Action Classification Research Articles

Classification of human actions using 3D skeleton data: A performance comparison between classical machine learning and deep learning models

Classification of human actions using 3D skeleton data: A performance comparison between classical machine learning and deep learning models

Corporate moral hazard and ESG performance: Unveiling the influence of gross enforcement actions and ownership dynamics in US multinational corporations

AbstractExisting research often highlights the impact of several categories of corporate enforcement action on corporate performance, thus overlooking the impossibility of isolating the impacts of other concurrent enforcement actions. The current study employed the gross value and volume of corporate enforcement actions to examine the influence of corporate moral hazard (CMH) on environmental, social, and governance performance (ESGP) along with ownership dynamics. The S&P data from 2018 to 2022 were analyzed, covering 1925 observations of US multinational corporations (MNCs) based on various aspects of corporate governance (CG). Institutional, insider, and family ownership were applied as proxies for ownership structure. CSRHub ratings represented ESGP, while Sustainalytics ratings were used for robustness. The analysis involved various econometric models, including the feasible generalized least squares model (FGLSM). Resultantly, CMH negatively affected ESGP, whereas CG mechanisms revealed a positive influence. Contrary to existing literature, the size of corporate law cases is a stronger proxy for CMH than the amount of enforcement actions. Although institutional owners decrease the number of enforcement actions, they encounter substantial penalties. Above‐average insiders reduce lawsuits and exert significant control over penalties, while below‐average insiders result in heavier penalties and lawsuits to an extent. Families are the most positively influential owners; having more family owners increases penalties and ESGP but reduces lawsuit size, particularly when ownership exceeds the mean percentage.

Advancing Incremental Few-Shot Video Action Recognition with Cluster Compression and Generative Separation

Few-Shot Class Incremental Learning (FSCIL) is a trending topic in deep learning, addressing the need for models to incrementally learn novel classes, particularly in real-world scenarios where continuously emerging classes come with limited labeled samples. However, the majority of FSCIL research has been dedicated to image classification and object recognition tasks, with limited attention given to video action classification. In this paper, we present a new Cluster Compression and Generative Separation (CCGS) method for Incremental Few-Shot Video Action Recognition (iFSVAR), which introduces contrastive learning to boost the degree of class separation in the base session. Simultaneously, it creates numerous fine-grained classes with diverse semantics, effectively filling the unallocated representation space. Experimental results on UCF101, Kinetics, and Something-Something-V2[Formula: see text]demonstrate the effectiveness of the framework.

A Two-Stream Method for Human Action Recognition Using Facial Action Cues

Human action recognition (HAR) is a critical area in computer vision with wide-ranging applications, including video surveillance, healthcare monitoring, and abnormal behavior detection. Current HAR methods predominantly rely on full-body data, which can limit their effectiveness in real-world scenarios where occlusion is common. In such situations, the face often remains visible, providing valuable cues for action recognition. This paper introduces Face in Action (FIA), a novel two-stream method that leverages facial action cues for robust action recognition under conditions of significant occlusion. FIA consists of an RGB stream and a landmark stream. The RGB stream processes facial image sequences using a fine-spatio-multitemporal (FSM) 3D convolution module, which employs smaller spatial receptive fields to capture detailed local facial movements and larger temporal receptive fields to model broader temporal dynamics. The landmark stream processes facial landmark sequences using a normalized temporal attention (NTA) module within an NTA-GCN block, enhancing the detection of key facial frames and improving overall recognition accuracy. We validate the effectiveness of FIA using the NTU RGB+D and NTU RGB+D 120 datasets, focusing on action categories related to medical conditions. Our experiments demonstrate that FIA significantly outperforms existing methods in scenarios with extensive occlusion, highlighting its potential for practical applications in surveillance and healthcare settings.

