Human Understanding Research Articles

Adaptive Behavior of Collaborative Robots: Review and Investigation of Human Predictive Ability.

With the increasing demand for product variety and customization in manufacturing, the need for flexible production and assembly systems, particularly for small and medium-sized enterprises (SMEs), is growing. Collaborative human-robot assembly (HRA) has emerged as a promising solution to enhance competitiveness by leveraging the strengths of both humans and robots. However, the inherent variability in human performance can disrupt the smooth interaction and processes in HRA systems. Artificial intelligence (AI) methods offer opportunities to develop adaptive robots that can dynamically adjust to the behavior and needs of their human partners. While the adaptivity of collaborative robots promises significant advantages, it may also introduce challenges in predicting the robot’s behavior and actions from a human perspective. Uncertainty due to ambiguity and confusion arising from the difficulty in understanding the automatisms of the robot’s actions could lead to psychological stress for human partners, impacting their well-being, trust, acceptance, and performance. Especially as production tends to integrate the concept of Industry 5.0, which focuses on the development of human-centered systems, addressing these challenges becomes crucial. This paper provides a comprehensive review of previous work on the adaptive behavior of collaborative robots, aiming to define the concept and identify the variables and mechanisms that make robot behavior adaptive. The review is completed by a study with 40 participants to investigate the effect of the degree of adaptation in robot behavior on human predictive ability. The results showed a significant effect, with an average matching of only 49% between participants’ expectations and the robot’s actual adaptation actions. Participants with previous robot experience showed improved but limited predictive ability (20% relative increase). The findings highlight significant difficulties in anticipating adaptive robot behavior, especially in more complex adaptive scenarios and for decision-making processes, underscoring the need for enhanced transparency to facilitate better human understanding and acceptance in collaborative robotic manufacturing environments.

Advancements in medical research using fetal sheep: Implications for human health and treatment methods.

Sheep are typically considered as industrial animals that provide wool and meals. However, they play a significant role in medical research in addition to their conventional use. Notably, sheep fetuses are resistant to surgical invasions and can endure numerous manipulations, such as needle puncture and cell transplantation, and surgical operations requiring exposure beyond the uterus. Based on these distinguishing characteristics, we established a chimeric sheep model capable of producing human/monkey pluripotent cell-derived blood cells via the fetal liver. Furthermore, sheep have become crucial as human fetal models, acting as platforms for developing and improving techniques for intrauterine surgery to address congenital disorders and clarifying the complex pharmacokinetic interactions between mothers and their fetuses. This study emphasizes the significant contributions of fetal sheep to advancing human disease understanding and treatment strategies, highlighting their unique characteristics that are not present in other animals.

Facial Action Unit Representation Based on Self-Supervised Learning With Ensembled Priori Constraints.

Facial action units (AUs) focus on a comprehensive set of atomic facial muscle movements for human expression understanding. Based on supervised learning, discriminative AU representation can be achieved from local patches where the AUs are located. Unfortunately, accurate AU localization and characterization are challenged by the tremendous manual annotations, which limits the performance of AU recognition in realistic scenarios. In this study, we propose an end-to-end self-supervised AU representation learning model (SsupAU) to learn AU representations from unlabeled facial videos. Specifically, the input face is decomposed into six components using auto-encoders: five photo-geometric meaningful components, together with 2D flow field AUs. By constructing the canonical neutral face, posed neutral face, and posed expressional face gradually, these components can be disentangled without supervision, therefore the AU representations can be learned. To construct the canonical neutral face without manually labeled ground truth of emotion state or AU intensity, two priori knowledge based assumptions are proposed: 1) identity consistency, which explores the identical albedos and depths of different frames in a face video, and helps to learn the camera color mode as an extra cue for canonical neutral face recovery. 2) average face, which enables the model to discover a 'neutral facial expression' of the canonical neutral face and decouple the AUs in representation learning. To the best of our knowledge, this is the first attempt to design self-supervised AU representation learning method based on the definition of AUs. Substantial experiments on benchmark datasets have demonstrated the superior performance of the proposed work in comparison to other state-of-the-art approaches, as well as an outstanding capability of decomposing input face into meaningful factors for its reconstruction. The code is made available at https://github.com/Sunner4nwpu/SsupAU.

