pinecone/core-2020-05-10-deduplication huggingface dataset:
A distributed optimization method for the geographically distributed data centres problem

The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method
Explanation-based weighted degree

The weighted degree heuristic is among the state of the art generic variable ordering strategies in constraint programming. However, it was often observed that when using large arity constraints, its efficiency deteriorates significantly since it loses its ability to discriminate variables. A possible answer to this drawback is to weight a conflict set rather than the entire scope of a failed constraint. We implemented this method for three common global constraints (AllDifferent, Linear Inequality and Element) and evaluate it on instances from the MiniZinc Challenge. We observe that even with simple explanations, this method outperforms the standard Weighted Degree heuristic
A rescaled method for RBF approximation

In the recent paper [8], a new method to compute stable kernel-based
interpolants has been presented. This \textit{rescaled interpolation} method
combines the standard kernel interpolation with a properly defined rescaling
operation, which smooths the oscillations of the interpolant. Although
promising, this procedure lacks a systematic theoretical investigation. Through
our analysis, this novel method can be understood as standard kernel
interpolation by means of a properly rescaled kernel. This point of view allow
us to consider its error and stability properties
A consensus approach to sentiment analysis

There are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnosing of an illness or parliament members looking for consensus on a specific law being passed. In\ud
this article we present a method that utilises Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion from a number of Sentiment Analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers. Previously determined sentence intensity polarity by different SA classification methods are used as input to a specific IOWA operator. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier `most' (IOWA_most) proved to yield superior results compared to those achieved when utilising other techniques commonly\ud
applied when some sort of averaging is needed, such as arithmetic mean or median techniques
A Dynamic Logic Framework for Abstract Argumentation: Adding and Removing Arguments

A dynamic framework, based on the Dynamic Logic of Propositional Assignments (DL-PA), has recently been proposed for Dung's abstract argument system. This framework allows the addition and the removal of attacks, and the modification of the acceptance status of arguments. We here extend this framework in order to capture the addition and the removal of arguments. We then apply the framework on an access control case, where an agent engages in an argued dialogue to access some information controlled by another agent
Temporal analysis of social media response to live events: The Milano fashion week

Social media response to catastrophic events, such as natural disasters or terrorist attacks, has received a lot of attention. However, social media are also extremely important in the context of planned events, such as fairs, exhibits, festivals, as they play an essential role in communicating them to fans, interest groups, and the general population. These kinds of events are geo-localized within a city or territory and are scheduled within a public calendar. We consider a specific scenario, the Milano Fashion Week (MFW), which is an important event in our city. We focus our attention on the spreading of social content in time, measuring the delay of the event propagation. We build different clusters of stakeholders (fashion brands), we characterize several features of time propagation and we correlate it to the popularity of involved actors. We show that the clusters by time and popularity are loosely correlated, and therefore the time response cannot be easily inferred. This motivates the development of a predictor through supervised learning in order to anticipate the space cluster of a new brand
Equivalence of Probabilistic mu-Calculus and p-Automata

An important characteristic of Kozen’s µ-calculus is its strong\ud
connection with parity alternating tree automata. Here, we show that the\ud
probabilistic µ-calculus µ^p-calculus and p-automata (parity alternating\ud
Markov chain automata) have an equally strong connection. Namely,\ud
for every µ^p-calculus formula we can construct a p-automaton that accepts\ud
exactly those Markov chains that satisfy the formula. For every\ud
p-automaton we can construct a µ^p-calculus formula satisfied in exactly\ud
those Markov chains that are accepted by the automaton. The translation\ud
in one direction relies on a normal form of the calculus and in the\ud
other direction on the usage of vectorial µ^p-calculus. The proofs use the\ud
game semantics of µ^p-calculus and automata to show that our translations\ud
are correct.Peer-reviewedPost-print22nd International Conference on Implementation and Application of Automat
Climate Change: Warming Impacts on Marine Biodiversity

