pinecone/core-2020-05-10-deduplication huggingface dataset:
Adaptation of an Evaluation System for e-Health Environments

Proceedings of: 14th International Conference, KES 2010, Cardiff, UK, September 8-10, 2010The increase in ageing of European population implies a high cost in economy and society in any European country and it can be reduced if we pay attention and develop home care systems. Evaluation of these systems is a critical and challenging issue but seldom tackled. It is important before evaluating a system to figure out what is the evaluation goal. In our case, such a goal is to evaluate enhanced user experience and beyond the evaluation goal it is also a central concern about what to evaluate. In this paper we propose a multi-agent home care system where we describe how agents coordinate their decisions to provide e-services to patients when at home after hospitalization. Finally we center our proposal on the adaptation of an evaluation system, previously developed, to support the challenges of an e-Health environment and also the multi-user evaluation. These evaluation methods (online/offline) will provide user's (patients, patient's relatives and healthcare professionals) feedback into the system.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/
TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/
TIC-1485) and DPS2008-07029-C02-02.Publicad
The MINWii Project : Renarcissization of Patients Suffering from Alzheimer's Disease Through Video Game-Based Music Therapy

International audienceMINWii, a new serious video game targeting Alzheimer and demented patients, is a simple Music Therapy tool usable by untrained care givers. Its objective is to improve patients' self-image (renarcissization) to reduce behavioral symptoms, which are an important cause of institutionalization. With MINWii, elderly gamers use Wiimotes to improvise or play prede ned songs on a virtual keyboard. We detail our design process, which addresses the speci c features of dementia: this iterative re nement scheme, built upon qualitative, small scale experiments in a therapeutic environment, led to a shift of MINWii's original focus from creativity to reminiscence. A large majority of our patients, with mild to moderate dementia, expressed a strong interest in our system, which was con rmed by feedback from the care givers. A fully controlled usability study of MINWii is currently under way, which should lead to future research assessing its actual therapeutic impact
Positional Communication and Private Information in Honeybee Foraging Models

Honeybees coordinate foraging efforts across vast areas through a complex system of advertising and recruitment. One mechanism for coordination is the waggle dance, a movement pattern which carries positional information about food sources. However, recent evidence suggests that recruited foragers may not use the dance’s positional information to the degree that has traditionally been believed. We model bee colony foraging to investigate the value of sharing food source position information in different environments. We find that in several environments, relying solely on private information about previously encountered food sources is more efficient than sharing information. Relying on private information leads to a greater diversity of forage sites and can decrease over-harvesting of sources. This is beneficial in environments with small quantities of nectar per flower, but may be detrimental in nectar-rich environments. Efficiency depends on both the environment and a balance between exploiting high-quality food sources and oversubscribing them.Engineering and Applied Science
Analysis of Computer Science Communities Based on DBLP

It is popular nowadays to bring techniques from bibliometrics and
scientometrics into the world of digital libraries to analyze the collaboration
patterns and explore mechanisms which underlie community development. In this
paper we use the DBLP data to investigate the author's scientific career and
provide an in-depth exploration of some of the computer science communities. We
compare them in terms of productivity, population stability and collaboration
trends.Besides we use these features to compare the sets of topranked
conferences with their lower ranked counterparts.Comment: 9 pages, 7 figures, 6 table
The role and future challenges of wireless communication networks for cooperative autonomous city vehicles

This paper elaborates on the use of future wireless communication networks for autonomous city vehicles. After addressing the state of technology, the paper explains the autonomous vehicle control system architecture and the Cybercars-2 communication framework; it presents experimental tests of communication-based real-time decision making; and discusses potential applications for communication in order to improve the localization and perception abilities of autonomous vehicles in urban environments
Money laundering and FATF compliance by the international community

This paper examines the anti-money laundering systems of Australia, the United Arab Emirates (UAE), the United Kingdom (UK) and the United States of America (USA), the extent to which they have implemented the Financial Action Task Force (FATF) recommendations, and how compliance with these recommendations is affected by local cultural and economic factors. The paper makes use of FATF evaluation reports to compare the countries’ compliance; it examines some of the underlying cultural considerations and culture-specific ethical issues that affect the extent of compliance, and how cultural and ethical considerations may affect good governance. The findings indicate that the UK and the USA are the most advanced with regards to their compliance with the FATF recommendations and Australia and the UAE less so. The UAE is in particular found to be least compliant. We relate this finding to previous work on how a country’s legal and financial systems develop in line with its religion, culture and socio-economic situation, and examine how such local factors have affected the UAE’s financial and anti-money laundering and combating the financing of terrorism (AML/CFT) systems. This research will be of interest to policy-makers and government agencies involved in addressing money laundering and its successful detection and prosecution
The applicability of context-based multicast: a shopping centre scenario

