Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Front Matter - Soft Computing for Data Mining Applications

Efficient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the capability of computers to search huge amounts of data in a fast and effective manner. However, the data to be analyzed is imprecise and afflicted with uncertainty. In the case of heterogeneous data sources such as text, audio and video, the data might moreover be ambiguous and partly conflicting. Besides, patterns and relationships of interest are usually vague and approximate. Thus, in order to make the information mining process more robust or say, human-like methods for searching and learning it requires tolerance towards imprecision, uncertainty and exceptions. Thus, they have approximate reasoning capabilities and are capable of handling partial truth. Properties of the aforementioned kind are typical soft computing. Soft computing techniques like Genetic

Race & Mobility in the Digital Periphery: New Urban Frontiers of Migration Control

Global movement and information technologies are changing practices of bordering, globally. Such matters are now also substantially urban. As cities of refuge rely increasingly on tech companies to develop digital urban infrastructures for accessing information, services, and socioeconomic life at large, they are also inviting the border closer to cities and migrant bodies. This marks a convergence of Silicon Valley logics, austere and xenophobic migration management practices, and racial capitalism. In New York City, infrastructural technologies such as sophisticated public Wi-Fi, smart ID cards, and digitalised city services, in an environment prone to deportation raids, has led to deep information “panics”. In Berlin, a combination of civil society and private sector technology initiatives have produced a deluge of largely unused or distrusted information services, job-matching, house-sharing, social credit, and identity management tools in the name of refugees. In lieu of mitigating conditions of displacement, these practices compound analogue borders by engaging in a practice of digitally fusing borders to racialised characteristics, resulting in symbolic, material, and epistemic forms of technological marginalization. Through following and documenting how migrant communities navigate and experience these digital urban interventions, and the logics of those who develop them, this dissertation 1) highlights how migrant bodies and urban spaces become contested spaces in the battle for racial capital – a frontier in which technology actors are chiefly concerned with reconstituting conceptions of race for power and profit, and; 2). unveils how digital urban infrastructures interact with subtle practices of racialised bordering. Drawing on an analytical lens rooted predominantly in the Black radical tradition, critical development and migration studies, and science and technology studies, it challenges the paradigms of techno-solutionism and techno-chauvinism, as well as critical digital studies that has tended to treat race and racialism as a symptom of, rather than as integral to, the technology industry. By extension, the field of migration has also tended to impart greater weight to the positive affordances of technology in contexts of displacement, in absence of the critical voice of would-be recipients and “users”. By attending to the frontiers of racial capitalism and increasing technology deployments, I advance the idea of the ‘digital periphery’ to make sense of how urban migrant environments and subjectivities are commodified and ‘datafied’. As a concept, the digital periphery allows for the rapid advance of technology upon displaced populations to be disaggregated. It reveals an inseparable and mutually constitutive entanglement of race, borders, and migration, advancing racial capitalism beyond its conventional physical and spatial limitations.This dissertation was completed with the financial support of the Jo Cox PhD Studentship at Pembroke College, University of Cambridge, and the Cambridge Commonwealth, European and International Trust

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Post Memes

Art-form, send-up, farce, ironic disarticulation, pastiche, propaganda, trololololol, mode of critique, mode of production, means of politicisation, even of subjectivation -- memes are the inner currency of the internet’s circulatory system. Independent of any one set value, memes are famously the mode of conveyance for the alt-right, the irony left, and the apoliticos alike,  and they are impervious to many economic valuations: the attempts made in co-opting their discourse in advertising and big business have made little headway, and have usually been derailed by retaliative meming. Post-Memes: Seizing the Memes of Production takes advantage of the meme’s subversive adaptability and ripeness for a focused, in-depth study. Pulling together the interrogative forces of a raft of thinkers at the forefront of tech theory and media dissection, this collection of essays paves a way to articulating the semiotic fabric of the early 21st century’s most prevalent means of content posting, and aims at the very seizing of the memes of production for the imagining and creation of new political horizons.With contributions from Scott and McKenzie Wark, Patricia Reed, Jay Owens, Thomas Hobson and Kaajal Modi, Dominic Pettman, Bogna M. Konior, and Eric Wilson, among others, this essay volume offers the freshest approaches available in the field of memes studies and inaugurates a new kind of writing about the newest manifestations of the written online. The book aims to become the go-to resource for all students and scholars of memes, and will be of the utmost interest to anyone interested in the internet’s most viral phenomenon

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing