Human Action Research Articles

Comparing Optimizer Strategies For Enhancing Emotion Classification In IndoBERT Models

Emotions are one of the reactions of human when they receive physical or verbal action. Every human action is based on emotion. Every opinion expressed in the comments column also contains the author's emotions. This research aims to classify five emotions, Marah, Takut, Senang, Cinta, and Sedih and evaluate the performance of three commonly used optimizer, Adam, RMSProp, and Nadam. The processed data used IndoBERT model for Indonesian text classification. The research purpose to search the best optimizer for text classification. The result shows classification used Adam Optimizer 90,21%, RMSProp Optimizer 82.11, and Nadam Optimizer 88.61%. The Adam optimizer applied to the IndoBERT model yielded the best results. This shows a significant improvement from previous studies, which had emotion classification.

A century of urban landslides: the legacy and consequences of altering riverbank landscapes

Urban landslides are a deadly and costly hazard. Human actions, such as altering the grade, composition, and vegetation-cover of hillslopes, can increase the threat of mass movements. Here, we use an interdisciplinary approach to examine the spatial distribution, timing, and cause of landslides affecting a state highway and adjacent buildings along the top of a steep, urban riverbank in the mid-latitude, humid-temperate state of Vermont. Using over 100 years of mapping, photographs, and written records, we demonstrate that most mass movements in our field area occurred on slopes over-steepened by the addition of uncompacted artificial fill – added without engineering considerations. Emplaced atop glacial and post-glacial sediment with low hydraulic conductivity, the fill, having little to no cohesion, expanded buildable areas, but the new infrastructure sat on unstable ground. Over the following decades, repeated failures, (n=20), mostly shallow translational landslides in fill material along with several deeper-seated rotational slides, sent buildings, trees, and segments of road into the river below. Solutions include incentivizing the removal of structures built on fill and limiting further filling activities through changes in zoning regulations and more effective enforcement of existing municipal codes. The approach we use provides a framework for similar geographic settings and can inform urban planning and risk assessment. Thematic collection: This article is part of the Engineering Geology and Hydrogeology of the Anthropocene collection available at: https://www.lyellcollection.org/topic/collections/engineering-geology-and-hydrogeology-of-the-anthropocene Supplementary material: https://doi.org/10.6084/m9.figshare.c.7103608

From remote to urbanized: Dispersal of antibiotic-resistant bacteria under the aspect of anthropogenic influence

Antibiotic resistance is a growing global concern, but our understanding of the spread of resistant bacteria in remote regions remains limited. While some level of intrinsic resistance likely contributes to reduced susceptibility to antimicrobials in the environment, it is evident that human actions, particularly the (mis)use of antibiotics, play a significant role in shaping the environmental resistome, even in seemingly distant habitats like glacier ice sheets.Our research aims to bridge this knowledge gap by investigating the direct influence of human activities on the presence of antibiotic-resistant bacteria in various habitats. To achieve a comprehensive assessment of anthropogenic impact across diverse and seemingly isolated sampling sites, we developed an innovative approach utilizing Corine Land Cover data and heatmaps generated from sports activity trackers. This method allowed us to make meaningful comparisons across relatively pristine environments.Our findings indicate a noteworthy increase in culturable antibiotic-resistant bacteria with heightened human influence, as evidenced by our analysis of glacier, snow, and lake water samples. Notably, the most significant concentrations of antibiotic-resistant and multidrug-resistant microorganisms were discovered in two highly impacted sampling locations, namely the Tux Glacier and Gas Station Ellmau.

A motion-aware and temporal-enhanced Spatial–Temporal Graph Convolutional Network for skeleton-based human action segmentation

Action segmentation task is an important approach for understanding the actions from the video. Most of the conventional action recognition tasks can recognize only a single action from a given input video, thus we need to input a pre-trimmed video containing only one type of action. In contrast, temporal action segmentation (TAS) aims to segment a temporally untrimmed video sequence by time. Consequently, it has wider application prospects in various fields. Previously proposed TAS-based methods use only RGB color video as input to segment the actions, but RGB video is not robust against diverse backgrounds. Whereas skeleton-based features are more resilient as they do not incorporate any background information but there has been limited research exploring this feature modality. To this end, we propose a motion-aware and temporal-enhanced spatial–temporal graph convolutional network for the skeleton-based human action segmentation. Our framework contains a motion-aware module, multi-scale temporal convolutional network, temporal-enhanced graph convolutional network module and a refinement module. Our method can efficiently capture the motion information and long-range dependencies using skeleton features while improving temporal modeling. We have conducted experiments using four publicly available datasets to demonstrate the effectiveness of our introduced method. The code is available at https://github.com/11yxk/openpack.

