19 research outputs found
EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop
Editor of the proceedings of EVALITA 2016
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace
Framework for collaborative knowledge management in organizations
Nowadays organizations have been pushed to speed up the rate of industrial transformation to high value products and services. The capability to agilely respond to new market demands became a strategic pillar for innovation, and knowledge management could support organizations to achieve that goal. However, current knowledge management approaches tend to be over complex or too academic, with interfaces difficult to manage, even more if cooperative handling is required. Nevertheless, in an ideal framework, both tacit and explicit knowledge management should be addressed to achieve knowledge handling with precise and semantically meaningful definitions. Moreover, with the increase of Internet usage, the amount of available information explodes. It leads to the observed progress in the creation of mechanisms to retrieve useful knowledge from the huge existent amount of information sources. However, a same knowledge representation of a thing could mean differently to different people and applications.
Contributing towards this direction, this thesis proposes a framework capable of gathering the knowledge held by domain experts and domain sources through a knowledge management system and transform it into explicit ontologies. This enables to build tools with advanced reasoning capacities with the aim to support enterprises decision-making processes. The author also intends to address the problem of knowledge transference within an among organizations. This will be done through a module (part of the proposed framework) for domain’s lexicon establishment which purpose is to represent and unify the understanding of the domain’s used semantic
A treatise on Web 2.0 with a case study from the financial markets
There has been much hype in vocational and academic circles surrounding the emergence of
web 2.0 or social media; however, relatively little work was dedicated to substantiating the
actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the
term web 2.0 assumes a certain interpretation of web history, including enough progress in
certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in
support of this development, and there has been a considerable amount of enthusiasm in the
literature. Much research has been busy evaluating current use of web 2.0, and analysis of the
user generated content, but an objective and thorough assessment of what web 2.0 really stands
for has been to a large extent overlooked. More recently the idea of collective intelligence
facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a
more unified approach and work in the area of collective intelligence is needed.
This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and
what caused it to emerge; providing a rich literature review on the topic, a review of existing
taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of
the collective intelligence potential that emerges from application usage. Finally, a framework
for harnessing collective intelligence in a more systematic manner is proposed.
In addition to the presented results, novel methodologies are also introduced throughout this
work. In order to provide interesting insight but also to illustrate analysis, a case study of the
recent financial crisis is considered. Some interesting results relating to the crisis are revealed
within user generated content data, and relevant issues are discussed where appropriate
Learning-based Segmentation for Connectomics
Recent advances in electron microscopy techniques make it possible to acquire highresolution, isotropic volume images of neural circuitry. In connectomics, neuroscientists seek to obtain the circuit diagram involving all neurons and synapses in such a volume image. Mapping neuron connectivity requires tracing each and every neural process through terabytes of image data. Due to the size and complexity of these volume images, fully automated analysis methods are desperately needed. In this thesis, I consider automated, machine learning-based neurite segmentation approaches based on a simultaneous merge decision of adjacent supervoxels.
- Given a learned likelihood of merging adjacent supervoxels, Chapter 4 adapts a probabilistic graphical model which ensures that merge decisions are consistent
and the surfaces of final segments are closed. This model can be posed as a multicut optimization problem and is solved with the cutting-plane method. In order to scale to large datasets, a fast search for (and good choice of) violated cycle constraints is crucial. Quantitative experiments show that the proposed closed-surface regularization significantly improves segmentation performance.
- In Chapter 5, I investigate whether the edge weights of the previous model can be chosen to minimize the loss with respect to non-local segmentation quality measures (e.g. Rand Index). Suitable w are obtained from a structured learning approach. In the Structured Support Vector Machine formulation, a novel fast enumeration scheme is used to find the most violated constraint. Quantitative experiments show that structured learning can improve upon unstructured methods. Furthermore, I introduce a new approximate, hierarchical and blockwise optimization approach for large-scale multicut segmentation. Using this method, high-quality approximate solutions for large problem instances are found quickly.
- Chapter 6 introduces another novel approximate scheme for multicut segmentation -- Cut, Glue&Cut -- which is based on the move-making paradigm. First, the graph is recursively partitioned into small regions (cut phase). Then, for any two adjacent regions, alternative cuts of these two regions define possible moves (glue&cut phase). The proposed algorithm finds segmentations that are { as measured by a loss function { as close to the ground-truth as the global optimum found by exact solvers, while being significantly faster than existing methods.
- In order to jointly label resulting segments as well as to label the boundaries between segments, Chapter 7 proposes the Asymmetric Multi-way Cut model, a variant of Multi-way Cut. In this new model, within-class cuts are allowed for some labels, while being forbidden for other labels. Qualitative experiments show when such a formulation can be beneficial. In particular, an application to joint neurite and cell organelle labeling in EM volume images is discussed.
- Custom software tools that can cope with the large data volumes common in the field of connectomics are a prerequisite for the implementation and evaluation of novel segmentation techniques. Chapter 3 presents version 1.0 of ilastik, a joint effort of multiple researchers. I have co-written its volume viewing component, volumina. ilastik provides an interactive pixel classification work
ow on largerthan-RAM datasets as well as a semi-automated segmentation module useful for acquiring gold standard segmentations. Furthermore, I describe new software for
dealing with hierarchies of cell complexes as well as for blockwise image processing operations on large datasets.
The different segmentation methods presented in this thesis provide a promising direction towards reaching the required reliability as well as the required data throughput
necessary for connectomics applications
The 1991 3rd NASA Symposium on VLSI Design
Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2
Project management in social data science : integrating lessons from research practice and software engineering
Online platforms, transaction processing systems, mobile sensors and other novel
sources of data have shaped many areas of social research. The emerging discipline
of social data science is subject to questions of epistemology, politics, ethics and
responsibility, while the practice of doing social data science raises significant
project management issues that include logistics, team communication, software
system integration and stakeholder engagement. Keeping track of such a multitude
of individual concerns while maintaining an overview of a social data science project
as a whole is not trivial. This calls for provision of appropriate guidance for holistic
project management.
The project management issues in social data science are strikingly similar to
those arising in software engineering. In this thesis, I adapt a particular software
engineering project management tool – the SEMAT Essence model (Jacobson
et al., 2013) – to the needs of social data science. This model offers a holistic
management approach by addressing key project aspects, including the often
overlooked yet crucially important ones such as maintaining stakeholder engagement
and establishing the ways of working. The SEMAT Essence is a progress tracking
model and does not assume any specific work process, which is valuable given the
great diversity of social data science projects.
To achieve this goal, I study the practice of doing social data science through
participant observation of social data science projects and by providing ethnographic
accounts for those. Using the ethnographic findings and the basic content and
structure of the SEMAT model, I develop the Social Science Scorecard Deck – an
agile project management tool for social data science. To assess the Scorecard Deck,
I use the tool in management of a social data science project and then subject the
tool to external validation by interviewing experts in social data science
BIG DATA и анализ высокого уровня : материалы конференции
В сборнике опубликованы результаты научных исследований и разработок в области BIG DATA and Advanced Analytics для оптимизации IT-решений и бизнес-решений, а также тематических исследований в области медицины, образования и экологии