Sequential Learning based IaaS Composition

We propose a novel IaaS composition framework that selects an optimal set of requests according to provider’s qualitative preferences . Decision variables are included in the temporalconditional preference networks (TempCP-net) to represent qualitativepreferences for both short-term and long-term consumers . The global preferenceranking is computed using a \textit{k}-d tree indexingbased temporal similarity measure approach .…

Localization Distillation for Object Detection

Knowledge distillation (KD) has witnessed its powerful ability in learningcompact models in deep learning field, but it is still limited in distillinglocalization information for object detection . Existing KD methods for objectdetection mainly focus on mimicking deep features between teacher model and student model .…

Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning

Communication is a important factor that enables agents to work cooperatively in multi-agent reinforcement learning (MARL) Most previous work uses continuous communication whose high representational capacity comes at the expense of interpretability . Allowing agents to learn their own discrete message protocol emerged from a variety of domains can increase theinterpretability for human designers and other agents .…

vrCAPTCHA Exploring CAPTCHA Designs in Virtual Reality

With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA designs for VR devices . In this paper, we present four traditional two-dimensional (2D) CAPTCHAs in VR . Then, based on the three-dimensional interactioncharacteristics of VR devices, we propose two vrCAPTCHA design prototypes .…

Enabling the Network to Surf the Internet

Few-shot learning is challenging due to the limited data and labels . We develop a framework that enables themodel to surf the Internet, which implies that the model can collect andannotate data without manual effort . We demonstratethe superiority of the proposed framework with experiments on miniImageNet,tieredImageNet and Omniglot .…

Memory based Deep Reinforcement Learning for POMDP

A promising characteristic of Deep Reinforcement Learning (DRL) is itsability to learn optimal policy in an end-to-end manner without relying on feature engineering . Most approaches assume a fully observable statespace, i.e. fully observable Markov Decision Process (MDP) In real-worldrobotics, this assumption is unpractical, because of sensor issues such assensors’ capacity limitation and sensor noise .…

Neural content aware collaborative filtering for cold start music recommendation

State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from available listening data . These approaches inherently face the cold-startproblem, as they cannot recommend novel songs with no listening history . Content-aware recommendation addresses this issue by incorporating contentinformation about the songs on top of collaborative filtering .…

The Logical Options Framework

Logical Options Framework (LOF) learns policies that are satisfying, optimal, and composable . LOF efficiently learns policies thatsatisfy tasks by representing the task as an automaton and integrating it into learning and planning . We evaluate LOF on four tasks in discrete and continuous domains, including a 3D pick-and-place environment .…

A predictive safety filter for learning based racing control

The growing need for high-performance controllers in safety-critical applications like autonomous driving has been motivating the development offormal safety verification techniques . Tothis end, we provide a principled procedure to compute a safe and invariant setfor nonlinear dynamic bicycle models using efficient convex approximationtechniques .…

Actionable Principles for Artificial Intelligence Policy Three Pathways

This paper proposes a novel framework for the development of Actionable Principles for AI . The approach acknowledges therelevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes . The paper proposes the following threepropositions for the formation of such a prototype framework: (1) preliminarylandscape assessments; (2) multi-stakeholder participation and cross-sectoralfeedback; and, (3) mechanisms to support implementation andoperationalizability.…

Directional Bias Amplification

Mitigating bias in machine learning systems requires refining our understanding of bias propagation pathways . A metric formeasuring bias amplification was introduced in the seminal work by Zhao et al. We introduceand analyze a new, decoupled metric for measuring bias amplification,$\text{BiasAmp}_{\rightarrow}$ (Directional Bias Amplification) We provide suggestions about its measurement by cautioning against predicting sensitive attributes, encouraging the use ofconfidence intervals due to fluctuations in the fairness of models across runs,and discussing the limitations of what this metric captures .…

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

This research proposes a new integrated framework for identifying safelanding locations and planning in-flight divert maneuvers . The proposed framework wasable to achieve 94.8% of successful landing in highly challenging landingsites where over 80$\%$ of the area around the initial target lading point ishazardous, by effectively updating the target landing site and feedback controlgain during descent .…

Explaining Safety Failures in NetKAT

This work introduces a concept of explanations with respect to the violation of safe behaviours within software defined networks . In our setting, a safe behaviour is characterised by aNetKAT policy, or program, which does not enable forwarding packets from aningress i to an undesirable egress e .…

Density Sketches for Sampling and Estimation

We introduce Density sketches (DS) as a succinct online summary of the datadistribution . DS can accurately estimate point wise probability density . DS also provides a capability to sample unseen novel data from the underlying data distribution . DS construction is an online algorithm.…

Unsupervised semantic discovery through visual patterns detection

We propose a new fast fully unsupervised method to discover semanticpatterns . Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail . We provide both qualitativeand quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency .…

Comparing Completion Time Accuracy and Satisfaction in Virtual Reality vs Desktop Implementation of the Common Coordinate Framework Registration User Interface CCF RUI

