Automatic Data Research Articles

Data pricing with privacy loss compensation for cyber‐physical systems: A Stackelberg game based approach

AbstractThe integration of sensors in cyber‐physical systems has given rise to data markets, where data owners can offer their sensing data for sale to potential buyer. However, determining the optimal data price in such markets is a complex issue, which demands a careful consideration of the interests of all parties involved, as well as the potential privacy loss for data sellers. By taking privacy loss into account, this paper proposes a fair compensation model for data sellers and formulates the pricing problem as a Stackelberg game. An automatic data pricing algorithm is developed to calculate the optimal price maximizing the joint benefits of the data sellers and the buyer where the privacy loss of the data sellers are compensated reasonably. Numerical simulations validate the effectiveness of the proposed pricing model in balancing benefits and privacy loss.

Robust H-K Curvature Map Matching for Patient-to-CT Registration in Neurosurgical Navigation Systems

Image-to-patient registration is a coordinate system matching process between real patients and medical images to actively utilize medical images such as computed tomography (CT) during surgery. This paper mainly deals with a markerless method utilizing scan data of patients and 3D data from CT images. The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. However, if a proper initial location is not set up, the conventional ICP algorithm has the disadvantages that it takes a long converging time and also suffers from the local minimum problem during the process. We propose an automatic and robust 3D data registration method that can accurately find a proper initial location for the ICP algorithm using curvature matching. The proposed method finds and extracts the matching area for 3D registration by converting 3D CT data and 3D scan data to 2D curvature images and by performing curvature matching between them. Curvature features have characteristics that are robust to translation, rotation, and even some deformation. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient’s scan data using the ICP algorithm.

Guest Editorial: Blockchain and decentralised solutions for social good

Guest Editorial: Blockchain and decentralised solutions for social good

Automatic Data Curation and Analysis Pipeline for Electrochemical Impedance Spectroscopy Measurements Conducted on Solid Oxide Cell Stacks

In this work, we apply data from Electrochemical Impedance Spectroscopy (EIS) measurements, conducted on a Solid Oxide Cell (SOC) stack, to an automatic data curation and evaluation pipeline. Latter is developed to enable the use of historic data from EIS measurements conducted on SOC stack experiments for Machine Learning (ML) models. We show that the proposed procedure can curate parasitic, inductive impedances, obtained as a common effect of measurements on stack level. In addition, drifts induced by temperature and by steam supply gradients during the EIS measurement can be compensated. The results are experimentally validated on a two-layer SOC stack. For extraction of feature values for subsequent ML models distribution of relaxation times (DRT) deconvolution and equivalent circuit modeling (ECM) are used. To determine a suitable regularization parameter for DRT deconvolution, a variance test is implemented.

People, Process, and Technology Maturity Levels for Successful Technology Implementation by State Departments of Transportation

The demand for safe, reliable, and higher-quality infrastructure systems often increases the complexity of transportation construction projects and necessitates more comprehensive evaluation methods. This often leads to the incorporation of technologies for oversight and management. With a focus on effective technology implementation, this research stretches beyond simply evaluating technologies to consider integrating the technology with personnel and procedures at departments of transportation (DOTs). Drawing from literature and a survey of state DOT personnel, this study applies and evaluates maturity models for people, process, and technology with regard to technology implementation at DOTs. The findings support the need for understanding technology implementation in the aspects of people, process, and technology maturity to improve the probability of implementation success. The framework allows DOTs to move toward higher maturity levels and improve the likelihood of implementation success. The study’s objective is to assess the importance of people, process, and technology as implementation dimensions that state DOTs need to focus on for successfully implementing technologies. The idea and scope of the paper can be extended to any new technology implementation efforts at DOTs, including Automatic Identification and Data Capture technologies, emerging sensing and wireless technologies, safety technologies, and others. The maturity models presented enable organizations to audit and benchmark their status for technology implementation, and the relative levels allow these organizations to advance from basic to advanced stages. This paper introduces three maturity models for people, process, and technology, their adaptation to DOT operations, and their possible future use, as presented through preliminary survey results. The final result of this study provides a maturity model methodology for successful technology implementation and a self-assessment tool for state DOTs to gauge and improve their current maturity levels. Preliminary validation of these products was conducted via a workshop of subject matter experts, and the results indicate that state DOTs can use these products to self-assess their organization’s maturity and devise a plan as required for improved technology implementation.

