Search

Autism is a developmental disorder that manifests in early childhood and persists throughout life, profoundly affecting social behavior and hindering the acquisition of learning and social skills in those diagnosed. As technological advancements progress, an increasing array of technologies is being utilized to support the education of students with Autism Spectrum Disorder (ASD), aiming to improve their educational outcomes and social capabilities. Numerous studies on autism intervention have highlighted the effectiveness of social robots in behavioral treatments. However, research on the integration of social robots into classroom settings for children with autism remains sparse. This paper describes the design and implementation of a group experiment in a collective classroom setting mediated by the NAO robot. The experiment involved special education teachers and the NAO robot collaboratively conducting classroom activities, aiming to foster a dynamic learning environment through interactions among teachers, the robot, and students. Conducted in a special education school, this experiment served as a foundational study in anticipation of extended robot-assisted classroom sessions. Data from the experiment suggest that ASD students in classrooms equipped with the NAO robot exhibited notably better performance compared to those in regular classrooms. The humanoid features and body language of the NAO robot captivated the students' attention, particularly during talent shows and command tasks, where students demonstrated heightened engagement and a decrease in stereotypical repetitive behaviors and irrelevant minor movements commonly observed in regular settings. Our preliminary findings indicate that the NAO robot significantly enhances focus and classroom engagement among students with ASD, potentially improving educational performance and fostering better social behaviors.

Differentially Private Stochastic Gradient Descent (DP-SGD) and its variants have been proposed to ensure rigorous privacy for fine-tuning large-scale pre-trained language models. However, they rely heavily on the Gaussian mechanism, which may overly perturb the gradients and degrade the accuracy, especially in stronger privacy regimes (e.g., the privacy budget $\epsilon < 3$). To address such limitations, we propose a novel Language Model-based Optimal Differential Privacy (LMO-DP) mechanism, which takes the first step to enable the tight composition of accurately fine-tuning (large) language models with a sub-optimal DP mechanism, even in strong privacy regimes (e.g., $0.1\leq \epsilon<3$). Furthermore, we propose a novel offline optimal noise search method to efficiently derive the sub-optimal DP that significantly reduces the noise magnitude. For instance, fine-tuning RoBERTa-large (with 300M parameters) on the SST-2 dataset can achieve an accuracy of 92.20% (given $\epsilon=0.3$, $\delta=10^{-10}$) by drastically outperforming the Gaussian mechanism (e.g., $\sim 50\%$ for small $\epsilon$ and $\delta$). We also draw similar findings on the text generation tasks on GPT-2. Finally, to our best knowledge, LMO-DP is also the first solution to accurately fine-tune Llama-2 with strong differential privacy guarantees. The code will be released soon and available upon request., Comment: 18 pages, 15 figures

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management.

With the development and widespread application of digital image processing technology, image splicing has become a common method of image manipulation, raising numerous security and legal issues. This paper introduces a new splicing image detection algorithm based on the statistical characteristics of natural images, aimed at improving the accuracy and efficiency of splicing image detection. By analyzing the limitations of traditional methods, we have developed a detection framework that integrates advanced statistical analysis techniques and machine learning methods. The algorithm has been validated using multiple public datasets, showing high accuracy in detecting spliced edges and locating tampered areas, as well as good robustness. Additionally, we explore the potential applications and challenges faced by the algorithm in real-world scenarios. This research not only provides an effective technological means for the field of image tampering detection but also offers new ideas and methods for future related research.

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects..

