pinecone/core-2020-05-10-deduplication huggingface dataset:
Takeuti's Well-Ordering Proof: Finitistically Fine?

If it could be shown that one of Gentzen's consistency proofs for pure number theory could be shown to be finitistically acceptable, an important part of Hilbert's program would be vindicated. This paper focuses on whether the transfinite induction on ordinal notations needed for Gentzen's second proof can be finitistically justified. In particular, the focus is on Takeuti's purportedly finitistically acceptable proof of the well-ordering of ordinal notations in Cantor normal form.\ud
     The paper begins with a historically informed discussion of finitism and its limits, before introducing Gentzen and Takeuti's respective proofs. The rest of the paper is dedicated to investigating the finitistic acceptability of Takeuti's proof, including a small but important fix to that proof. That discussion strongly suggests that there is a philosophically interesting finitist standpoint that Takeuti's proof, and therefore Gentzen's proof, conforms to
Megaprojects as an instrument of urban planning and development: example of Belgrade Waterfront

The research analyzed the theoretical and methodological background of urban megaprojects (UMPs) as instrument of urban planning and development, with specific reference to the Belgrade Waterfront Project (BWP). In analysis we combined a contextually appropriate ap-proach, some elements of the phronetic planning approach and the benchmarking analysis of megaproject planning and development. BWP induced a change of the institutional framework (introduction of specific legal and policy instruments), as a key source of future changes in the metropolitan tissue. Preliminary impact assessment of the BWP indicates: slow development & economic effects, low transparency, social inequalities, marginal social mobilization and weak networks between the key actors, public funds overuse, impact on law-making, displacement impacts, high public financial risk, deep urban transformations, environmental impacts, medium-technological modernization, etc. This research highlights the differences in the political, institutional, social and economic environment that shape the BWP. It provides recommendations for future research and application, improvement of planning approach and continuing in-depth analysis for managing the undesirable consequences of the UMPs, including the determination of the interplay between different pools of power.Slična verzija rada bila je prezentovana na konferenciji UNESCO (2016) pod istim naslovom Megaprojects as an Instrument of Urban Planning and Development: Example of Belgrade Waterfront Project, in UNESCO Chair in Technologies for Development: From Innovation to Social Impact, UNESCO Chair Conference on Technologies for Development: From Innovation to Social Impact, 2-4 May 2016, Ecole Polytechnique Federale de Lausanne - EPFL, Cooperation & Development Center - CODEV, Lausanne, Switzerland, pp.104, http://cooperation.epfl.ch/files/content/sites/cooperation/files/Tech4Dev%202016/Tech4Dev2016_Brochure_14Apr_WebVersion.pdfEditors: Silvia Hostettler, Samira Najih Besson, Jean-Claude Bola
Bayesian support for Evolution: detecting phylogenetic signal in a
  subset of the primate family

The theory of evolution states that the diversity of species can be explained
by descent with modification. Therefore, all living beings are related through
a common ancestor. This evolutionary process must have left traces in our
molecular composition. In this work, we present a randomization procedure in
order to determine if a group of 5 species of the primate family, namely,
macaque, guereza, orangutan, chimpanzee and human, has retained these traces in
its molecules. Firstly, we present the randomization methodology through two
toy examples, which allow to understand its logic. We then carry out a DNA data
analysis to assess if the group of primates contains phylogenetic information
which links them in a joint evolutionary history. This is carried out by
monitoring a Bayesian measure, called marginal likelihood, which we estimate by
using nested sampling. We found that it would be unusual to get the
relationship observed in the data among these primate species if they had not
shared a common ancestor. The results are in total agreement with the theory of
evolution
A Neural Network model with Bidirectional Whitening

We present here a new model and algorithm which performs an efficient Natural
gradient descent for Multilayer Perceptrons. Natural gradient descent was
originally proposed from a point of view of information geometry, and it
performs the steepest descent updates on manifolds in a Riemannian space. In
particular, we extend an approach taken by the "Whitened neural networks"
model. We make the whitening process not only in feed-forward direction as in
the original model, but also in the back-propagation phase. Its efficacy is
shown by an application of this "Bidirectional whitened neural networks" model
to a handwritten character recognition data (MNIST data).Comment: 16page
Towards Collaborative Conceptual Exploration

In domains with high knowledge distribution a natural objective is to create
principle foundations for collaborative interactive learning environments. We
present a first mathematical characterization of a collaborative learning
group, a consortium, based on closure systems of attribute sets and the
well-known attribute exploration algorithm from formal concept analysis. To
this end, we introduce (weak) local experts for subdomains of a given knowledge
domain. These entities are able to refute and potentially accept a given
(implicational) query for some closure system that is a restriction of the
whole domain. On this we build up a consortial expert and show first insights
about the ability of such an expert to answer queries. Furthermore, we depict
techniques on how to cope with falsely accepted implications and on combining
counterexamples. Using notions from combinatorial design theory we further
expand those insights as far as providing first results on the decidability
problem if a given consortium is able to explore some target domain.
Applications in conceptual knowledge acquisition as well as in collaborative
interactive ontology learning are at hand.Comment: 15 pages, 2 figure
Sequential Decision Making Under Uncertainty: Ordinal Uninorms vs. the Hurwicz Criterion

This paper focuses on sequential decision problems under uncertainty, i.e. sequential problems where no probability distribution on the states that may follow an action is available. New qualitative criteria are proposed that are based on ordinal uninorms, namely R∗ and R∗. Like the Hurwicz criterion, the R∗ and R∗ uninorms arbitrate between pure pessimism and pure optimism, and generalize the Maximin and Maximax criteria. But contrarily to the Hurwicz criterion they are associative, purely ordinal and compatible with Dynamic Consistency and Consequentialism. This latter important property allow the construction of an optimal strategy in polytime, following an algorithm of Dynamic Programming
A unified hyperbolic formulation for viscous fluids and elastoplastic
  solids

We discuss a unified flow theory which in a single system of hyperbolic
partial differential equations (PDEs) can describe the two main branches of
continuum mechanics, fluid dynamics, and solid dynamics. The fundamental
difference from the classical continuum models, such as the Navier-Stokes for
example, is that the finite length scale of the continuum particles is not
ignored but kept in the model in order to semi-explicitly describe the essence
of any flows, that is the process of continuum particles rearrangements. To
allow the continuum particle rearrangements, we admit the deformability of
particle which is described by the distortion field. The ability of media to
flow is characterized by the strain dissipation time which is a characteristic
time necessary for a continuum particle to rearrange with one of its
neighboring particles. It is shown that the continuum particle length scale is
intimately connected with the dissipation time. The governing equations are
represented by a system of first order hyperbolic PDEs with source terms
modeling the dissipation due to particle rearrangements. Numerical examples
justifying the reliability of the proposed approach are demonstrated.Comment: 6 figure
Data-driven elicitation, assessment and documentation of quality requirements in agile software development

Quality Requirements (QRs) are difficult to manage in agile software development. Given the pressure to deploy fast, quality concerns are often sacrificed for the sake of richer functionality. Besides, artefacts as user stories are not particularly well-suited for representing QRs. In this exploratory paper, we envisage a data-driven method, called Q-Rapids, to QR elicitation, assessment and documentation in agile software development. Q-Rapids proposes: 1) The collection and analysis of design and runtime data in order to raise quality alerts; 2) The suggestion of candidate QRs to address these alerts; 3) A strategic analysis of the impact of such requirements by visualizing their effect on a set of indicators rendered in a dashboard; 4) The documentation of the requirements (if finally accepted) in the backlog. The approach is illustrated with scenarios evaluated through a questionnaire by experts from a telecom company.Peer ReviewedPostprint (author's final draft
Analysis of performance in Depth Based Routing for Underwater Wireless Sensor Networks

In the last decade, Underwater Wireless Sensor Networks (UWSNs) have been widely studied because of their peculiar aspects that distinguish them from common wireless terrestrial networks. In fact, most UWSNs use acoustic instead of radio-frequency based communications, and nodes are subject to high mobility caused by water currents. As a consequence, specialized routing algorithms have been developed to tackle this challenging scenario. Depth based Routing (DBR) is one of the first protocols that have been developed to this aim, and is still widely adopted in actual implementations of UWSNs. In this paper we propose a stochastic analysis that aims at evaluating the performance of UWSNs using DBR in terms of expected energy consumption and expected end-to-end delay. Under a set of assumptions, we give expressions for these performance indices that can be evaluated efficiently, and hence they can be adopted as the basis for optimizing the configuration parameters of the protocol
Performance Degradation and Cost Impact Evaluation of Privacy Preserving Mechanisms in Big Data Systems

Big Data is an emerging area and concerns managing datasets whose size is beyond commonly used software tools ability to capture, process, and perform analyses in a timely way. The Big Data software market is growing at 32% compound annual rate, almost four times more than the whole ICT market, and the quantity of data to be analyzed is expected to double every two years.\ud
Security and privacy are becoming very urgent Big Data aspects that need to be tackled. Indeed, users share more and more personal data and user-generated content through their mobile devices and computers to social networks and cloud services, losing data and content control with a serious impact on their own privacy. Privacy is one area that had a serious debate recently, and many governments require data providers and companies to protect users’ sensitive data. To mitigate these problems, many solutions have been developed to provide data privacy but, unfortunately, they introduce some computational overhead when data is processed.\ud
The goal of this paper is to quantitatively evaluate the performance and cost impact of multiple privacy protection mechanisms. A real industry case study concerning tax fraud detection has been considered. Many experiments have been performed to analyze the performance degradation and additional cost (required to provide a given service level) for running applications in a cloud system
Lessons Learned with Laser Scanning Point Cloud Management in Hadoop HBase