The Registration User Interface (RUI) was developed to allow stakeholders in the Human BiomolecularAtlas Program (HuBMAP) to register tissue blocks by size, position, and orientation . The RUI has been used by tissue mapping centers across the HuBMAPconsortium to register a total of 45 kidney, spleen, and colon tissue blocks .…

Durable Top K Instant Stamped Temporal Records with User Specified Scoring Functions

A way of finding interesting or exceptional records from instant-stampedtemporal data is to consider their “durability,” or, intuitively speaking, how well they compare with other records that arrived earlier or later . For example, people are naturally fascinatedby claims with long durability, such as: “On January 22, 2006, Kobe Bryantdropped 81 points against Toronto Raptors.…

A Survey on Consortium Blockchain Consensus Mechanisms

Consensus algorithm is an agreement to validate the correctness of blockchain transactions . Unlike a public blockchain, a consortium blockchain does not contend with the issues of creating a resource-savingglobal consensus protocol . This paper presents the mechanisms of these and other consensus protocols, and analyzes and compares their advantages and drawbacks .…

Learning Augmented Sketches for Hessians

Sketching is a dimensionality reduction technique where one compresses amatrix by linear combinations that are typically chosen at random . A line ofwork has shown how to sketch the Hessian to speed up each iteration in a secondorder method . Such sketches usually depend only on the matrix at hand, but one could instead learn a distribution on sketching matrices that is optimized for the specific distribution of input matrices .…

The non positive circuit weight problem in parametric graphs a fast solution based on dioid theory

In this paper, we design an algorithm thatsolves the Non-positive Circuit weight Problem (NCP) on this class ofparametric graphs . The proposed algorithm isbased on max-plus algebra and formal languages and runs faster than otherexisting approaches . It achieves strongly polynomial time complexity$\mathcal{O}(n^4)$ (where $n$ is the number of nodes in the graph) The proposed algorithms are based on max plus algebra, and run faster than existing approaches .…

Decentralized conjugate gradients with finite step convergence

The decentralized solution of linear systems of equations arises as asubproblem in optimization over networks . Typical examples include the KKTsystem corresponding to equality constrained quadratic programs in distributedoptimization algorithms or in active set methods . This note presents a tailoredstructure-exploiting decentralized variant of the conjugate gradient method .…

Modern Koopman Theory for Dynamical Systems

The field of dynamical systems is being transformed by the mathematical tools emerging from modern computing and data science . Koopman spectral theory has emerged as a dominant perspective over the past decade . This linear representation of nonlinear dynamics has tremendous potential to enable the prediction,estimation, and control of non linear systems with standard textbook methods developed for linear systems .…

Object Detection in Aerial Images A Large Scale Benchmark and Challenges

The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-boxannotations collected from 11,268 aerial images . Previous challenges run on DOTA have attracted more than 1300 teams worldwide . We believe that the expanded large-scale DOTA datasets, the extensive baselines, the code library and the challenges can facilitate thedesigns of robust algorithms and reproducible research on the problem of object detection in aerial images.…

Cellular Automata and Kan Extensions

In this paper, we formalize the sense in which the application ofcellular automaton to partial configuration is a natural extension of its local transition function through the categorical notion of Kan extension . The two possible ways to do such an extension and the ingredients involved intheir definition are related through Kan extensions in many ways .…

Research on False Data Injection Attacks in VSC HVDC Systems

The false data injection (FDI) attack is a crucial form of cyber-physical security problems facing cyber power systems . There is noresearch revealing the problem of FDI attacks facing voltage source converterbased high voltage direct current transmission (VSC-HVDC) systems . And finally, the modified IEEE-14 bus system is used to demonstrate that attackers are capable of disrupting the operation security of converter stations in VSC- HVDC systems by FDI attack strategies .…

4D Panoptic LiDAR Segmentation

Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments . We propose 4D panopticLiDAR segmentation to assign a semantic class and a temporally-consistentinstance ID to a sequence of 3D points . We process multiple point clouds in parallel and resolve point-to-instance associations, effectively alleviating the need forexplicit temporal data association .…

Classification with abstention but without disparities

Classification with abstention has gained a lot of attention in recent years as it allows to incorporate human decision-makers in the process . Yet,abstention can amplify disparities and lead to discriminatorypredictions . The goal of this work is to build a general purpose classificational algorithm, which is able to abstain from prediction, while avoiding disparateimpact .…

An Enhanced Prohibited Items Recognition Model

We proposed a new modeling method to promote the performance of prohibited items recognition via X-ray images . We found the scales of some items are too small to be recognized which encumber the model performance . The Convolutional Block Attention Module(CBAM)and rescoring mechanism has been assembled into the model .…

On the Impact of Interpretability Methods in Active Image Augmentation Method

Deep neural network models achieve awe-inspiring results in a wide range of applications of computer vision . When combining ADA with GradCam, the U-Net model presented animpressive fast convergence . The results show that all methods achieve similar performance at the end of training, but when combining ADA and GradCam the model presented animation fast convergence, according to the paper .…