## Asymptotic Security by Model based Incident Handlers for Markov Decision Processes

This study investigates general model-based incident handler’s asymptoticbehaviors in time against cyber attacks to control systems . It is shown that the defender’s belief on existence of an attacker converges over time for any attacker’s strategy provided that thestochastic dynamics of the control system is known to the defender .…

## A Challenge Obfuscating Interface for Arbiter PUF Variants against Machine Learning Attacks

Physical unclonable functions are promising candidates as security primitives for resource-constrained IoT devices . Arbiter PUFs (APUFs) are a group of delay-based PUFs which are highly lightweight in resource requirements but suffer from high susceptibility to machine learning attacks . The challenge input interface incurs low resource overhead and improves PUFs’ resistance against machine-learning attacks .…

## DIALED Data Integrity Attestation for Low end Embedded Devices

The central challenge is how to securelydetect software exploits with minimal overhead . DIALED works in tandem with a companion CFA scheme to detect all (currently known) types of runtime software exploits at low-cost . Such attacks exploit softwarevulnerabilities to corrupt intermediate computation results stored in datamemory, changing neither the program code nor its control flow .…

## An Offline Delegatable Cryptocurrency System

Offline delegation offers an efficient way to exchange coins . However, in such an approach, the coins thathave been delegated confront the risk of being spent twice . In DelegaCoin, an owner can delegate his coinsthrough offline-transactions without interacting with the blockchain network .…

## CNN vs ELM for Image Based Malware Classification

Research in the field of malware classification often relies on machinelearning models that are trained on high-level features, such as opcodes,function calls, and control flow graphs . Extracting such features is costly, since disassembly or code execution is generally required .…

## Coining goldMEDAL A New Contribution to Data Lake Generic Metadata Modeling

Big data has revolutionized data exploitation practices and led to the emergence of new concepts . Data lakes have emerged as largeheterogeneous data repositories that can be analyzed by various methods . Anefficient data lake requires a metadata system that addresses the many problemsarising when dealing with big data .…

## RDMA is Turing complete we just did not know it yet

Remote Direct Memory Access(RDMA) NICs (RNICs) are one such device, allowing applications to offloadremote memory accesses . RDMA still requires CPU intervention for complex offloads, beyond simple remote memory access . RedN can outperform one and two-sided RDMA implementations by up to 3x and 7.8x for key-value get operations and .performance…

## A Multi Tenant Framework for Cloud Container Services

Virtual-Cluster is a new multi-tenant framework that extends Kubernetes . VirtualCluster provides both control plane and data plane isolations while sharing the underlying compute resources among tenants . The new framework preserves API compatibility by avoiding modifying the KuberNETes core components .…

## Opportunistic Federated Learning An Exploration of Egocentric Collaboration for Pervasive Computing Applications

Federated learning has gained attention for its ability to trainglobally shared models on users’ private data without requiring the users to share their data directly . Instead of learning in isolation, these models opportunistically incorporate the learned experiences of other devices they encounteropportunistically .…

## Distributed Learning for Melanoma Classification using Personal Health Train

Skin cancer is the most common cancer type . Usually, patients with suspicionof cancer are treated by doctors without any aided visual inspection . Data centralization does not often comply with local data protection regulations due to its sensitive nature .…

## SCHeMa Scheduling Scientific Containers on a Cluster of Heterogeneous Machines

SCHeMa is an open-source platform that facilitates the execution and reproducibility of computational analysis on heterogeneous clusters . It uses containerization, experiment packaging, workflow management, and machine learning technologies . SCMa is the latest open source platform to combine these technologies with machine learning and containerization to facilitate the work of scientists in this direction .…

## Towards Accommodating Real time Jobs on HPC Platforms

Increasing data volumes in scientific experiments necessitate the use of HPC resources for data analysis . Current HPC systems are typically batch-scheduled underpolicies in which an arriving job is run immediately only if enough resources are available; otherwise, it is queued .…

