Reviewerly/exHarmony huggingface dataset:
Heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game

Ant colony optimization (ACO) is prone to get into the local optimum and has a slow convergence speed when it is applied to the traveling salesman problem (TSP). Therefore, for overcoming the drawbacks of ACO, a heterogeneous ant colony optimization based on adaptive interactive learning and non-zero-sum game is proposed. Firstly, three subpopulations with different characteristics are constructed into heterogeneous ant colony to enhance the performance of the ant colony. Secondly, the adaptive interactive learning mechanism is adopted when the algorithm diversity decreases, in which the objects to be communicated are selected adaptively according to the population similarity. In this mechanism, the way of communication is to pair the inferior individuals with the superior individuals, which enlarges the searching range and speeds up the convergence speed. Finally, an elite information exchange strategy based on non-zero-sum game is adopted when the algorithm falls into local optimum, in which each subpopulation selects the partners for elite information exchange according to the normalized comprehensive evaluation operator, which is helpful for each subpopulation to select the most appropriate strategy for getting out of the local optimal. Through this model, the accuracy of the solution is further improved. The data that used for this experiment is from the TSPLIB library under MATLAB simulation with various ranges of TSP datasets. Experimental results indicate that the proposed algorithm has a higher quality solution and faster convergence speed in solving the traveling salesman problem.
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization

In this paper, we present a denoising sequence-to-sequence (seq2seq) autoencoder via contrastive learning for abstractive text summarization. Our model adopts a standard Transformer-based architecture with a multi-layer bi-directional encoder and an auto-regressive decoder. To enhance its denoising ability, we incorporate self-supervised contrastive learning along with various sentence-level document augmentation. These two components, seq2seq autoencoder and contrastive learning, are jointly trained through fine-tuning, w hich i mproves the performance of text summarization with regard to ROUGE scores and human evaluation. We conduct experiments on two datasets and demonstrate that our model outperforms many existing benchmarks and even achieves comparable performance to the state-of-the-art abstractive systems trained with more complex architecture and extensive computation resources.
Applying Augmented reality to enhance physics laboratory experience: does learning anxiety matter?

Augmented reality (AR) recently shows great potential to facilitate students' learning, especially their learning performance and motivation. However, few studies considered learners' emotional factors such as learning anxiety, which may influence the learning results. This study aims to explore how different experimental conditions (AR & Non-AR) may affect the learning performance and motivation of students with different levels of learning anxiety. An AR-based magnetism experimental tool entitled "MagAR" was proposed to support junior high school students' physics learning. The results indicated that, when compared to traditional experimental materials, AR can effectively improve students' learning performance and motivation. Moreover, regardless of their anxiety, students in AR condition performed better on transfer test than those in Non-AR group. AR can also significantly motivate students with high anxiety. Besides, all students hold a positive attitude towards MagAR.
Survey on profiling age and gender of text authors

Author profiling from text documents has become a popular task in latest years, in natural language applications. Author profiling is important for various domains such as advertising, marketing, forensics, and security. This survey focuses on profiling age and gender, the two features, which are probably the most researched profile attributes. In this paper, we present an overview of representative studies and datasets of the field (including those organized by PAN) with several significant leaps. Due to the increasing use of deep learning (DL) methods in recent years, we have also reviewed several DL systems that profile authors’ age and gender. Most age and gender datasets contain blog posts or Twitter messages written in English, Spanish or Arabic. There are also several relevant datasets written in Dutch, Italian, Portuguese, Turkish, and Russian. There is no consistency and no uniformity in the datasets concerning to the number and types of their documents, the division into training, dev, and test sets, the types of the applied preprocessing methods, and the quality measures used to evaluate the classification results. A prominent interesting finding is that the best age accuracy results are not as high as we might have expected taking into account relatively simple types of classification especially by gender (only 2 categories) when a large number of teams have competed over the years. Another interesting finding that repeats itself in various classification tasks is that classical ML methods are still better than DL methods for age and gender classification tasks. Most classical systems used word unigrams and bigrams and character 3–4-5-grams. Several systems also used various types of stylistic features. While many earlier systems did not apply preprocessing methods, most recent systems applied several preprocessing methods, e.g., lowercase conversion and replacement of various strings (e.g., URLs, LF characters, and User Mentions). We also suggest several potential future issues in age and gender profiling research.
FG-GAN: A Fine-Grained Generative Adversarial Network for Unsupervised SAR-to-Optical Image Translation

Synthetic aperture radar (SAR) and optical sensing are two important means of Earth observation. SAR can be used for all-day and all-weather Earth observation, but it has the disadvantages of speckle noise and geometric distortion, which are not conducive to human eye recognition. Optical image conforms to the characteristics of human visual observation, but it is easily affected by climate and time. Therefore, to integrate the advantages of the two, researchers have carried out extensive work on SAR-to-optical (S2O) image translation. Most of the existing methods for S2O image translation are supervised and need paired training samples, limiting its large-scale application in remote sensing field. Thus, we give priority to an unsupervised S2O image translation method. Meanwhile, we find that the images generated by unsupervised methods suffer from significant detail deficiencies. To solve this problem, we propose a fine-grained generative adversarial network (FG-GAN) introducing three strategies to enhance the detailed information in generated optical images. First, we design an unbalanced generator (UBG) with complex encoder networks and relatively simple decoder networks. The complex encoder extracts abundant feature information, while the decoder obtains key details by filtering these features. Second, to match the learning ability of the generator, we present a multiscale discriminator (MSD) to enhance the discriminant ability of the network. Third, we propose a comprehensive normalization group (CNG) to promote the physical representation consistency of SAR and optical images. Extensive experiments have been conducted, and the results show that our method is superior to the state-of-the-art (SOTA) methods on both subjective and objective evaluation indicators. Moreover, our FG-GAN has a significant improvement on classification accuracy, indicating its potential in facilitating the performance of practical remote sensing tasks.
Human Body Pose Intelligent Estimation Based on BlazePose

Intelligent physical fitness testing has always been the focus of researchers&#x0027; attention, because of the large number of people participating in physical fitness testing and each physical fitness testing needs a lot of manpower and time, so the market has a strong demand for automated physical fitness testing equipment. In this paper, computer vision and deep learning methods are combined to extract key pose points from video information obtained by the camera using BlazePose. Then, filtering and feature extraction are carried out on these key points of attitude data and three machine learning methods are used to classify these data. The experimental results show that the action recognition rate obtained by ANN algorithm is 97.5&#x0025;, and the accuracy of counting algorithm is 97.9&#x0025;, which has great practical application value.
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named Delin is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to k NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of Delin in improving the generalization performance of well-established partial label learning algorithms.
Visualizing the Scripts of Data Wrangling With Somnus

Data workers use various scripting languages for data transformation, such as SAS, R, and Python. However, understanding intricate code pieces requires advanced programming skills, which hinders data workers from grasping the idea of data transformation at ease. Program visualization is beneficial for debugging and education and has the potential to illustrate transformations intuitively and interactively. In this article, we explore visualization design for demonstrating the semantics of code pieces in the context of data transformation. First, to depict individual data transformations, we structure a design space by two primary dimensions, i.e., key parameters to encode and possible visual channels to be mapped. Then, we derive a collection of 23 glyphs that visualize the semantics of transformations. Next, we design a pipeline, named Somnus, that provides an overview of the creation and evolution of data tables using a provenance graph. At the same time, it allows detailed investigation of individual transformations. User feedback on Somnus is positive. Our study participants achieved better accuracy with less time using Somnus, and preferred it over carefully-crafted textual description. Further, we provide two example applications to demonstrate the utility and versatility of Somnus.
Hybrid Deep Learning Framework for Reduction of Mixed Noise via Low Rank Noise Estimation

