
Free Energy Node Embedding via Generalized Skipgram with Negative Sampling
A widely established set of unsupervised node embedding methods can be i...
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Additive Link Metrics Identification: Proof of Selected Lemmas and Propositions
This is a technical report, containing all the lemma and proposition pro...
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Identification of Additive Link Metrics: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in the fol...
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Efficient Identification of Additive Link Metrics: Theorem Proof and Evaluations
This is a technical report, containing all the theorem proofs and additi...
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Partial Network Identifiability: Theorem Proof and Evaluation
This is a technical report, containing all the theorem proofs and additi...
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Link Identifiability with Two Monitors: Proof of Selected Theorems
This is a technical report, containing all the theorem proofs in paper "...
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Fundamental Theories in Node Failure Localization
This is a technical report, containing all the theorem proofs in paper "...
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Failure Localization Capability: Theorem Proof and Evaluation
This is a technical report, containing all the theorem proofs and additi...
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Node Failure Localization: Theorem Proof
This is a technical report, containing all the theorem proofs in paper "...
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Distributed Scheduling using Graph Neural Networks
A fundamental problem in the design of wireless networks is to efficient...
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Adaptive Contention Window Design using Deep Qlearning
We study the problem of adaptive contention window (CW) design for rando...
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Resource Allocation in Onedimensional Distributed Service Networks with Applications
We consider assignment policies that allocate resources to users, where ...
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On the Analysis of Spatially Constrained Power of Two Choice Policies
We consider a class of power of two choice based assignment policies for...
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You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning
As Deep Packet Inspection (DPI) middleboxes become increasingly popular,...
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Unsupervised Joint knode Graph Representations with Compositional EnergyBased Models
Existing Graph Neural Network (GNN) methods that learn inductive unsuper...
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An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks
Deep neural networks have been shown to be vulnerable to membership infe...
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Measurementdriven Security Analysis of Imperceptible Impersonation Attacks
The emergence of Internet of Things (IoT) brings about new security chal...
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Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
There has been a recent surge in research on adversarial perturbations t...
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GraphCL: Contrastive SelfSupervised Learning of Graph Representations
We propose Graph Contrastive Learning (GraphCL), a general framework for...
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Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm
Many tasks regarding the monitoring, management, and design of communica...
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A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm
Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algor...
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Resource Sharing in the Edge: A Distributed BargainingTheoretic Approach
The growing demand for edge computing resources, particularly due to inc...
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Let's Share: A GameTheoretic Framework for Resource Sharing in Mobile Edge Clouds
Mobile edge computing seeks to provide resources to different delaysens...
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SENSE: Semantically Enhanced Node Sequence Embedding
Effectively capturing graph node sequences in the form of vector embeddi...
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Quickest Detection of Growing Dynamic Anomalies in Networks
The problem of quickest growing dynamic anomaly detection in sensor netw...
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MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design
In distributed softwaredefined networks (SDN), multiple physical SDN co...
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A GameTheoretic Framework for Resource Sharing in Clouds
Providing resources to different users or applications is fundamental to...
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Attributiondriven Causal Analysis for Detection of Adversarial Examples
Attribution methods have been developed to explain the decision of a mac...
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Resource Allocation in Onedimensional Distributed Service Networks
We consider assignment policies that allocate resources to users, where ...
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A queueingtheoretic model for resource allocation in onedimensional distributed service network
We consider assignment policies that allocate resources to requesting us...
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A GameTheoretic Approach to MultiObjective Resource Sharing and Allocation in Mobile Edge Clouds
Mobile edge computing seeks to provide resources to different delaysens...
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Adversarial Perturbations Against RealTime Video Classification Systems
Recent research has demonstrated the brittleness of machine learning sys...
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MultiArmed Bandits on Unit Interval Graphs
An online learning problem with side information on the similarity and d...
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Joint Data Compression and Caching: Approaching Optimality with Guarantees
We consider the problem of optimally compressing and caching data across...
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The Internet of Battle Things
The battlefield of the future will be densely populated by a variety of ...
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Optimal Energy Consumption with Communication, Computation, Caching and QoIGuarantee
Energy efficiency is a fundamental requirement of modern data communicat...
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Optimal Energy Tradeoff among Communication, Computation and Caching with QoIGuarantee
Many applications must ingest and analyze data that are continuously gen...
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Outlier Detection from Network Data with Subnetwork Interpretation
Detecting a small number of outliers from a set of data observations is ...
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Modeling Group Dynamics Using Probabilistic Tensor Decompositions
We propose a probabilistic modeling framework for learning the dynamic p...
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Crafting Adversarial Input Sequences for Recurrent Neural Networks
Machine learning models are frequently used to solve complex security pr...
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Extending Detection with Forensic Information
For over a quarter century, securityrelevant detection has been driven ...
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Practical BlackBox Attacks against Machine Learning
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vul...
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The Limitations of Deep Learning in Adversarial Settings
Deep learning takes advantage of large datasets and computationally effi...
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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Deep learning algorithms have been shown to perform extremely well on ma...
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