Reviewerly/exHarmony huggingface dataset:
An Improved YOLOv5 Underwater Detector Based on an Attention Mechanism and Multi-Branch Reparameterization Module

Underwater target detection is a critical task in various applications, including environmental monitoring, underwater exploration, and marine resource management. As the demand for underwater observation and exploitation continues to grow, there is a greater need for reliable and efficient methods of detecting underwater targets. However, the unique underwater environment often leads to significant degradation of the image quality, which results in reduced detection accuracy. This paper proposes an improved YOLOv5 underwater-target-detection network to enhance accuracy and reduce missed detection. First, we added the global attention mechanism (GAM) to the backbone network, which could retain the channel and spatial information to a greater extent and strengthen cross-dimensional interaction so as to improve the ability of the backbone network to extract features. Then, we introduced the fusion block based on DAMO-YOLO for the neck, which enhanced the system’s ability to extract features at different scales. Finally, we used the SIoU loss to measure the degree of matching between the target box and the regression box, which accelerated the convergence and improved the accuracy. The results obtained from experiments on the URPC2019 dataset revealed that our model achieved an mAP@0.5 score of 80.2%, representing a 1.8% and 2.3% increase in performance compared to YOLOv7 and YOLOv8, respectively, which means our method achieved state-of-the-art (SOTA) performance. Moreover, additional evaluations on the MS COCO dataset indicated that our model’s mAP@0.5:0.95 reached 51.0%, surpassing advanced methods such as ViDT and RF-Next, demonstrating the versatility of our enhanced model architecture.
An improved multi-strategy beluga whale optimization for global optimization problems

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.
Mobile Robot Path Planning Algorithm Based on RRT_Connect

Targeting some problems of the RRT_Connect path planning algorithm, such as average search and low efficiency, proposes an improved RRT_Connect algorithm that may optimize the searched nodes and parts of planned paths. Firstly, an improved RRT_Connect algorithm based on destination and searched node bias strategy is proposed. Secondly, an improved RRT_Connect algorithm is put forward for the optimization of the searched nodes and some planned paths to deal with the problem of low quality reflected in the improved RRT_Connect path planning algorithm, and the optimization for the cost of path planning by figuring out valid new nodes and parent nodes of adjacent nodes within a certain range. On this basis, the path planning algorithm is verified by simulation and actual experiments. It is shown by the experimental results that the improved RRT_Connect algorithm proposed in this paper can not only shorten the time and length of path planning but also decrease the number of search iterations and nodes.
Student behavior recognition for interaction detection in the classroom environment

With the development of multimedia technologies, surveillance videos and other multimedia data have received widespread attention in several fields. Surveillance videos can monitor students' learning statuses in real time. However, the current action recognition methods for teaching have limitations. First, the ethical privacy of AI and education makes public datasets on student behavior scarce. Therefore, based on the summarization of seven typical student behaviors in the classroom, course videos were obtained from the smart classroom to generate a dataset of student behavior. Compared with existing student behavior recognition datasets, the proposed dataset is distinguished by cluttered backgrounds, crowded scenes, and occlusions. Second, relational reasoning using existing methods is not ideal for distinguishing between students' body parts and small objects in a cluttered background; the interactive utilization rate of different relational features is low, and it cannot take advantage of the complementarity of different relational features, resulting in poor performance of interaction action recognition. Therefore, the attention-based relational reasoning module strengthens the interactive representation between small objects and human body parts. At the same time, considering that there is a certain complementary relationship between relational features, this study constructs a relational feature fusion module which models a human-to-human interaction relationship built upon supporting human's body part and surrounding context. Finally, the reconstructed features and human-appearance features were fused to achieve accurate interactive action recognition. Through an experimental comparison between the proposed and current mainstream algorithms on the generated student behavior dataset, it was verified that the proposed model achieves state-of-the-art performance in action recognition.
Model-centric transfer learning framework for concept drift detection

Concept drift refers to the inevitable phenomenon that influences the statistical features of the data stream. Detecting concept drift in data streams quickly and precisely remains challenging, and failure to detect it will render model trained on historical data ineffective. Current drift detection methods suffer from the following problems: detection delay, many false detected drifts, and rarely utilize the deep neural network directly in the field of concept drift detection, which is considerably competent at addressing the classification problems of data stream. Furthermore, the output of the model is usually taken as a metric to detect drift, while changes in the model parameters are often ignored which contain highly useful information. In this paper, we propose a model-centric framework for concept drift detection that uses deep neural network to detect concept drift by focusing on the change in the model itself, rather than the model output. In addition, transfer learning is developed to accelerate the drift detection process and reduce the computational complexity by freezing parts of the network. To further reduce false detected drifts, we propose long and short time windows method to determine the real drift from the potential detected drift. Experiments with real-world and artificial datasets have been undertaken to demonstrate the effectiveness of the proposed framework.
Lifelong nnU-Net: a framework for standardized medical continual learning

As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net-widely regarded as the best-performing segmenter for multiple medical applications-and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark.
Extended natural neighborhood for SMOTE and its variants in imbalanced classification

Imbalanced data classification is a challenging issue encountered in many practical applications. Synthetic minority oversampling technique (SMOTE) and its variants are popular resampling methods. However, in most of these methods, the neighborhood determined by k-nearest neighbor (kNN) cannot reflect the local distribution precisely, leading to the generation of noisy examples. To solve this problem, we propose a neighborhood concept without parameter k called extended natural neighbor (ENaN), which is derived from natural neighbor (NaN). ENaN unites kNN and reverse kNN to determine neighbors adaptively according to the sample distribution. Compared to NaN, ENaN explores broad neighborhoods, which facilitates to improve the quality of generated examples. ENaN-based SMOTE (ENaNSMOTE) can improve the sample distribution obtained by SMOTE and NaNSMOTE. Extensive experiments using 30 synthetic and 20 real-world datasets prove the effectiveness of ENaN in SMOTE and its variants.
Artificial Intelligence and Applications

Artificial intelligence (AI) has been employed since the 1950s in a number of industries, including manufacturing, logistics, finance, and healthcare. AI is being utilised more and more to automate operations that formerly required human intervention as a result of recent developments in machine learning (ML). AI can be used to automate decision-making procedures and increase the precision of predictions and recommendations. AI can, for instance, be used to determine which people are most likely to contract specific diseases, to suggest the best treatments for those diseases, and to automate the process of approving or rejecting insurance claims, in short healthcare. AI can also be used to identify product flaws and then automatically fix them, enhancing the effectiveness of manufacturing operations. AI can be applied to logistics to improve delivery routes and forecast demand for certain products. AI has a wide range of possible uses, and the opportunities are limitless. AI will probably grow more pervasive in our lives as machine learning continues to progress, altering the way we work, play, and live. In this paper, we will elaborate on the application of artificial intelligence in daily life, such as education, e-shopping, navigation, etc. AI has open vast number of opportunities and increased human efficiency. This has been studied through an intensive literature review concerning matters and applications of AI.
Meta-Learning-Based Degradation Representation for Blind Super-Resolution

Blind image super-resolution (blind SR) aims to generate high-resolution (HR) images from low-resolution (LR) input images with unknown degradations. To enhance the performance of SR, the majority of blind SR methods introduce an explicit degradation estimator, which helps the SR model adjust to unknown degradation scenarios. Unfortunately, it is impractical to provide concrete labels for the multiple combinations of degradations (e. g., blurring, noise, or JPEG compression) to guide the training of the degradation estimator. Moreover, the special designs for certain degradations hinder the models from being generalized for dealing with other degradations. Thus, it is imperative to devise an implicit degradation estimator that can extract discriminative degradation representations for all types of degradations without requiring the supervision of degradation ground-truth. To this end, we propose a Meta-Learning based Region Degradation Aware SR Network (MRDA), including Meta-Learning Network (MLN), Degradation Extraction Network (DEN), and Region Degradation Aware SR Network (RDAN). To handle the lack of ground-truth degradation, we use the MLN to rapidly adapt to the specific complex degradation after several iterations and extract implicit degradation information. Subsequently, a teacher network MRDAT is designed to further utilize the degradation information extracted by MLN for SR. However, MLN requires iterating on paired LR and HR images, which is unavailable in the inference phase. Therefore, we adopt knowledge distillation (KD) to make the student network learn to directly extract the same implicit degradation representation (IDR) as the teacher from LR images. Furthermore, we introduce an RDAN module that is capable of discerning regional degradations, allowing IDR to adaptively influence various texture patterns. Extensive experiments under classic and real-world degradation settings show that MRDA achieves SOTA performance and can generalize to various degradation processes.
Hierarchical graph attention network for temporal knowledge graph reasoning

Temporal knowledge graphs (TKGs) reasoning has attracted increasing research interest in recent years. However, most of the existing TKGs reasoning models aim to learn a dynamic entity representation by binding timestamps information with the entities, neglecting to learn adaptive entity representation that is valuable to the query from relevant historical facts. To this end, we propose a Hierarchical Graph Attention neTwork (HGAT) for the TKGs reasoning task. Specifically, we design a hierarchical neighbor encoder to model the time-oriented and task-oriented roles of the entities. The time-aware mechanism is developed in the first layer to differentiate the contributions of query-relevant historical facts at different timestamps to the query. The designed relation-aware attention is used in the second layer to discern the contributions of the structural neighbors of an entity. Through this hierarchical encoder, our model can absorb valuable knowledge effectively from the relevant historical facts, and thus learn more expressive adaptive entity representation for the query. Finally, we evaluate our model performance on four TKGs datasets and justify its superiority against vaerious state-of-the-art baselines.
Image encryption algorithm based on Hilbert sorting vector and new spatiotemporal chaotic system

The design of the lattice coupling mode in current spatiotemporal chaotic systems lacks dynamic characteristics. When it is applied to the cryptosystem, its chaotic performance defects weaken the security of the cryptosystem. In this context, it is urgent to design new coupling rules and secure cryptosystems based on fractal and chaos theory. In this paper, a kind of sorting vector based on the fractal sorting matrix and fractal curve, the Hilbert sorting vector (HSV), is proposed creatively, and its iterative generation process is introduced. HSV is irregular and infinitely iterable. According to the requirements of the actual situation, HSV has a flexible adjustment vector length, which improves the multiplicity and efficiency of changing information positions. Then, HSV is used to reconstitute the the interaction of nodes during iteration in a new spatiotemporal chaotic system named Hilbert-sorting-vector coupled map lattice (HSVCML). Using this new sorting vector, the dynamic characteristics of spatiotemporal chaotic systems can be effectively improved. This is proved by comparing their Lyapunov exponent, Kolmogorov-Sinai entropy, bifurcation diagram, and information entropy with the coupled map lattice (CML). Moreover, the rich spatio-temporal behaviours of HSVCML are studied. Therefore, HSVCML is more appropriate for image encryption than CML. Finally, HSV is combined with a spatiotemporal chaotic system to frame an image encryption method. For the purpose of proving the effectiveness and security of this algorithm against different types of attacks, a large number of tests related to security analysis and time complexity analysis are carried out. Simulation results prove that our encryption algorithm is more secure and efficient than the previous algorithms and can resist various attacks.
Research on Assembly Method of Threaded Fasteners Based on Visual and Force Information

Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This paper introduces a novel algorithm for detecting the position and orientation of threaded holes and a new method for tightening bolts. First, the elliptic arc fitting method and the three-point method are used to estimate the initial position and orientation of the threaded hole, and the force impact caused by switching from the free space to the constrained space during bolt tightening is solved. Second, by monitoring the deformation of passive compliance, the position information is introduced into the control process to better control the radial force between the bolt and the threaded hole in the tightening process. The constant force controller and orientation compliance controller are designed according to the adaptive control theory. A series of experiments are carried out. The results show that the proposed method can estimate the initial position and orientation of an M24 bolt with an average position error of 0.36 mm, 0.43 mm and 0.46 mm and an orientation error of 0.65°, 0.46° and 0.59°, and it can tighten the bolt with a success rate of 98.5%.
A Reconfigurable CNN-Based Accelerator Design for Fast and Energy-Efficient Object Detection System on Mobile FPGA

In limited-resource edge computing circumstances such as on mobile devices, IoT devices, and electric vehicles, the energy-efficient optimized convolutional neural network (CNN) accelerator implemented on mobile Field Programmable Gate Array (FPGA) is becoming more attractive due to its high accuracy and scalability.In recent days, mobile FPGAs such as the Xilinx PYNQ-Z1/Z2 and Ultra96, definitely have the advantage of scalability and flexibility for the implementation of deep learning algorithm-based object detection applications.It is also suitable for battery-powered systems, especially for drones and electric vehicles, to achieve energy efficiency in terms of power consumption and size aspect.However, it has the low and limited performance to achieve real-time processing.In this article, optimizing the accelerator design flow in the register-transfer level (RTL) will be introduced to achieve fast programming speed by applying low-power techniques on FPGA accelerator implementation.In general, most accelerator optimization techniques are conducted on the system level on the FPGA.In this article, we propose the reconfigurable accelerator design for a CNN-based object detection system on the register-transfer level on mobile FPGA.Furthermore, we present RTL optimization design techniques that will be applied such as various types of clock gating techniques to eliminate residual signals and to deactivate the unnecessarily active block.Based on the analysis of the CNN-based object detection architecture, we analyze and classify the common computing operation components from the Convolutional Neuron Network, such as multipliers and adders.We implement a multiplier/adder unit to a universal computing unit and modularize it to be suitable for a hierarchical structure of RTL code.The proposed system design was tested with Resnet-20 which has 23 layers and it was trained with the dataset, CIFAR-10 which provides a test set of 10,000 images in several formats, and the weight data we used for this experiment was provided from Tensil.Experimental results show that the proposed design process improves the power efficient consumption, hardware utilization, and throughput by 16%, up to 58%, and 15%, respectively.
A neural machine code and programming framework for the reservoir computer

Abstract From logical reasoning to mental simulation, biological and artificial neural systems possess an incredible capacity for computation. Such neural computers offer a fundamentally novel computing paradigm by representing data continuously and processing information in a natively parallel and distributed manner. To harness this computation, prior work has developed extensive training techniques to understand existing neural networks. However, the lack of a concrete and low-level machine code for neural networks precludes us from taking full advantage of a neural computing framework. Here we provide such a machine code along with a programming framework by using a recurrent neural network—a reservoir computer—to decompile, code and compile analogue computations. By decompiling the reservoir’s internal representation and dynamics into an analytic basis of its inputs, we define a low-level neural machine code that we use to program the reservoir to solve complex equations and store chaotic dynamical systems as random-access memory. We further provide a fully distributed neural implementation of software virtualization and logical circuits, and even program a playable game of pong inside of a reservoir computer. Importantly, all of these functions are programmed without requiring any example data or sampling of state space. Finally, we demonstrate that we can accurately decompile the analytic, internal representations of a full-rank reservoir computer that has been conventionally trained using data. Taken together, we define an implementation of neural computation that can both decompile computations from existing neural connectivity and compile distributed programs as new connections.