pinecone/core-2020-05-10-deduplication huggingface dataset:
An Energy-aware Hybrid Particle Swarm Optimization Algorithm for Spiking Neural Network Mapping

Recent approaches to improving the scalability of Spiking\ud
Neural Networks (SNNs) have looked to use custom architectures to im-\ud
plement and interconnect the neurons in the hardware. The Networks-\ud
on-Chip (NoC) interconnection strategy has been used for the hardware\ud
SNNs and has achieved a good performance. However, the mapping be-\ud
tween a SNN and the NoC system becomes one of the most urgent chal-\ud
lenges. In this paper, an energy-aware hybrid Particle Swarm Optimiza-\ud
tion (PSO) algorithm for SNN mapping is proposed, which combines the\ud
basic PSO and Genetic Algorithm (GA). A Star-Subnet-Based-2D Mesh\ud
(2D-SSBM) NoC system is used for the testing. Results show that the\ud
proposed hybrid PSO algorithm can avoid the premature convergence\ud
to local optimum, and effectively reduce the energy consumption of the\ud
hardware NoC systems
An Efficient Hardware Architecture for Multilayer Spiking Neural Networks

Spiking Neural Network (SNN) is the most recent computa\ud
tional model that can emulate the behaviors of biological neuron system.\ud
This paper highlights and discusses an efficient hardware architecture for\ud
the hardware SNNs, which includes a layer-level tile architecture (LTA)\ud
for the neurons and synapses, and a novel routing architecture (NRA)\ud
for the interconnections between the neuron nodes. In addition, a visu\ud
alization performance monitoring platform is designed, which is used as\ud
functional verification and performance monitoring for the SNN hard\ud
ware system. Experimental results demonstrate that the proposed archi\ud
tecture is feasible and capable of scaling to large hardware multilayer\ud
SNNs
Deep Neural Network with l2-norm Unit for Brain Lesions Detection

Automated brain lesions detection is an important and very challenging
clinical diagnostic task because the lesions have different sizes, shapes,
contrasts, and locations. Deep Learning recently has shown promising progress
in many application fields, which motivates us to apply this technology for
such important problem. In this paper, we propose a novel and end-to-end
trainable approach for brain lesions classification and detection by using deep
Convolutional Neural Network (CNN). In order to investigate the applicability,
we applied our approach on several brain diseases including high and low-grade
glioma tumor, ischemic stroke, Alzheimer diseases, by which the brain Magnetic
Resonance Images (MRI) have been applied as an input for the analysis. We
proposed a new operating unit which receives features from several projections
of a subset units of the bottom layer and computes a normalized l2-norm for
next layer. We evaluated the proposed approach on two different CNN
architectures and number of popular benchmark datasets. The experimental
results demonstrate the superior ability of the proposed approach.Comment: Accepted for presentation in ICONIP-201
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative
  Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to
their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN
that uses unsupervised learning to extract discriminative features from speech
signals, which can subsequently be used in a classifier. The architecture
consists of a spiking convolutional/pooling layer followed by a fully connected
spiking layer for feature discovery. The convolutional layer of leaky,
integrate-and-fire (LIF) neurons represents primary acoustic features. The
fully connected layer is equipped with a probabilistic spike-timing-dependent
plasticity learning rule. This layer represents the discriminative features
through probabilistic, LIF neurons. To assess the discriminative power of the
learned features, they are used in a hidden Markov model (HMM) for spoken digit
recognition. The experimental results show performance above 96% that compares
favorably with popular statistical feature extraction methods. Our results
provide a novel demonstration of unsupervised feature acquisition in an SNN
Automated Planning Techniques for Robot Manipulation Tasks Involving Articulated Objects

The goal-oriented manipulation of articulated objects plays an important role in real-world robot tasks. Current approaches typically pose a number of simplifying assumptions to reason upon how to obtain an articulated object’s goal configuration, and exploit ad hoc algorithms. The consequence is two-fold: firstly, it is difficult to generalise obtained solutions (in terms of actions a robot can execute) to different target object’s configurations and, in a broad sense, to different object’s physical characteristics; secondly, the representation and the reasoning layers are tightly coupled and inter-dependent. \ud
\ud
In this paper we investigate the use of automated planning techniques for dealing with articulated objects manipulation tasks. Such techniques allow for a clear separation between knowledge and reasoning, as advocated in Knowledge Engineering. We introduce two PDDL formulations of the task, which rely on conceptually different representations of the orientation of the objects. Experiments involving several planners and increasing size objects demonstrate the effectiveness of the proposed models, and confirm its exploitability when embedded in a real-world robot software architecture
Attentive Models in Vision: Computing Saliency Maps in the Deep Learning Era

