3,689 research outputs found
Scholarly literature of software Startups in web of science (WOS) database
No full textThe dataset houses the scholarly research in web of science (WOS) database about software startups
Towards Sustainability of Project-based Learning in Higher Education
No full textTowards Sustainability of Project-based Learning in Higher Education
Determination of structural changes and phase transformations in boron carbide by static and dynamic studies
No full textRecent transmission electron microscopy results demonstrate that the failure of B4C is commensurate with the segregation of boron icosahedra embedded in amorphous carbon in 2–3 nm wide amorphous bands along the (113) lattice direction, in good agreement with our recent theoretical results. Boron carbide is generally composed of multiple polytypes of B4C which have the same primitive lattice parameters but differ from each other by the location of the boron and carbon atoms in the unit cells. The unit cells are formed by a 12-atom B12-nCn icosahedron and a 3-atom (C3-nBn) chain. Our theoretical results indicate that one polytype, B12(C3), whose formation is responsible for
the failure of the entire material. This anomalous and poorly understood glass-like behavior in boron carbide has been the subject of research since its discovery over 70 years ago. The characterization of disorder in hot pressed and powder boron carbide samples
is therefore of primary interest. The research work has focused on characterization techniques which can be used at a micrometric sampling size so that individual powder grains of the material can be utilized. Specifically, micro-Raman and electrical
conductivity measurements can be used with micrometric gap cells to understand the disorder in B4C.The results also demonstrate that it is possible to induce transformations in boron carbide using electric fields that are comparable with those obtained under shock and nanoindentation. Our calculations present a hypothesis which can provide a solution to prevent the
premature failure of B4C. A route to achieve suppression of the B12(CCC) polytype without significantly affecting the elastic constants is via low concentration Silicon (Si) doping of B4C. Suppression of B12(CCC) by Si doping has implications towards
development of boron carbide armor with improved properties for protection against high velocity threats. In order to achieve this, nanostructures (nanowires, nanorods, etc.) of Sidoped boron carbide have been synthesized using a Solid-Liquid-Solid (SLS) growth
mechanism. The resulting structures have been characterized by SEM, TEM and Raman
spectroscopy and consolidated to evaluate their mechanical properties. In addition, the application of nanowires in a transparent and thermally conducting nanocomposite is demonstrated.Ph.D.Includes abstractVitaIncludes bibliographical referencesby Varun Gupt
Permutation Strikes Back: The Power of Recourse in Online Metric Matching
No full textIn this paper, we study the online metric matching with recourse (OMM-Recourse) problem. Given a metric space with k servers, a sequence of clients is revealed online. A client must be matched to an available server on arrival. Unlike the classical online matching model where the match is irrevocable, the recourse model permits the algorithm to rematch existing clients upon the arrival of a new client. The goal is to maintain an online matching with a near-optimal total cost, while at the same time not rematching too many clients.
For the classical online metric matching problem without recourse, the optimal competitive ratio for deterministic algorithms is 2k-1, and the best-known randomized algorithms have competitive ratio O(log² k). For the much-studied special case of line metric, the best-known algorithms have competitive ratios of O(log k). Improving these competitive ratios (or showing lower bounds) are important open problems in this line of work.
In this paper, we show that logarithmic recourse significantly improves the quality of matchings we can maintain online. For general metrics, we show a deterministic O(log k)-competitive algorithm, with O(log k) recourse per client, an exponential improvement over the 2k-1 lower bound without recourse. For line metrics we show a deterministic 3-competitive algorithm with O(log k) amortized recourse, again improving the best-known O(log k)-competitive algorithms without recourse. The first result (general metrics) simulates a batched version of the classical algorithm for OMM called Permutation. The second result (line metric) also uses Permutation as the foundation but makes non-trivial changes to the matching to balance the competitive ratio and recourse.
Finally, we also consider the model when both clients and servers may arrive or depart dynamically, and exhibit a simple randomized O(log n)-competitive algorithm with O(log Δ) recourse, where n and Δ are the number of points and the aspect ratio of the underlying metric. We remark that no non-trivial bounds are possible in this fully-dynamic model when no recourse is allowed
A Family of Experiments to Evaluate the Effects of Mindfulness on Software Engineering Students: The MetaMind Dataset
No full textContext: Software Engineering students are often excellent developers although they may occasionally encounter difficulties with certain tasks such as conceptual modelling, in which mental clarity plays an essential role. With several years of experience in practising mindfulness (a meditation technique that calm the mind to see with clarity), we hypothesise that several weeks of continued mindfulness practise may increase the performance of students regarding conceptual modelling since the proven benefits of mindfulness include increased concentration and mental clarity. In order to ascertain this hypothesis, a family of controlled experiments, involving 130 students, has been carried out at the University of Seville over three consecutive academic years. Subsequent to the analysis of the individual experiments, a meta—analysis was conducted. Aim: This chapter helps understand how various datasets have been generated and traces their evolution across the family of experiments and the resulting meta—analysis. Not only does the process include the integration of datasets into a single target dataset with the complete set of data, but it also improves the structure of the dataset and adapts it to several statistical analysis tools. Method: Data collection was manually carried out during the individual experiments by means of gathering the scores of the students attained in conceptual modelling exercises. In order to obtain the complete but also simple dataset of the family of experiments, certain dataset columns were necessarily selected and renamed. New relevant information was also added to form a conclusive meta—analysis. The dataset was adapted to the type and format of SPSS for the execution of a further analysis, complementary to the main study in R. The MetaMind dataset is the set of datasets involved in the present research. The MetaMind dataset has become a helpful resource for the independent reproducibility of the results, to ascertain the evolution of the process across a family of experiments, and to enable external replications of the controlled experiment.Ministerio de Ciencia e Innovación HORATIO (RTI2018-101204–B–C21)Junta de Andalucía APOLO (US-1264651)Junta de Andalucía EKIPMENTPLUS (P18–FR–2895
Crowdsourcing and probabilistic decision-making in software engineering Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series./ [edited by] Varun Gupta.
No full textIncludes bibliographical references and index."This book examines crowd-based software engineering. It explores effective solutions, automation supports, and case studies about crowd-based software engineering"--Chapter 1. Markov decision theory-based crowdsourcing software process model -- Chapter 2. I-way: a cloud-based recommendation system for software requirement reusability -- Chapter 3. Requirement-based test approach and traceability for high-integrity airborne embedded systems -- Chapter 4. A systematic literature review on risk assessment and mitigation approaches in requirement engineering -- Chapter 5. Agile Team measurement to review the performance in global software development -- Chapter 6. Improving construction management through advanced computing and decision making -- Chapter 7. An investigation on quality perspective of software functional artifacts -- Chapter 8. An analysis of UI/UX designing with software prototyping tools -- Chapter 9. Improving financial estimation in construction management through advanced computing and decision making -- Chapter 10. Independent verification and validation of FPGA-based design for airborne electronic applications.1 online resource
Look Before, Before You Leap: Online Vector Load Balancing with Few Reassignments
No full textIn this paper we study two fully-dynamic multi-dimensional vector load balancing problems with recourse. The adversary presents a stream of n job insertions and deletions, where each job j is a vector in ℝ^d_{≥ 0}. In the vector scheduling problem, the algorithm must maintain an assignment of the active jobs to m identical machines to minimize the makespan (maximum load on any dimension on any machine). In the vector bin packing problem, the algorithm must maintain an assignment of active jobs into a number of bins of unit capacity in all dimensions, to minimize the number of bins currently used. In both problems, the goal is to maintain solutions that are competitive against the optimal solution for the active set of jobs, at every time instant. The algorithm is allowed to change the assignment from time to time, with the secondary objective of minimizing the amortized recourse, which is the average cardinality of the change of the assignment per update to the instance.
For the vector scheduling problem, we present two simple algorithms. The first is a randomized algorithm with an O(1) amortized recourse and an O(log d/log log d) competitive ratio against oblivious adversaries. The second algorithm is a deterministic algorithm that is competitive against adaptive adversaries but with a slightly higher competitive ratio of O(log d) and a per-job recourse guarantee bounded by Õ(log n + log d log OPT). We also prove a sharper instance-dependent recourse guarantee for the deterministic algorithm.
For the vector bin packing problem, we make the so-called small jobs assumption that the size of all jobs in all the coordinates is O(1/log d) and present a simple O(1)-competitive algorithm with O(log n) recourse against oblivious adversaries.
For both problems, the main challenge is to determine when and how to migrate jobs to maintain competitive solutions. Our central idea is that for each job, we make these decisions based only on the active set of jobs that are "earlier" than this job in some ordering ≺ of the jobs
Model Ensembling for Constrained Optimization
No full textMany instances of decision making under objective uncertainty can be decomposed into two steps: predicting the objective function and then optimizing for the best feasible action under the estimate of the objective vector. We study the problem of ensembling models for optimization of uncertain linear objectives under arbitrary constraints. We imagine we are given a collection of predictive models mapping a feature space to multi-dimensional real-valued predictions, which form the coefficients of a linear objective that we would like to optimize. We give two ensembling methods that can provably result in transparent decisions that strictly improve on all initial policies. The first method operates in the "white box" setting in which we have access to the underlying prediction models and the second in the "black box" setting in which we only have access to the induced decisions (in the downstream optimization problem) of the constituent models, but not their underlying point predictions. They are transparent or trustworthy in the sense that the user can reliably predict long-term ensemble rewards even if the instance by instance predictions are imperfect
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