Domain-specific Framework Research Articles

Single Window Search System for Libraries using VuFind

The main objective of this study is to develop methods for enhancing the search capabilities of library collections by integrating various technological elements. Implementing this functional enhancement will provide users access to modern services and resources. The single-window search mechanism is created and built on the VuFind open-source discovery tool. The common access window has been created after properly configuring ILS-OPAC Koha, CONTENTdm, Digital Commons, DSpace, EPrints, Fedora, Greenstone, Omeka, and OPUS. It is based on LAMP architecture and uses Apache Solr, Tomcat, and Apache Tika to create indexes for easy document search through VuFind. This study suggests a new way to easily search and collect data from different domain-specific cluster tools using a single interface. This domain-specific framework provides an ideal environment for libraries to develop large-scale library discovery services. The originality of this paper lies in creating a prototype integrated framework to facilitate the easy installation and configuration of VuFind for integrating domain-specific software in both ILS and IDR.

SADFusion: A multi-scale infrared and visible image fusion method based on salient-aware and domain-specific

Image fusion intends to generate an informative image with maximally possible features and details of the source images. However, the existing deep-learning fusion methods are rarely considering the discrepancy of visible and infrared (IVIF) modalities, besides losing control of balance in preserving the texture details and thermal target, owing to the identical structure and parameters of multi-modality feature extractors’ designing and weak domain guidance of network training. In this paper, a novel multi-scale fusion network based on the salient-aware and domain-specific methods is proposed, termed as SADFusion. To boost the multi-modality feature extracting performance of our method, the proposed network adopts dual encoding with short connection and multi-scale structure. Moreover, the domain-specific framework and corresponding training strategy are designed to achieve the identical encoding for different image modalities. Moreover, multi-scale attention fusion modules (MSFAF modules) are proposed to effectively fuse the extracted complementary features at every scale. Finally, we construct the specific salient-aware loss to guide the model to trade off the preserving necessary information, by utilizing salient modality features as pixel-to-pixel intensity and gradient maps. Experiments based on the public datasets demonstrate the superiority of our method over the state-of-art fusion methods, which particularly highlights the targets and retains the effective information.

Efficient Optimization of a Regional Water Elevation Model With an Automatically Generated Adjoint

AbstractCalibration of unknown model parameters is a common task in many ocean model applications. We present an adjoint‐based optimization of an unstructured mesh shallow water model for the Baltic Sea. Spatially varying bottom friction parameter is tuned to minimize the misfit with respect to tide gauge sea surface height (SSH) observations. A key benefit of adjoint‐based optimization is that computational cost does not depend on the number of unknown variables. Adjoint models are, however, typically very laborious to implement. In this work, we leverage a domain specific language framework in which the discrete adjoint model can be obtained automatically. The adjoint model is both exactly compatible with the discrete forward model and computationally efficient. A gradient‐based quasi‐Newton method is used to minimize the misfit. Optimizing spatially‐variable parameters is typically an under‐determined problem and can lead to over‐fitting. We employ Hessian‐based regularization to penalize the spatial curvature of the friction field to overcome this problem. The SSH dynamics in the Baltic Sea are simulated for a 3‐month period. Optimization of the bottom friction parameter results in significant improvement of the model performance. The results are especially encouraging in the complex Danish Straits region, highlighting the benefit of unstructured meshes. Domain specific language frameworks enable automated model analysis and provide easy access to adjoint modeling. Our application shows that this capability can be enabled with few efforts, and the optimization procedure is robust and computationally efficient.

IEEE Special Issue on Innovative R&D Toward the Exascale Era

This special issue on Innovative research and development toward the exascale era explores new foundational and translational research toward enabling exascale computing for emerging scientific and societal challenges. Exascale computing is defined as the capability to perform 1018 operations per second. Productively harnessing such a scale of processing, storage, and networking capabilities for diverse domains— including high-performance computing (HPC) simulations, artificial intelligence (AI), and extreme data-driven computing— relies on not only revitalizing existing parallel and distributed computing technologies but also innovating new solutions. Papers in this special issue explore diverse topics in research encompassing parallel, distributed, and heterogeneous systems for exascale, including advances in applications, programming environments, runtimes, libraries, innovative algorithms, domain-specific frameworks, systems architecture, performance analysis, data processing, and networking technologies.