The DELAD initiative for sharing language resources on speech disorders

Sharing research data between researchers is becoming a requirement for open science in the field of clinical phonetics and speech and language pathology. An initiative entitled DELAD was started in 2015 to provide researchers a platform to discuss ongoing technical, legal and ethics issues related to sharing recordings of speech disorders, and to assist them in archiving their data for sharing with colleagues in the field. The ultimate goal of data sharing is to enhance research and teaching in the area of speech disorders. This paper first presents DELAD and its history and previous accomplishments. Then the recent work achieved by the DELAD steering group in providing information or recommendations in three areas are discussed: (1) speech annotation and automatic annotation mining, (2) consent and data storage, and (3) a role-play activity for learning data protection assessment. DELAD is an active community and researchers who share similar interests are welcome to join the (ad)venture.

Mapping the In-Motion Emotional Urban Experiences: An Evidence-Based Method

Urban settings affect the experience of people in places and the measurement of such urban experiences is the focus of this article. The ‘experiential Environmental Impact Assessment—exp-EIA©’ method and its application to the Città Studi area in Milan are presented. The method couples urban studies with environmental psychology and ICT for educational purposes with a sample of architecture students divided in two groups (N = 18). Experiential data are collected via a dedicated app installed on the participants’ mobile devices, and an automatic data processing and analysis produces spatialized results creating maps of the overall urban experience. In particular, the emotional reaction of participants through a ten-minute walking path is assessed. A group activity focused on the link between urban features and subjective evaluation of places by participants is combined with the results obtained via the application of the circumplex model of affect. Results show that the path is characterized by two main emotional experiences, passing from a pleasant to an unpleasant experience within the short walk in both groups, offering some insights on the urban design of the area. The results are part of the teaching process to increase students’ awareness about experiential design, yet the process is conceived also as a tool for professionals.

Classification of inbound and outbound ships using convolutional neural networks

In general, a single scalar hydrophone cannot determine the orientation of an underwater acoustic target. However, through a study of sea trial experimental data, the authors found that the sound field interference structures of inbound and outbound ships differ owing to changes in the topography of the shallow continental shelf. Based on this difference, four different convolutional neural networks (CNNs), AlexNet, visual geometry group, residual network (ResNet), and dense convolutional network (DenseNet), are trained to classify inbound and outbound ships using only a single scalar hydrophone. Two datasets, a simulation and a sea trial, are used in the CNNs. Each dataset is divided into a training set and a test set according to the proportion of 40% to 60%. The simulation dataset is generated using underwater acoustic propagation software, with surface ships of different parameters (tonnage, speed, draft) modeled as various acoustic sources. The experimental dataset is obtained using submersible buoys placed near Qingdao Port, including 321 target ships. The ships in the dataset are labeled inbound or outbound using ship automatic identification system data. The results showed that the accuracy of the four CNNs based on the sea trial dataset in judging vessels’ inbound and outbound situations is above 90%, among which the accuracy of DenseNet is as high as 99.2%. This study also explains the physical principle of classifying inbound and outbound ships by analyzing the low-frequency analysis and recording diagram of the broadband noise radiated by the ships. This method can monitor ships entering and leaving ports illegally and with abnormal courses in specific sea areas.

Design of College Students' physical health monitoring APP based on sports health big data

AbstractAt present, the living habits of college students are relatively poor, and the amount of exercise is reduced, leading to their physical fitness getting worse and worse. Therefore, people began to study the physical health monitoring of college students. Machine learning and high‐performance computing in medical applications provide technical support for intelligent medical technology. With the rapid development of computer network, human beings have entered the information and digital era, and sports health big data has become more and more popular. The combination of sports health big data and student physical health monitoring technology, in a sense, can realize automatic data processing through intelligent medical and sports health database and other information technologies, thus promoting the popularization of health monitoring technology. However, the current health monitoring equipment has many problems, such as complex collection, low accuracy and limited processing of health data. To solve this problem, this paper developed an Application (APP) based on sports health big data technology that can monitor multiple vital signs such as human heart rate and body temperature, and analyze the physical health of college students, so that college students can easily understand their health status in daily life, so as to promote the healthy development of students' physique and encourage them to actively participate in physical exercise. The experiment proved that, in the heart rate monitoring, when the speed is 6 km/h, the error rate of the college students' physical health monitoring APP designed in this paper is 8.15%. The average accuracy rate of student steps monitoring is 97.12%. This showed the accuracy and availability of the APP's monitoring function for human vital signs. It has certain application value and significance to help students improve their physical quality.

DCServCG: A data-centric service code generation using deep learning

Modern software development paradigms, including Service-Oriented Architecture (SOA), tend to make use of available services e.g., web service Application Programming Interfaces (APIs) to generate new software. Thus, for the further advancement of SOA, the development of accurate automatic tasks, such as service discovery and composition, is necessary. Most of these automated tasks rely heavily on web service metadata annotation. The lack of machine-readable documentation and structured metadata reduces the accuracy and volume of automatic data annotation, negatively affecting the performance of automated SOA tasks. This study aims to propose automatic code completion for improving web service-based systems by identifying and capturing service usage collected from public repositories that share Open Source Software (OSS). To this end, a Data-Centric Service Code Generation (DCServCG) model is proposed to improve old-fashioned, general-purpose code generators that neglect essential service-based code characteristics e.g., sequence overlap and bias issues. DCServCG takes advantage of the data-centric concept, i.e., conditional text generation, to overcome the mentioned issues. We have evaluated the approach from the point of view of language modeling metrics. The obtained results indicate that the usage of the data-centric approach reduces perplexity by 1.125. Moreover, the DCServCG model uses de-noising and conditional text generation, which is trained on the transformer by distilling the knowledge, DistilGPT2 (82M parameters) trained faster and its perplexity is 0.363 lower than ServCG (124M parameters) without de-noising and conditional text generation, which lower perplexity value indicates better model generalization performance.

PO-03-052 FAILURE OF IMPLANTABLE CARDIAC MONITOR CONNECTIVITY: LESSONS LEARNED AND IMPLICATIONS FOR CLINICAL CARE

Implantable cardiac monitors (ICMs) are capable of automatic daily data transmission. Multiple data transmission methods are available with recent emphasis on Bluetooth technology coupled with a smartphone-based app. However, the robustness of daily connectivity remains poorly understood. Evaluate ICM connectivity over a 90-day period and to determine if the data transmission method impacts connectivity. We enrolled all patients with an ICM (LINQII, Medtronic, Minneapolis) being followed in our practice as of September 1, 2022. All patients were prospectively followed for 90-days. Daily connectivity was determined based on an automated tool developed for our remote monitoring platform (91Life, New York) which retrieved the patient’s daily ECG, if available, from CareLink. We divided patients into 4 groups – always connected and maximal consecutive disconnected days of 1-3, 4-7, and >7 days. In addition, we compared the connectivity for the 3 types of transmitters: MyCareLink RelayTM bedside communicator; MyCareLink HeartTM mobile app on a patient owned phone; or MyCareLink HeartTM mobile app on a dedicated Medtronic provided phone. We followed 673 patients. The communication relay was the home communicator in 349 (52%), personal phone in 256 (38%), and Medtronic provided phone in 68 (10%) patients. There were isolated days with significant drop in connectivity (Figure, left, *) as well as one day where there was no patient connectivity (**). Overall, the average daily connectivity for the entire cohort was 89%; however, only 24% of patients achieved 100% continuous connectivity and 21% of patients were disconnected for >7 consecutive days (Table). This was significantly more likely when patients transmitted using a bedside communicator (Figure, right). (X2=52.7; df=2; p<0.00001) While overall daily connectivity of the LINQII ICM was 89%, uninterrupted connectivity was observed in only a quarter of patients over a 3-month observation period and approximately 20% were disconnected for more than a week at a time. There were days with unexplained large drops in connectivity. Finally, although there has been a major push to move to Bluetooth technology coupled with a smartphone for data transmission, we found this mode to be much less reliable than the traditional bedside communicator. These data have important implications for managing patients that need daily ECG monitoring to guide clinical decision making.Tabled 1Connectivity Patterns for Medtronic LINQll Implantable Cardiac Monitors100% Connected (n,%)1-3 Days Disconnected (n,%)4-7 Days Disconnected (n/%)>7 Days Disconnected (n,%)161, 24%277,41%94, 14%141, 21% Open table in a new tab

Machine learning-based detection of transportation infrastructure internal defects using ground-penetrating radar: a state-of-the-art review

Abstract Early detection of internal defects is crucial to ensure the long-term performance and stability of transportation infrastructure. Researchers and practitioners have developed various nondestructive testing (NDT) methods for this purpose. Among them, the ground-penetrating radar (GPR) technique has been widely implemented due to its advantages such as large coverage, traffic-speed survey, and rich subsurface information. In addition, machine learning (ML) algorithms have been frequently applied to achieve automatic GPR data interpretations, which are essential for field applications. However, the fundamental concepts, architectures, and appropriate application scenarios of these algorithms are often questionable to practitioners and researchers. This paper presents a state-of-the-art review of ML applications in the internal defect detection of transportation infrastructure using GPR. In particular, pavements and bridges are covered. The basic knowledge of GPR working principles and ML algorithms is documented. The critical features of the ML algorithms for each detection task are presented. The drawbacks that may hinder the application of ML algorithms using GPR are indicated, including the insufficiency of labeled GPR data, unavailability of GPR dataset, customized ML architecture, and field validation. Finally, possible transfer learning, integrated robotic platform, and data fusion with other NDT methods are discussed. This review paper is expected to serve as a reference for practitioners to choose appropriate ML algorithms to detect internal defects in transportation infrastructure using GPR.

MEMS-IMU automatic calibration system design and implementation

Aiming at the problems of traditional calibration, long calibration cycles, and low efficiency of traditional error calibration methods for MEMS inertial devices, a MEMS IMU automatic calibration system is proposed. Firstly, the error mechanism of MEMS-IMU is analyzed, the error model is established, and the calibration scheme is designed. Then, the automatic control of the turntable and the automatic data acquisition of MEMS inertial devices are realized by designing the data interaction mechanism between the system equipment. Finally, the least square method is used to identify the model parameters. The experimental results show that this method can meet the requirements of multi-position and multi-rate calibration. After compensation, the relative errors of the MEMS gyroscope and accelerometer are 0.04% and 0.15%, respectively, and the cross-coupling error of the MEMS gyroscope is reduced to about 0.1 °/s. At the same time, the whole calibration process is easy to operate, which greatly shortens the calibration time and improves the calibration efficiency.

Analisis Pengaruh Experiential Marketing, Emotional Branding, dan Service Quality terhadap Kepuasan Konsumen Uniqlo Store Ciputra World Mall Surabaya

This study aimed to determine whether experimental marketing, emotional branding, and service quality variables have a partial and simultaneous influence on customer satisfaction at the Uniqlo store Ciputra World Mall Surabaya. Determination of the sample of respondents in this study using purposive sampling with 100 respondents with data collection techniques using Google form and then using the SPSS 25 automatic data analysis software. The results of this study indicate that: (1) emotional branding and service quality simultaneously have a positive and significant effect on customer satisfaction at Uniqlo store Ciputra World Mall Surabaya. While experimental marketing has a positive but not significant effect on consumer satisfaction at Uniqlo store Ciputra World Mall Surabaya; (2) experimental marketing has a partial but not significant positive effect on customer satisfaction at Uniqlo store Ciputra World Mall Surabaya; (3) emotional branding has a positive and significant effect partially on customer satisfaction at Uniqlo store ciputra world mall Surabaya; (4) service quality has a partially positive and significant effect on customer satisfaction at Uniqlo store Ciputra World Mall Surabaya.&#x0D; Keywords: Experimental Marketing, Emotional Branding, Service Quality, Consumer Satisfaction

One-click bending stiffness: Robust and reliable automatic calculation of moment–curvature relation in a cantilever bending test

The cantilever bending test is one of the simplest and most widely used methods to estimate the bending stiffness of textile materials. A nonlinear moment–curvature relationship can be computed from a single image of a cantilevered textile specimen. However, the calculation of curvature involves second-order differentiation of noisy data, which leads to noise amplification. Traditionally, this is handled by subjectively choosing one of many functions to fit the data or by manual tuning of fitting parameters. The user choices ultimately lead to uncertainties in the data fit. This paper presents a novel automatic data processing method for the cantilever test using smoothing splines with automatic parameter selection through cross-validation. The method is verified on a simulated deflection curve with known bending stiffness and then used to characterise real textile specimens. Finally, the method is validated by simulating the deflection curve using the computed stiffness. This method makes it possible, for the first time, to accurately predict the textile curvature even in the presence of severe noise, without needing user inputs prone to human error. The code used for this paper is freely available with sample data on the repository at https://doi.org/10.5281/zenodo.7376939.

Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation

Background: The concept of customer orientation, which is based on a set of fundamental beliefs that prioritize the interests of the customer, requires companies to detect these interests in order to maintain a high level of quality in their products or services. Furthermore, there are several indicators of customer orientation, and one of them is their opinion or taste, which provides valuable feedback for businesses. With the rapid development of social media, customers can express emotions, thoughts, and opinions about services or products that may not be easily conveyed in the real world. Objective: The objective of this study is to detect customer orientation towards product or service quality, as expressed in online or social media. Additionally, the study showcases the novelty and superiority of the annotation process used for detecting customer orientation classifications. Methods: This study employs a method to compare the classification performance of the Vader lexicon annotation process with manual annotation. To accomplish this, a dataset from the Amazon website will be analyzed and classified using the Support Vector Machine algorithm. The objective of this method is to determine the level of customer orientation present within the dataset. To evaluate the effectiveness of the Vader lexicon, the study will compare the results of manual and automatic data annotation. Results: The results showed that customer orientation towards product or service quality has a predominantly positive value, comprising up to 76% of the total responses analyzed. Conclusion: The findings demonstrate that using Vader in the annotation process results in superior accuracy values compared to manual annotation. Specifically, the accuracy value increased from 86% to 88.57%, indicating that Vader could be a reliable tool for annotating text. Therefore, future studies should consider using Vader as a classifier or integrating it into the annotation process to further enhance its performance. Keywords: Classification, Customer, Orientation, Text analysis, Vader lexicon,

Surveillance practice and automatic data algorithm of sniffing telemetry for SO2 emissions from ship exhaust in Tianjin Port

Aiming at regulating SO2 emissions from ships, ship exhaust telemetry (SET) can be used as a low-cost and high-efficiency technology for the pre-detection of the S content of ship fuel. The sniffing method is the most accurate SET technology. In this study, eight SETs based on the sniffing method were built in Tianjin Port. The concentrations of CO2, SO2, and NOx in ship exhausts were monitored from 1 May to 31 August 2021. An automatic data algorithm was developed to identify peak signals of CO2, SO2, and NOx, calculate the S content of ship fuel, and trace emissions from ships. First, an automatic recognition algorithm for a gas-sniffing signal based on the incremental correlation threshold was established. Second, an automatic calculation algorithm for the S content of ship fuel based on the time series of SO2 peak was generated. Finally, the traceability-locking algorithm for emissions from ships was created by calculating the plume path integral. The recognition rate of the wave-crest signal was demonstrated to be more than 95%, and the positioning error of the ship was estimated at ∼100 m, which could satisfy the accuracy requirements of ship identification. Overall, the peak data of the curves of the gas concentrations monitored (n = 24,292) were obtained during the four-month operation of the SETs. The detection rate of ship exhaust was 62.2%, and the emission reduction of SO2 was 44.6 tons. The accuracy and validation of the algorithm were tested in surveillance practice in Tianjin Port. The SET and automatic data algorithm for SO2 emissions significantly promoted the efficiency of supervision and the emission reduction of SO2. Thus, a continuous online, unattended, and low-cost supervision strategy for the S content of ship fuel without boarding was established.

An automatic data quality approach to assess semantic data from cultural heritage institutions

AbstractIn recent years, cultural heritage institutions have been exploring the benefits of applying Linked Open Data to their catalogs and digital materials. Innovative and creative methods have emerged to publish and reuse digital contents to promote computational access, such as the concepts of Labs and Collections as Data. Data quality has become a requirement for researchers and training methods based on artificial intelligence and machine learning. This article explores how the quality of Linked Open Data made available by cultural heritage institutions can be automatically assessed. The results obtained can be useful for other institutions who wish to publish and assess their collections.

An Intelligent Approach for Automating Data Inputs on Web Portals for Several Apex / Statutory Bodies in the Field Education using Robotics and Machine Learning

ABSTRACT-World is upgrading day by day and education system is going side by side using that technologies. Automatic data inputs is a data entry process which means taking information from different sites and sorting them out in a particular section. This is very important part for any business and automating process helps this work easy as it is quite tough to do all the work for a single person. It is very essential for one student to get knowledge about machine language as it will be helpful for them in the future. Machine learning technology helps to keep all the data up to date and also for cyber security. Children are always finding something which is easy to remember and funny, hence learning new thing using robotic function helps them a lot. After COVID-19, most of the education filed has started online classes and using MI and robotic things, it is easy for them to gaining knowledge that are not able to attend schools. Keywords: Robots, machine language, data, education, methods, upgrade

Visual Detection and Association Tracking of Dim Small Ship Targets from Optical Image Sequences of Geostationary Satellite Using Multispectral Radiation Characteristics

By virtue of the merits of wide swath, persistent observation, and rapid operational response, geostationary remote sensing satellites (e.g., GF-4) show tremendous potential for sea target system surveillance and situational awareness. However, ships in such images appear as dim small targets and may be affected by clutter, reef islands, clouds, and other interferences, which makes the task of ship detection and tracking intractable. Considering the differences in visual saliency characteristics across multispectral bands between ships and jamming targets, a novel approach to visual detecting and association tracking of dense ships based on the GF-4 image sequences is proposed in this paper. First, candidate ship blobs are segmented in each single-spectral image of each frame through a multi-vision salient features fusion strategy, to obtain the centroid position, size, and corresponding spectral grayscale information of suspected ships. Due to the displacement of moving ships across multispectral images of each frame, multispectral association with regard to the positions of ship blobs is then performed to determine the final ship detections. Afterwards, precise position correction of detected ships is implemented for each frame in image sequences via multimodal data association between GF-4 detections and automatic identification system data. Last, an improved multiple hypotheses tracking algorithm with multispectral radiation and size characteristics is put forward to track ships across multi-frame corrected detections and estimate ships’ motion states. Experiment results demonstrate that our method can effectively detect and track ships in GF-4 remote sensing image sequences with high precision and recall rate, yielding state-of-the-art performance.