Automatic Support Research Articles

Vocabulary mapping for archaeological infrastructure

This article reports and reflects on vocabulary mapping techniques, tools and experience from the ARIADNE European archaeological infrastructure projects, where the widely differing terminology of subject indexing in the different partner languages posed significant challenges for effective data integration. The Getty Art & Architecture Thesaurus is employed as a central spine vocabulary for partners to map their native vocabularies and term lists – a hub structure enables a multilingual search capability via vocabulary mapping. Mappings are expressed via SKOS mapping relationships and output as structured JSON for use in the overall data aggregation process and in the ARIADNE portal. The approach followed offers some automatic support for final intellectual judgement. The method can be characterised as providing lexical support in an interactive tool that aims to intuitively visualise semantic context. The experience of partners in producing the vocabulary mappings is discussed in light of previous work in this area. Reflections on lessons learned both for the immediate project and for vocabulary mapping in general contribute to the conclusions. Future search functionality could take account of available vocabulary mappings via a range of search options, such as query expansion including compound mappings and mapping types. Further work on mapping guidelines and metadata is recommended.

Explaining Deep Learning Models Applied in Histopathology: Current Developments and the Path to Sustainability.

The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts. Following the study we observed that histopathological deep learning models are still underused and that the pathologists do not trust them. Also we need to point out that in order to get a sustainable use of deep learning we need to get the experts to trust the models. In order to do that, they need to understand how the results are generated and how this information correlates with their prior knowledge and for obtaining this they can use the methods highlighted in this study.

Digital pathology structure and deployment in Veneto: a proof-of-concept study

Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients’ clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools.

Automated Social Media Text Clustering Based on Financial Ontologies

Social media networks provide an aggregation of news and content, allowing users to share and discuss topics of greatest interest to them. Users can enrich the news by providing context and opinions that are useful to other users. Understanding topics of interest sheds light on the collective thinking of a group of individuals and offers important insights for exploring a given field. Among the fields of interest on social media networks, finance stands out. Automatically identifying and organizing the main issues that users discuss can be useful for multiple purposes, e.g., identifying the preferred types of loans could be useful for refining targeted advertising. Our work aims to identify and organize the topics discussed on a social media network that are related to the financial sector. For this, we propose an approach that consists of analyzing posts from Reddit communities oriented to finance. First, posts were gathered and cleaned to remove punctuation, links, and images. Then, textual similarity was computed to match posts with classes from dedicated ontologies designed for the financial sector. Finally, the populated ontology was analyzed to identify clusters of concepts. The results showed that the proposed approach and corresponding tool can summarize topics from a large number of Reddit posts using the identified classes. Over 70% of posts were linked to ontologies when considering both posts and comments, which shows that the automatic support given to posts related to financial concepts had a high degree of success.

Special Issue on Real-Time Diagnosis Algorithms in Biomedical Applications and Decision Support Tools

The use of automatic support tools in daily clicnical practice is increasing continuously [...]

Research on an Intelligent Mining Complete System of a Fully Mechanized Mining Face in Thin Coal Seam.

The mining environment of thin coal seam working faces is generally harsh, the labor intensity is high, and the production efficiency is low. Previous studies have shown that thin coal seam mining finds it difficult to follow machines, does not have complete sets of equipment, has a low degree of automation, and has difficult system co-control, which easily causes production safety accidents. In order to effectively solve the problems existing in thin coal seam mining, Binhu Coal Mine has established intelligent fully mechanized mining and actively explored automatic coal cutting, automatic support following, and intelligent control. The combination of an SAC electro-hydraulic control system and SAP pumping station control system has been applied in 16,108 intelligent fully mechanized coal mining faces, which realizes the automatic following of underground support and the control of adjacent support, partition support, and group operation; the automatic coal cutting of the shearer is realized by editing the automatic coal-cutting state of the shearer and adjusting the automatic parameters. A centralized control center is set up, which realizes the remote control and one-button start-stop of working face equipment. Through a comparative analysis of 16,108 intelligent fully mechanized mining faces and traditional fully mechanized mining faces, it is found that intelligent fully mechanized mining faces have obvious advantages in terms of equipment maintenance, equipment operation mode, and working face efficiency, which improve the equipment and technical mining level of thin coal seam. The application of intelligent mining in Binhu Coal Mine has a great and far-reaching impact on the development of thin coal seam mining technology in China.

Alerts and Collections for Automating Patients' Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study.

Electronic health record (EHR) data from multiple providers often exhibit important but convoluted and complex patterns that patients find hard and time-consuming to identify and interpret. However, existing patient-facing applications lack the capability to incorporate automatic pattern detection robustly and toward supporting making sense of the patient's EHR data. In addition, there is no means to organize EHR data in an efficient way that suits the patient's needs and makes them more actionable in real-life settings. These shortcomings often result in a skewed and incomplete picture of the patient's health status, which may lead to suboptimal decision-making and actions that put the patient at risk. Our main goal was to investigate patients' attitudes, needs, and use scenarios with respect to automatic support for surfacing important patterns in their EHR data and providing means for organizing them that best suit patients' needs. We conducted an inquisitive research-through-design study with 14 participants. Presented in the context of a cutting-edge application with strong emphasis on independent EHR data sensemaking, called Discovery, we used high-level mock-ups for the new features that were supposed to support automatic identification of important data patterns and offer recommendations-Alerts-and means for organizing the medical records based on patients' needs, much like photos in albums-Collections. The combined audio recording transcripts and in-study notes were analyzed using the reflexive thematic analysis approach. The Alerts and Collections can be used for raising awareness, reflection, planning, and especially evidence-based patient-provider communication. Moreover, patients desired carefully designed automatic pattern detection with safe and actionable recommendations, which produced a well-tailored and scoped landscape of alerts for both potential threats and positive progress. Furthermore, patients wanted to contribute their own data (eg, progress notes) and log feelings, daily observations, and measurements to enrich the meaning and enable easier sensemaking of the alerts and collections. On the basis of the findings, we renamed Alerts to Reports for a more neutral tone and offered design implications for contextualizing the reports more deeply for increased actionability; automatically generating the collections for more expedited and exhaustive organization of the EHR data; enabling patient-generated data input in various formats to support coarser organization, richer pattern detection, and learning from experience; and using the reports and collections for efficient, reliable, and common-ground patient-provider communication. Patients need to have a flexible and rich way to organize and annotate their EHR data; be introduced to insights from these data-both positive and negative; and share these artifacts with their physicians in clinical visits or via messaging for establishing shared mental models for clear goals, agreed-upon priorities, and feasible actions.

Neuro-control adjustment of conveyor belt tension

Goal.Identification of ways to improve the quality indicators of conveyor operation during their design and construction using modern automation elements, automatic control systems, and the application of neural controller systems for automatic support of optimal technological processes during material transportation. Research methodology. The proposed methodology involves supervisory training of a neural controller system for automatic tensioning of the conveyor belt using the MATLAB software package. Research results. A sequence for building and training an imitation neural control system for regulating (supporting) the optimal tension of the conveyor belt has been developed and investigated using the MATLAB software package. It was established that for systems containing elements with significant time constants, it is advisable to experimentally determine the ratio of the number of training stages to the number of segments during supervisory training of the neural controller. Scientific novelty. The article proposes the construction of an imitation model of a tension control system for a conveyor belt using artificial intelligence and the implementation of a neural controller. This approach allows for preliminary tuning of the developed control system and its utilization in the design phase of similar objects. Practical significance. The sequence for constructing an imitation model of an optimal tension control neural system for a conveyor belt can be applied in the design and investigation of similar mechanisms. The system is adaptable and enhances reliability while maintaining desired performance parameters. The model can be recommended for preliminary determination of neural controller parameters, regulator system tuning, obtaining predicted transient processes, and improving work productivity.

A model-driven approach to adopt good practices for agile process configuration and certification

ContextThe agile development paradigm has been extensively adopted in the industry. This adoption is highly dependent on the knowledge and good practices applied by most experienced practitioners in organizations. Hence, it would be valuable to count on appropriate support to preserve and systematically use this expert knowledge in configuring agile development processes aligned with organizational standards. ObjectiveThis paper presents a model-driven approach for representing and selecting good practices to configure agile practices in development processes aligned with organizational development practices and quality standards. MethodWe define a conceptual approach for configuring agile development processes that fulfills enterprise good practices and external quality standards. This approach was implemented in a tool suite and applied to an industrial development scenario related to ISO 9001 certification. ResultsThe approach was implemented in a model-driven tool that provides automatic support for identifying good practices when configuring agile development processes. The tool also verifies consistency with development methods and quality standards, such as ISO 9001. ConclusionsThe results obtained from the industrial application indicate that practitioners can reuse expert knowledge to configure agile development processes aligned with quality certifications. Moreover, the approach also facilitates the tailoring of agile practices into concrete development processes that take advantage of organizational good practices.

Usability and transparency in the design of a tool for automatic support for web accessibility validation

The importance of guaranteeing accessible Web applications is becoming widely recognised, and supported by national and international legislation. This implies an increasing need for large scale validations, which can be achieved only through automatic support. Thus, there is a need for a new generation of accessibility validation tools able to check against the continuously evolving guidelines and report any issues revealed, considering that their results will be used by many people with varied backgrounds and goals. Such tools should be able to support monitoring of Web sites’ accessibility, provide user-friendly information suitable for various purposes, and be transparent in terms of what they are actually able to evaluate in order to elicit the right expectations from their users. They should also consider how the technologies for implementing Web sites have evolved in recent years. In this paper, we provide a description of the requirements and design dimensions that characterise such tools in order to address emerging needs, providing indications on whether and how several existing validation tools support them, and present how we have addressed and implemented them in a specific tool. We also report on a first usability test of such tool, which has provided encouraging feedback.

The Impact of Fan Culture on China’s Film Industry

This paper discusses the impact of fan culture on the Chinese film industry. The continuous advancement of technology has changed the nature of ‘fan’ culture, which is now a daily presence in public life and, both directly and indirectly, exerts influence on the mass entertainment industry. A new, distinctive industry that has emerged from this new culture is fan films, or commercial films oriented toward a specific ‘fandom’ and that seeks to take advantage of a franchise’s cultural capital, reaping the benefits of fan attention. The ‘fan film’ model has even begun to influence the Chinese film industry, with films featuring certain idols gaining automatic support from a large number of fans, regardless of film quality. However, public opinion of these films is often polarised, For the main audiences of commercial and fan films, they will think that these movies that meet their tastes deserve a high rating. But there is also a part of the movie-viewing group that thinks the production and content of these movies are not sophisticated and deep enough to deserve a good rating. To understand this complex phenomenon, a deeper look into fan culture is necessary, specifically its characteristics, impact on the film industry, and how the film industry in turn manipulates fan culture to its benefit. This study will therefore seek to see how movies, stars and fans work together to promote China’s film industry using both sociological and communicative analysis.

Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier.

The Swiss classification of surgical interventions (CHOP) has to be used in daily practice by physicians to classify clinical procedures. Its purpose is to encode the delivered healthcare services for the sake of quality assurance and billing. For encoding a procedure, a code of a maximal of 6-digits has to be selected from the classification system, which is currently realized by a rule-based system composed of encoding experts and a manual search in the CHOP catalog. In this paper, we will investigate the possibility of automatic CHOP code generation based on a short query to enable automatic support of manual classification. The wide and deep hierarchy of CHOP and the differences between text used in queries and catalog descriptions are two apparent obstacles for training and deploying a learning-based algorithm. Because of these challenges, there is a need for an appropriate classification approach. We evaluate different strategies (multi-class non-terminal and per-node classifications) with different configurations so that a flexible modular solution with high accuracy and efficiency can be provided. The results clearly show that the per-node binary classification outperforms the non-terminal multi-class classification with an F1-micro measure between 92.6 and 94%. The hierarchical prediction based on per-node binary classifiers achieved a high exact match by the single code assignment on the 5-fold cross-validation. In conclusion, the hierarchical context from the CHOP encoding can be employed by both classifier training and representation learning. The hierarchical features have all shown improvement in the classification performances under different configurations, respectively: the stacked autoencoder and training examples aggregation using true path rules as well as the unified vocabulary space have largely increased the utility of hierarchical features. Additionally, the threshold adaption through Bayesian aggregation has largely increased the vertical reachability of the per node classification. All the trainable nodes can be triggered after the threshold adaption, while the F1 measures at code levels 3-6 have been increased from 6 to 89% after the threshold adaption.

Toward automatic support for leading court debates: a novel task proposal & effective approach of judicial question generation

Toward automatic support for leading court debates: a novel task proposal & effective approach of judicial question generation

Workspace analysis and motion control strategy of robotic mine anchor drilling truck manipulator based on the WOA-FOPID algorithm

The manipulator is the key component of the anchor drilling robot to automatically complete the anchoring operation underground. Due to the complexity of its motion equation and the limitations of its control strategy, the real-time pose and the positioning accuracy of the manipulator are inferior, which seriously restricts the safety, efficiency, and speed of roadway excavation. In order to improve the positioning accuracy and realize the optimal efficiency of the manipulator, this article designs a manipulator structure with four degrees of freedom. With the help of the D-H method and the intelligent parameter setting method, this article carries out the basic theoretical research on the kinematics and the fractional order FOPID control algorithm of the manipulator of the mining roof bolter, and formulates a manipulator motion control strategy. At the same time, combined with numerical simulations and field experiments, we explore the robustness and control efficiency of the hydraulic system of the manipulator under the working conditions of a harsh environment and limited space, and reveal that the intelligent optimization algorithm can control the motion state of the manipulator more accurately and stably after the parameters of the fractional order FOPID controller are positively determined. This study effectively solved the dynamic model uncertainty caused by time-varying internal parameters and external loads of the hydraulic servo system, optimized and reconstructed the structure and motion coefficient parameters of the manipulator, and revealed the control mechanism of a precise spatial positioning and online trajectory planning of the hydraulic servo system of the manipulator. Compared with the traditional PID control algorithm, this algorithm has a faster response speed and better expected track tracking ability. This research lays a theoretical foundation for the precise positioning and automatic support of the manipulator, and also provides a reference for the design of similar motion control algorithms.

Enabling SAT single pilot operations: tactical separation system design advancements in the COAST project

PurposeThis paper aims to describe the advancements of the activities that have been carried out, in the Cost-Optimized Avionics SysTem (COAST) project, to complete the design and in-flight demonstration of the Tactical Separation System (TSS), which is an automatic support system to the pilot’s decision-making, onboard on small air transport (SAT) vehicles under single pilot operations, in the separation management.Design/methodology/approachIn the framework of the Clean Sky 2 funded project COAST, some enabling technologies for single pilot operations in the EASA CS-23 category vehicles are designed and demonstrated in flight. Among the relevant flight management technologies addressed in the project, the specific one devoted to the real-time support to pilot’s decision-making in maintaining the vehicle self-separation is the TSS, designed by the Italian Aerospace Research Centre.FindingsThe TSS design started in the year 2016 and has been completed in the year 2021 after successful in-flight demonstration in the dedicated flight test campaign. The system has been validated by means of several simulation campaigns and finally demonstrated its effectiveness in providing its intended functionalities (situational awareness, conflict detection, conflict resolution) to the pilot in real flight trials, involving the presence of real conflicting aircraft.Originality/valueThe TSS contributes enabling the implementation of single pilot operations in CS-23 category vehicles, thanks to the possibility to support the pilot with provision of consolidated traffic picture, detection of conflicting surrounding traffic and suggestion of suitable conflict resolution manoeuvre real-time during the flight, through dedicated human–machine interface designed on purpose. The TSS supports the new separation modes that are envisaged in the future SESAR ATM target concept, with particular reference to the possible delegation of the separation responsibility to the pilot.

Control and regulation of the density of technical fluids during their transportation by sea specialized vessels

The object of research is the process of transporting drilling slurry through specialized marine vessels of the Platform Supply Vessels class. The subject of research is the sedimentation stability of the drilling slurry along the height of the cargo tank, which is proposed to be defined as a relative change in vertical density near the surface and bottom of the cargo tank. The studies were carried out on a specialized sea vessel with a displacement of 7320 tons. The design of the vessel provided for the reception and transportation of drilling slurry in four cargo tanks located on the port and starboard sides of the vessel. It has been experimentally established that during the 48-hour transportation, the density of the drilling slurry in the bottom part increases to 19.7 %; decrease in density on the surface – up to 7.8 %; decrease in the sedimentation resistance of the drilling slurry along the depth of the cargo tank – up to 29.85 %. A variant of modernization of the drilling slurry transportation system by installing additional circulation pumps providing forced circulation of the drilling slurry between cargo tanks is proposed. By using programmable microcontrollers (performing turning on/off the circulation pumps), it is possible to provide the following conditions for transporting the drilling fluid: an increase in the density of the drilling fluid in the bottom part up to 0.3 %; decrease in density on the surface – up to 0.25 %; decrease in the sedimentation resistance of the drilling slurry along the depth of the cargo tank – up to 8.01 %. It has been experimentally established that the creation of additional circulation and automatic support of the sedimentation resistance of the drilling fluid in the range of 2–7 % contributes to: – increasing the relative performance of cargo pumps from 38–55 % to 92–96 %; – reducing the time of pumping drilling slurry from cargo tanks to the drilling platform from 7.1 to 3.2 hours; – maintaining the technical condition of equipment, pipelines and elements of the drilling slurry transportation and pumping system.

Development of a convolutional neural network for the identification and the measurement of the median nerve on ultrasound images acquired at carpal tunnel level

BackgroundDeep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel.MethodsConsecutive patients with rheumatic and musculoskeletal disorders were recruited. Transverse US images were acquired at the carpal tunnel inlet, and the CSA was manually measured. Anatomical variants were registered. The dataset consisted of 246 images (157 for training, 40 for validation, and 49 for testing) from 103 patients each associated with manual annotations of the nerve boundary. A Mask R-CNN, state-of-the-art CNN for image semantic segmentation, was trained on this dataset to accurately localize and segment the median nerve section. To evaluate the performances on the testing set, precision (Prec), recall (Rec), mean average precision (mAP), and Dice similarity coefficient (DSC) were computed. A sub-analysis excluding anatomical variants was performed. The CSA was automatically measured by the algorithm.ResultsThe algorithm correctly identified the median nerve in 41/49 images (83.7%) and in 41/43 images (95.3%) excluding anatomical variants. The following metrics were obtained (with and without anatomical variants, respectively): Prec 0.86 ± 0.33 and 0.96 ± 0.18, Rec 0.88 ± 0.33 and 0.98 ± 0.15, mAP 0.88 ± 0.33 and 0.98 ± 0.15, and DSC 0.86 ± 0.19 and 0.88 ± 0.19. The agreement between the algorithm and the sonographer CSA measurements was excellent [ICC 0.97 (0.94–0.98)].ConclusionsThe developed algorithm has shown excellent performances, especially if excluding anatomical variants. Future research should aim at expanding the US image dataset including a wider spectrum of normal anatomy and pathology. This deep learning approach has shown very high potentiality for a fully automatic support for US assessment of carpal tunnel syndrome.

Real-time motion onset recognition for robot-assisted gait rehabilitation

BackgroundMany patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users’ movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total).MethodsTo predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification.ResultsTesting five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of 86.83% pm 6.2% and 92.01% are achieved on testing using LOPOCV and test data (30%, participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = 0.08 %).ConclusionsA neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.

Design of satellite ground fault diagnosis system based on rule base

The automation and intelligentization of fault diagnosis system for satellite ground system directly affect the success of tasks and the reliability of the system. This paper designed a fault diagnosis system based on rule base, which contained satellite ground system failure rule base, failure model, abnormal and alarm mechanism. Software implementation has been verified by actual project, it shows that the fault diagnosis system based on rule base can improve the capacity of fault management functions, real-time monitoring and automatic fault diagnosis support system. In addition, fault analysis and location can enhance the automation level and efficiency of satellite fault diagnosis, make efficient and reliable diagnosis of remote sensing satellite receiving system, raise the success rate of satellite data receiving, and have good practicability and popularization.

CAM Planning for Automatic Support Removal with Robotic Chiseling

In this paper, a new CAM planning strategy for removal of support structures is presented. Since such a task is usual in post-processing of parts fabricated with additive manufacturing, the developed approach is about a toolpath planning algorithm suitable for generic robotic cells that can be equipped with a chiseling holder. The 6-axis toolpath generation was used to define spatial paths of the tool tip and proper orientation of the chisel tool. The chisel can be orientated with a certain angle to the machined surface ensuring a gouge-free and collision-free positioning. Removability of different support structures was experimentally validated.