Hasil untuk "Human evolution"

Wenge Xu, Foroogh Hajiseyedjavadi, Kurtis Weir et al.

External Human-Machine Interfaces (eHMIs) have been proposed to facilitate communication between Automated Vehicles (AVs) and pedestrians. However, no attention was given to Deaf and Hard-of-Hearing (DHH) people. We conducted a formative study through focus groups with 6 DHH people and 6 key stakeholders (including researchers, assistive technologists, and automotive interface designers) to compare proposed eHMIs and extract key design requirements. Subsequently, we investigated the effects of visual and auditory eHMI in a virtual reality user study with 32 participants (16 DHH). Results from our scenario suggesting that (1) DHH participants spent more time looking at the AV; (2) both visual and auditory eHMIs enhanced trust, usefulness, and perceived safety; and (3) only visual eHMIs reduced the time to step into the road, time looking at the AV, gaze time, and percentage looking at active visual eHMI components. Lastly, we provided five practical implications for making eHMI inclusive of DHH people.

Sandra Díaz, Unai Pascual

Abstract The notion of reciprocity between humans and the rest of the living world is receiving increasing attention in the environmental sciences and science–policy international bodies. Here we first discuss different meanings of reciprocity, then we discuss this notion in relation to the conceptual framework of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), to date the most prominent international mechanism informing the science–policy interface on living nature. We show that the notion of human–nature reciprocity is recognized and is explicitly included in the IPBES conceptual framework. However, to date, it has received comparatively little attention. To overcome this, we argue that, rather than creating new separate ad‐hoc categories that risk compromising the internal consistency and pluralism of the IPBES conceptual framework, co‐created across different disciplines, worldviews and policy frames, a more fruitful path would be to interpret all its components in a reciprocity light, with stronger emphasis on the human shaping of, and practices of care towards the rest of the living world. Such attention to reciprocity should contribute to the evolution of IPBES and related science–policy initiatives, by incorporating a plurality of perspectives, while still maintaining the framework operational by the continued engagement of multiple disciplines and stakeholders. In terms of policy and action, this would involve more attention to pre‐existing practices of care for nature—of which we provide a few illustrative examples—and new practices inspired by them or created afresh. Read the free Plain Language Summary for this article on the Journal blog.

Haiwei Xue, Xiangyang Luo, Zhanghao Hu et al.

Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars that seamlessly dance to music. However, existing surveys in this field focus on individual methods, lacking a comprehensive overview of the entire generative process. This paper addresses this gap by providing an in-depth survey of human motion video generation, encompassing over ten sub-tasks, and detailing the five key phases of the generation process: input, motion planning, motion video generation, refinement, and output. Notably, this is the first survey that discusses the potential of large language models in enhancing human motion video generation. Our survey reviews the latest developments and technological trends in human motion video generation across three primary modalities: vision, text, and audio. By covering over two hundred papers, we offer a thorough overview of the field and highlight milestone works that have driven significant technological breakthroughs. Our goal for this survey is to unveil the prospects of human motion video generation and serve as a valuable resource for advancing the comprehensive applications of digital humans. A complete list of the models examined in this survey is available in Our Repository https://github.com/Winn1y/Awesome-Human-Motion-Video-Generation.

Haotian Li, Yun Wang, Huamin Qu

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Aimee Allen, Tom Drummond, Dana Kulić

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.

Yuxuan Xue, Xianghui Xie, Margaret Kostyrko et al.

Generating realistic and controllable 3D human avatars is a long-standing challenge, particularly when covering broad attribute ranges such as ethnicity, age, clothing styles, and detailed body shapes. Capturing and annotating large-scale human datasets for training generative models is prohibitively expensive and limited in scale and diversity. The central question we address in this paper is: Can existing foundation models be distilled to generate theoretically unbounded, richly annotated 3D human data? We introduce InfiniHuman, a framework that synergistically distills these models to produce richly annotated human data at minimal cost and with theoretically unlimited scalability. We propose InfiniHumanData, a fully automatic pipeline that leverages vision-language and image generation models to create a large-scale multi-modal dataset. User study shows our automatically generated identities are undistinguishable from scan renderings. InfiniHumanData contains 111K identities spanning unprecedented diversity. Each identity is annotated with multi-granularity text descriptions, multi-view RGB images, detailed clothing images, and SMPL body-shape parameters. Building on this dataset, we propose InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body shape, and clothing assets. InfiniHumanGen enables fast, realistic, and precisely controllable avatar generation. Extensive experiments demonstrate significant improvements over state-of-the-art methods in visual quality, generation speed, and controllability. Our approach enables high-quality avatar generation with fine-grained control at effectively unbounded scale through a practical and affordable solution. We will publicly release the automatic data generation pipeline, the comprehensive InfiniHumanData dataset, and the InfiniHumanGen models at https://yuxuan-xue.com/infini-human.

Azizul Zahid, Jie Fan, Farong Wang et al.

Understanding action correspondence between humans and robots is essential for evaluating alignment in decision-making, particularly in human-robot collaboration and imitation learning within unstructured environments. We propose a multimodal demonstration learning framework that explicitly models human demonstrations from RGB video with robot demonstrations in voxelized RGB-D space. Focusing on the "pick and place" task from the RH20T dataset, we utilize data from 5 users across 10 diverse scenes. Our approach combines ResNet-based visual encoding for human intention modeling and a Perceiver Transformer for voxel-based robot action prediction. After 2000 training epochs, the human model reaches 71.67% accuracy, and the robot model achieves 71.8% accuracy, demonstrating the framework's potential for aligning complex, multimodal human and robot behaviors in manipulation tasks.

Dallin L. Cordon, Shaden Moss, Marc Killpack et al.

This work represents an initial benchmark of a large-scale soft robot performing physical, collaborative manipulation of a long, extended object with a human partner. The robot consists of a pneumatically-actuated, three-link continuum soft manipulator mounted to an omni-directional mobile base. The system level configuration of the robot and design of the collaborative manipulation (co-manipulation) study are presented. The initial results, both quantitative and qualitative, are directly compared to previous similar human-human co-manipulation studies. These initial results show promise in the ability for large-scale soft robots to perform comparably to human partners acting as non-visual followers in a co-manipulation task. Furthermore, these results challenge traditional soft robot strength limitations and indicate potential for applications requiring strength and adaptability.

Indrani Chakraborty, Richard T. Olsson, Richard L. Andersson et al.

In glucose biofuel cells (G-BFCs), glucose oxidation at the anode and oxygen reduction at the cathode yield electrons, which generate electric energy that can power a wide range of electronic devices. Research associated with the development of G-BFCs has increased in popularity among researchers because of the eco-friendly nature of G-BFCs (as related to their construction) and their evolution from inexpensive bio-based materials. In addition, their excellent specificity towards glucose as an energy source, and other properties, such as small size and weight, make them attractive within various demanding applied environments. For example, G-BFCs have received much attention as implanted devices, especially for uses related to cardiac activities. Envisioned pacemakers and defibrillators powered by G-BFCs would not be required to have conventional lithium batteries exchanged every 5–10 years. However, future research is needed to develop G-BFCs demonstrating more stable power consistency and improved lifespan, as well as solving the challenges in converting laboratory-made implantable G-BFCs into implanted devices in the human body. The categorization of G-BFCs as a subcategory of different biofuel cells and their performance is reviewed in this article.

Faisal Mehmood, Xin Guo, Enqing Chen et al.

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data.

Jianbin Jiao, Xina Cheng, Weijie Chen et al.

3D human pose estimation captures the human joint points in three-dimensional space while keeping the depth information and physical structure. That is essential for applications that require precise pose information, such as human-computer interaction, scene understanding, and rehabilitation training. Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content. Given the remarkable self-attention mechanism of transformers, capable of capturing the spatial-temporal correlation from multi-view video datasets, we propose a multi-stage framework for 3D sequence-to-sequence (seq2seq) human pose detection. Firstly, the spatial module represents the human pose feature by intra-image content, while the frame-image relation module extracts temporal relationships and 3D spatial positional relationship features between the multi-perspective images. Secondly, the self-attention mechanism is adopted to eliminate the interference from non-human body parts and reduce computing resources. Our method is evaluated on Human3.6M, a popular 3D human pose detection dataset. Experimental results demonstrate that our approach achieves state-of-the-art performance on this dataset. The source code will be available at https://github.com/WUJINHUAN/3D-human-pose.

Kristian González Barman, Simon Lohse, Henk de Regt

We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can address challenges along the way. The paper concludes with an agenda for change, i.e. concrete, actionable steps to improve LLM development.

Jianyuan Ni, Hao Tang, Syed Tousiful Haque et al.

The combination of increased life expectancy and falling birth rates is resulting in an aging population. Wearable Sensor-based Human Activity Recognition (WSHAR) emerges as a promising assistive technology to support the daily lives of older individuals, unlocking vast potential for human-centric applications. However, recent surveys in WSHAR have been limited, focusing either solely on deep learning approaches or on a single sensor modality. In real life, our human interact with the world in a multi-sensory way, where diverse information sources are intricately processed and interpreted to accomplish a complex and unified sensing system. To give machines similar intelligence, multimodal machine learning, which merges data from various sources, has become a popular research area with recent advancements. In this study, we present a comprehensive survey from a novel perspective on how to leverage multimodal learning to WSHAR domain for newcomers and researchers. We begin by presenting the recent sensor modalities as well as deep learning approaches in HAR. Subsequently, we explore the techniques used in present multimodal systems for WSHAR. This includes inter-multimodal systems which utilize sensor modalities from both visual and non-visual systems and intra-multimodal systems that simply take modalities from non-visual systems. After that, we focus on current multimodal learning approaches that have applied to solve some of the challenges existing in WSHAR. Specifically, we make extra efforts by connecting the existing multimodal literature from other domains, such as computer vision and natural language processing, with current WSHAR area. Finally, we identify the corresponding challenges and potential research direction in current WSHAR area for further improvement.

alireza rezwani

The topic of humanity and philosophical anthropology have been indispensable in human history, and a true depiction of man's status in the creation is foremost in theistic religions. In the lofty view of Allama Javadi Amoli, the essence of humanity is described as "the theist living being" who is perfected on the foundation of religious doctrines with the possible telos of the arch of human ascention being "the Comprehensive man", a doctrine treated in the literature by such philosophers and mystics as Ibn Arabi and Mulla Sadra who consider the Comprehensive man as the theophony of all Divine attributes of perfection and beauty, and the credit for innovation of the term "Comprehensive man" in the Islamic tradition going to Ibn Arabi.In this study which provides a descriptive and analytic account with a special focus on the thoughts of Javadi Amoli, the necessity of perfect human and its relation to Divine vicegerency lead to the conclusion that the purpose of creating the cosmos is the perfect human, and achieving the perfect humanity is a requisite for Divine absolute vicegerency. In this respect, the perfect human is certainly the vicegerent of God such that the world is terminated if it is devoided of the perfect human.Extended abstractIntroductionHuman is combined with a physical body and a divine soul and has a special reflection on himself in the world of creation. And in terms of special talents such as intellect, will, lust, and evolution, he has been placed in the prostrate position of the divine angels. He can become the de facto vicegerency of God in the world to the extent of following and developing the power of reason and the special application of free will and obedience to God's commands. This privilege and position is specific to humans in a kind of way.Regarding this truth and prominence of human, there are conflicting opinions among thinkers, especially about the position of the divine vicegerency of man; Because, recognizing and recognizing the existence called "Human" throughout history among thinkers, especially philosophers and mystics, has been a concern that cannot be hidden and overlooked, and it is also discussed in most schools of thought that how far is the ultimate ascension and ascension of human. A Comprehensive man being with characteristics such as governorship, perfection, and vicegerency is a clear and outstanding example of God's vicegerency, and the last degree of the ascension of existing perfection shines a possibility, and it is the ideal point of discussion of some thinkers such as Ibn Arabi and the developers of his thought; The originator of such a term is described in the Islamic literature of Ibn Arabi.In this study which provides a descriptive and analytic account with a special focus on the thoughts of Javadi Amoli, the necessity of perfect human and its relation to Divine vicegerency lead to the conclusion that the purpose of creating the cosmos is the perfect human, and achieving the perfect humanity is a requisite for Divine absolute vicegerency. In this respect, the perfect human is certainly the vicegerent of God such that the world is terminated if it is devoided of the perfect human.In the mystical literature of Mulla Sadra and Imam Khomeini, the Comprehensive man being has been explored and revised. Allamah Javadi Amoli, with his new approach to human and his unprecedented approach to its definition, succeeds in removing human from the circle of the old definition (reasoning animal) and in the light of high Quranic, exegetical and philosophical knowledge, he clings to man with a new approach to man " the theist living being " introduces; Especially, in his philosophical and interpretative literature, he has an expanded view on the connection between man and the perfect man, and he considers the perfect man to be associated with the divine vicegerency, to introduce the place of the world empty of the perfect man as equal to the end of the world's life, that is the perfect man is definitely the actual and actual vicegerent of God,  but every vicegerent’s is not perfect human.MethodThe current research is descriptive-analytical typical and the method of collecting information is in the form of a library, and its cumulation information  in is such as books, both printed and electronic, and articles and treatises, and its purpose is to reread Allamah Javadi Amoli's thought about the perfect human being and its necessity with the vicegerency is divine and explain who the perfect man is and how to achieve this position, On the one side, and on the other abutment what is the connection and disconnection between the perfect man and the divine-actual vicegerent, what is the scope and extent of the vicegerency? Who are they, and how far is the territory of the vicegerency?FindingsIn this essay, these questions have been answered. In the end, it has reached such results and conclusions that the truth of the vicegerent is the appearance of the successor in the vicegerent in the qualities of perfection as much as possible, and the example of the atom and perfection of the vicegerent is the perfect human being, because the purpose and ideal of the creation of the world is the perfect human being. Because, creation without a perfect human being is incomplete and null; The perfect human being is the embodiment and manifestation of the atom and the perfection of the perfect and current attributes of vicegerency of him(God). The ability of the position of divine vicegerent includes all human beings in some way is institutionalized in the institution of all mankind, and the release of that power requires an action that is an agent and a excitant, which consists of right knowledge and righteous action. The principle of vicegerent includes all  of legal persons, not real.Concusions:In this study which provides a descriptive and analytic account with a special focus on the thoughts of Javadi Amoli, the necessity of perfect human and its relation to Divine vicegerency lead to the conclusion that the purpose of creating the cosmos is the perfect human, and achieving the perfect humanity is a requisite for Divine absolute vicegerency. In this respect, the perfect human is certainly the vicegerent of God such that the world is terminated if it is devoided of the perfect human.In this study which provides a descriptive and analytic account with a special focus on the thoughts of Javadi Amoli, the necessity of perfect human and its relation to Divine vicegerency lead to the conclusion that the purpose of creating the cosmos is the perfect human, and achieving the perfect humanity is a requisite for Divine absolute vicegerency. In this respect, the perfect human is certainly the vicegerent of God such that the world is terminated if it is devoided of the perfect human.

Robert Białoskórski

The purpose of this article is to clarify the essence and nature of geopolitics as a social science and academic discipline. The author's position is that geopolitics is an academic discipline within the field of social sciences, and its purpose is to study how political phenomena are influenced by geographic conditions that change over time and space. Analyzing the evolution of geopolitics to date, the author posits that its nature is unchanging. In his opinion, geopolitics is still the same in its nature, while the geographical conditions (as well as the ways of understanding them), which act as explanatory factors in geopolitics, change. These changes, in turn, are a consequence of the processes of socio-economic development, especially successive industrial revolutions. It is due to the rapid development of information technology that the activity of political actors in cyberspace has been added to the list of geographical factors influencing political reality. The author postulates that the relevance of human information activity in cyberspace makes it necessary to distinguish another subdiscipline of geopolitics, namely information geopolitics.

Parag Khanna, Elmira Yadollahi, Mårten Björkman et al.

Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot's explanation level at a certain round but also the prior information they received from the robot.

Watcharapong Hongjamrassilp, Roger Zhang, B. Natterson-Horowitz et al.

Glaucoma, an eye disorder caused by elevated intraocular pressure (IOP), is the leading cause of irreversible blindness in humans. Understanding how IOP levels have evolved across animal species could shed light on the nature of human vulnerability to glaucoma. Here, we studied the evolution of IOP in mammals and birds and explored its life history correlates. We conducted a systematic review, to create a dataset of species-specific IOP levels and reconstructed the ancestral states of IOP using three models of evolution (Brownian, Early burst, and Ornstein–Uhlenbeck (OU)) to understand the evolution of glaucoma. Furthermore, we tested the association between life history traits (e.g., body mass, blood pressure, diet, longevity, and habitat) and IOP using phylogenetic generalized least squares (PGLS). IOP in mammals and birds evolved under the OU model, suggesting stabilizing selection toward an optimal value. Larger mammals had higher IOPs and aquatic birds had higher IOPs; no other measured life history traits, the type of tonometer used, or whether the animal was sedated when measuring IOP explained the significant variation in IOP in this dataset. Elevated IOP, which could result from physiological and anatomical processes, evolved multiple times in mammals and birds. However, we do not understand how species with high IOP avoid glaucoma. While we found very few associations between life history traits and IOP, we suggest that more detailed studies may help identify mechanisms by which IOP is decoupled from glaucoma. Importantly, species with higher IOPs (cetaceans, pinnipeds, and rhinoceros) could be good model systems for studying glaucoma-resistant adaptations.

Nicole Robinson, Jason Williams, David Howard et al.

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.