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A highly pH-responsive gelatin film incorporating purple cabbage anthocyanin (PCA) and chondroitin sulphate (CS)/tannic acid (TA) was developed. Co-pigmentation of PCA via CS/TA improved its photothermal stability and visibility of color change in gelatin film. The morphological and structural properties of CS-PCA and TA-PCA films revealed that a more stable network was formed as new hydrogen bonds were generated by the co-pigmentation. Meanwhile, the co-pigmentation improved film's mechanical and hydrophobic properties, expressed as higher tensile strength (16.65 and 17.97 Mpa) and lower water vapor permeability (1.45 and 1.41) in CS-PCA and TA-PCA films, compared to PCA film. CS-PCA and TA-PCA films showed distinct color transitions for chilled fish fillets during storage. Total color difference (ΔE) of CS-PCA and TA-PCA films correlated well with the deterioration indexes of total volatile base nitrogen (TVB-N). All the results provided a novel pH-sensitive intelligent packaging strategy by co-pigmenting CS/TA with PCA for freshness monitoring.

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Objectives: This study assessed emotion recognition skills in school-age children in wartime conditions in Ukraine. Methods: An online survey based on the concept of basic emotions was administrated to a sample of 419 schoolchildren from Ukraine and a control group of 310 schoolchildren from the Czech Republic, aged 8 to 12. Results: There is no difference in judging the intensity of anger and fear by Ukrainian children, compared with the control group. There is no evidence that the emotions of anger, fear, and sadness were better recognized in the Ukrainian group. Children from Ukraine were better at recognizing positive emotions than Czech children. Conclusion: Increased risks of threats and wartime experience do not impair the accuracy of identification of emotions like fear or the assessment of intensity of basic emotions by children who experience war in Ukraine. Still, it is important to continue studying the long-term consequences of military conflicts in order to deepen the understanding of their impact on human mental functioning.

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In repeated-event paradigms where participants are asked to recall details of a sequence of similar instances they viewed/experienced previously, more accurate details are typically recalled from the first and final instances (i.e., long-term primacy and recency effects). Participants likely encode distinct attributes of details of the boundary instances that subsequently facilitate source monitoring. To date, most repeated event research has measured memory performance via free-/cued-recall paradigms; we examined delayed memory for repeated events using the recognition paradigm. In two preregistered experiments, participants viewed four videos, and after a delay completed a recognition task. In Experiment 1 (N = 168, between-subjects), participants decided whether an item was old (i.e., presented in any video) or new, or whether an item was presented in video 1/2/3/4 or was new. In Experiment 2 (N = 160, within-subjects), the old/new decision was followed by an instance attribution decision. Old items were recognised faster in the old/new task compared to the instance-attribution task. In the instance-attribution task, items from the boundary instances were accurately attributed faster compared to items from the middle instances. We found further evidence for primacy (and recency) effects in measures of confidence, memory judgments, recognition accuracy and discriminability, and confidence-accuracy calibration.

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This study investigated the associations among conduct problems, callous-unemotional (CU) traits, and indices of emotion recognition accuracy and emotion recognition bias obtained from human faces. Impairments in emotion recognition were considered within broader, impaired emotional and social functioning. The sample consisted of 293 middle-school students (51.19% girls; M age = 12.97 years, SD = 0.88 years). In general, CU traits were associated with less accuracy in recognizing emotions, especially fearful and angry faces, and such deficits in emotional recognition were not associated with conduct problems independent of CU traits. These results support the importance of studying potential deficits in the recognition of emotions other than fear. Furthermore, our results support the importance of considering the role of CU traits when studying emotional correlates of conduct problems. For children scoring high on CU traits, the emotion recognition accuracy of anger was low irrespective of the level of conduct problems, whereas in children scoring low on CU traits, less accuracy in recognizing emotions was related to increases in conduct problems. Finally, our results support the need for research to not only focus on accuracy of emotional recognition but also test whether there are specific biases leading to these inaccuracies. Specifically, CU traits were associated not only with lower accuracy in recognizing fearful faces but also with a tendency to interpret fearful faces as angry. This suggests that the emotional deficit associated with CU traits is not just a deficit in empathic concern toward others distress but also includes a tendency to overinterpret emotions as potential threats to oneself.

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The Deese-Roediger and McDermott (DRM) paradigm and visually guided saccade tasks are both prominent research tools in their own right. This study introduces a novel DRM-Saccade paradigm, merging both methodologies. We used rule-based saccadic eye movements whereby participants were presented with items at test and were asked to make a saccade to the left or right of the item to denote a recognition or non-recognition decision. We measured old/new recognition decisions and saccadic latencies. Experiment 1 used a pro/anti saccade task to a single target. We found slower saccadic latencies for correct rejection of critical lures, but no latency difference between correct recognition of studied items and false recognition of critical lures. Experiment 2 used a two-target saccade task and also measured corrective saccades. Findings corroborated those from Experiment 1. Participants adjusted their initial decisions to increase accurate recognition of studied items and rejection of unrelated lures but there were no such corrections for critical lures. We argue that rapid saccades indicate cognitive processing driven by familiarity thresholds. These occur before slower source-monitoring is able to process any conflict. The DRM-Saccade task could effectively track real-time cognitive resource use during recognition decisions.

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We demonstrate an InGaZnO (IGZO)-based synaptic transistor with a TiO2buffer layer. The structure of the synaptic transistor with TiO2inserted between the Ti metal electrode and an IGZO semiconductor channel O2trapping layer produces a large hysteresis window, which is crucial for achieving synaptic functionality. The Ti/TiO2/IGZO synaptic transistor exhibits reliable synaptic plasticity features such as excitatory post-synaptic current, paired-pulse facilitation, and potentiation and depression, originating from the reversible charge trapping and detrapping in the TiO2layer. Finally, the pattern recognition accuracy of Modified National Institute of Standards and Technology handwritten digit images was modeled using CrossSim simulation software. The simulation results present a high image recognition accuracy of ∼89%. Therefore, this simple approach using an oxide buffer layer can aid the implementation of high-performance synaptic devices for neuromorphic computing systems.

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The ability to recognize and work with patients' emotions is considered an important part of most psychotherapy approaches. Surprisingly, there is little systematic research on psychotherapists' ability to recognize other people's emotional expressions. In this study, we compared trainee psychotherapists' nonverbal emotion recognition accuracy to a control group of undergraduate students at two time points: at the beginning and at the end of one and a half years of theoretical and practical psychotherapy training. Emotion recognition accuracy (ERA) was assessed using two standardized computer tasks, one for recognition of dynamic multimodal (facial, bodily, vocal) expressions and one for recognition of facial micro expressions. Initially, 154 participants enrolled in the study, 72 also took part in the follow-up. The trainee psychotherapists were moderately better at recognizing multimodal expressions, and slightly better at recognizing facial micro expressions, than the control group at the first test occasion. However, mixed multilevel modeling indicated that the ERA change trajectories for the two groups differed significantly. While the control group improved in their ability to recognize multimodal emotional expressions from pretest to follow-up, the trainee psychotherapists did not. Both groups improved their micro expression recognition accuracy, but the slope for the control group was significantly steeper than the trainee psychotherapists'. These results suggest that psychotherapy education and clinical training do not always contribute to improved emotion recognition accuracy beyond what could be expected due to time or other factors. Possible reasons for that finding as well as implications for the psychotherapy education are discussed.

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Introduction: Psychotherapists' emotional and empathic competencies have a positive influence on psychotherapy outcome and alliance. However, it is doubtful whether psychotherapy education in itself leads to improvements in trainee psychotherapists' emotion recognition accuracy (ERA), which is an essential part of these competencies. Methods: In a randomized, controlled, double-blind study (N = 68), we trained trainee psychotherapists (57% psychodynamic therapy and 43% cognitive behavioral therapy) to detect non-verbal emotional expressions in others using standardized computerized trainings - one for multimodal emotion recognition accuracy and one for micro expression recognition accuracy - and compared their results to an active control group one week after the training (n = 60) and at the one-year follow up (n = 55). The participants trained once weekly during a three-week period. As outcome measures, we used a multimodal emotion recognition accuracy task, a micro expression recognition accuracy task and an emotion recognition accuracy task for verbal and non-verbal (combined) emotional expressions in medical settings. Results: The results of mixed multilevel analyses suggest that the multimodal emotion recognition accuracy training led to significantly steeper increases than the other two conditions from pretest to the posttest one week after the last training session. When comparing the pretest to follow-up differences in slopes, the superiority of the multimodal training group was still detectable in the unimodal audio modality and the unimodal video modality (in comparison to the control training group), but not when considering the multimodal audio-video modality or the total score of the multimodal emotion recognition accuracy measure. The micro expression training group showed a significantly steeper change trajectory from pretest to posttest compared to the control training group, but not compared to the multimodal training group. However, the effect vanished again until the one-year follow-up. There were no differences in change trajectories for the outcome measure about emotion recognition accuracy in medical settings. Discussion: We conclude that trainee psychotherapists' emotion recognition accuracy can be effectively trained, especially multimodal emotion recognition accuracy, and suggest that the changes in unimodal emotion recognition accuracy (audio-only and video-only) are long-lasting. Implications of these findings for the psychotherapy education are discussed.

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The relationship between decoding ability (Emotion recognition accuracy, ERA) for negative and positive emotion expressions from only video, only audio and audio-video stimuli and the skill to understand peoples' unspoken thoughts and feelings (Empathic accuracy, EA) was tested. Participants (N = 101) from three groups (helping professionals with and without therapy training as well as non-helping professionals) saw or heard recordings of narrations of a negative event by four different persons. Based on either audio-video or audio-only recordings, the participants indicated for given time points what they thought the narrator was feeling and thinking while speaking about the event. A Bayesian regression model regressing group and ERA scores on EA scores was showing weak support only for the EA scores for ratings of unspoken feelings from audio only recordings. In a subsample, the quality of self-experienced social interactions in everyday life was assessed with a diary. The analysis of ERA and EA scores in relation to diary scores did not indicate much correspondence. The results are discussed in terms of relations between skills in decoding emotions using different test paradigms and contextual factors.

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In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min-max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

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The purpose of the study is to improve the performance of intelligent football training. Based on deep learning (DL), the training of football players and detection of football robots are analyzed. First, the research status of the training of football players and football robots is introduced, and the basic structure of the neuron model and convolutional neural network (CNN) and the mainstream framework of DL are mainly expounded. Second, combined with the spatial stream network, a CNN-based action recognition system is constructed in the context of artificial intelligence (AI). Finally, by the football robot, a field line detection model based on a fully convolutional network (FCN) is proposed, and the effective applicability of the system is evaluated. The results demonstrate that the recognition effect of the dual-stream network is the best, reaching 92.8%. The recognition rate of the timestream network is lower than that of the dual-stream network, and the maximum recognition rate is 88%. The spatial stream network has the lowest recognition rate of 86.5%. The processing power of the four different algorithms on the dataset is stronger than that of the ordinary video set. The recognition rate of the time-segmented dual-stream fusion network is the highest, which is second only to the designed network. The recognition rate of the basic dual-stream network is 88.6%, and the recognition rate of the 3D CNN is the lowest, which is 86.2%. Under the intelligent training system, the recognition accuracy rates of jumping, kicking, grabbing, and starting actions range to 97.6, 94.5, 92.5, and 89.8% respectively, which are slightly lower than other actions. The recognition accuracy rate of passing action is 91.3%, and the maximum upgrade rate of intelligent training is 25.7%. The pixel accuracy of the improved field line detection of the model and the mean intersection over union (MIoU) are both improved by 5%. Intelligent training systems and the field line detection of football robots are more feasible. The research provides a reference for the development of AI in the field of sports training.

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Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

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The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate indium gallium zinc oxide (IGZO) synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.

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The reliable conductance modulation of synaptic devices is key when implementing high-performance neuromorphic systems. Herein, we propose a floating gate IGZO synaptic device with an aluminum trapping layer to investigate the correlation between its diverse electrical parameters and pattern recognition accuracy. Basic synaptic properties such as excitatory postsynaptic current, paired pulse facilitation, long/short term memory, and long-term potentiation/depression are demonstrated in the IGZO synaptic transistor. The effects of pulse tuning conditions associated with the pulse voltage magnitude, interval, duration, and cycling number of the applied pulses on the conductance update are systematically investigated. It is discovered that both the nonlinearity of the conductance update and cycle-to-cycle variation should be critically considered using an artificial neural network simulator to ensure the high pattern recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwritten digit images. The highest recognition rate of the MNIST handwritten dataset is 94.06% for the most optimized pulse condition. Finally, a systematic study regarding the synaptic parameters must be performed to optimize the developed synapse device.

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For urban traffic, traffic accidents are the most direct and serious risk to people's lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.

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It has repeatedly been argued that individual differences in personality influence emotion processing, but findings from both the facial and vocal emotion recognition literature are contradictive, suggesting a lack of reliability across studies. To explore this relationship further in a more systematic manner using the Big Five Inventory, we designed two studies employing different research paradigms. Study 1 explored the relationship between personality traits and vocal emotion recognition accuracy while Study 2 examined how personality traits relate to vocal emotion recognition speed. The combined results did not indicate a pairwise linear relationship between self-reported individual differences in personality and vocal emotion processing, suggesting that the continuously proposed influence of personality characteristics on vocal emotion processing might have been overemphasized previously.

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The conflicting findings from the few studies conducted with regard to gender differences in the recognition of vocal expressions of emotion have left the exact nature of these differences unclear. Several investigators have argued that a comprehensive understanding of gender differences in vocal emotion recognition can only be achieved by replicating these studies while accounting for influential factors such as stimulus type, gender-balanced samples, number of encoders, decoders, and emotional categories. This study aimed to account for these factors by investigating whether emotion recognition from vocal expressions differs as a function of both listeners' and speakers' gender. A total of N = 290 participants were randomly and equally allocated to two groups. One group listened to words and pseudo-words, while the other group listened to sentences and affect bursts. Participants were asked to categorize the stimuli with respect to the expressed emotions in a fixed-choice response format. Overall, females were more accurate than males when decoding vocal emotions, however, when testing for specific emotions these differences were small in magnitude. Speakers' gender had a significant impact on how listeners' judged emotions from the voice. The group listening to words and pseudo-words had higher identification rates for emotions spoken by male than by female actors, whereas in the group listening to sentences and affect bursts the identification rates were higher when emotions were uttered by female than male actors. The mixed pattern for emotion-specific effects, however, indicates that, in the vocal channel, the reliability of emotion judgments is not systematically influenced by speakers' gender and the related stereotypes of emotional expressivity. Together, these results extend previous findings by showing effects of listeners' and speakers' gender on the recognition of vocal emotions. They stress the importance of distinguishing these factors to explain recognition ability in the processing of emotional prosody.

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A novel sparsity-based stochastic pooling which integrates the advantages of max-pooling, average-pooling and stochastic pooling is introduced. The proposed pooling is designed to balance the advantages and disadvantages of max-pooling and average-pooling by using the degree of sparsity of activations and a control function to obtain an optimized representative feature value ranging from average value to maximum value of a pooling region. The optimized representative feature value is employed for probability weights assignment of activations in normal distribution. The proposed pooling also adopts weighted random sampling with a reservoir for the sampling process to preserve the advantages of stochastic pooling. This proposed pooling is evaluated on several standard datasets in deep learning framework to compare with various classic pooling methods. Experimental results show that it has good performance on improving recognition accuracy. The influence of changes to the feature parameter on recognition accuracy is also investigated.

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In two studies, the robustness of anger recognition of bodily expressions is tested. In the first study, video recordings of an actor expressing four distinct emotions (anger, despair, fear, and joy) were structurally manipulated as to image impairment and body segmentation. The results show that anger recognition is more robust than other emotions to image impairment and to body segmentation. Moreover, the study showed that arms expressing anger were more robustly recognised than arms expressing other emotions. Study 2 added face blurring as a variable to the bodily expressions and showed that it decreased accurate emotion recognition-but more for recognition of joy and despair than for anger and fear. In sum, the paper indicates the robustness of anger recognition in multileveled deteriorated bodily expressions.

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