Reviewerly/exHarmony huggingface dataset:
Describe Fashion Products via Local Sparse Self-Attention Mechanism and Attribute-Based Re-Sampling Strategy

In this paper, we convert conventional image captioning task into a new paradigm in the fashion domain, fashion item captioning. This task requires the ability of associating a group of item angles and generating a longer and more fine-grained description. To link several images in a more efficient way, we propose a local sparse self-attention mechanism(LSAM), which only enables one region to interact with those in the adjacent area and allows a lighter architecture with fewer layers. To capture more subtle details of the item, an attribute-based re-sampling strategy(ARS) is introduced to enhance the learning on those low-frequency but content-related attribute words. Besides, existing fashion datasets are limited in the quality of annotations and single fashion style. To bridge this gap, we propose a novel dataset for fashion item captioning, termed Fashion Item Captioning Dataset(FICD). FICD provides a meaningful complement to the existing fashion datasets, which comprises 294K images, 62K real-world product descriptions with diverse linguistic styles, rich attributes and categories. It is worth mentioning that we also annotate an attribute-level sentence for each item, which enables the FICD to be used not only for fashion item captioning but also for other fashion-related tasks. Moreover, the complex structure is even more restrictive for application to real-world scenarios. Without bells and whistles, our framework is simply designed with an end-to-end manner. Extensive experiments demonstrate the effectiveness of our LSAM-ARS. More remarkably, LSAM-ARS achieves state-of-the-art performance on FACAD and FICD datasets, with the CIDEr-D score being increased from 65.4% to 81.8%, 69.8% to 77.4%, respectively.
An Efficient Connected-Component Labeling Algorithm for 3-D Binary Images

Conventional voxel-based algorithms for labeling connected components in 3D binary images use the same mask to process all object voxels. To reduce the number of times that neighboring voxels are checked when object voxels are processed, we propose an algorithm that uses two different masks for processing two different types of object voxels. One type of mask is used when the voxel preceding the object voxel being processed is an object voxel, and the other type is used otherwise. In either case, an optimal order is used for checking the voxels in the corresponding mask. Experimental results demonstrate that our proposed algorithm checked fewer voxels, and was more efficient than conventional algorithms.
n-Dimensional Chaotic Time Series Prediction Method

Chaotic time series have been involved in many fields of production and life, so their prediction has a very important practical value. However, due to the characteristics of chaotic time series, such as internal randomness, nonlinearity, and long-term unpredictability, most prediction methods cannot achieve high-precision intermediate or long-term predictions. Thus, an intermediate and long-term prediction (ILTP) method for n-dimensional chaotic time series is proposed to solve this problem. Initially, the order of the model is determined by optimizing the preprocessing and constructing the joint calculation strategy, so that the observation sequence can be decomposed and reorganized accurately. Furthermore, the RBF neural network is introduced to construct a multi-step prediction model of future sequences, with a feedback recursion mechanism. Compared with the existing prediction methods, the error of the ILTP method can be reduced by 1–6 orders of magnitude, and the prediction step can be increased by 10–20 steps. The ILTP method can provide reference technology for the application of time series prediction with chaotic characteristics.
BORU-BENZERİ YAPAY SİNİR AĞI KARŞILAŞTIRMA MİMARİLERİNİN EĞİTİMİ HAKKINDA BİR TEORİK ARAŞTIRMA VE POPULAR EĞİTİM ALGORİTMALARIN PERFORMANS KARŞILAŞTIRILMALARI

Architectures of neural networks affect the training performance of artificial neural networks. For more consistent performance evaluation of training algorithms, hard-to-train benchmarking architectures should be used. This study introduces a benchmark neural network architecture, which is called pipe-like architecture, and presents training performance analyses for popular Neural Network Backpropagation Algorithms (NNBA) and well-known Metaheuristic Search Algorithms (MSA). The pipe-like neural architectures essentially resemble an elongated fraction of a deep neural network and form a narrowed long bottleneck for the learning process. Therefore, they can significantly complicate the training process by causing the gradient vanishing problems and large training delays in backward propagation of parameter updates throughout the elongated pipe-like network. The training difficulties of pipe-like architectures are theoretically demonstrated in this study by considering the upper bound of weight updates according to an aggregated one-neuron learning channels conjecture. These analyses also contribute to Baldi et al.'s learning channel theorem of neural networks in a practical aspect. The training experiments for popular NNBA and MSA algorithms were conducted on the pipe-like benchmark architecture by using a biological dataset. Moreover, a Normalized Overall Performance Scoring (NOPS) was performed for the criterion-based assessment of overall performance of training algorithms.
Robust Image Watermarking Using LWT and Stochastic Gradient Firefly Algorithm

