Reviewerly/exHarmony huggingface dataset:
Approximation of Nonlinear Functionals Using Deep ReLU Networks

In recent years, functional neural networks have been proposed and studied in order to approximate nonlinear continuous functionals defined on $$L^p([-1, 1]^s)$$ for integers $$s \ge 1$$ and $$1\le p<\infty $$ . However, their theoretical properties are largely unknown beyond universality of approximation or the existing analysis does not apply to the rectified linear unit (ReLU) activation function. To fill in this void, we investigate here the approximation power of functional deep neural networks associated with the ReLU activation function by constructing a continuous piecewise linear interpolation under a simple triangulation. In addition, we establish rates of approximation of the proposed functional deep ReLU networks under mild regularity conditions. Finally, our study may also shed some light on the understanding of functional data learning algorithms.
A Low-Cost High-Performance Montgomery Modular Multiplier Based on Pipeline Interleaving for IoT Devices

Modular multiplication is a crucial operation in public-key cryptography systems such as RSA and ECC. In this study, we analyze and improve the iteration steps of the classic Montgomery modular multiplication (MMM) algorithm and propose an interleaved pipeline (IP) structure, which meets the high-performance and low-cost requirements for Internet of Things devices. Compared to the classic pipeline structure, the IP does not require a multiplexing processing element (PE), which helps shorten the data path of intermediate results. We further introduce a disruption in the critical path to complete an iterative step of the MMM algorithm in two clock cycles. Our proposed hardware architecture is implemented on Xilinx Virtex-7 Series FPGA, using DSP48E1, to realize the multiplier. The implemented results show that the modular multiplication of 1024 bits by 2048 bits requires 1.03 μs and 2.13 μs, respectively. Moreover, our area–time–product analysis reveals a favorable outcome compared to the state-of-the-art designs across a 1024-bit and 2048-bit modulus.
High-Performance and Lightweight AI Model for Robot Vacuum Cleaners with Low Bitwidth Strong Non-Uniform Quantization

Artificial intelligence (AI) plays a critical role in the operation of robot vacuum cleaners, enabling them to intelligently navigate to clean and avoid indoor obstacles. Due to limited computational resources, manufacturers must balance performance and cost. This necessitates the development of lightweight AI models that can achieve high performance. Traditional uniform weight quantization assigns the same number of levels to all weights, regardless of their distribution or importance. Consequently, this lack of adaptability may lead to sub-optimal quantization results, as the quantization levels do not align with the statistical properties of the weights. To address this challenge, in this work, we propose a new technique called low bitwidth strong non-uniform quantization, which largely reduces the memory footprint of AI models while maintaining high accuracy. Our proposed non-uniform quantization method, as opposed to traditional uniform quantization, aims to align with the actual weight distribution of well-trained neural network models. The proposed quantization scheme builds upon the observation of weight distribution characteristics in AI models and aims to leverage this knowledge to enhance the efficiency of neural network implementations. Additionally, we adjust the input image size to reduce the computational and memory demands of AI models. The goal is to identify an appropriate image size and its corresponding AI models that can be used in resource-constrained robot vacuum cleaners while still achieving acceptable accuracy on the object classification task. Experimental results indicate that when compared to the state-of-the-art AI models in the literature, the proposed AI model achieves a 2-fold decrease in memory usage from 15.51 MB down to 7.68 MB while maintaining the same accuracy of around 93%. In addition, the proposed non-uniform quantization model reduces memory usage by 20 times (from 15.51 MB down to 0.78 MB) with a slight accuracy drop of 3.11% (the classification accuracy is still above 90%). Thus, our proposed high-performance and lightweight AI model strikes an excellent balance between model complexity, classification accuracy, and computational resources for robot vacuum cleaners.
Image Restoration with Fractional-Order Total Variation Regularization and Group Sparsity

In this paper, we present a novel image denoising algorithm, specifically designed to effectively restore both the edges and texture of images. This is achieved through the use of an innovative model known as the overlapping group sparse fractional-order total variation regularization model (OGS-FOTVR). The OGS-FOTVR model ingeniously combines the benefits of the fractional-order (FO) variation domain with an overlapping group sparsity measure, which acts as its regularization component. This is further enhanced by the inclusion of the well-established L2-norm, which serves as the fidelity term. To simplify the model, we employ the alternating direction method of multipliers (ADMM), which breaks down the model into a series of more manageable sub-problems. Each of these sub-problems can then be addressed individually. However, the sub-problem involving the overlapping group sparse FO regularization presents a high level of complexity. To address this, we construct an alternative function for this sub-problem, utilizing the mean inequality principle. Subsequently, we employ the majorize-minimization (MM) algorithm to solve it. Empirical results strongly support the effectiveness of the OGS-FOTVR model, demonstrating its ability to accurately recover texture and edge information in images. Notably, the model performs better than several advanced variational alternatives, as indicated by superior performance metrics across three image datasets, PSNR, and SSIM.
Navigation of Apple Tree Pruning Robot Based on Improved RRT-Connect Algorithm

Pruning branches of apple trees is a labor-intensive task. Pruning robots can save manpower and reduce costs. A full map of the apple orchard with collision-free paths, which is navigation planning, is essential. To improve the navigation efficiency of the apple tree pruning robot, an improved RRT-Connect algorithm was proposed. Firstly, to address the disadvantage of randomness in the expansion of the RRT-Connect algorithm, a goal-biased strategy was introduced. Secondly, to shorten the path length, the mechanism of the nearest node selection was optimized. Finally, the path was optimized where path redundancy nodes were removed, and Bezier curves were used to deal with path sharp nodes to further reduce the path length and improve the path smoothness. The experimental results of apple orchard navigation show that the improved algorithm proposed in this paper can cover the whole apple orchard, and the path length is 32% shorter than that of the RRT-Connect algorithm. The overall navigation time is 35% shorter than that of the RRT-Connect algorithm. This shows that the improved algorithm has better adaptability and planning efficiency in the apple orchard environment. This will contribute to the automation of orchard operations and provide valuable references for future research on orchard path planning.
Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class

Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus on one of the currently most limiting factor of the field: the (non-)availability of labeled data. Based on three common medical imaging modalities (bone marrow microscopy, gastrointestinal endoscopy, dermoscopy) and publicly available data sets, we analyze the performance of self-supervised DL within the self-distillation with no labels (DINO) framework. After learning an image representation without use of image labels, conventional machine learning classifiers are applied. The classifiers are fit using a systematically varied number of labeled data (1-1000 samples per class). Exploiting the learned image representation, we achieve state-of-the-art classification performance for all three imaging modalities and data sets with only a fraction of between 1% and 10% of the available labeled data and about 100 labeled samples per class.
Path Planning Algorithm Based on Obstacle Clustering Analysis and Graph Search

Path planning is receiving considerable interest in mobile robot research; however, a large number of redundant nodes are typically encountered in the path search process for large-scale maps, resulting in decreased algorithmic efficiency. To address this problem, this paper proposes a graph search path planning algorithm that is based on map preprocessing for creating a weighted graph in the map, thus obtaining a structured search framework. In addition, the reductions in the DBSCAN algorithm were analyzed. Subsequently, the optimal combination of the minPts and Eps required to achieve an efficient and accurate clustering of obstacle communities was determined. The effective edge points were then found by performing obstacle collision detection between special grid nodes. A straight-line connection or A* planning strategy was used between the effective edge points to establish a weighted, undirected graph that contained the start and end points, thereby achieving a structured search framework. This approach reduces the impact of map scale on the time cost of the algorithm and improves the efficiency of path planning. The results of the simulation experiments indicate that the number of nodes to be calculated in the search process of the weighted graph decreases significantly when using the proposed algorithm, thus improving the path planning efficiency. The proposed algorithm offers excellent performance for large-scale maps with few obstacles.
Quantum computing for transport network design problems

Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.
A few-shot rare wildlife image classification method based on style migration data augmentation

Camera traps are widely used in ecological studies to capture detailed shots because they rarely disturb wildlife. However, camera traps can obtain only a small fraction of the images of rare wildlife living in wild habitats and nature reserves, often just a flash of them. Thus, the classification of rare wildlife images faces the problems of small samples, incomplete subjects, and complex backgrounds. We propose a data augmentation method for classifying these images based on a cycle-consistent adversarial network that uses two discriminators and two generators. Images augmented by manual data are input to one generator, and stylized images are input to the other generator, constrained by cycle consistency loss. The two discriminators determine whether a generated image is true or false, resulting in stylized image data. The method can accurately distinguish between similar-appearing species. A model trained by our method was used to classify six rare wildlife species with a classification accuracy of 92.2% and an F1 score of 93.3%. The deep learning techniques widely used in wildlife image recognition require large-sample datasets for adequate accuracy. Our study better solves the problem facing small-sample datasets, when large datasets of rare wildlife are not available. The proposed method can provide a reference for promoting rare wildlife conservation through digitalization and intelligence.
Intrinsic feature extraction for unsupervised domain adaptation

Purpose Unsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively. Design/methodology/approach IFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain. Findings Experimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets. Research limitations/implications Limitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared. Originality/value This paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.
MPDNet: An underwater image deblurring framework with stepwise feature refinement module

In this study, a general network model called multi-progressive image deblurring network is proposed to correct blurring artifacts and local imaging details in underwater images. As a solution to nonuniform image distortion, a deformable convolution module was designed to enrich the encoded information of the image representation. Using a stepwise feature refinement module, multi-progressive image deblurring network can reduce the loss of contextual information to produce a more realistic underwater image for subsequent applications. Constructing a loss function based on multi-scale content can help the model improve image perception quality. We conducted experimental evaluations on large-scale image deblurring benchmark datasets, such as GoPro and HIDE, achieving excellent results with 32.84 dB and 31.03 dB peak signal-to-noise ratio, respectively, using the proposed method. Subsequently, a detailed optimization comparison was conducted on the in-house underwater image deblurring dataset. Multi-progressive image deblurring network obtained higher-quality, clearer images. Compared with the current state-of-the-art image deblurring algorithms, the proposed model achieved significant results with a 6.6% increase in deblur performance in peak signal-to-noise ratio. Finally, we conducted ablation experiments to evaluate the effectiveness of all the modules in the proposed framework.
Linear-time computation of DAWGs, symmetric indexing structures, and MAWs for integer alphabets

The directed acyclic word graph (DAWG) of a string y of length n is the smallest (partial) DFA which recognizes all suffixes of y with only O(n) nodes and edges. In this paper, we show how to construct the DAWG for the input string y from the suffix tree for y, in O(n) time for integer alphabets of polynomial size in n. In so doing, we first describe a folklore algorithm which, given the suffix tree for y, constructs the DAWG for the reversed string yˆ in O(n) time. Then, we present our algorithm that builds the DAWG for y in O(n) time for integer alphabets, from the suffix tree for y. We also show that a straightforward modification to our DAWG construction algorithm leads to the first O(n)-time algorithm for constructing the affix tree of a given string y over an integer alphabet. Affix trees are a text indexing structure supporting bidirectional pattern searches. We then discuss how our constructions can lead to linear-time algorithms for building other text indexing structures, such as linear-size suffix tries and symmetric CDAWGs in linear time in the case of integer alphabets. As a further application to our O(n)-time DAWG construction algorithm, we show that the set MAW(y) of all minimal absent words (MAWs) of y can be computed in optimal, input- and output-sensitive O(n+|MAW(y)|) time and O(n) working space for integer alphabets.
On the Quality of Synthetic Generated Tabular Data