In this chapter, the effects of temperature change—as a main aspect of climate change—on marine biodiversity are assessed. Starting from a general discussion of species responses to temperature, the chapter presents how species respond to warming. These responses comprise adaptation and phenotypic plasticity as well as range shifts. The observed range shifts show more rapid shifts at the poleward range edge than at the equator-near edge, which probably reflects more rapid immigration than extinction in a warming world. A third avenue of changing biodiversity is change in species interactions, which can be altered by temporal and spatial shifts in interacting species. We then compare the potential changes in biodiversity to actual trends recently addressed in empirical synthesis work on local marine biodiversity, which lead to conceptual issues in quantifying the degree of biodiversity change. Finally we assess how climate change impacts the protection of marine environments
Privacy Assessment of Software Architectures based on Static Taint
  Analysis

Privacy analysis is critical but also a time-consuming and tedious task. We
present a formalization which eases designing and auditing high-level privacy
properties of software architectures. It is incorporated into a larger policy
analysis and verification framework and enables the assessment of commonly
accepted data protection goals of privacy. The formalization is based on static
taint analysis and makes flow and processing of privacy-critical data explicit,
globally as well as on the level of individual data subjects. Formally, we show
equivalence to traditional label-based information flow security and prove
overall soundness of our tool with Isabelle/HOL. We demonstrate applicability
in two real-world case studies, thereby uncovering previously unknown
violations of privacy constraints in the analyzed software architectures
A Language for the Declarative Composition of Concurrent Protocols

A recent study of bugs in real-world concurrent and distributed systems found
that, while implementations of individual protocols tend to be robust, the
composition of multiple protocols and its interplay with internal computation
is the culprit for most errors. Multiparty Session Types and Choreographic
Programming are methodologies for developing correct-by-construction concurrent
and distributed software, based on global descriptions of communication flows.
However, protocol composition is either limited or left unchecked. Inspired by
these two methodologies, in this work we present a new language model for the
safe composition of protocols, called Procedural Choreographies (PC). Protocols
in PC are procedures, parameterised on the processes that enact them.
Procedures define communications declaratively using global descriptions, and
programs are written by invoking and composing these procedures. An
implementation in terms of a process model is then mechanically synthesised,
guaranteeing correctness and deadlock-freedom. We study PC in the settings of
synchronous and asynchronous communications, and illustrate its expressivity
with some representative examples
On Predicting Geolocation of Tweets using Convolutional Neural Networks

In many Twitter studies, it is important to know where a tweet came from in
order to use the tweet content to study regional user behavior. However,
researchers using Twitter to understand user behavior often lack sufficient
geo-tagged data. Given the huge volume of Twitter data there is a need for
accurate automated geolocating solutions. Herein, we present a new method to
predict a Twitter user's location based on the information in a single tweet.
We integrate text and user profile meta-data into a single model using a
convolutional neural network. Our experiments demonstrate that our neural model
substantially outperforms baseline methods, achieving 52.8% accuracy and 92.1%
accuracy on city-level and country-level prediction respectively
Stigmergy-based modeling to discover urban activity patterns from
  positioning data

Positioning data offer a remarkable source of information to analyze crowds
urban dynamics. However, discovering urban activity patterns from the emergent
behavior of crowds involves complex system modeling. An alternative approach is
to adopt computational techniques belonging to the emergent paradigm, which
enables self-organization of data and allows adaptive analysis. Specifically,
our approach is based on stigmergy. By using stigmergy each sample position is
associated with a digital pheromone deposit, which progressively evaporates and
aggregates with other deposits according to their spatiotemporal proximity.
Based on this principle, we exploit positioning data to identify high density
areas (hotspots) and characterize their activity over time. This
characterization allows the comparison of dynamics occurring in different days,
providing a similarity measure exploitable by clustering techniques. Thus, we
cluster days according to their activity behavior, discovering unexpected urban
activity patterns. As a case study, we analyze taxi traces in New York City
during 2015
2-State 2-Symbol Turing Machines with Periodic Support Produce Regular Sets

We say that a Turing machine has periodic support if there is an infinitely repeated word to the left of the input and another infinitely repeated word to the right. In the search for the smallest universal Turing machines, machines that use periodic support have been significantly smaller than those for the standard model (i.e. machines with the usual blank tape on either side of the input). While generalising the model allows us to construct smaller universal machines it makes proving decidability results for the various state-symbol products that restrict program size more difficult. Here we show that given an arbitrary 2-state 2-symbol Turing machine and a configuration with periodic support the set of reachable configurations is regular. Unlike previous decidability results for 2-state 2-symbol machines, here we include in our consideration machines that do not reserve a transition rule for halting, which further adds to the difficulty of giving decidability results
Evaluating the perception of the decision quality in web-based group decision support systems: a theory of satisfaction

