Different Levels Of Granularity Research Articles

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.

Language Modeling on Location-Based Social Networks

The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space–time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places.

Conceptual Design for Assembly methodology formalization: systems installation analysis and manufacturing information integration in the design and development of aircraft architectures

In recent years, the air transport market has experienced strong growth, increasing the demand for new civil aircraft, challenging the actual production rate of aerospace industries. The bottleneck of the production for the aviation industry lies in the capability of the manufacturing and assembly facilities to fulfill the module arrangement in the current design. The development of optimized product architecture requires the implementation of design for assembly principles at the conceptual design phase closing the gap between the design and the production departments. The study proposes a Conceptual Design for Assembly (CDfA) methodology which aims at the assessment of aircraft systems installation and assembly at the early phase of product development (conceptual design). The CDfA methodology allows comparing assembly performance of different aircraft architectures identifying critical modules and interfaces as well as assembly/installation issues. The methodology is based on a specific framework (hierarchical structure) which is characterized by levels, domains, and attributes. Levels enable the analysis of product architectures at different levels of granularity, splitting the global analysis into sub-problems (problem discretization). Domains and attributes are defined with a knowledge-based engineering approach considering available information at the conceptual design phase and production criteria. A complex system (the nose fuselage of a commercial aircraft) was chosen as a case study to test the robustness of the methodology in relation to the assembly performance observed within the manufacturing facilities. Results revealed the architectural elements (modules and interfaces) that contribute to inefficient assembly operations, as well as the rationales enabling to elaborate alternative architectures for an improved product industrial efficiency.

Optical Components for Transport Network Enabling The Path to 6G

The evolution beyond 5G towards 6G will accelerate the transformations begun by 5G to deploy new approaches to define delivery services. The network should be accessible anywhere, always-on and with the guarantee performances. This poses new challenges to the transport network that should assure available connectivity among the functions at different levels of granularity. The transport network should be so flexible to guarantee on-demand the connectivity among the processing functions that automatically will be required and, at the same time, should assure extreme performance. Optical network and Dense Wavelength Division Multiplexing technology are relevant candidates to match high bandwidth and low latency, but new challenges are required to assure the suitable cost and size for the wireless access networks. The current optical technology, suitable used in aggregation and long-haul transport network, cannot be simply downsized to be used in the access area, but novel paradigm should be considered for the optics as well. Integrated photonics seems a promising direction to realize low cost and low size optical components for the transport network. Moreover, this technology enables a certain level of remote tunability and reconfiguration of optical equipment that allow to simplify and reduce cost of installation in field and operation of the transport network. In this paper relevant optical components for transport networks are analyzed using the integrated photonics to assess the technology challenges.

Adaptable Closed-Domain Question Answering Using Contextualized CNN-Attention Models and Question Expansion

In closed-domain Question Answering (QA), the goal is to retrieve answers to questions within a specific domain. The main challenge of closed-domain QA is to develop a model that only requires small datasets for training since large-scale corpora may not be available. One approach is a flexible QA model that can adapt to different closed domains and train on their corpora. In this paper, we present a novel versatile reading comprehension style approach for closed-domain QA (called CA-AcdQA). The approach is based on pre-trained contextualized language models, Convolutional Neural Network (CNN), and a self-attention mechanism. The model captures the relevance between the question and context sentences at different levels of granularity by exploring the dependencies between the features extracted by the CNN. Moreover, we include candidate answer identification and question expansion techniques for context reduction and rewriting ambiguous questions. The model can be tuned to different domains with a small training dataset for sentence-level QA. The approach is tested on four publicly-available closed-domain QA datasets: Tesla (person), California (region), EU-law (system), and COVID-QA (biomedical) against nine other QA approaches. Results show that the ALBERT model variant outperforms all approaches on all datasets with a significant increase in Exact Match and F1 score. Furthermore, for the Covid-19 QA in which the text is complicated and specialized, the model is improved considerably with additional biomedical training resources (an F1 increase of 15.9 over the next highest baseline).

Semantically Query All (Squerall): A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

In terms of data formats, codecs, and storage capacity, the previous two decades have seen a phenomenal progress. Rather than needing to adjust to one's application demands to the restricted storage solutions available in the past, there is now a wide range of options to pick from in order to best satisfy an application's needs. As a result, there is massive volume of data available in many forms and formats that, when linked and searched together, may yield significant knowledge and insights. We offer data warehouse as a solution and Big data as a solution in this study in order to handle heterogeneous data. However, both of these techniques have drawbacks when it comes to handling diverse data. Afterwards we propose another framework (Squerall), which relies on the ideas of Ontology-Based Data Access (OBDA) to allow querying of diverse heterogeneous sources using a single query language, SPARQL. In Squerall original data is queried on the fly, with no prior data materialization or modification. Squerall, in particular, enables the distributed aggregate and combining of massive data sets. It comes with five data sources out of the box, and it may be programmatically expanded to include other sources and query engines. The framework includes user interfaces for creating required inputs and for assisting non-SPARQL specialists through the process of writing SPARQL queries. It declares data mappings and transformations using RML and FnO, and employs Spark and Presto as query engines. The initiative underscored the importance of developing in this framework, technologies, and processes that enable for decentralized Big Data administration. Furthermore, demonstrating the feasibility and usefulness of OBDA on top of the growing NoSQL movement has a beneficial impact on Semantic Web principles acceptance. This demonstrates Squerall's importance and contribution to the organization.

CDFA method: a way to assess assembly and installation performance of aircraft system architectures at the conceptual design.

This paper describes an engineering design methodology, called conceptual design for assembly (CDFA) in the context of aircraft development, to assess aircraft systems’ installation during conceptual phase, in relation to industrial performance objectives. The methodology is based on a given framework (hierarchical structure) which includes a set of attributes, collected in recognized domains that characterize the aircraft systems installation. The framework of the CDFA methodology enables to analyze product architectures at different levels of granularity, splitting the global analysis into sub-problems (problem discretization) with the aim to help architects and designers to identify product architecture weaknesses in terms of fit for assembly performances. The CDFA methodology was applied on a complex system (the nose-fuselage of a commercial aircraft) presenting a high number of criticalities both for the product and its assembly operations. Results identified the architectural components leading to the less efficient assembly operations and the rationales enabling to elaborate alternative architectures for an improved product industrial efficiency.

Key-Components for Digital Twin Modeling With Granularity: Use Case Car-as-a-Service

Digital technologies are changing the way people interact with the world. The Digital Twin (DT) is one of the key enablers of Industry 4.0. It provides a virtual representation of an observable element of the real world. These elements can be both physical objects such as devices or non-physical such as interactions and processes. Digitalization of the real world enables new business models, transforming traditional products into services, as for instance, the Car-as-a-Service (CaaS). To integrate all components that will be part of systems like CaaS or Smart Cities, it is necessary to have well-defined standards for modeling and defining an architecture especially taking into consideration the granularity level of the system. This paper proposes the main components needed for building DT-based systems with different levels of granularity. These components have been arranged in layers to specify the concerns of each part of the system. A case study has been developed to demonstrate the modeling and the deployment of the Digital Twin, highlighting how this concept can be one of the key enablers for CaaS.

The whole prefrontal cortex is premotor cortex.

We propose that the entirety of the prefrontal cortex (PFC) can be seen as fundamentally premotor in nature. By this, we mean that the PFC consists of an action abstraction hierarchy whose core function is the potentiation and depotentiation of possible action plans at different levels of granularity. We argue that the apex of the hierarchy should revolve around the process of goal-selection, which we posit is inherently a form of optimization over action abstraction. Anatomical and functional evidence supports the idea that this hierarchy originates on the orbital surface of the brain and extends dorsally to motor cortex. Accordingly, our viewpoint positions the orbitofrontal cortex in a key role in the optimization of goal-selection policies, and suggests that its other proposed roles are aspects of this more general function. Our proposed perspective will reframe outstanding questions, open up new areas of inquiry and align theories of prefrontal function with evolutionary principles. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

SWSR: A Chinese dataset and lexicon for online sexism detection

Online sexism has become an increasing concern in social media platforms as it has affected the healthy development of the Internet and can have negative effects in society. While research in the sexism detection domain is growing, most of this research focuses on English as the language and on Twitter as the platform. Our objective here is to broaden the scope of this research by considering the Chinese language on Sina Weibo. We propose the first Chinese sexism dataset – Sina Weibo Sexism Review (SWSR) dataset –, as well as a large Chinese lexicon SexHateLex made of abusive and gender-related terms. We introduce our data collection and annotation process, and provide an exploratory analysis of the dataset characteristics to validate its quality and to show how sexism is manifested in Chinese. The SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type, which can be exploited, among others, for building computational methods to identify and investigate finer-grained gender-related abusive language. We conduct experiments for the three sexism classification tasks making use of state-of-the-art machine learning models. Our results show competitive performance, providing a benchmark for sexism detection in the Chinese language, as well as an error analysis highlighting open challenges needing more research in Chinese NLP. The SWSR dataset and SexHateLex lexicon are publicly available.11https://doi.org/10.5281/zenodo.4773875.

