinical practices.
Mapping the Global South: Equal-Area Projections for Choropleth Maps
Venue: arXiv cs.HC 2020
Year: 2020

Choropleth maps are among the most common visualization techniques used to present geographical data. These maps require an equal-area projection but there are no clear criteria for selecting one. We collaborated with 20 social scientists researching on the Global South, interested in using choropleth maps, to investigate their design choices according to their research tasks. We asked them to design world choropleth maps through a survey, and analyzed their answers both qualitatively and quantitatively. The results suggest that the design choices of map projection, center, scale, and color scheme, were influenced by their personal research goals and the tasks. The projection was considered the most important choice and the Equal Earth projection was the most common projection used. Our study takes the first substantial step in investigating projection choices for world choropleth maps in applied visualization research.
Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation
Venue: arXiv cs.CL 2020
Year: 2020

With the ever-increasing number of digital music and vast music track features through popular online music streaming software and apps, feature recognition using the neural network is being used for experimentation to produce a wide range of results across a variety of experiments recently. Through this work, the authors extract information on such features from a popular open-source music corpus and explored new recognition techniques, by applying unsupervised Hebbian learning techniques on their single-layer neural network using the same dataset. The authors show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances their proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, authors put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of their work. The authors hope to discover and gather new information about this particular classification technique and its performance, and further understand future potential directions and prospects that could improve the art of computational music feature recognition.
Online Search With a Hint
Venue: arXiv cs.DS 2020
Year: 2020

The linear search problem, informally known as the cow path problem, is one of the fundamental problems in search theory. In this problem, an immobile target is hidden at some unknown position on an unbounded line, and a mobile searcher, initially positioned at some specific point of the line called the root, must traverse the line so as to locate the target. The objective is to minimize the worst-case ratio of the distance traversed by the searcher to the distance of the target from the root, which is known as the competitive ratio of the search. In this work we study this problem in a setting in which the searcher has a hint concerning the target. We consider three settings in regards to the nature of the hint: i) the hint suggests the exact position of the target on the line; ii) the hint suggests the direction of the optimal search (i.e., to the left or the right of the root); and iii) the hint is a general k-bit string that encodes some information concerning the target. Our objective is to study the Pareto-efficiency of strategies in this model. Namely, we seek optimal, or near-optimal tradeoffs between the searcher's performance if the hint is correct (i.e., provided by a trusted source) and if the hint is incorrect (i.e., provided by an adversary).
A Multisite, Report-Based, Centralized Infrastructure for Feedback and Monitoring of Radiology AI/ML Development and Clinical Deployment
Venue: arXiv cs.LG 2020
Year: 2020

An infrastructure for multisite, geographically-distributed creation and collection of diverse, high-quality, curated and labeled radiology image data is crucial for the successful automated development, deployment, monitoring and continuous improvement of Artificial Intelligence (AI)/Machine Learning (ML) solutions in the real world. An interactive radiology reporting approach that integrates image viewing, dictation, natural language processing (NLP) and creation of hyperlinks between image findings and the report, provides localized labels during routine interpretation. These images and labels can be captured and centralized in a cloud-based system. This method provides a practical and efficient mechanism with which to monitor algorithm performance. It also supplies feedback for iterative development and quality improvement of new and existing algorithmic models. Both feedback and monitoring are achieved without burdening the radiologist. The method addresses proposed regulatory requirements for post-marketing surveillance and external data. Comprehensive multi-site data collection assists in reducing bias. Resource requirements are greatly reduced compared to dedicated retrospective expert labeling.
Interactive and Explainable Point-of-Interest Recommendation using Look-alike Groups
Venue: arXiv cs.DB 2020
Year: 2020

Recommending Points-of-Interest (POIs) is surfacing in many location-based applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make recommendations. However these systems still lack customizability (incorporating session-based user interactions with the system) and contextuality (incorporating the situational context of the user), particularly in cold start situations, where nearly no user information is available. In this paper, we propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets. LikeMind reformulates the problem of POI recommendation, as recommending explainable look-alike groups (and their POIs) which are in line with user's interests. LikeMind frames the task of POI recommendation as an exploratory process where users interact with the system by expressing their favorite POIs, and their interactions impact the way look-alike groups are selected out. Moreover, LikeMind employs "mindsets", which capture actual situation and intent of the user, and enforce the semantics of POI interestingness. In an extensive set of experiments, we show the quality of our approach in recommending relevant look-alike groups and their POIs, in terms of efficiency and effectiveness.
On Polyhedral Realization with Isosceles Triangles
Venue: arXiv cs.CG 2020
Year: 2020

Answering a question posed by Joseph Malkevitch, we prove that there exists a polyhedral graph, with triangular faces, such that every realization of it as the graph of a convex polyhedron includes at least one face that is a scalene triangle. Our construction is based on Kleetopes, and shows that there exists an integer $i$ such that all convex $i$-iterated Kleetopes have a scalene face. However, we also show that all Kleetopes of triangulated polyhedral graphs have non-convex non-self-crossing realizations in which all faces are isosceles. We answer another question of Malkevitch by observing that a spherical tiling of Dawson (2005) leads to a fourth infinite family of convex polyhedra in which all faces are congruent isosceles triangles, adding one to the three families previously known to Malkevitch. We prove that the graphs of convex polyhedra with congruent isosceles faces have bounded diameter and have dominating sets of bounded size.
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
Venue: arXiv cs.AI 2020
Year: 2020

Knowledge-based Visual Question Answering (KVQA) requires external knowledge beyond the visible content to answer questions about an image. This ability is challenging but indispensable to achieve general VQA. One limitation of existing KVQA solutions is that they jointly embed all kinds of information without fine-grained selection, which introduces unexpected noises for reasoning the correct answer. How to capture the question-oriented and information-complementary evidence remains a key challenge to solve the problem. Inspired by the human cognition theory, in this paper, we depict an image by multiple knowledge graphs from the visual, semantic and factual views. Thereinto, the visual graph and semantic graph are regarded as image-conditioned instantiation of the factual graph. On top of these new representations, we re-formulate Knowledge-based Visual Question Answering as a recurrent reasoning process for obtaining complementary evidence from multimodal information. To this end, we decompose the model into a series of memory-based reasoning steps, each performed by a G raph-based R ead, U pdate, and C ontrol ( GRUC ) module that conducts parallel reasoning over both visual and semantic information. By stacking the modules multiple times, our model performs transitive reasoning and obtains question-oriented concept representations under the constrain of different modalities. Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts. We achieve a new state-of-the-art performance on three popular benchmark datasets, including FVQA, Visual7W-KB and OK-VQA, and demonstrate the effectiveness and interpretability of our model with extensive experiments.
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR frames into neural networks as point clouds and match pairs in the learned feature space. In contrast, motivated by the success of image based feature extractors, we propose to transfer the LiDAR frames to image space and reformulate the problem as image feature extraction. With the help of scale-invariant feature transform (SIFT) for feature extraction, we are able to generate matched keypoint pairs (MKPs) that can be precisely returned to the 3D space. A convolutional neural network pipeline is designed for LiDAR odometry estimation by extracted MKPs. The proposed scheme, namely LodoNet, is then evaluated in the KITTI odometry estimation benchmark, achieving on par with or even better results than the state-of-the-art.
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
Venue: arXiv cs.CV 2020
Year: 2020

In the globalized economic world, it has become important to understand the purpose behind infrastructural and construction initiatives occurring within developing regions of the earth. This is critical when the financing for such projects must be coming from external sources, as is occurring throughout major portions of the African continent. When it comes to imagery analysis to research these regions, ground and aerial coverage is either non-existent or not commonly acquired. However, imagery from a large number of commercial, private, and government satellites have produced enormous datasets with global coverage, compiling geospatial resources that can be mined and processed using machine learning algorithms and neural networks. The downside is that a majority of these geospatial data resources are in a state of technical stasis, as it is difficult to quickly parse and determine a plan for request and processing when acquiring satellite image data. A goal of this research is to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives could take, allowing for a directed monitoring via narrowed and targeted satellite imagery requests. By utilizing photogrammetric techniques on available satellite data to create 3D Meshes and Digital Surface Models (DSM) we hope to effectively predict transport routes. In understanding the potential directions that largescale transport infrastructure will take through predictive modeling, it becomes much easier to track, understand, and monitor progress, especially in areas with limited imagery coverage.
WorkflowHub: Community Framework for Enabling Scientific Workflow Research and Development -- Technical Report
Venue: arXiv cs.DC 2020
Year: 2020

Scientific workflows are a cornerstone of modern scientific computing. They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored/processed at heterogeneous, distributed resources. The workflow research and development community has employed a number of methods for the quantitative evaluation of existing and novel workflow algorithms and systems. In particular, a common approach is to simulate workflow executions. In previous work, we have presented a collection of tools that have been used for aiding research and development activities in the Pegasus project, and that have been adopted by others for conducting workflow research. Despite their popularity, there are several shortcomings that prevent easy adoption, maintenance, and consistency with the evolving structures and computational requirements of production workflows. In this work, we present WorkflowHub, a community framework that provides a collection of tools for analyzing workflow execution traces, producing realistic synthetic workflow traces, and simulating workflow executions. We demonstrate the realism of the generated synthetic traces by comparing simulated executions of these traces with actual workflow executions. We also contrast these results with those obtained when using the previously available collection of tools. We find that our framework not only can be used to generate representative synthetic workflow traces (i.e., with workflow structures and task characteristics distributions that resembles those in traces obtained from real-world workflow executions), but can also generate representative workflow traces at larger scales than that of available workflow traces.
Distinctive 3D local deep descriptors
Venue: arXiv cs.CV 2020
Year: 2020

We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignment. Point cloud patches are extracted, canonicalised with respect to their estimated local reference frame and encoded into rotation-invariant compact descriptors by a PointNet-based deep neural network. DIPs can effectively generalise across different sensor modalities because they are learnt end-to-end from locally and randomly sampled points. Because DIPs encode only local geometric information, they are robust to clutter, occlusions and missing regions. We evaluate and compare DIPs against alternative hand-crafted and deep descriptors on several indoor and outdoor datasets consisting of point clouds reconstructed using different sensors. Results show that DIPs (i) achieve comparable results to the state-of-the-art on RGB-D indoor scenes (3DMatch dataset), (ii) outperform state-of-the-art by a large margin on laser-scanner outdoor scenes (ETH dataset), and (iii) generalise to indoor scenes reconstructed with the Visual-SLAM system of Android ARCore. Source code: https://github.com/fabiopoiesi/dip.
Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning
Venue: arXiv cs.CL 2020
Year: 2020

With the large volume of unstructured data that increases constantly on the web, the motivation of representing the knowledge in this data in the machine-understandable form is increased. Ontology is one of the major cornerstones of representing the information in a more meaningful way on the semantic Web. The current ontology repositories are quite limited either for their scope or for currentness. In addition, the current ontology extraction systems have many shortcomings and drawbacks, such as using a small dataset, depending on a large amount predefined patterns to extract semantic relations, and extracting a very few types of relations. The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications. This paper suggests new types of semantic relations and points out of using deep learning (DL) models for semantic relation extraction.
Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT
Venue: arXiv cs.LG 2020
Year: 2020

As Artificial Intelligent (AI) technology advances and increasingly large amounts of data become readily available via various Industrial Internet of Things (IIoT) projects, we evaluate the state of the art of predictive maintenance approaches and propose our innovative framework to improve the current practice. The paper first reviews the evolution of reliability modelling technology in the past 90 years and discusses major technologies developed in industry and academia. We then introduce the next generation maintenance framework - Intelligent Maintenance, and discuss its key components. This AI and IIoT based Intelligent Maintenance framework is composed of (1) latest machine learning algorithms including probabilistic reliability modelling with deep learning, (2) real-time data collection, transfer, and storage through wireless smart sensors, (3) Big Data technologies, (4) continuously integration and deployment of machine learning models, (5) mobile device and AR/VR applications for fast and better decision-making in the field. Particularly, we proposed a novel probabilistic deep learning reliability modelling approach and demonstrate it in the Turbofan Engine Degradation Dataset.
Railgun: streaming windows for mission critical systems
Venue: arXiv cs.DC 2020
Year: 2020

Some mission critical systems, such as fraud detection, require accurate, real-time metrics over long time windows on applications that demand high throughputs and low latencies. As these applications need to run "forever", cope with large and spiky data loads, they further require to be run in a distributed setting. Unsurprisingly, we are unaware of any distributed streaming system that provides all those properties. Instead, existing systems take large simplifications, such as implementing sliding windows as a fixed set of partially overlapping windows, jeopardizing metric accuracy (violating financial regulator rules) or latency (breaching service agreements). In this paper, we propose Railgun, a fault-tolerant, elastic, and distributed streaming system supporting real-time sliding windows for scenarios requiring high loads and millisecond-level latencies. We benchmarked an initial prototype of Railgun using real data, showing significant lower latency than Flink, and low memory usage, independent of window size.