Output Data Types Research Articles

User Preferences on a Generative AI User Interface Through a Choice Experiment

Since the launch of ChatGPT in November 2022, the number of services providing generative AI has been steadily increasing. As different services enter the market, the generative AI interfaces users experience become more diverse. However, none of the services has yet established itself as the dominant tool, and which interface component affects user preferences the most has yet to be identified. This study investigates user preferences based on the interface of generative AI services currently on the market. We investigated users’ preferred interface components by setting the output data type, generative style, output variation, reference style, and generation history provided by the current generative AI service as properties. We collected data from 500 users through a survey and conducted conjoint analysis. Users preferred the provision of 10 generation histories the most, and the second most preferred the provision of reference style in footnote format. In addition, it was found that there was no preference for the creative generative style, which can be interpreted as users being aware of the problem of hallucination in generative AI. The results of this study will help future generative AI services design interfaces that consider user experience.

Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks

This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.

A model calibration procedure for count response

In recent years, there are numerous articles about computer model calibration. But these articles mainly focus on continuous output variables for the convenience of the derivation. However, there are some other types of output data in the application area, such as binary data and count data, the former of which has been investigated very well in the area of computer model calibration and applied to cell adhesion study successfully. Inspired by the existing methods for binary data, we propose a model calibration procedure for count response, and derive the consistency and asymptotic normality of the proposed estimator of the calibration parameters by the theory of empirical process. Some simulations are conducted to verify the good performance of the proposed method.

TRAINING A NEURAL NETWORK TO CALCULATE THE CLOSEST POINT OF APPROACH

The process of training a neural network on calculation of closest point of approach (CPA) between two ships, and testing its performance and accuracy is described in the paper. The architecture of the neural network, the type of input and output data, and creation of training data set are also described in the paper. Feed Forward Neural Networks with Backpropagation algorithm are used; training method is Supervised with Levenberg-Marquardt algorithm. The input data are positions, courses and speeds of vessels in a certain area, the output data are Closest Points of Approach (CPA) between them. The process of writing a script in MATLAB software environment is described. The script allows a user to generate training data with any number of vessels in an area. Comparison of the time spent on CPA calculation using formulas and using neural networks is carried out. It has been proven that when processing large data arrays, the CPA calculation with neural networks is much faster than by means of formulas. After neural networks training process and the calculations results comparison, one neural network with mean squared error of 0.21 is chosen. It can be used for CPA calculations in MATLAB-based simulations. In the future this network might become a base for a collision-avoidance neural network system, which will allow vessels to manoeuvre safely in order to avoid collisions in a certain area.

Analysis of modern software for processing the results of deformation monitoring of buildings and structures

Observations of the deformations of buildings and structures are the part of geodetic monitoring, which is carried out both during the construction of the facility and its operational period. Analysis and mathematical processing of the obtained geodetic measurements makes it possible to predict the dynamics of the development of deformation components in the structures of buildings, which makes it possible to prevent undesirable consequences. This process is automated with the help of special software systems, but not all of them allow performing the processes of predicting changes in the features and parameters of controlled objects. The article analyzes modern software systems for processing the results of deformation monitoring of buildings and structures that differ in purpose, tasks to be solved, and the type of output data, their advantages and disadvantages are identified, and the possibility of their application for forecasting tasks is assessed.

An overview of machine learning methods for monotherapy drug response prediction.

For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.

Ecosystem Services Assessment Tools for Regenerative Urban Design in Oceania

Tools that spatially model ecosystem services offer opportunities to integrate ecology into regenerative urban design. However, few of these tools are designed for assessing ecosystem services in cities, meaning their application by designers is potentially limited. This research reviews and compares a range of ecosystem services assessment tools to find those that are most suited for the urban context of Oceania. The tool classification includes considerations of type of input and output data, time commitment, and necessary skills required. The strengths and limitations of the most relevant tools are further discussed alongside illustrative case studies, some collected from literature and one conducted as part of this research in Wellington, Aotearoa using the Land Utilisation and Capability Indicator (LUCI) tool. A major finding of the research is that from the 95 tools reviewed, only four are judged to be potentially relevant for urban design projects. These are modelling tools that allow spatially explicit visualisation of biophysical quantification of ecosystem services. The ecosystem services assessed vary among tools and the outputs’ reliability is often highly influenced by the user’s technical expertise. The provided recommendations support urban designers and architects to choose the tool that best suits their regenerative design project requirements.

Construction of Pseudo CRS frontier for a negative data using RTS model of Allahyar & A modified multiplier BCC model

Performance measurement of Decision Making Units (DMU) possessing an array of positive and negative type of input and output data has been an extensively researched topic in Data Envelopment Analysis. However, assessment of Returns to Scale (RTS) under negative data problem has only been attainable after the deliberation of a Variable Returns to Scale assumption. Steps referred earlier were indeed purported a solution around the vicinity of the Decision Making Unit under examination to predict the nature of the Returns to Scale of a firm. The extant investigation extends the research of Allahyar and Malkhalifeh [Comput. Ind. Eng. 82 (2015) 78–81] to identify a Pseudo Constant Returns to Scale Frontier for a negative data problem along with the new origin based on the provided data. However, this approach seems to be ineffective to create a frontier for multiple input output scenario. In this regard, a new variation of Multiplier form of BCC model is proposed here to detect the new origin for the sake of designing the Pseudo CRS frontier. Small examples are added for the elaboration of the CRS efficient DMUs using methods described by Allahyar and Malkhalifeh [Comput. Ind. Eng. 82 (2015) 78–81] to identify the new origin from the Multiplier form of BCC model. This outcome is finally validated with the aid of the multiplier model of Sahoo et al. [Eur. J. Oper. Res. 255 (2016) 545–558].

Simplified Seismic Vulnerability Assessment Methods: A Comparative Analysis with Reference to Regional School Building Stock in Italy

The paper compares several simplified methods proposed in the literature for assessing the seismic vulnerability of existing buildings. Type and number of input and output data, limitations of use for different structural typologies, and complexity of use are examined for each methodology to identify the most suitable for assessing the vulnerability of a given class of buildings, based on the available data, the computational effort, and the type of vulnerability judgment. The selected methods were applied to a sample of school buildings located in the province of Naples (Italy). Data were available due to a digital platform and were used to verify the possibility of providing reliable large scale vulnerability judgments based on a reduced set of information, without carrying out additional surveys. The most simplified methods were applied to a sample of about a thousand of buildings, while more detailed methods, needing more information, were applied to a smaller sample. The comparison between the results obtained from different methods allows highlighting advantages and weaknesses of each, so as to identify the convenience in their use according to the specific available information and the objectives of the analysis, finally to evaluate which is more or less safe.

A Comparison of Mainstream Genotyping Platforms for the Evaluation and Use of Barley Genetic Resources.

We compared the performance of two commonly used genotyping platforms, genotyping-by-sequencing (GBS) and single nucleotide polymorphism-arrays (SNP), to investigate the extent and pattern of genetic variation within a collection of 1,000 diverse barley genotypes selected from the German Federal ex situ GenBank hosted at IPK Gatersleben. Each platform revealed equivalent numbers of robust bi-allelic SNPs (39,733 and 37,930 SNPs for the 50K SNP-array and GBS datasets respectively). A small overlap of 464 SNPs was common to both platforms, indicating that the methodologies we used selectively access informative polymorphism in different portions of the barley genome. Approximately half of the GBS dataset was comprised of SNPs with minor allele frequencies (MAFs) below 1%, illustrating the power of GBS to detect rare alleles in diverse germplasm collections. While desired for certain applications, the highly robust calling of alleles at the same SNPs across multiple populations is an advantage of the SNP-array, allowing direct comparisons of data from related or unrelated studies. Overall MAFs and diversity statistics (π) were higher for the SNP-array data, potentially reflecting the conscious removal of markers with a low MAF in the ascertainment population. A comparison of similarity matrices revealed a positive correlation between both approaches, supporting the validity of using either for entire GenBank characterization. To explore the potential of each dataset for focused genetic analyses we explored the outcomes of their use in genome-wide association scans for row type, growth habit and non-adhering hull, and discriminant analysis of principal components for the drivers of sub-population differentiation. Interpretation of the results from both types of analysis yielded broadly similar conclusions indicating that choice of platform used for such analyses should be determined by the research question being asked, group preferences and their capabilities to extract and interpret the different types of output data easily and quickly. Access to the requisite infrastructure for running, processing, analyzing, querying, storing, and displaying either datatype is an additional consideration. Our investigations reveal that for barley the cost per genotyping assay is less for SNP-arrays than GBS, which translates to a cost per informative datapoint being significantly lower for the SNP-array.

Строгая типизация в событийной микросервисной парадигме

Microservice architecture is a popular approach for building various systems. It consists of various dedicated small-sized components from a solid application. Components work together and provide the required business-logic but each one can be implemented with the most suitable technique. Moreover, overloaded parts can be scaled easily. However, data transfer layer does not guarantee compatibility of services during their development. After changing input or output data type of any microservice of the system there are too little ways to keep up compatibility of all other services. The problem is especially urgent for asynchronous communication because existing systems and standards work mostly with synchronous RPC or REST interoperation or cover high-level description of business-logic. One approach to guarantee compatibility of all services in the system is a preliminary check of data types before the services start to work. The previous paper considers realization of the mentioned approach with the dedicated service that checks types, connects services using description of the interfaces. The current paper covers further development of the approach: a system built with preliminary check of data types supports only asynchronous communication and all checks are provided by a message broker. We have implemented and compared simplified approach with alternative ways and have come to the conclusion that described way solves the problem of type inconsistency in a microservice event-driven architecture in a more convenient way than alternatives.

КЛАССИФИКАЦИЯ ЛИНГВИСТИЧЕСКИХ ТЕХНОЛОГИЙ

The paper identifies different types of linguistic technologies depending on the input and output data type, language system level, means and forms of communication, contingent of users. The author describes the peculiarities of the functioning of linguistic programs, systems and applications, the input of which is the text in the natural language. Special attention is paid to the applications for the statistical analysis of text documents. It is shown that the identified technologies can be used as the configurational characteristics of linguistic software.

Prediction of short-term PV power output and uncertainty analysis

Due to the intermittency and uncertainty in photovoltaic (PV) power outputs, not only deterministic point predictions (DPPs), but also associated prediction Intervals (PIs) are important information for promoting the application of PV in practice, especially when grid connection continues to grow. While there are few studies focused on quantifying the uncertainty of forecasting PV power outputs, this paper developed a novel two-stage model to quantify the PIs of PV power outputs. In the first stage, three different neural networks, namely Generalized Regression Neural Network (GRNN), Extreme Learning Machine Neural Network (ELMNN) and Elman Neural Network (ElmanNN), were integrated using the Genetic Algorithms optimized Back Propagation (GA-BP) method to develop a Weight-Varying Combination Forecast Mode (WVCFM) model. The WVCFM model was applied to generate DPPs. In the second stage, a nonparametric kernel density estimation (NKDE) method was adopted to estimate the PIs regarding the statistical distribution of the errors of the DPPs derived in the first stage. The proposed method was tested using four types of PV output and weather data measured from a 15 kW grid-connected PV system. The cover percentage of prediction intervals (PICPs) were computed under the confidence level of 95%, 90%, 85% and 80%, respectively. The results imply that the two-stage model proposed in the paper outperforms conventional forecast methods in terms of prediction of short-term PV power outputs and associated uncertainties.

Using a human thermoregulation model as a tool for design and refurbishment of industrial spaces for human occupancy

Requirements for industrial work conditions are determined by human occupancy, building construction and production processes and they should ensure the prevention and removal of heat and noxious substances from the working space. Till now costly and time-consuming experimental methods in existing buildings and fast but less reliable simulation methods at the design stage were used, which both treat the human factor as minor. In the present study we propose a user-centered methodology which combined the versatility of the thermo-physiological modeling and the reliability of in-situ measurements based on an example of a typical industrial hall.Based on the results we observed insufficient operative temperature for winter and summer months due to the missing HVAC system in the hall. As a consequence, sweat excretion, thermal sensation and skin wettedness exceeded their limits for physiological strain and thermal comfort during the summer months, and skin temperature decreased distinctly at the face and hands but was still above the limit for pain or non-freezing cold injury in the winter months.This methodology can be used for a wide range of thermal environments, activities and clothing. It eliminates the need for the exposure of human beings and provides information on physiological data, which are difficult or impractical to measure in a field study (e.g. sweat excretion, skin and core temperatures, thermal sensation). The methodology can be useful to reveal zones with distractive effect or are inappropriate for human exposition. Finally, this approach also allows using a single method and providing continuous output information for a variety of environments without needing to change approach and type of output data.

Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

Time interval and time length are two important indexes when analyzing the active output data of photovoltaic (PV) power stations. When the time interval is constant, the length of time is too small, and the included information is less, resulting in a lack and distortion of information; it the length of time is too large, the included information is redundant and complicated, resulting in unnecessary increases of storage capacity and calculation. Therefore, it is important to determine the appropriate length of data for the analysis of PV output data. In this paper, firstly, the output data of a PV power station is analyzed statistically, and the preliminary conclusions for time length selection are obtained by autocorrelation analysis. Based on the weather characteristics, clustering analysis methods and statistical principles are used to analyze the data and optimal sample capacity estimation, respectively, for different types of photovoltaic output data and determine the required data time length at the time of analyzing the PV power plant output data, the relationship between energy storage capacity demand and data length is investigated, the rationality of the length of the selected time is verified. Meanwhile, the energy storage system capacity configuration based on the optimal data time length is given. The results show that the requirement of data volume of energy storage system capacity configuration can be met when the time length of the PV output data is 23 days.

Processing and Unification of Environmental Noise Data from Road Traffic with Spatial Dimension Collected through Mobile Phones

Noise measurement using mobile phones is now developed very well. While there are some good applications for the measurement of noise from road traffic, thus on processing of measured data is only paid a very little attention. The data, however, are burdened by specific errors and for further work with them it is necessary to adjust and determine their uncertainty. One of the biggest problems is inaccuracy in position versus the noise source and the shortest length of measurement that can be regarded as representative. Imprecision in terms of location can be determined by calculating the variance of possible distance from the noise source, which for measurement of traffic noise requires a map-matching data points both transverse to the street (sidewalk) network and in the longwise direction. During typical urban measurements, this error can even reach 7 - 10 dB. Three basic types of algorithms for the calculation of uncertainty and positional correction based on the type of input and output data (raster, vector, vector-oriented) were tested. Uncertainty in the variability of the measurement data is necessary to determine from the number of passing vehicles per time unit. The presented solutions are implemented in the Mobile Noise system.

Automatic Composition of Heterogeneous Models Based on Semantic Web Services

As an important function of a distributed decision support system, model composition aims to aggregate model functions to solve complex decision problems. Most existing methods on model composition only apply to the models which have the same type of input and output data so that they can be linked together directly. Those methods are inadequate for the heterogeneous models, since a heterogeneous model may have different types of input and output data that are represented in either qualitative or quantitative manner. This paper aims to address the problem of heterogeneous model composition by employing the techniques based on semantic web services and artificial intelligence planning. In this paper, the heterogeneous model composition problem is converted to the problem of planning in nondeterministic domains under partial observability. An automatic composition method is presented to generate the composite model based on the planning as model checking technique. The experiment results are also presented in this paper to show the feasibility and capability of our approach in dealing with the complex problems involving heterogeneous models.

A SEVA soft sensor method based on self-calibration model and uncertainty description algorithm

Soft sensors are widely used to estimate process variables that are difficult to measure online. However, due to poor quality of input data and deterioration of prediction model as time passes, soft sensors make poor performance. We have been constructing a principal component analysis (PCA) model before performing a prediction. Furthermore, the just-in-time (JIT) learning model has been improved and served as prediction model for self validating (SEVA) soft sensors. The proposed soft sensor not only carries out internal quality assessment but also generates multiple types of output data, including the prediction values (PV), input sensor status (ISS), validated measurement (VM), output sensor status (OSS) and the uncertainty values (UV) which represent the credibility of soft sensors' output. The effectiveness of the proposed SEVA soft sensors is demonstrated through a case study of a wastewater treatment process.

An ontological-based knowledge organization for bioinformatics workflow management system

Motivation and Objectives In the field of Computer Science, ontologies represent formal structures to define and organize knowledge of a specific application domain (Chandrasekaran et al., 1999). An ontology is composed of entities, called classes, and relationships among them. Classes are characterized by features, called attributes, and they can be arranged into a hierarchical organization. Ontologies are a fundamental instrument in Artificial Intelligence for the development of Knowledge-Based Systems (KBS). With its formal and well defined structure, in fact, an ontology provides a machine-understandable language that allows automatic reasoning for problems resolution. Typical KBS are Expert Systems (ES) and Decision Support Systems (DSS). ESs gather and formalize the knowledge of a human expert of a domain in order to produce inferences and recommendations given an initial query. DSSs are more interactive KBS, in the sense they offer support, rather than replacement, for the decision making process during the execution of a task, suggesting one possible strategy or tool given a set of initial conditions. DSSs are mainly adopted in the clinical field, where they are called Clinical DSS (CDSS). Ontology specification, structure and organization are then of fundamental importance for the development of a KBS. In this paper we present an improvement of our ontological approach for knowledge organization in DSS design. In our previous publication (Fiannaca et al., 2012) we defined a paradigm for ontology specification named Data Problem Solver (DPS) and we showed how our approach can be applied to bioinformatics domain, modeling the Protein-Protein Interaction Network extraction scenario. In the proposed approach, we aim at integrating into our ontology the concept of Workflow as a set of processes. Our main objective is to provide a general schema in order to add the functionalities and capability of a DSS to the more recent Workflow Management Systems, that especially in bioinformatics, with the Taverna workbench (Hull et al., 2006), represent a powerful instrument for researchers. We called our extended ontological approach Data Problem Solver Workflow (DPSW).

An ontological-based knowledge organization for bioinformatics workflow management system

An ontological-based knowledge organization for bioinformatics workflow management system