Watermarking of digital images is required in diversified applications ranging from medical imaging to commercial images used over the web.Usually, the copyright information is embossed over the image in the form of a logo at the corner or diagonal text in the background.However, this form of visible watermarking is not suitable for a large class of applications.In all such cases, a hidden watermark is embedded inside the original image as proof of ownership.A large number of techniques and algorithms are proposed by researchers for invisible watermarking.In this paper, we focus on issues that are critical for security aspects in the most common domains like digital photography copyrighting, online image stores, etc.The requirements of this class of application include robustness (resistance to attack), blindness (direct extraction without original image), high embedding capacity, high Peak Signal to Noise Ratio (PSNR), and high Structural Similarity Matrix (SSIM).Most of these requirements are conflicting, which means that an attempt to maximize one requirement harms the other.In this paper, a blind type of image watermarking scheme is proposed using Lifting Wavelet Transform (LWT) as the baseline.Using this technique, custom binary watermarks in the form of a binary string can be embedded.Hu's Invariant moments' coefficients are used as a key to extract the watermark.A Stochastic variant of the Firefly algorithm (FA) is used for the optimization of the technique.Under a prespecified size of embedding data, high PSNR and SSIM are obtained using the Stochastic Gradient variant of the Firefly technique.The simulation is done using Matrix Laboratory (MATLAB) tool and it is shown that the proposed technique outperforms the benchmark techniques of watermarking considering PSNR and SSIM as quality metrics.394
Operation-Based Refactoring-Aware Merging: An Empirical Evaluation

Dealing with merge conflicts in version control systems is a challenging task for software developers. Resolving merge conflicts is a time-consuming and error-prone process, which distracts developers from important tasks. Recent work shows that refactorings are often involved in merge conflicts and that refactoring-related conflicts tend to be larger, making them harder to resolve. In the literature, there are two refactoring-aware merging techniques that claim to automatically resolve refactoring-related conflicts; however, these two techniques have never been empirically compared. In this paper, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefMerge</monospace> , a rejuvenated Java-based design and implementation of the first technique, which is an operation-based refactoring-aware merging algorithm. We compare <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefMerge</monospace> to Git and the state-of-the-art graph-based refactoring-aware merging tool, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IntelliMerge</monospace> , on 2,001 merge scenarios with refactoring-related conflicts from 20 open-source projects. We find that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefMerge</monospace> resolves or reduces conflicts in 497 (25%) merge scenarios while increasing conflicting LOC in only 214 (11%) scenarios. On the other hand, we find that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IntelliMerge</monospace> resolves or reduces conflicts in 478 (24%) merge scenarios but increases conflicting LOC in 597 (30%) merge scenarios. We additionally conduct a qualitative analysis of the differences between the three merging algorithms and provide insights of the strengths and weaknesses of each tool. We find that while <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IntelliMerge</monospace> does well with ordering and formatting conflicts, it struggles with class-level refactorings and scenarios with several refactorings. On the other hand, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefMerge</monospace> is resilient to the number of refactorings in a merge scenario, but we find that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefMerge</monospace> introduces conflicts when inverting move-related refactorings.
Simultaneously Training and Compressing Vision-and-Language Pre-training Model

Model compression is an essential step for large-scale pre-training models toward practical application and deployment on the edge device. However, when conventional compression methods following ‘pre-training then compressing’ two-phase pipeline are applied to Vision-and-Language Pre-training (VLP) models, it will lead to a high calculation and memory overhead. In this work, we break the two-phase pipeline and propose an efficient and effective one-phase VLP model compression mechanism, named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">REDUCER</b> , which stands for ‘simultaneously training and comp <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RE</b> ssing’ VLP model via progressive mo <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DU</b> le repla <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ing and n <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> twork <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> ewiring. Specifically, REDUCER consists of three insightful designs. Firstly, we design a one-phase compression framework to train and compress the VLP model simultaneously to avoid the extra calculation and memory cost caused by an isolated model compression phase in the conventional two-phase pipeline. Secondly, we propose an adaptive progressive module replacing mechanism to compress the model depth free from explicit knowledge distillation losses, relieving the multi-task optimization problems. Thirdly, we integrate pruning techniques into VLP model compression to simultaneously compress the model in width and depth. Overall, we obtain a lightweight VLP model with only one pre-training phase, and it is the first one-phase compression method for VLP models. Extensive experiments have been conducted on representative VLP models, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , ClipBERT and VICTOR, and the experimental results show a superior trade-off between performance and efficiency.
Deep Reinforcement Clustering

Deep clustering has attracted plentiful attention in various domains owning to the superior performance. However, the previous deep clustering methods are guided by pre-specified clustering strategies that lack sustained explorations of data structures, degrading recognition of intrinsic patterns hidden in data. To address this challenge, deep reinforcement clustering (DRC) is proposed to learn an adaptive partition policy for pattern mining, which can fully explore structure knowledge of data in an adaptive manner. DRC is defined as a Markov decision process of data partitions, which chooses the optimal cluster prototype for data via maximizing the cumulative reward in state transition of environment. To implement the definition, a Bernoulli action prototype is devised to capture decision distributions in the transition of states, where the heavy-tailed Cauchy distribution precisely measures the structure divergences of data. Furthermore, a reward maximizing policy is designed to guide sustained explorations of data structures, which ensures intra-cluster compactness and inter-cluster separation of data partitions. Finally, extensive experiments are conducted on eight benchmark datasets, and the results demonstrate that DRC outperforms the state-of-the-art baseline methods.
HLSAQ: Hybrid Security Model for Quantum Networks

