Reviewerly/exHarmony huggingface dataset:
Editing Out-of-Domain GAN Inversion via Differential Activations

Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic changes from a global perspective, i.e., the relative gap between the features of edited and unedited images. With the aid of the generated Diff-CAM mask, a coarse reconstruction can intuitively be composited by the paired original and edited images. In this way, the attribute-irrelevant regions can be survived in almost whole, while the quality of such an intermediate result is still limited by an unavoidable ghosting effect. Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction. Extensive experiments exhibit superiorities over the state-of-the-art methods, in terms of qualitative and quantitative evaluations. The robustness and flexibility of our method is also validated on both scenarios of single attribute and multi-attribute manipulations. Code is available at https://github.com/HaoruiSong622/Editing-Out-of-Domain .
Automatic Fall Detection using Deep Neural Networks with Aggregated Residual Transformation

The advancement of inertial sensor technology and the growing prevalence of intelligent wearables (including smart-watches, smart bands, and other intelligent devices) have aided in applying science and technology to automated Fall Detection Systems (FDSs). Over the past decade, wearable-based FDSs has garnered considerable scientific attention. In this context, ma-chine learning (ML) techniques have demonstrated remarkable efficacy in differentiating between falls and typical motions or Activities of Daily Living (ADLs) using the data recorded by wearable inertial sensors. Unfortunately, in most research, the effectiveness of machine learning classifiers was restricted by feature extraction and selection processes that relied on human-crafted decisions. A deep neural network was established to en-hance the capacity of fall detection, which combines convolutional layers and aggregated residual transformation in this study. The TensorFlow framework trained the proposed model to identify and categorize 15 special events: three distinct falls and twelve ADLs. On a publicly available benchmark dataset (UMAFall), the proposed model was analyzed and compared to existing baseline models. The findings indicate that the proposed model is superior to other models, with an accuracy of 96.72%.
OpBoost

Vertical Federated Learning (FL) is a new paradigm that enables users with non-overlapping attributes of the same data samples to jointly train a model without directly sharing the raw data. Nevertheless, recent works show that it's still not sufficient to prevent privacy leakage from the training process or the trained model. This paper focuses on studying the privacy-preserving tree boosting algorithms under the vertical FL. The existing solutions based on cryptography involve heavy computation and communication overhead and are vulnerable to inference attacks. Although the solution based on Local Differential Privacy (LDP) addresses the above problems, it leads to the low accuracy of the trained model. This paper explores to improve the accuracy of the widely deployed tree boosting algorithms satisfying differential privacy under vertical FL. Specifically, we introduce a framework called OpBoost. Three order-preserving desensitization algorithms satisfying a variant of LDP called distance-based LDP (dLDP) are designed to desensitize the training data. In particular, we optimize the dLDP definition and study efficient sampling distributions to further improve the accuracy and efficiency of the proposed algorithms. The proposed algorithms provide a trade-off between the privacy of pairs with large distance and the utility of desensitized values. Comprehensive evaluations show that OpBoost has a better performance on prediction accuracy of trained models compared with existing LDP approaches on reasonable settings. Our code is open source.
SOCNet: A Lightweight and Fine-Grained Object Recognition Network for Satellite On-Orbit Computing

High-quality remote sensing images not only provide opportunities for deep learning-based image interpretation, but also challenge satellite edge devices for storage, processing and downlinking to the ground server. Satellite on-orbit object recognition is an effective measure to solve this challenge. However, there is an insurmountable gap between the extremely limited computing and storage resources of satellites and the demand for those of deep learning. To solve this problem, a fast and lightweight intelligent satellite-on-orbit computing network (SOCNet) is proposed. First, the overall network architecture based on the idea of flat multi-branch feature extraction is proposed to accelerate model inference and reduce the network depth. Second, we propose the idea of exchanging a larger receptive field for network depth and combine the idea of depthwise separable convolution to further reduce the amount of parameters. We design a feature extraction method of coupled fine-coarse-grained for efficient feature extraction. Finally, global average pooling is used for feature fusion to further reduce network parameters amount and computational complexity. We have performed multiple sets of ablation experiments and some state-of-the-art comparison experiments on the server and carried out experimental verification on the NVIDIA Jetson TX2 mobile device to mimic the resource-constrained environment on the satellite. SOCNet with parameters of 0.21 MB, model size of 1.22 MB and computation cost of 50.81 MB floating point operations has achieved 99% comprehensive accuracy, 2.45 ms latency and 408 images per second throughput on 30 types of aircraft datasets with the 224 × 224 remote sensing images.
The Surprisingly Straightforward Scene Text Removal Method with Gated Attention and Region of Interest Generation: A Comprehensive Prominent Model Analysis

Scene text removal (STR), a task of erasing text from natural scene images, has recently attracted attention as an important component of editing text or concealing private information such as ID, telephone, and license plate numbers. While there are a variety of different methods for STR actively being researched, it is difficult to evaluate superiority because previously proposed methods do not use the same standardized training/evaluation dataset. We use the same standardized training/testing dataset to evaluate the performance of several previous methods after standardized re-implementation. We also introduce a simple yet extremely effective Gated Attention (GA) and Region-of-Interest Generation (RoIG) methodology in this paper. GA uses attention to focus on the text stroke as well as the textures and colors of the surrounding regions to remove text from the input image much more precisely. RoIG is applied to focus on only the region with text instead of the entire image to train the model more efficiently. Experimental results on the benchmark dataset show that our method significantly outperforms existing state-of-the-art methods in almost all metrics with remarkably higher-quality results. Furthermore, because our model does not generate a text stroke mask explicitly, there is no need for additional refinement steps or sub-models, making our model extremely fast with fewer parameters. The dataset and code are available at https://github.com/naver/garnet .
SOSum

Stack Overflow (SO) is becoming an indispensable part of modern software development workflow. However, given the limited time, attention, and memory capacity of programmers, navigating SO posts and comparing different solutions is time-consuming and cumbersome. Recent research has proposed to summarize SO posts to concise text to help programmers quickly assess the relevance and quality of SO posts. Yet there is no large dataset of high-quality SO post summaries, hindering the development and evaluation of post summarization techniques. We present SOSum, a dataset of 2,278 popular SO answer posts with manually labeled summative sentences. Questions in SOSum cover 669 tags with a median view count of 253K and a median post score of 17. This dataset will foster research on sentence-level summarization of SO posts and has the potential to facilitate text summarization research on other types of textual software artifacts such as programming tutorials.
Stereo Magnification with Multi-Layer Images

Representing scenes with multiple semitransparent colored layers has been a popular and successful choice for real-time novel view synthesis. Existing approaches infer colors and transparency values over regularly spaced layers of planar or spherical shape. In this work, we introduce a new view synthesis approach based on multiple semitransparent layers with scene-adapted geometry. Our approach infers such representations from stereo pairs in two stages. The first stage produces the geometry of a small number of data-adaptive layers from a given pair of views. The second stage infers the color and transparency values for these layers, producing the final representation for novel view synthesis. Importantly, both stages are connected through a differentiable renderer and are trained end-to-end. In the experiments, we demonstrate the advantage of the proposed approach over the use of regularly spaced layers without adaptation to scene geometry. Despite being orders of magnitude faster during rendering, our approach also outperforms the recently proposed IBRNet system based on implicit geometry representation.
SecureCam: Selective Detection and Encryption Enabled Application for Dynamic Camera Surveillance Videos

Using dynamic surveillance cameras for security has significantly increased the privacy concerns for captured individuals. Malicious users may misuse these videos by performing Replay and/or Man-in-the-Middle attacks during storage or recording over the network. Considering these risks, this paper proposes an effective security application SecureCam based on selective detection (focused moving objects) and protection using encryption. For object detection, this paper implements a novel low computational unsupervised learning algorithm, i.e., Motion-Fusion (MF) for more precise motion detection in the mobile camera videos. After that, selective encryption (SE) is applied by the lightweight Chacha20 cipher to the detected video parts. Proposed SecureCam is extensively evaluated based on performance analysis, security analysis and computational complexity. For object detection, the comparative evaluation shows that the MF algorithm outperforms traditional state-of-the-art dense optical flow (DOF) algorithm with an average (mean) difference increase: in the accuracy of 54%; and in the precision of 42% making it computationally effective for such videos. The visual results with 21% encryption space ratio (ESR) indicate that the videos are sufficiently protected against identification. Overall comparative evaluation with existing approaches also affirm the significance and utility of proposed SecureCam for Internet of Multimedia Things (IoMT) environment.
PlanarRecon: Realtime 3D Plane Detection and Reconstruction from Posed Monocular Videos