Designing a collection tree using Omeka

PurposeThe purpose of this study is to look into the three major new innovative components such as collection tree, tags cloud and geolocation for developing digital library system. This study aims to develop and design an integrated framework for enhancing this services.Design/methodology/approachThis study will develop a single purpose-driven framework for the domain. It will vary user-friendly architecture on expanding the collection tree based on a high-level operating system and plugins. Now software programs are available in the Omeka Web repository. The whole integrated framework has been designed based on the Linux Operating platform and LAMP architecture towards depends on proper installation and configuration of the “Collection Tree” module with Omeka for both the administration and user interfaces.FindingsWith this integrated structure, keyword cloud users will have easy access to objects and full-text content. Because it can save readers time, the collection tree is helpful in them. This integrated framework for constructing and designing the collection tree for the digital library allows geolocation-based searches from multiple collections.Originality/valueThe integrated domain-specific framework has been designed and developed for the libraries. So, it is feasible to provide better library services in inter-operability and crosswalk through the Omeka collection tree interface. It increases the advanced search mechanism for users using this innovative module and techniques towards creating collection trees based on tag clouds and geolocation for library professionals and advanced level users from multiple collections of hierarchy.

PACS.i - A Complexity Theory based Framework for Battle Management

A Platform based on Complexity Theory will consist of domain specific framework, dynamics, and systems. PACS.i is a framework through modern combat missions can be conveniently managed. PACS stand for Planner, Analyst, Commander and Soldier. While Planner and Analyst define pre and post battle view, soldier models frog’s view of combat in execution and commander bird’s view of same. These roles should handle Population, Spatial, Temporal and Causal dynamics (with their sub-classes). Also four types of Systems - regular, complex, chaos-based and stochastic can realize these architectures with varied benefits and losses. This paper explores the relation between roles, dynamics, and systems. PACS.i framework is result of synergy between all three of these.

Lightweight Cyber Security for Decision Support in Information Security Risk Assessment

Cyber-Security in the Internet of Things (IoT) is a major concern for information exploitation which hinder the growth of information system. To address security levels and issues, security risk assessment is considered an effective tool for system security, products, process, and readiness. Effective system vulnerabilities guidance is involved in the prioritization of security risk assessment. At present, the differential equation provides a significant tool for risk assessment. However, for second-order derivatives, the error rate is higher which impacts on overall risk assessment model. To overcome those limitations, this paper presented Decision Support Light Weight Risk Assessment Model (DSLiRAM). The proposed DSLiRAM is the domain-specific framework for security assessment. The proposed DSLiRAM is adopted in four stages for the specification of practices applied for cybersecurity and organizational characteristics. The proposed DSLiRAM includes a fuzzy differential equation with a second-order derivative. To minimize error rate Taylor series expansion is integrated with Fredholm for risk assessment. The proposed DSLiRAM is examined in three scenarios, RT server, BPCS, and HMI. Analysis of results stated that the proposed DSLiRAM significantly predicts risk and prevents the attack.

A Comprehensive State-of-the-Art Survey on Data Visualization Tools: Research Developments, Challenges and Future Domain Specific Visualization Framework

Data visualization is a powerful skill for the demonstration of meaningful data insights in an interactive and effective way. In this survey article, we collected 70 articles from last five years (2017-2022) to identify, classify, and investigate the various scopes, aspects and theories of data visualization. We also investigated the powerful applications of data visualization in various domains and fields such as visualization apps for health sector, Internet of things (IoTs), business dashboards, urban traffic management, smart buildings and environmental data visualization. However, after thorough investigation and classification, we conclude that, a comprehensive study is still missing about interactive, effective and efficient data visualization survey explaining basic current state-of-the-art best interactive visualization techniques, web-based tools and platforms, best performance theories, data structures and algorithms. In this survey article, we perform a thorough investigation to fill the gap on theoretical, analytical, statistical models and techniques for improving the performance of visualization. Current primary and domain specific future challenges are also reviewed, and related future research directions and opportunities are recommended.

Aligning Information Technology and Supply Chain

Information technology (IT) has a critical importance. Information technology governance (ITG) is the system that allows enterprises to master the complexity of IT in order to maximize its use and to foster the competitive advantage. Control objectives for information and related technology (COBIT) is a well-known IT governance (ITG) framework that groups IT best practices. There is little research about the correlation between COBIT and domain specific frameworks, like supply chain operation reference (SCOR) for supply chain (SC). The paper proposes an approach to map SCOR's components to COBIT with ArchiMate, which is a standard language to model enterprise architecture (EA). The mapping can simplify, by visualization, the complexity of both frameworks. It can also provide a view on possible overlaps and synergies between SCOR and COBIT. Two detailed illustrations are presented. The evaluation is done according the Bunge-Wand-Weber method. An example of the utility of the mapping is done for a real case scenario.