The electromagnetic form factors $G_{\rm E}$ and $G_{\rm M}$ of the proton and neutron in the timelike region are extracted in a study of the processes $e^+ e^-\to \bar{p}p$ and $e^+ e^-\to \bar{n}n$. The reaction amplitude is evaluated within the distorted wave Born approximation, with the interaction of the antinucleon-nucleon ($\bar{N}N$) pair taken into account. The latter is constructed within $SU(3)$ chiral effective field theory up to the next-to-leading order. An excellent description of the $e^+ e^-\to \bar{N}N$ data in the energy region from the $\bar{N}N$ threshold up to center-of-mass energies $E_{\rm cm}=2.2$~GeV is achieved. Results for the electromagnetic form factors $G_{\rm E}, G_{\rm M}$, $G_{\rm E}/G_{\rm M}$, and the subtracted effective form factors, $G_{\rm osc}$, are provided. These can be helpful for further studies of the properties of the nucleons., Comment: 37 pages, 10 figures

With the rapid advancement of artificial intelligence technology, AI-enabled image recognition has emerged as a potent tool for addressing challenges in traditional environmental monitoring. This study focuses on the detection of floating objects in river and lake environments, exploring an innovative approach based on deep learning. By intricately analyzing the technical pathways for detecting static and dynamic features and considering the characteristics of river and lake debris, a comprehensive image acquisition and processing workflow has been developed. The study highlights the application and performance comparison of three mainstream deep learning models -SSD, Faster-RCNN, and YOLOv5- in debris identification. Additionally, a detection system for floating objects has been designed and implemented, encompassing both hardware platform construction and software framework development. Through rigorous experimental validation, the proposed system has demonstrated its ability to significantly enhance the accuracy and efficiency of debris detection, thus offering a new technological avenue for water quality monitoring in rivers and lakes

In this paper, the reaction of electron-positron annihilation into $\Lambda_c^+\bar{\Lambda}_c^-$ is investigated. The $\Lambda_c^+\bar{\Lambda}_c^-$ scattering amplitudes are obtained by solving the Lippmann-Schwinger equation. The contact, annihilation, and two pseudoscalar-exchange potentials are taken into account in the spirit of the chiral effective field theory. The amplitudes of $e^+e^-\to \Lambda_c^+\bar{\Lambda}_c^-$ are constructed by the distorted wave Born approximation method, with the final state interactions of the $\Lambda_c^+\bar{\Lambda}_c^-$ re-scattering implemented. By fitting to the experimental data, the unknown couplings are fixed, and high-quality solutions are obtained. With these amplitudes, the individual electromagnetic form factors in the timelike region, $G_E^{\Lambda_c}$, $G_M^{\Lambda_c}$, and their ratio, $G_E^{\Lambda_c}/G_M^{\Lambda_c}$, are extracted. Both modulus and phases are predicted. These individual electromagnetic form factors reveal new insights into the properties of the $\Lambda_c$. The separated contributions of the Born term, contact, annihilation, as well as the two pseudoscalar exchange potentials to the electromagnetic form factors are isolated. It is found that the Born term dominates the whole energy region. The contact term plays a crucial role in the enhancement near the threshold, and the annihilation term is essential in generating the fluctuation of the electromagnetic form factors., Comment: 9 pages, 7 figures

This paper introduces a new multi-modal model based on the Transformer architecture and tensor product fusion strategy, combining BERT's text vectors and ViT's image vectors to classify students' psychological conditions, with an accuracy of 93.65%. The purpose of the study is to accurately analyze the mental health status of students from various data sources. This paper discusses modal fusion methods, including early, late and intermediate fusion, to overcome the challenges of integrating multi-modal information. Ablation studies compare the performance of different models and fusion techniques, showing that the proposed model outperforms existing methods such as CLIP and ViLBERT in terms of accuracy and inference speed. Conclusions indicate that while this model has significant advantages in emotion recognition, its potential to incorporate other data modalities provides areas for future research.