A Thematic Study of Nyaya Philosophy with Research Methodology

This thematic study explores the intricate components of Nyaya philosophy and its methodological implications for educational research. This review examines the conceptual distinctions in ontological and epistemological perspectives currently employed within the theoretical framework of educational research. Nyaya philosophy, rooted in the universal truths of Hindu philosophy, serves as a foundation for developing research methodologies. Its sixteen divisions of Padarthas support four primary modes of knowledge acquisition: perception, inference, comparison, and testimony. Nyaya subtly steers researchers towards theoretical generalization in research design, emphasizing the importance of incorporating inductive and deductive procedures in the theoretical processes of educational research. Nyāya philosophy enhances research rigor through its ontological and epistemological principles. By using Nyāya’s categories of existence, substance, quality, and action—research questions are clearly defined. Nyāya’s logical rigor in data analysis ensures valid, consistent conclusions, aiding critical evaluation. Theory building is strengthened by its causation principles and categories, leading to comprehensive theories. Nyāya’s principles also improve the critical evaluation of literature, helping researchers assess and build upon existing knowledge systematically, achieving greater clarity and validity in their studies.

Relationally competent attitudes and actions: a systematic review of general practice literature

Objective To explore core elements from Teachers’ Relational Competence in general practice literature regarding building relationships in consultations, specifying actions doctors take to create and maintain relationship quality with patients. This systematic literature review aims to map and propose a similar framework for the doctor-patient relationship. Background The doctor-patient relationship, a well-researched yet complex field, often lacks clear descriptions. In recent definitions of patient-centred medicine, the responsibility of this relationship falls on the doctor, though how both relationship and responsibility is enacted needs clarification. Pedagogical literature on the student-teacher relationship provides a framework for relational competence, incorporating the needs and interactions between teacher and student and their alignment with institutional goals. Methods A systematic review of two databases yielded 1256 hits. After screening, 15 studies were included and assessed. A qualitative synthesis was conducted through iterative and thematic deductive analysis. Results Four relationally competent attitudes identified were: Attention to emotion, Devotion, Mentalization, and Time-oriented presence. Four relationally competent actions identitfied were: Being open, Attunement, Offering Support, and Using humor. Additionally, Trust and Continued connectedness were found as components of both attitudes and actions. Conclusion An explanatory framework for professional relational competence for GPs includes concrete actions and specific attitudes before and during consultations. These consist of four key attitudes and four categories of actions with several subgroups of actions. Two additional components to the framework was found.

Displaced person(s): the production of a powerful political category

ABSTRACT Recent migration research suggests using “displaced persons” as a broader, more inclusive term than “refugees”, affording us an opportunity to recall the historical genesis and transformation of the concept of “displaced persons”. The concept emerged in the 1940s in the context of the control of forced migration. This article traces the evolution of the term “displaced person”, arguing it was created as a “category of action” for bureaucratic and political purposes, rather than to describe social realities. Understanding this historical context reveals more broadly how such categories have always been tools for political control, underscoring the need for reflexivity in migration studies.

Antimicrobial Peptides Derived from Bacteria: Classification, Sources, and Mechanism of Action against Multidrug-Resistant Bacteria

Antimicrobial peptides (AMPs) are short, usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi, and other pathogens. Bacterial AMPs, or bacteriocins, can be produced from Gram-negative and Gram-positive bacteria via ribosomal synthesis to eliminate competing organisms. Bacterial AMPs are vital in addressing the increasing antibiotic resistance of various pathogens, potentially serving as an alternative to ineffective antibiotics. Bacteriocins have a narrow spectrum of action, making them highly specific antibacterial compounds that target particular bacterial pathogens. This review covers the two main groups of bacteriocins produced by Gram-negative and Gram-positive bacteria, their modes of action, classification, sources of positive effects they can play on the human body, and their limitations and future perspectives as an alternative to antibiotics.

An Empirical and Deep Learning Investigation of Working Conditions and Musculoskeletal Outcomes in Wheat Farmers

Background: In developing countries, various farming activities are performed manually with the help of traditional hand tools. Therefore, agriculture is recognized as one of the risky occupations Objective: This research study aims to identify the critical working conditions of wheat farmers in Rajasthan state, India. Method: Data were collected through the survey conducted on 75 randomly selected wheat farmers of Rajasthan. The survey questionnaire gathered information related to demographic, occupational, and musculoskeletal discomfort faced by the farmers. In addition, a deep learning-based posture detection study of the workers was performed to assess the postural risks through a rapid upper limb assessment score. Result: The collected data were analyzed further for fruitful insights. The survey outcomes showed that awkward posture (41%) and repetitive movement (35%) were the most reported reasons for the severe risks of musculoskeletal disorders among farmers. The posture evaluationbased study outcomes showed that approximately 51% of subjects lie in the action category 4, which shows the higher level of risks in the activities performed by farmers. Conclusion: It is suggested to apply the principles of physical ergonomics in the agriculture sector and spread awareness among the farmers about the agriculture risks associated with farming activities.

