Low Level Of Granularity Research Articles

Development of an Integrated Computing Platform for Measuring, Predicting, and Analyzing the Profile-Specific Fixity of Railway Tracks

The current measures for railway track fixity in the UK’s railway remain at a relatively low level of granularity. This paper presents a pilot proof-of-concept study on the development of an integrated computing framework for improving the measurement, prediction, and analysis of profile-specific track fixity in the context of the UK’s rail network. The framework is aimed to produce a data integration and mining tool, which can determine track fixity parameters for any given section of track. In this study, we propose to measure track movement based on point cloud data and assess the track fixity by a set of parameters, such as the direction and rate of the track movement relative to the plane of the rail within a certain period. We seek to integrate a data mining algorithm into the framework to predict these parameters, given vast amounts of disparate and heterogeneous data of potential influencing factors in the area. From the study, we have developed a prototype framework, which allows the rapid implementation of data workflows with the necessary functionality. The feasibility of the prototype was demonstrated by training a random forest model on real data from an approximately 80-km section of the East Coast Main Line, southeast of Edinburgh, Scotland. The modeling results indicate that the curvature, cant, and maximum speed of trains are among the key factors that impact on, and are critical for predicting and analyzing, the profile-specific track fixity.

The relationship between income per capita and access to meningococcal serogroup B vaccination in Spain: an ecological correlation study

Background: Invasive meningococcal disease (IMD) remains a public health concern because of its serious consequences, which mainly affect infants and young children. In Spain, Neisseria meningitidis serotype B accounts for >40% of IMD cases. The multicomponent meningococcal serogroup B vaccine (4CMenB) is available privately in the absence of national public funding in Spain. This study evaluates whether potential inequities in access to 4CMenB exist among families because of financial disparities. Methods: An ecological correlation study was carried out using aggregate birth data, average income per capita and average number of 4CMenB vaccine units dispensed at province and municipal levels, including municipalities with >50,000 inhabitants. The base-case scenario used birth data from 2018, 4CMenB uptake from March 2018 to February 2019 and income per capita from 2017. The Spearman correlation test was used to compute bivariate correlation coefficients (rs) between the ratio of 4CMenB uptake per birth and income per capita. Sensitivity analyses were carried out to study the impact of observational period, territorial assignment and target population on base-case results. Results: At province level, no correlation between mean income per capita and 4CMenB units was observed. At municipality level, a statistically significant positive correlation was observed between 4CMenB uptake per birth and average income per capita (rs = 0.22; p = 0.007). The results were robust under sensitivity analyses. Conclusions: Despite the low level of granularity, these results suggest that access to the 4CMenB vaccine in Spain is associated with the income level of families at municipal level, producing inequities in the context of no national public funding. Keywords: Ecological correlation study, Meningococcal serogroup B vaccine, Vaccine uptake in Spain, Inequities in vaccine access, Financial impact.

TreatmentBank: Plazi's strategies and its implementation to most efficiently liberate data from scholarly publications

Plazi's TreatmentBank is a research infrastructure and partner of the recent European Union-funded Biodiversity Community Integrated Knowledge Library (BiCIKL) project to provide a single knowledge portal to open, interlinked and machine-readable, findable, accessible, interoperable and reusable (FAIR) data. Plazi is liberating published biodiversity data that is trapped in so-called flat formats, such as portable document format (PDF), to increase its FAIRness. This can pose a variety of challenges for both data mining and curation of the extracted data. The automation of such a complex process requires internal organization and a well established workflow of specific steps (e.g., decoding of the PDF, extraction of data) to handle the challenges that the immense variety of graphic layouts existing in the biodiversity publishing landscape can impose. These challenges may vary according to the origin of the document: scanned documents that were not initially digital, need optical character recognition in order to be processed. Processing a document can either be an individual, one-time-only process, or a batch process, in which a template for a specific document type must be produced. Templates consist of a set of parameters that tell Plazi-dedicated software how to read and where to find key pieces of information for the extraction process, such as the related metadata. These parameters aim to improve the outcome of the data extraction process, and lead to more consistent results than manual extraction. In order to produce such templates, a set of tests and accompanying statistics are evaluated, and these same statistics are constantly checked against ongoing processing tasks in order to assess the template performance in a continuous manner. In addition to these steps that are intrinsically associated with the automated process, different granularity levels (e.g., low granularity level might consist of a treatment and its subsections versus a high granularity level that includes material citations down to named entities such as collection codes, collector, collecting date) were defined to accommodate specific needs for particular projects and user requirements. The higher the granularity level, the more thoroughly checked the resulting data is expected to be. Additionally, steps related to the quality control (qc), such as the “pre-qc”, “qc” and “extended qc” were designed and implemented to ensure data quality and enhanced data accuracy. Data on all these different stages of the processing workflow are constantly being collected and assessed in order to improve these very same stages, aiming for a more reliable and efficient operation. This is also associated with a current Data Architecture plan to move this data assessment to a cloud provider to promote real-time assessment and constant analyses of template performance and processing stages as a whole. In this talk, the steps of this entire process are explained in detail, highlighting how data are being used to improve these steps towards a more efficient, accurate, and less costly operation.