GAN-Based Data Augmentation Strategy for Sensor Anomaly Detection in Industrial Robots

In the current industry world, the industrial robot has emerged as a critical device to make the manufacturing process more efficient through automation. However, abnormal operation of industrial robots caused by sensor failures may interrupt the entire manufacturing process, thereby increasing production costs. In this paper, we first propose a domain-specific framework consisting of offline training and online inference to effectively detect anomalies in the scenario of industrial robotic sensors. In a nutshell, the framework can be identified in three folds: (1) the offline training obtaining historical data from the database to train the time series and anomaly detection models; (2) the online inference deploying the offline trained models for online anomaly detection in real-time; (3) the incremental learning updating the online model for a new type of anomalies. We then propose an improved Generative Adversarial Networks (GANs) named MSGAN with the adaptive update strategy mechanism based on WGAN-GP to generate fake anomaly samples, improving anomaly detection accuracy. Specifically, the Wasserstein distance with the gradient penalty is introduced to improve training stability and sample quality. Moreover, adapting the generator’s complexity in robotic sensors, an adaptive update strategy based on the loss change ratio is adopted to speed up training convergence. Extensive experiments based on real robotic sensor datasets demonstrate the effectiveness of the proposed framework. Moreover, the results demonstrate that the detection accuracy can be improved by the synthetic samples based on the proposed MSGAN algorithm.

JCOGIN: a programming framework for particle transport on combinatorial geometry

Domain-specific programming frameworks are usually effective to simplify the development of large-scale applications on supercomputers. This paper introduces a parallel programming framework named JCOGIN for particle transport on combinatorial geometry. JCOGIN provides a combinatorial geometry data model and a patch-based parallel computing model to manage the data distribution in parallel computing and implements the hybrid parallelism of the domain decomposition and the particle parallelism on MPI/OpenMP to overcome the bottleneck of huge memory demand and long computational time. The application programming interface of JCOGIN can support users to quickly develop their parallel particle transport applications. Based on this framework, users only need to write serial codes for large-scale numerical simulations on modern supercomputers. The parallel efficiency of applications based on JCOGIN can reach up to 80% on hundreds of thousands of CPU cores.

A domain-specific language framework for farm management information systems in precision agriculture

Farm management information system (FMIS) is an important element of precision agriculture to support the decision making process in the agricultural business. Developing FMIS is not trivial and requires the proper design and implementation models for supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders. To cope with these challenges, a Domain-specific language (DSL) framework for the design and development of precision-agriculture FMISs is proposed and evaluated. The DSL framework is developed based on a domain-driven design approach in which a feature diagram is provided that represents the common and variant features of the precision agriculture domain. The key requirements for the DSL framework are discussed, the scope of the DSL is defined, and the set of DSLs for supporting FMISs is presented. A controlled experiment demonstrates that the proposed DSL framework is easy to learn and to use, and is effective in developing high-quality system models for precision agriculture.

Comparison of data storage and analysis throughput in the light of high energy physics experiment MACE

Abstract High Energy Physics (HEP) Experiments produce large amounts of data. The data produced in these experiments are in the range of terabytes and petabytes. The explosion of data has posed a challenge in data capture, storage, data integrity, searching, querying, visualization and analysis. This has led to the development of domain-specific file formats like FITS, HDF5, analysis frameworks like ROOT, storage architectures like relational and NoSQL databases, and parallel and distributed data handling methodologies. In this paper, we investigate the read–write performance by comparing the HEP domain-specific framework ROOT and a NoSQL database Berkeley DB in the context of a gamma-ray Cerenkov experiment to meet the requirement of real-time data analysis. Major Atmospheric Cerenkov Experiment (MACE) is a 21 m gamma-ray telescope set up by BARC at HANLE, India. It will generate a few hundred gigabytes of data per observational night. Aiming at the real-time analysis of the data we have developed a dynamic reading mechanism by implementing a binary type provider for data retrieval from the Berkeley DB database. Data analysis queries were performed and compared both in ROOT files using ROOT query methods and in Berkeley DB using Language Integrated Queries (LINQ). Finally, a generic framework facilitating the online analysis of the data is proposed in this paper.

Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis.