Sustaining superior visibility within digital platforms through inside and outside competitive action repertoires

Although often credited with leveling the competitive playing field, platforms pose novel challenges for millions of complementors within their ecosystems. This study explores the tactics complementors use to maintain superior visibility on these platforms. Building on competitive repertoire theory, we conceptualize two categories of competitive actions that capture the dual environmental complexity faced by complementors: inside and outside competitive moves. We assemble a unique dataset from a leading food delivery platform in Europe, providing a comprehensive view of complementors’ competitive repertoires and visibility over ten months. We find that complementors’ inside competitive repertoires with high volume and complexity are associated with sustained superior visibility. However, we also find that complementors whose competitive repertoires diverge from those of their competitors are more likely to exit the superior visibility strata. Additionally, we identify outside action repertoires as a second pathway to differentiation, built on complementors’ idiosyncratic resources and less dependent on platform architecture and rules.

Categorical claims about voluntary renewable energy actions are not supported by categorical evidence

Abstract Voluntary renewable energy procurement by retail electricity customers could play a critical role in achieving clean energy objectives. Research could optimize that role by providing accurate and nuanced information about how different voluntary actions most effectively drive renewable energy deployment. Recent literature has made categorical assertions that broad classes of voluntary actions are weakly or not impactful. This Perspective argues that these categorical assertions are not supported by equally categorical evidence. A closer examination shows the evidence is less conclusive and less generalizable than stakeholders may presume. Voluntary renewable energy buyers would benefit from more nuanced analysis of the impacts of different energy procurement actions across distinct contexts.

Application of the Student Facilitator and Explaining Model Assisted by Domino Media on Interest in Learning History

Study This aim For apply a sparse learning model or not enough used by most teachers in schools, as for the model that has become focus researcher in study This is a Students Facilitator and Explanation learning model with with the help of domino media as the current media This exist among generation young Indonesians. As for the application of this model and media For see there is change interest Study history the original student still very low in class XI IPS at SMAN 1 Want Jaya. Study This background behind problem from exists problems faced by teachers when teach ie student No show joy moment learning, encouragement student For do activity in learning very low, students No own will or desire For solve problem in a way independent, they are not enough show exists involvement active and inclined passive like No There is will For move, and it turns out matter This refer to problem low interest Study seen history from indicators shown. In answering problems, researchers use approach quantitative with type Classroom Action Research, research action class done as effort in answer existing problems through stages systematic ie planning, implementation action class and observation. Implementation study taking place as many as 2 cycles, where cycle second is form refinement and evaluation from failure or shortages that occur in study cycle first, deep study this is what happened object study are teachers and students class XI IPS 1 SMAN 14 Medan, engineering analysis of the data used is using descriptive analysis techniques namely data obtained from observations teachers and students, as well as interview results will be analyzed descriptively. Assessment of student interest in learning will be calculated using the DP (Descriptive Percentage) formula by (Rukajat, 2018) . If score from results observation reach or exceeded the targets set determined, then interest Study student after the implementation of the student facilitator and explaining learning model assisted by domino media is stated increase. Next, the instrument used that is sheet teacher and student observations, as well documentation. Based on results study action classes that have been carried out on students class XI IPS 1 at SMAN 14 Medan in 2 cycles, obtained results namely in cycle I results related teacher observations The application of the student facilitator and explaining learning model assisted by domino media was 67% implemented, meanwhile increase interest Study students 63% in cycle I however Not yet fulfil indicator 80% success for teacher observation and 75% for interest Study students, so continued in cycle II. Implementability application of learning models student facilitator and explaining assisted by domino media By teachers 93%, meanwhile increase interest Study 80% of students in Cycle II showed increase has fulfil indicator success that has been set. So that application of the student facilitator and explaining learning model assisted by domino media increase interest Study student class XI IPS 1 SMAN 14 Medan.