Improved strategies of relation extraction based on graph convolutional model on tree structure for web information processing

In the Industry 4.0/5.0 era, information integration is employed to fuse information from different companies to facilitate interoperation. However, information extraction is an important preprocessing phase that must be performed prior to integrating data from different contexts. Relation extraction, which is an element of information extraction, is typically the basis of many upper-level applications, e.g., information visualization and inference. Some current models may not fully consider the complementary effect of information at different levels of granularity featured by different neural networks. In this paper, two improvement relation extraction strategies based on the graph convolutional model on tree structure (GCNTree) are proposed. The first strategy integrates a hierarchical attention mechanism and correlation analysis between subjects and objects to generate sentence and entity vectors, respectively. The second strategy merges a named-entity recognition subnetwork with GCNTree to realize joint learning of relation and entity extraction. Experimental results demonstrate that the proposed strategies are comparable to state-of-the-art methods.11Our code will be available at https://github.com/yangshuodelove/.

VeriFly: On-the-fly Assertion Checking via Incrementality

AbstractAssertion checking is an invaluable programmer’s tool for finding many classes of errors or verifying their absence in dynamic languages such as Prolog. For Prolog programmers, this means being able to have relevant properties, such as modes, types, determinacy, nonfailure, sharing, constraints, and cost, checked and errors flagged without having to actually run the program. Such global static analysis tools are arguably most useful the earlier they are used in the software development cycle, and fast response times are essential for interactive use. Triggering a full and precise semantic analysis of a software project every time a change is made can be prohibitively expensive. This is specially the case when complex properties need to be inferred for large, realistic code bases. In our static analysis and verification framework, this challenge is addressed through a combination of modular and incremental (context- and path-sensitive) analysis that is responsive to program edits, at different levels of granularity. In this tool paper, we present how the combination of this framework within an integrated development environment (IDE) takes advantage of such incrementality to achieve a high level of reactivity when reflecting analysis and verification results back as colorings and tooltips directly on the program text – the tool’s VeriFly mode. The concrete implementation that we describe is Emacs-based and reuses in part off-the-shelf “on-the-fly” syntax checking facilities (flycheck). We believe that similar extensions are also reproducible with low effort in other mature development environments. Our initial experience with the tool shows quite promising results, with low latency times that provide early, continuous, and precise assertion checking and other semantic feedback to programmers during the development process. The tool supports Prolog natively, as well as other languages by semantic transformation into Horn clauses.

Adaptive Change-Point Detection for Studying Human Locomotion.

This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a refined analysis of the successive phases within a gait protocol. Thanks to a training procedure, the algorithm can be applied to a wide range of protocols and handles different levels of granularity. The method is tested on a cohort of 15 healthy subjects performing a complex protocol composed of different activities and shows promising results for the automated and adaptive study of human gait and activity.Clinical relevance- A new approach to study human activity and locomotion in Free-Living Environments FLEs through an adaptive change-point detection which isolates homogeneous phases.

Rastreia Saúde: A Spatiotemporal Disease Tracking System through Open Unstructured Data and GIS

Automated disease tracking has become an increasingly important tool today. This article describes the prototype of a disease tracking system for the Brazilian territory, preliminarily tested at the state level, in Paraná, and at the municipal level, in Curitiba. This study aims to extract and present relevant information in the health segment from unstructured data, extracted from news portals. The system generates data that allows analysis at different levels of granularity, from small municipalities to the national level. The results of the study shows the viability of the system and allows the authors to identify some patterns in the processed data.

Hierarchical sequential three-way decision model

Knowledge acquisition is one of the important issues in granular computing. In recent years, scholars have paid much attention to this problem and proposed the rule-based acquisition algorithms. However, a large number of the decision rules mined by the existing algorithms are not comprehensible. At the same time, the long detailed rules are easy to lead to over-fitting. In order to generate simpler and easier-to-comprehensible rules and improve human decision-making, a hierarchical sequential three-way decision model is proposed by combining sequential three-way decisions with hierarchical rough set model. Specifically, we generalize the concepts of the conditional attributes through the concept hierarchy tree, design the multi-hierarchical decision table with multiple levels of granularity, and illustrate the corresponding algorithm to acquire the generalized rules step by step. The experimental results demonstrate that the proposed model can mine hierarchical sequential three-way decision rules under different levels of granularity.

