Tootfinder

This https://arxiv.org/abs/2310.19545 has been replaced.
link: https://scholar.google.com/scholar?q=a

MENTOR: Human Perception-Guided Pretraining for Increased Generalization
Incorporating human perception into training of convolutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in the model architecture) and how to efficiently incorporate always-limited human perceptual data into training strategies of models. In this paper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr increased geneRalization), which addresses this question through two un…

Here's my latest Human Meme podcast episode: Exploding Head Syndrome: Unraveling the Mysteries of Sleep, Perception, and Human Experience - http://sites.libsyn.com/84966/exploding-head-syndrome-unraveling-the-my…

Emotional Tandem Robots: How Different Robot Behaviors Affect Human Perception While Controlling a Mobile Robot
Julian Kaduk, Friederike Weilbeer, Heiko Hamann
https://arxiv.org/abs/2403.03746

Emotional Tandem Robots: How Different Robot Behaviors Affect Human Perception While Controlling a Mobile Robot
In human-robot interaction (HRI), we study how humans interact with robots, but also the effects of robot behavior on human perception and well-being. Especially, the influence on humans by tandem robots with one human controlled and one autonomous robot or even semi-autonomous multi-robot systems is not yet fully understood. Here, we focus on a leader-follower scenario and study how emotionally expressive motion patterns of a small, mobile follower robot affect the perception of a human operat…

High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition by Spiking Neural Network
Youfang Han, Wei Zhao, Xiangjin Chen, Xin Meng
https://arxiv.org/abs/2403.06998

High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition by Spiking Neural Network
Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve …

Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations
Mahjabin Nahar, Haeseung Seo, Eun-Ju Lee, Aiping Xiong, Dongwon Lee
https://arxiv.org/abs/2404.03745

Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations
The widespread adoption and transformative effects of large language models (LLMs) have sparked concerns regarding their capacity to produce inaccurate and fictitious content, referred to as `hallucinations'. Given the potential risks associated with hallucinations, humans should be able to identify them. This research aims to understand the human perception of LLM hallucinations by systematically varying the degree of hallucination (genuine, minor hallucination, major hallucination) and examin…

Task-conditioned adaptation of visual features in multi-task policy learning
Pierre Marza, Laetitia Matignon, Olivier Simonin, Christian Wolf
https://arxiv.org/abs/2402.07739

Task-conditioned adaptation of visual features in multi-task policy learning
Successfully addressing a wide variety of tasks is a core ability of autonomous agents, which requires flexibly adapting the underlying decision-making strategies and, as we argue in this work, also adapting the underlying perception modules. An analogical argument would be the human visual system, which uses top-down signals to focus attention determined by the current task. Similarly, in this work, we adapt pre-trained large vision models conditioned on specific downstream tasks in the contex…

WixUp: A General Data Augmentation Framework for Wireless Perception in Tracking of Humans
Yin Li, Rajalakshmi Nandakumar
https://arxiv.org/abs/2405.04804 …

WixUp: A General Data Augmentation Framework for Wireless Perception in Tracking of Humans
Recent advancements in wireless perception technologies, including mmWave, WiFi, and acoustics, have expanded their application in human motion tracking and health monitoring. They are promising alternatives to traditional camera-based perception systems, thanks to their efficacy under diverse conditions or occlusions, and enhanced privacy. However, the integration of deep learning within this field introduces new challenges such as the need for extensive training data and poor model generaliza…

Task Presentation and Human Perception in Interactive Video Retrieval
Nina Willis, Abraham Bernstein, Luca Rossetto
https://arxiv.org/abs/2405.04279 https:…

Task Presentation and Human Perception in Interactive Video Retrieval
Interactive video retrieval is a cooperative process between humans and retrieval systems. Large-scale evaluation campaigns, however, often overlook human factors, such as the effects of perception, attention, and memory, when assessing media retrieval systems. Consequently, their setups fall short of emulating realistic retrieval scenarios. In this paper, we design novel task presentation modes based on concepts in media memorability, implement the pipelines necessary for processing target vid…

