Reviewerly/exHarmony huggingface dataset:
Research on Target Encirclement Strategy of Amphibious Spherical Robots in 3D Underwater Environments

The cooperative trapping of multiple amphibious robots in a 3D underwater environment is a comprehensive research topic that mainly includes the generation of dynamic alliances for trapping and path planning. To address this problem, this paper first proposes a “trap point occupancy strategy” to realize the generation of dynamic alliances among multiple robots during the trapping process. Secondly, based on this, an improved gray wolf algorithm is proposed in this paper to implement robot target guidance and adaptive obstacle avoidance functions, used for the path planning of the robot’s arrival at the trapping point during the trapping process, thus completing the entire trapping task. Then, this paper conducts simulation experiments in a 3D underwater environment with static obstacles, and the results confirm that the proposed cooperative trapping algorithm has high efficiency and robustness. Finally, the innovations and all the work in the article are summarized, and prospects are put forward.
Learning Transformations to Reduce the Geometric Shift in Object Detection

The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains.
Segmentation of Marine Targets Instance Based on Improved Mask R-CNN

Instance segmentation is the classification and segmentation of targets in images by pixel-level masks. The method for instance segmentation, Mask R-CNN, performs well in the task of segmenting targets such as pedestrians and vehicles. However, it still has the problem of low accuracy for small targets and marine targets segmentation with complex contours, and information loss occurs in the process of feature extraction and fusion of images. In this paper, an improved Mask R-CNN algorithm is proposed. Feature pyramid network (FPN) is enhanced, and the attention mechanism is added between the backbone and the feature pyramid network to solve the problem of the loss of low-level semantic information in the process of feature fusion. The research involves constructing a marine targets instance segmentation dataset, learning on the dataset, and comparing the improved algorithm with the original algorithm by coco index. The results show that the improved algorithm has obvious improvement in accuracy, has strong robustness.
Safe and Efficient Motion Planning for Material Transportation Robots considering Intention Prediction of Obstacles

To effectively address labor shortages in construction sites, material transportation robots, a type of construction robots, need to be both safe and efficient. In environments where robots and workers share the same space, motion planning algorithms that consider only the static environment may operate in a way that is inefficient and dangerous. In this work, motion planning is carried out with reference to the prediction of the obstacle movement intentions, thus avoiding unnecessary obstacle avoidance behaviors. We implemented our approach on a robot in a simulated environment and demonstrated that it can successfully navigate efficiently and safely in dynamic and complex construction environments.
Intelligent interface of the machine based on knowledge graph

The machine intelligent interface is a user interface that involves some aspect of artificial intelligence or computational intelligence to support the device sense, communication, and processing data for autonomy. This article proposed the implementation of an intelligent interface for the machine. It is based on the knowledge graph (KG) technology, integrating AI algorithms, and service behaviors to users, which we argue are three critical factors for an advanced interface. As for implementation, firstly, we introduced the construction of the machine KG and the definition of user request modes. Defining user request modes could restrict interact scope into domain area and in case access beyond the domain. Then, we combine semantic parsing and information retrieval to process multiple user request modes. Our implemented intelligent interface has the advantages of ensuring accuracy and interpretability oriented to core definition requests and flexibility oriented to random complex requests.
An Efficient RRT*-based Path Planning for Mobile Robot with Fast Convergence Rate

In recent decades, sampling-based path planning methods have garnered increasing attention from researchers due to their excellent performance, particularly in nonlinear complex environments and high-dimensional spaces. As a classical sampling-based algorithm, RRT*, claims to achieve an optimal solution, but it has a slow convergence rate or takes an infinite amount of time to do so. To address this limitation, this study proposes a novel algorithm called D-RRT*. D-RRT* employs the concept of dynamic region sampling to enhance the efficiency of path generation by leveraging the tree environment as heuristic information. In contrast to the conventional uniform sampling approach, dynamic region sampling strategy increases the density of sampled points in the forward region near the target point, which significantly improves the search efficiency for feasible paths. Secondly, once the initial path is found, the algorithm extracts the directly accessible nodes of the path as key nodes. The key nodes are positioned to facilitate the discovery of the optimal path. By optimizing these key nodes, not only does the algorithm improve memory utilization, but it also reduces the number of iterations required to converge towards the optimal solution. Finally, the state-of-the art RRT-based algorithm, such as RRT*, Q-RRT* and F-RRT*, are used for comparison in two representative maps including real environments. Statistical analyses are conducted to demonstrate the robustness of the proposed algorithm. Simulation results indicated that the proposed algorithm yields better solutions for solving path planning problems in terms of algorithm convergence speed and path length.
Sparse and Private Distributed Matrix Multiplication with Straggler Tolerance

This paper considers the problem of outsourcing the multiplication of two private and sparse matrices to untrusted workers. Secret sharing schemes can be used to tolerate stragglers and guarantee information-theoretic privacy of the matrices. However, traditional secret sharing schemes destroy all sparsity in the offloaded computational tasks. Since exploiting the sparse nature of matrices was shown to speed up the multiplication process, preserving the sparsity of the input matrices in the computational tasks sent to the workers is desirable. It was recently shown that sparsity can be guaranteed at the expense of a weaker privacy guarantee. Sparse secret sharing schemes with only two output shares were constructed. In this work, we construct sparse secret sharing schemes that generalize Shamir’s secret sharing schemes for a fixed threshold t = 2 and an arbitrarily large number of shares. We design our schemes to provide the strongest privacy guarantee given a desired sparsity of the shares under some mild assumptions. We show that increasing the number of shares, i.e., increasing straggler tolerance, incurs a degradation of the privacy guarantee. However, this degradation is negligible when the number of shares is comparably small to the cardinality of the input alphabet.
Achievable Resolution Limits of Noisy Adaptive 20 Questions Estimation for Multiple Targets

We study the problem of adaptive search for multiple targets using the framework of 20 questions estimation under the query-dependent noise channel. Specifically, we propose an adaptive query strategy that combines the ideas of non-adaptive search for multiple targets using random coding by Zhou, Bai and Hero (TIT, 2022) and adaptive search for one target using variable-length coding with feedback by Zhou and Hero (ISIT, 2021). Our main contribution is the derivation of the non-asymptotic and second-order asymptotic performance of our adaptive query procedure. Furthermore, we discuss the benefit of adaptivity, analytically and numerically, by comparing our results with the non-adaptive counterparts.
An Early-Termination Method for the Welch-Berlekamp Algorithm

This paper presents an early-termination method for the Welch-Berlekamp algorithm. Specifically, if e ≤ t (where e is the number of errors and t is the error correction capability), the Welch–Berlekamp algorithm can be terminated at latest at the completion of the (t + e)-th iteration. Based on the early-termination mechanism, a new variant of the Welch–Berlekamp algorithm called eFDMA is presented, and a systolic architecture is designed for the eFDMA algorithm. This provides an efficient implementation for the key equation solver for a new class of Reed–Solomon codes recently proposed by Lin et al. [9].
A Transfer Learning Application on the Reliability of Psychological Drugs' Comments

As digitalization and the Internet stay emerging concepts by gaining popularity, the accuracy of personal reviews/opinions will be a critical issue. This circumstance also particularly applies to patients taking psychological drugs, where accurate information is crucial for other patients and medical professionals. In this study, we analyze drug reviews from drugs.com to determine the effectiveness of reviews for psychological drugs. Our dataset includes over 200,000 drug reviews, which we labeled as positive, negative, or neutral according to their rating scores. We apply machine learning (ML) models, including Logistic Regression, Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) algorithms, to predict the sentiment class of each review. Our results demonstrate an F1-Weighted score of 85.3% for the LSTM model. However, by applying the transfer learning technique, we further improved the F1 score (nearly 3% increase) obtained by the LSTM model. Our findings proved that there is no contextual difference between the comments made by the patients suffering from psychological or other diseases.
RONO: Robust Discriminative Learning with Noisy Labels for 2D-3D Cross-Modal Retrieval

Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval becomes popular with a burst of 2D and 3D data. However, this problem is challenging given the heterogeneous structure and semantic discrepancies. Moreover, imperfect annotations are ubiquitous given the ambiguous 2D and 3D content, thus inevitably producing noisy labels to degrade the learning performance. To tackle the problem, this paper proposes a robust 2D-3D retrieval framework (RONO) to robustly learn from noisy multimodal data. Specifically, one novel Robust Discriminative Center Learning mechanism (RDCL) is proposed in RONO to adaptively distinguish clean and noisy samples for respectively providing them with positive and negative optimization directions, thus mitigating the negative impact of noisy labels. Besides, we present a Shared Space Consistency Learning mechanism (SSCL) to capture the intrinsic information inside the noisy data by minimizing the cross-modal and semantic discrepancy between common space and label space simultaneously. Comprehensive mathematical analyses are given to theoretically prove the noise tolerance of the proposed method. Furthermore, we conduct extensive experiments on four 3D-model multimodal datasets to verify the effectiveness of our method by comparing it with 15 state-of-the-art methods. Code is available at https://github.com/penghu-cs/RONO.
A Sentiment Analysis Approach for Identifying Users’ Security and Privacy Perception of Metaverse In Twitter

Metaverse technology has gained the attention of researchers and professionals due to its numerous potential applications. It serves as a virtual world that connects people socially and offers them an immersive experience. However, we lack knowledge about how users perceive this technology, particularly regarding their optimism or pessimism towards it. Of specific concern is users’ feelings about their security and privacy in the virtual world. To address this gap, we analyzed over 300,000 tweets about Metaverse, including more than 1,000 tweets that mentioned security and privacy keywords. The study aimed to understand how people perceive Metaverse security and privacy by applying sentiment analysis to the collected tweets. The results indicate that approximately 59% of users had a positive view of their security in the Metaverse, while around 66% had a positive view of their privacy. However, the study revealed that people were uncertain about these two factors and raised legal and ethical concerns about the technology. The study’s findings highlight research and practical implications for the future development of the Metaverse.
Generic Decoding of Restricted Errors

Several recently proposed code-based cryptosystems base their security on a slightly generalized version of the classical (syndrome) decoding problem. Namely, in the so-called restricted (syndrome) decoding problem, the error values stem from a restricted set. In this paper, we propose new generic decoders, that are inspired by subset sum solvers and tailored to the new setting. The introduced algorithms take the restricted structure of the error set into account in order to utilize the representation technique efficiently. This leads to a considerable decrease in the security levels of recently published code-based cryptosystems.
HerA Scheme: Secure Distributed Matrix Multiplication via Hermitian Codes

We consider the problem of secure distributed matrix multiplication (SDMM), where a user has two matrices and wishes to compute their product with the help of N honest but curious servers under the security constraint that any information about either A or B is not leaked to any server. This paper presents a new scheme that considers the inner product partition for matrices A and B. Our central technique relies on encoding matrices A and B in a Hermitian code and its dual code, respectively. We present the Hermitian Algebraic (HerA) scheme, which employs Hermitian codes and characterizes the partitioning and security capacities given entries of matrices belonging to a finite field with q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> elements. We showcase that this scheme performs the secure distributed matrix multiplication in a significantly smaller finite field and expands security allowances compared to the existing results in the literature.
Smart City Traffic Monitoring:YOLOv7 Transfer Learning Approach for Real-Time Vehicle Detection

Real-time vehicle detection is a critical component of traffic monitoring, with significant implications for smart city applications. Accurate and efficient detection of vehicles can improve traffic flow and reduce congestion. This paper presents a real-time vehicle detection system based on Deep Learning (DL) techniques, using the YOLOv7 object detection framework. The system was trained on a novel dataset with a diverse range of vehicles, including different sizes, orientations, and lighting conditions, to improve object detection accuracy. To reduce the required training time and computing resources, Transfer learning is utilized to fine-tune two variants of YOLOv7, YOLOv7-x and YOLOv7-tiny. The results of the experiment revealed that the YOLOv7-x achieved a mean Average Precision (mAP) rate of 96.7%, while the YOLOv7-tiny achieved 89.3%. Furthermore, when the models were fed with a video stream, the YOLOv7-tiny achieved 57 Frames Per Second (FPS), whereas the YOLOv7-x achieved 27 FPS. As a result, the YOLOv7-tiny is more suitable for resource-constrained devices, such as those frequently utilized in IoT applications, due to its smaller model size, lower computational requirements, and higher FPS rate with acceptable accuracy. On the other hand, if higher accuracy is the priority, the YOLOv7-x model should be considered. The proposed frameworks help to improve the effectiveness of traffic management systems, leading to more efficient and sustainable transportation in smart cities.
Design and Implementation of Proactive Multi-Type Context-Aware Recommender System for Patients Suffering Diabetes

This paper presents a design and implementation of a multi-type, proactive and context-aware recommender system that supports a healthy lifestyle for patients who suffer Diabetes mellitus. The main features of this recommender system includes the consideration of users’ context while generating recommendations, its ability to recommend multi-types in the same time. The recommendation types include food, drink, physical exercise, and medication. Furthermore, the proposed recommender system is proactive, where the recommendations are pushed to the users, based on the context, without explicit query by them. A prototype was developed for the system, as well as simple Android mobile application. Artificial Neural Network was used to train the system. The results show an overall accuracy of 89.5%.
MEDIC: Remove Model Backdoors via Importance Driven Cloning

We develop a novel method to remove injected backdoors in deep learning models. It works by cloning the benign behaviors of a trojaned model to a new model of the same structure. It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models. The set of important neurons varies for each input, depending on their magnitude of activations and their impact on the classification result. We theoretically show our method can better recover benign functions of the backdoor model. Meanwhile, we prove our method can be more effective in removing back-doors compared with fine-tuning. Our experiments show that our technique can effectively remove nine different types of backdoors with minor benign accuracy degradation, outper-forming the state-of-the-art backdoor removal techniques that are based on fine-tuning, knowledge distillation, and neuron pruning. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Mind the Label Shift of Augmentation-based Graph OOD Generalization

Out-of-distribution (OOD) generalization is an important issue for Graph Neural Networks (GNNs). Recent works employ different graph editions to generate augmented environments and learn an invariant GNN for generalization. However, the label shift usually occurs in augmentation since graph structural edition inevitably alters the graph label. This brings inconsistent predictive relationships among augmented environments, which is harmful to generalization. To address this issue, we propose LiSA, which generates label-invariant augmentations to facilitate graph OOD generalization. Instead of resorting to graph editions, LiSA exploits Label-invariant Subgraphs of the training graphs to construct Augmented environments. Specifically, LiSA first designs the variational subgraph generators to extract locally predictive patterns and construct multiple label-invariant subgraphs efficiently. Then, the subgraphs produced by different generators are collected to build different augmented environments. To promote diversity among augmented environments, LiSA further introduces a tractable energy-based regularization to enlarge pair-wise distances between the distributions of environments. In this manner, LiSA generates diverse augmented environments with a consistent predictive relationship and facilitates learning an invariant GNN. Extensive experiments on node-level and graph-level OOD benchmarks show that LiSA achieves impressive generalization performance with different GNN backbones. Code is available on https://github.com/Samyu0304/LiSA.
Development of a Lightweight Real-Time Application for Dynamic Hand Gesture Recognition

Hand Gesture Recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and network architectures with varying levels of efficacy. Such frameworks maximized performance at the expense of increased hardware and computational requirements. These drawbacks can be tackled by transforming the relatively complex dynamic hand gesture recognition task into a simpler image classification task. This paper presents a skeleton-based HGR framework that implements data-level fusion for encoding spatiotemporal information from dynamic gestures into static representational images. Said static images are subsequently processed by a custom, end-to-end trainable multi-stream CNN architecture for gesture classification. Our framework reduces the hardware and computational requirements of the HGR task while remaining competitive with the state-of-the-art on the CNR, FPHA, LMDHG, SHREC2017, and DHG142S benchmark datasets. We demonstrated the practical utility of our framework by creating a lightweight real-time application that makes use of skeleton data extracted from RGB video streams captured by a standard inbuilt PC webcam. The application operates successfully with minimal CPU and RAM footprint while achieving 93.46% classification accuracy with approximately 2s latency at 15 frames per second.
Lightweight Network for Masked Face Recognition Based on Improved Dual Attention Mechanism

More and more people are wearing masks to avoid contracting COVID-19. However, masked occlusion will lead to the loss of facial features. The universal face recognition model cannot recognize the masked face accurately and quickly. Masked face recognition (MFR) has become a very urgent challenge. Based on MobileNetV2, this paper will replace the average pooling in the attention module with depth-separable convolution, also the normalization operation is improved by replacing the Relu activation function with Prelu. The improved dual attention module is introduced to redistribute the weight parameters of bottleneck. The results show that the accuracy of mask-LFW and mask-AgeDB data sets reached 90% and 91% respectively, and the model size is reduced to one tenth of the other common model size. It is proved that the improved network can effectively reduce the occlusion interference, reduce the calculation amount, and improve the robustness of the system.
Action Primitive Classification of Sewing Operations toward Training Unskilled Workers

In this study, we proposed a system for measuring hand posture and fingertip force for training unskilled workers who sew with sewing machines. Furthermore, we classified sewing behaviors by constructing deep learning models using time-series color image data, hand posture, and fingertip force as inputs. Our experiments demonstrated that the classification model based on color images is the highest accuracy. Meanwhile, using the fingertip force-based model can not only classify sewing actions but can also extract the operator’s motion data in detail.