Interface Framework Research Articles

BIM-Based Human Machine Interface (HMI) Framework for Energy Management

This study proposes a Building Information Modeling (BIM)-based Human Machine Interface (HMI) framework for intuitive space-based energy management. The BIM-based HMI supports building managers with a method of linking data between BIM and Building Energy Management System (BEMS), which are heterogeneous systems, and provides space-based real-time energy monitoring. This study also proposes a BIM and BEMS data linking framework for systematic BIM-based HMI development. Towards this end, the BIM-based HMI framework was defined after deriving the considerations and requirements necessary for linking the energy control point and BIM through a questionnaire designed by practitioners. Through case analysis, the authors implemented BIM-based HMI and analyzed its effects. The results of the analysis confirmed the positive effects (3.9/5.0) on the connectivity of BIM-based HMI, the benefits (4.3/5.0) for real-time data monitoring, the system function expandability, and the BIM-based spatial intuitiveness.

A universal closed-loop brain–machine interface framework design and its application to a joint prosthesis

Brain–machine interface (BMI) system offers the possibility for the brain communicating with external devices (such as prostheses) according to the electroencephalograms, but there are few BMI frameworks available for flexible design systems. In this paper, first of all, inspired by the single-joint information transmission (SJIT) model, a Wiener-filter-based decoder and an auxiliary controller based on model predictive control strategy are designed to rebuild the information pathways between the brain and prosthesis. Specifically, the decoder is used to decode the neuron activities from cerebral cortex, and the auxiliary controller is used to calculate control inputs, which injected to the SJIT model as feedback information. Then, a universal closed-loop BMI framework available for designing flexible systems is proposed and formulated on the basis of the brain model, decoder, auxiliary controller and prosthesis, and it can well recovery the motor function of prosthesis. Finally, a simulation and another experiment are designed to show that the presented closed-loop BMI framework is feasible and can track the target trajectory accurately, and the presented framework with actual prosthesis can successfully achieve the target position along the target trajectory.

Cement strength prediction using cloud-based machine learning techniques

ABSTRACT This paper describes a cloud-based software framework to predict cement strength for 2 days, 7 days and 28 days. Levenbarg-Marquardt back-propagation-artificial neural network (LMBP-ANN) is used to build a prediction model. This ANN model uses 70% of data for training (70%, 212 data records), testing (15%, 46 data records) and for validation (15%, 46 data records). A total of 16 significant input parameters are considered for the cement strength prediction. The user interface and software framework are built using the Python programming language. Multiple Python packages are used for the implementation of the ANN model. The cloud server having Ubuntu operating system has been used to host the web application for prediction of cement strength. The software application is tested using real-time data from various cement industries. The prediction of the cement strength of the proposed ANN-based software application appears to be very similar to those currently generated in experimental data in the cement manufacturing industry. The adequacy of the developed model based on the back-propagation ANN algorithm is confirmed as the Pearson correlation of experimental value and predicted value. The calculated value of R for experimentations on the data is 0.82539 and is 0.6813.

Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework.

BackgroundAn estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse.ObjectiveThe aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse.MethodsWe developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation).ResultsOur technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network.ConclusionsThe proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.

Методи та інструменти розробки інформаційної системи валідації результатів неформального навчання

The publication describes methods and tools for development an information system (IS) for acceptance of the non-formal and informal learning results on base of computer ontologies. ESCO ontology of the multilingual skills classifier is used as a prototype of domain ontology that provides knowledge for developed IS. We propose the main stages of development of IS for validation of the results of non-formal learning that include creation of an ontology schema, process of integration of the obtained ontology into the RDF repository, development of application architecture and creation of user interface. The modern approaches to design and development of knowledge-oriented distributed applications intended for functioning in the open Web environment are analyzed, the existing methods and software tools for the presentation and analysis of knowledge and their suitability for solving the task are considered. We propose an innovative method of IS development based on the use of such elements of the Semantic Web project as ontologies, Web services and software agents. The paper describes the tools used in process of development of IS for validation of non-formal learning outcomes. In particular, we analyse Neo4j database management system serving the GraphDB database, the specifics of connectors and SPARQL requests to the data stored in the RDF repository and the tools used for web server creation. Comparison of PHP frameworks for web applications is performed in consideration of task requirements. Functional modeling of IS in order to determine its main functionalities is performed, and the DFD data flow diagrams of system are designed. The benefits of Laravel software are established on base of the analysis of such criteria as security, readiness to installation of plugins and libraries, support for the MVC (Model-View-Controller) concept. User interface is developed to ensure user dialogue with IS. Authers analyse software tools oriented on development of user interface and select React framework that works efficiently with all software tools selected for IS development on the previous stages of analysis.

