Recognition Paradigm Research Articles

Prompt Assessment of Facial Expressions to Foresee Behavioral Aptitudes in Video Interviews

This endeavor seeks to engineer a sophisticated real-time image and video processor, augmented with an advanced artificial intelligence (AI) agent, capable of prognosticating a candidate's behavioral competencies through the nuanced analysis of their facial expressions. This achievement leverages a real-time, video-recorded interview utilizing a histogram of oriented gradients and support vector machine (HOG-SVM) in conjunction with convolutional neural network (CNN) recognition. Diverging from the traditional paradigm of emotional state recognition, this avant-garde prototype system is designed to autonomously decipher a job candidate’s behaviors through their micro expressions, grounded in the behavioral ecology view of facial displays (BECV), within the framework of employment interviews. This manuscript introduces a pioneering methodology for evaluating a candidate's performance in a video interview. It elucidates a comprehensive study of sentiment analysis and eye-tracking techniques, whereby results can be synthesized on a single display to facilitate the selection of the most suitable candidate for hire. An empirical study was executed at a enterprise, wherein video recordings and competency evaluations were gathered from the organization's staff and hiring executives. The findings revealed that our proposed system exhibits superior predictive capabilities compared to traditional human-structured interviews, personality assessments, occupational interest evaluations, and assessment center. Consequently, our innovative methodology can be effectively employed as a screening tool, leveraging a personal-value-based competency framework. Key Words: Behavioural ecology view of facial displays (BECV) · Convolutional neural network (CNN) · Employment selection · Histogram of oriented gradients (HOG) · Real-time image and video processing · Support vector machine (SVM), , Eye Tracking Technique, AVI(Automatic Visual Inspection).

Advancing skeleton-based human behavior recognition: multi-stream fusion spatiotemporal graph convolutional networks

In the realm of daily human interactions, a rich tapestry of behaviors and actions is observed, encompassing a wealth of informative cues. In the era of burgeoning big data, extensive repositories of images and videos have risen to prominence as the primary conduits for disseminating information. Grasping the intricacies of human behaviors depicted within these multimedia contexts has evolved into a pivotal quandary within the domain of computer vision. The technology of behavior recognition finds its practical application across domains such as human-computer interaction, intelligent surveillance, and anomaly detection, exhibiting a robust blend of pragmatic utility and scholarly significance. The present study introduces an innovative human body behavior recognition framework anchored in skeleton sequences and multi-stream fused spatiotemporal graph convolutional networks. Developed upon the foundation of graph convolutional networks, this method encompasses three pivotal refinements tailored to ameliorate extant challenges. First and foremost, in response to the complex task of capturing distant interdependencies among nodes within graph convolutional networks, we incorporate a spatial attention module. This module adeptly encapsulates long-term node interdependencies via precision-laden positional information, thus engendering interconnections that span diverse temporal and spatial contexts. Subsequently, to elevate the discernment of channel information within the network and to optimize the allocation of attention across distinct channels, we introduce a channel attention mechanism. This augmentation fortifies the discernment of motion-related features. Lastly, confronting the lacuna of information gaps prevalent within single-stream data, we deploy a multi-stream fusion methodology to fortify model outputs, ultimately fostering more precise prognostications concerning action classifications. Empirical results bear testament to the efficacy of the proposed multi-stream fused spatiotemporal graph convolutional network paradigm for skeleton-centric behavior recognition, evincing a pinnacle recognition accuracy of 96.0% on the expansive NTU-RGB+D skeleton dataset, alongside a zenithal accuracy of 37.3% on the Kinetics-Skeleton dataset—emanating from RGB data and furthered through pose estimation.

Inhibition of the Integrated Stress Response Prevents Natural Forgetting and Corrects Accelerated Forgetting Associated with Epilepsy.

