Different Levels Of Granularity Research Articles

The meaning distributions on different levels of granularity

The meaning distributions of certain linguistic forms generally follow a Zipfian distribution. However, since the meanings can be observed and classified on different levels of granularity, it is thus interesting to ask whether their distributions on different levels can be fitted by the same model and whether the parameters are the same. In this study, we investigate three quasi-prepositions in Shanghainese, a dialect of Wu Chinese, and test whether the meaning distributions on two levels of granularity can be fitted by the same model and whether the parameters are close. The results first show that the three models proposed by modern quantitative linguists can both achieve a good fit for all cases, while both the exponential (EXP) model and the right-truncated negative binomial (RTBN) models behave better than the modified right-truncated Zipf-Alekseev distribution (MRTZA), in terms of the consistency of the goodness of fit, parameter change, rationality, and simplicity. Second, the parameters of the distributions on the two levels and the curves are not exactly the same or even close to each other. This has supported a weak view of the concept of ‘scaling’ in complex sciences. Finally, differences are found to lie between the distributions on the two levels. The fine-grained meaning distributions are more right-skewed and more non-linear. This is attributed to the openness of the categories of systems. The finer semantic differentiation behaves like systems with open set of categories, while the coarse-grained meaning distribution resembles those having a close set of few categories.

A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

A Scalable Framework to Analyze Data from Heterogeneous Sources at Different Levels of Granularity

Editorial to the Inaugural Issue

Dear editors, reviewers, authors, readers, and other members of the whole scientific and professional community, Welcome to this inaugural issue of Information Dynamics and Applications (IDA)! On behalf of the editorial office, I am writing with great pleasure and sincere privilege to present the first issue of the journal IDA. In the last few years, there is a growing necessity for information dynamics, especially with the extensive application of artificial intelligence in virtually all fields of science, economy, and life. It can be observed that many conflicting indicators are involved in the complex nature and society systems. The conflicting aims need to be balanced by developing and applying intelligent management decision models, which represents a significant challenge in uncertain environments, given the limited resources, time, and funds available. In addition, an important task in modern society is the possibility to make adequate decisions in real time through quantitative and qualitative analysis. Such analysis enables decision-makers to make real-time decisions by looking at the real needs of the companies and the market at any moment. This first issue aims to give answers to many important questions related to the scope of the journal. This issue consists of five very interesting high-quality papers, which apply decision models in various fields: in the Integration of Ontology Transformation into Hidden Markov Model, in the Geographic Routing Protocols in Wireless Sensor Network, in the Heterogeneous Sources at Different Levels of Granularity. In addition, digital marketing, quality models, MCDM model, and intelligent models related to information dynamics are discussed in this issue, shedding new lights on various management problems. Information Dynamics and Applications (IDA) (ISSN 2958-1486) is a fully refereed international open access journal, which publishes original research results in all subjects related to the dynamics and applications of information technology. The mission of the journal is to promote interdisciplinary research centered around the key issues of information processing, storage, and transmission. We welcome original submissions in various forms, including reviews, regular research papers, and short communications as well as Special Issues on particular topics. Our special interest lies in new analytical and application techniques of information technology in different fields of science and engineering. We follow principle of international diversity regarding the editorial board, reviewers and authors. Therefore, we invite and welcome submissions from around the world. All information related to IDA journal are available in our website: https://www.acadlore.com/journals/ida.

A Framework for Modeling and Interpreting Patient Subgroups Applied to Hospital Readmission: Visual Analytical Approach.

A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient's risk of an adverse outcome and compare its accuracy with and without patient subgroup information. The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) (COPD: n=29,016; CHF: n=51,550; THA/TKA: n=16,498). For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility. In each condition, the visual analytical model identified patient subgroups that were statistically significant (Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated (Rand Index=0.92, 0.94, 0.89; P<.001, <.001, <.01), and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups (mean accuracy=99.6%, 99.34%, 99.86%). In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement (0.059, 0.11) but not as measured by the C-statistic or integrated discrimination improvement. Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond.

An Adaptive Partitioning and Multi-Granularity Network for Video-Based Person Re-Identification

Person re-identification (Re-ID) is a key technology used in the field of intelligent surveillance. The existing Re-ID methods are mainly realized by using convolutional neural networks (CNNs), but the feature information is easily lost in the operation process due to the down-sampling structure design in CNNs. Moreover, CNNs can only process one local neighbourhood at a time, which makes the global perception of the network poor. To overcome these shortcomings, in this study, we apply a pure transformer to a video-based Re-ID task by proposing an adaptive partitioning and multi-granularity (APMG) network framework. To enable the pure transformer structure better at adapting to the Re-ID task, we propose a new correlation-adaptive partitioning (CAP) of feature embedding modules that can adaptively partition person images according to structural correlations and thus retain the structure and semantics of local feature information in the images. To improve the Re-ID performance of the network, we also propose a multi-granularity (MG) module to better capture people feature information at different levels of granularity. We performed validation trials on three video-based benchmark datasets. The results show that the network structure based on the pure transformer can adapt to Re-ID tasks well, and our APMG network outperforms other state-of-the-art methods.

A method for documenting architectural solutions of computing platforms

The article describes a method for documenting the principles of functioning and internal organization of computing platforms, including reconfigurable computing systems and non-standard processor architectures. The novelty is in using of unified tools to describe: the design process and the computing process, hardware, software and tools, computing components of different granularity. The proposed approach is to describe computing platform as an ideal model that represents abstract algorithms for fulfilling functional requirements without specifying of how to implement it. Then the iterative model refinement follows including selection of physical implementation options, specifying the technological stacks, and additional mechanisms that provide the specified system qualities. A feature of the method is a kernel used for structuring information, classifying and describing the computational mechanisms the system consists of. The kernel includes elements common for different systems and is based on the analysis of a large number of computing architectures. The method describes the principles of the organization of platforms which are usually not considered together. These are: generalized processors with classical architecture which is an evolution of the von Neumann principles; systems based on microcontrollers; operating systems; large- and small-granular reconfigurable systems; specialized processors and accelerators; artificial neural networks. The proposed method can be used to structure information in both traditional and rapidly developing areas: reconfigurable systems and specialized processors. Based on the method, it is possible to create a common database of computing mechanisms suitable for use in different functional units of the system and at different levels of granularity. The results of the work can be useful for system architects to describe complex computing mechanisms consisting of software, hardware and dynamically generated adaptive “intelligent” components which will simplify their reuse and can be used to generate new architectural solutions. Also, the proposed method can be used in the process of training specialists, for a visual demonstration of the basic principles of computer technology.

Atlas-based data integration for mapping the connections and architecture of the brain.

Detailed knowledge about the neural connections among regions of the brain is key for advancing our understanding of normal brain function and changes that occur with aging and disease. Researchers use a range of experimental techniques to map connections at different levels of granularity in rodent animal models, but the results are often challenging to compare and integrate. Three-dimensional reference atlases of the brain provide new opportunities for cumulating, integrating, and reinterpreting research findings across studies. Here, we review approaches for integrating data describing neural connections and other modalities in rodent brain atlases and discuss how atlas-based workflows can facilitate brainwide analyses of neural network organization in relation to other facets of neuroarchitecture.

A service lens on cybersecurity continuity and management for organizations’ subsistence and growth

PurposeThis paper proposes a holistic, proactive and adaptive approach to cybersecurity from a service lens, given the continuously evolving cyber-attack techniques, threat and vulnerability landscape that often overshadow existing cybersecurity approaches.Design/methodology/approachThrough an extensive literature review of relevant concepts and analysis of existing cybersecurity frameworks, standards and best practices, a logical argument is made to produce a dynamic end-to-end cybersecurity service system model.FindingsCyberspace has provided great value for businesses and individuals. The COVID-19 pandemic has significantly motivated the move to cyberspace by organizations. However, the extension to cyberspace comes with additional risks as traditional protection techniques are insufficient and isolated, generally focused on an organization's perimeter with little attention to what is out there. More so, cyberattacks continue to grow in complexity creating overwhelming consequences. Existing cybersecurity approaches and best practices are limited in scope, and implementation strategies, differing in strength and focus, at different levels of granularity. Nevertheless, the need for a proactive, adaptive and responsive cybersecurity solution is recognized.Originality/valueThis paper presents a model that promises proactive, adaptive and responsive end-to-end cybersecurity. The proposed cybersecurity continuity and management model premised on a service system, leveraging on lessons learned from existing solutions, takes a holistic analytical view of service activities from source (service provider) to destination (Customer) to ensure end-to-end security, whether internally (within an organization) or externally.

Occlusion detection of traffic signs by voxel-based ray tracing using highly detailed models and MLS point clouds of vegetation

Visibility analysis plays a vital role in the design and placing of traffic signs in the urban street environment. This work investigates the occlusion detection of traffic lights and traffic signs caused by vegetation. The presented analysis method is built upon the inputs from the expected situation reflected by a highly detailed 3D city model and the as-is situation captured by 3D Mobile Laser Scanning (MLS). The model contains the location and orientation of streets, traffic lights, and traffic signs; the measurements add detail on irregular-shaped and morphing objects such as vegetation, respectively. The analysis covers the visibility of traffic lights and traffic signs by ray-tracing in an occupancy grid that is generated by the voxelization of the space. The voxels facilitate the distinction between occupied and empty space. The identification of unknown volumes is added and considered in the decision process, to cope with the regions invisible to the sensor. As output, we provide a visibility metric and detailed 3D space descriptions on different levels of granularity, including the knowledge of the semantic classes of traversed voxels. During the whole process, the awareness of unknown volumes is added to an otherwise binary decision between visible and invisible targets. Experiments are conducted on the TUM-MLS-2016 dataset. Results demonstrate that the proposed method is feasible for the detection of occlusions by vegetation in the street scenario, and reveal that the identification of unknown volumes proves necessary for a reliable interpretation of the measurements. • Agent-based visibility analysis of traffic installations. • Combination of a street environment model with actual MLS measurements of vegetation. • Hence solving that realistic volumetric modeling of trees is often not present in city models. • Inclusion of information on volumes not covered by measurements. • The analysis gains an additional level of confidence depending on lines of sight traveling through space of unknown occupation.

129. Standardization of cancer terminology in the Mondo Disease Ontology

Biomedical ontologies provide a standard computable representation of knowledge that can be used to integrate and navigate large amounts of heterogeneous data for downstream computational analysis and knowledge discovery. The Mondo Disease Ontology (Mondo) is a semantic resource that integrates several existing disease terminologies, provides precise, curated semantic mappings between them, and unifies them into one coherent classification of diseases. Cancer concepts in Mondo are formally classified into a hierarchical representation, which can be used to annotate data at different levels of granularity. Neoplasm is the top-level class and subtypes (subclasses) are largely aligned with the NCIt neoplasm branch, with malignant neoplasms (cancers) being classified as subtypes. Classes are grouped in multiple ways, such as by anatomical entities affected (such as cardiovascular or digestive system neoplasms), by malignancy (benign, pre-malignant, or malignant, or onset (childhood neoplasm). In addition, Mondo has representations of susceptibilities to cancers and hereditary neoplastic syndromes such as Lynch Syndrome and Li-Fraumeni syndrome. Mondo provides mappings to other disease resources such as the National Cancer Institute Thesaurus (NCIt), as well as cancer terminology from Orphanet, OMIM, Disease Ontology, and others. Mondo precisely annotates each mapping using strict semantics, so that we know when two diseases are precisely equivalent or merely closely related. Mondo is iteratively developed and revised and we invite the community to contribute to Mondo; visit github.com/monarch-initiative/mondo for details.

Privacy-Preserving in-bed pose monitoring: A fusion and reconstruction study

Background and objectives:Recently, in-bed human pose estimation has attracted the interest of researchers due to its relevance to a wide range of healthcare applications. Compared to the general problem of human pose estimation, in-bed pose estimation has several inherent challenges, the most prominent being frequent and severe occlusions caused by bedding. In this paper we explore the effective use of images from multiple non-visual and privacy-preserving modalities such as depth, long-wave infrared (LWIR) and pressure maps for the task of in-bed pose estimation in two settings. First, we explore the effective fusion of information from different imaging modalities for better pose estimation. Secondly, we propose a framework that can estimate in-bed pose estimation when visible images are unavailable, and demonstrate the applicability of fusion methods to scenarios where only LWIR images are available. Method:We analyze and demonstrate the effect of fusing features from multiple modalities. For this purpose, we consider four different techniques: (1) Addition, (2) Concatenation, (3) Fusion via learned modal weights, and 4) End-to-end fully trainable approach; with a state-of-the-art pose estimation model. We also evaluate the effect of reconstructing a data-rich modality (i.e., visible modality) from a privacy-preserving modality with data scarcity (i.e., long-wavelength infrared) for in-bed human pose estimation. For reconstruction, we use a conditional generative adversarial network. Results:We conduct experiments on a publicly available dataset for feature fusion and visible image reconstruction. We conduct ablative studies across different design decisions of our framework. This includes selecting features with different levels of granularity, using different fusion techniques, and varying model parameters. Through extensive evaluations, we demonstrate that our method produces on par or better results compared to the state-of-the-art. Conclusion:The insights from this research offer stepping stones towards robust automated privacy-preserving systems that utilize multimodal feature fusion to support the assessment and diagnosis of medical conditions.

Generalized multigranulation sequential three-way decision models for hierarchical classification

Hierarchical classification is an important research hotspot in machine learning due to the widespread existence of data with hierarchical class structures. The existing sequential three-way decision models mainly constructed the hierarchical condition information granules via concept hierarchy tree to discuss the three probabilistic regions for flat classification. However, in real-world applications, one may face not only the tree-structured data with hierarchical condition attributes but also more often the multi-level data with hierarchical decision attribute (hierarchical class labels). How to obtain acceptable decisions under different levels of granularity is the most important issue within the multi-level and multi-view data. To this end, we construct a generalized hierarchical decision table and propose a generalized hierarchical multigranulation sequential three-way decision model by combining multi-granularity and sequential three-way decisions. Specifically, we first design a generalized hierarchical decision table using concept hierarchy trees of all conditional attributes and decision attribute, and explore some basic properties. Then we decompose and aggregate condition and decision granules under different levels of granularity, propose the optimistic and pessimistic generalized hierarchical multigranulation three-way decision models to update the three probabilistic regions for flat and hierarchical classification, and discuss the relationships between these two models. Finally, the experimental results demonstrate that the proposed models are more suitable for different applications. These models will provide a novel insight and enrich the development of multigranulation three-way decisions from the perspective of multi-level and multi-view.

Verification Approach for Refactoring Transformation Rules of State-Based Models

With the increased adoption of Model-Driven Engineering (MDE), where models are being used as the primary artifact of software, it is apparent that greater attention to the quality of the models is necessary. Traditionally, refactoring is used to enhance the quality of software systems at the source-code level; however, applying refactoring at the model level will have a more significant improvement on the system. After refactoring a model, proving that it still preserves its original behavior is crucial. In this paper, we present a process for applying refactoring transformations to the Extended Finite State Machine (EFSM) models using verified transformation rules that have been proven to preserve the model's original behavior. We provide a simplified three-step verification approach that can be used to prove that a transformation rule will generate a transformed model that is semantically equivalent to the original model. To do this, we formally define semantical equivalence at three different levels of granularity: models, sub-models, and transitions. Additionally, we introduce five model transformation rules and we demonstrate how our verification approach is used to prove the correctness of these rules. Finally, we present two case studies where we apply the proposed transformation process which adopts the five verified transformation rules. Using model testing, we show that applying a sequence of transformations using the verified transformation rules will keep both the original and the transformed model semantically equivalent. Additionally, the case studies show that model transformation can be used to enhance certain pre-defined model characteristics.

OA31 Successes and challenges in harmonising 4 national Juvenile Idiopathic Arthritis cohorts: an example from CLUSTER consortium

Introduction/BackgroundThe CLUSTER consortium aims to identify biomarkers and strata that improve personalised treatments for JIA/JIA-uveitis. By bringing together knowledge and data, CLUSTER can conduct novel analyses in this rare, heterogeneous disease. Data harmonisation across existing JIA cohorts facilitates new, larger datasets that would otherwise take years to collect; however, challenges exist as datasets are often collected autonomously. Here we present progress towards a large-scale, unique JIA data resource, bringing together treatment data from 4 real-world JIA treatment studies.Description/MethodFour studies (CAPS, CHARMS, BCRD and BSPAR-ETN; the latter two being part of the UK JIA Biologics register) contributed data into CLUSTER.We created two clinical datasets of JIA patients starting first-line methotrexate (MTX) or tumour necrosis factor inhibitors (TNFi). Variables were selected based on a previously developed core dataset, accounting for different levels of granularity across studies. The same inclusion and exclusion criteria were agreed for both datasets, designed to allow for combined analysis of these.OpenPseudonymiser software encrypted NHS numbers - these were matched cross-study to identify duplicates and checked against known duplicate lists. Errors in NHS numbers and existing duplicate matches were identified and corrected. Each NHS number was assigned a CLUSTER ID, meaning 1 child has the same ID across all relevant studies such that children contributing similar data across multiple studies could be identified.Discussion/ResultsA total of 7013 records (from 5435 individuals) were identified; of which 2882 (41%, corresponding to 1304 individuals) represented the same child across >1 study. 197 individuals had duplicate records within 1 study, 961 in 2 studies, 142 in 3, and 4 children had duplicate records in all 4 studies.After removing 350 MTX and 605 TNFi duplicate entries, the final datasets contain 2899 and 2401 unique MTX and TNFi patients respectively; 1018 are in both datasets having received both treatments. Missingness across core outcome variables ranged from 10% (active joint count MTX timepoint 2) to 60% (physician VAS TNFi timepoint 2) and was not improved through combining datasets with duplicate entries. Specificity in some variables was lost to allow integration by combining data using least common denominators (e.g. ethnicity captured as Caucasian/Non-Caucasian, despite more specific categories available in some studies). Key learning points/ConclusionCombining data across studies has achieved dataset sizes rarely seen in JIA, which is invaluable to progressing research into personalised treatments and disease outcomes. However, losing specificity in some variables and missingness (a known challenge in observational data) and their impact on future analyses requires further consideration. Ongoing work includes identifying patients with both clinical and biological data that can be combined for more in-depth analyses. Both datasets are available for researchers to use via the CLUSTER Consortium Data Management Committee.

Plinian Core: The long and winding road

Plinian Core (PliC) is a set of vocabulary terms that can be used to describe different aspects of biological species information. Under "biological species information" all kinds of properties or traits related to taxa—biological and non-biological—are included. Thus, for instance, terms pertaining to descriptions, legal aspects, conservation, management, demographics, nomenclature, or related resources are incorporated. Plinian Core started as a collaborative project between Instituto Nacional de Biodiversidad (INBio, Costa Rica) and the Global Biodiversity Information Facility Spain (GBIF Spain) in 2005. In successive years, the National Commission for Knowledge and Use of Biodiversity (CONABIO, Mexico), the National Network of Open Biodiversity Data (SiB Colombia)/the Humboldt Institute (Colombia), the University of Granada (Spain) and the University of São Paulo (USP, Brazil) were also involved. In 2012. PliC aligned with the Biodiversity Information Standards (TDWG) and an umbrella Interest Group was created (Species Information IG, Pando 2017b) with developments carried out by its corresponding TDWG Task Group (Plinian Core Task Group 2020b). PliC design requirements include: ease of use, being self-contained, able to support data integration from multiple databases, and ability to handle different levels of granularity. Since its early stages, PliC was formalized as an XML Schema. Its terms can be grouped in its current version as follows: Metadata Base elements Record metadata Nomenclature and classification Taxonomic description Natural history Invasive species Habitat and distribution Demography and threats Uses, management and conservation Miscellaneous: associated party, measurement or fact, references and ancillary data Metadata Base elements Record metadata Nomenclature and classification Taxonomic description Natural history Invasive species Habitat and distribution Demography and threats Uses, management and conservation Miscellaneous: associated party, measurement or fact, references and ancillary data During its years of existence, PliC has been implemented in several real-life contexts (e.g., Spanish Ministry for the Ecological Transition and the Demographic Challenge 2020, CRBIO 2020, SIB Colombia 2020), and a set of extensions for using PliC in GBIF Integrated Publishing Toolkit (IPT) was developed (Plinian Core Task Group 2020a). PliC has been featured in some TDWG conferences, either to explore and explain how it relates to other TDWG standards (Pando 2018), how PliC handles specific information aspects (Pando 2017a), or how it may be used in biodiversity portals (Vargas et al. 2018, Vargas et al. 2019). Recent work has focused on how to represent terms and codify structure as an XML Schema (XSD) under the current TDWG Documentation Standard (SDS, Vocabulary Maintenance Specification Task Group 2017). Challenges identified in the process comprise: how to handle terms used in different contexts, how to preserve the semantic context of terms reflected in the XSD hierarchy in the SDS flat layout, and how to refer to terms and definitions defined in other data specifications. These are the areas to focus our contribution.

Path-scan: A novel clustering algorithm based on core points and connexity

A new clustering algorithm Path-scan aiming at discovering natural partitions is proposed. It is based on the idea that a (k,ɛ) coreset of the original data base represented by core and support patterns can be path-connected via a density differential approach. The Path-scan algorithm is structured in two main parts producing a connectivity matrix where partitions can be extracted at different levels of granularity. The first one aims to identify and select core and support points while the second one extracts connected components of core points and clusters with the help of support points. A simulation experiment based on synthetic and real world data sets was conducted to show the effectiveness of the proposed method.

Synchronization phenomena in dual-transistor spiking oscillators realized experimentally towards physical reservoirs

Transistor-based chaotic oscillators are known to realize highly diverse dynamics despite having elementary circuit topologies. This work investigates, numerically and experimentally using a ring network, a recently-introduced dual-transistor circuit that generates neural-like spike trains. A multitude of non-trivial effects are observed as a function of the supply voltage and coupling strength, including pattern formation under incomplete synchronization and sensitivity to additional long-distance links. Globally-applied noise exerts a synchronizing effect that interacts with the other control parameters. When the network is partitioned in halves at different levels of granularity, their interplay gives rise to adversarial route-to-synchronization phenomena. These results highlight the generative ability of this circuit and motivate its consideration towards the future realization of physical reservoirs.

Combining multiple granularity variability in a software product line approach for web engineering

Web engineering involves managing a high diversity of artifacts implemented in different languages and with different levels of granularity. Technological companies usually implement variable artifacts of Software Product Lines (SPLs) using annotations, being reluctant to adopt hybrid, often complex, approaches combining composition and annotations despite their benefits. This paper proposes a combined approach to support fine and coarse-grained variability for web artifacts. The proposal allows web developers to continue using annotations to handle fine-grained variability for those artifacts whose variability is very difficult to implement with a composition-based approach, but obtaining the advantages of the composition-based approach for the coarse-grained variable artifacts. A combined approach based on feature modeling that integrates annotations into a generic composition-based approach. We propose the definition of compositional and annotative variation points with custom-defined semantics, which is resolved by a scaffolding-based derivation engine. The approach is evaluated on a real-world web-based SPL by applying a set of variability metrics, as well as discussing its quality criteria in comparison with annotations, compositional, and combined existing approaches. Our approach effectively handles both fine and coarse-grained variability. The mapping between the feature model and the web artifacts promotes the traceability of the features and the uniformity of the variation points regardless of the granularity of the web artifacts. Using well-known techniques of SPLs from an architectural point of view, such as feature modeling, can improve the design and maintenance of variable web artifacts without the need of introducing complex approaches for implementing the underlying variability.

A simple and effective iterated greedy algorithm for structural balance in signed networks

Recently, problems related to structural balance have received widespread attention in the research fields of signed networks, such as signed network partition, community detection and correlation clustering. Structural balance problems aim to find a partition of a signed network such that the frustration index, the sum of positive edges between subsets and negative edges within subsets, is minimized, which is known to be nondeterministic polynomial-time (NP)-complete and remains an open challenge. Many heuristic and meta-heuristic algorithms are developed to minimize the frustration index. However, most extant algorithms require quite a few parameters and are not efficient enough for large signed networks. In this study, we present a simple and effective iterated greedy (IG) algorithm for solving the structural balance problems with the objective of frustration minimization. In the algorithm, a constructive greedy heuristic is proposed to generate and reconstruct solutions to the problem with high efficiency. A two-stage local search procedure is designed to exploit the search space at different levels of granularity. An adaptive destruction method is developed to enhance the exploitation ability of the algorithm in the early stage and maintain the exploration ability later. Additionally, two acceleration methods are proposed to help the algorithm get a better performance in large signed networks. The experimental results indicate that the proposed IG algorithm outperforms other meta-heuristics in the literature especially in terms of the computational time.

Visualization of aggregated information to support class-level software evolution

Context:Software is inherently prone to constant change, especially in the source code, making it difficult for developers to keep track of changes performed over time and to fully understand their implications. Objective:To this end, we present an Eclipse plug-in for visualizing heterogeneous information, collected from multiple sources, at different levels of granularity. This visualization provides a single graphical representation of a system’s change histories over multiple versions, allowing developers to identify the previous and present dependencies in the system, while adding new or removing and modifying the current functionalities of a software. Summarizing and associating the relevant changes in a single graph, further supports developers, not familiar with the overall system, to conduct a self-study and explore the systems design and changes of its functionality over time. Method:Our tool, DejaVu, initially infers and further visualizes change scenarios that have been applied to a given class in source code, across multiple versions of a software. DejaVu additionally augments the change information with prior commits from GitHub repositories, as well as associated issues from Jira issue tracking system. Evaluation:As part of the evaluation, we conducted a controlled experiment, recruiting participants with research or industrial programming experiences. The participants were asked to investigate and assess a set of change stories with and without the use of the DejaVu. As such, we empirically evaluated the impact of DejaVu in alleviating developers’ understanding of code class-level changes across multiple versions. Results:Our results showed an average of 52% reduction in completion time and a 51% increase in correctness of several change-comprehension tasks once, users adopted DejaVu in comparison to the manual completion of the same tasks. A student’s t-test verified the significant improvement in time and correctness of the tasks with p-values of 0.01 and 0.002. Conclusion:Visualizing aggregated information from multiple sources provides developers with a more comprehensive intuition of the change and its rationale, facilitating software maintenance tasks.