Estimating the focus of attention of a person looking at an image or a video is a crucial step which can enhance many vision-based inference mechanisms: image segmentation and annotation, video captioning, autonomous driving are some examples. The early stages of the attentive behavior are typically bottom-up; reproducing the same mechanism means to find the saliency embodied in the images, i.e. which parts of an image pop out of a visual scene. This process has been studied for decades in neuroscience and in terms of computational models for reproducing the human cortical process. \ud
In the last few years, early models have been replaced by deep learning architectures, that outperform any early approach compared against public datasets. \ud
In this paper, we propose a discussion on why convolutional neural networks (CNNs) are so accurate in saliency prediction. We present our DL architectures which combine both bottom-up cues and higher-level semantics, and incorporate the concept of time in the attentional process through LSTM recurrent architectures. Eventually, we present a video-specific architecture based on the C3D network, which can extracts spatio-temporal features by means of 3D convolutions to model task-driven attentive behaviors. The merit of this work is to show how these deep networks are not mere brute-force methods tuned on massive amount of data, but represent well-defined architectures which recall very closely the early saliency models, although improved with the semantics learned by human ground-thuth
Use of mobile apps for teaching and research - implications for digital literacy

This paper reports on the results of an online survey about mobile application (app) use for academic purposes, i.e. teaching and research, by Higher Degree Research (HDR) students and academic staff at one of the eight New Zealand universities. Two thirds of the 138 respondents reported they used apps for academic purposes. In teaching, apps were reported to be used as a means to push information to students. In research, apps appeared to be used to self-organise, collaborate with colleagues, store information, and to stay current with research. This paper presents the survey results and discusses implications for personal information management in education context and opportunities for university library services
The practice of enterprise modeling : 10th IFIP WG 8.1. Working Conference, PoEM 2017, Leuven, Belgium, November 22-24, 2017, Proceedings

This volume constitutes the proceedings of the 10th IFIP WG 8.1 Conference on the Practice of Enterprise Modeling held in November 2017 in Leuven, Belgium. The conference was created by the International Federation for Information Processing (IFIP) Working Group 8.1 to offer a forum for knowledge transfer and experience sharing between the academic and practitioner communities.
The 20 full papers and 4 short papers accepted were carefully reviewed and selected from 70 submissions. They include research results, practitioner/experience reports and work-in-progress papers and were presented in 8 sessions covering diverse topics related to enterprise modelling and its application in practice
Renewing terraces and drystone walls of Algarvian Barrocal. Cultural and touristic values

This paper aims to enhance the functions of terraces and drystone walls as structural elements and distinctive factors of the traditional rural zone known as the barrocal of the Algarve, south of Portugal. In fact, such elements define the character of the landscape that forms the background of a traditional touristic region along the coast. The core of this paper will be the knowledge of construc-tion processes, spatial distribution, and hydrological, ecological, economic and social functions, once it is quite necessary for the Algarve region. Some projects and studies have been developed in the Mediterranean context, but there is not enough research on this subject in the south of Portugal. Secondly, the social meaning, or acknowledgement of the landscape characterized by drystone wall structures, will be the key for finding real possibilities of renewing the terraces. Therefore, the landscape will be assumed as a common good. In this subject, we take into account the potential role and sensibility of tourists and resident popu-lation. Beyond static patrimonial statutes, we can design some practical possibil-ities to enhance plastic features and new uses leading to an acknowledgement of the Algarvian drystone wall structure, referred as a part of Mediterranean coastal landscapes. Some examples that can add cultural and touristic value could be: recreation areas for urbanites, new cooperative forms of agriculture, hobby farm-ing, workshops about harvesting traditional products, circuits and pedestrian paths, and interchange with similar Mediterranean regions defining broader cir-cuits.info:eu-repo/semantics/publishedVersio
An empirical analysis of smart contracts: platforms, applications, and
  design patterns

Smart contracts are computer programs that can be consistently executed by a
network of mutually distrusting nodes, without the arbitration of a trusted
authority. Because of their resilience to tampering, smart contracts are
appealing in many scenarios, especially in those which require transfers of
money to respect certain agreed rules (like in financial services and in
games). Over the last few years many platforms for smart contracts have been
proposed, and some of them have been actually implemented and used. We study
how the notion of smart contract is interpreted in some of these platforms.
Focussing on the two most widespread ones, Bitcoin and Ethereum, we quantify
the usage of smart contracts in relation to their application domain. We also
analyse the most common programming patterns in Ethereum, where the source code
of smart contracts is available.Comment: WTSC 201
Partial spreads and vector space partitions