Design and Application of Language Translation System Resource Platform on Basis of Artificial Intelligence

In daily lives, people often use translators for cross-language translation, but the current translation system has the problem of translation accuracy, and its translation speed also has some limitations. Artificial intelligence has begun to develop, and the language translation system resource platform based on artificial intelligence has gradually attracted attention. The design and application of this platform are of great significance for providing high-quality and efficient language translation services. This research proposes and demonstrates a language translation system resource platform based on artificial intelligence technology. The platform is based on artificial intelligence technology for hardware resources, network resources, and the use of artificial intelligence algorithms to achieve language translation that is accurate and smooth. Through experimental testing, the results showed that the translation accuracy of the system platform reached 95-99%. The platform provides quality and efficient language translation services which can promote and facilitate human cross-language communication and understanding.

Real-Time Speaker Identification and Subtitle Overlay with Multithreaded Audio Video Processing

Active Speaker Detection is pivotal in a multitude of applications, particularly in processing live Audio-Video (AV) streams. Current implementations predominantly focus on processing saved video files, limiting their real-time applicability. Addressing this gap, the proposed model leverages a multi-threading-based system to detect active speakers in live AV streams. This system forms a critical component in an innovative software solution designed to generate real-time subtitles and elegantly overlay them aside from the active speaker. This feature is especially beneficial for individuals with hearing impairments and facilitates the transcription of foreign languages into English, thereby improving human interaction and understanding. Our approach stands out for its ability to process live AV streams promptly for immediate speaker identification and subtitle overlay, marking a significant advancement in real-time communication assistance.

THE ELUCIDATION OF ALBERT CAMUS’ PHILOSOPHY OF ABSURDISM

The philosophical concept of absurdism has been one of the difficult notions to understand in a realm of philosophy. It deals mainly with the inherent contradiction between the human longing for meaning in life and the belief that there is no inherent purpose to the universe. According to this philosophy, human life is absurd because people are constantly seeking meaning and purpose in a universe that is irrational. However, one must embrace and experience life despite its inherent absurdity, finding joy and satisfaction in action amidst the futility of existence. This belief system advocates for individuals to identify their own purpose in life, acknowledge the absurdity of reality, and experience joy in living. Nevertheless, individuals use beliefs or ideologies to avoid or reject the harsh realities of life known as philosophical suicide. Philosophical suicide involves religious or spiritual beliefs that pledge eternal life or rebirth, or blind obedience to a specific political or philosophical principle that shields individuals from evil. Ultimately, philosophical suicide can lead to a superficial and thoughtless life without real meaning or purpose. Hence, there is an advocation to rebel. The idea of rebellion is a way to challenge established beliefs and systems of thought. This requires questioning authority, norms and societal expectations in the quest for truth (and/or personal freedom). ‘Rebels’ in philosophy sought to expand human knowledge and understanding by questioning established doctrines and challenging accepted truths. It is an opportunity for innovation and transformation, as it challenges conventional thought while paving the way for human progress and advancement. Therefore, a rebel must be resilient in whatever decision he makes. Resilience is the ability of a person to recover from failure, overcome obstacles, and adapt to change. It is all about having the inner strength, the means and the attitude to rise above challenges. Hence, for one to build a life worth living, he or she must possess resilience, which is the belief of perseverance, courage, and self-awareness.

Polarity Loss: Improving Visual-Semantic Alignment for Zero-Shot Detection.

Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behavior, zero-shot object detection (ZSD) aims to recognize and localize "unseen" object instances by using only their semantic information. The model is first trained to learn the relationships between visual and semantic domains for seen objects, later transferring the acquired knowledge to totally unseen objects. This setting gives rise to the need for correct alignment between visual and semantic concepts so that the unseen objects can be identified using only their semantic attributes. In this article, we propose a novel loss function called "polarity loss" that promotes correct visual-semantic alignment for an improved ZSD. On the one hand, it refines the noisy semantic embeddings via metric learning on a "semantic vocabulary" of related concepts to establish a better synergy between visual and semantic domains. On the other hand, it explicitly maximizes the gap between positive and negative predictions to achieve better discrimination between seen, unseen, and background objects. Our approach is inspired by embodiment theories in cognitive science that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary), and visual perception (seen/unseen object images). We conduct extensive evaluations on the Microsoft Common Objects in Context (MS-COCO) and Pascal Visual Object Classes (VOC) datasets, showing significant improvements over state of the art. Our code and evaluation protocols available at: https://github.com/salman-h-khan/PL-ZSD_Release.

The people's game: evolutionary perspectives on the behavioural neuroscience of football fandom.

Association football (soccer) is the world's most popular sport. Transculturally, fans invest significant resources following their teams, suggesting underlying psychological universals with evolutionary origins. Although evolutionary science can help illuminate the ultimate causes of human behaviour, there have been limited modern evolutionary perspectives on football fandom. In this paper, we consider evolutionary perspectives on football fandom from a behavioural neuroscientific standpoint. We discuss how the appeal of football may arise through the low-scoring and highly variable outcomes of games; we relate this to the neuroscience of reward prediction errors and motivation. We highlight recent research on the psychobiological responses to ritual, including endorphin release, which may reduce anxiety and facilitate group bonding. We discuss the prosocial and anxiety-sublimating effects of the matchday ritual and argue that football may be a special case whereby ritual behaviour does have a small effect on the outcome of interest. We discuss the psychology of ingroup and outgroup effects of fandom and argue that, although resource scarcity can sometimes lead to aggression, that larger inter-group effects can be positive. We comment on the socioemotional developmental aspects of football fandom, and note how group identification may lead to displays of sacrifice. We finish with a discussion of whether, in the era of social prescribing, football could be seen as a psychiatrist's tool. We conclude with suggestions on how the positive aspects of football can be emphasised through evolutionary perspectives, and how future research on football fandom may inform evolutionary understanding of humans writ large.

Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding.

Previous research has demonstrated the potential of using pre-trained language models for decoding open vocabulary Electroencephalography (EEG) signals captured through a non-invasive Brain-Computer Interface (BCI). However, the impact of embedding EEG signals in the context of language models and the effect of subjectivity, remain unexplored, leading to uncertainty about the best approach to enhance decoding performance. Additionally, current evaluation metrics used to assess decoding effectiveness are predominantly syntactic and do not provide insights into the comprehensibility of the decoded output for human understanding. We present an end-to-end architecture for non-invasive brain recordings that brings modern representational learning approaches to neuroscience. Our proposal introduces the following innovations: 1) an end-to-end deep learning architecture for open vocabulary EEG decoding, incorporating a subject-dependent representation learning module for raw EEG encoding, a BART language model, and a GPT-4 sentence refinement module; 2) a more comprehensive sentence-level evaluation metric based on the BERTScore; 3) an ablation study that analyses the contributions of each module within our proposal, providing valuable insights for future research. We evaluate our approach on two publicly available datasets, ZuCo v1.0 and v2.0, comprising EEG recordings of 30 subjects engaged in natural reading tasks. Our model achieves a BLEU-1 score of 42.75%, a ROUGE-1-F of 33.28%, and a BERTScore-F of 53.86%, achieving an increment over the previous state-of-the-art by 1.40%, 2.59%, and 3.20%, respectively.

O peso da ecologia nativo-cêntrica africana na ecodessacralização

Abstract The ecological crisis is one of the environmental issues affecting the planet, and it is caused by eco-desacralization, a concept defined by environmental skepticism that relegates the divine identities of the environment. Doubts about the authenticity of environmental degradation or climate change and desert encroachment bear a strong anthropocentric view of nature. Skepticism may have deepened the human understanding of the natural world, but it has also destroyed the environment. In this study it is argued that eco-desacralization is environmental avarice, and steps must be taken to shift the strong anthropocentric perception of the natural world. To overcome this, we unearth African conservation ethics, deeply rooted in native-centric ecology. This concept reconstructs negative attitudes for the benefit of humanity. This study concludes that the resacralization of the environment by the teaching and learning of native-centric ecology in schools can mitigate the impacts of the ecological crisis.

