pinecone/core-2020-05-10-deduplication huggingface dataset:
Learning features for tissue classification with the classification restricted Boltzmann machine

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Performance of automated tissue classification in medical imaging depends on the choice of descriptive features. In this paper, we show how restricted Boltzmann machines (RBMs) can be used to learn features that are especially suited for texture-based tissue classification. We introduce the convolutional classification RBM, a combination of the existing convolutional RBM and classification RBM, and use it for discriminative feature learning. We evaluate the classification accuracy of convolutional and non-convolutional classification RBMs on two lung CT problems. We find that RBM-learned features outperform conventional RBM-based feature learning, which is unsupervised and uses only a generative learning objective, as well as often-used filter banks. We show that a mixture of generative and discriminative learning can produce filters that give a higher classification accuracy
Shifting processes with cyclically exchangeable increments at random

We propose a path transformation which applied to a cyclically exchangeable
increment process conditions its minimum to belong to a given interval.
  This path transformation is then applied to processes with start and end at
zero. It is seen that, under simple conditions, the weak limit as epsilon tends
to zero of the process conditioned on remaining above minus epsilon exists and
has the law of the Vervaat transformation of the process.
  We examine the consequences of this path transformation on processes with
exchangeable increments, L\'evy bridges, and the Brownian bridge.Comment: 14 pages and 3 figure
Oceania: Antidepressant Medicinal Plants

Despite having the smallest land mass of the World Wildlife Fund (WWF)-declared ecozones, Oceania is amongst the most diverse floral regions of the world. Geographically, the region consists of Australia and New Guinea as the largest land masses, as well as the islands of Melanesia, Micronesia and Polynesia. Due to the island nature of the region, the flora has developed in isolation in various climatic conditions within the region, resulting in an extremely high degree of endemism. Furthermore, the harsh climatic conditions in some regions have resulted in a wealth of unique phytochemicals not found in plants from other regions globally. Coupled with possibly the world’s oldest continuous human inhabitation on the Australian mainland and a diversity of cultures in other Oceania regions, this has led to complex and sophisticated ethnopharmacological systems. Medicinal plants with unique properties have long been recognised by indigenous Oceania populations, and this lore has been passed from generation to generation. Whilst often not well recorded, there is a wealth of knowledge of the medicinal value of the regions’ floral species for all types of therapeutic purposes. This chapter focuses on the plants of the region with known antidepressant uses and/or those plants which have phytochemistry consistent with antidepressant properties. This is by no means an exhaustive list, but instead serves to highlight some of the best known examples (e.g. kava-kava) and discuss examples of plants with established antidepressant mechanisms. For example, whilst we discuss the calmative properties of the Australian plant Backhousia citriodora, many other aromatic plants with similar essential oil components and thus similar therapeutic properties exist in the region and are not discussed here for the sake of brevity. Furthermore, despite the high degree of endemism of Oceania flora, several well-known species (e.g. Areca catechu L. and Nelumbo nucifera Gaertn.) have wide geographic ranges. Indeed, whilst native to Oceania, A. catechu is better known as a component of the pharmacopoeias of other regions (e.g. India). However, these species also make an important contribution to Oceanic antidepressant medicinal plants and are therefore discussed in this chapter.Griffith Sciences, School of Natural SciencesFull Tex
Robustness Modelling and Verification of a Mix Net Protocol

Re-encryption Mix Nets are used to provide anonymity by passing encrypted messages through a collection of servers which each permute and re-encrypt messages. They are used in secure electronic voting protocols because they provide a combination of anonymity and verifiability. The use of several peers also provides for robustness, since a Mix Net can run even in the presence of a minority of dishonest or incorrectly behaving peers. However, in practice the protocols for peers to decide when to exclude a peer are complex distributed algorithms, and it is non-trivial to gain confidence that the Mix Net will be robust and live in the presence of faulty or malicious peers. In this paper we model and analyse the algorithm used by Ximix, a particular Mix Net implementation, using the CSP process algebra and the FDR model checker. We model and analyse the protocol in the presence of a realistic intruder based on Roscoe and Goldsmith’s perfect Spy. We show that in the current implementation the protocol does not satisfy the robustness requirement. Finally, we propose a method of making it robust, and verify in FDR that the proposed solution is sound and provides this robustness. Along the way, we highlight the omissions and deviations from the original RPC proposal; Mix Net protocols are extremely fragile, and small and seemingly benign changes may result in security flaws. Our experimental results show that, with our modification, Ximix guarantees termination and produces a correct output in the presence of an intruder who can corrupt a minority of mix servers
On-line Context Aware Physical Activity Recognition from the Accelerometer and Audio Sensors of Smartphones

