Reviewerly/exHarmony huggingface dataset:
A Recursive tree-structured neural network with goal forgetting and information aggregation for solving math word problems

Most existing state-of-the-art neural network models for math word problems use the Goal-driven Tree-Structured decoder (GTS) to generate expression trees. However, we found that GTS does not provide good predictions for longer expressions, mainly because it does not capture the relationships among the goal vectors of each node in the expression tree and ignores the position order of the nodes before and after the operator. In this paper, we propose a novel Recursive tree-structured neural network with Goal Forgetting and information aggregation (RGFNet) to address these limits. The goal forgetting and information aggregation module is based on ordinary differential equations (ODEs) and we use it to build a sub-goal information feedback neural network (SGIFNet). Unlike GTS, which uses two-layer gated-feedforward networks to generate goal vectors, we introduce a novel sub-goal generation module. The sub-goal generation module could capture the relationship among the related nodes (e.g. parent nodes, sibling nodes) using attention mechanism. Experimental results on two large public datasets i.e. Math23K and Ape-clean show that our tree-structured model outperforms the state-of-the-art models and obtains answer accuracy over 86%. Furthermore, the performance on long-expression problems is promising.1
AMDS symbol-pair codes from repeated-root cyclic codes

Symbol-pair codes are proposed to guard against pair-errors in symbol-pair read channels. The minimum symbol-pair distance is of significance in determining the error-correcting capability of a symbol-pair code. One of the central themes in symbol-pair coding theory is the constructions of symbol-pair codes with the largest possible minimum symbol-pair distance. Maximum distance separable (MDS) and almost maximum distance separable (AMDS) symbol-pair codes are optimal and sub-optimal regarding the Singleton bound, respectively. In this paper, six new classes of AMDS symbol-pair codes are explicitly constructed through repeated-root cyclic codes. Remarkably, one class of such codes has unbounded lengths and the minimum symbol-pair distance of another class can reach 13.
MEQA: Manifold embedding quality assessment via anisotropic scaling and Kolmogorov-Smirnov test

Manifold learning methods unfold the manifold structures and embed them in a lower-dimensional space. The quality of such an embedding should be measured both qualitatively and quantitatively. The proposed manifold embedding quality assessment (MEQA) method does so by taking into account of local and global structure preservation as both are important traits of an embedding. To measure the local structure preservation MEQA uses two transformations. Initially anisotropic scaling, rotation and translation are incorporated to measure the closeness between the original and the embedded data points. In the next stage, rigid transformation is incorporated to quantify the previous transformation which involved anisotropic scaling. For quantifying the global structure preservation, the Kolmogorov-Smirnov test is applied in a distributed manner over each dimension and averaged over them. To establish the superiority of MEQA we conducted several studies over standard synthetic and real-life datasets across separate existing feature extraction techniques.
Multiple camera styles learning for unsupervised person re-identification

Most clustering-based unsupervised person re-identification methods usually first assign pseudo-labels to pedestrian images using a clustering algorithm and then train a network using the labeled pedestrian images in each iteration. However, clustering algorithms may produce some clusters that only contain pedestrian images from fewer cameras, thus resulting in these clusters having fewer camera styles. Training a network using the pedestrian images from these clusters may degrade the capability of the network to retrieve pedestrians across cameras. Moreover, unsupervised learning methods usually utilize only contrastive loss that is based on self-supervised learning. However, lack of metric loss with mandatory guidance from labels may lead the network to fail to learn sufficiently the discriminative information of pedestrians. To address these issues, we propose a multiple camera styles learning (MCSL) method for unsupervised person re-identification. Specifically, we design a novel cluster filtration module (CFM) to ensure the clustering algorithm to keep the focus on pedestrian identities rather than camera styles by filtering out the clusters with fewer camera styles. In addition, we also design a multi-granularity feature metric (MGFM) loss to introduce metric learning with labels into unsupervised learning. The effectiveness of the proposed MCSL is demonstrated by the experimental results (Rank-1 accuracy) on Market1501 (93.7%), DukeMTMC(86.0%) and MSMT17 (71.12%), significantly reducing the gap between unsupervised and supervised performance on person re-identification.
An innovative data collection method to eliminate the preprocessing phase in web usage mining

The underlying data source for web usage mining (WUM) is commonly thought to be server logs. However, access log files ensure quite limited data about the clients. Identifying sessions from this messy data takes considerable effort, and operations performed for this purpose do not always yield excellent results. Also, this data cannot be used for web analytics efficiently. This study proposes a method for user tracking, session management, and collecting web usage data. The method is mainly based on an innovative approach for using collected data for web analytics extraction as the data source in web usage mining. An application-based API has been developed with a different strategy from conventional client-side methods to obtain and process log data. The log data has been successfully gathered by integrating the technique into an enterprise web application. The results reveal that the homogeneous data collected and stored with this method is more convenient to browse, filter, and process than web server logs. This structured data can be used effortlessly as a reliable data source for high-performance web usage mining activity, real-time web analytics, machine learning algorithms, or a functional recommendation system.
Marine ship instance segmentation by deep neural networks using a global and local attention (GALA) mechanism

Marine ships are the transport vehicle in the ocean and instance segmentation of marine ships is an accurate and efficient analysis approach to achieve a quantitative understanding of marine ships, for example, their relative locations to other ships or obstacles. This relative spatial information is crucial for developing unmanned ships to avoid crashing. Visible light imaging, e.g. using our smartphones, is an efficient way to obtain images of marine ships, however, so far there is a lack of suitable open-source visible light datasets of marine ships, which could potentially slow down the development of unmanned ships. To address the problem of insufficient datasets, here we built two instance segmentation visible light datasets of marine ships, MariBoats and MariBoatsSubclass, which could facilitate the current research on instance segmentation of marine ships. Moreover, we applied several existing instance segmentation algorithms based on neural networks to analyze our datasets, but their performances were not satisfactory. To improve the segmentation performance of the existing models on our datasets, we proposed a global and local attention mechanism for neural network models to retain both the global location and semantic information of marine ships, resulting in an average segmentation improvement by 4.3% in terms of mean average precision. Therefore, the presented new datasets and the new attention mechanism will greatly advance the marine ship relevant research and applications.
Use of Facebook fan pages in Polish academic libraries (2009–2022)

The article presents the results of a quantitative comparative analysis of the activity of 59 Polish public university libraries on the social networking site Facebook (2009–2022). The study aimed to assess the use of Facebook by university libraries of different types and to evaluate the popularity of the content shared by these institutions based on the number of posts shared and the users' reactions to them. Fanpage Karma, a commercial online tool for monitoring social media monitoring was used to collect and statistically analyze the data from individual fan pages. The findings showed that overall, the degree of social media activity was not dependent on the type of library. Although the existence of Facebook is not new in the area of social networking, its use in Polish libraries has not yet become widespread. The phenomenon of the lack of library fan pages on social media is worrying given that several of these tertiary level libraries had no fan pages on any kind of social media. Nevertheless, for those that had a Facebook fan page, it was discovered that it was one of the primary tools for communicating with users, promoting library and information services, and other library activities.
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval

The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}). The zero-shot sketch-based image retrieval (\underline{ZS-SBIR}) is more generic and practical but poses an even greater challenge because of the additional knowledge gap between the seen and unseen categories. To simultaneously mitigate both gaps, we propose an \textbf{A}pproaching-and-\textbf{C}entralizing \textbf{Net}work (termed "\textbf{ACNet}") to jointly optimize sketch-to-photo synthesis and the image retrieval. The retrieval module guides the synthesis module to generate large amounts of diverse photo-like images which gradually approach the photo domain, and thus better serve the retrieval module than ever to learn domain-agnostic representations and category-agnostic common knowledge for generalizing to unseen categories. These diverse images generated with retrieval guidance can effectively alleviate the overfitting problem troubling concrete category-specific training samples with high gradients. We also discover the use of proxy-based NormSoftmax loss is effective in the zero-shot setting because its centralizing effect can stabilize our joint training and promote the generalization ability to unseen categories. Our approach is simple yet effective, which achieves state-of-the-art performance on two widely used ZS-SBIR datasets and surpasses previous methods by a large margin.
Dense Concatenation Memory Network for Aspect Level Sentiment Analysis

Aspect-level sentiment analysis is a fundamental task in sentiment analysis. It has many practical applications in product reviews, takeaway evaluations, and public opinion supervision. Over the past few years, previous studies have made remarkable progress. However, the existing methods still have some areas for improvement in aspect processing and feature fusion. In this study, we propose an aspect-context dense concatenation model (ACDC) to merge deep semantic information from various aspects and contexts. ACDC first captures the interaction information between the aspect and context using an interaction matrix. Subsequently, a dense concatenation mechanism is used to extract and integrate features at different levels. In addition, we introduce two loss functions to eliminate the interference information and overfitting. On the Restaurant2014 and Laptop2014 datasets, the experimental results show that our proposed model achieves state-of-the-art performance in terms of accuracy and MF1. Furthermore, comparative experiments are conducted to verify the effectiveness of the proposed model.
Aligning Comments to News Articles on a Budget

Disagreement among text annotators as a part of a human (expert) labeling process produces noisy labels, which affect the performance of supervised learning algorithms for natural language processing. Using only high agreement annotations introduces another challenge: the data imbalance problem. We study this challenge within the problem of relating user comments to the content of a news article. We show that traditional techniques for learning from imbalanced data, such as oversampling, using weighted loss functions, or assigning weak labels using crowdsourcing, may not be sufficient for modeling complex temporal relationships between news articles and user comments. In this study, we propose a framework for aligning comments and articles (1) from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">imbalanced news data</i> characterized with (2) different degrees of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">annotator agreement</i> , under (3) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constrained budget</i> for human labeling and computing resources. Within the framework, we propose a Semi-Automatic Labeling solution based on Human-AI collaboration. We compare our proposed technique with traditional data imbalance handling techniques and synthetic data generation on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">article-comment alignment problem</i> , where the goal is to determine a category of an article-comment pair that represents how relevant the comment is to the article. Finding an effective and efficient solution is essential because it is time-consuming and prohibitively costly to manually label a sufficiently large amount of article-comment pairs based on the semantic understanding of an article and its comments. We discover that the Human-AI collaboration outperforms all alternative techniques by 17% of article-comment alignment accuracy. When there is no time or budget for re-labeling some article-comment pairs, we found that synonym augmentation is a reasonable alternative. We also provide a detailed analysis of the effect of humans in the loop and the use of unlabeled data.
Mine-Distill-Prototypes for Complete Few-Shot Class-Incremental Learning in Image Classification

Recently, few-shot learning (FSL) has received increasing attention because of difficulties in sample collection in some application scenarios, such as maritime surveillance using synthetic aperture radar (SAR) or infrared images. In real situations of such scenarios, it is a common requirement that the model can recognize novel classes incrementally, namely class-incremental learning (CIL). Considering the above requirement, a novel problem that recognizes novel classes incrementally when both the base and novel class samples are scarce is proposed in this article. It is called complete few-shot CIL (C-FSCIL) for distinguishing from the FSCIL that assumes sufficient samples of base classes. Specifically, the following challenges of C-FSCIL are focused on: 1) distance measurement is used for recognizing novel classes incrementally, but the encoder is difficult to be learned well when base class samples are scarce, making some features unsuitable for calculating the distance, decreasing the performance and 2) the catastrophic forgetting problem becomes more difficult to be alleviated than that in FSCIL because of the scarcity of base class samples. To tackle both challenges, mine-distill-prototypes (MDP) algorithm is proposed, which consists of two parts: 1) prototypes-distillation (PD) network is proposed to learn to distill the features and prototypes into a lower dimensional in which ineffective features are eliminated and 2) the prototypes-weight (PW) network and the prototypes-selection (PS) training strategy are proposed for the catastrophic forgetting problem, which aims to capture the relationship between the base and novel prototypes. The superior performance of the proposed algorithm is demonstrated by the experiments on three datasets.
A Novel Hybrid Model for Docker Container Workload Prediction

The emergence of containers dramatically simplifies and facilitates the development and deployment of applications. More and more enterprises deploy their applications on the container cloud platform. For cloud service providers, an effective container workload prediction method is a must to achieve efficient utilization of cloud resources. However, the existing methods are either rarely based on container load characteristics or cannot make accurate real-time predictions. In this paper, we propose a Docker container workload proactive prediction method using a hybrid model combining triple exponential smoothing and long short-term memory (LSTM), which not only can capture both short-term and long-term dependencies in container resource time series but also smooth the container resource utilization data. In order to improve the prediction accuracy of the hybrid model, those two single models are combined using the mean absolute percentage error (MAPE) method. Besides, we design a real-time Docker workload prediction system for the hybrid model. Our experiments show that the mean absolute percentage error of the hybrid model is decreased by an average of 3.24%, 12.18%, 13.42%, 43.45%, and 50.69% compared with the LSTM, the triple exponential smoothing, ES-ARIMA, Bayesian Ridge Regression and BiLSTM with an acceptable time and computational cost overhead.
Introducing 3D Thumbnails to Access 360-Degree Videos in Virtual Reality

360° videos provide an immersive experience, especially when watched in virtual reality (VR). Yet, even though the video data is inherently three-dimensional, interfaces to access datasets of such videos in VR almost always use two-dimensional thumbnails shown in a grid on a flat or curved plane. We claim that using spherical and cube-shaped 3D thumbnails may provide a better user experience and be more effective at conveying the high-level subject matter of a video or when searching for a specific item in it. A comparative study against the most used existing representation, that is, 2D equirectangular projections, showed that the spherical 3D thumbnails did indeed provide the best user experience, whereas traditional 2D equirectangular projections still performed better for high-level classification tasks. Yet, they were outperformed by spherical thumbnails when participants had to search for details within the videos. Our results thus confirm a potential benefit of 3D thumbnail representations for 360-degree videos in VR, especially with respect to user experience and detailed content search and suggest a mixed interface design providing both options to the users. Supplemental materials about the user study and used data are available at https://osf.io/5vk49/.
Parallelized Control-Aware Motion Planning With Learned Controller Proxies

Kinodynamic motion planning enables autonomous robots to find efficient paths while minimizing energy expenditure and avoiding hazards in the environment. However, during plan execution, the controller may deviate from the collision-free path found by the planner due to discrepancies between planning and control, causing inaccurate estimation of path costs and potentially collisions with obstacles. While this can be mitigated by incorporating the vehicle controller into planning, these approaches are generally bottlenecked by the high computation cost of simulating the vehicle dynamics and controller. This letter presents the Parallel Closed-Loop RRT* motion planner that uses a fast neural network controller as a substitute for a computationally-demanding controller during planning. Using a neural network controller and parallelizing the planning process makes closed-loop planning tractable for vehicles with nonlinear dynamics and significantly reduces planning time. Experiments on a simulated underwater vehicle with a model predictive controller demonstrate that our approach yields feasible plans that are more likely to be successfully executed without collisions compared to planners that do not consider the controller.
Solar PV Power Estimation and Upscaling Forecast Using Different Artificial Neural Networks Types: Assessment, Validation, and Comparison

According to its various features, the solar photovoltaics (PV) system is realized as a significant promising energy source to cope with energy shortcomings and environmental impacts like contamination. Therefore, it is mandatory to estimate and predict the output power for prediction intervals to avoid any power outage or urgent disturbances in the utility grid. These are challenging tasks as the solar PV output power depends on the weather variables data such as temperature and solar radiation. In this article, the estimation and forecast of solar PV output power are investigated with an upscaling method using three different types of artificial neural networks (ANNs) in order to reduce the estimation errors in current types of ANNs. The multilayer feedforward neural network (MLFFNN), recurrent neural network (RNN), and nonlinear autoregressive exogenous (NARX) model neural network (NARXNN) are applied to estimate and forecast the total output power of four real solar PV substations in Egypt. Hence both the surface temperature and the solar radiation of each PV substation are applied as the inputs of each designed NN, whereas the total output power of the four PV substations is its output. For the training and effectiveness investigation procedures of each applied ANNs, the data of two months (60 days) are attained and collected from these four PV substations. Here, the data of the first 45 days are applied to train the three designed NNs, while the data from the remaining 15 days, which are not applied for the training, are used to check the effectiveness and the generalization capability of the trained NNs. Hence, the estimation process is considered a prior step for the forecast of the output power. Therefore, upscaling method is utilized for assessing and forecasting a regional solar PV output power because of the limited number of monitored plants and applied data. The results provide evidence that the trained NNs are running very well and efficiently to estimate the power correctly. The performance of the MLFFNN is the best compared with the other NNs, whereas the NARXNN’s performance is the lowest one. The MLFFNN achieves the lowest mean squared error (MSE) of 0.27533 and the lowest absolute approximation error of 0.2099 MWh. Finally, the assessment and comparison among the three trained NNs and other techniques in recently published articles are highlighted and presented which reveal the performance superiority of the three trained NNs compared to other ANNs.