## FedGP Correlation Based Active Client Selection for Heterogeneous Federated Learning

Client-wise heterogeneity is one of the major issues that hinder effective training in federated learning (FL) Since the data distribution on each clientmay differ dramatically, the client selection strategy can largely influencethe convergence rate of the FL process . FedGP can improve convergence rates by $1.3\sim2.3 \times$ and$1.2\sim1.4\times$ on FMNIST and CIFAR-10 .…

## Improving Editorial Workflow and Metadata Quality at Springer Nature

Smart Topic Miner (STM) assists Springer Nature editorial team in annotating volumes of all books covering conference proceedings in Computer Science . Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year .…

## Ontology Based Recommendation of Editorial Products

Smart Book Recommender (SBR) was developed by The Open University in collaboration with Springer Nature . It supports their Computer Science editorial team in selecting products to market at specific venues . SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products .…

## The combinatorial game nofil played on Steiner Triple Systems

We introduce an impartial combinatorial game on Steiner triple systems calledNofil . Players move alternately, choosing points of the triple system . If a player is forced to fill a block on their turn, they lose . The game Nofil can be thought of in terms of play on a corresponding hypergraph .…

## Note on the offspring distribution for group testing in the linear regime

The group testing problem is concerned with identifying a small set of $k$infected individuals in a large population of $n$ people . A test comes back positiveif and only if at least one individual is infected . In this note, we laygroundwork for analysing belief propagation for group testing .…

## Why Do Local Methods Solve Nonconvex Problems

Non-convex optimization is ubiquitous in modern machine learning . Problem is NP-hard in the worst case, but optimization quality in practice is not an issue . Most of the local minima of the practically-used objective functions are approximately global minima .…

## Single Sample Prophet Inequalities Revisited

The study of the prophet inequality problem in the limited information regimewas initiated by Azar et al. [SODA’14] in the pursuit of prior-independentposted-price mechanisms . As they show, $O(1)$-competitive policies areachievable using only a single sample from the distribution of each agent .…

## Online Stochastic Matching Poisson Arrivals and the Natural Linear Program

We study the online stochastic matching problem . For maximizing the cardinality of the matching, we give a $0.711$-competitive online algorithm . When the offline vertices are weighted, we introduce a$0.7009$- competitive online algorithm for maximizing the total weight of thematched online vertices .…

## Isolating Cuts Bi Submodularity and Faster Algorithms for Global Connectivity Problems

Li and Panigrahi obtained the first deterministic algorithm for the global minimum cut of a weighted undirected graph that runs in time $o(mn)$. They introduced an elegant and powerful technique to find isolatingcuts for a terminal set in a graph via a small number of $s$-$t$ minimum cutcomputations .…

## On the Uniform Distribution of Regular Expressions

In this paper we study a set of expressions that avoid a givenabsorbing pattern . It is shown that, although this set is significantly smallerthan the standard one, the asymptotic average estimates for the size of the . automaton for these expressions does not differ from the standardcase .…

## Failure Tolerant Contract Based Design of an Automated Valet Parking System using a Directive Response Architecture

The architecture is demonstrated on amodular automated valet parking (AVP) system . The contracts for the different components in the AVP system are explicitly defined, implemented, and validated against a Python implementation . This paper aims to extend the contract-based design approach using a directive-response architecture to reactivity to failure scenarios .…

## Deep Implicit Moving Least Squares Functions for 3D Reconstruction

Point set is a flexible and lightweight representation widely used for 3D deep learning . However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shapegeneration . In this work, we turn discrete point sets into smooth surfaces by introducing the well-known implicit moving least-squares (IMLS) surfaceformulation, which naturally defines locally implicit functions on point sets .…

## Robust Stochastic Stability with Applications to Social Distancing in a Pandemic

The theory of learning in games has extensively studied situations where agents respond dynamically to each other in light of a fixed utility function . However, in many settings of interest, agent utility functions themselves vary as a result of past agent choices .…

## Individual Altruism Cannot Overcome Congestion Effects in a Global Pandemic Game

A key challenge in responding to public health crises such as COVID-19 is the difficulty of predicting the results of feedback interconnections between the disease and society . We study the game-theoretic equilibria that emerge from this setup when the population is composed of either selfish or altruistic individuals .…

