pinecone/core-2020-05-10-deduplication huggingface dataset:
Optimization of a stand - alone renewable energy system for a small load requirement

Optimization of a stand-alone Renewable Energy (RE) system involves selecting the best RE resources and components, and sizing the system accordingly to get the most efficient and cost-effective solution. Design and optimization of an RE power system to serve the lighting in a University of the South Pacific car park was carried out using HOMER software and compared to manual calculations. Resource analysis showed that on average the site received 3.8 kWh m−2 day−1 of solar energy, with 1,387 full sun hours annually. Monthly average wind speed of 3.88 m s−1 at 10 m above ground level extrapolated to 15 m (the hub height of the wind turbine) resulted in an average wind speed of 4 m s−1, with power density of 70 Wm−2. With this wind resource, a Whisper 100 wind turbine would be in operation for approximately 50 % of the time in the year. The complementary nature of solar and wind resources showed good potential for a solar-wind hybrid system. In this study three possible systems—a PV system, a wind power system, and a hybrid power system (PV-wind)—were analyzed. It was found that a hybrid system is the best and most cost-effective option, as it is able to provide reliable power whilst minimizing the need for battery storage compared to a single RE power system. The optimum system comprised 0.270 kWp PV combined with a 900 W Whisper wind turbine with total battery storage capacity of 440 Ah at 12 V. Manual calculations yielded results similar to the HOMER simulations
Situating COIN in the cloud (Invited Paper).

We start from the view that the central theme of the research at the core of coordination, organization, institutions and norms, is whether the social structures and mechanisms, that have emerged over time, can be adapted and applied to artificial societies of programs and perhaps more significantly, to mixed societies of humans and programs -and how The means by which the social constraints that guide and regulate behaviour are acquired and represented remains an open problem. If recent experiences in information retrieval and natural language processing are plausible indicators, the statistical may yet oust the logical. Technology aside, it is clear that for socio-technical systems, that integrate human and software components, we may expect the adoption of, or the illusion of observation of, and support for human social conventions. The growing migration to cloud computing of the services that make up current pervasive, social applications suggests near-term developments emerging from the same platform(s). Thus, the question considered here is, what pathways, opportunities and challenges exist for the development, wider use and validation of COIN technologies to help realize socio-technical systems that better meet human requirements. As examples of specific enabling technologies, we review current developments in resource-oriented architecture, complex event processing and stream reasoning and observe how COIN technologies might integrate with them
An agent-based simulation approach to comparative analysis of enforcement mechanisms

Incentive-based enforcement can be an effective mechanism for fostering cooperation in open distributed systems. The strength of such systems is the absence of a central controlling instance, but at the same time, they do depend upon (voluntary) regulation to achieve system goals, creating a potential "tragedy of the commons". Many different mechanisms have been proposed, both in the multi-agent systems and the social science communities, to solve the commons problem by using incentive-based enforcement. This paper advocates the use of agent-based simulation to carry out detailed comparative analysis of competing enforcementmechanisms, by providing common settings, the environment and the basis for comprehensive statistical analysis. To advance this argument, we take the case study of wireless mobile grids, a future generation mobile phone concept, to ground our experiments and analyse three different enforcement approaches: police entities, image information and a well-known existing reputation mechanism. The contribution of this paper is not the enforcement mechanisms themselves, but their comparison in a common setting through which we demonstrate by simulation and statistical analysis that enforcement can improve cooperation and that a relatively small percentage (of the population as a whole) of police agents outperforms (under the chosen metrics) image- and reputation-based approaches. Hence, qualified conclusions may be drawn for the application of such mechanisms generally in open distributed systems
Handling change in normative specifications

Normative frameworks provide a means to address the governance of open systems, offering a mechanism to express responsibilities and permissions of the individual participants with respect to the entire system without compromising their autonomy. In order to meet requirements careful design is crucial. Tools that support the design process can be of great benefit. In this paper, we describe and illustrate a methodology for elaborating normative specifications. We utilise use-cases to capture desirable and undesirable system behaviours, employ inductive logic programming to construct elaborations, in terms of revisions and extensions, of an existing (partial) normative specification and provide justifications as to why certain changes are better than others. The latter can be seen as a form of impact analysis of the possible elaborations, in terms of critical consequences that would be preserved or rejected by the changes. The main contributions of this paper is a (semi) automated process for controlling the elaboration of normative specifications and a demonstration of its effectiveness through a proof-of-concept case study
Using Event-Based Style for Developing M2M Applications

