Wayback Machine | EPFL Graph Search

The Wayback Machine is a digital archive of the World Wide Web founded by the Internet Archive, a nonprofit based in San Francisco, California. Created in 1996 and launched to the public in 2001, it allows the user to go "back in time" to see how websites looked in the past. Its founders, Brewster Kahle and Bruce Gilliat, developed the Wayback Machine to provide "universal access to all knowledge" by preserving archived copies of defunct web pages. Launched on May 10, 1996, the Wayback Machine had saved more than 38.2 million web pages at the end of 2009. , the Wayback Machine has archived 833 billion web pages. History The Wayback Machine began archiving cached web pages in 1996. One of the earliest known pages was archived on May 10, 1996, at 2:08p.m. (UTC). Internet Archive founders Brewster Kahle and Bruce Gilliat launched the Wayback Machine in San Francisco, California, in October 2001, primarily to address the problem of web content vanishing whenever it gets chang

Online Dating Quantification Practices: A Human-Machine Learning Process

Jessica Carolina Pidoux

The phenomenon of online dating via web and mobile phone applications involves several actors: graphical interfaces, developers, algorithmic systems for user matching, and users. These actors have been studied in parallel by the social sciences and by computer science. While computer science research focuses on the design of more efficient recommendation systems and on the analysis of behavioral patterns, research in the humanities and social sciences is interested in the effects of platforms and their computational practices on the formation of affective relationships.This thesis explores how the mediation relations between the different actors of dating help to commonly establish the human-machine communication. Every actor learns in his-her own way a new conception of dating, mirroring the machineâs algorithmic efficiency. The study of these new dynamics of communication is bound to become key to software and algorithmic studies, for which the human-machine relation-ship has hitherto remained invisible in the study of dating platforms, or incomplete when it comes to analyzing developers' practices or algorithmic systems without taking into account the final uses of the platforms. Using a combination of qualitative and quantitative concepts and methodologies from sociology and data science, this thesis contributes to the comparative analysis of four actors who have a profound impact on the experience of online dating: graphical interfaces, developers, algorithmic matchmaking systems, and users. This is a study of more than 320 variables collected in more than 20 dating applications, both English and French, used in Switzerland and around the world. These variables mediate between the design of profile recommendation systems and their final use for dating. A case study is conducted on the massively-adopted dating application Tinder, which acts as an industry innovator, capable of establishing design conventions (e.g., swiping). This thesis further investigates, on the one hand, the development practices of nine founders and developers working in the industry, and on the other hand, the practices of 40 users of 26 different dating platforms. The results show that the interaction with dating platforms involves a collective learning process of experimentation that allows for communication between humans and machines. This fast-paced communication favors trial-and-error practices: actors either learn to adapt to the machine, or on the contrary to overcome it by accepting the mutual tolerance stemming from the uncertainty of actions. Learning is possible through the supposed social conventions of the actors, some of which are made explicit and quantifiable through graphical interfaces. This thesis contributes to the described research field with a theoretical framework and empirical data which can guide future research on online sociability at the intersection of computer and social sciences, like in the digital humanities.

EPFL2021

Object-oriented approach for dynamic system modelling and simulation

Adel Besrour

Software engineering always cares to provide solutions for building applications as close as possible to what they should be, according to the requirements and the final users needs. Systems behavior simulation is a very common application to virtually reproduce and often predict the real-world behavior. Simulation is one of the most operational research tool in a large variety of engineering and scientific domains: Transport, telecommunication, medicine, chemical processes, physics, etc. The complexity of such application is relative to the increasing complexity of the systems. In this context, it is relevant to bring together different tools and formalisms such as markovian chain, Petri nets, etc., to improve the existent approaches and so to answer the simulations performances needs. The principle objective of this thesis is to bring together techniques from software engineering and safety engineering in order to improve the state of the art of modeling and simulation of dynamic systems in the industrial context. In addressing this objective, this work initially involves defining the essential limitations of the used formalisms, methods and tools regarding from one hand the software engineering modeling and simulation techniques and from the other hand the existent risk analysis methodologies. This work is conducted with respect to the problem of danger identification, considering the context of the complex systems behavior and their interaction with the human operator. In software engineering, it is well known that Petri/high-level nets have attractive characteristics to be used in systems simulation and behavior prediction such as the natural graphical representation, and their well-defined semantic. They are well-suited for the description of complex situations with concurrency (interleaving and true concurrency depending on the underlying semantics), conflict and confusion. However, the absence of structuring capabilities has been one of the main criticisms raised against Petri nets/high-level nets. Thus, there have been many attempts to introduce structuring principles in nets of this kind [BCM88] [Kie89] [JR91]. The attractive characteristics of Petri/high-level nets have prompted researchers to enrich these formalisms with object-oriented features. CO-OPN (Concurrent Object-Oriented Petri Net) approach, brings together the power of both Petri/high-level nets and object-orientation techniques, it has been devised so as to offer an adequate framework for the specification and design of large scale concurrent system [BG91]. CO-OPN, as a powerful modeling tool, has been used in a limited way to simulate systems. This work aims to provide a CO-OPN extension to allow a more realistic systems' simulation. Actually, its simulator semantic uses to be a suitable approach for modeling near closed systems and software components, because they need to loose coupling with external world. But, when we model more realistic problems like industrial processes, where human interaction is a relevant event, this approach is not sufficient to catch all system activity attributes. Moreover, the CO-OPN interpretation process does not allow interaction with the object internal states. This work provides a new solution to overcome CO-OPN simulation limitations and a set of prototypes to assist dynamic systems simulations. Furthermore, this work has been conducted in a Risk Analysis (RA) context, a domain where computer-based simulations research are of utmost interest. Actually, classical approaches used to address complex workplace hazard in a limited way, using checklists or sequence models. Moreover, the use of single oriented methods, such as AEA (man-oriented), FMEA (machine oriented) or HAZOP (process oriented), is not satisfactory to overcome the increasing sophistication of industrial processes. The automation of a part of the analysis process as well as the multiple-oriented approach allowed by dynamic modeling may indeed enhance significantly the analysis completeness and reduce the time analyzing time. This work, based on Object Oriented Petri net formalism (CO-OPN), propose an alternative multi-oriented approach where existent methods limitations have been criticized to develop a dynamic model, MORM (Man-machine Occupational Risk Modeling). A real industrial system (metal wire making process) has been specified to implement the different approach steps (system identification, model application, system simulation, system analysis).

EPFL2005

Comparing machines and humans on a visual categorization test

Charles Dubout, François Fleuret, Ting Li

Automated scene interpretation has benefited from advances in machine learning, and restricted tasks, such as face detection, have been solved with sufficient accuracy for restricted settings. However, the performance of machines in providing rich semantic descriptions of natural scenes from digital images remains highly limited and hugely inferior to that of humans. Here we quantify this "semantic gap" in a particular setting: We compare the efficiency of human and machine learning in assigning an image to one of two categories determined by the spatial arrangement of constituent parts. The images are not real, but the category-defining rules reflect the compositional structure of real images and the type of "reasoning" that appears to be necessary for semantic parsing. Experiments demonstrate that human subjects grasp the separating principles from a handful of examples, whereas the error rates of computer programs fluctuate wildly and remain far behind that of humans even after exposure to thousands of examples. These observations lend support to current trends in computer vision such as integrating machine learning with parts-based modeling.

2011