Lotka's Law and Computer Science Literature

2007 ◽  
Vol 30 (1) ◽  
pp. 51-54 ◽  
Author(s):  
T. Radhakrishnan ◽  
R. Kernizan
Keyword(s):  
Computer Science ◽  
Science Literature ◽  
Lotka’S Law

scholarly journals The composition and formation of effective teams: computer science meets organizational psychology

2018 ◽  
Vol 33 ◽  
Author(s):  
Ewa Andrejczuk ◽  
Rita Berger ◽  
Juan A. Rodriguez-Aguilar ◽  
Carles Sierra ◽  
Víctor Marín-Puchades
Keyword(s):  
Computer Science ◽  
Team Performance ◽  
Organizational Psychology ◽  
Team Formation ◽  
Science Literature ◽  
Effective Teams ◽  
Human Agent ◽  
Wide Range ◽  
Psychology Literature ◽  
Cross Fertilization

AbstractNowadays the composition and formation of effective teams is highly important for both companies to assure their competitiveness and for a wide range of emerging applications exploiting multiagent collaboration (e.g. crowdsourcing, human-agent collaborations). The aim of this article is to provide an integrative perspective on team composition, team formation, and their relationship with team performance. Thus, we review the contributions in both the computer science literature and the organizational psychology literature dealing with these topics. Our purpose is twofold. First, we aim at identifying the strengths and weaknesses of the contributions made by these two diverse bodies of research. Second, we aim at identifying cross-fertilization opportunities that help both disciplines benefit from one another. Given the volume of existing literature, our review is not intended to be exhaustive. Instead, we have preferred to focus on the most significant contributions in both fields together with recent contributions that break new ground to spur innovative research.

scholarly journals Relational data paradigms: What do we learn by taking the materiality of databases seriously?

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172093483
Author(s):  
Andrea K Thomer ◽  
Karen M Wickett
Keyword(s):  
Computer Science ◽  
Scholarly Communication ◽  
Information Organization ◽  
Relational Data ◽  
Science Literature ◽  
Scientific Databases ◽  
Database Construction ◽  
Data Practices ◽  
Close Analysis ◽  
Relational Systems

Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ from or align with the materialities of conventional relational systems? What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects? To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities— and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms.

scholarly journals A unifying framework for iterative approximate best-response algorithms for distributed constraint optimization problems

2011 ◽  
Vol 26 (4) ◽  
pp. 411-444 ◽  
Author(s):  
Archie C. Chapman ◽  
Alex Rogers ◽  
Nicholas R. Jennings ◽  
David S. Leslie
Keyword(s):  
Computer Science ◽  
Optimization Problems ◽  
Potential Games ◽  
Science Literature ◽  
Stochastic Algorithm ◽  
Constraint Optimization ◽  
Distributed Constraint Optimization ◽  
Best Response ◽  
Game Theoretic ◽  
Constraint Optimization Problems

AbstractDistributed constraint optimization problems (DCOPs) are important in many areas of computer science and optimization. In a DCOP, each variable is controlled by one of many autonomous agents, who together have the joint goal of maximizing a global objective function. A wide variety of techniques have been explored to solve such problems, and here we focus on one of the main families, namely iterative approximate best-response algorithms used as local search algorithms for DCOPs. We define these algorithms as those in which, at each iteration, agents communicate only the states of the variables under their control to their neighbours on the constraint graph, and that reason about their next state based on the messages received from their neighbours. These algorithms include the distributed stochastic algorithm and stochastic coordination algorithms, the maximum-gain messaging algorithms, the families of fictitious play and adaptive play algorithms, and algorithms that use regret-based heuristics. This family of algorithms is commonly employed in real-world systems, as they can be used in domains where communication is difficult or costly, where it is appropriate to trade timeliness off against optimality, or where hardware limitations render complete or more computationally intensive algorithms unusable. However, until now, no overarching framework has existed for analyzing this broad family of algorithms, resulting in similar and overlapping work being published independently in several different literatures. The main contribution of this paper, then, is the development of a unified analytical framework for studying such algorithms. This framework is built on our insight that when formulated as non-cooperative games, DCOPs form a subset of the class of potential games. This result allows us to prove convergence properties of iterative approximate best-response algorithms developed in the computer science literature using game-theoretic methods (which also shows that such algorithms can also be applied to the more general problem of finding Nash equilibria in potential games), and, conversely, also allows us to show that many game-theoretic algorithms can be used to solve DCOPs. By so doing, our framework can assist system designers by making the pros and cons of, and the synergies between, the various iterative approximate best-response DCOP algorithm components clear.

Introduction

2010 ◽  
pp. 1-12 ◽  
Author(s):  
Monica Chis
Keyword(s):  
Software Engineering ◽  
Computer Science ◽  
Evolutionary Computation ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Science Literature ◽  
Computation Techniques

This chapter aims to present a part of the computer science literature in which the evolutionary computation techniques, optimization techniques and other bio-inspired techniques are used to solve different search and optimization problems in the area of software engineering.

False friends in english‐Spanish translations in computer science literature

Perspectives ◽  
1998 ◽  
Vol 6 (1) ◽  
pp. 47-59 ◽  
Author(s):  
Mariona Sabaté‐Carrové ◽  
Carlos Iván Chesñevar
Keyword(s):  
Computer Science ◽  
Science Literature

scholarly journals Privacy Protection from Sampling and Perturbation in Survey Microdata

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Natalie Shlomo ◽  
Chris J. Skinner
Keyword(s):  
Computer Science ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Science Literature ◽  
Confidential Information ◽  
Social Surveys ◽  
Statistical Disclosure Limitation ◽  
Statistical Disclosure ◽  
Key Variables ◽  
Statistical Agencies

Statistical agencies release microdata from social surveys as public-use files after applying statistical disclosure limitation (SDL) techniques. Disclosure risk is typically assessed in terms of identification risk, where it is supposed that small counts on cross-classified identifying key variables, i.e. a key, could be used to make an identification and confidential information may be learnt. In this paper we explore the application of definitions of privacy from the computer science literature to the same problem, with a focus on sampling and a form of perturbation which can be represented as misclassification. We consider two privacy definitions: differential privacy and probabilistic differential privacy. Chaudhuri and Mishra (2006) have shown that sampling does not guarantee differential privacy, but that, under certain conditions, it may ensure probabilistic differential privacy. We discuss these definitions and conditions in the context of survey microdata. We then extend this discussion to the case of perturbation. We show that differential privacy can be ensured if and only if the perturbation employs a misclassification matrix with no zero entries. We also show that probabilistic differential privacy is a viable alternative to differential privacy when there are zeros in the misclassification matrix. We discuss some common examples of SDL methods where in some cases zeros may be prevalent in the misclassification matrix.

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