We present PlanarRecon - a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video. Unlike previous works that detect planes in 2D from a single image, PlanarRecon incrementally detects planes in 3D for each video fragment, which consists of a set of key frames, from a volumetric representation of the scene using neural networks. A learning-based tracking and fusion module is designed to merge planes from previous fragments to form a coherent global plane reconstruction. Such design allows Planar-Recon to integrate observations from multiple views within each fragment and temporal information across different ones, resulting in an accurate and coherent reconstruction of the scene abstraction with low-polygonal geometry. Experiments show that the proposed approach achieves state-of-the-art performances on the ScanNet dataset while being real-time. Code is available at the project page: https://neu-vi.github.io/planarrecon/.
ETHSeg: An Amodel Instance Segmentation Network and a Real-world Dataset for X-Ray Waste Inspection

Waste inspection for packaged waste is an important step in the pipeline of waste disposal. Previous methods either rely on manual visual checking or RGB image-based inspection algorithm, requiring costly preparation procedures (e.g., open the bag and spread the waste items). Moreover, occluded items are very likely to be left out. Inspired by the fact that X-ray has a strong penetrating power to see through the bag and overlapping objects, we propose to perform waste inspection efficiently using X-ray images without the need to open the bag. We introduce a novel problem of instance-level waste segmentation in X-ray image for intelligent waste inspection, and contribute a real dataset consisting of 5,038 X-ray images (totally 30,881 waste items) with high-quality annotations (i.e., waste categories, object boxes, and instance-level masks) as a benchmark for this problem. As existing segmentation methods are mainly designed for natural images and cannot take advantage of the characteristics of X-ray waste images (e.g., heavy occlusions and penetration effect), we propose a new instance segmentation method to explicitly take these image characteristics into account. Specifically, our method adopts an easy-to-hard disassembling strategy to use high confidence predictions to guide the segmentation of highly overlapped objects, and a global structure guidance module to better capture the complex contour information caused by the penetration effect. Extensive experiments demonstrate the effectiveness of the proposed method. Our dataset is released at WIXRayNet.
Digitalization of education in the modern society: problems, experience, prospects

The article analyzes the digitalization of modern education. Its relevance is determined by the role of education in the life of the modern society, which is based on the principles and values of the post-industrial era. Every year digital technologies play an increasingly important role in the improvement of national education, contributing to the enhancement of the quality of training of teachers-to-be and bringing education closer to science. At the same time, these technologies require an analysis of their impact on the society. In this regard, the study of digitalization of education and its social consequences appears to be a very relevant area of scientific research. To achieve the goal, methods of social cognition, the analysis of philosophical, sociological, pedagogical literature, the synthesis and modeling of socio-cultural processes have been used. The main problems of the digitalization of education have been identified: dehumanization of educational relations and further dehumanization of all other social relations, the ability of students to be creative, the growth of pragmatism and individualism based on the values of personal comfort and consumption. The prospects for the digitalization of the education system have been determined: instrumental prospects – equipping educational institutions with high-quality software, information systems that provide access to educational resources, the introduction of information (distance) technologies, online learning, changing the requirements for teachers and students, the formation of new educational structures , inevitable changes in the usual forms and methods of education; value-meaningful prospects – finding a harmonious unity with the best traditions of national education. Solving practical issues related to the implementation of the state policy of digitalization of various spheres of social relations, which contribute to the coordination of the efforts of scientists, politicians, civil society institutions for the socio-economic and cultural development of the information society, is of crucial importance.
A Tight Upper Bound for the Maximal Length of MDS Elliptic Codes

Determining the maximal length of MDS codes with certain dimension has been an interesting research topic in coding theory. The objective of this paper is to derive an upper bound for the maximal length of MDS elliptic codes over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> with dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\leq k\leq \frac {q+1-2\sqrt {q}}{10}$ </tex-math></inline-formula> . For such a range of dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> , our result improves an earlier bound of Munuera and gives an affirmative solution to the conjecture of Li, Wan, and Zhang. Most notably, the proposed upper bound is tight for odd <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> in the sense that it can be achieved by some well-designed MDS elliptic codes.
Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Process (HDP) Models on News Data

