A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions

Stakeholders make various types of decisions with respect to requirements, design, management, and so on during the software development life cycle . These decisions are typically not well documented and classified due to limited human resources, time, and budget . In this paper, we aimed at automaticallyclassifying decisions into five types to help stakeholders better document and understand decisions .…

Artificial compressibility methods for the incompressible Navier Stokes equations using lowest order face based schemes on polytopal meshes

We investigate artificial compressibility (AC) techniques for the timediscretization of the incompressible Navier-Stokes equations . AC timestepping uncouples at each time step thevelocity update from the pressure update . We consider both first-order and second-order time schemes and either an implicit or an explicit treatment of the nonlinear convection term .…

Reachability of Black Box Nonlinear Systems after Koopman Operator Linearization

Reachability analysis of nonlinear dynamical systems is a challenging andcomputationally expensive task . Computing the reachable states for linear systems, in contrast, can often be done efficiently in high dimensions . The Koopman operator links the behaviors of a nonlinear system to a linear system embedded in a higher dimensional space, with an additional set of so-calledobservable variables .…

Formalizing the Four layer Metamodeling Stack Potential and Benefits

Enterprise modeling deals with the increasing complexity of processes and systems by operationalizing model content and by linking complementary modelsand languages . To enable this amplification and turn models intocomputer-processable structures a comprehensive formalization is needed . Thispaper presents a generic formalism based on typed first-order logic and provides a perspective on the potential and benefits arising for a variety of research issues in conceptual modeling .…

Explaining Behavioural Inequivalence Generically in Quasilinear Time

We provide a generic algorithm for constructing formulae that distinguishbehaviourally inequivalent states in systems of various transition types . The algorithm builds on an existing coalgebraic partition refinement algorithm . It runs in time O((m + n) log n) on systems with n states and m transitions,and the same asymptotic bound applies to the size of the formula itconstructs .…

Pattern Complexity of Aperiodic Substitutive Subshifts

This paper aims to better understand the link between aperiodicity in subshifts and pattern complexity . We prove aquadratic lower bound on their pattern complexity for a class of substitutive sub-shifts . We also prove that the recent bound ofKari and Moutot, showing that any aperiodic subshift has pattern complexity atleast $mn+1$, is optimal for fixed $m$ and $n$.…

Enumeration of parallelogram polycubes

In this paper, we enumerate parallelogram polycubes according to several parameters . After establishing a relation between Multiple Zeta Function and Dirichlet generating function . We also give an explicit formula and an ordinary generating function of parallelograms according to thewidth, length and depth .…

Ensemble Feature Extraction for Multi Container Quality Diversity Algorithms

Quality-Diversity algorithms search for large collections of diverse and high-performing solutions . They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning orrobotics tasks . We show that this approach produces solutions that are more diverse than those produced by single-representation approaches .…

Distributed Quantum Computing with QMPI

Practical applications of quantum computers require millions of physical qubits and it will be challenging for individual quantum processors to reach such qubit numbers . We introduce an extension of the Message Passing Interface (MPI) to enable high-performanceimplementations of distributed quantum algorithms .…

A Constant factor Approximation for Weighted Bond Cover

Only three cases of minor-closed ${\calF}$ are known to admit constant-factor approximations . We study the problem for the class $\cal F$ of graphs, under theequivalent setting of the \textsc{Weighted $c$-Bond Cover} problem . In the first case, we tame the graph by replacing the protrusion with a special-purpose weighted gadget .…

Accessibility Across Borders

Cultural differences influence user preferences and interaction methods . We believe that it is equally important to apply this inquiry to digital accessibility and how accessibility fits within the design process around the world . We hope that this inquiry will also be applied to how digital accessibility fits into design process .…

Multi agent consensus with heterogeneous time varying input and communication delays in digraphs

This paper investigates the distributed consensus tracking control problem for general linear multi-agent systems (MASs) with external disturbances andheterogeneous time-varying input and communication delays . An extended LMI is proposed which, in conjunction with the rest of the LMIs, results in a solution with a larger upper bound on delays than what would befeasible without it .…

Three Shades of Partial Protection in Elastic Optical Networks

Partial protection strategies based on the observation that in failureevents, a service can tolerate a certain amount of degradation and therefore by reducing the protection traffic in the network, better spectrum utilization could be attained . Such concept has been widely studied in the traditional WDM context and yet has been somehow faded due to the fact that fixed transmissiontechnologies allow small room for spectral improvement .…

Reversible cellular automata in presence of noise rapidly forget everything

We consider reversible and surjective cellular automata perturbed with noise . We show that in the presence of positive additive noise, the cellularautomaton forgets all the information regarding its initial configurationexponentially fast . In particular, the state of a finite collection of cellswith diameter n becomes indistinguishable from pure noise after O(log n) timesteps .…

MLCheck Property Driven Testing of Machine Learning Models

In recent years, we observe an increasing amount of software with machinelearning components being deployed . We propose property-driven testing of machinelearning models . Our approach MLCheck encompasses (1) a language for propertyspecification, and (2) a technique for systematic test case generation .…

Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High Frequency Time Series

High-Frequency (HF) signal are ubiquitous in the industrial world and are of great use for monitoring of industrial assets . We propose a fullyunsupervised deep learning framework that is able to extract meaningful andsparse representation of raw HF signals . We embed in our architecture important properties of the fast discrete wavelet transformation (FDWT) such as the Cascade algorithm, the quadrature mirror filter property that relatestogether the wavelet, the scaling and transposed filter functions, and thecoefficient denoising .…

