Automatic Processing Research Articles

Perceptual anchoring: Children with dyslexia benefit less than controls from contextual repetitions in speech processing

ObjectivesIndividuals with dyslexia perceive and utilize statistical features in the auditory input deficiently. The present study investigates whether affected children also benefit less from repeating context tones as perceptual anchors for subsequent speech processing. MethodsIn an event-related potential study, eleven-year-old children with dyslexia (n = 21) and without dyslexia (n = 20) heard syllable pairs, with the first syllable either receiving a constant pitch (anchor) or variable pitch (no-anchor), while second syllables were identical across conditions. ResultsChildren with and without dyslexia showed smaller auditory P2 responses to constant-pitch versus variable-pitch first syllables, while only control children additionally showed smaller N1 and faster P1 responses. This suggests less automatic processing of anchor repetitions in dyslexia. For the second syllables, both groups showed faster P2 responses following anchor than no-anchor first syllables, but only controls additionally showed smaller P2 responses. ConclusionsChildren with and without dyslexia show differences in anchor effects. While both groups seem to allocate less attention to speech stimuli after contextual repetitions, children with dyslexia display less facilitation in speech processing from acoustic anchors. SignificanceAltered anchoring in the linguistic domain may contribute to the difficulties of individuals with dyslexia in establishing long-term representations of speech.

Servo control technology for automatic screw-tightening process of robotic arm based on multi-vision sensor

Servo control technology for automatic screw-tightening process of robotic arm based on multi-vision sensor

Advances in Research on Tool Wear Online Monitoring Method

Background: With the continued advancement of industrial internet technology, mechanical manufacturing is increasingly developing towards automation and intelligence. As a result, monitoring the manufacturing process has become an essential requirement for intelligent manufacturing. As one of the fundamental components of cutting processes, tools are inevitably subject to wear and damage during use. Therefore, tool wear monitoring plays a crucial role in modern manufacturing. Introduction: With the development of the manufacturing industry, the requirement for automation manufacturing is higher and higher. In the process of automatic processing, unmanned processing and adaptive processing, it is not only required to be able to know the accurate wear state of the tool in the process real-time but also required to change the milling parameters according to the wear state of the tool, in order to optimize the productivity and processing quality. The tool monitoring system can effectively reduce the operating cost of workshop production and improve the reliability of intelligent workshop and flexible production lines. Method: This article summarizes commonly used online monitoring methods mentioned by articles and patents, such as cutting force, vibration, acoustic emission, temperature, current, and power signals. Each monitoring method is analyzed in terms of its principles, advantages and disadvantages, signal acquisition equipment, and research status. The article also identifies current issues and future development directions. Results: As modern manufacturing technology continues to develop rapidly, unmanned factories have become a significant feature of the manufacturing industry. Consequently, the need for tool wear condition monitoring technology is becoming increasingly urgent. Although tool condition monitoring technology has made significant progress over the past twenty years and has been applied in actual production, several issues need to be addressed to make tool wear condition monitoring systems mo. Conclusion: This serves as a reference for theoretical research and application of online monitoring of tool wear in intelligent manufacturing systems.

Editorial: Symposium on Movements of the Mind by Wayne Wu

Movements of the Mind offers a compelling and empirically grounded theory of agency and attention, arguing that the structure of action depends on the capacity of attention. This capacity is also central to the explanation of how memory can be geared towards immediate action, as memory 'for work', because attention is essential for the solution of selection problems that require working memory. This seemingly simple structure has substantial and broad consequences for various aspects of cognition, allowing for the integration of automatic processing and top-down routines with the articulation of physical and mental action. This account of agency and attention is also applicable to non-human cognition.

Study on the Interactivity of Controlled and Automatic Information Processing in L2 Reading

Study on the Interactivity of Controlled and Automatic Information Processing in L2 Reading

Automatic processing of unattended mean emotion: Evidence from visual mismatch responses

The mean emotion from multiple facial expressions can be extracted rapidly and precisely. However, it remains debated whether mean emotion processing is automatic which can occur under no attention. To address this question, we used a passive oddball paradigm and recorded event-related brain potentials when participants discriminated the changes in the central fixation while a set of four faces was presented in the periphery. The face set consisted of one happy and three angry expressions (mean negative) or one angry and three happy expressions (mean positive), and the mean negative and mean positive face sets were shown with a probability of 20% (deviant) and 80% (standard) respectively in the sequence, or the vice versa. The cluster-based permutation analyses showed that the visual mismatch negativity started early at around 92 ms and was also observed in later time windows when the mean emotion was negative, while a mismatch positivity was observed at around 168- 266 ms when the mean emotion was positive. The results suggest that there might be different mechanisms underlying the processing of mean negative and mean positive emotions. More importantly, the brain can detect the changes in the mean emotion automatically, and ensemble coding for multiple facial expressions can occur in an automatic fashion without attention.

“I can join a sport class, but school has to come first”: adolescent athletes’ dual career construction styles in lower secondary sport schools

ABSTRACT In this longitudinal qualitative study, we explored the automatic and controlled processes underpinning adolescent athletes’ dual career (DC) construction styles in lower secondary school and how these styles influenced the continuation of sports in upper secondary education. We conducted semi-structured interviews with 16 Finnish adolescent lower secondary school DC athletes (nine females, seven males) aged 14.5 years at baseline, interviewed them one year later, and then analyzed the data using reflexive thematic analysis based on dual-process theory. Most adolescent athletes (11 of 16) constructed their DCs in Grade 8 (T1) using a contrapuntal DC construction style, attempting to balance sports and academic studies. Eleven adolescent athletes maintained their DC styles, but five shifted their career styles between T1 and T2. Exemplifying the dynamic and individual construction of their DCs, seven adolescents had a contrapuntal style, three a dissonant style, and six a monophonic style in Grade 9 (T2). Four of sixteen continued their DCs in sports-oriented upper secondary education, all of them exhibiting a contrapuntal style. The remaining 12, representing all 3 styles, transitioned to general upper secondary schools. Dual-process theory facilitated the identification of specific automatic and controlled processes that influenced decision-making among adolescents with different career construction styles. This understanding can aid in tailoring individual DC services to effectively support adolescents in balancing their school and sports pursuits during lower secondary school and assist them in transitioning to their preferred study options in upper secondary school.

Automatic and Semi-automatic Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) is the most commonly used method for solving multi-criteria decision-making problems worldwide. Although AHP offers many advantages for problems with several alternatives and criteria, the pairwise comparisons require considerable effort. State-of-the-art methodologies have demonstrated that AHP is suitable for automating decisions on tabular data. Considering it, this paper proposes a new approach to decision-making in both automated and semi-automated ways. For the automated approach, formulas are proposed for the normalization and computation of criteria weights. For the semi-automated approach, functions are proposed to convert normalized tabular data into values on the Saaty scale. These values are then used to automatically construct pairwise comparison matrices. This approach allows decision-makers to generate such matrices when necessary, thereby minimizing or even eliminating the effort required for pairwise comparisons. Simulations demonstrate the differences obtained depending on the use of the conversion function. Comparisons with state-of-the-art methods reveal that the proposed approach is compatible with existing methodologies. The viability of the proposed methodology is also explored through problems of selecting genotypes/varieties of agricultural crops, showing its viability in real problems. The obtained results show that the proposed approach produces results similar to other decision-making methods.

Evidence Against Effects of Cultural Group and Prior Knowledge on Feature Binding in Working Memory.

Feature binding is the process of integrating features, such as colour and shape, into object representations. A persistent question in the literature concerning whether feature binding is an automatic or resource-demanding process may depend on unitisation, that is, whether the to-be-bound information is intrinsic (belonging to) or extrinsic (contextual). Given extensive evidence showing that Easterners may process information more holistically than Westerners, such cultural differences may be useful to understand the fundamental processes of feature binding in visual working memory (WM). Accordingly, we recruited British and Chinese participants to complete a visual WM task wherein to-be-remembered colours were integrated within (i.e., intrinsic binding) or as backgrounds (i.e., extrinsic binding) of to-be-remembered shapes (Experiments 1 and 2). Experiment 2 further investigated the role of prior knowledge in long-term memory to facilitate feature binding in WM. During retrieval, participants decided among three probes: a target, a lure (i.e., recombination of the presented features), and a new colour/shape. Hierarchical Bayesian multinomial processing tree models were fit to the data to estimate parameters representing binding and item memory. The current results suggest that intrinsic and extrinsic binding memory are similar between the two cultural groups, with no prior knowledge benefits for either intrinsic or extrinsic binding for either cultural group. This result conflicts with the Analytic and Holistic framework and suggests that there are no cultural differences or prior knowledge benefits in feature binding.

Enhancing handwritten text recognition accuracy with gated mechanisms

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages. The gated mechanism enables the model to capture long-term dependencies, retain relevant context, handle variable length sequences, mitigate error propagation, and adapt to contextual variations. The pipeline involves preprocessing the handwritten text images, extracting features, modeling the sequential dependencies using the gated mechanism, and decoding the output into readable text. The training process utilizes annotated datasets and optimization techniques to minimize transcription discrepancies. HTR using a gated mechanism has found applications in digitizing historical documents, automatic form processing, and real-time transcription. The results show improved accuracy and robustness compared to traditional HTR approaches. The advancements in HTR using a gated mechanism open up new possibilities for effectively recognizing and transcribing handwritten text in various domains. This research does a better job than the most recent iteration of the HTR system when compared to five different handwritten datasets (Washington, Saint Gall, RIMES, Bentham and IAM). Smartphones and robots are examples of low-cost computing devices that can benefit from this research.

An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data.

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Self-Regulation in Eating Behaviors: The Role of Executive Function in Response to Food Stimuli.

Human nutrition is characterized by both automatic and self-regulated processes. One of the dimensions that may be employed in monitoring eating behaviors is the cognitive control played by the executive functions (EFs). The role of EFs in affecting eating behaviors has been assessed in empirical studies, but further analyses are needed in a current society characterized by a food-abundant environment. Accordingly, this study attempted to evaluate the association between weight status and executive functions in response to food-related stimuli. One hundred and forty-four young adults took part in the study (25.7% in overweight condition). The participants completed a set of computerized cognitive tasks to assess cognitive and motor inhibition and working memory in two different conditions: (i) classic versions of the tasks and (ii) modified versions with food cues. The results indicate that food stimuli may influence executive performance and that there is an association between food cue-related executive functioning, particularly in the domain of motor inhibition, and working memory. These results suggest that self-regulation in nutrition may involve executive control. Although further studies are needed, this work suggests the importance of a multidimensional perspective in the analysis of eating behaviors and the relevance of EFs in monitoring our approach to food stimuli in an environmental context.

Accuracy of Automatic Processing of Speech-Language Pathologist and Child Talk During School-Based Therapy Sessions.

This study examines the accuracy of Interaction Detection in Early Childhood Settings (IDEAS), a program that automatically transcribes audio files and estimates linguistic units relevant to speech-language therapy, including part-of-speech units that represent features of language complexity, such as adjectives and coordinating conjunctions. Forty-five video-recorded speech-language therapy sessions involving 27 speech-language pathologists (SLPs) and 56 children were used. The F measure determines the accuracy of IDEAS diarization (i.e., speech segmentation and speaker classification). Two additional evaluation metrics, namely, median absolute relative error and correlation, indicate the accuracy of IDEAS for the estimation of linguistic units as compared with two conditions, namely, Oracle (manual diarization) and Voice Type Classifier (existing diarizer with acceptable accuracy). The high F measure for SLP talk data suggests high accuracy of IDEAS diarization for SLP talk but less so for child talk. These differences are reflected in the accuracy of IDEAS linguistic unit estimates. IDEAS median absolute relative error and correlation values for nine of the 10 SLP linguistic unit estimates meet the accuracy criteria, but none of the child linguistic unit estimates meet these criteria. The type of linguistic units also affects IDEAS accuracy. IDEAS was tailored to educational settings to automatically convert audio recordings into text and to provide linguistic unit estimates in speech-language therapy sessions and classroom settings. Although not perfect, IDEAS is reliable in automatically capturing and returning linguistic units, especially in SLP talk, that are relevant in research and practice. The tool offers a way to automatically measure SLP talk in clinical settings, which will support research seeking to understand how SLP talk influences children's language growth.

A New Automatic Process Based on Generative Design for CAD Modeling and Manufacturing of Customized Orthosis

As is widely recognized, advancements in new design and rapid prototyping techniques such as CAD modeling and 3D printing are pioneering individualized medicine, facilitating the implementation of new methodologies for creating customized orthoses. The aim of this paper is to develop a new automatic technique for producing personalized orthoses in a straightforward manner, eliminating the necessity for doctors to collaborate directly with technicians. A novel design method for creating customized wrist orthoses has been implemented, notably featuring a generative algorithm for the parametric modeling of the orthosis. To assess the efficacy of the developed algorithm, a case study was conducted involving the design and rapid prototyping of a wrist orthosis using Fused Deposition Modeling (FDM) technology. Subsequently, the developed algorithm was tested by clinicians and patients. The results obtained indicate that the implemented algorithm is user-friendly and could potentially enable non-expert users to design customized orthoses. These results introduce innovative elements of originality within the CAD modeling, offering promising solutions to the challenges associated with the design and production of customized orthoses. Future developments could consist of a better investigation regarding the parameters that influence the accuracy of the scanning and of the printing processes.

Causal Roles of Ventral and Dorsal Neural Systems for Automatic and Control Self-Reference Processing: A Function Lesion Mapping Study.

Background: Humans perceive and interpret the world through the lens of self-reference processes, typically facilitating enhanced performance for the task at hand. However, this research has predominantly emphasized the automatic facet of self-reference processing, overlooking how it interacts with control processes affecting everyday situations. Methods: We investigated this relationship between automatic and control self-reference processing in neuropsychological patients performing self-face perception tasks and the Birmingham frontal task measuring executive functions. Results: Principal component analysis across tasks revealed two components: one loaded on familiarity/orientation judgments reflecting automatic self-reference processing, and the other linked to the cross task and executive function indicating control processing requirements. Voxel-based morphometry and track-wise lesion-mapping analyses showed that impairments in automatic self-reference were associated with reduced grey matter in the ventromedial prefrontal cortex and right inferior temporal gyrus, and white matter damage in the right inferior fronto-occipital fasciculus. Deficits in executive control were linked to reduced grey matter in the bilateral inferior parietal lobule and left anterior insula, and white matter disconnections in the left superior longitudinal fasciculus and arcuate fasciculus. Conclusions: The causal evidence suggests that automatic and control facets of self-reference processes are subserved by distinct yet integrated ventral prefrontal-temporal and dorsal frontal-parietal networks, respectively.

METHODS OF MATHEMATICAL ANALYSIS AND PROCESSING OF EEG SIGNALS: OVERVIEW OF THE CURRENT STATE AND LATEST TRENDS

Aim. With the help of retrospective analysis, establish the current level of achievements in the field of applying methods of mathematical analysis and processing of biomedical signals, in particular EEG, and characterize the main trends. Materials and methods. The research was conducted using theoretical methods, by studying and analyzing freely available literature, publications, materials of working groups of domestic and foreign scientists over the past few years. Results. The analysis of scientific works devoted to the development and development of EEG showed that over the past five years there has been a deepening of the tendency to separate the methods of improving the hardware and software parts of electroencephalographic complexes. The last of them, in turn, can be divided into several main directions. The first direction is the improvement of real-time EEG analysis methods, including automatic processing included in the software of EEG complexes. The second direction combines mathematical methods of signal analysis to detect existing pathologies and predict the onset of pathological conditions, such as statistical, spectral-correlation analysis, building mathematical models. The next direction brings together the application of mathematical methods for EEG signal processing and further use in deep learning, the use of convolutional neural networks to detect epileptic seizures, sleep disorders, differentiation of neuropsychiatric disorders, etc. The direction of using EEG in prosthetics deserves special attention. Methods of mathematical processing of the EEG signal are widely implemented in the development of bionic prostheses. The main achievements in these directions are considered in detail in the work. Conclusions. While the improvement of the material and technical part of the EEG is focused on the issues of improving the methods of recording biological signals, finding optimal methods of suppressing noise without losing the useful signal and does not currently have breakthrough results, the methods of analysis and processing of the already received signal have developed rapidly. The use of EEG to detect pathological conditions, the study of the neurophysiological basis of cognitive processes, etc., has always attracted the interest of scientists, but the use of neural networks for EEG processing and analysis and their implementation in robotics developments has given this direction a new impetus.

Threat-induced prosocial behavior: enhanced exogenous attention to protect others from harm

As social animals, humans tend to voluntarily engage in pro-social behavior to prevent harm to others. However, to what extent prosocial behavior can be reflected at the level of less voluntary cognitive processes remains unclear. Here, we examined how threat to others modulates exogenous attention. Fifty-four participants performed an exogenous spatial cueing task where the participant’s performance determined whether electric shocks would be delivered either to themselves or to their anonymous co-participant. Threat of shock to the co-participant elicited orienting and reorienting responses that were faster than in the safe condition and did not differ from performance when participants avoided shocks to themselves. This attentional improvement was not due to speed-accuracy trade off and was associated with arousal, i.e., increased pupil dilation in both threat conditions. Together, these findings suggest that pro-social behavior triggers automatic attentional processes which may be relevant for providing immediate help without relying on reflexive processes.

Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax

Objective. The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as ‘black boxes’ due to their lack of interpretability. This situation presents challenges in identifying reasons for misdiagnoses and improving accuracy, which leads to potential risks of misdiagnosis and delayed treatment. Therefore, how to enhance the interpretability of diagnostic models is crucial for improving patient outcomes and reducing treatment delays. So far, only limited researches exist on deep learning-based prediction of spontaneous pneumothorax, a pulmonary disease that affects lung ventilation and venous return. Approach. This study develops an integrated medical image analysis system using explainable deep learning model for image recognition and visualization to achieve an interpretable automatic diagnosis process. Main results. The system achieves an impressive 95.56% accuracy in pneumothorax classification, which emphasizes the significance of the blood vessel penetration defect in clinical judgment. Significance. This would lead to improve model trustworthiness, reduce uncertainty, and accurate diagnosis of various lung diseases, which results in better medical outcomes for patients and better utilization of medical resources. Future research can focus on implementing new deep learning models to detect and diagnose other lung diseases that can enhance the generalizability of this system.

Automatic welding seam tracking and real-world coordinates identification with machine learning method

In the automatic welding process, the precision of the welding position significantly influences welding quality, relying heavily on the accurate identification of the welding seam. However, in complex and unstructured welding environments, such as those with strong arc lights, welding splashes, and thermal-induced deformations, various disturbances present a significant challenge to identify the welding seam. This paper introduces a cost-effective welding seam tracking model constructed using a laser line and a fixed viewpoint single RGB camera. The model's primary objective is to detect a chain of welding-seam points in both captured images and real-time camera feeds. The paper presents the techniques for camera calibration, laser detection, and the use of artificial intelligence to establish a real-world coordinate frame. The process begins with camera calibration using the OpenCV library to filter noise and undistort images. Subsequently, the thresholding technique is employed for color image segmentation, isolating the laser sensor light on the workpiece. An algorithm is then introduced to detect welding spots. Finally, the image coordinates are converted to world coordinates using two machine learning methods: Random Forest Regression and Gradient Boosting Regression. Experimental demonstrations were conducted at different positions on the object to assess algorithm precision. The results indicate average deviations of 0.83 mm, 0.74 mm, and 0.77 mm for X, Y, and Z coordinates, respectively, which fall below the expected standard deviation of 3 mm. Despite the overall consistency of the program, slight noise from environmental lighting may still exist. In conclusion, the proposed welding seam tracking method and coordinate conversion program have proven effective and cost-efficient, with potential for future improvements.

Integrating multi-microphone devices and AI for noise event identification in unattended monitoring

This paper explores the use of multi-microphone devices and artificial intelligence (AI) for the identification of noise events in unattended noise monitoring. The system presented aids in the identification and localization of dominant sound sources using time difference of arrival techniques. By incorporating AI technology, the system's software automates the extraction of non-relevant sounds. The algorithms enable removal of undesired components, resulting in a more accurate measurement. This article discusses the benefits and limitations associated with employing automatic processes in noise monitoring and analysis software. Finally, a current application and prospects for future advancements in the field are presented.