Reviewerly/exHarmony huggingface dataset:
A Motion Planning Method for an Autonomous Underwater Vehicle with Kinematic Constraints

Motion planning is essential for the autonomous underwater vehicle (AUV) to achieve complete autonomous navigation in unknown environments. The two-layer motion planning framework, which combines path searching and trajectory parameterization, cannot deal with the non-zero initial state of AUV and is even unable to plan a dynamically feasible trajectory in an environment that needs reversing. In this paper, we combine the AUV motion state with the grid map and propose a path searching algorithm based on kinematics to ensure the correctness and dynamic feasibility of the path. By exploiting the advantages of the B-spline, the trajectory optimization strategy with a safety guarantee algorithm is proposed to ensure the safety of the final trajectory. Comparing with the other methods, the general applicability of this framework was demonstrated using a simulation system.
YOLOv7-Based Land and Underwater Target Detection and Recognition

There is a growing demand for marine resource development. Bionic amphibious robots can replace humans to conduct land and underwater exploration, which has important research significance. Deep learning has developed rapidly in recent years, and many kinds of target detection algorithms have emerged. We select one two-stage target detection algorithm Faster R-CNN, three single-stage target detection algorithms SSD, Centernet, and YOLOv7. We use each of these four algorithms to train the VOC2007 dataset in a deep learning environment. After the training is completed, these four models are evaluated and predicted separately. We find an algorithm that is most suitable for the amphibious robot applicationYOLOv7. Finally, we use the YOLOv7 model to detect the underwater dataset, and the results prove that the model is promising for detecting small underwater targets.
BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation

Scale-wise evaluation of object detectors is important for real-world applications. However, existing metrics are either coarse or not sufficiently reliable. In this paper, we propose novel scale-wise metrics that strike a balance between fineness and reliability, using a filter bank consisting of triangular and trapezoidal band-pass filters. We conduct experiments with two methods on two datasets and show that the proposed metrics can highlight the differences between the methods and between the datasets. Code is available at https://github.com/shinya7y/UniverseNet.
Imbalanced sentiment classification of online reviews based on SimBERT

The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods.
E-SAWM: A Semantic Analysis-Based ODF Watermarking Algorithm for Edge Cloud Scenarios

With the growing demand for data sharing file formats in financial applications driven by open banking, the use of the OFD (open fixed-layout document) format has become widespread. However, ensuring data security, traceability, and accountability poses significant challenges. To address these concerns, we propose E-SAWM, a dynamic watermarking service framework designed for edge cloud scenarios. This framework incorporates dynamic watermark information at the edge, allowing for precise tracking of data leakage throughout the data-sharing process. By utilizing semantic analysis, E-SAWM generates highly realistic pseudostatements that exploit the structural characteristics of documents within OFD files. These pseudostatements are strategically distributed to embed redundant bits into the structural documents, ensuring that the watermark remains resistant to removal or complete destruction. Experimental results demonstrate that our algorithm has a minimal impact on the original file size, with the watermarked text occupying less than 15%, indicating a high capacity for carrying the watermark. Additionally, compared to existing explicit watermarking schemes for OFD files based on annotation structure, our proposed watermarking scheme is suitable for the technical requirements of complex dynamic watermarking in edge cloud scenario deployment. It effectively overcomes vulnerabilities associated with easy deletion and tampering, providing high concealment and robustness.
Generating Chinese Event Extraction Method Based on ChatGPT and Prompt Learning

Regarding the scarcity of annotated data for existing event extraction tasks and the insufficient semantic mining of event extraction models in the Chinese domain, this paper proposes a generative joint event extraction model to improve existing models in two aspects. Firstly, it utilizes the content generation capability of ChatGPT to generate annotated data corpora for event extraction tasks and trains the model using supervised learning methods adapted to downstream tasks. Secondly, explicit entity markers and event knowledge are added to the text to construct generative input templates, enhancing the performance of event extraction. To validate the performance of this model, experiments are conducted on DuEE1.0 and Title2Event public datasets, and the results show that both data enhancement and prompt learning based on ChatGPT effectively improve the performance of the event extraction model, and the F1 values of the events extracted by the CPEE model proposed in this paper reach 85.1% and 59.9% on the two datasets, respectively, which are comparable to the existing models’ values of 1.3% and 10%, respectively; moreover, on the Title2Event dataset, the performance of different models on the event extraction task can be gradually improved as the data size of the annotated corpus of event extraction generated using ChatGPT increases.
Debiasing SHAP scores in random forests

Abstract Black box machine learning models are currently being used for high-stakes decision making in various parts of society such as healthcare and criminal justice. While tree-based ensemble methods such as random forests typically outperform deep learning models on tabular data sets, their built-in variable importance algorithms are known to be strongly biased toward high-entropy features. It was recently shown that the increasingly popular SHAP (SHapley Additive exPlanations) values suffer from a similar bias. We propose debiased or "shrunk" SHAP scores based on sample splitting which additionally enable the detection of overfitting issues at the feature level.
Pursuit of hidden rules behind the irregularity of nano capillary lithography by hybrid intelligence

Abstract Nature finds a way to leverage nanotextures to achieve desired functions. Recent advances in nanotechnologies endow fascinating multi-functionalities to nanotextures by modulating the nanopixel’s height. But nanoscale height control is a daunting task involving chemical and/or physical processes. As a facile, cost-effective, and potentially scalable remedy, the nanoscale capillary force lithography (CFL) receives notable attention. The key enabler is optical pre-modification of photopolymer’s characteristics via ultraviolet (UV) exposure. Still, the underlying physics of the nanoscale CFL is not well understood, and unexplained phenomena such as the “forbidden gap” in the nano capillary rise (unreachable height) abound. Due to the lack of large data, small length scales, and the absence of first principles, direct adoptions of machine learning or analytical approaches have been difficult. This paper proposes a hybrid intelligence approach in which both artificial and human intelligence coherently work together to unravel the hidden rules with small data. Our results show promising performance in identifying transparent, physics-retained rules of air diffusivity, dynamic viscosity, and surface tension, which collectively appear to explain the forbidden gap in the nanoscale CFL. This paper promotes synergistic collaborations of humans and AI for advancing nanotechnology and beyond.
Allow patents on AI-generated inventions — for the good of science

The current global mishmash of rules on whether innovations made using artificial intelligence are patentable impedes AI-rich fields such as drug discovery. The current global mishmash of rules on whether innovations made using artificial intelligence are patentable impedes AI-rich fields such as drug discovery.
Non-Invasive Intraoral Stand-Alone Tongue Control System Based on RSIC-V Edge Computing

The intelligent tongue control system is of great significance for assisting the independent life of patients with a limb disability. In order to more accurately control the assisted living equipment of incompetent patients and solve the power-loss problem of the intelligent tongue control system, this research designs a non-invasive pressure sensor array for tongue touch signal detection in the oral cavity and proposes a tongue control system based on RSIC-V edge computing. The system converts the tongue touch pressure data into specific control instructions on the edge of the RSIC-V chip and transmits them to the receiver, thus reducing the transmission of data. This study takes control of the wheelchair motor as the test object. In the experiment, the speed response time test, the center click task, and the power consumption experiment are carried out, whose results show that the adaptive fuzzy PID control algorithm has good robustness in the system; when the DC motor with a given speed of 750 r/min reaches the steady state, its rise time is 0.108 s and the adjustment time is 0.59 s. The dynamic power consumption of the non-intrusive intraoral stand-alone tongue control system proposed in this paper is found to be 3.745 MW, which is 11.5% lower than the total power consumption of the sTD system.
Axial Correlation Revivals and Number Factorization with Structured Random Waves

Number factorization is instrumental in cybersecurity and machine-learning applications, for example. Although quantum protocols have enabled remarkable breakthroughs here, their limitations have spurred classical alternatives undergirded by the physics of superposition of coherent waves, but these are extremely difficult to implement under realistic noisy environments. The authors propose and demonstrate with random optical waves a factoring protocol that is free from this shortcoming. Their theory also exposes a fundamental link between statistical optics and number theory. This protocol may trigger advances in factoring with acoustic and matter waves as well.
Text line extraction strategy for palm leaf manuscripts

Text line segmentation is an important step in the historical document image analysis pipeline to supply useful information for recognition, keyword spotting and indexing. Many handcrafted-based and learning-based approaches have been developed to cope with text line extraction challenges. In this work we present a hybrid technique which combines a convolutional-based denoising task and heuristic Seam Carving framework. We propose the following changes to the original Seam Carving: (1) We applied adaptive slice to anticipate miss-extraction on a short text during medial seam computation. (2) We applied a triple smoothing to find the best local maxima of the smoothed horizontal projection profile which represents candidate medial seams. (3) We utilized five post-processing steps aimed at reconstructing a more precise medial seam. Three different palm leaf data sets: old Sundanese, old Balinese, and old Khmer, have been used to compare Convolutional Seam Carving (CSC) to several baseline methods, including the original Seam Carving. Experimental results show that the proposed method outperforms other current handcrafted-based baselines on all three palm leaf manuscript (PLM) data sets. On the old Sundanese data sets, CSC can produce a significant improvement of the performance rate compared to all other baseline approaches, and it also enhance the measurement on old Balinese and old Khmer datasets. In the ablation study, we discovered that a foreground extraction step is not only able to reduce noises and color degradation but also provide better separation of text region. Following that, an adaptive slice and triple smoothing approach contribute to solve the text length variation problem. Finally, post-processing steps were effective in connecting discontinuous medial seam. The code has been published in https://github.com/erickpaulus/text-line-segmentation.
Parameterized deep reinforcement learning with hybrid action space for energy efficient data center networks

To ensure the delivery of high-performance and reliable services, data center networks (DCNs) are often over-provisioned for peak workload and traffic bursts. However, in real-world data centers, network traffic seldom reaches peak capacity of the network, resulting in significant energy waste. Traditional energy conservation approaches either suffer from high computational complexity and low solution quality, or their strategies cannot be dynamically adjusted to accommodate changes in data center network traffic. Deep reinforcement learning (DRL) provides an effective way to deal with these issues. However, most of the existing DRL-based schemes only consider either a continuous action space or a discrete action space, which greatly restricts the optimality of decisions. To solve these problems, this paper proposes a novel DRL-based DCN energy optimization framework, named SmartDCN. Specifically, SmartDCN consists of a traffic prediction module (TPM) and an energy optimization module (EOM). TPM incorporates an improved LSTM model JANET with an attention mechanism providing a high prediction accuracy, while EOM integrates our newly proposed parameterized DRL algorithm, named PAS-DQN, combining with the discrete-continuous hybrid action space. PAS-DQN implements a two-level control mechanism for the network, using TPM to predict future traffic in the data center as input. It is devoted to dynamically aggregating current traffic and makes tradeoffs between energy efficiency, performance, and robustness to optimize the network’s power consumption by dynamically calculating the minimum required network subset and turning off the non-involved network devices to achieve power savings. Experimental results show that SmartDCN significantly outperforms the existing state-of-the-art schemes in terms of energy savings under various network conditions.
Stable clinical risk prediction against distribution shift in electronic health records

The availability of large-scale electronic health record datasets has led to the development of artificial intelligence (AI) methods for clinical risk prediction that help improve patient care. However, existing studies have shown that AI models suffer from severe performance decay after several years of deployment, which might be caused by various temporal dataset shifts. When the shift occurs, we have access to large-scale pre-shift data and small-scale post-shift data that are not enough to train new models in the post-shift environment. In this study, we propose a new method to address the issue. We reweight patients from the pre-shift environment to mitigate the distribution shift between pre- and post-shift environments. Moreover, we adopt a Kullback-Leibler divergence loss to force the models to learn similar patient representations in pre- and post-shift environments. Our experimental results show that our model efficiently mitigates temporal shifts, improving prediction performance.
Ancient Textual Restoration Using Deep Neural Networks: A Literature Review

Ancient texts are important because they connect us with ancient civilizations, through which gain cultural, religious, and scientific knowledge, but these texts are often damaged over time due to environmental factors and the method of preservation that affects them, so the task of restoring these texts was one of the necessary tasks for specialists and researchers in ancient sciences. This paper provides an overview of the current state of research in this field. The review would examine the different approaches and methods used, the successes of these methods, and the challenges that need to be addressed in the future. The literature review would also cover the various types of datasets of ancient texts that have been restored using deep neural networks, such as manuscripts, inscriptions, and scrolls, as the different languages and the involved scripts.
Learning Detailed Radiance Manifolds for High-Fidelity and 3D-Consistent Portrait Synthesis from Monocular Image

A key challenge for novel view synthesis of monocular portrait images is 3D consistency under continuous pose variations. Most existing methods rely on 2D generative models which often leads to obvious 3D inconsistency artifacts. We present a 3D-consistent novel view synthesis approach for monocular portrait images based on a recent proposed 3D-aware GAN, namely Generative Radiance Manifolds (GRAM) [13], which has shown strong 3D consistency at multiview image generation of virtual subjects via the radiance manifolds representation. However, simply learning an encoder to map a real image into the latent space of GRAM can only reconstruct coarse radiance manifolds without faithful fine details, while improving the reconstruction fidelity via instance-specific optimization is time-consuming. We introduce a novel detail manifolds reconstructor to learn 3D-consistent fine details on the radiance manifolds from monocular images, and combine them with the coarse radiance manifolds for high-fidelity reconstruction. The 3D priors derived from the coarse radiance manifolds are used to regulate the learned details to ensure reasonable synthesized results at novel views. Trained on in-the-wild 2D images, our method achieves high-fidelity and 3D-consistent portrait synthesis largely outperforming the prior art. Project page: https://yudeng.github.io/GRAMInverter/
Coaching a Teachable Student

We propose a novel knowledge distillation framework for effectively teaching a sensorimotor student agent to drive from the supervision of a privileged teacher agent. Current distillation for sensorimotor agents methods tend to result in suboptimal learned driving behavior by the student, which we hypothesize is due to inherent differences between the input, modeling capacity, and optimization processes of the two agents. We develop a novel distillation scheme that can address these limitations and close the gap between the sensorimotor agent and its privileged teacher. Our key insight is to design a student which learns to align their input features with the teacher's privileged Bird's Eye View (BEV) space. The student then can benefit from direct supervision by the teacher over the internal representation learning. To scaffold the difficult sensorimotor learning task, the student model is optimized via a student-paced coaching mechanism with various auxiliary supervision. We further propose a high-capacity imitation learned privileged agent that surpasses prior privileged agents in CARLA and ensures the student learns safe driving behavior. Our proposed sensorimotor agent results in a robust image-based behavior cloning agent in CARLA, improving over current models by over 20.6% in driving score without requiring LiDAR, historical observations, ensemble of models, on-policy data aggregation or reinforcement learning.
Blowing in the Wind: CycleNet for Human Cinemagraphs from Still Images

Cinemagraphs are short looping videos created by adding subtle motions to a static image. This kind of media is popular and engaging. However, automatic generation of cinemagraphs is an underexplored area and current solutions require tedious low-level manual authoring by artists. In this paper, we present an automatic method that allows generating human cinemagraphs from single RGB images. We investigate the problem in the context of dressed humans under the wind. At the core of our method is a novel cyclic neural network that produces looping cinemagraphs for the target loop duration. To circumvent the problem of collecting real data, we demonstrate that it is possible, by working in the image normal space, to learn garment motion dynamics on synthetic data and generalize to real data. We evaluate our method on both synthetic and real data and demonstrate that it is possible to create compelling and plausible cinemagraphs from single RGB images.
SunStage: Portrait Reconstruction and Relighting Using the Sun as a Light Stage

A light stage uses a series of calibrated cameras and lights to capture a subject's facial appearance under varying illumination and viewpoint. This captured information is crucial for facial reconstruction and relighting. Unfortunately, light stages are often inaccessible: they are expensive and require significant technical expertise for construction and operation. In this paper, we present SunStage: a lightweight alternative to a light stage that captures comparable data using only a smartphone camera and the sun. Our method only requires the user to capture a selfie video outdoors, rotating in place, and uses the varying angles between the sun and the face as guidance in joint reconstruction of facial geometry, reflectance, camera pose, and lighting parameters. Despite the in-the-wild un-calibrated setting, our approach is able to reconstruct detailed facial appearance and geometry, enabling compelling effects such as relighting, novel view synthesis, and reflectance editing.
Discriminating Known from Unknown Objects via Structure-Enhanced Recurrent Variational AutoEncoder

Discriminating known from unknown objects is an important essential ability for human beings. To simulate this ability, a task of unsupervised out-of-distribution object detection (OOD-OD) is proposed to detect the objects that are never-seen-before during model training, which is beneficial for promoting the safe deployment of object detectors. Due to lacking unknown data for supervision, for this task, the main challenge lies in how to leverage the known in-distribution (ID) data to improve the detector's discrimination ability. In this paper, we first propose a method of Structure-Enhanced Recurrent Variational AutoEncoder (SR-VAE), which mainly consists of two dedicated recurrent VAE branches. Specifically, to boost the performance of object localization, we explore utilizing the classical Laplacian of Gaussian (LoG) operator to enhance the structure information in the extracted low-level features. Meanwhile, we design a VAE branch that recurrently generates the augmentation of the classification features to strengthen the discrimination ability of the object classifier. Finally, to alleviate the impact of lacking unknown data, another cycle-consistent conditional VAE branch is proposed to synthesize virtual OOD features that deviate from the distribution of ID features, which improves the capability of distinguishing OOD objects. In the experiments, our method is evaluated on OOD-OD, open-vocabulary detection, and incremental object detection. The significant performance gains over baselines show the superiorities of our method. The code will be released at https://github.com/AmingWu/SR-VAE.