Scalable Synthesis of LiF‐rich 3D Architected Li Metal Anode via Direct Lithium‐Fluoropolymer Pyrolysis to Enable Fast Li Cycling

Lithium metal anode holds an important position in fast‐charging batteries. But lithium dendrite issues tend to exacerbate at high currents. LiF can be considered as an effective way to improve the Li metal surface electrochemical stability to achieve high power and high energy. However, most of reported work are relying on in situ formation of a 2D LiF on Li metal in liquid electrolyte, which limits the scalability and plated Li quantity. Here, we address this challenge and report a scalable synthesis of LiF‐rich 3D architected Li metal anode via a direct pyrolysis of molten lithium and fluoropolymer to enable fast Li charging with high current density (20 mA cm−2) and high areal capacity (20 mAh cm−2). The 3D structure is synthesized by the pyrolysis of fluoropolymer with Li metal and results show high similarity to the pristine electrolyte‐derived solid‐electrolyte‐interphase (SEI). This concept using pyrolysis of fluoropolymer with Li‐containing active materials could be also extended to modify Li metal oxide cathode (e.g., LiNi0.5Mn1.5O4) for mixed conductive interphase and engineer Li solid ion conductors (e.g., Li garnet‐type oxides) for interface stabilization and framework design.

A Transfer Learning Framework for RSVP-based Brain Computer Interface.

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.

Towards the Design of Context-Aware Adaptive User Interfaces to Minimize Drivers’ Distractions

The usage of a smartphone while driving is a pervasive problem and has been acknowledged as a significant source of road accidents and crashes. Several solutions have been developed to control and minimize risky driving behavior. However, these solutions were mainly designed from the perspective of normal users to be used in a nondriving scenario. In a driving scenario, any deviation from these assumptions (e.g., touching or taping interfaces and looking to visual items) could impact driving performance. In this research paper, we aimed to design and develop a context-aware adaptive user interface framework to minimize driver distraction. The proposed framework is implemented in Android platform, namely, “DriverSense,” which is capable of adapting smartphone user interfaces based on contextual factors including driver preferences, environmental factors, and device usage in real time using adaptation rules. The proposed solution is evaluated both in real time using AutoLog application and through an empirical study by collecting data from 93 drivers through a mixed-mode survey using a questionnaire. Results obtained from AutoLog dataset show that performing activities on smartphone native interfaces while driving leads to abrupt changes in speed and steering wheel angle. However, minimal variations have been observed while performing activities on DriverSense interfaces. The results obtained from the empirical study show that the data are found to be internally consistent with 0.7 Cronbach’s alpha value. Furthermore, an Iterated Principal Factor Analysis (IPFA) retained 60 of a total of 61 measurement items with lower uniqueness values. The findings show that the proposed solution has significantly minimized the driver distractions and has positive perceptions in terms of usefulness, attitude, learnability and understandability, and user satisfaction.

A User Interface (UI) and User eXperience (UX) evaluation framework for cyberlearning environments in computer science and software engineering education

Despite the widespread availability and increasing use of cyberlearning environments, there remains a need for more research about their usefulness in undergraduate education, particularly in STEM education. The process of evaluating the usefulness of a cyberlearning environment is an essential measure of its success and is useful in assisting the design process and ensuring user satisfaction. Unfortunately, there are relatively few empirical studies that provide a comprehensive test of the usefulness of cyberlearning in education. Additionally, there is a lack of standards upon whose usefulness evaluators agree.In this research, we present multiple user studies that can be used to assess the usefulness of a cyberlearning environment used in Computer Science and Software Engineering courses through testing its usability and measuring its utility using user interface and user experience evaluations. Based on these assessments, we propose an evaluation framework to evaluate cyberlearning environments. To help illustrate the framework utility and usability evaluations, we explain them through an example SEP-CyLE (Software Engineering and Programming Cyberlearning Environment). The evaluation techniques used are cognitive walkthroughs with a think-aloud protocol and a heuristic evaluation survey. We further use a network-based analysis to find the statistically significant correlated responses in the heuristic evaluation survey with regard to the students’ perceptions of using SEP-CyLE.Our goal is to improve cyberlearning practice and to emphasize the need for evaluating cyberlearning environments with respect to its designated tasks and its users using UI/UX evaluations. Our experiments demonstrated participants were able to utilize SEP-CyLE efficiently to accomplish the tasks we posed to them and to enhance their software development concepts, specifically, software testing. We discovered areas of improvement in the visibility and navigation of SEP-CyLE's current design. We provide our recommendations for improving SEP-CyLE and provide guidance and possible directions for future research on designing cyberlearning environments for computer education.

A Novel Framework for Fog Computing: Lattice-Based Secured Framework for Cloud Interface

Interconnection and intercommunication between people, processes, and things have been enhanced with the development of the Internet of Things (IoT) and extended with fog computing for better efficiency. Fog computing improves the network services and circumvents the problem of escalated data management as an interface between cloud and terminal with the requirement of secure data transmission. In this article, a novel security framework is designed for fog computing intended for improving the security of IoT. This framework uses single point of aggregation from fog interface and lattice cryptographic approach to establish the security for the services. Azure cloud platform and Sage lattice cryptographic base have been employed to evaluate the performance of the framework. It is noticed that the Level 1-Level 2 (L1–L2) cache implementation in the system significantly improves the efficiency of the framework. The results show that the proposed framework is robust in preventing attacks on fog nodes and additionally provides an advantage for low communication overhead.

Lessons Learned While Migrating From Swing to JavaFX

The authors describe a case study of the migration of an interactive diagramming tool written in Java from the Swing Graphical User Interface framework to the more recent JavaFX framework. The study distills the authors' experience identifying what information was needed to support the migration effort, and how the information was ultimately discovered. The outcome is presented in a set of five lessons about the discrepancies between expectations and reality in the search for information when migrating software between major frameworks.

A Brain–Computer Interface Framework Based on Compressive Sensing and Deep Learning

Compression of brain–computer interface (BCI) signals is significant to reduce transmission bandwidth to cloud/remote servers and to minimize storage cost. Precise reconstruction of the compressed signal is also crucial as these data are further used for spike detection and/or classification. The conventional compressive sensing (CS) techniques to reconstruct the compressed BCI signals are computationally expensive. There are several existing techniques for CS reconstruction, including block-sparse Bayesian learning and block-based CS, which also work to replace a reconstruction methodology of CS in medical imaging with deep learning (DL) techniques. DL can be helpful in reconstructing compressed BCI signals, including Electroencephalography (EEG) and Electrocorticography (ECoG). Pertinent to that, in this work, a convolutional neural network (CNN) based reconstruction framework has been proposed to reconstruct spike signals that has been highly compressed using the CS technique. An accuracy of 91.62% has been achieved over signals compressed at 90% compression rate, when compared with original signals using a cross-correlation technique.

Protecting Unavoidable Attacks Inborder

Remote Integrated Network Sensors (WINS) .This thought should be possible bysensing the bloodgroup by utilizing their unique mark and nowprovide another observing and control ability formonitoring the fringes of the nation and coordinating the unique mark of the psychological militants and distinguishing their blood gathering while at the same time going into the outskirt. Utilizing this idea we can without much of a stretch distinguish a more interesting or a few psychological oppressors entering the fringe. The outskirt region is isolated into number of hubs. Every hub is in contact with one another and with the fundamental hub. The commotion created by the strides of the outside are gathered utilizing the sensor. The human fingerprints can be separated into three primary classes as circles, whorls, curves, as depicted.This detected sign is then changed over into power phantom thickness and the contrasted and reference estimation of our accommodation. In like manner the thought about worth is handled utilizing a chip, which imparts suitable signs to the principle hub. Consequently the outsider is distinguished at the fundamental hub. A progression of interface, signal handling, and correspondence frameworks have been actualized in smaller scale power CMOS circuits. A small scale power range analyzer has been created to empower low force activity of the whole WINS framework. In this way WINS require a Microwatt of intensity. Be that as it may, it is less expensive when contrasted with other security frameworks, for example, RADAR under use. It is even utilized for short separation delay. Henceforth it is sensibly quicker. On a worldwide scale, WINS will allow observing of land, water, and air assets for ecological checking. On a national scale, transportation frameworks, and fringes will be checked for effectiveness, wellbeing, and security.

LogoJS: a Javascript package for creating sequence logos and embedding them in web applications.

Sequence logos were introduced nearly 30 years ago as a human-readable format for representing consensus sequences, and they remain widely used. As new experimental and computational techniques have developed, logos have been extended: extra symbols represent covalent modifications to nucleotides, logos with multiple letters at each position illustrate models with multi-nucleotide features and symbols extending below the x-axis may represent a binding energy penalty for a residue or a negative weight output from a neural network. Web-based visualization tools for genomic data are increasingly taking advantage of modern web technology to offer dynamic, interactive figures to users, but support for sequence logos remains limited. Here, we present LogoJS, a Javascript package for rendering customizable, interactive, vector-graphic sequence logos and embedding them in web applications. LogoJS supports all the aforementioned logo extensions and is bundled with a companion web application for creating and sharing logos. LogoJS is implemented both in plain Javascript and ReactJS, a popular user-interface framework. The web application is hosted at logojs.wenglab.org. All major browsers and operating systems are supported. The package and application are open-source; code is available at GitHub. zhiping.weng@umassmed.edu. Supplementary data are available at Bioinformatics online.

Science-policy interplay on air pollution governance in China

Air quality in China is a major public health, social and economic concern. Air pollution governance and research in China have been increasingly active in the past decade, especially since 2013 when strict emission controls were implemented. Such emission control policies have been informed through dialogue between scientists and policy-makers on the sources and transport of air pollution in order to identify potential control measures. However, the process of making regulatory decisions about air pollution controls at this science-policy interface in China has rarely been analysed or discussed. We outline four classical science-policy models for making regulatory decisions proposed by scholars: (i) the decisionist model – whereby policy dictates what science and regulatory decisions are required; (ii) the technocratic model – where science dictates policy directly; (iii) the inverted decisionist model (where scientists advise policy-makers on what policy is required); and (iv) the co-evolutionary model (where policy-makers and scientists jointly create regulatory decisions). Boundary-actors play a key role in this co-evolutionary model. They operate as ‘gate-keepers’ between scientists and policy-makers. Most contemporary studies of the science-policy interface argue that the co-evolutionary model best captures the reality of how science and policy interact effectively to make regulatory decisions. To assess which of these models most closely resemble decision-making at the air pollution science-policy interface, we conducted a case study on “air quality climate services” and held workshops with Chinese scientists, decision-makers and stakeholders. A typology of existing scientific approaches to explore air quality climate science is presented. The workshop results show that the current air quality climate science-policy interplay occurs most strongly in accordance with the co-evolutionary model whereby the Beijing Climate Centre and the National Environmental Monitoring Centre operate as the key boundary actors between science and policy, specifically for a seasonal air pollution haze outlook service. We illustrate that current seasonal haze outlooks carefully avoiding quantification. We then present a conceptual framework of the air pollution science-policy interface in China, which captures the main participants and the interactive flow of information between them.

Biosecurity risk mapping and gap analysis in South East Asia

IntroductionIn today’s globalized world where travel is commonplace, a threat in one region can easily spread throughout the whole world. It is, therefore, essential for multi-disciplinary risk assessment, risk mitigation, and collaborative strategies to take place among various stakeholders to mitigate this. Any strategic plan to deal with biosecurity, therefore, needs to be a complete loop, top to bottom and bottom to top. This paper describes the results of Project 62, which involved mapping and biosecurity risk assessment in South East Asia. Materials and methodsThe mapping and biosecurity risk assessment activities for the participating partner countries was carried out in two phases. The first phase involved risk assessment by six partner-country bio-experts for their own countries and the second phase involved conducting a joint Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and risk assessment for the South East Asian region as whole, together with international bio-experts at a workshop. Results and discussionThe following key recommendations of the project show that biosecurity needs to be addressed through stakeholder engagement at multiple levels starting from the top echelons of the government to the worker who needs to recognize and understand the threats they might face: 1.Systematic analysis of existing information from BWC ISU Article 10, IHR capacities, JEE, UN Security Council Resolution 1540 Action Plan, EU CBRN NAP, and other relevant sources to develop a common understanding of the definition of biosecurity for all stakeholders in the region.2.Creation of programs for awareness building in biosecurity, not limiting them to laboratory biosecurity but using an all-inclusive approach to include border biosecurity, pandemic response, etc.3.Creation of a country-specific list of high-risk biological materials.4.Compulsory standardized biosecurity training, including responsible conduct in biosecurity research.5.Framework for one-health and security interface addressing biosecurity threats.6.Framework for ensuring information security in relation to biosecurity.7.Involvement of local/regional experts in the implementation of EU-CBRN CoE projects.