Class imbalance is a common issue while developing classification models. In order to tackle this problem, synthetic data have recently been developed to enhance the minority class. These artificially generated samples aim to bolster the representation of the minority class. However, evaluating the suitability of such generated data is crucial to ensure their alignment with the original data distribution. Utility measures come into play here to quantify how similar the distribution of the generated data is to the original one. For tabular data, there are various evaluation methods that assess different characteristics of the generated data. In this study, we collected utility measures and categorized them based on the type of analysis they performed. We then applied these measures to synthetic data generated from two well-known datasets, Adults Income, and Liar+. We also used five well-known generative models, Borderline SMOTE, DataSynthesizer, CTGAN, CopulaGAN, and REaLTabFormer, to generate the synthetic data and evaluated its quality using the utility measures. The measurements have proven to be informative, indicating that if one synthetic dataset is superior to another in terms of utility measures, it will be more effective as an augmentation for the minority class when performing classification tasks.
Vision-based virtual vibration sensor using error calibration convolutional neural network with signal augmentation

In this study, we propose a methodology for using image-based virtual vibration sensors to replace acceleration sensors in the vibration monitoring of targets. Most image-based vibration-monitoring techniques typically involve the installation of a non-contact-capture camera at a location unaffected by disturbances to photograph the outside of a target. However, these methods have several drawbacks, including the additional cost of installing sensors and the need to select appropriate installation locations. The proposed virtual vibration sensor addresses these limitations by combining signal extraction based on a contact-capture camera installed inside the target with a convolutional neural network for error calibration. By employing a camera using the contact-capture method, the installed cameras can be reused to integrate the functions of other sensors and reduce the total number of sensors required. Moreover, in the proposed method, a calibration convolutional neural network (CCNN) improves the limited accuracy of contact-capture cameras. Furthermore, the calibration performance of the CCNN was improved via signal augmentation even with the availability of limited data. The experimental results obtained using a quadcopter platform demonstrated the improved accuracy and effectiveness of the proposed virtual sensor compared to that of the actual sensors.
Secure auditing and deduplication with efficient ownership management for cloud storage

More and more users opt to outsource their data to the cloud due to its popularity, which also brings some problems. One is that users cannot know whether the data stored in the cloud is intact since they no longer have physical control over it. The other is that the data stored in the cloud are often duplicated, which clearly wastes cloud storage resources. To solve the above problems, cloud auditing technology and secure deduplication technology have been widely studied. Furthermore, cloud auditing schemes supporting secure deduplication have also been proposed. However, data ownership management is rarely considered in these schemes. Once the data ownership changes, the revoked user should not be able to obtain the original ciphertext stored in the cloud to recover the original data. To solve this problem, we propose a cloud auditing scheme that supports cross-user secure deduplication with efficient data ownership management. We design a lazy update strategy to reduce update frequency and delegate the update task to the cloud, which greatly reduces the computing cost of ownership management. When the data ownership changes, the cloud does not need to update the original ciphertext immediately. It will decide whether to update the original ciphertext based on the value of a preset flag. Experimental results and the security analysis demonstrate the effectiveness and security of the proposed scheme.
EFL Tertiary Learners' Perceptions on Ease of Use of Portable Devices in English Mobile Learning at a University in Ho Chi Minh City, Vietnam

As the remarkable breakthrough of the age of revolution 4.0, mobile learning (M-learning) appears among the sharp development of modern technology. Their clever features have been discovered to have accompanied the exponential growth of mobile devices to enhance students' learning whenever and wherever. Mobile-assisted language learning (MALL) has so been considered to benefit individuals' English proficiency. Students' attitudes on utilizing mobile devices for learning while being connected to a wireless network need to be carried out, even though learning through mobile devices is expected to apply appropriately to teaching and learning. Through a survey of 207 university EFL students using a questionnaire with 13 English-Vietnamese statements within 20 minutes, this article aims to exhibit EFL learners' behaviors and acceptance of the use of mobile devices in English learning at a university in Vietnam. The survey ran within two weeks in the first semester of the 2019–2020 school year at a University in Ho Chi Minh City, Vietnam. A quantitative approach was used to gather and analyze data from the questionnaire under SPSS version 20. Research findings demonstrated that learners' opinions regarding adopting portable technology in English learning are favorable. The learner supports this virtual learning thanks to mobile gadgets' smart functions offering convenient learning without demanding users' efforts to manipulate. Neither my current university firmly welcomes mobile learning applications to serve flexible education and training in the future.