## Are energy savings the only reason for the emergence of bird echelon formation

We analyze the conditions under which the emergence of frequently observedechelon formation can be explained solely by the maximization of energysavings . We provide conditions on the benefit functionunder which the frequently observed echelon formations cannot be Nashequilbriums or group optimums .…

## Towards Efficient Auctions in an Auto bidding World

Auto-bidding has become one of the main options for bidding in online advertisements . Advertisers only need to specify high-level objectives and leave the task of bidding to auto-bidders . In this paper, we propose a family of auctions with boosts to improve welfare in auto-bid environments with both return on ad spend constraints and budget constraints .…

## A relaxed inertial forward backward forward algorithm for Stochastic Generalized Nash equilibrium seeking

In this paper we propose a new operator splitting algorithm for distributed Nash equilibrium seeking under stochastic uncertainty, featuring relaxation andinertial effects . Our work is inspired by recent deterministic operatorsplitting methods, designed for solving structured monotone inclusion problems . The algorithm is derived from a forward-backward-forward scheme for solvingstructured monotones problems featuring a Lipschitz continuous andmonotone game operator .…

## The Value of Communication and Cooperation in a Two Server Service System

In 2015, Guglielmi and Badia discussed optimal strategies in a service system with two strategic servers . In their setup, each server can either be active or inactive and an active server can be requested totransmit a sequence of packets .…

## Online Market Equilibrium with Application to Fair Division

We focus on the case of online Fishermarkets: individuals have linear, additive utility and items drawn from adistribution arrive one at a time in an online setting . We show that our dynamics can also be used as an online item-allocation rule such that the time-averagedallocations and utilities converge to those of a corresponding static Fishermarket .…

## Exercise with Social Robots Companion or Coach

In this paper, we investigate the roles that social robots can take inphysical exercise with human partners . In related work, robots or virtualintelligent agents take the role of a coach or instructor whereas in other approaches they are used as motivational aids .…

## AQEyes Visual Analytics for Anomaly Detection and Examination of Air Quality Data

AQEyes is an integrated visual analytic system for efficiently monitoring, detecting, and examining anomalies in air quality data . The pipeline integrates an efficientunsupervised anomaly detection method that works without the use of labeleddata and overcomes the limitations of existing approaches .…

## How to Motivate and Engage Generation Clash of Clans at Work Emergent Properties of Business Gamification Elements in the Digital Economy

Average employee spends eleven cumulative years of their life at work . Less than one third of the workforce are actually engaged in their duties throughout their career . Using behavioural concepts derived from video games, and applying game designelements in business systems to motivate employees in the digital economy, is aconcept which has come to be recognised as Business Gamification .…

## Hierarchical Hyperedge Embedding based Representation Learning for Group Recommendation

In this work, we study group recommendation in a particular scenario, namelyOccasional Group Recommendation (OGR) Most existing works have addressed OGR by aggregating group members’ personal preferences to learn the grouprepresentation . We propose to leverage the user-user interactions to alleviatethe sparsity issue of user-item interactions, and design a GNN-basedrepresentation learning network to enhance the learning of individuals’preferences from their friends’ preferences .…

## Web Mining for Estimating Regulatory Blockchain Readiness

The regulatory framework of cryptocurrencies (and, in general, blockchaintokens) is of paramount importance . This framework drives nearly all key decisions in the respective business areas . In this work, a computational model is proposed for quantitatively estimating the regulatory stance of countries with respect to cryptocurrencies .…

## From Semantic Retrieval to Pairwise Ranking Applying Deep Learning in E commerce Search

We introduce deep learning models to the two most important stages in productsearch at JD.com, one of the largest e-commerce platforms in the world . We outline the design of a deep learning system that retrievessemantically relevant items to a query within milliseconds .…

## Meta ViterbiNet Online Meta Learned Viterbi Equalization for Non Stationary Channels

Meta-ViterbiNet is a DNN-aided symbol detector that tracks channel variations with reduced overhead . It outperforms the previous best approach by a margin of up to 0.6dB in bit error rate in various challenging scenarios . It is based on a model-based/data-driven equalizer that operates with a relatively small number of trainable parameters .…

## Information Freshness Analysis of Slotted ALOHA in Gilbert Elliot Channels

This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel . The penalty function that follows a power law of the time elapsed sincethe last received update, generalizing the age of information metric .…