Real estate price estimation in French cities using geocoding and machine learning

This paper reviews real estate price estimation in France, a market that has received little attention. We compare seven popular machine learning techniques by proposing a different approach that quantifies the relevance of location features in real estate price estimation with high and fine levels of granularity. We take advantage of a newly available open dataset provided by the French government that contains 5 years of historical data of real estate transactions. At a high level of granularity, we obtain important differences regarding the models’ prediction powers between cities with medium and high standards of living (precision differences beyond 70% in some cases). At a low level of granularity, we use geocoding to add precise geographical location features to the machine learning algorithm inputs. We obtain important improvements regarding the models’ forecasting powers relative to models trained without these features (improvements beyond 50% for some forecasting error measures). Our results also reveal that neural networks and random forest techniques particularly outperform other methods when geocoding features are not accounted for, while random forest, adaboost and gradient boosting perform well when geocoding features are considered. For identifying opportunities in the real estate market through real estate price prediction, our results can be of particular interest. They can also serve as a basis for price assessment in revenue management for durable and non-replenishable products such as real estate.

Automated in-situ determination of buildings’ global thermo-physical characteristics and air change rates through inverse modelling of smart meter and air temperature data

The advancement of smart metering and sensor technologies has opened the door to performing extensive in-situ measurements in buildings and a tendency to carry-out detailed energy and indoor climate monitoring, leading to the availability of the so-called “on-board monitoring data”. The data obtained through these measurements is of high value as it can be used for identification of parameters determining health, thermal comfort, and energy use. In this article, an occupied dwelling has been inspected and monitored for one year and the in-situ measurement and meteorological data are combined to feed a physic-based energy model. For the first time, the detailed data cleaning and filtering techniques are explained to give insight for future similar studies. The data is fed to a 1st – order circuit RC model, equivalent to the building’s thermal model. Next, using Genetic Algorithm in a stated optimization problem, Inverse Modelling has been applied to identify four main global thermo-physical characteristics of the building, with a special attention to the heat loss coefficient. The results are compared by analysing three feed data properties: granularity level, period length, and time period, resulting the best fit in the coldest periods. The outcomes have shown the importance of these data properties by revealing differences in the heat loss coefficient in different periods and the weakening of the heat capacitance effect when feeding the model with low granularity level data. The daily values of the heat loss coefficient are then applied in combination with construction data to determine the daily averages of hourly air change rates. Finally, the method has been evaluated in terms of accuracy and precision and the air change rates have been validated using CO2 concentration and wind velocity. Using this method, it is possible to determine buildings’ main global thermo-physical characteristics as well as the cold periods’ airborne heat losses.

Modeling and Planning with Macro-Actions in Decentralized POMDPs.

Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for decentralized multi-agent decision making under uncertainty. However, they typically model a problem at a low level of granularity, where each agent's actions are primitive operations lasting exactly one time step. We address the case where each agent has macro-actions: temporally extended actions that may require different amounts of time to execute. We model macro-actions as options in a Dec-POMDP, focusing on actions that depend only on information directly available to the agent during execution. Therefore, we model systems where coordination decisions only occur at the level of deciding which macro-actions to execute. The core technical difficulty in this setting is that the options chosen by each agent no longer terminate at the same time. We extend three leading Dec-POMDP algorithms for policy generation to the macro-action case, and demonstrate their effectiveness in both standard benchmarks and a multi-robot coordination problem. The results show that our new algorithms retain agent coordination while allowing high-quality solutions to be generated for significantly longer horizons and larger state-spaces than previous Dec-POMDP methods. Furthermore, in the multi-robot domain, we show that, in contrast to most existing methods that are specialized to a particular problem class, our approach can synthesize control policies that exploit opportunities for coordination while balancing uncertainty, sensor information, and information about other agents.

