Reviewerly/exHarmony huggingface dataset:
Imbalanced Classification Methods for Student Grade Prediction: A Systematic Literature Review

Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students’ performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy predictive model is challenging due to the dataset’s imbalanced nature, which caused biased results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field.
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images

We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view. This capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm where we sample and render virtual camera trajectories, including cyclic camera paths, allowing our model to learn stable view generation from a collection of single views. At test time, despite never having seen a video, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality. Our project webpage, including video results, is at https://infinite-nature-zero.github.io .
Meta Convolutional Neural Networks for Single Domain Generalization

In single domain generalization, models trained with data from only one domain are required to perform well on many unseen domains. In this paper, we propose a new model, termed meta convolutional neural network, to solve the single domain generalization problem in image recognition. The key idea is to decompose the convolutional features of images into meta features. Acting as “visual words”, meta features are defined as universal and basic visual elements for image representations (like words for documents in language). Taking meta features as reference, we propose compositional operations to eliminate irrelevant features of local convolutional features by an addressing process and then to reformulate the convolutional feature maps as a composition of related meta features. In this way, images are universally coded without biased information from the unseen domain, which can be processed by following modules trained in the source domain. The compositional operations adopt a regression analysis technique to learn the meta features in an online batch learning manner. Extensive experiments on multiple benchmark datasets verify the superiority of the proposed model in improving single domain generalization ability.
An Empirical Study of Challenges in Converting Deep Learning Models

There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually, DL models are developed and trained using DL frameworks like TensorFlow and PyTorch. Each framework has its own internal mechanisms/formats to represent and train DL models (deep neural networks), and usually those formats cannot be recognized by other frameworks. Moreover, trained models are usually deployed in environments different from where they were developed. To solve the interoperability issue and make DL models compatible with different frameworks/environments, some exchange formats are introduced for DL models, like ONNX and CoreML. However, ONNX and CoreML were never empirically evaluated by the community to reveal their prediction accuracy, performance, and robustness after conversion. Poor accuracy or non-robust behavior of converted models may lead to poor quality of deployed DL-based software systems. We conduct, in this paper, the first empirical study to assess ONNX and CoreML for converting trained DL models. In our systematic approach, two popular DL frameworks, Keras and PyTorch, are used to train five widely used DL models on three popular datasets. The trained models are then converted to ONNX and CoreML and transferred to two runtime environments designated for such formats, to be evaluated. We investigate the prediction accuracy before and after conversion. Our results unveil that the prediction accuracy of converted models are at the same level of originals. The performance (time cost and memory consumption) of converted models are studied as well. The size of models are reduced after conversion, which can result in optimized DL-based software deployment. We also study the adversarial robustness of converted models to make sure about the robustness of deployed DL-based software. Leveraging the state-of-the-art adversarial attack approaches, converted models are generally assessed robust at the same level of originals. However, obtained results show that CoreML models are more vulnerable to adversarial attacks compared to ONNX. The general message of our findings is that DL developers should be cautious on the deployment of converted models that may 1) perform poorly while switching from one framework to another, 2) have challenges in robust deployment, or 3) run slowly, leading to poor quality of deployed DL-based software, including DL-based software maintenance tasks, like bug prediction.
Automatic Pull Request Title Generation

Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging into another branch in a repository. A PR needs to be reviewed and approved by the core team of the repository before the changes are merged into the branch. Usually, reviewers need to identify a PR that is in line with their interests before providing a review. By default, PRs are arranged in a list view that shows the titles of PRs. Therefore, it is desirable to have a precise and concise title, which is beneficial for both reviewers and other developers. However, it is often the case that developers do not provide good titles; we find that many existing PR titles are either inappropriate in length (i.e., too short or too long) or fail to convey useful information, which may result in PR being ignored or rejected. Therefore, there is a need for automatic techniques to help developers draft high-quality titles.In this paper, we introduce the task of automatic generation of PR titles. We formulate the task as a one-sentence summarization task. To facilitate the research on this task, we construct a dataset that consists of 43,816 PRs from 495 GitHub repositories. We evaluated the state-of-the-art summarization approaches for the automatic PR title generation task. We leverage ROUGE metrics to automatically evaluate the summarization approaches and conduct a manual evaluation. The experimental results indicate that BART is the best technique for generating satisfactory PR titles with ROUGE-1, ROUGE-2, and ROUGE-L F1-scores of 47.22, 25.27, and 43.12, respectively. The manual evaluation also shows that the titles generated by BART are preferred.
Controllable Animation of Fluid Elements in Still Images

We propose a method to interactively control the animation of fluid elements in still images to generate cinemagraphs. Specifically, we focus on the animation of fluid elements like water, smoke, fire, which have the properties of repeating textures and continuous fluid motion. Taking inspiration from prior works, we represent the motion of such fluid elements in the image in the form of a constant 2D optical flow map. To this end, we allow the user to provide any number of arrow directions and their associated speeds along with a mask of the regions the user wants to animate. The user-provided input arrow directions, their corresponding speed values, and the mask are then converted into a dense flow map representing a constant optical flow map (F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</inf> ). We observe that F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</inf> , obtained using simple exponential operations can closely approximate the plausible motion of elements in the image. We further refine computed dense optical flow map F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</inf> using a generative-adversarial network (GAN) to obtain a more realistic flow map. We devise a novel UNet based architecture to autoregressively generate future frames using the refined optical flow map by forward-warping the input image features at different resolutions. We conduct extensive experiments on a publicly available dataset and show that our method is superior to the baselines in terms of qualitative and quantitative metrics. In addition, we show the qualitative animations of the objects in directions that did not exist in the training set and provide a way to synthesize videos that otherwise would not exist in the real world. Project url: https://controllable-cinemagraphs.github.io/
Self-Attention Memory-Augmented Wavelet-CNN for Anomaly Detection

Anomaly detection plays an important role in manufacturing quality control/assurance. Among approaches adopting computer vision techniques, reconstruction-based methods learn a content-aware mapping function that transfers abnormal regions to normal regions in an unsupervised manner. Such methods usually have difficulty in improving both the reconstruction quality and capacity for abnormal discovery. We observe that high-level semantic contextual features demonstrate a strong ability for abnormal discovery, while variational features help to preserve fine image details. Inspired by the observation, we propose a new abnormal detection model by utilizing features for different purposes depending on their frequency characteristics. The 2D-discrete wavelet transform (DWT) is introduced to obtain the low-frequency and high-frequency components of features and further used to generate the two essential features following different routing paths in our encoder process. To further improve the capacity for abnormal discovery, we propose a novel feature augmentation module that is informed by a customized self-attention mechanism. Extensive experiments are conducted on two popular datasets: MVTec AD and BTAD. The experimental results illustrate that the proposed method outperforms other state-of-the-art approaches in terms of the image-level AUROC score. In particular, our method achieves 100% of the image-level AUROC score on 8 out of 15 classes on the MVTec dataset.
Prediction Methods of Common Cancers in China Using PCA-ANN and DBN-ELM-BP

Accurate prediction of cancer cases is crucial for diagnosis of cancer at an early stage because a long-lasting chronic disease is harmful to both physical and mental health.While medical data about healthcare and health obtained from questionnaire, the true positive rate of cancers predicted by traditional methods is low.Machine learning can provide a pattern for classification for types of cancer (mainly including lung cancer, liver cancer, upper gastrointestinal cancer, lower gastrointestinal cancer and breast cancer) using instances of early questionnaire screening.The screening covered 3411 respondents in this study.Principal component analysis (PCA) is used to generate attributes, coupled with artificial neural network (ANN) technology to conduct cancer prediction by providing 28 attributes into models.While deep belief network (DBN) is used for unsupervised training and extracting relevant attributes.Extreme learning machine (ELM) optimizes DBN and conducts supervised classification.Back propagation (BP) algorithm conducts supervised fine tuning.Finally, PCA-ANN and DBN-ELM-BP common cancers prediction models are established.The training set and testing set of PCA-ANN model gives 35.29% and 37.5% sensitivity, 98.36% and 98.33% specificity, 97.01% and 97.85% accuracy, an area under the receiver operating characteristic curve (AUC) 0.7245 and 0.7221, respectively.While the training set and testing set of DBN-ELM-BP model gives 58.83% and 62.5% sensitivity, 98.31% and 98.52% specificity, 98.03% and 98.24% accuracy, AUC 0.7747 and 0.7238, respectively.The results show that DBN-ELM-BP model can provide a method to predict the possibility of common cancers, which is non-invasive and economical for clinicians to make diagnostic decisions.
Drivers and Inhibitors of Low Code Development Platform Adoption

When done right, the use of low code development promises a significant competitive advantage in the software development process for organizations. Thus, multiple vendors have created low code development platforms to ease the use of low code development. However, current research on low code development platforms mainly focuses on the technological aspects of the platforms but not on their adoption. Hence, it remains unclear what drives and inhibits the adoption of low code development platforms. We conducted a literature review and identified thirteen factors that inhibit the adoption and seven factors that drive it. We structure these factors along with the diffusion of innovation framework that helps to disentangle drivers and inhibitors. As a result, we provide an initial explanation of the adoption of low code development platforms. Nevertheless, we conclude that existing research on the adoption of low code development platforms is not specific enough to understand the phenomenon substantially. Further, for some factors (e.g., cost), there is a disagreement in the academic literature on whether they are drivers or inhibitors. Hence, we identify gaps and derive avenues for future research.
Multiview Stereo with Cascaded Epipolar RAFT

We address multiview stereo (MVS), an important 3D vision task that reconstructs a 3D model such as a dense point cloud from multiple calibrated images. We propose CER-MVS (Cascaded Epipolar RAFT Multiview Stereo), a new approach based on the RAFT (Recurrent All-Pairs Field Transforms) architecture developed for optical flow. CER-MVS introduces five new changes to RAFT: epipolar cost volumes, cost volume cascading, multiview fusion of cost volumes, dynamic supervision, and multiresolution fusion of depth maps. CER-MVS is significantly different from prior work in multiview stereo. Unlike prior work, which operates by updating a 3D cost volume, CER-MVS operates by updating a disparity field. Furthermore, we propose an adaptive thresholding method to balance the completeness and accuracy of the reconstructed point clouds. Experiments show that our approach achieves state-of-the-art performance on the DTU and Tanks-and-Temples benchmarks (both intermediate and advanced set). Code is available at https://github.com/princeton-vl/CER-MVS .
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos

With the assumption that a video dataset is multimodality annotated in which auditory and visual modalities both are labeled or class-relevant, current multimodal methods apply modality fusion or cross-modality attention. However, effectively leveraging the audio modality in vision-specific annotated videos for action recognition is of particular challenge. To tackle this challenge, we propose a novel audio-visual framework that effectively leverages the audio modality in any solely vision-specific annotated dataset. We adopt the language models (e.g., BERT) to build a semantic audio-video label dictionary (SAVLD) that maps each video label to its most K-relevant audio labels in which SAVLD serves as a bridge between audio and video datasets. Then, SAVLD along with a pretrained audio multi-label model are used to estimate the audio-visual modality relevance during the training phase. Accordingly, a novel learnable irrelevant modality dropout (IMD) is proposed to completely drop out the irrelevant audio modality and fuse only the relevant modalities. Moreover, we present a new two-stream video Transformer for efficiently modeling the visual modalities. Results on several vision-specific annotated datasets including Kinetics400 and UCF-101 validated our framework as it outperforms most relevant action recognition methods.
A Novel Construction of Substitution Box Based on Polynomial Mapped and Finite Field With Image Encryption Application

In the modern block cipher, the substitution box (S-box) is a nonlinear constituent that plays a substantial role to create the confusion in ciphertext.S-boxes with low value of differential uniformity and high value of nonlinearity are considered more secure against cryptanalysis attacks.For the construction of 8×8 S-boxes, an efficient and novel scheme is presented in this paper.This scheme based on polynomial mapped and finite field which work only for even integers without multiple of 4 in the range (2-254).Firstly, we take a quadratic polynomial mapped for the construction of S-box from the newly designed map.To keep the S-box bijective, swap each missing entries with repeating entries after that we acquire the initial box.Secondly, to increase the randomness of initial S-box special permutations of symmetric group S256 used and generated the proposed S-box.Lastly, to examine the validity of the suggested S-box we used various tests such as nonlinearity (NL), bit independence criteria (BIC), strict avalanche criteria (SAC), differential uniformity (DU) and linear approximation probability (LAP) which all certify algebraic properties of S-box.Moreover, the features of newly constructed S-box compared with recent S-boxes from literature which show superior performance against intruders' attacks.Further, S-box is utilized in image encryption scheme and apply MLC (majority logic criterion) and histogram analysis to examined the encryption quality.Our results shows that proposed S-box based encryption scheme is very good as compared to other encryption methods.
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose From Monocular Video

Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pixel depth and ego-motion from the unlabeled monocular video. In this paper, a novel unsupervised training framework is proposed with 3D hierarchical refinement and augmentation using explicit 3D geometry. In this framework, the depth and pose estimations are hierarchically and mutually coupled to refine the estimated pose layer by layer. The intermediate view image is proposed and synthesized by warping the pixels in an image with the estimated depth and coarse pose. Then, the residual pose transformation can be estimated from the new view image and the image of the adjacent frame to refine the coarse pose. The iterative refinement is implemented in a differentiable manner in this paper, making the whole framework optimized uniformly. Meanwhile, a new image augmentation method is proposed for the pose estimation by synthesizing a new view image, which creatively augments the pose in 3D space but gets a new augmented 2D image. The experiments on KITTI demonstrate that our depth estimation achieves state-of-the-art performance and even surpasses recent approaches that utilize other auxiliary tasks. Our visual odometry outperforms all recent unsupervised monocular learning-based methods and achieves competitive performance to the geometry-based method, ORB-SLAM2 with back-end optimization. The source codes will be released soon at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IRMVLab/HRANet</uri> .
Kayak and Sailboat Detection Based on the Improved YOLO with Transformer

In this paper, we aim at sailboat and kayak detection from digital images using deep learning. The main dataset is created by ourselves, we have collected the images at the harbour near our city, Auckland. In the sailboat and kayak detection, we search for a set of best parameters for the baseline of YOLOv5 model. In this paper, we propose a spate of backbone structures for the purpose of comparisons, we are able to find out the best structure for the kayak detection using our training dataset. Finally, we verify our proposed model and compare it with an existing well-trained model by using ensemble learning. All results are obtained based on a computer with GTX1060 6G RAM GPU.
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?

In Neural Architecture Search (NAS), reducing the cost of architecture evaluation remains one of the most crucial challenges. Among a plethora of efforts to bypass training of each candidate architecture to convergence for evaluation, the Neural Tangent Kernel (NTK) is emerging as a promising theoretical framework that can be utilized to estimate the performance of a neural architecture at initialization. In this work, we revisit several at-initialization metrics that can be derived from the NTK and reveal their key short-comings. Then, through the empirical analysis of the time evolution of NTK, we deduce that modern neural architectures exhibit highly non-linear characteristics, making the NTK-based metrics incapable of reliably estimating the performance of an architecture without some amount of training. To take such non-linear characteristics into account, we introduce Label-Gradient Alignment (LGA), a novel NTK-based metric whose inherent formulation allows it to capture the large amount of non-linear advantage present in modern neural architectures. With minimal amount of training, LGA obtains a meaningful level of rank correlation with the final test accuracy of an architecture. Lastly, we demonstrate that LGA, complemented with few epochs of training, successfully guides existing search algorithms to achieve competitive search performances with significantly less search cost. The code is available at: https://github.com/nute11amok/DemystifyingNTK.