18,691 research outputs found
Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform
Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation
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Autonomous QoS Management and Policing in Unmanaged Local Area Networks
The high increase of bandwidth-intensive applications like high definition video streaming in home and small office environments leads to QoS challenges in hybrid wired/wireless local area networks. These networks are often not QoS aware and may contain bottlenecks in their topology. In addition, they often have a hybrid nature due to the used access technology consisting of, for example, Ethernet, wireless, and PowerLAN links. In this paper, we present the research work on a novel autonomous system for hybrid QoS in local area networks, called QoSiLAN, which does not rely on network infrastructure support but on host cooperation and works independently of the access technology. We present a new QoS Signalling Protocol, policing and admission control algorithms, and a new lightweight statistical bandwidth prediction algorithm for autonomous resource management in LANs. This new QoS framework enables link based, access-medium independent bandwidth management without network support. We provide evaluation results for the novel bandwidth prediction algorithm as well as for the QoSiLAN framework and its protocol, which highlight the features, robustness, and the effectiveness of the proposed system
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Client-server-based LBS architecture: A novel positioning module for improved positioning performance
Permission to distribute obtained from publisher.This work presents a new efficient positioning module that operates over client-server LBS architectures. The
aim of the proposed module is to fulfil the position information requirements for LBS pedestrian applications
by ensuring the availability of reliable, highly accurate and precise position solutions based on GPS single
frequency (L1) positioning service. The positioning module operates at both LBS architecture sides; the client
(mobile device), and the server (positioning server). At the server side, the positioning module is responsible
for correcting user’s location information based on WADGPS corrections. In addition, at the mobile side,
the positioning module is continually in charge for monitoring the integrity and available of the position
solutions as well as managing the communication with the server. The integrity monitoring was based on
EGNOS integrity methods. A prototype of the proposed module was developed and used in experimental trials
to evaluate the efficiency of the module in terms of the achieved positioning performance. The positioning
module was capable of achieving a horizontal accuracy of less than 2 meters with a 95% confidence level
with integrity improvement of more than 30% from existing GPS/EGNOS services
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Multimodal Content Analysis for Effective Advertisements on YouTube
The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In this work, we seek to study the characteristics or
attributes that characterize an effective advertisement and recommend a useful
set of features to aid the designing and production processes of commercial
advertisements. We analyze the temporal patterns from multimedia content of
advertisement videos including auditory, visual and textual components, and
study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an
advertisement, and to recommend a useful set of features to advertisement
designers to make it more successful and approachable to users. Our proposed
framework employs the signal processing technique of cross modality feature
learning where data streams from different components are employed to train
separate neural network models and are then fused together to learn a shared
representation. Subsequently, a neural network model trained on this joint
feature embedding representation is utilized as a classifier to predict
advertisement effectiveness. We validate our approach using subjective ratings
from a dedicated user study, the sentiment strength of online viewer comments,
and a viewer opinion metric of the ratio of the Likes and Views received by
each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201
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