Transformer machine learning language model for auto-alignment of long-term and short-term plans in construction

In construction, master schedules and look-ahead plans are created at different times (monthly vs. weekly), by different personas (planner vs. superintendent), with different software (scheduling solution vs. spreadsheet), and at different levels of granularity (milestones vs. production details). Their full-alignment is essential for project coordination, progress updating, and payment application reviews, and its absence may lead to costly litigation. This paper presents the first attempt to automate linking look-ahead planning tasks to master-schedule activities following an NLP-based multi-stage ranking formulation. Our model employs distance-based matching for candidate generation and a Transformer architecture for final matching.11Access to the code and a sample of the dataset can be granted upon direct request from the authors. Validation results from real-world projects demonstrate that the method helps planners match look-ahead planning tasks to master schedule activities by presenting a list of top-five matches with a precision of 76.5%. We also show that the method helps superintendents create look-ahead plans from a master schedule by generating lists of tasks based on activity descriptions.

Variable Selection and Data Quality Challenges in Impact Assessments

The research is focused on the role of two related key concepts, namely variables and data, in the impact evaluations of public projects. A difficult task of the evaluators and researchers is to select the appropriate variables to ensure the best model of reality and satisfy the evaluation methods' needs. Therefore, the paper aims to look at the current knowledge and discuss how variables and data could be best used to connect the evaluation models, the particularities of the intervention with the potential of the advanced quantitative assessment methods. The results emphasise that evaluations operate with data with different levels of granularity, as required by the intervention logic. Structuring data in clusters and categories, performing evaluability assessments are useful in assessing data quality and limitations and improving them. In line with the existing literature, we demonstrate that data accessibility is a key constraint and imposes adjustment of the desired evaluation model to a feasible one. While Big Data and Open Data systems significantly improved data quality in evaluations in recent years, blockchain, as a ledger technology with default features related to decentralisation and security, is expected to bring large benefits to evaluation. For evaluators and policymakers, blockchain potential is an area of further research looking for additional advantages that could enhance the use of quantitative methods.

Direct and indirect links between children’s socio-economic status and education: pathways via mental health, attitude, and cognition

A child’s socio-economic environment can profoundly affect their development. While existing literature focusses on simplified metrics and pair-wise relations between few variables, we aimed to capture complex interrelationships between several relevant domains using a broad assessment of 519 children aged 7–9 years. Our analyses comprised three multivariate techniques that complimented each other, and worked at different levels of granularity. First, an exploratory factor analysis (principal component analysis followed by varimax rotation) revealed that our sample varied along continuous dimensions of cognition, attitude and mental health (from parallel analysis); with potentially emerging dimensions speed and socio-economic status (passed Kaiser’s criterion). Second, k-means cluster analysis showed that children did not group into discrete phenotypes. Third, a network analysis on the basis of bootstrapped partial correlations (confirmed by both cross-validated LASSO and multiple comparisons correction of binarised connection probabilities) uncovered how our developmental measures interconnected: educational outcomes (reading and maths fluency) were directly related to cognition (short-term memory, number sense, processing speed, inhibition). By contrast, mental health (anxiety and depression symptoms) and attitudes (conscientiousness, grit, growth mindset) showed indirect relationships with educational outcomes via cognition. Finally, socio-economic factors (neighbourhood deprivation, family affluence) related directly to educational outcomes, cognition, mental health, and even grit. In sum, cognition is a central cog through which mental health and attitude relate to educational outcomes. However, through direct relations with all components of developmental outcomes, socio-economic status acts as a great ‘unequaliser’.

A morphospace of functional configuration to assess configural breadth based on brain functional networks.

The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.

Fine-Grained Multi-label Sexism Classification Using a Semi-Supervised Multi-level Neural Approach

Sexism, a permeate form of oppression, causes profound suffering through various manifestations. Given the increasing number of experiences of sexism shared online, categorizing these recollections automatically can support the battle against sexism, since it can promote successful evaluations by gender studies researchers and government representatives engaged in policy making. In this paper, we examine the fine-grained, multi-label classification of accounts (reports) of sexism. To the best of our knowledge, we consider substantially more categories of sexism than any related prior work through our 23-class problem formulation. Moreover, we present the first semi-supervised work for the multi-label classification of accounts describing any type(s) of sexism. We devise self-training-based techniques tailor-made for the multi-label nature of the problem to utilize unlabeled samples for augmenting the labeled set. We identify high textual diversity with respect to the existing labeled set as a desirable quality for candidate unlabeled instances and develop methods for incorporating it into our approach. We also explore ways of infusing class imbalance alleviation for multi-label classification into our semi-supervised learning, independently and in conjunction with the method involving diversity. In addition to data augmentation methods, we develop a neural model which combines biLSTM and attention with a domain-adapted BERT model in an end-to-end trainable manner. Further, we formulate a multi-level training approach in which models are sequentially trained using categories of sexism of different levels of granularity. Moreover, we devise a loss function that exploits any label confidence scores associated with the data. Several proposed methods outperform various baselines on a recently released dataset for multi-label sexism categorization across several standard metrics.