Constant-dimension codes with the maximum possible minimum distance have been
studied under the name of partial spreads in Finite Geometry for several
decades. Not surprisingly, for this subclass typically the sharpest bounds on
the maximal code size are known. The seminal works of Beutelspacher and Drake
\& Freeman on partial spreads date back to 1975, and 1979, respectively. From
then until recently, there was almost no progress besides some computer-based
constructions and classifications. It turns out that vector space partitions
provide the appropriate theoretical framework and can be used to improve the
long-standing bounds in quite a few cases. Here, we provide a historic account
on partial spreads and an interpretation of the classical results from a modern
perspective. To this end, we introduce all required methods from the theory of
vector space partitions and Finite Geometry in a tutorial style. We guide the
reader to the current frontiers of research in that field, including a detailed
description of the recent improvements.Comment: 30 pages, 1 tabl
An overview and comparative analysis of Recurrent Neural Networks for
  Short Term Load Forecasting

The key component in forecasting demand and consumption of resources in a
supply network is an accurate prediction of real-valued time series. Indeed,
both service interruptions and resource waste can be reduced with the
implementation of an effective forecasting system. Significant research has
thus been devoted to the design and development of methodologies for short term
load forecasting over the past decades. A class of mathematical models, called
Recurrent Neural Networks, are nowadays gaining renewed interest among
researchers and they are replacing many practical implementation of the
forecasting systems, previously based on static methods. Despite the undeniable
expressive power of these architectures, their recurrent nature complicates
their understanding and poses challenges in the training procedures. Recently,
new important families of recurrent architectures have emerged and their
applicability in the context of load forecasting has not been investigated
completely yet. In this paper we perform a comparative study on the problem of
Short-Term Load Forecast, by using different classes of state-of-the-art
Recurrent Neural Networks. We test the reviewed models first on controlled
synthetic tasks and then on different real datasets, covering important
practical cases of study. We provide a general overview of the most important
architectures and we define guidelines for configuring the recurrent networks
to predict real-valued time series.Comment: Springer Briefs in Computer Science (ISBN 978-3-319-70338-1), 201
A Supervisory Control Algorithm Based on Property-Directed Reachability

We present an algorithm for synthesising a controller (supervisor) for a
discrete event system (DES) based on the property-directed reachability (PDR)
model checking algorithm. The discrete event systems framework is useful in
both software, automation and manufacturing, as problems from those domains can
be modelled as discrete supervisory control problems. As a formal framework,
DES is also similar to domains for which the field of formal methods for
computer science has developed techniques and tools. In this paper, we attempt
to marry the two by adapting PDR to the problem of controller synthesis. The
resulting algorithm takes as input a transition system with forbidden states
and uncontrollable transitions, and synthesises a safe and
minimally-restrictive controller, correct-by-design. We also present an
implementation along with experimental results, showing that the algorithm has
potential as a part of the solution to the greater effort of formal supervisory
controller synthesis and verification.Comment: 16 pages; presented at Haifa Verification Conference 2017, the final
  publication is available at Springer via
  https://doi.org/10.1007/978-3-319-70389-3_
Joining Jolie to Docker - Orchestration of Microservices on a
  Containers-as-a-Service Layer

Cloud computing is steadily growing and, as IaaS vendors have started to
offer pay-as-you-go billing policies, it is fundamental to achieve as much
elasticity as possible, avoiding over-provisioning that would imply higher
costs. In this paper, we briefly analyse the orchestration characteristics of
PaaSSOA, a proposed architecture already implemented for Jolie microservices,
and Kubernetes, one of the various orchestration plugins for Docker; then, we
outline similarities and differences of the two approaches, with respect to
their own domain of application. Furthermore, we investigate some ideas to
achieve a federation of the two technologies, proposing an architectural
composition of Jolie microservices on Docker Container-as-a-Service layer.Comment: 9 pages, 3 figure
AntibIoTic: Protecting IoT Devices Against DDoS Attacks

The 2016 is remembered as the year that showed to the world how dangerous
Distributed Denial of Service attacks can be. Gauge of the disruptiveness of
DDoS attacks is the number of bots involved: the bigger the botnet, the more
powerful the attack. This character, along with the increasing availability of
connected and insecure IoT devices, makes DDoS and IoT the perfect pair for the
malware industry. In this paper we present the main idea behind AntibIoTic, a
palliative solution to prevent DDoS attacks perpetrated through IoT devices
Towards an ontology for strategic decision making: The case of quality in rapid software development projects

Strategic decision making is the process of selecting a logical and informed choice from the alternative options based on key strategic indicators determining the success of a specific organization strategy. To support this process and provide a common underlying language, in this work, we present an empirically-grounded ontology to support different strategic decision-making processes and extend the ontology to cover the context of managing quality in Rapid Software Development projects. We illustrate the complete ontology with an example.Peer ReviewedPostprint (author's final draft
Queering the Virgin/Whore Binary: The Virgin Mary, the Whore of Babylon, and Sexual Violence

In this chapter, I seek to problematize the virgin/whore binary which, I argue, has enabled, normalized, and sustained violence against women. This binary positions women into two opposing categories: virgins or “good” women are those who express their sexuality only within culturally sanctioned and patriarchally defined boundaries such as marriage; all other women who fail to conform to this ideal are considered whores—morally corrupt and dangerously concupiscent “bad” women (Gottschall et  al. 2006). Previous studies have shown how this binary has been deployed to justify and perpetuate sexual violence against those who are labelled as whores or “sluts” (Asencio 1999; García 2006). The logic is that sexual violence against these women is deemed “acceptable” or “makes sense” because they have transgressed cultural norms around women’s purity and chastity. By being “promiscuous,” they are “asking for trouble”; it is therefore their own fault if they are sexually assaulted
Quantum Fully Homomorphic Encryption With Verification

Fully-homomorphic encryption (FHE) enables computation on encrypted data
while maintaining secrecy. Recent research has shown that such schemes exist
even for quantum computation. Given the numerous applications of classical FHE
(zero-knowledge proofs, secure two-party computation, obfuscation, etc.) it is
reasonable to hope that quantum FHE (or QFHE) will lead to many new results in
the quantum setting. However, a crucial ingredient in almost all applications
of FHE is circuit verification. Classically, verification is performed by
checking a transcript of the homomorphic computation. Quantumly, this strategy
is impossible due to no-cloning. This leads to an important open question: can
quantum computations be delegated and verified in a non-interactive manner? In
this work, we answer this question in the affirmative, by constructing a scheme
for QFHE with verification (vQFHE). Our scheme provides authenticated
encryption, and enables arbitrary polynomial-time quantum computations without
the need of interaction between client and server. Verification is almost
entirely classical; for computations that start and end with classical states,
it is completely classical. As a first application, we show how to construct
quantum one-time programs from classical one-time programs and vQFHE.Comment: 30 page
“God and Tonga Are My Inheritance!”: climate change impact on perceived spirituality, adaptation and lessons learnt from Kanokupolu, ‘Ahau, Tukutonga, Popua and Manuka in Tongatapu, Tonga

Climate change is a significant threat to health and well-being, in particular to people’s spiritual well-being, in coastal areas and communities. This paper describes the findings of a 2013 study on impacts of climate change on people’s spiritual well-being in 5 coastal communities in Tongatapu, Tonga: Kanokupolu, ‘Ahau, Tukutonga, Popua and Manuka, using a concurrent convergence parallel triangulation design. Information was collected from a group of 460 participants aged 15–75 via self-administered questionnaire, in-depth interview (IDI), focus group discussions (FGD) and key information interviews (KII). There was a statistically significant difference between people whose spirituality was affected and those unaffected (p < 0.005). People who were worried and whose physical well-being were affected, emerged as the most strongly affected (χ2(4) = 15.780, p < 0.005). The same factor was explored qualitatively using thematic analytical strategy, and concluded that climate change had affected people’s spiritual well-being, negatively. Lessons learnt from this paper will be useful for people invested in policy practice, and pastorate serving the cause of holistic Climate Change Adaptation (CCA) in Tonga: preaching, fasting and praying to God to reverse climate change and its impacts, help State and Church to adapt more effectively and comprehensively
Convex regularization of discrete-valued inverse problems

This work is concerned with linear inverse problems where a distributed
parameter is known a priori to only take on values from a given discrete set.
This property can be promoted in Tikhonov regularization with the aid of a
suitable convex but nondifferentiable regularization term. This allows applying
standard approaches to show well-posedness and convergence rates in Bregman
distance. Using the specific properties of the regularization term, it can be
shown that convergence (albeit without rates) actually holds pointwise.
Furthermore, the resulting Tikhonov functional can be minimized efficiently
using a semi-smooth Newton method. Numerical examples illustrate the properties
of the regularization term and the numerical solution
An SVD in Spherical Surface Wave Tomography

In spherical surface wave tomography, one measures the integrals of a
function defined on the sphere along great circle arcs. This forms a
generalization of the Funk--Radon transform, which assigns to a function its
integrals along full great circles. We show a singular value decomposition
(SVD) for the surface wave tomography provided we have full data. Since the
inversion problem is overdetermined, we consider some special cases in which we
only know the integrals along certain arcs. For the case of great circle arcs
with fixed opening angle, we also obtain an SVD that implies the injectivity,
generalizing a previous result for half circles in [Groemer, On a spherical
integral transform and sections of star bodies, Monatsh. Math.,
126(2):117--124, 1998]. Furthermore, we derive a numerical algorithm based on
the SVD and illustrate its merchantability by numerical tests