While big data technologies are growing rapidly and benefit a wide range of science and engineering domains, many barriers remain for the remote sensing community to fully exploit the benefits provided by these emerging powerful technologies. To overcome these barriers, this paper presents the in-depth experience gained when adopting a distributed computing framework – Hadoop HBase – for storage, indexing, and integration of large scale, high resolution laser scanning point cloud data. Four data models were conceptualized, implemented, and rigorously investigated to explore the advantageous features of distributed, key-value database systems. In addition, the comparison of the four models facilitated the reassessment of several well-known point cloud management techniques founded in traditional computing environments in the new context of the distributed, key-value database. The four models were derived from two row-key designs and two columns structures, thereby demonstrating various considerations during the development of a data solution for high-resolution, city-scale aerial laser scan for a portion of Dublin, Ireland. This paper presents lessons learned from the data model design and its implementation for spatial data management in a distributed computing framework. The study is a step towards full exploitation of powerful emerging computing assets for dense spatio-temporal data.The Hadoop cluster used for the work presented in this paper was provided by allocation TG-CIE170036 - Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-154856
Mobile digital storytelling in a Brazilian care home

Digital stories are short personal films made up of a series of still images with voiceover, music and text. The technical barriers to creating such stories are falling with the use of mobile apps which make it easy to assemble story elements as audiophoto narratives on a smartphone or tablet. In this case study, we explored the potential of mobile digital storytelling in a care home context. It was used for four weeks as form of multimedia communication between formal and informal carers inside and outside the home, and a care home resident suffering from dementia. The home was located in São Carlos, Brazil as part of a larger international project called Time Matters (UK and Brazil), in which Time stands for ‘This is me’. Fifteen digital stories were made by participants in the trial, which is about one for every visit of the researchers to the care home. Stories focused mainly on the resident; capturing aspects of everyday life discussed in Visit conversations (4), documenting Social events (3) inside or outside the home, recording Therapy sessions (3) with the resident or Health reports (3) by professional carers, and forming Media albums (2) of the residents’ art or life. In general, the technology was most useful for facilitating richer conversations with the resident and other participants, and stimulating greater expressivity and creativity in the resident herself. The desire to document the resident’s current life and interests in the home for later reminiscence by their family, stands in contrast to conventional reminiscence therapy and related digital systems. These use media artefacts to stimulate reminiscence of residents’ past life outside the home
Supporting audiography: Design of a system for sentimental sound recording, classification and playback

It is now commonplace to capture and share images in photography as triggers for memory. In this paper we explore the possibility of using sound in the same sort of way, in a practice we call audiography. We report an initial design activity to create a system called Audio Memories comprising a ten second sound recorder, an intelligent archive for auto-classifying sound clips, and a multi-layered sound player for the social sharing of audio souvenirs around a table. The recorder and player components are essentially user experience probes that provide tangible interfaces for capturing and interacting with audio memory cues. We discuss our design decisions and process in creating these tools that harmonize user interaction and machine listening to evoke rich memories and conversations in an exploratory and open-ended way
Meeting Future Energy Needs in the Hindu Kush Himalaya

As mentioned in earlier chapters, the HKH regions form the entirety of some countries, a major part of other countries, and a small percentage of yet others. Because of this, when we speak about meeting the energy needs of the HKH region we need to be clear that we are not necessarily talking about the countries that host the HKH, but the clearly delineated mountainous regions that form the HKH within these countries. It then immediately becomes clear that energy provisioning has to be done in a mountain context characterized by low densities of population, low incomes, dispersed populations, grossly underdeveloped markets, low capabilities, and poor economies of scale. In other words, the energy policies and strategies for the HKH region have to be specific to these mountain contexts
Twenty years of coordination technologies: State-of-the-art and perspectives

Since complexity of inter- and intra-systems interactions is steadily increasing in modern application scenarios (e.g., the IoT), coordination technologies are required to take a crucial step towards maturity. In this paper we look back at the history of the COORDINATION conference in order to shed light on the current status of the coordination technologies there proposed throughout the years, in an attempt to understand success stories, limitations, and possibly reveal the gap between actual technologies, theoretical models, and novel application needs
Application of the Bead Perturbation Technique to a Study of a Tunable 5
  GHz Annular Cavity