This paper analyzes the applicability of context-based multicast content distribution (CBMCD) on the example of realistic push- and videobased mobile advertising services at a shopping centre. The technical results of the simulation of the service scenario show that CBMCD significantly reduces the number of unicast streams and the total volume of traffic in the network. The results of the financial analysis show that these technical benefits can be translated into considerable financial benefits due to costs savings. Taken together, these results suggest that CBMCD can be an efficient, cost-saving network traffic management approach and the basis for lucrative push services
Kamouflage : loss-resistant password management

We introduce Kamouflage: a new architecture for building theft-resistant password managers. An attacker who steals a laptop or cell phone with a Kamouflage-based password manager is forced to carry out a considerable amount of online work before obtaining any user credentials. We implemented our proposal as a replacement for the built-in Firefox password manager, and provide performance measurements and the results from experiments with large real-world password sets to evaluate the feasibility and effectiveness of our approach. Kamouflage is well suited to become a standard architecture for password managers on mobile devices
Pretty good democracy for more expressive voting schemes

In this paper we revisit Pretty Good Democracy, a scheme for verifiable Internet voting from untrusted client machines. The original scheme was designed for first-past-the-post elections. Here, we show how Pretty Good Democracy can be extended to voting schemes in which the voter lists the candidates in their order of preference. Our scheme applies to elections using STV, IRV, Borda, or any other tallying scheme in which a vote is a list of candidates in preference order. We also describe an extension to cover Approval or Range voting
Defining Leadership as Process Reference Model: Translating Organizational Goals into Practice Using a Structured Leadership Approach

Effective leadership in organisations is important to the achievement of organizational objectives. Yet leadership is widely seen as a quality that individuals innately possess, and which cannot be learned. This paper makes two assertions; (a) that leadership is a skill that not only can be learned, but which can be formalized into a Process Reference Model that is intelligible to practitioners and be understood from an Enterprise Architecture perspective, and (b) that Process Reference Models in the strict sense can be redefined to include a new category of PRM called provisionally a Reference Model of Organisational Behavior, a new category of PRM which focuses on organisational behavior in pursuits of goals.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Live and Trustworthy Forensic Analysis of Commodity Production Systems

We present HyperSleuth, a framework that leverages the virtualization extensions provided by commodity hardware to securely perform live forensic analysis of potentially compromised production systems. HyperSleuth provides a trusted execution environment that guarantees four fundamental properties. First, an attacker controlling the system cannot interfere with the analysis and cannot tamper the results. Second, the framework can be installed as the system runs, without a   reboot and without loosing any volatile data. Third, the analysis performed is completely transparent to the OS and to an attacker. Finally, the analysis can be periodically and safely interrupted to resume normal execution of the system. On top of HyperSleuth we implemented three forensic analysis applications: a lazy physical memory dumper, a lie detector, and a system call tracer. The experimental evaluation we conducted demonstrated that even time consuming analysis, such as the dump of the content of the physical memory, can be securely performed without interrupting the services oered by the system
Towards Automatic Deduction and Event Reconstruction Using Forensic Lucid and Probabilities to Encode the IDS Evidence

We apply the theoretical framework and formal model of the observation tuple with the credibility weight for forensic analysis of the IDS data and the corresponding event reconstruction. Forensic Lucid - a forensic case modeling and specification language is used for the task. In the ongoing theoretical and practicalwork, Forensic Lucid is augmented with the Dempster-Shafer theory of mathematical evidence to include the credibility factors of the evidential IDS observations. Forensic Lucid's toolset is practically being implemented within the General Intensional Programming System (GIPSY) and the probabilisticmodel-checking tool PRISM as a backend to compile the Forensic Lucid model into the PRISM’s code and model-check it. This work may also help with further generalization of the testing methodology of IDSs
Compact Video Description for Copy Detection with Precise Temporal Alignment

International audienceThis paper introduces a very compact yet discriminative video description, which allows example-based search in a large number of frames corresponding to thousands of hours of video. Our description extracts one descriptor per indexed video frame by aggregating a set of local descriptors. These frame descriptors are encoded using a time-aware hierarchical indexing structure. A modified temporal Hough voting scheme is used to rank the retrieved database videos and estimate segments in them that match the query. If we use a dense temporal description of the videos, matched video segments are localized with excellent precision. Experimental results on the Trecvid 2008 copy detection task and a set of 38000 videos from YouTube show that our method offers an excellent trade-off between search accuracy, efficiency and memory usage
Object of Interest Detection by Saliency Learning

In this paper, we present a method for object of interest detection. This method is statistical in nature and hinges in a model which combines salient features using a mixture of linear support vector machines. It exploits a divide-and-conquer strategy by partitioning the feature space into sub-regions of linearly separable data-points. This yields a structured learning approach where we learn a linear support vector machine for each region, the mixture weights, and the combination parameters for each of the salient features at hand. Thus, the method learns the combination of salient features such that a mixture of classifiers can be used to recover objects of interest in the image. We illustrate the utility of the method by applying our algorithm to the MSRA Salient Object Database.Full Tex
Efficient Structure from Motion by Graph Optimization

We present an efficient structure from motion algorithm that can deal with large image collections in a fraction of time and effort of previous approaches while providing comparable quality of the scene and camera reconstruction. First, we employ fast image indexing using large image vocabularies to measure visual overlap of images without running actual image matching. Then, we select a small subset from the set of input images by computing its approximate minimal connected dominating set by a fast polynomial algorithm. Finally, we use task prioritization to avoid spending too much time in a few difficult matching problems instead of exploring other easier options. Thus we avoid wasting time on image pairs with low chance of success and avoid matching of highly redundant images of landmarks. We present results for several challenging sets of thousands of perspective as well as omnidirectional images
A high-quality video denoising algorithm based on reliable motion estimation

Although the recent advances in the sparse representations of images have achieved outstanding denosing results, removing real, structured noise in digital videos remains a challenging problem. We show the utility of reliable motion estimation to establish temporal correspondence across frames in order to achieve high-quality video denoising. In this paper, we propose an adaptive video denosing framework that integrates robust optical flow into a non-local means (NLM) framework with noise level estimation. The spatial regularization in optical flow is the key to ensure temporal coherence in removing structured noise. Furthermore, we introduce approximate K-nearest neighbor matching to significantly reduce the complexity of classical NLM methods. Experimental results show that our system is comparable with the state of the art in removing AWGN, and significantly outperforms the state of the art in removing real, structured noise.11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part II
Finding semantic structures in image hierarchies using Laplacian graph energy

Many segmentation algorithms describe images in terms of a hierarchy of regions. Although such hierarchies can produce state of the art segmentations and have many applications, they often contain more data than is required for an efficient description. This paper shows Laplacian graph energy is a generic measure that can be used to identify semantic structures within hierarchies, independently of the algorithm that produces them. Quantitative experimental validation using hierarchies from two state of art algorithms show we can reduce the number of levels and regions in a hierarchy by an order of magnitude with little or no loss in performance when compared against human produced ground truth. We provide a tracking application that illustrates the value of reduced hierarchies
Probabilistic approaches for modeling text structure and their application to text-to-text generation

Since the early days of generation research, it has been acknowledged that modeling the global structure of a document is crucial for producing coherent, readable output. However, traditional knowledge-intensive approaches have been of limited utility in addressing this problem since they cannot be effectively scaled to operate in domain-independent, large-scale applications. Due to this difficulty, existing text-to-text generation systems rarely rely on such structural information when producing an output text. Consequently, texts generated by these methods do not match the quality of those written by humans – they are often fraught with severe coherence violations and disfluencies.
In this chapter, I will present probabilistic models of document structure that can be effectively learned from raw document collections. This feature distinguishes these new models from traditional knowledge intensive approaches used in symbolic concept-to-text generation. Our results demonstrate that these probabilistic models can be directly applied to content organization, and suggest that these models can prove useful in an even broader range of text-to-text applications than we have considered here.National Science Foundation (U.S.) (CAREER grant IIS- 0448168)Microsoft Research. New Faculty Fellowshi
Indexing Temporal Data with Virtual Structure

Temporal and spatio-temporal data are present in many modern application systems, including monitoring moving objects. Such systems produce enormous volume of data, and therefore efficient indexing method is crucial. In this paper, we investigate and present a new concept based on virtual index structure, which can efficiently query such data. Concept is based on spatial representation of interval data and a recursive triangular decomposition of that space. The empirical performance of presented concept is demonstrated to be superior to its best known competitors.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Developing Model-Driven Software Product Lines

This thesis focuses on model-driven software product line development, which is the combination of the following two software development paradigms: (1) Model-Driven Engineering (MDE), which focuses on modeling software products and automating code generation from product models. In particular, Domain-Specific Modeling (DSM), as a technique in the arena of MDE, is about defining a Domain-Specific Language (DSL) and creating software product models using the language. (2) Software Product Line Engineering (SPLE), is a means to produce similar software products, by consolidating those into product lines to enable managed reuse.In a model-driven Software Product Line (SPL) which adopts DSM technique, products are represented as product models defined in a DSL. The variability (and commonality) of all intended products is specified in a product line model, typically using a variability modeling language. Based on the variability specified in the product line model, reusable model fragments specified using the base DSL, serving as the core assets of the product line, will be reused to derive all intended product models. This thesis provides methods for developing model-driven software product lines, in terms of development methodology, automated assistance and SPL evolution support.

Firstly, this thesis presents two results on the methodology for developing a modeldriven SPL: (1) A generic and separate variability modeling language, which can be used to specify a product line model defining how intended product models can vary from each other, both at the domain conceptual level and the realization level (model object level). (2) Guidelines on how to define a DSL that is suitable to serve as the base language for a model-driven SPL, if the base language of the product line does not exist yet. 

Secondly, this thesis reports on two results in providing automated tool support for model-driven product line development: (1) A method for synthesizing a product line model from a set of existing product models when the product line is not built from scratch. (2) A method for ensuring that all the product models that can be derived from the product line model are intended. 

Thirdly, this thesis reports on three results in providing support for evolving model-driven SPLs: (1) A method for augmenting the existing product line model when new product models need to be included. (2) A method for suggesting automatic update to the product line model when the core assets of the product line have been changed. (3) A method for calculating semantic difference between two model-driven SPLs. We illustrate the application of our approaches in various case studies in different domains, provided by both industry and academia. Different phases of SPL development and evolution can require substantial amount of manual efforts, of which productivity can be improved by adopting our automatic tool support. We show that 2 by following our approaches, model-driven SPLs can be developed and evolved in a systematic and efficient manner
The Reformulation-Optimization Software Engine

International audienceMost optimization software performs numerical computation, in the sense that the main interest is to find numerical values to assign to the decision variables, e.g. a solution to an optimization problem. In mathematical programming, however, a considerable amount of symbolic transformation is essential to solving difficult optimization problems, e.g. relaxation or decomposition techniques. This step is usually carried out by hand, involves human ingenuity, and often constitutes the "theoretical contribution" of some research papers. We describe a Reformulation- Optimization Software Engine (ROSE) for performing (automatic) symbolic computation on mathematical programming formulations
Efficient Evaluation of Large Polynomials

Minimizing the evaluation cost of a polynomial expression is a fundamental problem in computer science. We propose tools that, for a polynomial P given as the sum of its terms, compute a representation that permits a more efficient evaluation. Our algorithm runs in d(nt) [superscript O(1)] bit operations plus dt [superscript O(1)] operations in the base field where d, n and t are the total degree, number of variables and number of terms of P. Our experimental results show that our approach can handle much larger polynomials than other available software solutions. Moreover, our computed representation reduce the evaluation cost of P substantially
Correctness ensuring process configuration : an approach based on partner synthesis

A configurable process model describes a family of similar process models in a given domain. Such a model can be configured to obtain a specific process model\ud
that is subsequently used to handle individual cases, for instance, to process customer orders. Process configuration is notoriously difficult as there may be all kinds of interdependencies between configuration decisions.} In fact, an incorrect configuration may lead to behavioral issues such as deadlocks and livelocks. To address this problem, we present a novel verification approach inspired by the ``operating guidelines'' used for partner synthesis. We view the configuration process as an external service,\ud
and compute a characterization of all such services which meet particular requirements using the notion of configuration guideline. As a result, we can characterize all feasible configurations (i.\,e., configurations without behavioral problems) at design time, instead of repeatedly checking each individual configuration while configuring a process model