A closed-loop brain–machine interface framework design for motor rehabilitation

Brain–machine interfaces (BMIs) can be adopted to rehabilitate motor systems for disabled subjects by sensing cortical neuronal activities and creating new method. In this paper, to achieve the function of motor rehabilitation, two generalized BMI frameworks, including decoders, encoders and auxiliary controllers, are proposed and compared based on a classical single-joint information transmission model. Firstly, a decoder based on the Wiener filter and an encoder based on a network of spiking neurons are designed to compensate for the absent information pathway, and a charge-balanced intra-cortical microstimulation current is chosen as the input of the spiking neuron network; Secondly, to formulate closed-loop BMI frameworks, two auxiliary controllers are designed according to the strategy of model predictive control, where the controller inputs are the position of joint muscle trajectories and the average firing activity trajectories of perceived position vector neurons. Thirdly, considering that several integer parameters are included in the charge-balanced intra-cortical microstimulation current and that the optimization problem for solving the control inputs also includes these decision variables, a particle swarm optimization algorithm is adopted to solve the hard optimization problem. We compare the motor recovery effectiveness of the two presented frameworks through these simulations and choose the better framework for future BMI system design. The proposed frameworks provide a important theoretical guidance for designing BMI system applied in future life.

Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing

The increasing amount of marketing content in e-commerce websites results in the limited attention of users. For recommender systems, the way recommended items are presented becomes as important as the underlying algorithms for product selection. In order to improve the effectiveness of content presentation, marketing experts experiment with the layout and other visual aspects of website elements to find the most suitable solution. This study investigates those aspects for a recommending interface. We propose a framework for performance evaluation of a recommending interface, which takes into consideration individual user characteristics and goals. At the heart of the proposed solution is a deep neutral network trained to predict the efficiency a particular recommendation presented in a selected position and with a chosen degree of intensity. The proposed Performance Evaluation of a Recommending Interface (PERI) framework can be used to automate an optimal recommending interface adjustment according to the characteristics of the user and their goals. The experimental results from the study are based on research-grade measurement electronics equipment Gazepoint GP3 eye-tracker data, together with synthetic data that were used to perform pre-assessment training of the neural network.

Visualization for Micromagnetics With Synchronized Plotting

Micromagnetic simulations can present both time- and cost-savings, as complicated phenomena simulated a priori provide a valuable intuition in adjusting experimental parameters. However, they require insightful post-analysis, which can be tedious because local changes in magnetization occur rapidly. We have developed an application that allows for viewing multiple data objects such as graphs, 2-D layer visualizations, or 3-D graphic structures synchronized with each other. We believe that it could be of help for scientists who wish to investigate and verify the results obtained from simulation software like OOMMF or MuMax3 in a deeper context. In this article, we present a complete software suite built using the Qt framework for graphical user interface and the PyOpenGL framework for 3-D representation of magnetic structures.

Assimilation of Low-Peaking Satellite Observations Using the Coupled Interface Framework

Abstract Coupled data assimilation (DA) provides a consistent framework for assimilating satellite observations that are sensitive to several components of the Earth system. In this paper, we focus on low-peaking infrared satellite channels that are sensitive to the lower atmosphere and Earth surface temperature (EST) over both ocean and land. Our atmospheric hybrid-4DVAR system [the Navy Global Environmental Model (NAVGEM)] is extended to include the following: 1) variability in the sea surface temperature (both diurnal variability and climatological perturbations to the ensemble members), 2) the coupled Jacobians of the radiative transfer model for the infrared sensors, and 3) the coupled covariances between the EST and the atmosphere. Our coupling approach is found to improve forecast accuracy and to provide corrections to the EST that are in balance with the atmospheric analysis. The largest impact of the coupling is found on near-surface atmospheric temperature and humidity in the tropics, but the impact extends all the way to the stratosphere. The role of each coupling element on the performance of the global atmospheric circulation model is investigated. Inclusion of variability in the sea surface temperature has the strongest positive impact on the forecast quality. Additional inclusion of the coupled Jacobian and ensemble-based coupled covariances led to further improvements in scores and to modification of the corrections to the ocean boundary layer. Coupled DA had significant impact on latent and sensible heat fluxes over land, locations of western boundary currents, and along the ice edge.