Microwave cavities for a Sikivie-type axion search are subject to several
constraints. In the fabrication and operation of such cavities, often used at
frequencies where the resonator is highly overmoded, it is important to be able
to reliably identify several properties of the cavity. Those include
identifying the symmetry of the mode of interest, confirming its form factor,
and determining the frequency ranges where mode crossings with intruder levels
cause unacceptable admixture, thus leading to the loss of purity of the mode of
interest. A simple and powerful diagnostic for mapping out the electric field
of a cavity is the bead perturbation technique. While a standard tool in
accelerator physics, we have, for the first time, applied this technique to
cavities used in the axion search. We report initial results from an extensive
study for the initial cavity used in the HAYSTAC experiment. Two effects have
been investigated: the role of rod misalignment in mode localization, and
mode-mixing at avoided crossings of TM/TE modes. Future work will extend these
results by incorporating precision metrology and high-fidelity simulations.Comment: 6 pages, 4 figures, submitted to the 2nd Workshop on Microwave
  Cavities and Detectors for Axion Researc
Towards improving adaptability of capability driven development methodology in complex environment

We are triggered to incorporate adaptability in information system designs and methodologies corresponding to complex and unpredictable envi- ronment of today and tomorrow and to complex adaptive systems they are aimed for. Adaptability as non-functional requirement is being portrayed and investi- gated from broad multidisciplinary perspective that influences how dynamic business-IT alignment can be accomplished. Capability Driven Development methodology has supported delivering dynamic capabilities by providing context-aware self-adaptive platform in the CaaS project implementations, as our case study. Along with the already incorporated mechanisms, components that enable adaptability, there is open space for further evolutionary and deliberate change towards becoming truly appropriate methodology for dynamic recon- figurations of capabilities in organizations and business ecosystems that operate in complexity and uncertainty. The analysis and evaluation of adaptability of the CDD methodology through three dimensions (complexity of the external and internal environment, managerial profiling and artifact-integrated components) in this paper conclude with instigation of starting points towards achieving higher adaptability for complexity of the CDD methodology
The origin and evolution of syntax errors in simple sequence flow models in BPMN

How do syntax errors emerge? What is the earliest moment that potential syntax errors can be detected? Which evolution do syntax errors go through during modeling? A provisional answer to these questions is formulated in this paper based on an investigation of a dataset containing the operational details of 126 modeling sessions. First, a list is composed of the different potential syntax errors. Second, a classification framework is built to categorize the errors according to their certainty and severity during modeling (i.e., in partial or complete models). Third, the origin and evolution of all syntax errors in the dataset are identified. This data is then used to collect a number of observations, which form a basis for future research
Facial expression based emotion recognition using neural networks

Facial emotion recognition has been extensively studied over the last decade due to its various applications in the fields such as human-computer interaction and data analytics. In this paper, we develop a facial emotion recognition approach to classify seven emotional states (joy, sadness, surprise, anger, fear, disgust and neutral). Seventeen action units tracked by Kinect v2 sensor have been used as features. Classification of emotions was performed by artificial neural networks (ANNs). Six subjects took part in the experiment. We have achieved average accuracy of 95.8% for the case in which we tested our approach with the same volunteers took part in our data generation process. We also evaluated the performance of the network with additional volunteers who were not part of the training data and achieved 67.03% classification accuracy
Extended patch prioritization for depth filling within constrained exemplar-based RGB-D image completion.

We address the problem of hole filling in depth images, obtained from either active or stereo sensing, for the purposes of depth image completion in an exemplar-based framework. Most existing exemplar-based inpainting techniques, designed for color image completion, do not perform well on depth information with object boundaries obstructed or surrounded by missing regions. In the proposed method, using both color (RGB) and depth (D) information available from a common-place RGB-D image, we explicitly modify the patch prioritization term utilized for target patch ordering to facilitate improved propagation of complex texture and linear structures within depth completion. Furthermore, the query space in the source region is constrained to increase the efficiency of the approach compared to other exemplar-driven methods. Evaluations demonstrate the efficacy of the proposed method compared to other contemporary completion techniques
Human action recognition using fusion of depth and inertial sensors

In this paper we present a human action recognition system that utilizes the fusion of depth and inertial sensor measurements. Robust depth and inertial signal features, that are subject-invariant, are used to train independent Neural Networks, and later decision level fusion is employed using a probabilistic framework in the form of Logarithmic Opinion Pool. The system is evaluated using UTD-Multimodal Human Action Dataset, and we achieve 95% accuracy in 8-fold cross-validation, which is not only higher than using each sensor separately, but is also better than the best accuracy obtained on the mentioned dataset by 3.5%