Interesting paper, for those of us calibrating the threat from Large Language models https://www.nature.com/articles/s41598-024-53303-w

The current state of artificial intelligence generative language models is more creative than humans on divergent thinking tasks - Scientific Reports
The emergence of publicly accessible artificial intelligence (AI) large language models such as ChatGPT has given rise to global conversations on the implications of AI capabilities. Emergent research on AI has challenged the assumption that creative potential is a uniquely human trait thus, there seems to be a disconnect between human perception versus what AI is objectively capable of creating. Here, we aimed to assess the creative potential of humans in comparison to AI. In the present study…

Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
Zhonghan Zhao, Ke Ma, Wenhao Chai, Xuan Wang, Kewei Chen, Dongxu Guo, Yanting Zhang, Hongwei Wang, Gaoang Wang
https://arxiv.org/abs/2404.04619

Do We Really Need a Complex Agent System? Distill Embodied Agent into a Single Model
With the power of large language models (LLMs), open-ended embodied agents can flexibly understand human instructions, generate interpretable guidance strategies, and output executable actions. Nowadays, Multi-modal Language Models~(MLMs) integrate multi-modal signals into LLMs, further bringing richer perception to entity agents and allowing embodied agents to perceive world-understanding tasks more delicately. However, existing works: 1) operate independently by agents, each containing multip…

Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision
Mat\'ias Mattamala, Jonas Frey, Piotr Libera, Nived Chebrolu, Georg Martius, Cesar Cadena, Marco Hutter, Maurice Fallon
https://arxiv.org/abs/2404.07110

Wild Visual Navigation: Fast Traversability Learning via Pre-Trained Models and Online Self-Supervision
Natural environments such as forests and grasslands are challenging for robotic navigation because of the false perception of rigid obstacles from high grass, twigs, or bushes. In this work, we present Wild Visual Navigation (WVN), an online self-supervised learning system for visual traversability estimation. The system is able to continuously adapt from a short human demonstration in the field, only using onboard sensing and computing. One of the key ideas to achieve this is the use of high-d…

"'We know that blinks cause better vision,' but whether or not that is put to use for human perception remains to be seen. That is something that the researchers could test in future experiments—such as whether manipulating an environment to be more visually challenging results in more blinks, [Rolfs] says."
Very interesting, but please PLEASE remember not to blink in the presence of a Weeping Angel.
#neuroscience
#vision
#DoctorWho
#WeepingAngel
https://www.thetransmitter.org/vision/visual-perception-improves-in-the-blink-of-an-eye/

Cymatics Cup: Shape-Changing Drinks by Leveraging Cymatics
Weijen Chen, Yang Yang, Kao-Hua Liu, Yun Suen Pai, Junichi Yamaoka, Kouta Minamizawa
https://arxiv.org/abs/2404.06027

Cymatics Cup: Shape-Changing Drinks by Leveraging Cymatics
To enhance the dining experience, prior studies in Human-Computer Interaction (HCI) and gastrophysics have demonstrated that modifying the static shape of solid foods can amplify taste perception. However, the exploration of dynamic shape-changing mechanisms in liquid foods remains largely untapped. In the present study, we employ cymatics, a scientific discipline focused on utilizing sound frequencies to generate patterns in liquids and particles to augment the drinking experience. Utilizing s…

This https://arxiv.org/abs/2303.07446 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Conditions on detecting tripartite entangled state in psychophysical experiments
This paper explores the sensitivity of the human visual system to quantum entangled light. We examine the possibility of human subjects perceiving multipartite entangled state through psychophysical experiments. Our focus begins with a bipartite entangled state to make a comparative study with the literature by taking into account additive noise for false positive on bipartite entanglement perception by humans. After that, we limit our similar investigation to a tripartite entangled state for s…

H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations
Zishen Wan, Che-Kai Liu, Mohamed Ibrahim, Hanchen Yang, Samuel Spetalnick, Tushar Krishna, Arijit Raychowdhury
https://arxiv.org/abs/2404.04173

H3DFact: Heterogeneous 3D Integrated CIM for Factorization with Holographic Perceptual Representations
Disentangling attributes of various sensory signals is central to human-like perception and reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems. An elegant approach to represent this intricate factorization is via high-dimensional holographic vectors drawing on brain-inspired vector symbolic architectures. However, holographic factorization involves iterative computation with high-dimensional matrix-vector multiplications and suffers from non-convergence probl…

This https://arxiv.org/abs/2312.08864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception. However, the practical deployment of such deep VQA models is often limited due to their high computational complexity and large memory requirements. To address this issue, we aim to significantly reduce the model size and runtime of one of the state-of-the-art deep VQA methods, RankDVQA, by e…

Here's my latest Human Meme podcast episode: Finding the Depths of Perception: The Futility of Precision in Understanding Life - http://sites.libsyn.com/84966/finding-the-depths-of-perception-the-futility-of-precision-in…

This https://arxiv.org/abs/2401.11632 has been replaced.
link: https://scholar.google.com/scholar?q=a

What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders
In the past decade, deep learning (DL) models have gained prominence for their exceptional accuracy on benchmark datasets in recommender systems (RecSys). However, their evaluation has primarily relied on offline metrics, overlooking direct user perception and experience. To address this gap, we conduct a human-centric evaluation case study of four leading DL-RecSys models in the movie domain. We test how different DL-RecSys models perform in personalized recommendation generation by conducting…

This https://arxiv.org/abs/2403.01766 has been replaced.
link: https://scholar.google.com/scholar?q=a

Improving Visual Perception of a Social Robot for Controlled and In-the-wild Human-robot Interaction
Social robots often rely on visual perception to understand their users and the environment. Recent advancements in data-driven approaches for computer vision have demonstrated great potentials for applying deep-learning models to enhance a social robot's visual perception. However, the high computational demands of deep-learning methods, as opposed to the more resource-efficient shallow-learning models, bring up important questions regarding their effects on real-world interaction and user exp…

"In this way “AI” is deeply dehumanizing: Making the spaces and opportunities for people to grow and be human smaller and smaller. Applying a straitjacket of past mediocrity to our minds and spirits.
And that is what is being booed: The salespeople of mediocrity who’ve made it their mission to speak lies from power. The lie that only tech can and will save us. The lie that a bit of statistics and colonial, mostly white, mostly western data is gonna create a brilliant future. The l…

Smashing Frames
While the crowd at sxsw2024 booing a sizzle reel of people either promising the beauty of the future "AI" will bring or claiming it to be "without alternative" is funny and went viral for all the right reasons, this event speaks to a deeper shift in perception. #ai #genAI #luddism

Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance
Huakun Shen, Boyue Caroline Hu, Krzysztof Czarnecki, Lina Marsso, Marsha Chechik
https://arxiv.org/abs/2402.19401

Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance
While Neural Networks (NNs) have surpassed human accuracy in image classification on ImageNet, they often lack robustness against image corruption, i.e., corruption robustness. Yet such robustness is seemingly effortless for human perception. In this paper, we propose visually-continuous corruption robustness (VCR) -- an extension of corruption robustness to allow assessing it over the wide and continuous range of changes that correspond to the human perceptive quality (i.e., from the original …

Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception
Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong
https://arxiv.org/abs/2403.14896 …

Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception
The pervasive spread of misinformation and disinformation in social media underscores the critical importance of detecting media bias. While robust Large Language Models (LLMs) have emerged as foundational tools for bias prediction, concerns about inherent biases within these models persist. In this work, we investigate the presence and nature of bias within LLMs and its consequential impact on media bias detection. Departing from conventional approaches that focus solely on bias detection in m…

Physics-informed generative neural networks for RF propagation prediction with application to indoor body perception
Federica Fieramosca, Vittorio Rampa, Michele D'Amico, Stefano Savazzi
https://arxiv.org/abs/2405.02131

Physics-informed generative neural networks for RF propagation prediction with application to indoor body perception
Electromagnetic (EM) body models designed to predict Radio-Frequency (RF) propagation are time-consuming methods which prevent their adoption in strict real-time computational imaging problems, such as human body localization and sensing. Physics-informed Generative Neural Network (GNN) models have been recently proposed to reproduce EM effects, namely to simulate or reconstruct missing data or samples by incorporating relevant EM principles and constraints. The paper discusses a Variational Au…

Mixed or Misperceived Reality?
Aven Le Zhou, Lei Xi, Kang Zhang
https://arxiv.org/abs/2405.02338 https://arxiv.org/pdf/2405.02338

Mixed or Misperceived Reality?
"Surrealism Me" delves into Vilém Flusser's critique of media as mediators that often distort human perception of reality through an interactive virtual-embodying MR experience. It examines the obfuscating nature of media and reveals the constructed nature of media-projected realities, prompting a reevaluation of media's role and influence on our perception.

Correlation of Fr\'echet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant
Modan Tailleur, Junwon Lee, Mathieu Lagrange, Keunwoo Choi, Laurie M. Heller, Keisuke Imoto, Yuki Okamoto
https://arxiv.org/abs/2403.17508

Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant
This paper explores whether considering alternative domain-specific embeddings to calculate the Fréchet Audio Distance (FAD) metric can help the FAD to correlate better with perceptual ratings of environmental sounds. We used embeddings from VGGish, PANNs, MS-CLAP, L-CLAP, and MERT, which are tailored for either music or environmental sound evaluation. The FAD scores were calculated for sounds from the DCASE 2023 Task 7 dataset. Using perceptual data from the same task, we find that PANNs-WGM-…

The web is such a mess. I was trying to look up something about refresh rates and the human eye / perception. All sorts of shadey sounding domains at the top of the results. Pick the least shadey one and half way into the article is this incredible … infographic? I don’t even know what to call it. #ai is going great. How deeply sad.

Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts
Han Cui, Alfredo De Goyeneche, Efrat Shimron, Boyuan Ma, Michael Lustig
https://arxiv.org/abs/2405.02208

Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts
Image Quality Assessment (IQA) is essential in various Computer Vision tasks such as image deblurring and super-resolution. However, most IQA methods require reference images, which are not always available. While there are some reference-free IQA metrics, they have limitations in simulating human perception and discerning subtle image quality variations. We hypothesize that the JPEG quality factor is representatives of image quality measurement, and a well-trained neural network can learn to a…

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks
Ivan S. Maksymov
https://arxiv.org/abs/2404.10554

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks
On the surface, behavioural science and physics seem to be two disparate fields of research. However, a closer examination of problems solved by them reveals that they are uniquely related to one another. Exemplified by the theories of quantum mind, cognition and decision-making, this unique relationship serves as the topic of this chapter. Surveying the current academic journal papers and scholarly monographs, we present an alternative vision of the role of quantum mechanics in the modern stud…

A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro, Giuseppe Averta
https://arxiv.org/abs/2403.03037

A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks, to synergistically exploit them when learning n…

Impact of whole-body vibrations on electrovibration perception varies with target stimulus duration
Jan D. A. Vuik, Daan M. Pool, Y. Vardar
https://arxiv.org/abs/2404.18972 https://arxiv.org/pdf/2404.18972
arXiv:2404.18972v1 Announce Type: new
Abstract: This study explores the impact of whole-body vibrations induced by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens. Electrovibration holds promise as a technology for providing tactile feedback on future touchscreens, addressing usability challenges in vehicle cockpits. However, its performance under dynamic conditions, such as during whole-body vibrations induced by turbulence, still needs to be explored. We measured the absolute detection thresholds of 15 human participants for short- and long-duration electrovibration stimuli displayed on a touchscreen, both in the absence and presence of two types of turbulence motion generated by a motion simulator. Concurrently, we measured participants' applied contact force and finger scan speeds. Significantly higher (38%) absolute detection thresholds were observed for short electrovibration stimuli than for long stimuli. Finger scan speeds in the direction of turbulence, applied forces, and force fluctuation rates increased during whole-body vibrations due to biodynamic feedthrough. As a result, turbulence also significantly increased the perception thresholds, but only for short-duration electrovibration stimuli. The results reveal that whole-body vibrations can impede the perception of short-duration electrovibration stimuli, due to involuntary finger movements and increased normal force fluctuations. Our findings offer valuable insights for the future design of touchscreens with tactile feedback in vehicle cockpits.

This https://arxiv.org/abs/2302.14595 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

MateRobot: Material Recognition in Wearable Robotics for People with Visual Impairments
People with Visual Impairments (PVI) typically recognize objects through haptic perception. Knowing objects and materials before touching is desired by the target users but under-explored in the field of human-centered robotics. To fill this gap, in this work, a wearable vision-based robotic system, MateRobot, is established for PVI to recognize materials and object categories beforehand. To address the computational constraints of mobile platforms, we propose a lightweight yet accurate model M…

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
Shuo Liu, Kaining Ying, Hao Zhang, Yue Yang, Yuqi Lin, Tianle Zhang, Chuanhao Li, Yu Qiao, Ping Luo, Wenqi Shao, Kaipeng Zhang
https://arxiv.org/abs/2403.20194

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on a distinct capability, mirroring the cognitive progression from basic perception to logical reasoni…

Correlation of Fr\'echet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant
Modan Tailleur, Junwon Lee, Mathieu Lagrange, Keunwoo Choi, Laurie M. Heller, Keisuke Imoto, Yuki Okamoto
https://arxiv.org/abs/2403.17508

Correlation of Fréchet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependant
This paper explores whether considering alternative domain-specific embeddings to calculate the Fréchet Audio Distance (FAD) metric can help the FAD to correlate better with perceptual ratings of environmental sounds. We used embeddings from VGGish, PANNs, MS-CLAP, L-CLAP, and MERT, which are tailored for either music or environmental sound evaluation. The FAD scores were calculated for sounds from the DCASE 2023 Task 7 dataset. Using perceptual data from the same task, we find that PANNs-WGM-…

Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers
Till Aust, Eirini Balta, Argiro Vatakis, Heiko Hamann
https://arxiv.org/abs/2404.15213

Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers
One indicator of well-being can be the person's subjective time perception. In our project ChronoPilot, we aim to develop a device that modulates human subjective time perception. In this study, we present a method to automatically assess the subjective time perception of air traffic controllers, a group often faced with demanding conditions, using their physiological data and eleven state-of-the-art machine learning classifiers. The physiological data consist of photoplethysmogram, electroderm…

The web is such a mess. I was trying to look up something about refresh rates and the human eye / perception. All sorts of shadey sounding domains at the top of the results. Pick the least shadey one and half way into the article is this incredible … infographic? I don’t even know what to call it. #ai is going great. How deeply sad.

Transfer Learning from Whisper for Microscopic Intelligibility Prediction
Paul Best, Santiago Cuervo, Ricard Marxer
https://arxiv.org/abs/2404.01737 https:…

Transfer Learning from Whisper for Microscopic Intelligibility Prediction
Macroscopic intelligibility models predict the expected human word-error-rate for a given speech-in-noise stimulus. In contrast, microscopic intelligibility models aim to make fine-grained predictions about listeners' perception, e.g. predicting phonetic or lexical responses. State-of-the-art macroscopic models use transfer learning from large scale deep learning models for speech processing, whereas such methods have rarely been used for microscopic modeling. In this paper, we study the use of…

Predicting Perceived Gloss: Do Weak Labels Suffice?
Julia Guerrero-Viu, J. Daniel Subias, Ana Serrano, Katherine R. Storrs, Roland W. Fleming, Belen Masia, Diego Gutierrez
https://arxiv.org/abs/2403.17672

Predicting Perceived Gloss: Do Weak Labels Suffice?
Estimating perceptual attributes of materials directly from images is a challenging task due to their complex, not fully-understood interactions with external factors, such as geometry and lighting. Supervised deep learning models have recently been shown to outperform traditional approaches, but rely on large datasets of human-annotated images for accurate perception predictions. Obtaining reliable annotations is a costly endeavor, aggravated by the limited ability of these models to generalis…

Multi-View Active Sensing for Human-Robot Interaction via Hierarchically Connected Tree
Yuanjiong Ying, Xian Huang, Wei Dong
https://arxiv.org/abs/2403.12538

Multi-View Active Sensing for Human-Robot Interaction via Hierarchically Connected Tree
Comprehensive perception of human beings is the prerequisite to ensure the safety of human-robot interaction. Currently, prevailing visual sensing approach typically involves a single static camera, resulting in a restricted and occluded field of view. In our work, we develop an active vision system using multiple cameras to dynamically capture multi-source RGB-D data. An integrated human sensing strategy based on a hierarchically connected tree structure is proposed to fuse localized visual in…

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks
Ivan S. Maksymov
https://arxiv.org/abs/2404.10554

Quantum Mechanics of Human Perception, Behaviour and Decision-Making: A Do-It-Yourself Model Kit for Modelling Optical Illusions and Opinion Formation in Social Networks
On the surface, behavioural science and physics seem to be two disparate fields of research. However, a closer examination of problems solved by them reveals that they are uniquely related to one another. Exemplified by the theories of quantum mind, cognition and decision-making, this unique relationship serves as the topic of this chapter. Surveying the current academic journal papers and scholarly monographs, we present an alternative vision of the role of quantum mechanics in the modern stud…

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
Abanti Bhattacharjya, Md Manowarul Islam, Md Ashraf Uddin, Md. Alamin Talukder, AKM Azad, Sunil Aryal, Bikash Kumar Paul, Wahia Tasnim, Muhammad Ali Abdulllah Almoyad, Mohammad Ali Moni
https://

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for cur…

An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders
Youhua Li, Hanwen Du, Yongxin Ni, Yuanqi He, Junchen Fu, Xiangyan Liu, Qi Guo
https://arxiv.org/abs/2403.17372

An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders
Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi-modal signals, like text and images, has inspired researchers to delve into constructing SR from multi-modal information without using IDs. However, the complexity of multi-modal learning manifests in diverse feature extractors, fusion methods, and pre-trained models. Conseque…

Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
Jake Varley, Sumeet Singh, Deepali Jain, Krzysztof Choromanski, Andy Zeng, Somnath Basu Roy Chowdhury, Avinava Dubey, Vikas Sindhwani
https://arxiv.org/abs/2404.03570

Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon tasks over a large workspace. Our system is modular: it deploys state of the art Large Language Models for task planning,Vision-Language models for semantic perception, and Point Cloud transformers for grasping. With semantic and physical safety in mind, these modules are interfaced with a real-time trajectory optimiz…

Molecule Generation and Optimization for Efficient Fragrance Creation
Bruno C. L. Rodrigues, Vinicius V. Santana, Sandris Murins, Idelfonso B. R. Nogueira
https://arxiv.org/abs/2402.12134

Molecule Generation and Optimization for Efficient Fragrance Creation
This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired…

User Perception of Partially Automated Driving Systems: A Meaningful Human Control Perspective on the Perception among Tesla Users
Lucas Elbert Suryana, Sina Nordhoff, Simeon C. Calvert, Arkady Zgonnikov, Bart van Arem
https://arxiv.org/abs/2402.08080

User Perception of Partially Automated Driving Systems: A Meaningful Human Control Perspective on the Perception among Tesla Users
The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how …

Efficient and accurate neural field reconstruction using resistive memory
Yifei Yu, Shaocong Wang, Woyu Zhang, Xinyuan Zhang, Xiuzhe Wu, Yangu He, Jichang Yang, Yue Zhang, Ning Lin, Bo Wang, Xi Chen, Songqi Wang, Xumeng Zhang, Xiaojuan Qi, Zhongrui Wang, Dashan Shang, Qi Liu, Kwang-Ting Cheng, Ming Liu
https://arxiv.org/abs/2404…

Efficient and accurate neural field reconstruction using resistive memory
Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Replicating this capability in AI finds wide applications in medical imaging, AR/VR, and embodied AI, where input data is often sparse and computing resources are limited. However, traditional signal reconstruction methods on digital computers face both software and hardware challenges. On the software front, difficulti…

Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
Jake Varley, Sumeet Singh, Deepali Jain, Krzysztof Choromanski, Andy Zeng, Somnath Basu Roy Chowdhury, Avinava Dubey, Vikas Sindhwani
https://arxiv.org/abs/2404.03570

Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon tasks over a large workspace. Our system is modular: it deploys state of the art Large Language Models for task planning,Vision-Language models for semantic perception, and Point Cloud transformers for grasping. With semantic and physical safety in mind, these modules are interfaced with a real-time trajectory optimiz…

This https://arxiv.org/abs/2403.20194 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on a distinct capability, mirroring the cognitive progression from basic perception to logical reasoni…

This https://arxiv.org/abs/2309.12001 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Exploring Human's Gender Perception and Bias toward Non-Humanoid Robots
As non-humanoid robots increasingly permeate various sectors, understanding their design implications for human acceptance becomes paramount. Despite their ubiquity, studies on how to improve human interaction are sparse. Our investigation, conducted through two surveys, addresses this gap. The first survey emphasizes non-humanoid robots and human perceptions about gender attributions, suggesting that both design and perceived gender influence acceptance. Survey 2 investigates the effects of va…

Just Like Me: The Role of Opinions and Personal Experiences in The Perception of Explanations in Subjective Decision-Making
Sharon Ferguson, Paula Akemi Aoyagui, Young-Ho Kim, Anastasia Kuzminykh
https://arxiv.org/abs/2404.12558

Just Like Me: The Role of Opinions and Personal Experiences in The Perception of Explanations in Subjective Decision-Making
As large language models (LLMs) advance to produce human-like arguments in some contexts, the number of settings applicable for human-AI collaboration broadens. Specifically, we focus on subjective decision-making, where a decision is contextual, open to interpretation, and based on one's beliefs and values. In such cases, having multiple arguments and perspectives might be particularly useful for the decision-maker. Using subtle sexism online as an understudied application of subjective decisi…

This https://arxiv.org/abs/2403.20194 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMM_…

ConvBench: A Multi-Turn Conversation Evaluation Benchmark with Hierarchical Capability for Large Vision-Language Models
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on a distinct capability, mirroring the cognitive progression from basic perception to logical reasoni…

Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning
Zhaoxun Ju, Chao Yang, Hongbo Wang, Yu Qiao, Fuchun Sun
https://arxiv.org/abs/2402.17511

Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning
Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions. The ability to compose long-horizon tasks based on unconstrained language instructions necessitates the acquisition of a diverse set of general-purpose skills. However, acquiring inherent primitive skills in a coupled and long-horizon environment without external rewards or human supervision presents significant challenges. In this paper, w…

Molecule Generation and Optimization for Efficient Fragrance Creation
Bruno C. L. Rodrigues, Vinicius V. Santana, Sandris Murins, Idelfonso B. R. Nogueira
https://arxiv.org/abs/2402.12134

Molecule Generation and Optimization for Efficient Fragrance Creation
This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired…

The Duet of Representations and How Explanations Exacerbate It
Charles Wan, Rodrigo Belo, Leid Zejnilovi\'c, Susana Lavado
https://arxiv.org/abs/2402.08379

The Duet of Representations and How Explanations Exacerbate It
An algorithm effects a causal representation of relations between features and labels in the human's perception. Such a representation might conflict with the human's prior belief. Explanations can direct the human's attention to the conflicting feature and away from other relevant features. This leads to causal overattribution and may adversely affect the human's information processing. In a field experiment we implemented an XGBoost-trained model as a decision-making aid for counselors at a p…

This https://arxiv.org/abs/2310.12877 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Perceptual Assessment and Optimization of High Dynamic Range Image Rendering
High dynamic range (HDR) rendering has the ability to faithfully reproduce the wide luminance ranges in natural scenes, but how to accurately assess the rendering quality is relatively underexplored. Existing quality models are mostly designed for low dynamic range (LDR) images, and do not align well with human perception of HDR image quality. To fill this gap, we propose a family of HDR quality metrics, in which the key step is employing a simple inverse display model to decompose an HDR image…