Optimal heating of an indoor swimming pool

This work presents the derivation of a model for the heating process of the air of a glass dome, where an indoor swimming pool is located in the bottom of the dome . The problem can be reduced from a three-dimensional to a twodimensional one .…

A Rate Splitting Strategy to Enable Joint Radar Sensing and Communication with Partial CSIT

Joint radar and communication (RadCom) systems have attracted increased attention in recent years . Joint RadCom system is designed which marriesthe capabilities of a Multiple-Input Multiple-Output (MIMO) radar withRate-Splitting Multiple Access (RSMA) RSMA providesthe RadCom with more robustness, flexibility and user rate fairness compared to the baseline joint RadCom System based on Space Division Multiple Access(SDMA) System is designed in the presence of partial CSIT to maximize the Average Weighted Sum-Rate (AWSR) under QoS rate constraints and minimize the RadCom Beampattern Squared Error (BSE) against anideal MIMO radar beamp attern .…

Robotic Surgery With Lean Reinforcement Learning

Model-freereinforcement learning (RL) is a promising direction toward generalizableautomated surgical performance . This is the first time an RL-based agent is taught from visual data in a surgical robotics environment . We tackle the sampleinefficiency of RL using a simple-to-implement system which we termhybrid-batch learning (HBL), effectively adding a second, long-term replaybuffer to the Q-learning process .…

Makespan Trade offs for Visiting Triangle Edges

We study a primitive vehicle routing-type problem in which a fleet of $n$unitspeed robots start from a point within a non-obtuse triangle . The goal is to design robots’ trajectories so as to visit alledges of the triangle with the smallest visitation time makespan .…

Stream Compression of DLMS Smart Meter Readings

Smart electricity meters typically upload readings a few times a day . Utility providers aim to increase the upload frequency in order to access consumption information in near real time . The legacy compressors fail to provides savings on the low-bandwidth, high-cost data connection .…

Network models for nonlocal traffic flow

We present a network formulation for a traffic flow model with nonlocalvelocity in the flux function . The modeling framework includes suitablecoupling conditions at intersections to ensure maximum flux ordistribution parameters . We prove themaximum principle and the existence of weak solutions on networks .…

DeepMPCVS Deep Model Predictive Control for Visual Servoing

The simplicity of the visual servoing approach makes it an attractive option for tasks dealing with vision-based control of robots in many real-world applications . Attaining precise alignment for unseen environments pose a challenge to existing visual servoational approaches . The recent data-driven approaches face issues when generalizing to novel environments .…

Metadata Interpretation Driven Development

Despite decades of engineering and scientific research efforts, separation of concerns in software development remains not fully achieved . We show that business-domain codeinscriptions play an even larger role in this challenge than the crosscutting of concerns phenomenon . We introduce a new methodology, called MetadataInterpretation Driven Development (MIDD) that suggests a possible path to .…

The Matter of Chance Auditing Web Search Results Related to the 2020 U S Presidential Primary Elections Across Six Search Engines

We examine how six search engines filter and rank information in relation to queries on the U.S. 2020 presidential primary elections under the default -that is nonpersonalized – conditions . Our findings indicate substantialdifferences in the search results between search engines and multiplediscrepancies within the results generated for different agents using the samesearch engine .…

Distributionally robust risk map for learning based motion planning and control A semidefinite programming approach

The DR-risk map aims to assess the conditional value-at-risk (CVaR) of collision withobstacles whose movements are inferred by Gaussian process regression (GPR) The inferred distribution is subject to errors, making it difficult to accurately evaluate the CVaR of collision . To overcome this challenge, this tool measures the risk under the worst-case distribution in aso-called ambiguity set that characterizes allowable distribution errors .…

Tubal Matrix Analysis

The $2$-norm of a tubal matrix is equal to its largest T-singular value, multiplied with a coefficient, which is $1$ in the case of matrices . Further study on tubal matrices may reveal more links between matrix theory and tensor theory .…

Octopus A Zero Cost Architecture for Stream Network Monitoring

In this letter, we discuss Octopus: anarchitecture for stream network monitoring . This isespecially relevant for the developing world where Community Networks (CN) areincreasing in popularity and complexity . We discuss the use of an efficient low-cost monitoring platform to ensure users’ Quality of Experience (QoE) Octopus is an anarchistic approach to stream-streaming networks .…

General Knapsack Problems in a Dynamic Setting

The world is dynamic and changes over time, so any optimization problems must address this dynamic nature . In the multistage model we are given aseries of instance of an optimization problem, and a solution is provided foreach instance . The strive for continuous and similar solutions over time are quantified and integrated into the objective function .…

Abstract clones for abstract syntax

Abstract clonestraditionally describe first-order structures, but by equipping them with algebraic structure, one can further axiomatize second-order, variable-binding operators . We give a construction of free algebras and derive acorresponding induction principle . This provides a syntax-independent representation of simple type theories, such as the simply-typed $\lambda$-calculus, using the framework of abstract clones .…

SmoothI Smooth Rank Indicators for Differentiable IR Metrics

Information retrieval (IR) systems traditionally aim to maximize metrics built on rankings, such as precision or NDCG . However, thenon-differentiability of the ranking operation prevents direct optimization of such metrics in state-of-the-art neural IR models . To address this shortcoming, we propose SmoothI, a smooth approximation of rank indicators that serves as abasic building block to devise differentiable approximations of IR metrics .…