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 .…

Switching 3 edge colorings of cubic graphs

The chromatic index of a cubic graph is either 3 or 4 . Edge-Kempe switching can be used to transform edge-colorings of cubic graphs . It is further connected to cycle switching of Steiner triple systems, for example, for which an improvement of the classification algorithm is presented .…

Where and When Space Time Attention for Audio Visual Explanations

Recent advances in XAI provide explanations for models trained on still images . But when it comes to modeling multiplesensory modalities in a dynamic world, it remains underexplored how tomystify the mysterious dynamics of a complex multi-modal model . We propose a novel space-time attention network that uncovers the synergistic dynamics of audio and visual data overboth space and time .…

Playing Stochastically in Weighted Timed Games to Emulate Memory

Weighted timed games are two-player zero-sum games played in a timedautomaton equipped with integer weights . In such weighted timed games, Minmay need finite memory to play (close to) optimally . In this work, we allow the players to use stochastic decisions, both in the choice of the timing oftransitions and of timing delays .…

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 .…

Graph Pooling via Coarsened Graph Infomax

Existing graphpooling methods either suffer from high computational complexity or cannot capture the global dependencies between graphs before and after pooling . CoarsenedGraph Infomax Pooling (CGIPool) maximizes the mutual information betweenthe input and the coarsened graph of each pooling layer to preserve graph-leveldependencies .…

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 .…

MLP Mixer An all MLP Architecture for Vision

Convolutional Neural Networks (CNNs) are the go-to model for computer vision . Attention-based networks, such as the Vision Transformer, have also become popular . In this paper we show that while convolutions and attention are sufficient for good performance, neither of them are necessary .…

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 .…

Technical Report for Valence Arousal Estimation on Affwild2 Dataset

In this work, we describe our method for tackling the valence-arousalestimation challenge from ABAW FG-2020 Competition . The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affectivebehavior in real-life settings . We use MIMAMO Net to achieve information about micro-motion and macro-motion for improving videoemotion recognition .…

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 .…

Technology Review of Blockchain Data Privacy Solutions

This objective of this report is to review existing enterprise blockchaintechnologies that provide data privacy while leveraging the data integrity benefits of blockchain . The hashing algorithm was found to be the most usedcryptographic primitive in enterprise or changeover privacy solutions .…

Hallucination Improves Few Shot Object Detection

Learning to detect novel objects from few annotated examples is of greatpractical importance . A particularly challenging yet common regime occurs whenthere are extremely limited examples (less than three) One critical factor in improving few-shot detection is to address the lack of variation in training data .…

ZEN 2 0 Continue Training and Adaption for N gram Enhanced Text Encoders

Pre-trained text encoders have drawn sustaining attention in natural languageprocessing (NLP) They have shown their capability in obtaining promising results indifferent tasks . We propose topre-train n-gram-enhanced Encoders with a large volume of data and advanced techniques for training . We try to extend the encoder to different languages as well as different domains, where it is confirmed that the samarchitecture is applicable to these varying circumstances and new state-of-the-art performance is observed from a long list of NLP tasks across the languages and domains .…

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 .…

Effects of Quantization on the Multiple Round Secret Key Capacity

We consider the strong secret key (SK) agreement problem for the satellitecommunication setting . Legitimate receivers have access to an authenticated, noiseless, two-way, and public communication link . The noise variances for Alice’s and Bob’s measurement channels are both fixed to a value $Q1$, whereas the noise over Eve’s measurement channel has a unitvariance, so $Q$ represents a channel quality ratio .…

Area Rate Efficiency in Molecular Communications

We consider a multiuser diffusion-based molecular communication (MC) system . Multiple spatially distributed transmitter (TX)-receiver (RX) pairs establish point-to-point communication links employing the same type of signaling molecules . We propose a new performance metric, which we referto as area rate efficiency (ARE), that captures the tradeoff between the userdensity and IUI .…

Architecture of a Flexible and Cost Effective Remote Code Execution Engine

A cloud-based web service for remote code execution, that is easily extensible to support any number of programming languages and libraries . The service provides a fast, reproduciblesolution for small software experiments and is amenable to collaboration in a workplace (via sharable permalinks) The service is designed as a distributedsystem to reliably support a large number of users, and efficiently managecloud-hosting costs with predictive auto-scaling while minimizing SLAviolations .…

VQCPC GAN Variable length Adversarial Audio Synthesis using Vector Quantized Contrastive Predictive Coding

Generative Adversarial Networks are often adopted for the audio domain using fixed-size two-dimensionalspectrogram representations . VQCPC-GAN is an adversarial framework for synthesizing variable-length audio by exploiting Vector-Quantized Contrastive PredictiveCoding . The input noise z(characteristic in adversarial architectures) remains fixed over time, ensuring temporal consistency of global features of the generated content .…

Unsupervised Graph based Topic Modeling from Video Transcriptions

The model improvescoherence by exploiting neural word embeddings through a graph-based clusteringmethod . Unlike typical topic models, this approach works without knowing the true number of topics . Experimental results on the real-life multimodal dataset MuSe-CaR demonstrates that our approach extracts coherent and meaningfultopics, outperforming baseline methods .…

Large scale Taxonomy Induction Using Entity and Word Embeddings

TIEmb is an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings . We apply the approach on theWebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person andPlace domain .…

Two Stage Facility Location Games with Strategic Clients and Facilities

We consider non-cooperative facility location games where both facilities and clients act strategically and heavily influence each other . This contrasts established game-theoretic facility location models with non-strategic clientsthat simply select the closest opened facility . We focus on a natural client behavior similar to classical loadbalancing: our selfish clients aim for a distribution that minimizes their maximum waiting times for getting serviced, where a facility’s waiting timecorresponds to its total attracted client weight .…

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 .…

Canonical Saliency Maps Decoding Deep Face Models

The Canonical Saliency Maps highlight facial features responsible for the decision made by a deep face model on a given image . Image-level maps highlight facialfeatures responsible for a DNN decision, thus helping to understand how a . DNN made a prediction on the image .…

An Overview of Laser Injection against Embedded Neural Network Models

The deployment of models in a large variety of devices faces several obstacles related to trust and security . Fault Injection Analysis (FIA) are known to be very powerful with a large spectrum of attack vectors . Laser injection with state-of-the-art equipment, combined with theoretical evidences from Adversarial Machine Learning, highlights worryingthreats against the integrity of deep learning inference and claims that joinefforts from the theoretical AI and Physical Security communities are a urgent need .…

Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments

The proposed approach uses a map segmentation technique to decompose the environment map into smaller, tractable maps . A simple information gain function is computed to determine the best target region to search during eachiteration of the process . DDQN and A2C algorithms are extended with a stack ofLSTM layers and trained to generate optimal policies for the exploration andexploitation, respectively .…