A Criterion-Mining Method for Group Idea Selection -Increasing Consensus with Minimal Loss of Efficiency

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From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Intrusion Detection Systems Using Adaptive Regression Splines

Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDS) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given

When autoencoders meet recommender systems : COFILS approach

Collaborative Filtering to Supervised Learning (COFILS) transforms a Collaborative Filtering (CF) problem into classical Supervised Learning (SL) problem. Applying COFILS reduce data sparsity and make it possible to test a variety of SL algorithms rather than matrix decomposition methods. It main steps are: extraction, mapping and prediction. Firstly, a Singular Value Decomposition (SVD) generates a set of latent variables from a ratings matrix. Next, on the mapping phase, a new data set is generated where each sample contains a set of latent variables from an user and it rated item; and a target that corresponds the user rating for that item. Finally, on the last phase, a SL algorithm is applied. One problem of COFILS is it’s dependency on SVD, that is not able to extract non-linear features from data and it is not robust to noisy data. To address this problem, we propose switching SVD to a Stacked Denoising Autoencoder (SDA) on the first phase of COFILS. With SDA, more useful and complex representations can be learned in a Deep Network with a local denoising criterion. We test our novel technique, namely Deep Learning COFILS (DL-COFILS), on MovieLens, R3 Yahoo! Music and Movie Tweetings data sets and compare to COFILS, as a baseline, and state of the art CF techniques. Our results indicate that DL-COFILS outperforms COFILS for all the data sets and with an improvement up to 5.9%. Also, DL-COFILS achieves the best result for the MovieLens 100k data set and ranks on the top three algorithms for these data sets. Thus, we show that DL-COFILS represents an advance on COFILS methodology, improving it’s results and that is a suitable method for CF problem.Collaborative Filtering to Supervised Learning (COFILS) transforma um problema de filtragem colaborativa (CF) em um problema clássico de aprendizado supervisionado (SL). Sua aplicação reduz a esparsidade e torna possível a utilização de variados algoritmos de SL em oposição aos métodos de decomposição de matrizes. Primeiramente, a Decomposição em Valores Singulares (SVD) gera um conjunto de variáveis latentes a partir da matriz de avaliações. Na fase de mapeamento, um novo conjunto de dados é gerado, do qual cada amostra contém um conjunto de variáveis latentes de um usuário e do item avaliado; e um valor que corresponde a avaliação que o usuário atribuiu a esse item. Por fim, o algoritmo de SL é aplicado. Um ponto negativo do COFILS é sua dependência ao SVD, incapaz de extrair características não-lineares e sem robustez `a dados ruidosos. Nesse caso, propomos a troca do SVD por um Stacked Denoising Autoencoder (SDA). Com o uso de um SDA, representações mais úteis e complexas podem ser aprendidas em uma rede neural profunda com um critério local de remoção de ruído. Executamos nossa técnica, chamada Deep Learning COFILS (DL-COFILS), nos conjuntos de dados MovieLens, R3 Yahoo! Music e Movie Tweetings comparando os resultados com o COFILS padrão, como baseline, e demais técnicas de estado da arte de CF. Com os resultados obtidos, é possível mencionar que DL-COFILS supera COFILS para todos os conjuntos de dados, com uma melhora de até 5.9%. Além disso, o DLCOFILS alcança o melhor resultado para o MovieLens 100k e se encontra entre os três melhores algoritmos nos demais conjuntos de dados. Dessa forma, mostraremos que DL-COFILS representa um avanço na metodologia COFILS, melhorando seus resultados e se mostrando um método adequado para CF

Collaborative Tagging and Taxonomy by Vector Space Approach

Collaborative tagging or group tagging is tagging performed by a group of users usually to support in re-finding the items. The limberness of tagging allows users to classify their collections of items in the ways that they find useful, but the personalized variety of expressions can present challenges when searching and browsing. When users can liberally choose tags (users create and apply public tags to online items as different to selecting terms from a proscribed terminology based on the users feedback), the resulting metadata can consist of homonyms (the same tags used with dissimilar implication) and synonyms (multiple tags for the same concept) which may direct to inappropriate connections between items and wasteful searches for information about a subject. Collaborative tagging requires the enforcement of method that enables users to protect their privacy by allowing them to hide certain user-generated contents without making them useless for the purposes they have been provided in a given online service. This means that privacy-preserving mechanisms must not harmfully affect the service truthfulness and usefulness.The proposed approach defends the user privacy to a certain level by reducing the tags that make a user profile let somebody see partiality toward certain categories of interest or feedback

Distributed Load Testing by Modeling and Simulating User Behavior

Modern human-machine systems such as microservices rely upon agile engineering practices which require changes to be tested and released more frequently than classically engineered systems. A critical step in the testing of such systems is the generation of realistic workloads or load testing. Generated workload emulates the expected behaviors of users and machines within a system under test in order to find potentially unknown failure states. Typical testing tools rely on static testing artifacts to generate realistic workload conditions. Such artifacts can be cumbersome and costly to maintain; however, even model-based alternatives can prevent adaptation to changes in a system or its usage. Lack of adaptation can prevent the integration of load testing into system quality assurance, leading to an incomplete evaluation of system quality. The goal of this research is to improve the state of software engineering by addressing open challenges in load testing of human-machine systems with a novel process that a) models and classifies user behavior from streaming and aggregated log data, b) adapts to changes in system and user behavior, and c) generates distributed workload by realistically simulating user behavior. This research contributes a Learning, Online, Distributed Engine for Simulation and Testing based on the Operational Norms of Entities within a system (LODESTONE): a novel process to distributed load testing by modeling and simulating user behavior. We specify LODESTONE within the context of a human-machine system to illustrate distributed adaptation and execution in load testing processes. LODESTONE uses log data to generate and update user behavior models, cluster them into similar behavior profiles, and instantiate distributed workload on software systems. We analyze user behavioral data having differing characteristics to replicate human-machine interactions in a modern microservice environment. We discuss tools, algorithms, software design, and implementation in two different computational environments: client-server and cloud-based microservices. We illustrate the advantages of LODESTONE through a qualitative comparison of key feature parameters and experimentation based on shared data and models. LODESTONE continuously adapts to changes in the system to be tested which allows for the integration of load testing into the quality assurance process for cloud-based microservices

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

MetaRec: Meta-Learning Meets Recommendation Systems

Artificial neural networks (ANNs) have recently received increasing attention as powerful modeling tools to improve the performance of recommendation systems. Meta-learning, on the other hand, is a paradigm that has re-surged in popularity within the broader machine learning community over the past several years. In this thesis, we will explore the intersection of these two domains and work on developing methods for integrating meta-learning to design more accurate and flexible recommendation systems. In the present work, we propose a meta-learning framework for the design of collaborative filtering methods in recommendation systems, drawing from ideas, models, and solutions from modern approaches in both the meta-learning and recommendation system literature, applying them to recommendation tasks to obtain improved generalization performance. Our proposed framework, MetaRec, includes and unifies the main state-of-the-art models in recommendation systems, extending them to be flexibly configured and efficiently operate with limited data. We empirically test the architectures created under our MetaRec framework on several recommendation benchmark datasets using a plethora of evaluation metrics and find that by taking a meta-learning approach to the collaborative filtering problem, we observe notable gains in predictive performance