Representation Format Research Articles

Development of a Knowledge Base for Chronic Wound Management Using the Decision Model & Notation.

Chronic wounds have significant impacts on patient health-related quality of life (HRQoL) and the healthcare expenditures. Various complex decision-making scenarios arise from wound management. Clinical decision-making systems (CDSS) can assist in relieving healthcare providers in these complex decision-making processes and improve the quality of care. In our study, we used the Decision Model & Notation (DMN) standard as a knowledge representation format to implement a knowledge base for chronic wound material recommendation in phase-based therapy. The resulting decision model is theoretically consistent and sustainable. With this study, we also emphasized the need of a semantic interoperability framework. This opens further research possibilities regarding the improvement of the model and the interest of DMN for decision models in clinical fields.

VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling

Machine learning represents a milestone in data-driven research, including material informatics, robotics, and computer-aided drug discovery. With the continuously growing virtual and synthetically available chemical space, efficient and robust quantitative structure–activity relationship (QSAR) methods are required to uncover molecules with desired properties. Herein, we propose variable-length-array SMILES-based (VLA-SMILES) structural descriptors that expand conventional SMILES descriptors widely used in machine learning. This structural representation extends the family of numerically coded SMILES, particularly binary SMILES, to expedite the discovery of new deep learning QSAR models with high predictive ability. VLA-SMILES descriptors were shown to speed up the training of QSAR models based on multilayer perceptron (MLP) with optimized backpropagation (ATransformedBP), resilient propagation (iRPROP‒), and Adam optimization learning algorithms featuring rational train–test splitting, while improving the predictive ability toward the more compute-intensive binary SMILES representation format. All the tested MLPs under the same length-array-based SMILES descriptors showed similar predictive ability and convergence rate of training in combination with the considered learning procedures. Validation with the Kennard–Stone train–test splitting based on the structural descriptor similarity metrics was found more effective than the partitioning with the ranking by activity based on biological activity values metrics for the entire set of VLA-SMILES featured QSAR. Robustness and the predictive ability of MLP models based on VLA-SMILES were assessed via the method of QSAR parametric model validation. In addition, the method of the statistical H0 hypothesis testing of the linear regression between real and observed activities based on the F2,n−2 -criteria was used for predictability estimation among VLA-SMILES featured QSAR-MLPs (with n being the volume of the testing set). Both approaches of QSAR parametric model validation and statistical hypothesis testing were found to correlate when used for the quantitative evaluation of predictabilities of the designed QSAR models with VLA-SMILES descriptors.

A survey of historical document image datasets

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies.

On Uncertainty Representation Formats in Solving Measurement Problems

On Uncertainty Representation Formats in Solving Measurement Problems

Students’ conceptual understanding on vector topic in visual and mathematical representation: a comparative study

This study aims to compare students’ abilities in understanding vector topics in two representations, namely visual and mathematical. This is a comparative research. Data were collected by survey. The research was conducted on 191 first-year undergraduate students of physics and physics education. The research instrument consisted of 14 multiple choice questions (7 questions of mathematical representation and 7 questions of verbal representation). Data analysis was performed by determining descriptive statistics and paired sample t-test. The results showed that the students’ ability to understand vector concepts with mathematical representations was better than those in verbal representations. Students’ mean score in verbal and mathematical representation formats are 33.91 and 59.17. Based on the results of the paired sample t-test obtained t = −12.96 and sig. = 0.00. These results indicate that the students’ ability to understand vector concepts in verbal and mathematical representation formats is significantly different. This study’s results came that students’ understanding of vector concepts still depends on the representation of the questions because their understanding is not coherent. Based on these findings, vector learning should be focused on the meaning of vectors in various representations, and connecting the meanings of vector operations in various representations, not only on mathematics.

Standardization of Human Metabolic Stoichiometric Models: Challenges and Directions

Genome-scale metabolic network models are of great importance in systems biology research, as they are used in metabolic activity dynamics studies and provide the metabolic level representation in multi-omic investigations. Especially for human, accurate metabolic network reconstruction is important in biomedical research and drug discovery. Today, there exist many instances of the human metabolic network as a whole and in its tissue-specific versions. Some are improved updates of models reconstructed from the same research team, while others are combinations of models from various teams, in an effort to include all available information from genome annotation and omic datasets. A major challenge regarding the human stoichiometric models in particular is the standardization of the reconstruction methods, representation formats and model repositories. Stoichiometric model standardization will enable the educated selection of the model that better fits the goals of a study, the direct comparison of results from various flux analysis studies and the identification of model sections that require reconsideration and updating with respect to the annotation of the human genome and proteome. Standardized human metabolic models aligned to the human genome will be a very useful tool in multi-omic studies, enabling the direct and consistent integration of the metabolic with the gene regulation and protein interaction networks. In this work, we provide a thorough overview of the current collection of human metabolic stoichiometric models, describe the current issues regarding their direct comparison and alignment in the context of the various model repositories, exposing the standardization needs, and propose potential solutions.

System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation.

BackgroundClinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model.ObjectiveHere we present a novel approach based on dynamic guideline visualization that incorporates the individual patient’s current treatment context.MethodsWe derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine.ResultsWe implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians.ConclusionsThe novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.

Cognitive Biases in Risk Communication during Post-Flight Debrief

ABSTRACT Objective This paper aims to identify cognitive biases in risk communication among pilots during flight debrief. We evaluate the effect of framing language, representation format, and parameter type measured on how pilots interpret flight information. Background Advancements in technology have created an opportunity for a data-centric approach to flight instruction. The understanding of virtual debrief formats, consisting of visualizations of flight data, relies on the pilot perceiving the information displayed accurately. Cognitive biases may impact how pilots perceive the information and therefore affect how they react to it in future flights. Method We used flight data to design ways to communicate the information and developed a display prototype for the user. We asked pilots to evaluate eight different representations through a scenario-based survey. Pilots reviewed a flight and rated how risky they thought it was and the likelihood of their debrief resulting in any behavioral changes in upcoming flights. Results Most significantly, the graphical representation format caused pilots to report a lower risk for the same flight, compared to the numerical representation format. Conclusion The analysis of the survey has shown that debrief representation affects risk perception, but not necessarily willingness to change unsafe behaviors. Respondents were highly motivated to change unsafe behaviors, independent of how the information was presented to them. Future work should develop specific guidelines to be followed when designing debrief products.

Hardware Optimizations of the X-ray Pre-Processing for Interventional Computed Tomography Using the FPGA

In computed tomography imaging, the computationally intensive tasks are the pre-processing of 2D detector data to generate total attenuation or line integral projections and the reconstruction of the 3D volume from the projections. This paper proposes the optimization of the X-ray pre-processing to compute total attenuation projections by avoiding the intermediate step to convert detector data to intensity images. In addition, to fulfill the real-time requirements, we design a configurable hardware architecture for data acquisition systems on FPGAs, with the goal to have a “on-the-fly” pre-processing of 2D projections. Finally, this architecture was configured for exploring and analyzing different arithmetic representations, such as floating-point and fixed-point data formats. This design space exploration has allowed us to find the best representation and data format that minimize execution time and hardware costs, while not affecting image quality. Furthermore, the proposed architecture was integrated in an open-interface computed tomography device, used for evaluating the image quality of the pre-processed 2D projections and the reconstructed 3D volume. By comparing the proposed solution with the state-of-the-art pre-processing algorithm that make use of intensity images, the latency was decreased 4.125×, and the resources utilization of ∼6.5×, with a mean square error in the order of 10−15 for all the selected phantom experiments. Finally, by using the fixed-point representation in the different data precisions, the latency and the resource utilization were further decreased, and a mean square error in the order of 10−1 was reached.

An Ontology and Data Converter from RDF to the i2b2 Data Model.

In a national effort aiming at cross-hospitals data interoperability, the Swiss Personalized Health Network elected RDF as preferred data and meta-data representation format. Yet, most clinical research software solutions are not designed to interact with RDF databases. We present a modular Python toolkit allowing easy conversion from RDF graphs to i2b2, adaptable to other common data models (CDM) with reasonable efforts. The tool was designed with feedback from clinicians in both oncology and laboratory research.

Energy-Efficient Convolution Module With Flexible Bit-Adjustment Method and ADC Multiplier Architecture for Industrial IoT

Offloading the unprecedented growing data to the edge exhibits a mainstream trend in the Industrial Internet of Things (IIoT) era, delivering far-reaching impacts in all aspects of our daily lives, including transportation, health care, and entertainment. However, voluminous data analyzes and processing at the edge will unavoidably raise the edge processor's burden and dramatically expand its design complexity and energy dissipation. This article proposes a flexible bit-adjustment-based energy-efficient convolution module with an approximate divide-and-conquer (ADC) multiplier for compact and low-power edge processor design. The maximum distribution search technique is utilized to exploit the optimal fixed-point representation format for both input and output of the convolution module. The neural network manifests the same precision as a 32-b floating-point multiplication deploying the determined representation formats. An ADC multiplier is proposed to realize the convolution module by eliminating the high-bit multiplication between weights and feature maps. The dynamic power consumption of the ADC multiplier-based convolution module with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q(6, 9)$</tex-math></inline-formula> input and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q(7, 8)$</tex-math></inline-formula> output representation formats is 3.85% lower than that of the 16-b signed multiplication circuit. Furthermore, the dynamic power consumption with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q(6, 9)$</tex-math></inline-formula> input is capable of being decreased by 15.38% for the 16-b convolution if the output is represented by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q(1, 14)$</tex-math></inline-formula> format and by up to 39.93% for the 64-b multiplication. The practical verification system of the convolution module working on a field-programmable gate array evaluation board exhibits an outstanding low-power characteristic.

Students’ Gender Proficiency in Multiple Representational Format and Mathematical Reasoning in Senior School Physics Problem-Solving

This study investigated multiple representation formats and mathematical reasoning to unravel physics concepts and laws in problem-solving. Four hundred and sixty (460) students from forty-six (46) co-educational schools in Kwara State- Nigeria, participated in the study. Using a random sampling technique 230 males and 230 females were selected. The two instruments used are Physics Multiple Representational Test and the Physics Mathematical Reasoning Test. Two research questions and two hypotheses guided the study. Results of the analyses through frequency count, percentages, and t-test revealed a low mean value for mathematical reasoning and the representational format of students. The male recorded a higher mean value in multiple representation formats and mathematical reasoning than their female counterparts. It concluded that students' mathematical reasoning and multiple representational formats in problem-solving were low, and the female students were the most affected when it comes to low performance. The study recommended a curriculum review to accommodate an introductory course on multiple representations. Also, an innovative program that enhances mathematical reasoning and promotes the relationship between mathematics and physics is in the curriculum. Extracurricular activities that could stimulate female interests in representational format and mathematical reasoning, should be introduced in schools.

Non-symbolic Ratio Reasoning in Kindergarteners: Underlying Unidimensional Heuristics and Relations With Math Abilities.

Although it is thought that young children focus on the magnitude of the target dimension across ratio sets during binary comparison of ratios, it is unknown whether this is the default approach to ratio reasoning, or if such approach varies across representation formats (discrete entities and continuous amounts) that naturally afford different opportunities to process the dimensions in each ratio set. In the current study, 132 kindergarteners (Mage = 68 months, SD = 3.5, range = 62–75 months) performed binary comparisons of ratios with discrete and continuous representations. Results from a linear mixed model revealed that children followed an additive strategy to ratio reasoning—i.e., they focused on the magnitude of the target dimension across ratio sets as well as on the absolute magnitude of the ratio set. This approach did not vary substantially across representation formats. Results also showed an association between ratio reasoning and children’s math problem-solving abilities; children with better math abilities performed better on ratio reasoning tasks and processed additional dimensions across ratio sets. Findings are discussed in terms of the processes that underlie ratio reasoning and add to the extant debate on whether true ratio reasoning is observed in young children.

A novel normalization algorithm to facilitate pre-assessment of Covid-19 disease by improving accuracy of CNN and its FPGA implementation

COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus. These medical images are remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean–Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique, the algorithm of proposed method is applied to chest X-ray and Sars-Cov-2 computed tomography images dataset. The normalized X-ray images with MVSR are used to recognize Covid-19 virus via Convolutional Neural Network (CNN) model. At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then all the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format on FPGA platform. The experimental platform consists of Zynq-7000 Development FPGA Board and VGA monitor to display the both original and MVSR normalized chest X-ray images. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed with MVSR normalization technique. The proposed MVSR normalization technique increased the classification accuracy of the CNN model from 83.01, to 96.16% for binary class of chest X-ray images.

Semantic Modeling of a VLC-Enabled Task Automation Platform for Smart Offices

The evolution of ambient intelligence has introduced a range of new opportunities to improve people’s well-being. One of these opportunities is the use of these technologies to enhance workplaces and improve employees’ comfort and productivity. However, these technologies often entail two major challenges: the requirement for fast and reliable data transmission between the vast number of devices connected simultaneously, and the interoperability between these devices. Conventional communication technologies present some drawbacks in these kinds of systems, such as lower data rates and electromagnetic interference, which have prompted research into new wireless communication technologies. One of these technologies is visible light communication (VLC), which uses existing light in an environment to transmit data. Its characteristics make it an up-and-coming technology for IoT services but also aggravate the interoperability challenge. To facilitate the continuous communication of the enormous amount of heterogeneous data generated, highly agile data models are required. The semantic approach tackles this problem by switching from ad hoc application-centric representation models and formats to a formal definition of concepts and relationships. This paper aims to advance the state of the art by proposing a semantic vocabulary for an intelligent automation platform with VLC enabled, which benefits from the advantages of VLC while ensuring the scalability and interoperability of all system components. Thus, the main contributions of this work are threefold: (i) the design and definition of a semantic model for an automation platform; (ii) the development of a prototype automation platform based on a VLC-based communication system; and (iii) the integration and validation of the proposed semantic model in the VLC-based automation platform.

FangTianSim: High-Level Cycle-Accurate Resistive Random-Access Memory-Based Multi-Core Spiking Neural Network Processor Simulator.

Realization of spiking neural network (SNN) hardware with high energy efficiency and high integration may provide a promising solution to data processing challenges in future internet of things (IoT) and artificial intelligence (AI). Recently, design of multi-core reconfigurable SNN chip based on resistive random-access memory (RRAM) is drawing great attention, owing to the unique properties of RRAM, e.g., high integration density, low power consumption, and processing-in-memory (PIM). Therefore, RRAM-based SNN chip may have further improvements in integration and energy efficiency. The design of such a chip will face the following problems: significant delay in pulse transmission due to complex logic control and inter-core communication; high risk of digital, analog, and RRAM hybrid design; and non-ideal characteristics of analog circuit and RRAM. In order to effectively bridge the gap between device, circuit, algorithm, and architecture, this paper proposes a simulation model—FangTianSim, which covers analog neuron circuit, RRAM model and multi-core architecture and its accuracy is at the clock level. This model can be used to verify the functionalities, delay, and power consumption of SNN chip. This information cannot only be used to verify the rationality of the architecture but also guide the chip design. In order to map different network topologies on the chip, SNN representation format, interpreter, and instruction generator are designed. Finally, the function of FangTianSim is verified on liquid state machine (LSM), fully connected neural network (FCNN), and convolutional neural network (CNN).

Compression strategies for digital holograms in biomedical and multimedia applications

While 60 years of successful application of holography is celebrated in this special issue, efficient representation and compression of holographic data has received relatively little attention in research. Notwithstanding this observation, and particularly due to the digitization that is also penetrating the holographic domain, interest is growing on how to efficiently compress holographic data such that interactive exchange of content, as well as digital storage can be facilitated proficiently. This is a particular challenge, not only because of its interferometric nature and the various representation formats, but also the often extremely large data volumes involved in pathological, tomographic, or high-end visualization applications. In this paper, we provide an overview of the state of the art in compression techniques and corresponding quality metrics for various practical applications in digital holography. We also consider the future by analyzing the emerging trends for addressing the key challenges in this domain.

Standardized and extensible reference data model for clinical research in Living Labs

Over the last few years, Living Labs (LL) have emerged as resilient research and innovation infrastructures to facilitate and promote activities, which can deal with the complexity of health and wellbeing research. Such systems integrate multiple types of data, including personal info or generated by ICT tools and devices (i.e., wearable sensors). Healthcare data models have the ability to integrate such data and focus on relevant content that will follow a specific data representation template that can be extended based on the needs of each LL. In this study, we propose a reference metamodel based on existing schemas to improve the efficiency of the LL research by pursuing a labor-intensive workflow with a hierarchical structure that simplifies the transformation process and minimizes data variation. OmH library has been used as the basis of the metamodel along with other standards in healthcare to design a complete and straightforward data reference model for LL. The presented data model has been designed within the Vitalise (H2020-INFRAIA 2021-2024) project and visualizes representations of the collected data elements and their relations. The main purpose is to provide an extensible data model that: 1) harmonizes representation formats of the information exchanged that will be used from the LL, 2) empowers other LL to develop solutions that adhere to this common definition and 3) provides a shared, common schema. The designed model will precisely represent and handle data from different devices or platforms (i.e., heart rate, BMI) and efficiently describe the collected dataset to produce meaningful metadata.

Transforming data-centric eXtensible markup language into relational databases using hybrid approach

eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.

Transforming data-centric eXtensible markup language into relational databases using hybrid approach

eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.