LOCALIZATION METHOD OF HUMAN SKELETAL SEQUENCE MOVEMENTS IN ICE SPORTS

A method based on kernel extreme learning machine is proposed for accurate localization and recognition of human bones. In this method, the human skeleton is divided into 16 key nodes based on physiology, and the human skeleton model is formed by connecting lines between the nodes. The sequence images of human actions are taken as input and fed into the input and hidden layers of the extreme learning machine. During the learning and training process, a kernel function is used to construct a kernel matrix to complete weight allocation. The experimental study was conducted on the action sequences of figure skaters, and the results showed that our method achieved an accuracy rate of over 90% in recognizing human bones, with individual sequences achieving an accuracy rate of over 95%, which is superior to convolutional neural network (CNN) and long short-term memory (LSTM) methods.

What to Blame? Self-Serving Attribution Bias with Multi-Dimensional Uncertainty

Abstract People often receive feedback influenced by external factors, yet little is known about how this affects self-serving biases. Our theoretical model explores how multi-dimensional uncertainty allows additional degrees of freedom for self-serving bias. In our primary experiment, feedback combining an individual’s ability and a teammate’s ability leads to biased belief updating. However, in a follow-up experiment with a random fundamental replacing the teammate, unbiased updating occurs. A validation experiment shows that belief distortion is greater when outcomes originate from human actions. Overall, our experiments highlight how multi-dimensional environments can enable self-serving biases.

“¡Abajo los médanos!”: Orígenes del conflicto entre dunas costeras y humanos en la ciudad de Veracruz (siglos XVI-XIX)

The aim of this article is to analyze, from the perspective of environmental history and of history of science, the relationship between médanos (coastal dunes) and humans in the city and port of Veracruz based on the sand removal project of 1880 and through following the conflict between both actors from the 16th to 19th centuries. This conflict had its origin in the construction of a negative vision of thecoastal dunes of Veracruz by colonizers, physicians and naturalists, which implied that this coastal ecosystem had to be destroyed due to being infertile, unhealthy (mainly associated with yellow fever) and hindering modernity. Thus, the configuration of the colonialist, Eurocentric and anthropocentric project of modernity is on the basis of the said negative vision. Through a methodology that combines following and the Actor-Network Theory, besides the use of various sources (press, correspondence, historical maps, a relación geográfica, medical treatises and studies and urbanistic and naturalist works), the collective of human and non-human actors involved in the mentioned conflict is recreated.

Beyond land use planning and ecosystem services assessment with the conservation use potential framework: A study in the Upper Rio das Velhas basin, Brazil

Human actions can damage the ecosystems and affect the services depending on them, with ample detrimental consequences. In earlier studies, the Conservation Use Potential (PCU) framework proved useful in assessing the capacity for aquifer recharge, suitable land uses and resistance to erosion at the river basin scale. On the other hand, the joint analysis of PCU and land uses allowed identifying the adequacy of current uses in relation to suitability (natural uses) in various basins. This was especially useful from the management perspective in basins with environmental conflicts, where current uses differed from suitability, because the PCU indicated how and where the conflicts should be mitigated. Besides the use as management tool, the PCU has potential to shed light over environmental issues such as ecosystem services, but that was not tempted so far. The aim of this work was therefore to bridge that knowledge gap and frame the PCU's application from the standpoint of Ecosystem Services (ES) assessment. We demonstrated how the PCU could be used to improve provision (recharge), support (sustainable agriculture) and regulation (resistance to erosion) services in a specific basin with land use conflicts (the Upper Rio das Velhas basin, located in Minas Gerais, Brazil), through the planning of suitable uses. It was noted that the studied basin is mostly composed of Very Low, Low and Medium potentials. These classes occur because steep slopes, fragile soils and lithologies with high denudation potential and low nutrient supply dominate in the basin. On the other hand, urban sprawl has a negative impact on all ES, while maintaining agricultural areas with appropriate management can effectively regulate erosion. As per the current results, the premise of using the PCU as joint management-environmental tool was fully accomplished, and is recommended a basis for public policy design and implementation in Brazil and elsewhere.

Beyond an anthropocentric view of praxis: towards education for planetary well-being

A number of philosophical perspectives, such as deep ecology, posthumanism, and new materialisms, to name a few, have challenged the deep-rooted anthropocentric assumptions about human exceptionalism. Yet these non-anthropocentric perspectives must still find a place for human action; they require clear conceptualisations of human action and agency. It is generally acknowledged that human beings have a weighty moral responsibility for correcting the current global ecological crisis. For more than two millennia, theories of praxis supplied conceptualisations of action for the good of humankind. In this paper, we argue that the non-anthropocentric perspectives can be substantially extended and enhanced by a new theory of transformative praxis that breaks through the anthropocentric limit imposed by the notion of ‘the good for humankind’ to embrace collective human action for planetary well-being. We call this approach a praxis orientation to environmental education.

Neoliberal contradictions, necrocapitalist nightmares: questions of human agency and free will in Aḥmad Saʿdāwī’s Frankenstein in Baghdad

ABSTRACT This article argues that the major fantastic conceit, the Whatsitname (the shisma), in Ahmad Sa'dāwī's novel, Frankenstein in Baghdad, functions as an allegory for the repressed totality of necrocapitalist rationality in the postcolonial global South. Sa'dāwī's novel seemingly privileges postmodern and neoliberal ideologemes such as individual choice that blame the death and violence in Iraq on people's actions and representations. Nonetheless, the novel paradoxically attributes the human condition and the consequences of human actions to invisible structural forms of power and larger-than-life supernatural powers namely, the Whatsitsname as an allegory for necrocapitalism, that ultimately undermine human agency and freedom. I will examine the ways in which this tension is articulated in the novel through the production of the fantasy of a unified nation, homoerotic desire, and class politics. The novel ultimately reflects Iraqi subjects' deeper ontological anxieties about the circulation of their bodies in the necrocapitalist market.

High highs, low lows: Artists and anti-environments in the twenty-first century

From antiquity to contemporary times, artists have served as society’s defence against unbridled technological adoption. As stressed by Marshall McLuhan, artists are those individuals who aid society in understanding the conditions of our time. In literate times the poet and the painter assumed such responsibilities, but in the digital era the artist becomes something altogether different. McLuhan defined the artists broadly as an individual in any field who was able to provide insightful information into the human actions and consciousness of their time. Similarly, Paglia argued analysts must combine ‘high with popular art, the noble with the sleazy’ (1991: 34). Thus, we are left with two pressing questions: who are the artists of the millennium? Can they still save us? The following article sought to trace who we should be considered artists in the twenty-first century and exactly what they have accomplished, or failed to accomplish, in making society aware of shifting sense ratios. Though might bemoan the obsolescence of authenticity and aura in art, Paglia astutely points out the ‘popular culture reclaims what high culture shuts out’ (1991: 34). Therefore, a survey of both high and popular art will probe the abilities and limits of artists and their art to make society aware of the effects of technological change.

Automating the Human Action of First-Trimester Biometry Measurement from Real-World Freehand Ultrasound

Objective: Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution only represents part of a human task which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in free-hand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three sub-actions.Methods: This paper considers how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid Convolutional Neural Network (CNN) architecture design, a classification-regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes which contain the mid-sagittal view of the fetus are sampled at multiple timestamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown-rump length (CRL) estimate is calculated as the mean CRL from these multiple frames.Results: Our fully automated method shows a high correlation with clinical expert CRL measurement (Pearson ρ=0.92, R-Squared (R2)=0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos.Conclusion: A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards and ensuring precise biometric measurements.

How Text-to-Image Generative AI Is Transforming Mediated Action.

This article examines the intricate relationship between humans and text-to-image generative models (generative artificial intelligence/genAI) in the realm of art. The article frames that relationship in the theory of mediated action-a well-established theory that conceptualizes how tools shape human thoughts and actions. The article describes genAI systems as learning, cocreating, and communicating, multimodally capable hybrid systems that distill and rely on the wisdom and creativity of massive crowds of people and can sometimes surpass them. Those systems elude the theoretical description of the role of tools and locus of control in mediated action. The article asks how well the theory can accommodate both the transformative potential of genAI tools in creative fields and art, and the ethics of the emergent social dynamics it generates. The article concludes by discussing the fundamental changes and broader implications that genAI brings to the realm of mediated action and, ultimately, to the very fabric of our daily lives.

Outdoor Assessment of Airborne Microorganisms During the COVID-19 Outbreak in Madinah City, KSA

The COVID-19 outbreak was a serious challenge for countries around the globe. With the objective of mitigating the spread of the virus, both national and international health organizations swiftly enacted quarantine measures across numerous cities around the globe. This presented a unique chance to evaluate the consequences of human actions on the quality of the air. This study aimed to investigate airborne microbial levels in different outdoor locations in Al-Madinah City, Kingdom of Saudi Arabia (KSA), during the COVID-19 pandemic by comparing lockdown against non-lockdown conditions. Twelve outdoor locations were investigated in terms of microbial total counts using the open plate method during and after the COVID-19 lockdown. Environmental factors that could affect the airborne microbial load, including humidity, temperature and wind speed, were recorded during the assessment. The means of the total colony-forming units (CFU) for each cubic meter (m3) of air were calculated. Lockdown restrictions caused significant decreases in the biological contaminants in all locations compared with the numbers after the pandemic. Gram-positive bacteria represented most of the samples, with fewer fungal strains detected. The outdoor average total bacterial counts ranged between 0.00±0.00-8337.50±248.98 CFU/m3, compared with 2903.75±407.60-19722.50±475.03 CFU/m3 after the pandemic. The mean concentrations of total fungi were lower than those of bacteria and ranged between 0.00±0.00-143.75±131.75 CFU/m3 during the COVID-19 lockdown and were elevated after the lockdown to reach 28.75±49.80-776.25±298.78 CFU/m3. Based on the available data, there are no studies comparing outdoor microbial counts during and after the COVID-19 pandemic. Therefore, this research offers additional perspectives on the air quality experienced amidst the COVID-19 pandemic and the subsequent implementation of lockdown measures and could serve as a valuable resource for monitoring and implementing measures to control air pollution.

Brain-Computer Interface: The Construction of Artificial Highways

One of the nifty advent of technology in terms of neurobionics is the Brain-Computer Interface (BCI). It will be no wrong to regard its principle somewhat likely to a real-time chip robot that deciphers human impulses, traversing on the nerve highways, into commands that modulate human actions which have been rendered paradoxical due to any physical or congenital nervous injuries. BCI technology has empowered the disabled, enabling them to operate cursors and smart home devices using their minds. The famous NeuraLink is emerging to be an advocate of the latest BCI technology, having performed a new brain implant successfully; but deep research puts forward the name of Synchron as an instigator of BCI industry. 
 A tailed-electrode, with sixteen electric arrays, is pushed up-stream near to the brain’s motor cortex region via a small incision made in the jugular vein [1]. Upon reaching the destination it tubes out against the interior wall of the blood vessel, functioning as an antenna to detect the tissues’ electrical activity of the proximal cortex region such as moving a leg or scrolling through a screen. These signals are demodulated into muscle-translatable commands by a transmitter fitted in the chest, that disseminates the waves to an external digital device which performs the specified actions through a connected effector device. Primeval BCIs incorporated a mesh of protruding wires. Trials are underway to replace it with a home-based BCI setup. For now, fifty severely paralyzed people, including brain-stem stroke patients, have received these stents with a few Australians and Americans roaming around freely with implants in their blood vessels as part of BCI contemporary testing. 
 Despite such bamboozling invention by Synchron, it has its limitations owing to the inferiority of brain signals espied across the curtains of membranes in way. A Synchron user can only scroll through the web pages. NeuraLink enters the competition by offering a 1024 electrodes’ matrix of ultrathin conducting bio-friendly fibers, integrated onto 1 centimeter’s cube film that can be inserted by a robot [1]. In January 2024 the first brain-film implant was performed on a brain-disabled patient which is showing signs of recovery. BCI current research revolves around inculcating the electrode bearing agent(s) directly into the brain regions without invasive procedures, which will be a breakthrough in neurosciences of the current era. 
 Despite of its unprecedented utility, the BCI system also opens the gate for controversial scrutiny. For instance, there might be an undesired compulsion on BCI industry to escalate the amplitudes of cognition, memory, motor functions, and intellect even of normal individuals. Peculiarities like infrared vision and memory augmentation pose a danger towards informed consent and privacy. An environment of disparity, ascribable to the unequal approach of the interface, may be created that will lead to socio-political issues of genocide [2]. 
 The anatomy of the human brain has this inherent tendency to reject any foreign agent playing with its natural traffic, thus for how long these electrodes can co-exist normally with human biology is a debate not yet started. In research a robot sensitive to empathy was generated and named ‘cobot’. With the use of an electroencephalographic (EEG) sensor worn by the human agent, its BCI was built. The cobot responds to the threat and passes it on to the recipient human agent, creating a virtuous cycle of caring and security. Consequently, when the patient was exposed to unsettling motion, an increase in the EEG signal was recorded [3]. The brain-computer interface fails to stamp out the placebo effect associated with the recovery. Is it really the chip or the unwavering willingness of the human mind to adapt and thrive ?

Charles S. Pierce's Semiotic Study of the Last Days in QS. Al-Haqqah: Trichotomy Relations (Icons, Indexes and Symbols)

This research aims to examine the meaning of the Day of Judgment in Surah Al-Haqqah and analyze the vocabulary within it through Peirce’s semiotic study. According to Charles Sanders Peirce’s semiotics, signs based on their relationship with their objects are divided into three categories: icons, indexes, and symbols. Surah Al-Haqqah depicts a highly tense atmosphere and narrates the consequences for those who disobey in the world. It clearly illustrates human actions, both good and bad, as no creature can hide from His presence. This research employs a qualitative approach. The analysis reveals that out of the fifty-two verses in Surah Al-Haqqah, there are six quotations related to the Day of Judgment categorized as icons. Additionally, there are ten index quotations scattered throughout the surah. Finally, among the fifty-two verses, the most frequently found type of symbol is found in seventeen verses.

An Optimized Deep LSTM Model for Human Action Recognition

An Optimized Deep LSTM Model for Human Action Recognition

In the Footsteps of the Silk Road: Czech-Kyrgyz Geo-environmental Project

Kyrgyzstan can be represented, geomorphologically-speaking, by a transect between the Fergana lowlands and the Tien Shan highlands and is an outstanding area for the study of paleoclimatic conditions relating to climatic changes. These changes have been crucial for the behaviour of past cultures in this area, especially due to the presence of the Silk Road. A Czech environmental team, covering geology, geomorphology, pedology, paleoecology, archaeobotany, malacology, osteology and many other disciplines, has been following up previous survey fieldwork undertaken in this area. Since 2021, the expeditions in the south-eastern Kyrgyzstan (Osh Region) have been aiming at the structure and settlement pattern development in the contact zone between the fertile Fergana basin and the steppe environment at the foothills of the Pamir-Alai and Tian-Shan Mountains, from prehistory until the present, including the material testimony of life on the ancient and medieval Silk Road. This work is a part of an agreement between the University of West Bohemia in Pilsen, the Osh State University and the Institute of History, Archaeology and Ethnology named after B. Dzamgyrchinov of the National Academy of Sciences of the Kyrgyz Republic. One of the major challenges of the ongoing geoarchaeological and palaeoecological research is to link climate changes and changes forced by human action with the transformation of settlement and landscape patterns.

Spreading Relations Rather Than Chemicals in Melanesian Gardens

The strand of environmentalism that focuses on biodiversity as produced by the ecosystem places natural agency beyond human control. In Melanesia, where plants, animals, and men cannot radically be distinguished from one another, this standpoint seems too radical. Agronomic studies have shown that Melanesian gardens possess a profusion of biodiversity superior to the so-called natural one. This article therefore attempts to show that Melanesian commonality between people and plants and the correlative association of plants with humans are at the root of this profusion. In sum, rather than placing nature outside the reach of humans, the Melanesian case suggests that biodiversity is best served by human ritual actions during which vegetal materialize the social relations which link the different people.

MFCANN: A feature diversification framework based on local and global attention for human activity recognition

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network’s ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively.