Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

In lifelong learning systems, especially those based on artificial neural networks, one of the biggest obstacles is the severe inability to retain old knowledge as new information is encountered. This phenomenon is known as catastrophic forgetting. In this article, we propose a new kind of connectionist architecture, the Sequential Neural Coding Network, that is robust to forgetting when learning from streams of data points and, unlike networks of today, does not learn via the immensely popular back-propagation of errors. Grounded in the neurocognitive theory of predictive processing, our model adapts its synapses in a biologically-plausible fashion, while another, complementary neural system rapidly learns to direct and control this cortex-like structure by mimicking the task-executive control functionality of the basal ganglia. In our experiments, we demonstrate that our self-organizing system experiences significantly less forgetting as compared to standard neural models and outperforms a wide swath of previously proposed methods even though it is trained across task datasets in a stream-like fashion. The promising performance of our complementary system on benchmarks, e.g., SplitMNIST, Split Fashion MNIST, and Split NotMNIST, offers evidence that by incorporating mechanisms prominent in real neuronal systems, such as competition, sparse activation patterns, and iterative input processing, a new possibility for tackling the grand challenge of lifelong machine learning opens up.Comment: Key updates including results on standard benchmarks, e.g., split mnist/fmnist/not-mnist. Task selection/basal ganglia model has been integrate

CHORUS Deliverable 4.3: Report from CHORUS workshops on national initiatives and metadata

Minutes of the following Workshops: • National Initiatives on Multimedia Content Description and Retrieval, Geneva, October 10th, 2007. • Metadata in Audio-Visual/Multimedia production and archiving, Munich, IRT, 21st – 22nd November 2007 Workshop in Geneva 10/10/2007 This highly successful workshop was organised in cooperation with the European Commission. The event brought together the technical, administrative and financial representatives of the various national initiatives, which have been established recently in some European countries to support research and technical development in the area of audio-visual content processing, indexing and searching for the next generation Internet using semantic technologies, and which may lead to an internet-based knowledge infrastructure. The objective of this workshop was to provide a platform for mutual information and exchange between these initiatives, the European Commission and the participants. Top speakers were present from each of the national initiatives. There was time for discussions with the audience and amongst the European National Initiatives. The challenges, communalities, difficulties, targeted/expected impact, success criteria, etc. were tackled. This workshop addressed how these national initiatives could work together and benefit from each other. Workshop in Munich 11/21-22/2007 Numerous EU and national research projects are working on the automatic or semi-automatic generation of descriptive and functional metadata derived from analysing audio-visual content. The owners of AV archives and production facilities are eagerly awaiting such methods which would help them to better exploit their assets.Hand in hand with the digitization of analogue archives and the archiving of digital AV material, metadatashould be generated on an as high semantic level as possible, preferably fully automatically. All users of metadata rely on a certain metadata model. All AV/multimedia search engines, developed or under current development, would have to respect some compatibility or compliance with the metadata models in use. The purpose of this workshop is to draw attention to the specific problem of metadata models in the context of (semi)-automatic multimedia search

Challenges for MapReduce in Big Data

In the Big Data community, MapReduce has been seen as one of the key enabling approaches for meeting continuously increasing demands on computing resources imposed by massive data sets. The reason for this is the high scalability of the MapReduce paradigm which allows for massively parallel and distributed execution over a large number of computing nodes. This paper identifies MapReduce issues and challenges in handling Big Data with the objective of providing an overview of the field, facilitating better planning and management of Big Data projects, and identifying opportunities for future research in this field. The identified challenges are grouped into four main categories corresponding to Big Data tasks types: data storage (relational databases and NoSQL stores), Big Data analytics (machine learning and interactive analytics), online processing, and security and privacy. Moreover, current efforts aimed at improving and extending MapReduce to address identified challenges are presented. Consequently, by identifying issues and challenges MapReduce faces when handling Big Data, this study encourages future Big Data research

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by projects from the Spanish Ministry of Education and Competitivity TIN2015-65100-R and DIIM2.0 (PROMETEOII/2014/001)

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams

Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. We describe a Web service that leverages this framework to track political memes in Twitter and help detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We present some cases of abusive behaviors uncovered by our service. Finally, we discuss promising preliminary results on the detection of suspicious memes via supervised learning based on features extracted from the topology of the diffusion networks, sentiment analysis, and crowdsourced annotations