This https://arxiv.org/abs/2309.08508 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

MOSAIC: Learning Unified Multi-Sensory Object Property Representations for Robot Learning via Interactive Perception
A holistic understanding of object properties across diverse sensory modalities (e.g., visual, audio, and haptic) is essential for tasks ranging from object categorization to complex manipulation. Drawing inspiration from cognitive science studies that emphasize the significance of multi-sensory integration in human perception, we introduce MOSAIC (Multimodal Object property learning with Self-Attention and Interactive Comprehension), a novel framework designed to facilitate the learning of uni…

This https://arxiv.org/abs/2403.06998 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition by Spiking Neural Network
Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve …

This https://arxiv.org/abs/2309.10787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models often focus on a limited set of tasks, and generalization abilities of learned representations are unclear. To this end, we propose the AV-SUPERB benchmark that enables general-purpose evaluation of unimodal audio/visual and bimodal fusion representations on 7 datasets covering 5 audio-visual tasks in speech and audio p…

This https://arxiv.org/abs/2403.06998 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

High-speed Low-consumption sEMG-based Transient-state micro-Gesture Recognition by Spiking Neural Network
Gesture recognition on wearable devices is extensively applied in human-computer interaction. Electromyography (EMG) has been used in many gesture recognition systems for its rapid perception of muscle signals. However, analyzing EMG signals on devices, like smart wristbands, usually needs inference models to have high performances, such as low inference latency, low power consumption, and low memory occupation. Therefore, this paper proposes an improved spiking neural network (SNN) to achieve …

To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
Daniel Tanneberg, Felix Ocker, Stephan Hasler, Joerg Deigmoeller, Anna Belardinelli, Chao Wang, Heiko Wersing, Bernhard Sendhoff, Michael Gienger
https://arxiv.org/abs/2403.12533

To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain si…

This https://arxiv.org/abs/2309.10787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models often focus on a limited set of tasks, and generalization abilities of learned representations are unclear. To this end, we propose the AV-SUPERB benchmark that enables general-purpose evaluation of unimodal audio/visual and bimodal fusion representations on 7 datasets covering 5 audio-visual tasks in speech and audio p…

Characterizing and modeling harms from interactions with design patterns in AI interfaces
Lujain Ibrahim, Luc Rocher, Ana Valdivia
https://arxiv.org/abs/2404.11370

Characterizing and modeling harms from interactions with design patterns in AI interfaces
The proliferation of applications using artificial intelligence (AI) systems has led to a growing number of users interacting with these systems through sophisticated interfaces. Human-computer interaction research has long shown that interfaces shape both user behavior and user perception of technical capabilities and risks. Yet, practitioners and researchers evaluating the social and ethical risks of AI systems tend to overlook the impact of anthropomorphic, deceptive, and immersive interface…

This https://arxiv.org/abs/2404.10849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving
The current autonomous stack is well modularized and consists of perception, decision making and control in a handcrafted framework. With the advances in artificial intelligence (AI) and computing resources, researchers have been pushing the development of end-to-end AI for autonomous driving, at least in problems of small searching space such as in highway scenarios, and more and more photorealistic simulation will be critical for efficient learning. In this research, we propose a novel game-b…

A Survey on Integration of Large Language Models with Intelligent Robots
Yeseung Kim, Dohyun Kim, Jieun Choi, Jisang Park, Nayoung Oh, Daehyung Park
https://arxiv.org/abs/2404.09228

A Survey on Integration of Large Language Models with Intelligent Robots
In recent years, the integration of large language models (LLMs) has revolutionized the field of robotics, enabling robots to communicate, understand, and reason with human-like proficiency. This paper explores the multifaceted impact of LLMs on robotics, addressing key challenges and opportunities for leveraging these models across various domains. By categorizing and analyzing LLM applications within core robotics elements -- communication, perception, planning, and control -- we aim to provi…