Innate values describe agents' intrinsic motivations, which reflect their inherent interests and preferences to pursue goals and drive them to develop diverse skills satisfying their various needs. The essence of reinforcement learning (RL) is learning from interaction based on reward-driven (such as utilities) behaviors, much like natural agents. It is an excellent model to describe the innate-values-driven (IV) behaviors of AI agents. Especially in multi-agent systems (MAS), building the awareness of AI agents to balance the group utilities and system costs and satisfy group members' needs in their cooperation is a crucial problem for individuals learning to support their community and integrate human society in the long term. This paper proposes a hierarchical compound intrinsic value reinforcement learning model -- innate-values-driven reinforcement learning termed IVRL to describe the complex behaviors of multi-agent interaction in their cooperation. We implement the IVRL architecture in the StarCraft Multi-Agent Challenge (SMAC) environment and compare the cooperative performance within three characteristics of innate value agents (Coward, Neutral, and Reckless) through three benchmark multi-agent RL algorithms: QMIX, IQL, and QTRAN. The results demonstrate that by organizing individual various needs rationally, the group can achieve better performance with lower costs effectively., Comment: This paper was accepted by the 38th AAAI 2024 workshop: "Cooperative Multi-Agent Systems Decision-Making and Learning: From Individual Needs to Swarm Intelligence"

The optical manipulation of nanoparticles on superlubricity surfaces is investigated. The research revealed that, due to the near-zero static friction and extremely low dynamic friction at superlubricity interfaces, the maximum intensity for controlling the optical field can be less than 100 W/cm$^2$, which is nine orders of magnitude lower than controlling nanoparticles on traditional interfaces. The controlled nanoparticle radius can be as small as 5 nm, which is more than one order of magnitude smaller than nanoparticles controlled through traditional optical manipulation. Manipulation can be achieved in sub-microsecond to microsecond timescales. Furthermore, the manipulation takes place on solid surfaces and in non-liquid environments, with minimal impact from Brownian motion. By appropriately increasing dynamic friction, controlling light intensity, or reducing pressure, the effects of Brownian motion can be eliminated, allowing for the construction of microstructures with a size as small as 1/75 of the wavelength of light. This enables the control of super-resolution optical microstructures. The optical super-resolution manipulation of nanoparticles on superlubricity surfaces will find important applications in fields such as nanofabrication, photolithography, optical metasurface, and biochemical analysis.

In recent years, edge computing has served as a paradigm that enables many future technologies like AI, Robotics, IoT, and high-speed wireless sensor networks (like 5G) by connecting cloud computing facilities and services to the end users. Especially in medical and healthcare applications, it provides remote patient monitoring and increases voluminous multimedia. From the robotics angle, robot-assisted therapy (RAT) is an active-assistive robotic technology in rehabilitation robotics, attracting many researchers to study and benefit people with disability like autism spectrum disorder (ASD) children. However, the main challenge of RAT is that the model capable of detecting the affective states of ASD people exists and can recall individual preferences. Moreover, involving expert diagnosis and recommendations to guide robots in updating the therapy approach to adapt to different statuses and scenarios is a crucial part of the ASD therapy process. This paper proposes the architecture of edge cognitive computing by combining human experts and assisted robots collaborating in the same framework to help ASD patients with long-term support. By integrating the real-time computing and analysis of a new cognitive robotic model for ASD therapy, the proposed architecture can achieve a seamless remote diagnosis, round-the-clock symptom monitoring, emergency warning, therapy alteration, and advanced assistance., Comment: This paper was accepted by the 38th AAAI 2024 workshop: "Cooperative Multi-Agent Systems Decision-Making and Learning: From Individual Needs to Swarm Intelligence"

Using effective Lagrangians constrained by the heavy quark spin symmetry and chiral symmetry, for the light quarks, we analyze the $D^0 D^0\pi^+$, $\bar{D}^0D^0\pi^0$ and $D^0\bar{D}^{*0}$ invariant mass spectra. Performing a simultaneous analysis of the doubly charmed and charm-anti-charm states gives further insights into the nature of the $T^+_{cc}$ and $\chi^0_{c1}(3872)$, exotic hadrons. We find that both states lie below the respective $DD^*$/$D\bar{D}^*$ thresholds. Also, the contributions of the triangle and box diagrams are negligible., Comment: 5 pages, 5 figures

As a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field, the utility is used to evaluate the level of individual needs, preferences, and interests. Especially for decision-making and learning in multi-agent/robot systems (MAS/MRS), a suitable utility model can guide agents in choosing reasonable strategies to achieve their current needs and learning to cooperate and organize their behaviors, optimizing the system's utility, building stable and reliable relationships, and guaranteeing each group member's sustainable development, similar to the human society. Although these systems' complex, large-scale, and long-term behaviors are strongly determined by the fundamental characteristics of the underlying relationships, there has been less discussion on the theoretical aspects of mechanisms and the fields of applications in Robotics and AI. This paper introduces a utility-orient needs paradigm to describe and evaluate inter and outer relationships among agents' interactions. Then, we survey existing literature in relevant fields to support it and propose several promising research directions along with some open problems deemed necessary for further investigations., Comment: Preprint

Chemists have been pursuing the general mathematical laws to explain and predict molecular properties for a long time. However, most of the traditional quantitative structure-activity relationship (QSAR) models have limited application domains, e.g., they tend to have poor generalization performance when applied to molecules with parent structures different from those of the trained molecules. This paper attempts to develop a new QSAR method that is theoretically possible to predict various properties of molecules with diverse structures. The proposed deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR) methods consist of three essentials: (1) A large number of molecule entities with activity data as training objects and responses; (2) three-dimensional electron cloud density (ECD) or related field data by the accurate density functional theory methods as input descriptors; (3) a deep learning model that is sufficiently flexible and powerful to learn the large data described above. DECAR and DFAR are used to distinguish 977 sweet and 1965 non-sweet molecules (with 6-fold data augmentation) and the classification performance is demonstrated to be significantly better than the traditional least squares support vector machine (LS-SVM) models using traditional descriptors. DECAR and DFAR would provide a possible way to establish a widely applicable, cumulative, and shareable artificial intelligence-driven QSAR system. They are likely to promote the development of an interactive platform to collect and share the accurate ECD and field data of millions of molecules with annotated activities. With enough input data, we envision the appearance of several deep networks trained for various molecular activities. Finally, we could anticipate a single DECAR or DFAR network to learn and infer various properties of interest for chemical molecules., Comment: 21 pages, 4 figures, 1 table

Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance., Comment: This paper is accepted by the ACM Symposium on Applied Computing (SAC) 2023 Technical Track on Intelligent Robotics and Multi-Agent Systems (IRMAS)

Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy decomposition approach based on the Bayesian chain rule to separate an intricate policy into several simple sub-policies and organize their relationships as Bayesian strategy networks (BSN). We integrate this approach into the state-of-the-art DRL method -- soft actor-critic (SAC) and build the corresponding Bayesian soft actor-critic (BSAC) model by organizing several sub-policies as a joint policy. We compare the proposed BSAC method with the SAC and other state-of-the-art approaches such as TD3, DDPG, and PPO on the standard continuous control benchmarks -- Hopper-v2, Walker2d-v2, and Humanoid-v2 -- in MuJoCo with the OpenAI Gym environment. The results demonstrate that the promising potential of the BSAC method significantly improves training efficiency., Comment: Accepted by the Elsevier Science 14th International Conference on Ambient Systems, Networks and Technologies (ANT 2023). arXiv admin note: substantial text overlap with arXiv:2208.06033, arXiv:2302.13132

The needs describe the necessities for a system to survive and evolve, which arouses an agent to action toward a goal, giving purpose and direction to behavior. Based on Maslow hierarchy of needs, an agent needs to satisfy a certain amount of needs at the current level as a condition to arise at the next stage -- upgrade and evolution. Especially, Deep Reinforcement Learning (DAL) can help AI agents (like robots) organize and optimize their behaviors and strategies to develop diverse Strategies based on their current state and needs (expected utilities or rewards). This paper introduces the new hierarchical needs-driven Learning systems based on DAL and investigates the implementation in the single-robot with a novel approach termed Bayesian Soft Actor-Critic (BSAC). Then, we extend this topic to the Multi-Agent systems (MAS), discussing the potential research fields and directions., Comment: Accepted by the 37th AAAI Conference on Artificial Intelligence and Robotics Bridge Program. arXiv admin note: text overlap with arXiv:2208.06033