AN ONLINE FRAMEWORK FOR CIVIL UNREST PREDICTION USING TWEET STREAM BASED ON TWEET WEIGHT AND EVENT DIFFUSION

Twitter is one of most popular Internet-based social networking platform to share feelings, views, and opinions. In recent years, many researchers have utilized the social dynamic property of posted messages or tweets to predict civil unrest in advance. However, existing frameworks fail to describe the low granularity level of tweets and how they work in offline mode. Moreover, most of them do not deal with cases where enough tweet information is not available. To overcome these limitations, this article proposes an online framework for analyzing tweet stream inpredicting future civil unrest events. The framework filters tweet stream and classifies tweets using linear Support Vector Machine (SVM) classifier. After that, the weight of the tweet is measured and distributed among extracted locations to update the overall weight in each location in a day in a fully online manner. The weight history is then used to predict the status of civil unrest in a location. The significant contributions of this article are (i) A new keyword dictionary with keyword score to quantify sentiment in extracting the low granularity level of knowledge (ii) A new diffusion model for extracting locations of interest and distributing the sentiment among the locations utilizing the concept of information diffusion and location graph to handle locations with insufficient information (iii) Estimating the probability of civil unrest and determining the stages of unrest in upcoming days. The performance of the proposed framework has been measured and compared with existing logistic regression based predictive framework. The results showed that the proposed framework outperformed the existing framework in terms of F1 score, accuracy, balanced accuracy, false acceptance rate, false rejection rate, and Matthews correlation coefficient.

The FARC’s militaristic blueprint

ABSTRACTThis article intends to explain the outstanding sequence of success and failure exhibited by the FARC, the main Colombian guerrilla since the 1980s. It claims that such sequence is unintelligible unless the adoption by the FARC of a militaristic organizational blueprint at its 1982 7th Conference is taken into account. By building itself like an army, the FARC could boost its combat capacity, maintain its structural integrity, and develop powerful mechanisms that held the whole structure together. At the same time, the militarization of the FARC also entailed significant risks and costs like political isolation and high personnel turnover. After describing the militaristic blueprint, the article compares the FARC with other irregular forces that operated in the Colombian context – a comparison which is important to understand the specificity of the FARC trajectory, as well as the benefits and costs involved in it. The analysis highlights the critical role of organizational dimensions in the explanation of civil war outcomes, and suggests that at least for some problems organizational dynamics should be observed at a low level of granularity.

Information Security Risk Assessment A Practical Approach with a Mathematical Formulation of Risk

Risk management methodologies, such as Mehari, Ebios, CRAMM and SP 800-30 (NIST) use a common step based on threat, vulnerability and probability witch are typically evaluated intuitively using verbal hazard scales such as low, medium, high. Because of their subjectivity, these categories are extremely difficult to assign to threats, vulnerabilities and probability, or indeed, to interpret with any degree of confidence. The purpose of the paper is to propose a mathematical formulation of risk by using a lower level of granularity of its elements: threat, probability, criteria used to determine an asset‘s value, exposure, frequency and existing protection measure. General Terms Security risk assessment, risk management system, framework, audit, information system.

M2LGP: Mining Multiple Level Gradual Patterns

Gradual patterns have been studied for many years as they contain precious information. They have been integrated in many expert systems and rule-based systems, for instance to reason on knowledge such as {\\em ''the greater the number of turns, the greater the number of car crashes\}. In many cases, this knowledge has been considered as a rule {\\em ''the greater the number of turns $\\rightarrow$ the greater the number of car crashes\} Historically, works have thus been focused on the representation of such rules, studying how implication could be defined, especially fuzzy implication. These rules were defined by experts who were in charge to describe the systems they were working on in order to turn them to operate automatically. More recently, approaches have been proposed in order to mine databases for automatically discovering such knowledge. Several approaches have been studied, the main scientific topics being: how to determine what is an relevant gradual pattern, and how to discover them as efficiently as possible (in terms of both memory and CPU usage). However, in some cases, end-users are not interested in raw level knowledge, and are rather interested in trends. Moreover, it may be the case that no relevant pattern can be discovered at a low level of granularity (e.g. city), whereas some can be discovered at a higher level (e.g. county). In this paper, we thus extend gradual pattern approaches in order to consider multiple level gradual patterns. For this purpose, we consider two aggregation policies, namely horizontal and vertical.

MBTDD: MODEL BASED TEST DRIVEN DEVELOPMENT

Test Driven Development (TDD), as a quality promotion approach, suffers from some shortages that discourage its usage. One of the most challenging shortcomings of TDD is the low level of granularity and abstraction. This may lead to production of software that is not acceptable by the end users. Additionally, exploiting of TDD is not applicable in the enterprise systems development. To overcome this defect, we have merged TDD with Model Based Testing (MBT) and suggested a framework named Model Based Test Driven Development (MBTDD). According to TDD, writing test cases comes before programming, and based on our improved method of TDD, modeling precedes writing test cases. To validate the applicability of the proposed framework, we have implemented a use case of Human Resource Management (HRM) system by means of MBTDD. The empirical results of using MBTTD show that our proposed method overwhelms existing deficiencies of TDD.

