## Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

Model pruning is an effective technique to reduce computation overhead for deep neural networks . Modern recommendation systems are thirsty for model capacity due to the demand for handling big data . Pruning a recommendation model at scale results in a smaller model capacity and lower accuracy .…

## 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 .…

## Training Quantized Neural Networks to Global Optimality via Semidefinite Programming

In this work, we introduce a convex optimization strategy to train quantized NNs with polynomial activations . Our method leverages hiddenconvexity in two-layer neural networks from the recent literature, semidefinitelifting, and Grothendieck’s identity . We show that certainquantized NN problems can be solved to global optimality in polynnomine-time in Polynomial-time .…

## 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 .…

## Moving Towards Centers Re ranking with Attention and Memory for Re identification

Re-ranking utilizes contextual information to optimize the initial rankinglist of person or vehicle re-identification (re-ID) This paper proposes a re-ranking networkto predict the correlations between the . probe and top-ranked neighbor samples . The process is equivalent to moving independent embeddings toward theidentity centers, improving cluster compactness .…

## One Model for All Quantization A Quantized Network Supporting Hot Swap Bit Width Adjustment

Model quantization has been successfully applied in many practical applications . When the precision of quantization is adjusted, it is necessary to fine-tune the model or minimize the quantization noise . We propose a method to train a model for all quantization that supports diverse bit-widths .…

## YAPS Your Open Examination System for Activating and emPowering Students

There are numerous e-assessment systems devoted to specific domains underdiverse license models . Cost, extensibility, and maintainability are relevant issues for an institution . Ease of use and inclusion into courses are main concerns . For students the user experience and fast transparent feedback plus “better” tests are most important .…

## Deep learning based coupled flow geomechanics surrogate model for CO _2 sequestration

A deep-learning-based surrogate model capable of predicting flow andgeomechanical responses in CO2 storage operations is presented and applied . The surrogate model is trained topredicting the 3D CO2 saturation and pressure fields in the storage aquifer, and 2D displacement maps at the Earth’s surface .…

## 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 .…

## mil benchmarks Standardized Evaluation of Deep Multiple Instance Learning Techniques

Multiple-instance learning is a subset of weakly supervised learning where labels are applied to sets of instances rather than the instances themselves . This paper introduces a series of multiple-instances learning benchmarks . The benchmarks are available from PyPi as mil-benchmarksand on GitHub .…

## 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 .…

## Secure Virtual Mobile Small Cells A Stepping Stone Towards 6G

As 5th Generation research reaches the twilight, the research community must look beyond 5G and look towards the 2030 connectivity landscape, namely 6G . Virtual mobile small cells (MSC) are created on demand and can harness radio and networking capability locally reducing protocol signaling latency and overhead .…

## 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 .…

## Combining Supervised and Un supervised Learning for Automatic Citrus Segmentation

Citrus segmentation is a key step of automatic citrus picking . Most current image segmentation approaches achieve good segmentation results bypixel-wise segmentation . These methods require alarge amount of annotated data, and do not consider continuous temporalchanges of citrus position in real-world applications .…

## YAPS Your Open Examination System for Activating and emPowering Students

There are numerous e-assessment systems devoted to specific domains underdiverse license models . Cost, extensibility, and maintainability are relevant issues for an institution . Ease of use and inclusion into courses are main concerns . For students the user experience and fast transparent feedback plus “better” tests are most important .…

## 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 .…

## When Can Accessibility Help An Exploration of Accessibility Feature Recommendation on Mobile Devices

Many users, especially older adults, are not aware of accessibility features or do not know which combination to use . Automated recommendation could help people find beneficial accessibility features, authors say . Their work demonstrates the need to increase awareness of existing accessibility features on mobile devices .…

## The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT

Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations . The synergy of complex event processing (CEP) and machinelearning (ML) has been developed actively in the last years in IIoT to identify patterns in heterogeneous data streams and fuse raw data into tangible facts .…

## 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 .…