Exploring Fish Antimicrobial Peptides (Amps): Classification, Biological Activities, and Mechanisms of Action

Exploring Fish Antimicrobial Peptides (Amps): Classification, Biological Activities, and Mechanisms of Action

Preventive Maintenance Planning for Machines Computer Numerical Control (CNC) Type Hurco Vmx24 to Improve Reliability Using the Reability Centered Maintenance (RCM) Method (Case Study)

Abstract: This paper presents the results of the analysis of Computer Numerical Control (CNC) machine components that result in decreased machine efficiency in the production system with the Reliability Centered Maintenance (RCM) approach. Utilized to improve the performance of machine components. The machine has not been maintained regularly so that at any time the machine can experience damage which results in losses to the company's income. so that an analysis is designed for maintenance planning management activities with the methodology of identifying and analyzing failure factors using the Failure Mode Effect Analysis (FMECA) worksheet, Logic Tree Analysis (LTA), and finally the Action Road Map. The results of the analysis carried out are divided into three categories of maintenance actions, namely, Condition Direction (CD) maintenance, Time Directed (TD) maintenance, Finding Failure (FF) maintenance with a periodic checking interval of 2 times within a period of 12 days.

Action Classification from Motion Flow

Action Classification from Motion Flow

Centralizers of Hamiltonian finite cyclic group actions on rational ruled surfaces

Let M = ( M , ω ) M=(M,\omega ) be either S 2 × S 2 S^2 \times S^2 or C P 2 # C P 2 ¯ \mathbb {C}P^2\# \overline {\mathbb {C}P^2} endowed with any symplectic form ω \omega . Suppose a finite cyclic group Z n \mathbb {Z}_n is acting effectively on ( M , ω ) (M,\omega ) through Hamiltonian diffeomorphisms, that is, there is an injective homomorphism Z n ↪ H a m ( M , ω ) \mathbb {Z}_n\hookrightarrow Ham(M,\omega ) . In this paper, we investigate the homotopy type of the group S y m p Z n ( M , ω ) Symp^{\mathbb {Z}_n}(M,\omega ) of equivariant symplectomorphisms. We prove that for some infinite families of Z n \mathbb {Z}_n actions satisfying certain inequalities involving the order n n and the symplectic cohomology class [ ω ] [\omega ] , the actions extend to either one or two toric actions, and accordingly, that the centralizers are homotopically equivalent to either a finite dimensional Lie group, or to the homotopy pushout of two tori along a circle. Our results rely on J J -holomorphic techniques, on Delzant’s classification of toric actions, on Karshon’s classification of Hamiltonian circle actions on 4 4 -manifolds, and on the Chen-Wilczyński classification of smooth Z n \mathbb {Z}_n -actions on Hirzebruch surfaces.

Public leadership meta-skills in the 2020s

IMPACT Looking at public leadership from a meta-skills point of view is a new way to make visible what matters in public leadership in the 2020s. The significance of this topic lies in the contribution it makes to understanding and improving public leadership in the age of prevailing public policy complexities and austerity. This article explains why investing in leadership meta-skills is not only crucial for employees and organizations but also directly impacts the public sector's capacity for renewal, the quality of public policy and the ability of the public sector to deliver good services to citizens. The article provides directly applicable results for public leaders regardless of administrative branch; the findings can be applied anywhere where public value is created. The four categories of leadership actions and practices presented in this article help the public sector to succeed in the transition towards people-centred, cross-sectoral and future-driven policy outcomes.

A New Approach to Detect Driver Distraction to Ensure Traffic Safety and Prevent Traffic Accidents: Image Processing and MCDM

One of the factors that threaten traffic safety and cause various traffic problems is distracted drivers. Various studies have been carried out to ensure traffic safety and, accordingly, to reduce traffic accidents. This study aims to determine driver-distraction classes and detect driver violations with deep learning algorithms and decision-making methods. Different driver characteristics are included in the study by using a dataset created from five different countries. Weight classification in the range of 0–1 is used to determine the most important classes using the AHP method, and the most important 9 out of 23 classes are determined. The YOLOv8 algorithm is used to detect driver behaviors and distraction action classes. The YOLOv8 algorithm is examined according to performance-measurement criteria. According to mAP 0.5:0.95, an accuracy rate of 91.17% is obtained. In large datasets, it is seen that a successful result is obtained by using the AHP method, which is used to reduce transaction complexity, and the YOLOv8 algorithm, which is used to detect driver distraction. By detecting driver distraction, it is possible to partially avoid traffic accidents and the negative situations they create. While detecting and preventing driver distraction makes a significant contribution to traffic safety, it also provides a significant improvement in traffic accidents and traffic congestion, increasing transportation efficiency and the sustainability of cities. It also serves sustainable development goals such as energy efficiency and reducing carbon emissions.

Evidence-based guidelines for the interpretation of the 9-item Concise Health Risk Tracking - Self-Report (CHRT-SR9) measure of suicidal risk.

The 9-item Concise Health Risk Tracking - Self-Report (CHRT-SR9) is a widely used patient-reported outcome measure of suicidal risk. The goal of this article is to provide an evidence-based interpretation of the CHRT-SR9 total score in terms of four clinically actionable categories of suicidal risk (none, mild, moderate, and severe). Data from two large programs involving adolescents and adults were combined in this paper. In these studies, the CHRT-SR9 was anchored against an independent measure of suicidal risk, the suicide item (Item #9) in the Patient Health Questionnaire (PHQ-9), with categories 0 (none), 1 (mild), 2 (moderate), and 3 (severe). In the combined data (n = 1945), we calculated the cumulative percentage of data across these four categories and the percentile score of the CHRT-SR9 total score that corresponded to these percentages; from this, we developed ranges of the CHRT-SR9 total score that corresponded to the four categories of Item #9 of PHQ-9. We also calculated similar ranges for two broad subscales of the CHRT-SR9 total score; Propensity and Suicidal Thoughts. To assess the robustness of our findings, we repeated the analysis at another timepoint across studies. Findings indicated that the CHRT-SR9 total score (range: 0-36) can be categorized as none (0-14), mild (15-21), moderate (22-26), and severe (27-36). Similar categories were calculated for the Propensity and Suicidal Thoughts subscales. The findings were the same when repeated at another timepoint. This categorization of the CHRT-SR9 total score can place patients into clinically meaningful and actionable categories of suicidal risk.

Beyond balance: The role of the Vestibular system in action recognition

BackgroundAction recognition is a fundamental aspect of human interaction. This process is mediated by the activation of shared sensorimotor representations during action execution and observation. Although complex movements involving balance or head and trunk rotations require vestibular signals for effective execution, their role in the recognition of others' actions is still unknown. ObjectiveTo investigate the causal involvement of the vestibular system in the discrimination of actions performed by others and whether this is influenced by motor familiarity. MethodsIn a single-blind design involving 25 healthy participants, Galvanic Vestibular Stimulation (GVS) was administered during an Action Discrimination Task (ADT), in which videos of actions categorized as vestibular/non-vestibular and familiar/unfamiliar were presented. Following each video, participants were required to identify the climax of the previously viewed action between two image options, using a two-alternative forced choice paradigm. The ADT was performed in active and sham GVS conditions, with left or right anodal montages. Response Times (RTs), Accuracy, and subjective motor familiarity were recorded for each action category. ResultsIn sham GVS condition, an overall familiarity effect was observed, where RTs for familiar actions were faster than RTs for unfamiliar ones, regardless of vestibular engagement (p < .001; ηp2 = .80). Conversely, under active GVS, a selective interference of the identification of vestibular familiar actions was observed compared to sham. Specifically, GVS prolonged RTs for recognizing familiar vestibular actions (p = .004, d = .59) while concurrently enhancing visual sensitivity (d’) for the same actions (p = .03, r = .21). ConclusionThese findings demonstrate the contribution of the vestibular system to action recognition. GVS disrupted the sensorimotor representation of vestibular actions and led to increased reliance on an alternative processing system focused on visual analysis of limb positions. This dissociation provides valuable insights for future investigations into the complex relationship between vestibular signals and cognitive processes involved in action identification, essential for developing innovative GVS interventions, particularly for individuals with sensorimotor or vestibular disorders.