MotivationAccurate morphological models of brain vasculature are key to modeling and simulating cerebral blood flow in realistic vascular networks. This in silico approach is fundamental to revealing the principles of neurovascular coupling. Validating those vascular morphologies entails performing certain visual analysis tasks that cannot be accomplished with generic visualization frameworks. This limitation has a substantial impact on the accuracy of the vascular models employed in the simulation.ResultsWe present VessMorphoVis, an integrated suite of toolboxes for interactive visualization and analysis of vast brain vascular networks represented by morphological graphs segmented originally from imaging or microscopy stacks. Our workflow leverages the outstanding potentials of Blender, aiming to establish an integrated, extensible and domain-specific framework capable of interactive visualization, analysis, repair, high-fidelity meshing and high-quality rendering of vascular morphologies. Based on the initial feedback of the users, we anticipate that our framework will be an essential component in vascular modeling and simulation in the future, filling a gap that is at present largely unfulfilled.Availability and implementationVessMorphoVis is freely available under the GNU public license on Github at https://github.com/BlueBrain/VessMorphoVis. The morphology analysis, visualization, meshing and rendering modules are implemented as an add-on for Blender 2.8 based on its Python API (application programming interface). The add-on functionality is made available to users through an intuitive graphical user interface, as well as through exhaustive configuration files calling the API via a feature-rich command line interface running Blender in background mode.Supplementary information Supplementary data are available at Bioinformatics online.

Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype

To derive meaningful insights from voluminous healthcare data, it is essential to convert it into machine understandable knowledge. Currently, machine understandable domain specific healthcare knowledge curation framework does not exist for complex neurological diseases such as subarachnoid hemorrhage stroke. We envisage futuristic clinical decision support systems and tools backed with such knowledge will aide in complex neurological disease prognosis, diagnosis, and treatment. Existing knowledge graphs (KGs) only contain concepts and relationships between them and offer this knowledge to information extraction and knowledge management applications. However, the proposed domain-specific automated KG curation framework enables extraction of concepts, relationships, individual and cohort graphs, and predictive knowledge. By employing ontology-based information extraction, ensemble learning and word embedding based on skip-gram techniques on structured and unstructured data from electronic health records of 1025 patients with an intracranial aneurysm, this paper proposes a novel fully automated framework to curate knowledge graph, consisting of concepts, different hierarchical and non-hierarchical relationships, and predictive rules for prediction of subarachnoid hemorrhage. The evaluation shows that proposed framework achieves 78% precision and 71% recall respectively, for concept extraction from clinical text. Taxonomic relationships evaluation had precision and recall of 68%, and 95%, respectively. Evaluation of knowledge to predict unruptured status using validation dataset shows accuracy, precision, recall, of 73%, 76%, and 90% respectively.

Domain-specific requirements analysis framework: ontology-driven approach

Requirement engineering (RE) is the first track in the course of software development that represents and analyses the problem domain for suitable solutions. This improves the quality of the software development and decreases the risk of budget overrun, delays and project failure. However, traditional methods are inadequate to address different problems in RE. Prime challenges among those are lack of interoperability between heterogeneous requirements models, less focus on validation of requirements, and inability to reuse similar requirement specification in different domains. To solve these challenges, semantic approaches are needed to represent commonalities and variability in requirements across different applications and domains. This paper is aimed to address this issue. A requirement analysis framework named as Ontology-driven requirement analysis (ODRA) is proposed in this paper. Proposed ontology-driven specification provides common semantics towards different requirements. Proposed framework also comprises Formal Scenario descriptions and cause-effect-dependency graphs to conduct an early and a detailed analysis of requirements. It is capable to represent 5W1H characteristics (What, Why, Who, When, Where and How) of requirements. Further, it devises a set of key validation rules and a customized rule-based reasoner to validate consistency, unambiguity, and traceability in requirement specifications.

LiSRA: Lightweight Security Risk Assessment for decision support in information security

Information security risk assessment frameworks support decision-makers in assessing and understanding the risks their organisation is exposed to. However, there is a lack of lightweight approaches. Most existing frameworks require security-related information that are not available and that are very challenging to gather. So they are not suitable in practice, especially for small and medium-sized enterprises (SMEs) who often lack in data and in security knowledge. On the other hand, other explicit SME approaches have far less informative value than the proposed framework. Moreover, many approaches only provide extensive process descriptions that are challenging for SMEs. In order to overcome this challenge, we propose LiSRA, a lightweight, domain-specific framework to support information security decision-making. It is designed with a two-sided input where domain experts initially provide domain-specific information (e.g. attack scenarios for a specific domain), whereupon users can focus on specifying their security practices and organisational characteristics by entering information that many organisations have already collected. This information is then linked to attack paths and to the corresponding adverse impacts in order to finally assess the total risk. Moreover, LiSRA can be used to get transparent recommendations for future security activities and presents detailed insights on the mitigating effects of each recommendation. The security activities are being evaluated taking into account the security activities already in place, and also considering the dependencies between multiple overlapping activities that can be of complementary, substitutive or dependent nature. Both aspects are ignored by most existing evaluation approaches which can lead to an over-investment in security. A prototype has been implemented, and the applicability of the framework has been evaluated with performance and robustness analyses and with initial qualitative evaluations.

Ethical Living and Work Self Efficacy Beliefs of Academicians of Higher Education in ASIA: A Key Determinant of One's Belief in One's Ability to Achieve the Desired Result in a Precise State of Affairs

Ethical academicians are perfectly virtuous. They always strive for greater virtue and follow strictly the moral stands of their profession. The ethical living and self-efficacy are important to them because of being fair and honest in their academics. Determinants of ethics include knowledge, values, attitude and intention. The domain-specific framework developed by Verbeke et al. (2004) has been considered as fundamental for identifying the dimensionality of work Self-efficacy and ethical challenges of academicians. A comprehensive literature review is undertaken regarding the concept of work Self-efficacy to assess workers' confidence and their ethical living in the workplace. This article examines theoretically and analytically the antecedent processes and information cues involved in the formation of work self-efficacy. Theoretical and numerical analysis of the key determinants of work self-efficacy increases the understanding of moral values, truthful fair and honest. Factors which decisively affect ethical living were identified from literature collected from the academicians who are working in the Five Regions of Asia - Central Asia (Tajikistan, Uzbekistan, Kazakhstan, Turkmenistan, Kyrgyzstan) East Asia (China, Mongolia, North Korea, South Korea, Japan, Hong Kong, Taiwan, Macau) South Asia (Sri Lanka, Bangladesh, India, Afghanistan, Pakistan, Bhutan, Nepal, the Maldives) through Google classroom. Methods of Statistical Analysis of self-efficacy data are descriptive statistics, Pearson Correlation Coefficient and Kolmogorov-Smirvnos normality test and Kruskal–Wallis one-way analysis of variance and Principal Component Analysis. Positive, mastery experiences give academicians a sense of accomplishment when they have faced a challenge ethically. Positive Zeal during Academic interaction, vicarious experiences that occur when academician see others succeed and feel an increased sense of their own ability to succeed. Sincere & deeper self, mingling with students, Social persuasion increase a teacher’s sense of confidence and ability to succeed. A proper plan of action has drawn special attention, and inferences pertaining to future research are discussed at the end of the critique.

A General Domain Specific Feature Transfer Framework for Hybrid Domain Adaptation

Heterogeneous domain adaptation needs supplementary information to link up different domains. However, such supplementary information may not always be available in real cases. In this paper, a new problem setting called hybrid domain adaptation is investigated. It is a special case of heterogeneous domain adaptation, in which different domains share some common features, but also have their own domain specific features. We leverage upon common features instead of supplementary information to achieve effective adaptation. We propose a general domain specific feature transfer framework, which can link up different domains using common features and simultaneously reduce domain divergences. Specifically, we learn the translations between common features and domain specific features. Then, we cross-use the learned translations to transfer the domain specific features of one domain to another domain. Finally, we compose a homogeneous space in which the domain divergences are minimized. We instantiate the general framework to a linear case and a nonlinear case. Extensive experiments verify the effectiveness of the two cases.

A Pattern-Based Development Approach for Interaction Flow Modeling Language

Development and deployment technologies for data-intensive web applications have considerably evolved in the last years. Domain-specific frameworks or model-driven web engineering approaches are examples of these technologies. They have made possible to face implicit problems of these systems such as quick evolving business rules or severe time-to-market requirements. Both approaches propose the automation of redundant development tasks as the key factor for their success. The implementation of CRUD operations is a clear example of repetitive and recurrent task that may be automated. However, although web application frameworks have provided mechanisms to automate the implementation of CRUD operations, model-driven web engineering approaches have generally ignored them, so automation has not been properly faced yet. This paper presents a pattern-based development approach for the Interaction Flow Modeling Language as a way to finally automate repetitive specification tasks. Our approach is illustrated by defining and applying IFML patterns for CRUD operations. Additionally, a supporting tool, which enables automation, is shown. The suitability of our approach and the utility of its tool have been evaluated by its application into several real projects developed by a software company specialized in model-driven web application development. The results obtained present evidence of a significant productivity improvement obtained by the automation of the IFML specification of CRUD operations.