## On Sequential Bayesian Optimization with Pairwise Comparison

In this work, we study the problem of user preference learning on the exampleof parameter setting for a hearing aid . We propose to use an agent thatinteracts with a HA user, in order to collect the most informative data, andlearns user preferences for HA parameter settings .…

## Quantized Corrupted Sensing with Random Dithering

Corrupted sensing concerns the problem of recovering a high-dimensionalstructured signal from a collection of measurements that are contaminated by structured corruption and unstructured noise . In practical applications of digital signalprocessing, the quantization process is inevitable, which often leads tonon-linear measurements .…

## A Message Passing based Adaptive PDA Algorithm for Robust Radio based Localization and Tracking

We present a message passing algorithm for localization and tracking inmultipath-prone environments . The proposed adaptive probabilistic data association algorithminfers the position of a mobile agent using multiple anchors . The algorithm adapts in an online manner to both, the time-varyingsignal-to-noise-ratio and line-of-sight (LOS) existence probability of eachanchor .…

## Energy Efficient Resource Allocation in Massive MIMO NOMA Networks with Wireless Power Transfer A Distributed ADMM Approach

In multicell massive multiple-input multiple- input multiple-output (MIMO) non-orthogonalmultiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things(IoT) devices use their harvested energy to support uplink data transmission . To maximize theEE of the network, we propose a novel joint power, time, antenna selection, andsubcarrier resource allocation scheme .…

## Homomorphic encoders of profinite abelian groups

In this paper we investigate the structure of order controllable subgroups . We say that asubgroup $G_i :i\in\N\}$ is a family of finite Abelian groups . If $G$ is an ordercontrollable, shift invariant, group code over an abelian group $H$ then \$G#possesses a canonical generator set .…

## Graph Representation Learning by Ensemble Aggregating Subgraphs via Mutual Information Maximization

Graph Neural Networks have shown tremendous potential on dealing with garph data and achieved outstanding results in recent years . In some research areas,labelling data are hard to obtain for technical reasons, which necessitates the study of unsupervised and semi-superivsed learning on graphs .…

## Analysis of QoS in Heterogeneous Networks with Clustered Deployment and Caching Aware Capacity Allocation

In cellular networks, the densification of connected devices and basestations engender the ever-growing traffic intensity . We propose the allocation ofdownlink data transmission capacity according to the cases of requestedcontents which are either cached or non-cached in nearby nodes . The throughput and delay of the allocationsystem are derived based on the approximated sojourn time of the DiscriminatoryProcessor Sharing (DPS) queue .…

## Outage Probability Expressions for an IRS Assisted System with and without Source Destination Link for the Case of Quantized Phase Shifts

The outage probability (OP) at the destination of anintelligent reflecting surface (IRS) assisted communication system is studied in the presence of phase error due to quantization at the IRS when a)source-destination (SD) link is present and a) SD link is absent .…

## Comments on A Framework for Control System Design Subject to Average Data Rate Constraints

Theorem 4.1 in the 2011 paper “A Framework for Control System Design Subjectto Average Data-Rate Constraints” allows one to lower bound average operational data rates in feedback loops . Unfortunately, the proof is invalid . In this note we first state the theorem and explain why its proof is flawed,and then provide a correct proof under weaker assumptions .…

## ModGNN Expert Policy Approximation in Multi Agent Systems with a Modular Graph Neural Network Architecture

Recent work in the multi-agent domain has shown the promise of Graph NeuralNetworks (GNNs) to learn complex coordination strategies . We introduce ModGNN, adecentralized framework which serves as a generalization of GCNs, providing more flexibility . We evaluate an implementation ofModGNN against several baselines to test their hypothesis .…

## Multi Agent Off Policy TD Learning Finite Time Analysis with Near Optimal Sample Complexity and Communication Complexity

The finite-time convergence of off-policy TD learning has been studied recently, but such a type of convergence has not been well established for multi-agent learning in the multi-agents setting . This work develops two decentralized TD with correction (TDC) algorithms formulti-agent off-Policy TD learning under Markovian sampling .…