The future and success of organizations depend greatly on the quality
of every decision made. It is known that most of the decisions in organizations
are made in group. With the purpose to support the decision-makers anytime and
anywhere, Web-based Group Decision Support Systems have been studied. The
amount of Web-based Group Decision Support Systems incorporating automatic
negotiation mechanisms such as argumentation is increasing nowadays. Usually,
these systems/models are evaluated through mathematical proofs, number of
rounds or seconds to propose (reach) a solution. However, those techniques do
not say much in terms of decision quality. Here, we propose a model to predict
the decision-makers’ satisfaction (perception of the decision quality), specially
designed to deal with multi-criteria problems. Our model considers aspects such
as: alternatives comparison, style of behaviour, emotions, mood and expectations.
The proposed formulation matches the assumptions previously defined in
the literature.This work was supported by COMPETE Programme (operational programme for competitiveness)
within Project POCI-01-0145-FEDER-007043, by National Funds
through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for
Science and Technology) within the Projects UID/CEC/00319/2013,
UID/EEA/00760/2013, and the João Carneiro PhD Grant with the Reference
SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio
Decision process to manage renewable energy production in smart grid environment

This research work is related to the Electric Vehicle (EV) integration
in the electricity market, using the OpenADR open protocol for demand response.
The proposed solution integrates a local developed EV charging system and a
cloud management system, to coordinate the available energy produced from renewable
energy sources, taking into account its intermittent production and the
requirements of the EV charging process at home. Considering the smart mobility
paradigm, all transactions processes are available at mobile devices in real-time,
where users can define their usual behavior, configure the energy consumption
profile at home, establish new profiles for specific days according to the EV
charging process, and consult the historical transactions.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and
FCT – Fundação para a Ciência e Tecnologia within the Project Scope:
UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Developing an individualized survival prediction model for rectal cancer

This work presents a survivability prediction model for rectal cancer patients developed through machine learning techniques. The model was based on the most complete worldwide cancer dataset known, the SEER dataset. After preprocessing, the training data consisted of 12,818 records of rectal cancer patients. Six features were extracted from a feature selection process, finding the most relevant characteristics which affect the survivability of rectal cancer. The model constructed with six features was compared with another one with 18 features indicated by a physician. The results show that the performance of the six-feature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance.FCT - Fuel Cell Technologies Program (SFRH/BD/85291/2012)info:eu-repo/semantics/publishedVersio
On the dynamic suction pumping of blood cells in tubular hearts

Around the third week after gestation in embryonic development, the human
heart consists only of a valvless tube, unlike a fully developed adult heart,
which is multi-chambered. At this stage in development, the heart valves have
not formed and so net flow of blood through the heart must be driven by a
different mechanism. It is hypothesized that there are two possible mechanisms
that drive blood flow at this stage - Liebau pumping (dynamic suction pumping
or valveless pumping) and peristaltic pumping. We implement the immersed
boundary method with adaptive mesh refinement (IBAMR) to numerically study the
effect of hematocrit on the circulation around a valveless. Both peristalsis
and dynamic suction pumping are considered. In the case of dynamic suction
pumping, the heart and circulatory system is simplified as a flexible tube
attached to a relatively rigid racetrack. For some Womersley number (Wo)
regimes, there is significant net flow around the racetrack. We find that the
addition of flexible blood cells does not significantly affect flow rates
within the tube for Wo $\leq$ 10. On the other hand, peristalsis consistently
drives blood around the racetrack for all Wo and for all hematocrit considered.Comment: 21 pages, 19 figure
Shallow reading with Deep Learning: Predicting popularity of online
  content using only its title

With the ever decreasing attention span of contemporary Internet users, the
title of online content (such as a news article or video) can be a major factor
in determining its popularity. To take advantage of this phenomenon, we propose
a new method based on a bidirectional Long Short-Term Memory (LSTM) neural
network designed to predict the popularity of online content using only its
title. We evaluate the proposed architecture on two distinct datasets of news
articles and news videos distributed in social media that contain over 40,000
samples in total. On those datasets, our approach improves the performance over
traditional shallow approaches by a margin of 15%. Additionally, we show that
using pre-trained word vectors in the embedding layer improves the results of
LSTM models, especially when the training set is small. To our knowledge, this
is the first attempt of applying popularity prediction using only textual
information from the title
Recurrent Neural Networks for Online Video Popularity Prediction