International audienceActivity Recognition (AR) from smartphone sensors has be-come a hot topic in the mobile computing domain since it can provide ser-vices directly to the user (health monitoring, fitness, context-awareness) as well as for third party applications and social network (performance sharing, profiling). Most of the research effort has been focused on direct recognition from accelerometer sensors and few studies have integrated the audio channel in their model despite the fact that it is a sensor that is always available on all kinds of smartphones. In this study, we show that audio features bring an important performance improvement over an accelerometer based approach. Moreover, the study demonstrates the interest of considering the smartphone location for on-line context-aware AR and the prediction power of audio features for this task. Finally, an-other contribution of the study is the collected corpus that is made avail-able to the community for AR recognition from audio and accelerometer sensors
A Transformational Approach to Resource Analysis with Typed-Norms

In order to automatically infer the resource consumption of programs, analyzers track how data sizes change along a program's execution. Typically, analyzers measure the sizes of data by applying norms which are mappings from data to natural numbers that represent thesizes of the corresponding data. When norms are de_ned by taking typeinformation into account, they are named typed-norms. The main contributionof this paper is a transformational approach to resource analysiswith typed-norms. The analysis is based on a transformation of the programinto an intermediate abstract program in which each variable isabstracted with respect to all considered norms which are valid for itstype. We also sketch a simple analysis that can be used to automaticallyinfer the required, useful, typed-norms from programs
Software Development Support for Shared Sensing Infrastructures: A Generative and Dynamic Approach

International audienceSensors networks are the backbone of large sensing infras-tructures such as Smart Cities or Smart Buildings. Classical approaches suffer from several limitations hampering developers' work (e.g., lack of sensor sharing, lack of dynamicity in data collection policies, need to dig inside big data sets, absence of reuse between implementation platforms). This paper presents a tooled approach that tackles these issues. It couples (i) an abstract model of developers' requirements in a given infrastructure to (ii) timed automata and code generation techniques, to support the efficient deployment of reusable data collection policies on different infrastructures. The approach has been validated on several real-world scenarios and is currently experimented on an academic campus
Recovering Architectural Variability of a Family of Product Variants

A Software Product Line (SPL) aims at applying a pre-planned systematic reuse
of large-grained software artifacts to increase the software productivity and
reduce the development cost. The idea of SPL is to analyze the business domain
of a family of products to identify the common and the variable parts between
the products. However, it is common for companies to develop, in an ad-hoc
manner (e.g. clone and own), a set of products that share common
functionalities and differ in terms of others. Thus, many recent research
contributions are proposed to re-engineer existing product variants to a SPL.
Nevertheless, these contributions are mostly focused on managing the
variability at the requirement level. Very few contributions address the
variability at the architectural level despite its major importance. Starting
from this observation, we propose, in this paper, an approach to reverse
engineer the architecture of a set of product variants. Our goal is to identify
the variability and dependencies among architectural-element variants at the
architectural level. Our work relies on Formal Concept Analysis (FCA) to
analyze the variability. To validate the proposed approach, we experimented on
two families of open-source product variants; Mobile Media and Health Watcher.
The results show that our approach is able to identify the architectural
variability and the dependencies
Mining Software Components from Object-Oriented APIs

Object-oriented Application Programing Interfaces (APIs) support software
reuse by providing pre-implemented functionalities. Due to the huge number of
included classes, reusing and understanding large APIs is a complex task.
Otherwise, software components are admitted to be more reusable and
understandable entities than object-oriented ones. Thus, in this paper, we
propose an approach for reengineering object-oriented APIs into component-based
ones. We mine components as a group of classes based on the frequency they are
used together and their ability to form a quality-centric component. To
validate our approach, we experimented on 100 Java applications that used
Android APIs
Virtual embedded librarianship for information literacy teaching.

This paper, reports on the planning and preliminary results of an action research project undertaken for the redesign of an online distance learning information literacy (IL) module on the basis of virtual 'embedded librarianship'. The research project, which followed an action research design, brought together the IL module coordinator and an Academic Liaison Librarian, working at different institutions to collaboratively redesign the assessment and teaching of the module. Data were collected via a qualitative analysis of students' work and a series of open-ended questions addressed to students on the value of the approach followed. Students reacted positively to the embedded librarianship design and engaged constructively in situated learning. Challenges included time-zones differences, the contribution level of students and lack of confidence. The paper puts emphasis on educating future information professionals as embedded information literacy partners, promoting the development of transferable skills and a collaborative/sharing online working ethos
Towards Adult Information Literacy Assessment in Latvia: UNESCO Media and Information Literacy Competency Matrix in Practice

The study presents conclusions regarding the possibility of adapting
the UNESCO MIL Competency Matrix and developing methodology for
information literacy assessment of the adult population. During field research in
a sample territory (Kekava district) and within an adult population target group,
the levels of information literacy are assessed, and information literacy
education needs are clarified. The research results can be used for the
development of diagnostic instruments for regional growth, planning of adult
education, elaboration of information literacy training programmes, as well as
for self-evaluation of information literacy competencies. The study has been
carried out within the framework of the European Social Fund project
“Development of Innovative Diagnostic Instruments for Regional Growth”
Former students' views on the usefulness of conceptual modeling education

A big challenge for education and research in Requirements Engineering and Conceptual Modeling (RE/CM) is the need for much more empirical research about the use in practice of RE/CM, including the practical impact of CM education. Former students of RE/CM are potential prescriptors of the RE/CM concepts, methodologies and tools that they have learnt, but they are also conditioned by the current use of those same issues in practice. In this paper we focus on the views that former students of a RE/CM course have, now as young professionals, on the usefulness of the received CM education. We have surveyed over 70 former students to know their opinions on the usefulness of the education on a representative set of CM artifacts. Our results show that our former students find quite useful in general their received CM education, with different usefulness degrees for the various learned artifacts.Peer ReviewedPostprint (author’s final draft
Measuring Accuracy of Automated Parsing and Categorization Tools and
  Processes in Digital Investigations

This work presents a method for the measurement of the accuracy of evidential
artifact extraction and categorization tasks in digital forensic
investigations. Instead of focusing on the measurement of accuracy and errors
in the functions of digital forensic tools, this work proposes the application
of information retrieval measurement techniques that allow the incorporation of
errors introduced by tools and analysis processes. This method uses a `gold
standard' that is the collection of evidential objects determined by a digital
investigator from suspect data with an unknown ground truth. This work proposes
that the accuracy of tools and investigation processes can be evaluated
compared to the derived gold standard using common precision and recall values.
Two example case studies are presented showing the measurement of the accuracy
of automated analysis tools as compared to an in-depth analysis by an expert.
It is shown that such measurement can allow investigators to determine changes
in accuracy of their processes over time, and determine if such a change is
caused by their tools or knowledge.Comment: 17 pages, 2 appendices, 1 figure, 5th International Conference on
  Digital Forensics and Cyber Crime; Digital Forensics and Cyber Crime, pp.
  147-169, 201
Determining Training Needs for Cloud Infrastructure Investigations using
  I-STRIDE

As more businesses and users adopt cloud computing services, security
vulnerabilities will be increasingly found and exploited. There are many
technological and political challenges where investigation of potentially
criminal incidents in the cloud are concerned. Security experts, however, must
still be able to acquire and analyze data in a methodical, rigorous and
forensically sound manner. This work applies the STRIDE asset-based risk
assessment method to cloud computing infrastructure for the purpose of
identifying and assessing an organization's ability to respond to and
investigate breaches in cloud computing environments. An extension to the
STRIDE risk assessment model is proposed to help organizations quickly respond
to incidents while ensuring acquisition and integrity of the largest amount of
digital evidence possible. Further, the proposed model allows organizations to
assess the needs and capacity of their incident responders before an incident
occurs.Comment: 13 pages, 3 figures, 3 tables, 5th International Conference on
  Digital Forensics and Cyber Crime; Digital Forensics and Cyber Crime, pp.
  223-236, 201
Tensor categories and endomorphisms of von Neumann algebras (with
  applications to Quantum Field Theory)

Q-systems describe "extensions" of an infinite von Neumann factor $N$, i.e.,
finite-index unital inclusions of $N$ into another von Neumann algebra $M$.
They are (special cases of) Frobenius algebras in the C* tensor category of
endomorphisms of $N$. We review the relation between Q-systems, their modules
and bimodules as structures in a category on one side, and homomorphisms
between von Neumann algebras on the other side. We then elaborate basic
operations with Q-systems (various decompositions in the general case, and the
centre, the full centre, and the braided product in braided categories), and
illuminate their meaning in the von Neumann algebra setting. The main
applications are in local quantum field theory, where Q-systems in the
subcategory of DHR endomorphisms of a local algebra encode extensions
$A(O)\subset B(O)$ of local nets. These applications, notably in conformal
quantum field theories with boundaries, are briefly exposed, and are discussed
in more detail in two separate papers [arXiv:1405.7863, 1410.8848].Comment: v1: 54 pages. v2: 90 pages. Title changed by request of editor,
  numerous material added, especially an overview of applications in Quantum
  Field Theory; some corrections. v3: minor corrections to match the published
  version; Springer Briefs in Mathematical Physics, vol. 3, 201
Dynamic Load Balancing with Pair Potentials

International audienceWe present a new load balancing algorithm inspired by Molecular Dynamics Simulations. Our main motivation is to anticipate the rising costs of tasks-scheduling caused by the growth of the number of available cores on chips. This algorithm is based on a virtual decomposition of workload in Voronoï cells centered around computing units. The method used in this paper allows cores to virtually move in order to change their computing load. Cores displacements are result of forces computation (with pair potential): attractive or repulsive forces between cores are balanced by the cores computing load (total cost of Voronoï cell). Overcharged cores are more attractive than under-charged cores (which are then more repulsive). In this paper, we demonstrate the relevance of our approach by experimenting our algorithm with a high number of automatically-generated test cases, ranging from almost stable to quickly-evolving scenarii. In all cases, our algorithm is able to quickly converge to a distribution which maintains good locality properties
Planning Live-Migrations to Prepare Servers for Maintenance

International audienceIn a virtualized data center, server maintenance is a common but still critical operation. A prerequisite is indeed to relocate elsewhere the Virtual Machines (VMs) running on the production servers to prepare them for the maintenance. When the maintenance focuses several servers, this may lead to a costly relocation of several VMs so the migration plan must be chose wisely. This however implies to master numerous human, technical, and economical aspects that play a role in the design of a quality migration plan. In this paper, we study migration plans that can be decided by an operator to prepare for an hardware upgrade or a server refresh on multiple servers. We exhibit performance bottleneck and pitfalls that reduce the plan efficiency. We then discuss and validate possible improvements deduced from the knowledge of the environment peculiarities
Quantum spin chains and integrable many-body systems of classical
  mechanics

This note is a review of the recently revealed intriguing connection between
integrable quantum spin chains and integrable many-body systems of classical
mechanics. The essence of this connection lies in the fact that the spectral
problem for quantum Hamiltonians of the former models is closely related to a
sort of inverse spectral problem for Lax matrices of the latter ones. For
simplicity, we focus on the most transparent and familiar case of spin chains
on N sites constructed by means of the GL(2)-invariant R-matrix. They are
related to the classical Ruijsenaars-Schneider system of N particles, which is
known to be an integrable deformation of the Calogero-Moser system. As an
explicit example the case N=2 is considered in detail.Comment: 17 pages, misprints corrected, written for Proceedings of the
  International School and Workshop "Nonlinear Mathematical Physics and Natural
  Hazards", Sofia, Bulgaria, November 28 - December 2, 2013, to be published in
  Lecture Notes in Physic
Mining and Analyzing the Italian Parliament: Party Structure and
  Evolution

The roll calls of the Italian Parliament in the XVI legislature are studied
by employing multidimensional scaling, hierarchical clustering, and network
analysis. In order to detect changes in voting behavior, the roll calls have
been divided in seven periods of six months each. All the methods employed
pointed out an increasing fragmentation of the political parties endorsing the
previous government that culminated in its downfall. By using the concept of
modularity at different resolution levels, we identify the community structure
of Parliament and its evolution in each of the considered time periods. The
analysis performed revealed as a valuable tool in detecting trends and drifts
of Parliamentarians. It showed its effectiveness at identifying political
parties and at providing insights on the temporal evolution of groups and their
cohesiveness, without having at disposal any knowledge about political
membership of Representatives.Comment: 27 pages, 14 figure
A new image decomposition and reconstruction approach -- adaptive fourier decomposition

© Springer International Publishing Switzerland 2015. Fourier has been a powerful mathematical tool for representing a signal into an expression consist of sin and cos. Recently a new developed signal decomposition theory is proposed by Pro. Tao Qian named Adaptive Fourier Decomposition, which has the advantage in time frequency over Fourier decomposition and without the need for a fixed window size problem such as short-time frequency transform. Studies show that AFD can fast decompose signals into positive-frequency functions with good analytical properties. In this paper we apply AFD into image decomposition and reconstruction area first time in the literature, which shows a promising result and gives the fundamental prospect for image compression
Community detection based on links and node features in social networks

© Springer International Publishing Switzerland 2015. Community detection is a significant but challenging task in the field of social network analysis. Many effective methods have been proposed to solve this problem. However, most of them are mainly based on the topological structure or node attributes. In this paper, based on SPAEM [1], we propose a joint probabilistic model to detect community which combines node attributes and topological structure. In our model, we create a novel feature-based weighted network, within which each edge weight is represented by the node feature similarity between two nodes at the end of the edge. Then we fuse the original network and the created network with a parameter and employ expectation-maximization algorithm (EM) to identify a community. Experiments on a diverse set of data, collected from Facebook and Twitter, demonstrate that our algorithm has achieved promising results compared with other algorithms