The neural mechanisms underlying the natural and maladaptive forgetting of established memory remain largely unknown. Brain disease states might hijack the physiological forgetting mechanisms, resulting in maladaptive forgetting such as accelerated forgetting that contributes to cognitive decline in various neurologic conditions including epilepsy. Based on the key role of the integrated stress response (ISR) in memory storage and maintenance, we determined whether the ISR underpins natural and accelerated forgetting. Here, based on the object location recognition (OLR) and novel object recognition (NOR) paradigms in mice, we found that the ISR was activated while an established memory was naturally forgotten, which was denoted by increased levels of phosphorylated eukaryotic translation initiation factor 2α (eIF2α) and activating transcription factor 4 (ATF4), and reduced general protein synthesis. Multiple administrations of ISRIB, a small molecule ISR inhibitor, during the memory retention interval attenuated the ISR activation, and prevented the natural forgetting of established OLR and NOR memories. At the same time, a single injection of ISRIB has no effect on natural forgetting and memory retrieval. Moreover, administration of pentylenetetrazole (PTZ), an inducer of epileptic seizures, during the memory retention interval provoked the ISR activation and accelerated forgetting, which was corrected by ISRIB treatment. Together, our findings suggest that the ISR is critically involved in natural forgetting and accelerated forgetting associated with epilepsy, and pharmacological inhibition of the ISR may emerge as a novel intervention strategy for accelerated forgetting in patients with epilepsy.

Local and global self-attention enhanced graph convolutional network for skeleton-based action recognition

The current successful paradigm for skeleton-based action recognition is the combination of Graph Convolutional Networks (GCNs) modeling spatial correlations, and Temporal Convolution Networks (TCNs), extracting motion features. Such GCN-TCN-based approaches usually rely on local graph convolution operations, which limits their ability to capture complicated correlations among distant joints, as well as represent long-range dependencies. Although the self-attention originated from Transformers shows great potential in correlation modeling of global joints, the Transformer-based methods are usually computationally expensive and ignore the physical connectivity structure of the human skeleton. To address these issues, we propose a novel Local-Global Self-Attention Enhanced Graph Convolutional Network (LG-SGNet) to simultaneously learn both local and global representations in the spatial–temporal dimension. Our approach consists of three components: The Local-Global Graph Convolutional Network (LG-GCN) module extracts local and global spatial feature representations by parallel channel-specific global and local spatial modeling. The Local-Global Temporal Convolutional Network (LG-TCN) module performs a joint-wise global temporal modeling using multi-head self-attention in parallel with local temporal modeling. This constitutes a new multi-branch temporal convolution structure that effectively captures both long-range dependencies and subtle temporal structures. Finally, the Dynamic Frame Weighting Module (DFWM) adjusts the weights of skeleton action sequence frames, allowing the model to adaptively focus on the features of representative frames for more efficient action recognition. Extensive experiments demonstrate that our LG-SGNet performs very competitively compared to the state-of-the-art methods. Our project website is available at https://github.com/DingYyue/LG-SGNet.

Contrasting Deficits in the Function of the Phonological Loop Between Children With Attention Deficit Hyperactivity Disorder and Reading Disability.

Attention Deficit Hyperactivity Disorder (ADHD) and reading disability (RD) often co-occur. Impairments in the phonological loop of working memory are central to RD, but it is unclear whether this is linked to ADHD, especially in Chinese reading. Two experiments were conducted with Chinese children aged 7 to 12 (Experiment 1, n = 65; Experiment 2, n = 60). Immediate and delayed recognition paradigms were used to assess phonological encoding and rehearsal. Both the RD-only and comorbid (ADHD + RD) groups performed worse than the control and ADHD-only groups in response time and accuracy. Notably, the comorbid group performed similarly to the RD-only group, indicating that phonological loop deficits in comorbid children are likely due to RD, not ADHD. This highlights phonological loop function as the key to distinguishing between ADHD and RD.

Dynamic sampling of a surveillance state enables DNA proofreading by Cas9.

CRISPR-Cas9 has revolutionized genome engineering applications by programming its single-guide RNA, where high specificity is required. However, the precise molecular mechanism underscoring discrimination between on/off-target DNA sequences, relative to the guide RNA template, remains elusive. Here, using methyl-based NMR to study multiple holoenzymes assembled invitro, we elucidate a discrete protein conformational state which enables recognition of DNA mismatches at the protospacer adjacent motif (PAM)-distal end. Our results delineate an allosteric pathway connecting a dynamic conformational switch at the REC3 domain, with the sampling of a catalytically competent state by the HNH domain. Our NMR data show that HiFi Cas9 (R691A) increases the fidelity of DNA recognition by stabilizing this "surveillance state" for mismatched substrates, shifting the Cas9 conformational equilibrium away from the active state. These results establish a paradigm of substrate recognition through an allosteric protein-based switch, providing unique insights into the molecular mechanism which governs Cas9 selectivity.

Nociplastic Pain: A Critical Paradigm for Multidisciplinary Recognition and Management.

Our understanding of chronic pain has evolved significantly, shifting from a focus on peripheral damage to recognizing the central mechanisms underlying pain perception. This perspective article explores the concept of nociplastic pain, a term introduced by the International Association for the Study of Pain (IASP) in 2017, which describes pain arising from altered pain modulation within the central nervous system, without clear evidence of tissue damage or inflammation. The historical progression from fibrositis to fibromyalgia, and now to nociplastic pain, underscores the complexity of chronic pain syndromes and the need for a multidisciplinary approach to management. Nociplastic pain is characterized by central sensitization, leading to heightened pain sensitivity and often accompanied by comorbidities such as fatigue, sleep disturbances, and cognitive difficulties. Advances in neuroimaging have revealed altered connectivity within key brain networks, such as the default mode and salience networks, in patients with nociplastic pain, providing insights into the neural underpinnings of this condition. The article also addresses controversies surrounding the role of small fiber neuropathy and autonomic dysfunction in nociplastic pain, highlighting the ongoing debates in the field. The practical importance of recognizing nociplastic pain across various medical disciplines-including primary care, orthopedics, neurology, psychiatry, and rheumatology-is emphasized, with recommendations for integrating this knowledge into clinical practice. Emerging therapies, such as neurofeedback, hyperbaric oxygen therapy, and neuromodulation, offer new avenues for treatment, particularly for patients who do not respond to conventional approaches. The article calls for continued research into the mechanisms of nociplastic pain, the development of reliable diagnostic tools, and the exploration of novel therapeutic strategies to improve patient outcomes. The recognition and management of nociplastic pain are crucial for advancing the care of patients with chronic pain, necessitating interdisciplinary collaboration and a patient-centered approach.

A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

Helicity-directed recognition of bacterial phospholipid via radially amphiphilic antimicrobial peptides.

The fundamental differences in phospholipids between bacterial and mammalian cell membranes present remarkable opportunities for antimicrobial design. However, it is challenging to distinguish bacterial anionic phospholipid phosphatidylglycerol (PG) from mammalian anionic phosphatidylserine (PS) with the same net charge. Here, we report a class of radially amphiphilic α helix antimicrobial peptides (RAPs) that can selectively discriminate PG from PS, relying on the helix structure. The representative RAP, L10-MMBen, can direct the rearrangement of PG vesicles into a lamellar structure with its helix axis parallel to the PG membrane surface. The helical structure imparts both the thermodynamic and kinetic advantages of L10-MMBen/PG assembly, and the hiding of hydrophobic regions in RAPs is crucial for PG recognition. L10-MMBen exhibits high selectivity against bacteria depending on PG recognition, showing low in vivo toxicity and significant treatment efficacy in mice infection models. Our study introduces a helicity-direct bacterial phospholipid recognition paradigm for designing highly selective antimicrobial peptides.

Cross-cultural comparison of the neural correlates of true and false memory retrieval

ABSTRACT Prior work has shown Americans have higher levels of memory specificity than East Asians. Neuroimaging studies have not investigated mechanisms that account for cultural differences at retrieval. In this study, we use fMRI to assess whether mnemonic discrimination, distinguishing novel from previously encountered stimuli, accounts for cultural differences in memory. Fifty-five American and 55 Taiwanese young adults completed an object recognition paradigm testing discrimination of old targets, similar lures and novel foils. Mnemonic discrimination was tested by comparing discrimination of similar lures from studied targets, and results showed the relationship between activity in right fusiform gyrus and behavioural discrimination between target and lure objects differed across cultural groups. Parametric modulation analyses of activity during lure correct rejections also indicated that groups differed in left superior parietal cortex response to variations in lure similarity. Additional analyses of old vs. new activity indicated that Americans and Taiwanese differ in the neural activity supporting general object recognition in the hippocampus, left inferior frontal gyrus and middle frontal gyrus. Results are juxtaposed against comparisons of the regions activated in common across the two cultures. Overall, Americans and Taiwanese differ in the extent to which they recruit visual processing and attention modulating brain regions.

Determining underlying influences of data variability in the novel object recognition paradigm as used with young pigs.

