AutoGL A Library for Automated Graph Learning

Automated Graph Learning (AutoGL) is the first library for automated machine learning on graphs . AutoGL is open-source, easy-to-use, and flexible to be extended . We propose a machine learning pipeline for graph data containing four modules:auto feature engineering, model training, hyper-parameter optimization, andauto ensemble .…

Sublinear Time Nearest Neighbor Search over Generalized Weighted Manhattan Distance

Nearest Neighbor Search (NNS) over generalized weighted distance is fundamental to a wide range of applications . The Manhattan distance could be more practical than the Euclidean distance for high-dimensional NNS . We propose two novel sublinear time hashing schemes ($d_w^{l_1,.l_2$)-ALSH…

Supervised Feature Selection Techniques in Network Intrusion Detection a Critical Review

Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats . Feature Selection (FS) is a crucial pre-processing step in network management and, specifically, for the purposes of Network intrusion detection .…

ODT FLOW A Scalable Platform for Extracting Analyzing and Sharing Multi source Multi scale Human Mobility

In response to the soaring needs of human mobility data, we develop a scalable online platform for extracting, analyzing, and sharing multi-source multi-scale human mobility flows . Within the platform, an origin-destination-time (ODT) data model is proposed to work with scalable query engines to handle heterogenous mobility data in large volumes .…

Shuffler A Large Scale Data Management Tool for ML in Computer Vision

Shuffler is an open source tool that makes it easy to manage large computervision datasets . It stores annotations in a relational, human-readabledatabase . It defines over 40 data handling operations with annotationsthat are commonly useful in supervised learning applied to computer vision .…

Simple Optimal Algorithms for Random Sampling Without Replacement

We construct algorithms that are evensimpler, easier to implement, and have optimal space and time complexity . Consider the fundamental problem of drawing a simple random sample of size kwithout replacement from [n] := {1, . . . n}.…

A Novel Spatial Temporal Specification Based Monitoring System for Smart Cities

With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data . This leads to a need for checking city states against city requirements at runtime . In this paper, we develop anovel spatial-temporal specification-based monitoring system for smart cities .…

Velocity Skinning for Real time Stylized Skeletal Animation

We propose asimple, real-time solution for adding secondary animation effects on top of standard skinning . Our method takes a standard skeleton animation as input, along with skin mesh and rig weights . It then derives per-vertex deformations from the different linear and angularvelocities along the skeletal hierarchy .…

Compressive Neural Representations of Volumetric Scalar Fields

We present an approach for compressing volumetric scalar fields usingimplicit neural representations . Our approach represents a scalar field as alearned function, wherein a neural network maps a point in the domain to an output scalar value . By setting the number of weights of the neural network to be smaller than the input size, we achieve compressed representations of scalarfields .…

Dissecting the square into seven congruent parts

We give a computer-based proof of the following fact: If a square is tiled byseven convex tiles which are congruent among themselves, then the tiles arerectangles . This confirms a new case of a conjecture posed by Yuen, Zamfirescu .…

Disentangling Semantics and Syntax in Sentence Embeddings with Pre trained Language Models

Paraphrasepairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax . ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase,and a parse tree that specifies the target syntax .…

A Deep Learning Based Cost Model for Automatic Code Optimization

A novel deeplearning based cost model for automatic code optimization has been proposed in the Tiramisu compiler . The proposed model has only 16% of mean absolute percentage error in predicting speedups onfull programs . Unlike previous models, the proposed one does not rely on any heavy feature engineering .…

MIPT NSU UTMN at SemEval 2021 Task 5 Ensembling Learning with Pre trained Language Models for Toxic Spans Detection

This paper describes our system for SemEval-2021 Task 5 on Toxic SpansDetection . We developed ensemble models using BERT-based neural architectures and post-processing to combine tokens into spans . Our system obtained a F1-score of 67.55% on test data .…

A Preliminary Model for the Design of Music Visualizations

Music Visualization is basically the transformation of data from the aural to the visual space . There are a variety of music visualizations, across applications, present on the web . Models of Visualization include conceptualframeworks helpful for designing, understanding and making sense of visualizations .…

Jamming Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks . In this paper, we aim to find collision-free paths formultiple cellular-connected UAVs, while satisfying requirements of connectivity with ground base stations in the presence of a dynamic jammer .…

Unsupervised Learning of Explainable Parse Trees for Improved Generalisation

Recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate treerepresentation . In this work, we propose an attention mechanism over Tree-LSTMsto learn more meaningful and explainable parse tree structures . We alsodemonstrate the superior performance of our proposed model on natural languageinference, semantic relatedness, and sentiment analysis tasks .…

Multiple Run Ensemble Learning withLow Dimensional Knowledge Graph Embeddings

Link prediction using knowledgegraph embedding (KGE) models has gained significant attention for knowledgegraph completion . In this paper, we propose a simple but effective performance boosting strategy for KGE models by using multiple low dimensions in different rounds of the same model .…

NorDial A Preliminary Corpus of Written Norwegian Dialect Use

Norway has a large amount of dialectal variation, as well as a general tolerance to its use in the public sphere . There are, however, few available resources to study this variation and its change over time and in more informalareas, \eg on social media .…

The structure of online social networks modulates the rate of lexical change

New words are regularly introduced to communities, yet not all of these words remain in a community’s lexicon . Dense connections, the lack of local clusters and more external contacts promote lexical innovation and retention . Unlike offline communities, topic-based communities do not experience strong lexical levelling despite increased contact but accommodate more niche words .…

PPT Multicore Performance Prediction of OpenMP applications using Reuse Profiles and Analytical Modeling

PPT-Multicore builds upon our previous work towards amulticore cache model . We extract LLVM basic block labeled memory trace using an architecture-independent LLVM-based instrumentation tool only once in anapplication’s lifetime . The model uses the memory trace and other parameters from an instrumented sequentially executed binary .…

Constructing Contrastive samples via Summarization for Text Classification with limited annotations

Contrastive Learning has emerged as a powerful representation learning method . How to construct efficient contrastive samples through dataaugmentation is key to its success . Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks .…