Hasil untuk "Human evolution"

R. Feldman

K. Hawkes, J. O'connell, N. Jones et al.

M. Olivier

L. Vigilant, M. Stoneking, Henry Harpending et al.

W. McGrew

Ian T. Fiddes, G. A. Lodewijk, M. Mooring et al.

S. Carter

A. Bittles, M. Black

Hayes K. H. Luk, Xin Li, Joshua Fung et al.

Shortly after its emergence in southern China in 2002/2003, Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) was confirmed to be the cause of SARS. Subsequently, SARS-related CoVs (SARSr-CoVs) were found in palm civets from live animal markets in Guangdong and in various horseshoe bat species, which were believed to be the ultimate reservoir of SARSr-CoV. Till November 2018, 339 SARSr-CoV genomes have been sequenced, including 274 from human, 18 from civets and 47 from bats [mostly from Chinese horseshoe bats (Rhinolophus sinicus), n = 30; and greater horseshoe bats (Rhinolophus ferrumequinum), n = 9]. The human SARS-CoVs and civet SARSr-CoVs were collected in 2003/2004, while bat SARSr-CoVs were continuously isolated in the past 13 years even after the cessation of the SARS epidemic. SARSr-CoVs belong to the subgenus Sarbecovirus (previously lineage B) of genus Betacoronavirus and occupy a unique phylogenetic position. Overall, it is observed that the SARSr-CoV genomes from bats in Yunnan province of China possess the highest nucleotide identity to those from civets. It is evident from both multiple alignment and phylogenetic analyses that some genes of a particular SARSr-CoV from bats may possess higher while other genes possess much lower nucleotide identity to the corresponding genes of SARSr-CoV from human/civets, resulting in the shift of phylogenetic position in different phylogenetic trees. Our current model on the origin of SARS is that the human SARS-CoV that caused the epidemic in 2002/2003 was probably a result of multiple recombination events from a number of SARSr-CoV ancestors in different horseshoe bat species.

Thorsten Klößner, João Belo, Zekun Wu et al.

Interfaces for human oversight must effectively support users' situation awareness under time-critical conditions. We explore reinforcement learning (RL)-based UI adaptation to personalize alerting strategies that balance the benefits of highlighting critical events against the cognitive costs of interruptions. To enable learning without real-world deployment, we integrate models of users' gaze behavior to simulate attentional dynamics during monitoring. Using a delivery-drone oversight scenario, we present initial results suggesting that RL-based highlighting can outperform static, rule-based approaches and discuss challenges of intelligent oversight support.

Vasanth Reddy Baddam, Behdad Chalaki, Vaishnav Tadiparthi et al.

In social robot navigation, traditional metrics like proxemics and behavior naturalness emphasize human comfort and adherence to social norms but often fail to capture an agent's autonomy and adaptability in dynamic environments. This paper introduces human empowerment, an information-theoretic concept that measures a human's ability to influence their future states and observe those changes, as a complementary metric for evaluating social compliance. This metric reveals how robot navigation policies can indirectly impact human empowerment. We present a framework that integrates human empowerment into the evaluation of social performance in navigation tasks. Through numerical simulations, we demonstrate that human empowerment as a metric not only aligns with intuitive social behavior, but also shows statistically significant differences across various robot navigation policies. These results provide a deeper understanding of how different policies affect social compliance, highlighting the potential of human empowerment as a complementary metric for future research in social navigation.

Kate Letheren, Nicole Robinson

Humans and robots are increasingly working in personal and professional settings. In workplace settings, humans and robots may work together as colleagues, potentially leading to social expectations, or violation thereof. Extant research has primarily sought to understand social interactions and expectations in personal rather than professional settings, and none of these studies have examined negative outcomes arising from violations of social expectations. This paper reports the results of a 2x3 online experiment that used a unique first-person perspective video to immerse participants in a collaborative workplace setting. The results are nuanced and reveal that while robots are expected to act in accordance with social expectations despite human behavior, there are benefits for robots perceived as being the bigger person in the face of human rudeness. Theoretical and practical implications are provided which discuss the import of these findings for the design of social robots.

Ruben Janssens, Jens De Bock, Sofie Labat et al.

Detecting miscommunication in human-robot interaction is a critical function for maintaining user engagement and trust. While humans effortlessly detect communication errors in conversations through both verbal and non-verbal cues, robots face significant challenges in interpreting non-verbal feedback, despite advances in computer vision for recognizing affective expressions. This research evaluates the effectiveness of machine learning models in detecting miscommunications in robot dialogue. Using a multi-modal dataset of 240 human-robot conversations, where four distinct types of conversational failures were systematically introduced, we assess the performance of state-of-the-art computer vision models. After each conversational turn, users provided feedback on whether they perceived an error, enabling an analysis of the models' ability to accurately detect robot mistakes. Despite using state-of-the-art models, the performance barely exceeds random chance in identifying miscommunication, while on a dataset with more expressive emotional content, they successfully identified confused states. To explore the underlying cause, we asked human raters to do the same. They could also only identify around half of the induced miscommunications, similarly to our model. These results uncover a fundamental limitation in identifying robot miscommunications in dialogue: even when users perceive the induced miscommunication as such, they often do not communicate this to their robotic conversation partner. This knowledge can shape expectations of the performance of computer vision models and can help researchers to design better human-robot conversations by deliberately eliciting feedback where needed.

Qingfeng Miao, Xiaoyu Liu, Haibin Shi et al.

Examining lake-area evolution and influencing factors is essential for understanding global environmental and societal changes and supporting ecological sustainability. Inner Mongolia, China, given its unique geographical and climatic conditions, serves as a natural laboratory for investigating the complex coupling mechanisms of “climate–hydrology–humanities.” Accordingly, we analyzed data regarding annual area changes in 655 lakes across five basins obtained from Landsat, Sentinel-2, and pushbroom multispectral scanner (1987–2023), combined with meteorological, hydrological, and human factors affecting lake-area changes. Results indicated that lake areas varied from 4059.36 to 6489.46 km2 in 1987–2023, exhibiting an overall decline of 38.06 km2/a (R2 = 0.39, p < 0.001). This trend was nonlinear, exhibiting area expansion (1987–1998), rapid shrinkage (1998–2010), and stabilization after a slight rebound (2010–2023). Natural factors dominated lake-area dynamics in the Songhua and Northwest River Basins, while human activities, particularly agriculture, were key drivers in the Liaohe, Haihe, and Yellow River Basins. These findings provide critical insights into the drivers of lake-area changes and establish a scientific basis for developing effective water-resource management and ecological protection strategies.

Paweł Nowik

This article examines the emerging role of artificial intelligence (AI) auditing as a mechanism for promoting algorithmic accountability within the European Union’s labour law framework. Focusing on two key legislative instruments—the Artificial Intelligence Act (AI Act) and the Platform Work Directive (PWD)—the study presents a comparative analysis of their respective audit models. While the AI Act introduces a general, risk‑based approach to AI governance centred on ex ante conformity assessments, the PWD establishes a sector‑specific, rights‑based framework that emphasises transparency, human oversight, and worker participation in ex post evaluations of algorithmic management systems. Drawing on legal analysis and interdisciplinary literature, the article explores how each instrument operationalises AI auditing, with particular attention to procedural safeguards, institutional design, and enforcement mechanisms. It argues that, although the AI Act offers a more formalised audit structure, its reliance on internal assessments raises concerns regarding independence and effectiveness. Conversely, while the PWD lacks a mandatory external audit requirement, it compensates through participatory governance tools, including data protection impact assessments, transparency obligations, and individual redress rights.The article concludes that these complementary regulatory models collectively represent a significant normative development in embedding algorithmic accountability within EU labour law. However, their effectiveness will depend upon robust implementation, institutional capacity, and the evolution of audit practices that are not only technically rigorous but also legally enforceable and socially legitimate.

Md. Nazmus Sakib, Mohammad Abdul Jabber, Mohammad Younus et al.

Abstract Neural network has emerged as a transformative force reshaping various domains in response to the rapidly evolving technological landscape. This study aims to address the literature gap, delving into the current state of development, identifying key contributors, influential countries, and journals, and understanding publication trends. This bibliometrics literature review analysis comprehensively explores the cooperation between neural networks and human resource management (HRM). Through a bibliometric examination of 86 relevant articles from the Scopus database, this study employs bibliometric methodologies, network analysis, and content analysis to reveal research clusters and knowledge gaps though the use of R studio, Vosviewer, biblioshiney. The findings of this bibliometric analysis suggest that neural networks are a vital concept for HRM in recent years, with a large number of articles produced in the last 5 years, totaling 62 articles. This topic is a global concern, as contributions have come from countries across Europe, America, Asia, and Africa. The citation impact analysis and country collaboration analysis highlight the significant role played by Chinese and Indian researchers and institutions in advancing this research area. Thematic evaluation over time reveals the evolution of research themes, shifting from convolutional neural networks and forecasting to machine learning and artificial intelligence in the field of HRM. By bridging the gap between theory and practice, this research contributes to advancing HRM scholarship and facilitating the adoption of innovative HRM practices in organizations worldwide. These findings underscore the dynamic nature of the neural networks and HRM field and its potential for further scientific enrichment.

Ryan D. Hernandez, Joanna L. Kelley, Eyal Elyashiv et al.

Yousra Shleibik, Elijah Alabi, Christopher Reardon

Robots are now increasingly integrated into various real world applications and domains. In these new domains, robots are mostly employed to improve, in some ways, the work done by humans. So, the need for effective Human-Robot Teaming (HRT) capabilities grows. These capabilities usually involve the dynamic collaboration between humans and robots at different levels of involvement, leveraging the strengths of both to efficiently navigate complex situations. Crucial to this collaboration is the ability of robotic systems to adjust their level of autonomy to match the needs of the task and the human team members. This paper introduces a system designed to control attention using HRT through the use of ground robots and augmented reality (AR) technology. Traditional methods of controlling attention, such as pointing, touch, and voice commands, sometimes fall short in precision and subtlety. Our system overcomes these limitations by employing AR headsets to display virtual visual markers. These markers act as dynamic cues to attract and shift human attention seamlessly, irrespective of the robot's physical location.

Sujan Sarker, Haley N. Green, Mohammad Samin Yasar et al.

Collaborative robots are increasingly deployed alongside humans in factories, hospitals, schools, and other domains to enhance teamwork and efficiency. Systems that seamlessly integrate humans and robots into cohesive teams for coordinated and efficient task execution are needed, enabling studies on how robot collaboration policies affect team performance and teammates' perceived fairness, trust, and safety. Such a system can also be utilized to study the impact of a robot's normative behavior on team collaboration. Additionally, it allows for investigation into how the legibility and predictability of robot actions affect human-robot teamwork and perceived safety and trust. Existing systems are limited, typically involving one human and one robot, and thus require more insight into broader team dynamics. Many rely on games or virtual simulations, neglecting the impact of a robot's physical presence. Most tasks are turn-based, hindering simultaneous execution and affecting efficiency. This paper introduces CoHRT (Collaboration System for Human-Robot Teamwork), which facilitates multi-human-robot teamwork through seamless collaboration, coordination, and communication. CoHRT utilizes a server-client-based architecture, a vision-based system to track task environments, and a simple interface for team action coordination. It allows for the design of tasks considering the human teammates' physical and mental workload and varied skill labels across the team members. We used CoHRT to design a collaborative block manipulation and jigsaw puzzle-solving task in a team of one Franka Emika Panda robot and two humans. The system enables recording multi-modal collaboration data to develop adaptive collaboration policies for robots. To further utilize CoHRT, we outline potential research directions in diverse human-robot collaborative tasks.

D. Beasley, A. Koltz, J. Lambert et al.

Gastric acidity is likely a key factor shaping the diversity and composition of microbial communities found in the vertebrate gut. We conducted a systematic review to test the hypothesis that a key role of the vertebrate stomach is to maintain the gut microbial community by filtering out novel microbial taxa before they pass into the intestines. We propose that species feeding either on carrion or on organisms that are close phylogenetic relatives should require the most restrictive filter (measured as high stomach acidity) as protection from foreign microbes. Conversely, species feeding on a lower trophic level or on food that is distantly related to them (e.g. herbivores) should require the least restrictive filter, as the risk of pathogen exposure is lower. Comparisons of stomach acidity across trophic groups in mammal and bird taxa show that scavengers and carnivores have significantly higher stomach acidities compared to herbivores or carnivores feeding on phylogenetically distant prey such as insects or fish. In addition, we find when stomach acidity varies within species either naturally (with age) or in treatments such as bariatric surgery, the effects on gut bacterial pathogens and communities are in line with our hypothesis that the stomach acts as an ecological filter. Together these results highlight the importance of including measurements of gastric pH when investigating gut microbial dynamics within and across species.