The novel object recognition (NOR) paradigm is a cognitive test that has been used with many species to detect differences in ability. Various iterations of the paradigm have been implemented, making it difficult to compare results both within and across species. Interpretations of the results are equally diverse, threatening the integrity of the paradigm. These inconsistencies have prompted a deeper dive into the variability of the resultant data. For the purposes of this meta-analysis, data originated from 12 studies involving 367 pigs that were subjected to the same NOR paradigm beginning between postnatal days 21 and 24. The main cognitive measure from the NOR paradigm is recognition index (RI), which was the focus of most of the analyses in this meta-analysis. RI was chosen as the main outcome as it determines a pig's preference for novelty, an innate behavior of cognitively intact pigs. A histogram of RI values (range 0 to 1) showed a bimodal distribution skewed to the right, suggesting that the interpretation of positive performance on the task may need to be stricter. Correlational analyses proved that the number of investigations and investigation time with both the novel and familiar objects were the strongest predictors of resultant RI values. Objective data inclusion criteria were then considered to eliminate non-compliant pigs. Results indicated that requiring at least 5 s of investigation over a minimum of 3 investigations with the novel object reduced overall variability for RI with a concomitant increase in the mean. Further analyses showed that pigs preferred to spend more time with and interact more with the novel object across the entire testing trial, especially in the first minute. Together, these findings suggest that future interpretations of NOR should consider applying stricter statistical analyses as well as additional data processing, such as binning, with emphasis on novel object and familiar object investigation. Overall, modifications to the existing iterations of the NOR paradigm are necessary to improve paradigm reliability.

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis.

Recognizing familiar speakers from vocal streams is a fundamental aspect of human verbal communication. However, it remains unclear how listeners can still discern the speaker's identity in expressive speech. This study develops a memorization-based individual speaker identity recognition approach and an accompanying electroencephalogram (EEG) data analysis pipeline, which monitors how listeners recognize familiar speakers and tell unfamiliar ones apart. EEG data captures online cognitive processes during new versus old speaker distinction based on voice, offering a real-time measure of brain activity, overcoming limits of reaction times and accuracy measurements. The paradigm comprises three steps: listeners establish associations between three voices and their names (training); listeners indicate the name corresponding to a voice from three candidates (checking); listeners distinguish between three old and three new speaker voices in a two-alternative forced-choice task (testing). The speech prosody in testing was either confident or doubtful. EEG data were collected using a 64-channel EEG system, followed by preprocessing and imported into RStudio for ERP and statistical analysis and MATLAB for brain topography. Results showed an enlarged late positive component (LPC) was elicited in the old-talker compared to the new-talker condition in the 400-850 ms window in the Pz and other wider range of electrodes in both prosodies. Yet, the old/new effect was robust in central and posterior electrodes for doubtful prosody perception, whereas the anterior, central, and posterior electrodes are for confident prosody condition. This study proposes that this experiment design can serve as a reference for investigating speaker-specific cue-binding effects in various scenarios (e.g., anaphoric expression) and pathologies in patients like phonagnosia.

Multi-Derivational Parsing of Vague Languages- The New Paradigm of Syntactic Pattern Recognition.

The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

Vision-Language Models for Vision Tasks: A Survey.

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, leading to a laborious and time-consuming visual recognition paradigm. To address the two challenges, Vision-Language Models (VLMs) have been intensively investigated recently, which learns rich vision-language correlation from web-scale image-text pairs that are almost infinitely available on the Internet and enables zero-shot predictions on various visual recognition tasks with a single VLM. This paper provides a systematic review of visual language models for various visual recognition tasks, including: (1) the background that introduces the development of visual recognition paradigms; (2) the foundations of VLM that summarize the widely-adopted network architectures, pre-training objectives, and downstream tasks; (3) the widely-adopted datasets in VLM pre-training and evaluations; (4) the review and categorization of existing VLM pre-training methods, VLM transfer learning methods, and VLM knowledge distillation methods; (5) the benchmarking, analysis and discussion of the reviewed methods; (6) several research challenges and potential research directions that could be pursued in the future VLM studies for visual recognition.

Influence of degree of learning on rate of forgetting of tonal sequences.

Initial performance is frequently equated in studies that compare forgetting rates across groups. However, since the encoding capacity of different groups can be different, some procedures to match initial degree of learning need to be implemented, adding confounding variables such as longer exposures to the material, which would create memories of a different age. Slamecka and McElree Journal of Experimental Psychology: Learning, Memory, and Cognition,9, 384-397, (1983) and our previous work found that the rate of forgetting was independent from initial degree of learning using verbal material. The present study seeks to determine whether this pattern holds true when undertaken with nonverbal material. In two experiments, we manipulate initial degree of learning by varying the number of presentations of the material and studying the effect on the forgetting rates. A set of 30 tonal sequences were presented to young, healthy participants either once or three times. Forgetting was evaluated in a yes/no recognition paradigm immediately and 1 hour or 24 hours after the study phase. A different subset of 10 sequences was tested along with 10 nontargets at each retention interval. The results of these experiments showed that initial acquisition was modulated by the number of repetitions. However, the forgetting rates were independent of initial degree of learning. These results are in keeping with the pattern found by Slamecka and McElree, and in our own previous studies. They suggest that the pattern of parallel forgetting after different levels of initial learning is not limited to verbal material.

Honneth as a Reader of Sartre. On the Limits of the Honnethian Interpretation of Sartre’s Paradigm of Recognition

After tracing the way in which Honneth interprets Sartre’s theory of intersubjectivity and seeing how it is essentially based on the consideration of Being and Nothingness, this article aims to discuss some of the theses of the Sartrian paradigm of recognition that emerge in the Critique of Dialectical Reason, in particular those relating to the “groupe en fusion”, that elude the Honnethian interpretation. It can be concluded that Sartre’s last philosophical work is decisive in the effort to conceptualise recognition, because it offers a more articulate and complex understanding of the social world.

A data-efficient and easy-to-use lip language interface based on wearable motion capture and speech movement reconstruction.

Lip language recognition urgently needs wearable and easy-to-use interfaces for interference-free and high-fidelity lip-reading acquisition and to develop accompanying data-efficient decoder-modeling methods. Existing solutions suffer from unreliable lip reading, are data hungry, and exhibit poor generalization. Here, we propose a wearable lip language decoding technology that enables interference-free and high-fidelity acquisition of lip movements and data-efficient recognition of fluent lip language based on wearable motion capture and continuous lip speech movement reconstruction. The method allows us to artificially generate any wanted continuous speech datasets from a very limited corpus of word samples from users. By using these artificial datasets to train the decoder, we achieve an average accuracy of 92.0% across individuals (n = 7) for actual continuous and fluent lip speech recognition for 93 English sentences, even observing no training burn on users because all training datasets are artificially generated. Our method greatly minimizes users' training/learning load and presents a data-efficient and easy-to-use paradigm for lip language recognition.

Cluster Visualized Topic Modeling Paradigms for Recognition of Health-Related Topics Through a Machine Learning

Cluster Visualized Topic Modeling Paradigms for Recognition of Health-Related Topics Through a Machine Learning

Primacy (and recency) effects in delayed recognition of items from instances of repeated events

ABSTRACT 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.

The association between psychopathology, childhood trauma, and emotion processing.

Childhood trauma is highly prevalent and can have a negative impact on the development of socioemotional processes resulting in a higher vulnerability for mental disorders in adulthood. Previous studies have associated the severity of childhood trauma with deficits in social functioning, such as a negative attention bias, suggesting altered social information processing as a mechanism underlying the association between childhood trauma and transdiagnostic psychopathologies. In a cross-sectional setup with a total of 103 participants (26 with major depressive disorder, MDD; 24 with posttraumatic stress disorder, PTSD; 22 with somatic symptom disorder, SDD; and 31 healthy volunteers, HV), this study applied eye tracking in an emotion recognition paradigm. Reaction times, accuracy, and gaze behavior were analyzed for 4 different facial expressions as a function of self-reported childhood trauma and diagnosis. The aim was to investigate to what extent emotion processing is associated with (a) childhood trauma, (b) psychopathology, and (c) respective interacting effects. Patients showed higher reaction times and error rates overall in classifying emotions than HVs, especially for the recognition of anger and fear. Individuals with a diagnosis of PTSD and MDD were particularly slow in their response to these emotions. Higher scores of reported childhood trauma were associated with faster responses for classifying anger and fear and slower initiation of eye movements for SSD, MDD, and HVs for anger. These findings indicate that childhood trauma may contribute to attentional and information-processing biases relevant for social interaction. Identifying individual social deficits offers implications for tailored therapeutic interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).