International audienceIn this paper, we introduce how to write M2M applications by using INI, a programming language specified and implemented by ourselves that supports event-based style. With event-based programming, all M2M communication can be handled and scheduled. Programmers may use existing built-in events or define their own events. We apply our approach in a real M2M gateway, which allows gathering and exchanging information between sensors and machines in the network. The results shows that our work proposes a concise and elegant alternative and complement to industrial state-of-the-art languages such as Java or C/C++
Security evaluation of Rakaposhi Stream Cipher

Rakaposhi is a synchronous stream cipher, which uses three main components: a non-linear feedback shift register (NLFSR), a dynamic linear feedback shift register (DLFSR) and a non-linear filtering function (NLF). NLFSR consists of 128 bits and is initialised by the secret key K. DLFSR holds 192 bits and is initialised by an initial vector (IV). NLF takes 8-bit inputs and returns a single output bit. The work identifies weaknesses and properties of the cipher. The main observation is that the initialisation procedure has the so-called sliding property. The property can be used to launch distinguishing and key recovery attacks. The distinguisher needs four observations of the related (K,IV) pairs. The key recovery algorithm allows to discover the secret key K after observing 29 pairs of (K,IV). Based on the proposed related-key attack, the number of related (K,IV) pairs is 2(128 + 192)/4 pairs.\ud
\ud
Further the cipher is studied when the registers enter short cycles. When NLFSR is set to all ones, then the cipher degenerates to a linear feedback shift register with a non-linear filter. Consequently, the initial state (and Secret Key and IV) can be recovered with complexity 263.87.\ud
\ud
If DLFSR is set to all zeros, then NLF reduces to a low non-linearity filter function. As the result, the cipher is insecure allowing the adversary to distinguish it from a random cipher after 217 observations of keystream bits. There is also the key recovery algorithm that allows to find the secret key with complexity 2 54
Computational Methods for the Parallel 3D Simulation of Biochemical Kinetics at the Microscopic Scale

International audienceThis work takes place in the context of biochemical kinetics simulation for the understanding of complex biological systems such as hæmostasis. The classical approach, based on the numerical solving of differential systems, cannot satisfactorily handle local geometrical constraints, such as membrane binding events. To address this problem, we propose a particle-based system in which each molecular species is represented by a three-dimensional entity which diffuses and may undergo reactions. Such a system can be computationaly intensive, since a small time step and a very large number of entities are required to get significant results. Therefore, we propose a model that is suitable for parallel computing and that can especially take advantage of recent multicore and multiprocessor architectures. We present our particle-based system, detail the behaviour of our entities, and describe our parallel computing algorithms. Comparisons between simulations and theoretical results are exposed, as well as a performance evaluation of our algorithms
Combining Machine Learning Techniques and Natural Language Processing to Infer Emotions Using Spanish Twitter Corpus

Proceedings of: 11th Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 13). Salamanca, Spain, May 22-24, 2013.In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.This work was supported in part by Projects MEyC TEC2012-37832-C02-01, MEyC TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485)Publicad
Analysing facial regions for face recognition using forensic protocols

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38061-7_22Proceedings of International Workshops of Practical Applications of Agents and Multi-Agent Systems (PAAMS), held in 2013, Salamanca (Spain).This paper focuses on the analysis of automatic facial regions extraction for face recognition applications. Traditional face recognition systems compare just full face images in order to estimate the identity, here different facial areas of face images obtained from both uncontrolled and controlled environments are extracted from a person image. In this work, we study and compare the discriminative capabilities of 15 facial regions considered in forensic practice such as full face, nose, eye, eyebrow, mouth, etc. This study is of interest to biometrics because a more robust general-purpose face recognition system can be built by fusing the similarity scores obtained from the comparison of different individual parts of the face. To analyse the discriminative power of each facial region, we have randomly defined three population subsets of 200 European subjects (male, female and mixed) from MORPH database. First facial landmarks are automatically located, checked and corrected and then 15 forensic facial regions are extracted and considered for the study. In all cases, the performance of the full face (faceISOV region) is higher than the one achieved for the rest of facial regions. It is very interesting to note that the nose region has a very significant discrimination efficiency by itself and similar to the full face performance.This work has been partially supported by contract with Spanish Guardia Civil and projects BBfor2 (FP7-ITN-238803), Bio-Challenge (TEC2009-11186), Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and "Cátedra UAM-Telefónica"
Analysis of gait recognition on constrained scenarios with limited data information

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38061-7_23Proceedings of International Workshops of  Practical Applications of Agents and Multi-Agent Systems (PAAMS), held in 2013, Salamanca (Spain).This paper is focused on the assessment of gait recognition on a constrained scenario, where limited information can be extracted from the gait image sequences. In particular we are interested in assessing the performance of gait images when only the lower part of the body is acquired by the camera and just half of a gait cycle is available (SFootBD database). Thus, various state-of-the-art feature approaches have been followed and applied to the data. Results show that good recognition performance can be achieved using such limited data information for gait biometric. A comparative analysis of the influence of the quantity of data used in the training models has been carried out obtaining results of 8.6% EER for the case of using 10 data samples to train the models, and 5.7% of EER for the case of using 40 data for training. Also, a comparison with a standard and ideal gait database (USF database) is also carried out using similar experimental protocols. In this case 10 data samples are used for training achieving results of 3.6% EER. The comparison with a standard database shows that different feature approaches perform differently for each database, achieving best individual results with MPCA and EGEI methods for the SFootBD and the USF databases respectivelyThis work has been supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186), Bio-Shield (TEC2012-34881) and "Cátedra UAM-Telefonica". Rubén Vera-Rodríguez is supported by a Juan de la Cierva Fellowship from the Spanish MINECO
Extracting behavioural patterns from a negotiation game

The work presented focuses not only on the behavioural patterns that
influence the outcome of a negotiation, but also on the discovery of ways to predict
the type of conflict used in the process and the stress levels of the actors. After
setting up an experimental intelligent environment provided with sensors to capture
behavioural and contextual information, a set of relevant data was collected
and analysed, with the underlying objective of using the behavioural patterns (obtained
by statistical/probabilistic methods) as a basis to design and present plans
and suggestions to the associated participants. In sooth, these proposals may influence
in a positive way the course and outcome of a negotiation task in many
aspects. This work highlights the importance of knowledge in negotiation, as in
other social forms of interaction, providing also some new insights for informed
decision support in situations in which uncertainty and conflict may be present
Towards a framework for designing full model selection and optimization systems

People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem of how to choose a combination of data processing tools and algorithms for a given dataset. This problem is usually termed the Full Model Selection (FMS) problem. Extended from our previous work [10], in this paper, we introduce a framework for designing FMS algorithms. Under this framework, we propose a novel algorithm combining both genetic algorithms (GA) and particle swarm optimization (PSO) named GPS (which stands for GA-PSO-FMS), in which a GA is used for searching the optimal structure for a data mining solution, and PSO is used for searching optimal parameters for a particular structure instance. Given a classification dataset, GPS outputs a FMS solution as a directed acyclic graph consisting of diverse data mining operators that are available to the problem. Experimental results demonstrate the benefit of the algorithm. We also present, with detailed analysis, two model-tree-based variants for speeding up the GPS algorithm
Feature Level Multiple Model Fusion Using Multilinear Subspace Analysis with Incomplete Training Set and Its Application to Face Image Analysis

In practical applications of pattern recognition and computer vision, the performance of many approaches can be improved by using multiple models. In this paper, we develop a common theoretical framework for multiple model fusion at the feature level using multilinear subspace analysis (also known as tensor algebra). One disadvantage of the multilinear approach is that it is hard to obtain enough training observations for tensor decomposition algorithms. To overcome this difficulty, we adopted the M$^2$SA algorithm to reconstruct the missing entries of the incomplete training tensor. Furthermore, we apply the proposed framework to the problem of face image analysis using Active Appearance Model (AAM) to validate its performance. Evaluations of AAM using the proposed framework are conducted on Multi-PIE face database with promising results
Dynamic Filtering of Useless Data in an Adaptive Multi-Agent System : Evaluation in the Ambient Domain

Amadeus is an Adaptive Multi-Agent System whose goal is to observe and to learn users’ behaviour in an ambient system in order to perform their recurrent actions on their behalf. Considering the large number of devices (data sources) that generally compose ambient systems, performing an efficient learning in such a domain requires filtering useless data. This paper focuses on an extended version of Amadeus taking account this requirement and proposes a solution based on cooperative interactions between the different agents composing Amadeus. An evaluation of the performances of our system as well as the encouraging obtained results are then shown
GAMA : A Spatially Explicit, Multi-level, Agent-Based Modeling and Simulation Platform

Agent-based modeling is now widely used to investigate complex systems but still lacks integrated and generic tools to support the representation of features usually associated with real complex systems, namely rich, dynamic and realistic environments or multiple levels of agency. The GAMA platform has been developed to address such issues and allow modelers, thanks to the use of a high-level modeling language, to build, couple and reuse complex models combining various agent architectures, environment representations and levels of abstraction
A cognitive management framework for empowering the internet of things

This work presents a Cognitive Management framework for empowering the Internet of Things (IoT). This framework has the ability to dynamically adapt its behaviour, through self-management functionality, taking into account information and knowledge (obtained through machine learning) on the situation (e.g., internal status and status of environment), as well as policies (designating objectives, constraints, rules, etc.). Cognitive technologies constitute a unique and efficient approach for addressing the technological heterogeneity of the IoT and obtaining situation awareness, reliability and efficiency. The paper also presents a first indicative implementation of the proposed framework, comprising real sensors and actuators. The preliminary results of this work demonstrate high potential towards self-reconfigurable IoT. © The Author(s)
Freshness and Reactivity Analysis in Globally Asynchronous Locally Time-Triggered Systems

Critical embedded systems are often designed as a set of real-time tasks, running on shared computing modules, and communicating through networks. Because of their critical nature, such systems have to meet timing properties. To help the designers to prove the correctness of their system, the real-time systems community has developed numerous approaches for analyzing the worst case times either on the processors (e.g. worst case execution time of a task) or on the networks (e.g. worst case traversal time of a message). However, there is a growing need to consider the complete system and to be able to determine end-to-end properties. Such properties apply to a functional chain which describes the behavior of a sequence of functions, not necessarily hosted on a shared module, from an input until the production of an output. This paper explores two end-to-end properties: freshness and reactivity, and presents an analysis method based on Mixed Integer Linear Programming (MILP). This work is supported by the French National Research Agency within the Satrimmap project
Seismology of the Sun : Inference of Thermal, Dynamic and Magnetic Field
  Structures of the Interior

Recent overwhelming evidences show that the sun strongly influences the
Earth's climate and environment. Moreover existence of life on this Earth
mainly depends upon the sun's energy. Hence, understanding of physics of the
sun, especially the thermal, dynamic and magnetic field structures of its
interior, is very important. Recently, from the ground and space based
observations, it is discovered that sun oscillates near 5 min periodicity in
millions of modes. This discovery heralded a new era in solar physics and a
separate branch called helioseismology or seismology of the sun has started.
Before the advent of helioseismology, sun's thermal structure of the interior
was understood from the evolutionary solution of stellar structure equations
that mimicked the present age, mass and radius of the sun. Whereas solution of
MHD equations yielded internal dynamics and magnetic field structure of the
sun's interior. In this presentation, I review the thermal, dynamic and
magnetic field structures of the sun's interior as inferred by the
helioseismology.Comment: To be published in the proceedings of the meeting "3rd International
  Conference on Current Developments in Atomic, Molecular, Optical and Nano
  Physics with Applications", December 14-16, 2011, New Delhi, Indi
MapIt : a model based pattern recovery tool

Design patterns provide a means to reuse proven solutions during development, but also to identify good practices during analysis. These are particularly relevant in complex and critical software, such as is the case of ubiquitous and pervasive systems. Model Driven Engineering (MDE) presents a solution for this problem, with the usage of high level models. As part of an effort to develop approaches to the migration of applications to mobile contexts, this paper reports on a tool that identifies design patterns in source code. Code is transformed into both platform specific and independent models, and from these design patterns are inferred. MapIt, the tool which implements these functionalities is described.This work was partly funded by ERDF -- European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT -- Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-015095
Guidelines for modelling reactive systems with coloured Petri nets

This paper focus on the modelling of reactive systems, more
particularly, control systems. A set of guidelines is proposed in order
to build models that support analysis, simulation and prototyping. The
guidelines are split in two parts; the analysis of a problem is addressed
first, followed by the design with Coloured Petri Nets (CPNs). A smart
library example is used as case study. The models developed under this
approach turn out to be modular, parameterisable, configurable and
executable.FC