308 research outputs found
Minimum Density Hyperplanes
Associating distinct groups of objects (clusters) with contiguous regions of
high probability density (high-density clusters), is central to many
statistical and machine learning approaches to the classification of unlabelled
data. We propose a novel hyperplane classifier for clustering and
semi-supervised classification which is motivated by this objective. The
proposed minimum density hyperplane minimises the integral of the empirical
probability density function along it, thereby avoiding intersection with high
density clusters. We show that the minimum density and the maximum margin
hyperplanes are asymptotically equivalent, thus linking this approach to
maximum margin clustering and semi-supervised support vector classifiers. We
propose a projection pursuit formulation of the associated optimisation problem
which allows us to find minimum density hyperplanes efficiently in practice,
and evaluate its performance on a range of benchmark datasets. The proposed
approach is found to be very competitive with state of the art methods for
clustering and semi-supervised classification
Real Time Sentiment Change Detection of Twitter Data Streams
In the past few years, there has been a huge growth in Twitter sentiment
analysis having already provided a fair amount of research on sentiment
detection of public opinion among Twitter users. Given the fact that Twitter
messages are generated constantly with dizzying rates, a huge volume of
streaming data is created, thus there is an imperative need for accurate
methods for knowledge discovery and mining of this information. Although there
exists a plethora of twitter sentiment analysis methods in the recent
literature, the researchers have shifted to real-time sentiment identification
on twitter streaming data, as expected. A major challenge is to deal with the
Big Data challenges arising in Twitter streaming applications concerning both
Volume and Velocity. Under this perspective, in this paper, a methodological
approach based on open source tools is provided for real-time detection of
changes in sentiment that is ultra efficient with respect to both memory
consumption and computational cost. This is achieved by iteratively collecting
tweets in real time and discarding them immediately after their process. For
this purpose, we employ the Lexicon approach for sentiment characterizations,
while change detection is achieved through appropriate control charts that do
not require historical information. We believe that the proposed methodology
provides the trigger for a potential large-scale monitoring of threads in an
attempt to discover fake news spread or propaganda efforts in their early
stages. Our experimental real-time analysis based on a recent hashtag provides
evidence that the proposed approach can detect meaningful sentiment changes
across a hashtags lifetime
Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories
The cyber-physical convergence is opening up new business opportunities for
industrial operators. The need for deep integration of the cyber and the
physical worlds establishes a rich business agenda towards consolidating new
system and network engineering approaches. This revolution would not be
possible without the rich and heterogeneous sources of data, as well as the
ability of their intelligent exploitation, mainly due to the fact that data
will serve as a fundamental resource to promote Industry 4.0. One of the most
fruitful research and practice areas emerging from this data-rich,
cyber-physical, smart factory environment is the data-driven process monitoring
field, which applies machine learning methodologies to enable predictive
maintenance applications. In this paper, we examine popular time series
forecasting techniques as well as supervised machine learning algorithms in the
applied context of Industry 4.0, by transforming and preprocessing the
historical industrial dataset of a packing machine's operational state
recordings (real data coming from the production line of a manufacturing plant
from the food and beverage domain). In our methodology, we use only a single
signal concerning the machine's operational status to make our predictions,
without considering other operational variables or fault and warning signals,
hence its characterization as ``agnostic''. In this respect, the results
demonstrate that the adopted methods achieve a quite promising performance on
three targeted use cases
Detection of Fake Generated Scientific Abstracts
The widespread adoption of Large Language Models and publicly available
ChatGPT has marked a significant turning point in the integration of Artificial
Intelligence into people's everyday lives. The academic community has taken
notice of these technological advancements and has expressed concerns regarding
the difficulty of discriminating between what is real and what is artificially
generated. Thus, researchers have been working on developing effective systems
to identify machine-generated text. In this study, we utilize the GPT-3 model
to generate scientific paper abstracts through Artificial Intelligence and
explore various text representation methods when combined with Machine Learning
models with the aim of identifying machine-written text. We analyze the models'
performance and address several research questions that rise during the
analysis of the results. By conducting this research, we shed light on the
capabilities and limitations of Artificial Intelligence generated text
RTB Innovation Catalog - Method and Work Plan
This document describes the method for building RTB’s Innovation Catalog. We start by defining the
objectives of this research, the problems and the challenges we are addressing.
Most CGIAR innovations are documented in a way that does not favor their wider use. This has limited
the contribution of CGIAR innovations to the developmental challenges that CGIAR investors demand.
The goal of this research is to contribute to the CGIAR innovation management system that will enable
the deployment of innovations faster, at a larger scale, and a reduced cost, having a more significant
impact where they are needed the most.
The purpose of the Innovation Catalog is to document RTB innovations, in a way that is easily
accessible, and understandable. The Catalog will be user-friendly (see definition in Section 6.2).
Technical terms, indicators, and categories will be standardized. The type of language and depth of
information will be tailored to different types of users.
The RTB Innovation Catalog will be developed using a tailor-made Scaling Readiness framework.
Individual RTB innovations are the building blocks of the Innovation Catalog. Contextual information
and connection to innovation packages will be documented for a few of the innovations
Novel Docosahexaenoic Acid Ester of Phloridzin Inhibits Proliferation and Triggers Apoptosis in an In Vitro Model of Skin Cancer
Skin cancer is among the most common cancer types accompanied by rapidly increasing incidence rates, thus making the development of more efficient therapeutic approaches a necessity. Recent studies have revealed the potential role of decosahexaenoic acid ester of phloridzin (PZDHA) in suppressing proliferation of liver, breast, and blood cancer cell lines. In the present study, we investigated the cytotoxic potential of PZDHA in an in vitro model of skin cancer consisting of melanoma (A375), epidermoid carcinoma (A431), and non-tumorigenic (HaCaT) cell lines. Decosahexaenoic acid ester of phloridzin led to increased cytotoxicity in all cell lines as revealed by cell viability assays. However, growth inhibition and induction of both apoptosis and necrosis was more evident in melanoma (A375) and epidermoid carcinoma (A431) cells, whereas non-tumorigenic keratinocytes (HaCaT) appeared to be more resistant as detected by flow cytometry. More specifically, PZDHA-induced cell cycle growth arrest at the G2/M phase in A375 and A431 cells in contrast to HaCaT cells, which were growth arrested at the G0/G1 phase. Elevated intracellular generation of reactive oxygen species ROS was detected in all cell lines. Overall, our findings support the potential of PZDHA as a novel therapeutic means against human skin cancer
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