17,774 research outputs found
Forecasting bus passenger flows by using a clustering-based support vector regression approach
As a significant component of the intelligent transportation system, forecasting bus passenger
flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains
challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to
varied destinations and departure times. For this reason, a novel forecasting model named as affinity
propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear
simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based
intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each
cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally,
the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model
is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate
that the proposed model performs better than other peer models in terms of absolute percentage error and
mean absolute percentage error. It is recommended that the deterministic clustering technique with stable
cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio
Musical recommendations and personalization in a social network
This paper presents a set of algorithms used for music recommendations and
personalization in a general purpose social network www.ok.ru, the second
largest social network in the CIS visited by more then 40 millions users per
day. In addition to classical recommendation features like "recommend a
sequence" and "find similar items" the paper describes novel algorithms for
construction of context aware recommendations, personalization of the service,
handling of the cold-start problem, and more. All algorithms described in the
paper are working on-line and are able to detect and address changes in the
user's behavior and needs in the real time.
The core component of the algorithms is a taste graph containing information
about different entities (users, tracks, artists, etc.) and relations between
them (for example, user A likes song B with certainty X, track B created by
artist C, artist C is similar to artist D with certainty Y and so on). Using
the graph it is possible to select tracks a user would most probably like, to
arrange them in a way that they match each other well, to estimate which items
from a fixed list are most relevant for the user, and more.
In addition, the paper describes the approach used to estimate algorithms
efficiency and analyze the impact of different recommendation related features
on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys
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Adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity
UzimajuÄi u obzir nezadovoljavajuÄe djelovanje grupiranja srodnog Å”irenja algoritma grupiranja, kada se radi o nizovima podataka složenih struktura, u ovom se radu predlaže prilagodljivi nadzirani algoritam grupiranja srodnog Å”irenja utemeljen na strukturnoj sliÄnosti (SAAP-SS). Najprije se predlaže nova strukturna sliÄnost rjeÅ”avanjem nelinearnog problema zastupljenosti niskoga ranga. Zatim slijedi srodno Å”irenje na temelju podeÅ”avanja matrice sliÄnosti primjenom poznatih udvojenih ograniÄenja. Na kraju se u postupak algoritma uvodi ideja eksplozija kod vatrometa. Prilagodljivo pretražujuÄi preferencijalni prostor u dva smjera, uravnotežuju se globalne i lokalne pretraživaÄke sposobnosti algoritma u cilju pronalaženja optimalne strukture grupiranja. Rezultati eksperimenata i sa sintetiÄkim i s realnim nizovima podataka pokazuju poboljÅ”anja u radu predloženog algoritma u usporedbi s AP, FEO-SAP i K-means metodama.In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, an adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) is proposed in this paper. First, a novel structural similarity is proposed by solving a non-linear, low-rank representation problem. Then we perform affinity propagation on the basis of adjusting the similarity matrix by utilizing the known pairwise constraints. Finally, the idea of fireworks explosion is introduced into the process of the algorithm. By adaptively searching the preference space bi-directionally, the algorithmās global and local searching abilities are balanced in order to find the optimal clustering structure. The results of the experiments with both synthetic and real data sets show performance improvements of the proposed algorithm compared with AP, FEO-SAP and K-means methods
Compressive Sensing DNA Microarrays
Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. Lab experiments suggest that in order to achieve accurate hybridization profiling, consensus probe sequences are required to have sequence homology of at least 80% with all targets to be detected. Furthermore, out-of-equilibrium datasets are usually as accurate as those obtained from equilibrium conditions. Consequently, one can use CSMs in applications in which only short hybridization times are allowed
Passive characterization of sopcast usage in residential ISPs
AbstractāIn this paper we present an extensive analysis of traffic generated by SopCast users and collected from operative networks of three national ISPs in Europe. After more than a year of continuous monitoring, we present results about the popularity of SopCast which is the largely preferred application in the studied networks. We focus on analysis of (i) application and bandwidth usage at different time scales, (ii) peer lifetime, arrival and departure processes, (iii) peer localization in the world. Results provide useful insights into users ā behavior, including their attitude towards P2P-TV application usage and the conse-quent generated load on the network, that is quite variable based on the access technology and geographical location. Our findings are interesting to Researchers interested in the investigation of users ā attitude towards P2P-TV services, to foresee new trends in the future usage of the Internet, and to augment the design of their application. I
Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates
This study is a first, exploratory attempt to use quantitative semantics
techniques and topological analysis to analyze systemic patterns arising in a
complex political system. In particular, we use a rich data set covering all
speeches and debates in the UK House of Commons between 1975 and 2014. By the
use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show
that both members and parties feature specific roles within the system,
consistent over time, and extract global patterns indicating levels of
political cohesion. Our results provide a wide array of novel hypotheses about
the complex dynamics of political systems, with valuable policy applications
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