In this paper, an innovative hybridized deep learning framework (EN-CNN) is presented for image noise reduction where the noise originates from heterogeneous sources. More specifically, EN-CNN is applied to the benchmark natural images affected by a mixture of additive white gaussian noise (AWGN) and impulsive noise (IN). Reduction of mixed noise (AWGN and IN) is relatively more involved as compared to removing simply one type of noise. In fact, mitigating the impact of a mixture of multiple noise types becomes exceedingly challenging due to simultaneous presence of different noise statistics. Although, various effective deep learning approaches and the classical state-of-the-art approaches like WNNM have been used to suppress AWGN noise only, the same techniques are not suitable in case of mixed noise. In this context, EN-CNN can not only infer changed noise statistics but can also effectively eliminate residual noise. Firstly, EN-CNN employs the classical method of neighborhood filtering followed by non-local low rank estimation to respectively reduce IN noise and estimate the residual noise characteristics after reducing IN noise. As a result of this step, we obtain a pre-processed image with residual noise statistics. Secondly, convolutional neural network (CNN) is applied to the pre-processed image based on the noise statistics inferred in the first step. This two pronged strategy, in conjunction with the deep learning mechanism, effectively handles the mixed noise suppression. As a result, the suggested framework yields promising results as compared to various state-of-the-art approaches.
Single-shot Embedding Dimension Search in Recommender System

As a crucial component of most modern deep recommender systems, feature embedding maps high-dimensional sparse user/item features into low-dimensional dense embeddings. However, these embeddings are usually assigned a unified dimension, which suffers from the following issues: (1) high memory usage and computation cost. (2) sub-optimal performance due to inferior dimension assignments. In order to alleviate the above issues, some works focus on automated embedding dimension search by formulating it as hyper-parameter optimization or embedding pruning problems. However, they either require well-designed search space for hyperparameters or need time-consuming optimization procedures. In this paper, we propose a Single-Shot Embedding Dimension Search method, called SSEDS, which can efficiently assign dimensions for each feature field via a single-shot embedding pruning operation while maintaining the recommendation accuracy of the model. Specifically, it introduces a criterion for identifying the importance of each embedding dimension for each feature field. As a result, SSEDS could automatically obtain mixed-dimensional embeddings by explicitly reducing redundant embedding dimensions based on the corresponding dimension importance ranking and the predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic, meaning that it could be integrated into different base recommendation models. The extensive offline experiments are conducted on two widely used public datasets for CTR (Click Through Rate) prediction task, and the results demonstrate that SSEDS can still achieve strong recommendation performance even if it has reduced 90% parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription platform for practical recommendation services. The 7-day online A/B test results show that SSEDS can significantly improve the performance of the online recommendation model while reducing resource consumption.
Improved BLS based transformer fault diagnosis considering imbalanced samples

At present, the traditional machine learning diagnosis method has some problems, such as the imbalance of various fault types and the great difference of recognition effect between different fault types. In order to solve the problem of low accuracy of traditional machine learning diagnosis methods, a multilevel fault diagnosis model of transformer based on hierarchical classification, generalized learning and ensemble learning is established. According to the imbalanced degree of each category sample, the method established the corresponding classifier, and carried on the diagnosis step by step. At the first level, the broad learning system was selected to extract three generalized feature labels—normal, discharge and overheat, which were fused with the original parameter input to guide the nine more detailed state types. In the second-level classifier, EasyEnsemble learning method is used to generate multiple data-balanced training subsets through under-sampling, fully balancing the fault information of most classes and a few classes, and then synthesized the final classifier through parallel training sub-classifiers, avoiding the problem of missing data information due to under-sampling. Experimental results show that, compared with the traditional diagnosis methods, the proposed method improved the generalization characteristics of a few types of faults, improved the overall accuracy rate, and had a more accurate and balanced fault diagnosis effect of power transformer.
DMRVisNet: Deep Multihead Regression Network for Pixel-Wise Visibility Estimation Under Foggy Weather

Scene perception is essential for driving decision-making and traffic safety. However, fog, as a kind of common weather, frequently appears in the real world, especially in mountain areas, making it difficult to accurately observe the surrounding environments. Therefore, precisely estimating the visibility under foggy weather can significantly benefit traffic management and safety. To address this, most current methods use professional instruments outfitted at fixed locations on the roads to perform the visibility measurement; these methods are expensive and less flexible. In this paper, we propose an innovative end-to-end convolutional neural network framework to estimate the visibility leveraging Koschmieder’s law and the image data. The proposed method estimates the visibility by integrating the physical model into the proposed framework, instead of directly predicting the visibility value via the convolutional neural network. Moreover, we estimate the visibility as a pixel-wise visibility map against those of previous visibility measurement methods which solely predict a single value for the entire image. Thus, the estimated result of our method is more informative, particularly in uneven fog scenarios, which can benefit to developing a more precise early warning system for foggy weather, thereby better protecting the intelligent transportation infrastructure systems and promoting their development. To validate the proposed framework, a virtual dataset, FACI, containing 3,000 foggy images in different concentrations, is collected using the AirSim platform, which is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/coutyou/FoggyAirsimCityImages</uri> . Detailed experiments show that the proposed method achieves performance competitive to those of state-of-the-art methods.
Breaking Blockchain’s Communication Barrier With Coded Computation

Although blockchain, the supporting technology of various cryptocurrencies, has offered a potentially effective framework for numerous decentralized trust management systems, its performance is still sub-optimal in real-world networks. With limited bandwidth, the communication complexity for nodes to process a block scales with the growing network size and hence becomes the limiting factor of blockchain’s performance. In this paper, we suggest a re-design of existing blockchain systems, which addresses the issue of the communication burden. First, by employing techniques from Coded Computation, our scheme guarantees correct verification of transactions while reducing the bit complexity dramatically such that it grows logarithmically with the number of nodes. Second, with the adoption of techniques from Information Dispersal and State Machine Replication, the system is resilient to Byzantine faults and achieves linear message complexity. Third, we propose a novel 2-dimensional sharding strategy, which inherently supports cross-shard transactions, alleviating the need for complicated communication protocols between shards, while keeping the computation and storage benefits of sharding.
CNDesc: Cross Normalization for Local Descriptors Learning

For a long time, the local descriptors learning benefited from the use of L2 normalization, which projects the descriptor space onto the hypersphere. However, there is no free lunch in the world. Although hypersphere description space stabilizes the optimization and improves the repeatability of the descriptors, it causes the descriptors to have a denser distribution, which reduces the discrimination between descriptors and leads to some incorrect matches. To alleviate this problem, we propose the learnable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cross normalization</i> technology as an alternative to L2 normalization, which can achieve a consistent improvement in several of the current popular local descriptors. In addition, we propose an ER-Backbone that can efficiently reuse features in descriptors extraction and an IDC Loss that can provide an image-level description space distribution consistency constraint to further stimulate the performance of the local descriptors. Based on the above innovations, we provide a novel local descriptors extraction method named CNDesc. We perform experiments on image matching, homography estimation, 3D reconstruction, and visual localization tasks, and the results demonstrate that our CNDesc surpasses the current state-of-the-art local descriptors. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/vignywang/CNDesc</uri> .
Modeling and predicting students’ engagement behaviors using mixture Markov models

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope with extremely large datasets. As an additional feature, the proposal includes a threshold value to denote the number of courses that each discovered rule should satisfy. A post-processing step is also included so redundant subgroups can be removed. The experimental stage is carried out by considering de-identified data from the first year of 16 MITx and HarvardX courses on the edX platform. Experimental results demonstrate that the proposed MapReduce approach outperforms traditional sequential subgroup discovery approaches, achieving a runtime that is almost constant for different courses. Additionally, thanks to the final post-processing step, only interesting and not-redundant rules are discovered, hence reducing the number of subgroups in one or two orders of magnitude. Finally, the discovered subgroups are easily used by courses' instructors not only for descriptive purposes but also for additional tasks such as recommendation or personalization.