News has become a very important need in modern society. Almost every level of society needs information such as news. Online news gets the attention of writers because there are a lot of characteristics of the data available and can be used for analysis so as to get insights and current trends, such as on 4 news portals that are most frequently accessed by the public such as detik.com, okezone.com, kompas.corn, and tribunnews.com. Related research in this field about Trend Analysis and Topic modeling on news media data in Indonesia then discusses news data collection methods and data collection techniques news pre-processing text, and 2 Topic Modeling algorithms applied in this research, namely Latent Semantic Indexing (LSI) and Hierarchical Dirichlet Process (HDP). In this study, the author has succeeded in extracting topics from news data from media in Indonesia. Various topics show the effectiveness of a learning model producing topics from the LSI and HDP models. This research has implemented natural language preprocessing (NLP) to process news data so that it can be processed and used as data from modeling a topic. This study finds topics based on the model of coherence and LSI and HDP contained in the news data with a total of 29,437. The accuracy obtained is 87% compared to the model from LDA-KNN which can determine the topic with an accuracy of 72%. Hence, the Latent Semantic Indexing (LSI) model and Hierarchical Dirichlet Process (HDP) have better accuracy than the LDA-KNN model.
A Long-Tail Regularization Method for Traffic Sign Recognition Based on Parallel Vision

The long-tail effect is prevalent in the actual world, and the resulting long-tail problem hinders the development of intelligent vehicles towards a more reliable and safe direction. In previous studies, the theory of Long-tail Regularization (LoTR) based on parallel vision was proposed, and further research and experiments on the long-tail problem of traffic sign recognition are conducted in this paper. Specifically, in the process of long-tail regularization for traffic sign recognition, a novel generative adversarial network is designed and improved to generate images. By feeding the category-specific image from standard gallery into the proposed network, it can generate a large number of virtual images with the same category. Finally, the generated virtual images are added to the original dataset to regularize the long tail, and then the recognition accuracy is significantly improved.
Semi-Supervised Action Recognition From Temporal Augmentation Using Curriculum Learning

Semi-supervised learning for video action recognition is a very challenging research area. Existing state-of-the-art methods perform data augmentation on the temporality of actions, which are combined with the mainstream consistency-based semi-supervised learning framework FixMatch for action recognition. However, these approaches have the following limitations: (1) data augmentation based on video clips lacks coarse-grained and fine-grained representations of actions in temporal sequences, and the models have difficulty understanding synonymous representations of actions in different motion phases. (2) Pseudo labeling selection based on the constant thresholds lacks a “make-up curriculum” for difficult actions, that results in the low utilization of unlabeled data corresponding to difficult actions. To address the above shortcomings, we propose a semi-supervised action recognition via the temporal augmentation using curriculum learning (TACL) algorithm. Compared to previous works, TACL explores different representations of the same semantics of actions in temporal sequences for video and uses the idea of curriculum learning (CL) to reduce the difficulty of the model training process. First, for different action expressions with the same semantics, we designed the temporal action augmentation (TAA) for videos to obtain coarse-grained and fine-grained action expressions based on constant-velocity and hetero-velocity methods, respectively. Second, we construct a temporal signal to constrain the model such that fine-grained action expressions containing different movement phases have the same prediction results, and achieve action consistency learning (ACL) by combining the label and pseudo-label signals. Finally, we propose action curriculum pseudo labeling (ACPL), a loosely and strictly parallel dynamic threshold evaluation algorithm for selecting and labeling unlabeled data. We evaluate TACL on three standard public datasets: UCF101, HMDB51, and Kinetics. The combined experiments show that TACL significantly improves the accuracy of models trained on a small amount of labeled data and better evaluates the learning effects for different actions.
Learning Hierarchical Spatial-Temporal Graph Representations for Robust Multivariate Industrial Anomaly Detection

Multivariate time series anomaly detection is one of the most indispensable yet troublesome links in complex industrial processes. The main challenge lies in discovering the representative patterns for collective or contextual anomalies among interconnected sensory data streams, which has been largely hampered by inefficient spatial-temporal feature extraction and suboptimal decision criteria under the scarcity of positive training samples. This article goes beyond the common limitations of the existing methods, and novelly proposes Hierarchical Spatial-Temporal grAph Representation (HiSTAR). It processes the data with strong structural inductive biases through latent spatial-temporal graph modeling, yet requiring no predefined topological priors for the sensor network. A discriminative decision boundary is constructed by learning hierarchical normality-enclosing hyperspheres on the produced graph-structure representations. In this way, HiSTAR not only presents superior anomaly detection performance, but also provides consistent anomaly localization results. The efficacy of the proposed method is experimentally corroborated through three industrial case studies.