In this paper, we address the problem of popularity prediction of online
videos shared in social media. We prove that this challenging task can be
approached using recently proposed deep neural network architectures. We cast
the popularity prediction problem as a classification task and we aim to solve
it using only visual cues extracted from videos. To that end, we propose a new
method based on a Long-term Recurrent Convolutional Network (LRCN) that
incorporates the sequentiality of the information in the model. Results
obtained on a dataset of over 37'000 videos published on Facebook show that
using our method leads to over 30% improvement in prediction performance over
the traditional shallow approaches and can provide valuable insights for
content creators
Segmentation of anthropometric data of the Brazilian’ female population

The researches concerning the measurements of the human body in Brazil are still very few and the anthropometric data related with the Brazilian women is widely diversified due to some aspects such as the vastness of the country and a huge miscegenation of races. The results obtained through the utilization of a 3D body scanner were segmented into four categories, namely: age, region of origin, race and shape. The aim of this study is to provide more accurate and reliable measurements and information concerning the different types of body shapes for the clothing sizing systems directed to the industry specialized in the mass production of clothing, and, therefore, allow clothing to fit appropriately on the wearer’s body in such a way as to make her more confident and satisfied with the clothing available in the market but also access higher levels of comfort.We would like to acknowledge 2C2T-Science Center for Textile Technology
from University of Minho and Federal University of Technology – Parana (UTFPR). This
work is financed by FEDER funds through the Competitive Factors Operational Program
(COMPETE) POCI-01-0145-FEDER-007136 and by national funds through FCT-Portuguese
Foundation for Science and Technology, under the project UID/CTM/000264.info:eu-repo/semantics/publishedVersio
Garment design and engineering for hospital use

Garments used in hospitals play an important role in user’s comfort and health. This paper presents the developments of a patented design technology for medical garments aimed at users, caregivers, and health care providers. Traditional gowns lacking comfort and protection affect the patient’s dignity. Similarly, patients often use their own garments, which are not designed to provide the best comfort and protection if sensing capacity is affected. Additionally, they can interfere with caregivers’ daily tasks. The proposed pajamas consider these needs and allow an effective interaction between patient and caregiver without compromising the person’s self-esteem. The selection of functional textile fibers and the use of seamless production processes allow the design of products advantageous in the prevention of wounds and pressure ulcers, as pressure points are reduced and the micro-climate of the skin is managed in a more effective way, enhancing the levels of sensorial, physiological and ergonomic comfort.This work is financed by FEDER funds through the Competitive Factors
Operational Program (COMPETE) POCI-01-0145-FEDER-007136 and by national funds
through FCT-Portuguese Foundation for Science and Technology, under the project
UID/CTM/000264 and SFRH/BD/79762/2011.info:eu-repo/semantics/publishedVersio
Software Time Reliability in the Presence of Cache Memories

The use of caches challenges measurement-based timing analysis (MBTA) in critical embedded systems. In the presence of caches, the worst-case timing behavior of a system heavily depends on how code and data are laid out in cache. Guaranteeing that test runs capture, and hence MBTA results are representative of, the worst-case conflictive cache layouts, is generally unaffordable for end users. The probabilistic variant of MBTA, MBPTA, exploits randomized caches and relieves the user from the burden of concocting layouts. In exchange, MBPTA requires the user to control the number of runs so that a solid probabilistic argument can be made about having captured the effect of worst-case cache conflicts during analysis. We present a computationally tractable Time-aware Address Conflict (TAC) mechanism that determines whether the impact of conflictive memory layouts is indeed captured in the MBPTA runs and prompts the user for more runs in case it is not.The research leading to these results has received funding from the European\ud
Community's FP7 [FP7/2007-2013] under the PROXIMA Project (www.proximaproject.\ud
eu), grant agreement no 611085. This work has also been partially supported\ud
by the Spanish Ministry of Science and Innovation under grant TIN2015-\ud
65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially\ud
supported by the Ministry of Economy and Competitiveness under Ramon y\ud
Cajal postdoctoral fellowship number RYC-2013-14717.Peer ReviewedPostprint (author's final draft