274 research outputs found
A Prototype For Learning Privacy-Preserving Data Publising
Erinevad organisatsioonid, valitsusasutused, firmad ja üksikisikud koguvad andmeid, mida on võimalik hiljem uute teadmiste saamiseks andmekaeve meetoditega töödelda. Töötlejaks ei tarvitse olla andmete koguja. Sageli ei ole teada andmetöötleja usaldusväärsus, mistõttu on oluline tagada, et avalikustatud andmetest poleks enam võimalik tagantjärgi privaatseid isikuandmeid identifitseerida. Selleks, et isikuid ei oleks enam võimalik identifitseerida, tuleb enne andmete töötlejatele väljastamist rakendada privaatsust säilitavaid meetodeid. Käesolevas lõputöös kirjeldatakse erinevaid ohte privaatsusele, meetodeid nende ohtude ennetamiseks, võrreldakse neid meetodeid omavahel ja kirjeldatakse erinevaid viise, kuidas andmeidanonümiseerida. Lõputöö teiseks väljundiks on õpitarkvara, mis võimaldabtudengitel antud valdkonnaga tutvuda. Lõputöö viimases osas valideeritakse loodud tarkvara.Our data gets collected every day by governments and different organizations for data mining. It is often not known who the receiving part of data is and whether data receiver can be trusted. Therefore it is necessary to anonymize data in a way what it would be not possible to identify persons from released data sets. This master thesis will discuss different threats to privacy, discuss and compare different privacy-preserving methods to mitigate these threats. The thesis will give an overview of different possible implementations for these privacy-preserving methods. The other output of this thesis is educational purpose software that allows students to learn and practice privacy-preserving methods. The final part of this thesis is a validation of designed software
Adaptability, Cooperation and Reconfiguration in Very Complex Multiregional Network Organizations
There seems to be a general trend that the development of technologies which interact with human beings also enhances the knowledge of human functions. For example, with the development of color television systems progress in the knowledge of human color vision was also recorded. In return this new knowledge then helped in the design of even more efficient color television system.
A similar situation seems to reign in computer systems and computer networks. Managing different resources in computer systems by operational systems resembles somewhat the management of resources in an organization. The inference block in 5th generation computers may resemble human inference and is pursued by an artificial intelligence discipline. The study of cooperative features in computer systems and networks may bring us closer to understanding these processes in organizations or even in human societies at large. This happens because many causal relations are present in computer systems in clearer and sometimes more primitive forms, stripped of many of the accompanying but irrelevant (emotional) ingredients.
This Collaborative Paper is the continuation of an activity that started when Dr. Cifersky joined the Management and Technology Area of IIASA in 1882 as a participant in the Young Scientists Summer Program, under the supervision of Dr. R. Lee. The paper scans those problems in organizations which are evoked by the environment. It attempts to describe some of those processes which are taking place in complex organizations as a response to external influences, and identifies some of the impacts this may have on the organization's performance objectives.
The paper has not been edited and supplemented by a vocabulary, therefore it does not make easy reading. It uses terms common in organization research, computer systems (for example, communication protocol), or principles used in fail-safe computer systems (reconfiguration). The topic is interesting and stimulating and can contribute to further research at the Institute in this field
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
Ideological and Temporal Components of Network Polarization in Online Political Participatory Media
Political polarization is traditionally analyzed through the ideological
stances of groups and parties, but it also has a behavioral component that
manifests in the interactions between individuals. We present an empirical
analysis of the digital traces of politicians in politnetz.ch, a Swiss online
platform focused on political activity, in which politicians interact by
creating support links, comments, and likes. We analyze network polarization as
the level of intra- party cohesion with respect to inter-party connectivity,
finding that supports show a very strongly polarized structure with respect to
party alignment. The analysis of this multiplex network shows that each layer
of interaction contains relevant information, where comment groups follow
topics related to Swiss politics. Our analysis reveals that polarization in the
layer of likes evolves in time, increasing close to the federal elections of
2011. Furthermore, we analyze the internal social network of each party through
metrics related to hierarchical structures, information efficiency, and social
resilience. Our results suggest that the online social structure of a party is
related to its ideology, and reveal that the degree of connectivity across two
parties increases when they are close in the ideological space of a multi-party
system.Comment: 35 pages, 11 figures, Internet, Policy & Politics Conference,
University of Oxford, Oxford, UK, 25-26 September 201
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for low-rank factorization of massive
tensors with binary observations. The proposed model has the following key
properties: (1) in contrast to the models based on the logistic or probit
likelihood, using a zero-truncated Poisson likelihood for binary data allows
our model to scale up in the number of \emph{ones} in the tensor, which is
especially appealing for massive but sparse binary tensors; (2)
side-information in form of binary pairwise relationships (e.g., an adjacency
network) between objects in any tensor mode can also be leveraged, which can be
especially useful in "cold-start" settings; and (3) the model admits simple
Bayesian inference via batch, as well as \emph{online} MCMC; the latter allows
scaling up even for \emph{dense} binary data (i.e., when the number of ones in
the tensor/network is also massive). In addition, non-negative factor matrices
in our model provide easy interpretability, and the tensor rank can be inferred
from the data. We evaluate our model on several large-scale real-world binary
tensors, achieving excellent computational scalability, and also demonstrate
its usefulness in leveraging side-information provided in form of
mode-network(s).Comment: UAI (Uncertainty in Artificial Intelligence) 201
Negative emotions boost users activity at BBC Forum
We present an empirical study of user activity in online BBC discussion
forums, measured by the number of posts written by individual debaters and the
average sentiment of these posts. Nearly 2.5 million posts from over 18
thousand users were investigated. Scale free distributions were observed for
activity in individual discussion threads as well as for overall activity. The
number of unique users in a thread normalized by the thread length decays with
thread length, suggesting that thread life is sustained by mutual discussions
rather than by independent comments. Automatic sentiment analysis shows that
most posts contain negative emotions and the most active users in individual
threads express predominantly negative sentiments. It follows that the average
emotion of longer threads is more negative and that threads can be sustained by
negative comments. An agent based computer simulation model has been used to
reproduce several essential characteristics of the analyzed system. The model
stresses the role of discussions between users, especially emotionally laden
quarrels between supporters of opposite opinions, and represents many observed
statistics of the forum.Comment: 29 pages, 6 figure
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