A Granular Framework for Recognition of Arabic Handwriting

We propose a novel cognitively motivated unifying framework for Arabic handwriting recognition that takes into account the nature of the human reading process of Arabic handwriting. This Modular Granular Architecture tackles the problem by observing Arabic handwriting from both perceptual and linguistic points of view and hence analyzes the underlying input signal from different granularity levels. It is based on three levels of abstraction: a low granularity level that uses perceptual features called global visual indices, a medium granularity level that is the conventional recognition stage and a high granularity level that consists on morphological analysis dedicated to segmentation/recognition. The original idea is the effective use of Arabic word's morphology in the recognition not only in post-processing. This architecture carries well around the Arabic word's morphology, as typically in Arabic, the Arabic word's morphology is by excellence the logical structure (even semantic) of a given Arabic word, whereas the visual data constitute the physical geometric (topological) structure of a given word. We need to integrate both of them for an effective cooperative recognition of Arabic Handwriting. This framework subsumes the lexicon-driven approaches; in that it can recognize a word that does not exist within the lexicon.

Design reuse framework: a perspective for lean development

While the notion of reuse is around for a long time, its real potential is not exposed and exploited in product development. When reuse is applied through a knowledge centric process at low granularity level, it can help in reducing the development process wastes. In this article, a systematic and comprehensive reuse process framework is proposed by recycling the design knowledge and development artefacts in a seamless development process. In the proposed process framework, knowledge assets are created by decomposition, generalisation and parameterisation. These decomposed and atomised assets are used as reusable building blocks for the next level of product design. Requirements are modelled and testing is performed in virtual environment by auto generating the test cases and test vectors. Model-based development is demonstrated as an enabler of effective reuse. In this research, it is emphasised that model-based seamless development is instrumental for removing process waste and making the product development process lean.

Understanding through Structure: The Challenges of Information and Navigation Architecture in Cybercartography

The information-rich environments suggested by the concept of cybercartography present new challenges to the design and display of interactive geographically oriented information. These new challenges revolve around two basic issues: information architecture and information navigation models. A variety of information architectures are discussed in this article, along with an analysis of topologies such as the hierarchy (tree), linear sequence, matrix (grid), web-like, or hybrid. A three-tier information architecture model is proposed that varies in information granularity. The high-granularity level includes information units such as maps, articles, images, animations, video clips, and data graphs. The medium-granularity level includes integrated functional information units that are topical, task oriented, audience specific, or a hybrid. At such a level of information units, maps are no longer a stand-alone element. Finally, the low-granularity level includes top-level information architecture linking the various functional units. The advantage of these three granularity levels is that they enable an adaptable information architecture that can accommodate the addition of new content composed of the basic elements. While the way users navigate such an information architecture can be prescribed by the information structure, we demonstrate how navigation schemes can be independent of the information architecture and offer the user a greater diversity of interaction.

Intersection and Union Types for [formula omitted

This paper presents a notion of intersection and union type assignment for the calculus X, a substitution free language that can be used to describe the behaviour of functional programming languages at a very low level of granularity, and has first been defined in [Stéphane Lengrand. Call-by-value, call-by-name, and strong normalization for the classical sequent calculus. In Bernhard Gramlich and Salvador Lucas, editors, Electronic Notes in Theoretical Computer Science, volume 86. Elsevier, 2003, S. van Bakel, S. Lengrand, and P. Lescanne. The language §: computation and sequent calculus in classical logic. Submitted, 2004]. X has been designed to give a Curry-Howard-de Bruijn correspondence to the sequent calculus for classical logic.In this paper we will define a notion of sequent-style intersection type assignment on X that needs union types, and show that this notion is closed for both subject-reduction and subject-expansion. We will also show that it is an extension of the Strict system for lc.

Implementing [formula omitted

This paper presents a term graph rewriting system as an implementation for the X-calculus, a substitution-free language that can be used to describe the behaviour of functional programming languages at a very low level of granularity, and has first been defined in [Stéphane Lengrand. Call-by-value, call-by-name, and strong normalization for the classical sequent calculus. In Bernhard Gramlich and Salvador Lucas, editors, Electronic Notes in Theoretical Computer Science, volume 86. Elsevier, 2003; S. van Bakel, S. Lengrand, and P. Lescanne. The language X: circuits, computations and classical logic. Submitted, 2004]. Since the calculus has a notion of binding, in the context of sharing a notion of rebinding is introduced that helps avoid double binding of names.

A Formal Method for Analyzing Electronic Commerce Protocols

A formal method which can be used to analyze security properties such as accountability and fairness in electronic commerce protocols is presented. Compared with the previous work, the main contributions are the following. Firstly, a formal definition is given to the possession set of each protocol participant, and the initial possession set depends only on the environment. Secondly, the set of initial state assumptions is divided into three categories: basic assumptions, trust assumptions, and protocol comprehension assumptions, in order to avoid analysis errors caused by informal initial state assumptions. Thirdly, the set of trust assumptions is articulated by formal specification at a lower level of granularity, exposing the essence of the protocol. Fourthly, establishing an axiom system makes the new approach more rigorous and expressive.