“Dizer para viver”: trabalho escravo e a importância do relato para o resgate da confiança no mundo

Abstract The measures to contain and repress slave labor are based mainly on economic remedies. Indeed, the central good to be protected by the legal system in these situations is neglected: dignity. Given this scenario, it is through J. M. Bernstein's thesis (Torture and Dignity) on the possibility of a negative ethics based on the victim's report that this article focuses on investigating new ways of observing this crime, as well as identifying alternatives for repairing the crime. confidence in the world of these survivors. For this, as a general objective, we seek to reconstruct Bernstein's contribution to the understanding of the report as an alternative to repair the offense to dignity in cases of work analogous to slavery. As specific objectives, this work runs through three axes of appreciation: to analyze the applicability of Bernstein's concept of agency to the basic theoretical construction to the understanding of slave work; understand how the loss of confidence in the world is situated in the lives of survivors of slavery; and, finally, to establish how a theory based on the reports of these survivors can contribute to the rescue of these people's dignity. As results achieved, it was observed the need to insert the report and therapeutic listening as ways of recovering human dignity, as well as a more humane understanding of the procedure after the rescue based on this theory.

PoseScript: Linking 3D Human Poses and Natural Language.

Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained semantic information. Unfortunately, while human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. To address this issue, we have introduced the PoseScript dataset. This dataset pairs more than six thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. Additionally, to increase the size of the dataset to a scale that is compatible with data-hungry learning algorithms, we have proposed an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information, known as "posecodes", using a set of simple but generic rules on the 3D keypoints. These posecodes are then combined into higher level textual descriptions using syntactic rules. With automatic annotations, the amount of available data significantly scales up (100k), making it possible to effectively pretrain deep models for finetuning on human captions. To showcase the potential of annotated poses, we present three multi-modal learning tasks that utilize the PoseScript dataset. Firstly, we develop a pipeline that maps 3D poses and textual descriptions into a joint embedding space, allowing for cross-modal retrieval of relevant poses from large-scale datasets. Secondly, we establish a baseline for a text-conditioned model generating 3D poses. Thirdly, we present a learned process for generating pose descriptions. These applications demonstrate the versatility and usefulness of annotated poses in various tasks and pave the way for future research in the field. The dataset is available at https://europe.naverlabs.com/research/computer-vision/posescript/.

Residual gas noises in vacuum of optical interferometer for ground-based gravitational wave detection

Gravitational waves (GWs) are ripples in spacetime caused by most violent and energetic processes in the universe, such as the rapid motion of massive celestial bodies. The GWs carry energy when they propagate through the universe. The detection of GWs holds significance for advancing human understanding of the nature and driving scientific and technological progress. The continual upgrading and optimizing of GW detectors offer novel avenues for cosmic measurements. However, ground-based GW detectors based on a large interferometer necessitate addressing various noises which are harmful to the sensitivity of the GW detectors. Among these noises, the noise from residual gas in the light beam of the interferometer is a crucial factor to affect the sensitivity. Consequently, it is necessary to establish a vacuum system to shield the laser interferometer from the effects of gas flow. This paper focuses on China’s third-generation ground-based GWs detector, conducting theoretical analysis of the influence of residual gas noise on both a 20-meter arm-length prototype and a full-scale device with a 10-kilometer arm-length. In this paper, a theoretical model for the residual gas particles passing through the laser beam is established and the effect on the beam phase is analyzed. The theoretical simulations are performed to discover the relations between the residual gas noise and significant parameters such as gas pressure of the vacuum system, temperature, mass of residual gas particles, polarization rate of the residual gas, and the curvature radius of the test mass. The simulations indicate that when the residual gas pressure is below 2×10<sup>–6</sup> Pa, the GW detector can achieve the enough sensitivity, 10<sup>–24</sup> Hz<sup>–1/2</sup>, in a frequency range from 10 to 10<sup>3</sup> Hz. The findings of this research offer crucial theoretical insights for designing and constructing the vacuum systems in future third-generation GWs detector prototypes and full-scale devices.