The idea of the quantum internet is to provide an internet technology that can enable quantum communication between any two points on Earth. Researchers are proposing a theoretically large number of quantum networks, but practically they are still far from reality Yet the aspect of security can not be ignored as it has always remained a crucial aspect of every network or device. In this article, we are considering a classical/quantum hybrid network and proposing using classical layered architecture to enhance security in quantum networks. We have proposed a hybrid layered security architecture for a large-scale quantum network to handle security requirements and a few challenges. It also tackles open problems on the way to building a large-scale quantum network and a security detail in the near future.
Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

Abstract The echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.
Semi‐supervised multi‐label learning with missing labels by exploiting feature‐label correlations

Abstract The majority of multi‐learning techniques now in use presuppose that there will be enough labeled instances. But in real‐world applications, it is frequently the case that only partial labels are included for each training instance. This is either because getting a fully labeled training set takes a lot of time and effort or because doing so is expensive. Multi‐label learning with missing labels, on the other hand, has greater practical value. In this paper, we propose a brand‐new semi‐supervised multi‐label learning method (SMLMFC) that specifically addresses missing‐label scenarios. After successfully filling in the missing labels for instances using two‐stage label correlations, SMLMFC trains a semi‐supervised multi‐label classifier by imposing feature‐label correlation restrictions directly on the output of labels. The complex relationships between features and labels can be learned and implicitly captured through feature‐label correlations, in particular. The experimental results on a number of real‐world multi‐label datasets confirm that SMLMFC has strong competitiveness in comparison to other state‐of‐the‐art methods.
On the optimal binary classifier with an application

The alternative accumulated improvement curve stochastic order is a criterion for the comparison of the performance of classifiers that predict binary responses. An explicit optimal classifier for this criterion is obtained. That optimal classifier has the largest ROC and CAP curves and indexes, that is, it is also optimal for the criteria based on the comparison of such curves and indexes. An application of the results to the search of the best classifier to predict clients of a bank which will make a transaction in the future is developed.
Effect of Nursing Student’s Learning Presence and Self-regulated Learning Ability on Learning Satisfaction in Flipped Learning Environment

The purpose of this study is to evaluate the effect of learning presence and self-regulated learning ability on learning satisfaction of nursing students who studied flipped learning classes.The procedure of the flipped learning class was divided into learning outside the classroom and learning within the classroom, and it can be organized in detail by watching learning videos, pre-learning check activities, knowledge application activities, and finishing activities.The participants were 188 nursing students of the first year in the university.The data were collected June, 2022.The general characteristics of the subjects were analyzed for the frequency of numbers and percentages.The degree of learning presence, self-regulated learning ability, and learning satisfaction perceived by the subjects were descriptive statistics of the mean and standard deviation.Pearson correlation coefficients were calculated for the relationship between learning presence, self-regulated learning ability, and learning satisfaction.The effect of learning presence and self-regulated learning ability on learning satisfaction was analyzed by multiple regression analysis.The factors affecting learning satisfaction were cognitive presence and emotional reality.Recognition • super cognition control, motivation control, and behavior control had a significant effect on learning satisfaction.It will be possible to provide basic data for preparing measures to improve learning satisfaction, such as increasing the sense of reality in the learning situation and instilling confidence in the learners' own abilities.
Cinebot: more than just a robot, a laboratory

This work reports the design, construction and deployment of a mechatronic system, which, combined with a computer vision module, provides students and teachers with a tool to experience with concepts of kinematics of a particle throughout the usage of a robot, known as to Cinebot, which can be controlled from any smart device endowed with a stable internet connection. The methodology used for envisioning and developing the system was engineering design. The figure of merits of the prototype was evaluated under a linear motion scheme with constant speed (~0.10 m/s), showing errors between 0.08 and 3.2%.
An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II

In computer vision, convolutional neural networks have a wide range of uses.Images represent most of today's data, so it's important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However, when designing the network structure for a particular problem, you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand, different datasets require different combinations of models or hyperparameters, which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem, such as grid search limited to low-dimensional space and queuing by random selection.To address this issue, we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks (CNNs) using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms (NSGA) to improve the hyperparameters of the CNN model.In addition, different types and parameter ranges of existing genetic algorithms are used.A comparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous 5642 CMC, 2023, vol.74, no.3 methods in terms of classification accuracy, and the results are published in modern computing literature.
Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

Few previous Reversible Visible Watermarking (RVW) schemes have both good transparency and watermark visibility.An adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding (BTC) compressed domain, called TVB-RVW is proposed in this paper.A new mean image estimation method for BTC-compressed images is first developed with the help of Total Variation.Then, a visual perception factor computation model is devised by fusing texture and luminance characteristics.An adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC domain.Moreover, a lossless embedding method of the encrypted visible watermark is exploited to deter illegal watermark removal.The visible watermark can be removed since the visual perception factor and the estimated mean image remain unchanged before and after watermark embedding.Extensive experiments validate the superiority of the proposed algorithm over previous RVW schemes in BTC in terms of the visual quality of watermarked images and watermark visibility, and it can achieve a good balance between transparency and watermark visibility.