A Subatomic Proof System for Decision Trees

Insummary, by expanding the language of propositional logic, we make its prooftheory more regular and generate more proofs, some of which are very efficient . We design a proof system for propositional classical logic that integratestwo languages for Boolean functions .…

Three Commandments for Applying Photonic Network Coding To Optical Core Networks

The digital transformation has been underway, creating digital shadows of(almost) all physical entities and moving them to the Internet . The era ofInternet of Everything has therefore started to come into play, giving rise tounprecedented traffic growths . Optical core networks forming the backbone of Internet infrastructure have been under critical issues ofreaching the capacity limit of conventional fiber, a phenomenon widely referredas capacity crunch .…

Deterministic Rounding of Dynamic Fractional Matchings

We present a framework for deterministically rounding a dynamic fractional matching algorithm . This is the first dynamic matching algorithm that works on general graphs by using an algorithm for low-arboricity graphs as ablack-box subroutine . Our rounding scheme works by maintaining a good {\em matching-sparsifier} with bounded arboricity, and then applying the algorithm of Peleg and Solomon[SODA’16] to maintain a near-optimal matching in this low arboric graph .…

Enhancing Generalizability of Predictive Models with Synergy of Data and Physics

Wind farm needs prediction models for predictive maintenance. There is a need to predict values of non-observable parameters beyond ranges reflected in available data . Physics-based principles are combined with machinelearning algorithms through feature engineering, strong rules anddivide-and-conquer. The proposed synergy concept is illustrated with the windturbine blade icing prediction and achieves significant prediction accuracyacross different turbines .…

Isolation schemes for problems on decomposable graphs

The Isolation Lemma is a self-reduction scheme that allows one to assume that a given instance of a problem has a unique solution . In this paper, we study a setting that is more typical for $\mathsf{NP}$-complete problems, and obtain partial derandomizations in theform of significantly decreasing the number of required random bits .…

ABET Accreditation A Way Forward for PDC Education

ACM and IEEEComputer Society’s Computer Science curricular guidelines have recommendedexposure to PDC concepts since 2013 . PDC curricular content, lectures, and labs freely available for undergraduate computer science programs . Despite efforts, progress inensuring computer science students graduate with sufficient PDC exposure has been uneven .…

Classes of intersection digraphs with good algorithmic properties

An intersection digraph is a digraph where every verticle $v$ is represented by an ordered pair $S_v, T_v$ of sets such that there is an edge from $v $w$ to $W$ if $V$ and $T_w$ intersect . We introduce a novel framework of directed versions of locally checkable problems, that streamlinesthe definitions and the study of many problems in the literature andfacilitates their common algorithmic treatment .…

Dual Cross Central Difference Network for Face Anti Spoofing

Face anti-spoofing (FAS) plays a vital role in securing face recognitionsystems . Central difference convolution (CDC) has shown its excellent capacity for the FAS task via leveraging local gradientfeatures . However, aggregating central difference clues from allneighbors/directions simultaneously makes CDC redundant and sub-optimized .…

Representation Learning for Clustering via Building Consensus

Recent advances in deep clustering and unsupervisedrepresentation learning are based on the idea that different views of an inputimage must be closer in therepresentation space . Consensus Clustering usingUnsupervised Representation Learning (ConCURL) improves the clusteringperformance over state-of-the art methods on four out of five image datasets .…

Multipath Graph Convolutional Neural Networks

Graph convolution networks have recently garnered a lot of attention forrepresentation learning on non-Euclidean feature spaces . In this work, we propose a novel Multipath Graphconvolutional neural network that aggregates the output of multiple different shallow networks . Results show that the proposed method attains increased test accuracy but also requires fewer trainingepochs to converge .…

Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems ADAS

A unified system integrating a compact object detector and a surroundingenvironmental condition classifier for enhancing the robustness of objectdetection scheme in advanced driver assistance systems (ADAS) is proposed in this paper . The proposed system includes two main components: (1) a compactone-stage object detector which is expected to be able to perform at acomparable accuracy compared to state-of-the-art object detectors, and (2) an environmental condition detector that helps to send a warning signal to the cloud in case the self-driving car needs human actions due to the significance of the situation .…

Weak Multi View Supervision for Surface Mapping Estimation

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations . We learn theunderlying surface geometry of common categories, such as human faces, cars,and airplanes, given instances from those categories . Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding .…

A Survey on End User Robot Programming

As robots interact with a broader range of end-users, end-user robotprogramming has helped democratize robot programming . This article surveys work on work on robot programming, with a focus on program specification . The survey concludes by highlighting open directions for further investigation to enhance and widen the reach of robot programming systems .…

Walk in the Cloud Learning Curves for Point Clouds Shape Analysis

Discrete point cloud objects lack sufficient shape descriptors of 3Dgeometries . In this paper, we present a novel method for aggregatinghypothetical curves in point clouds . Sequences of connected points (curves) areinitially grouped by taking guided walks in the point clouds, and then aggregated back to augment their point-wise features .…

TimeGym Debugging for Time Series Modeling in Python

TimeGym Forecasting Debugging Toolkit is a Python library fortesting and debugging time series forecasting pipelines . It provides generic tests for forecasting pipelines fresh out of the box . The library enables forecasters to apply aTest-Driven Development approach to forecast modeling, using specified oraclesto generate artificial data with noise .…

Learning Traffic Speed Dynamics from Visualizations

We present a deep learning method to learn the macroscopic traffic speed dynamics from these space-time visualizations . We present the high-resolution traffic speed fields estimated for several freeway sections . We further demonstrate the quality and utility of the estimation by inferring vehicle trajectories from the estimated speedfields .…

Optimal Algorithms for Range Searching over Multi Armed Bandits

This paper studies a multi-armed bandit (MAB) version of the range-searching problem . In its basic form, range searching considers as input a set of points and a collection of (real) intervals . The current work addresses range searching with stochastic weights: eachpoint corresponds to an arm (that admits sample access) and the point’s weightis the (unknown) mean of the underlying distribution .…

An Empirical Review of Deep Learning Frameworks for Change Detection Model Design Experimental Frameworks Challenges and Research Needs

Visual change detection is one of the elementary tasks in computervision and video analytics . Applications include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance . The challenges in change detection include background fluctuations,illumination variation, weather changes, intermittent object motion, shadow, camera motion, and heterogeneous object shapes .…

NeuralLog a Neural Logic Language

The main goal of NeuralLog is to bridge logic programming and deep learning . The main advantages of neural networks are: to allow neural networks to be defined as logic programs; and to be able to handlenumeric attributes and functions .…

A Review on Oracle Issues in Machine Learning

Machine learning contrasts with traditional software development in that theoracle is the data, and the data is not always a correct representation of the problem that machine learning tries to model . We present a survey of the oracle issues found in machine learning and state-of-the-art solutions for dealing with these issues .…

CREAMS Copyrighted Cloud Media Sharing

The advent of the big data era drives the media data owner to seek help from the cloud platform for data hosting and sharing . Sharing media data through thecloud suffers three key security/privacy problems including the leakage of dataprivacy, the infringement on the data owner’s copyright, and the infringementon the user’s right .…

Evaluating Metrics for Standardized Benchmarking of Remote Presence Systems

The U.S. Department of Energy’s ARPA-E issued a research project called SCOTTIE – Systematic CommunicationObjectives and Telecommunications Technology Investigations and Evaluations . SCOTTie tests virtual and augmented reality platforms in a functionalcomparison with face-to-face (FtF) interactions to derive travel replacementthresholds for common industrial training scenarios .…

Simplified Klinokinesis using Spiking Neural Networks for Resource Constrained Navigation on the Neuromorphic Processor Loihi

C. elegans shows chemotaxis using klinokinesis where the worm senses the concentration based on a single concentration sensor to compute the concentration gradient to perform foraging through gradient ascent/descenttowards the target concentration . The biomimeticimplementation requires complex neurons with multiple ion channel dynamics aswell as interneurons for control .…

Leveraging Third Order Features in Skeleton Based Action Recognition

Recent skeleton-based actionrecognition methods extract features from 3D joint coordinates asspatial-temporal cues . We propose fusing third-orderfeatures in the form of angles into modern architectures, to robustly capture relationships between joints and body parts . This simple fusion with spatial-temporality graph neural networks achieves new state-of-the-artaccuracy in two large benchmarks, including NTU60 and NTU120, while employing fewer parameters and reduced run time .…

COMISR Compression Informed Video Super Resolution

Most video super-resolution methods focus on restoring high-resolution videoframes from low-resolution videos without taking into account compression . Most videos on the web or mobile devices are compressed, and the compression can be severe when the bandwidth is limited . In this paper, we propose a new compression-informed video-super-resolution model to restorehigh-resolution content without introducing artifacts caused by compression .…