Undercurrents of the River Duddon: Human Consciousness versus Nature

This essay examines the relationship between human consciousness and nature in William Wordsworth's "The River Duddon" sonnets. By adapting the method of literary analysis, Wordsworth’s portrayal of the River Duddon serves as a lens through which the boundaries of human understanding are examined, illustrating an unbridgeable gap between human consciousness and nature. After a brief introduction of research background, this essay elaborates on the River Duddon's unattainable origin as cloud’s child in deferral to both antiquity and industry, eco-Gothic wilderness in departure and return pattern, and nonperishable eternity compared with the transience of human. By deconstructing anthropocentric views prevalent in Romantic literature, the research emphasizes the urgent necessity of acknowledging nature's subjectivity and its independent existence beyond human perception. This essay not only revitalizes the poetic landscape of Wordsworth but also advocates for a non-human turn in literary criticism, promoting ecological awareness and respect for nature.

SEMAR (An Analysis of God's Form of Togetherness with Javanese Society)

This paper aims to find out by analytically describing Semar as a form of God's togetherness with Javanese society. In order to reach the desired goal, one question was asked: how is Semar a form of God's togetherness with the Javanese people? The method, or way to obtain Semar as a form of God's togetherness with Javanese society, is carried out using hermeneutics, semiotics, dialectics and referentics in accordance with existing forms, where Semar has words or language, symbols and behavior. Semar's approach as a form of togetherness between God and Javanese society is a makrifat philosophy, namely a human understanding of God. The result of this writing is that Semar is a form of God's togetherness with the Javanese people, his surah and sirah always chant: 1. God, 2. goodness, and 3. humans. Chanting God, in Surahs: 1). the gender is neither male nor female, 2). parents, 3). around him. In sirah: 1). namur kawula, 2). calm, 3). if the weapon farts. Chanting goodness, in the surah: 1). hands are accusing, 2). left hand grasps. In sirah: 1). namur kawula, 2). memayu hayuning Bawana, 3). patience, 4). wearing a pole. Chanting humans, a. in the surah: 1). with a white tip pointing upwards, 2). wearing a Kawung batik sarong, 3). wearing earrings, necklaces, bracelets and anklets. In sirah: 1). If you start saying: lae-lae mbegeg ugeg-ugeg sadilita heml-heml.

Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination.

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of seen primitives. Prior studies have attempted to either learn primitives individually (non-connected) or establish dependencies among them in the composition (fully-connected). In contrast, human comprehension of composition diverges from the aforementioned methods as humans possess the ability to make composition-aware adaptation for these primitives, instead of inferring them rigidly through the aforementioned methods. However, developing a comprehension of compositions akin to human cognition proves challenging within the confines of real space. This arises from the limitation of real-space-based methods, which often categorize attributes, objects, and compositions using three independent measures, without establishing a direct dynamic connection. To tackle this challenge, we expand the CZSL distance metric scheme to encompass complex spaces to unify the independent measures, and we establish an imaginary-connected embedding in complex space to model human understanding of attributes. To achieve this representation, we introduce an innovative visual bias-based attribute extraction module that selectively extracts attributes based on object prototypes. As a result, we are able to incorporate phase information in training and inference, serving as a metric for attribute-object dependencies while preserving the independent acquisition of primitives. We evaluate the effectiveness of our proposed approach on three benchmark datasets, illustrating its superiority compared to baseline methods. Our code is available at https://github.com/LanchJL/IMAX.

Modeling interpersonal perception in dyadic interactions: towards robot-assisted social mediation in the real world.

Social mediator robots have shown potential in facilitating human interactions by improving communication, fostering relationships, providing support, and promoting inclusivity. However, for these robots to effectively shape human interactions, they must understand the intricacies of interpersonal dynamics. This necessitates models of human understanding that capture interpersonal states and the relational affect arising from interactions. Traditional affect recognition methods, primarily focus on individual affect, and may fall short in capturing interpersonal dynamics crucial for social mediation. To address this gap, we propose a multimodal, multi-perspective model of relational affect, utilizing a conversational dataset collected in uncontrolled settings. Our model extracts features from audiovisual data to capture affective behaviors indicative of relational affect. By considering the interpersonal perspectives of both interactants, our model predicts relational affect, enabling real-time understanding of evolving interpersonal dynamics. We discuss our model's utility for social mediation applications and compare it with existing approaches, highlighting its advantages for real-world applicability. Despite the complexity of human interactions and subjective nature of affect ratings, our model demonstrates early capabilities to enable proactive intervention in negative interactions, enhancing neutral exchanges, and respecting positive dialogues. We discuss implications for real-world deployment and highlight the limitations of current work. Our work represents a step towards developing computational models of relational affect tailored for real-world social mediation, offering insights into effective mediation strategies for social mediator robots.

"Philosophy of Falsehood" A Research Review in the Light of Selected Qur'anic Interpretations

The "Philosophy of Falsehood" explores the intricate relationship between falsehood, perception, and truth within the context of selected Qur'anic interpretations. This research review delves into the philosophical underpinnings of falsehood as discussed in various Islamic commentaries, seeking to unravel the multifaceted dimensions of deception and its implications on human understanding. Drawing upon a diverse array of Qur'anic verses, the study critically examines the nuanced perspectives on falsehood and its consequences, considering the impact of distorted perceptions on moral and ethical frameworks. Moreover, the research highlights the dynamic interplay between falsehood and truth, shedding light on the transformative potential of discerning reality amidst misleading appearances. By synthesizing insights from classical and contemporary interpretations, the article aims to provide a comprehensive overview of the philosophical discourse surrounding falsehood in the Qur'an. This research review contributes to a deeper understanding of the Philosophy of Falsehood within Islamic theology, emphasizing the relevance of discernment and ethical considerations in navigating the complexities of truth and deception.

Suspicious Activity Detection

Abstract: Unpredictable activities is guessing a person's physical location or joint activity based on images or videos. The project will require the use of neural networks to detect human activities from CCTV images. Human behavior is one of the fundamental problems in computer vision and has been studied for over 15 years. This is important because the number of applications that can benefit from the search function is huge. For example, it is used in applications such as human prediction, video analysis, animal analysis and behavior understanding, language recognition, human relations-computer, and characters are less powerful. While low-cost depth sensors have limitations such as being limited to indoor use, their low-noise data and low depth make it difficult to estimate humans from depth images. Therefore, we plan to use neural networks to solve these problems. Analyzing human activity through image analysis is an active area of research in image processing and computer vision. Thanks to visual monitoring, human activities in public places such as bus stops, train stations, airports, banks, shopping malls, schools, parking lots and roads can be monitored in order to prevent attacks, theft, accidents, illegal parking, violence, fights and chain events. purse snatching etc. illegal and other activities. It is very difficult to monitor public places regularly, so there is a need for intelligent video surveillance that can monitor people's activities in real time and divide them into tasks, normal and abnormal activities; and can generate an alarm. Often research is done with images rather than video. Also, none of the published articles attempt to use CNNs to detect suspicious activity.

On a theory of self-structure–Part 1: the nature of narcissism

The self and its structure have been one of the most interesting research topics in psychology and psychoanalysis, which are related to the following important philosophical and scientific questions: "Where did 'I' come from?" and "How am 'I' formed and developed?" The contribution of psychoanalysis to human understanding of itself is indisputable. I believe that, in theory, the core purpose of psychoanalysis is to help humans answer these two fundamental questions. First, "Where did 'I' come from?" That is, the dynamic source of personality and the development of personality, which is the relationship between time and personality. Second, "How am 'I' formed and developed?" This has to do with the structural space of personality. Only by effectively answering these two questions can psychoanalysis further help human beings understand: "Why is 'my' mind 'sick'?" In this article, first I will try to understand the historical context of psychoanalytic theoretical development on narcissism by sorting out the relevant theories of predecessors. Second, I will try to formulate a new definition for narcissism, which becomes a new angle to understand self-structure. Furthermore, combining this new definition of narcissism, I try to use some clinical and non-clinical materials to explore the rationality and compatibility of this new definition.