Reviewerly/exHarmony huggingface dataset:
GAN Inversion: A Survey

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN model so that the image can be faithfully reconstructed from the inverted code by the generator. As an emerging technique to bridge the real and fake image domains, GAN inversion plays an essential role in enabling pretrained GAN models, such as StyleGAN and BigGAN, for applications of real image editing. Moreover, GAN inversion interprets GAN's latent space and examines how realistic images can be generated. In this paper, we provide a survey of GAN inversion with a focus on its representative algorithms and its applications in image restoration and image manipulation. We further discuss the trends and challenges for future research. A curated list of GAN inversion methods, datasets, and other related information can be found at https://github.com/weihaox/awesome-gan-inversion.
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis

The aspect-based sentiment analysis (ABSA) consists of two conceptual tasks, namely an aspect extraction and an aspect sentiment classification.Rather than considering the tasks separately, we build an end-to-end ABSA solution.Previous works in ABSA tasks did not fully leverage the importance of syntactical information.Hence, the aspect extraction model often failed to detect the boundaries of multi-word aspect terms.On the other hand, the aspect sentiment classifier was unable to account for the syntactical correlation between aspect terms and the context words.This paper explores the grammatical aspect of the sentence and employs the self-attention mechanism for syntactical learning.We combine part-of-speech embeddings, dependencybased embeddings and contextualized embeddings (e.g.BERT, RoBERTa) to enhance the performance of the aspect extractor.We also propose the syntactic relative distance to de-emphasize the adverse effects of unrelated words, having weak syntactic connection with the aspect terms.This increases the accuracy of the aspect sentiment classifier.Our solutions outperform the state-of-the-art models on SemEval-2014 dataset in both two subtasks.
A lightweight vehicles detection network model based on YOLOv5

Vehicle detection technology is of great significance for realizing automatic monitoring and AI-assisted driving systems. The state-of-the-art object detection method, namely, a class of YOLOv5, has often been used to detect vehicles. However, it suffers some challenges, such as a high computational load and undesirable detection rate. To address these issues, an improved lightweight YOLOv5 method is proposed for vehicle detection in this paper. In the presented method, C3Ghost and Ghost modules are introduced into the YOLOv5 neck network to reduce the floating-point operations (FLOPs) in the feature channel fusion process and enhance the feature expression performance. A convolutional block attention module (CBAM) is introduced to the YOLOv5 backbone network to select the information critical to the vehicle detection task and suppress uncritical information, thus improving the detection accuracy of the algorithm. Furthermore, CIoU_Loss is considered the bounding box regression loss function to accelerate the bounding box regression rate and improve the localization accuracy of the algorithm. To verify the performance of the proposed approach, we tested our model via two case studies, i.e., the PASCAL VOC dataset and MS COCO dataset. The results show that the detection precision of the proposed model increased 3.2%, the FLOPs decreased 15.24%, and the number of model parameters decreased 19.37% compared with those of the existing YOLOv5. Through case studies and comparisons, the effectiveness and superiority of the presented approach are demonstrated.
IBM: Cost of a Data Breach

Network SecurityVol. 2022, No. 8 Report AnalysisIBM: Cost of a Data BreachSteve Mansfield-DevineSteve Mansfield-DevineSearch for more papers by this authorSteve Mansfield-DevinePublished Online:24 Aug 2022https://doi.org/10.12968/S1353-4858(22)70049-9AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 August 2022Volume 2022Issue 8ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 24 August 2022 Published in print 1 August 2022 Information© MA Business Ltd.
Automatic Fault Diagnosis of Infrared Insulator Images Based on Image Instance Segmentation and Temperature Analysis

As an onsite condition monitoring method, an infrared inspection can help to discover and analyze abnormal temperature increases in power equipment. For improving the efficiency of the onsite diagnosis of insulators in substations, this article proposes an automatic diagnosis method using instance segmentation and temperature analysis of infrared insulator images. For developing this method, thousands of infrared images from field inspection databases were collected to establish an annotated data set of insulator images. With the aid of the Mask R-convolutional neural network (CNN), it was possible to extract multiple insulators automatically in the infrared images. Transfer learning, as well as the dynamic learning rate algorithm, were then employed to realize the training process of Mask R-CNN with the annotated image data set. The result of the testing experiment showed that the mean Average Precision (mAP) of the model is 0.77, and the frame per second (FPS) is 5.07, which indicated great identification accuracy and computing speed of the proposed model. Next, function fitting was realized to extract the temperature distribution of each insulator. Finally, to evaluate the condition of each insulator, rules, which are based on the related standards, were established using machine language. This is the first time that the machine could independently realize fault analysis of multiple insulators in the infrared images, which is a great attempt to adapt the development of the Internet of Things and the tendency of predictive maintenance. Moreover, because of the universality of the model algorithm used, automatic infrared fault diagnosis for other power equipment could also be performed in a similar manner, which has significant potential applicability in the area of power equipment diagnosis.
Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement

Colorectal polyp recognition is crucial for early colorectal cancer detection and treatment. Colonoscopy is always employed for colorectal polyp scanning. However, one out of four polyps may be ignored, due to the similarity of polyp and normal tissue. In this paper, we present a novel method called NeutSS-PLP for polyp region extraction in colonoscopy images using a short connected saliency detection network with neutrosophic enhancement. We first utilize the neutrosophic theory to enhance the quality of specular reflections detection in the colonoscopy images. We develop the local and global threshold criteria in the single-valued neutrosophic set (SVNS) domain and define the corresponding T (Truth), I (Indeterminacy), and F (Falsity) functions for each criterion. The well-built neutrosophic images are processed and employed for specular reflection detection and suppressing. Next, we introduce two-level short connections into the saliency detection network, aiming to take advantage of the multi-level and multi-scale features extracted from different stages of the network. Experimental results conducted on two public colorectal polyp datasets achieve 0.877 and 0.9135 mIoU for polyp extraction respectively, and our method performs better compared with several state-of-the-art saliency networks and semantic segmentation networks, which demonstrate the effectiveness of applying the saliency detection mechanism for colorectal polyp region extraction.
A Survey on Deep Semi-Supervised Learning

Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first present a taxonomy for deep semi-supervised learning that categorizes existing methods, including deep generative methods, consistency regularization methods, graph-based methods, pseudo-labeling methods, and hybrid methods. Then we provide a comprehensive review of 60 representative methods and offer a detailed comparison of these methods in terms of the type of losses, architecture differences, and test performance results. In addition to the progress in the past few years, we further discuss some shortcomings of existing methods and provide some tentative heuristic solutions for solving these open problems.
YOLOP: You Only Look Once for Panoptic Driving Perception

A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. Our model performs extremely well on the challenging BDD100K dataset, achieving state-of-the-art on all three tasks in terms of accuracy and speed. Besides, we verify the effectiveness of our multi-task learning model for joint training via ablative studies. To our best knowledge, this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS) and maintain excellent accuracy. To facilitate further research, the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.
Strategies for Pre-training Graph Neural Networks

Many domains in machine learning have datasets with a large number of related but different tasks. Those domains are challenging because task-specific labels are often scarce and test examples can be distributionally different from examples seen during training. An effective solution to these challenges is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective for improving many language and vision domains, pre-training on graph datasets remains an open question. Here, we develop a strategy for pre-training Graph Neural Networks (GNNs). Crucial to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs. We systematically study different pre-training strategies on multiple datasets and find that when ad-hoc strategies are applied, pre-trained GNNs often exhibit negative transfer and perform worse than non-pre-trained GNNs on many downstream tasks. In contrast, our proposed strategy is effective and avoids negative transfer across downstream tasks, leading up to 11.7% absolute improvements in ROC-AUC over non-pre-trained models and achieving state of the art performance.
A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms

Abstract Background In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negatives. Only part of the ROC curve and AUC are informative however when they are used with imbalanced data. Hence, alternatives to the AUC have been proposed, such as the partial AUC and the area under the precision-recall curve. However, these alternatives cannot be as fully interpreted as the AUC, in part because they ignore some information about actual negatives. Methods We derive and propose a new concordant partial AUC and a new partial c statistic for ROC data—as foundational measures and methods to help understand and explain parts of the ROC plot and AUC. Our partial measures are continuous and discrete versions of the same measure, are derived from the AUC and c statistic respectively, are validated as equal to each other, and validated as equal in summation to whole measures where expected. Our partial measures are tested for validity on a classic ROC example from Fawcett, a variation thereof, and two real-life benchmark data sets in breast cancer: the Wisconsin and Ljubljana data sets. Interpretation of an example is then provided. Results Results show the expected equalities between our new partial measures and the existing whole measures. The example interpretation illustrates the need for our newly derived partial measures. Conclusions The concordant partial area under the ROC curve was proposed and unlike previous partial measure alternatives, it maintains the characteristics of the AUC. The first partial c statistic for ROC plots was also proposed as an unbiased interpretation for part of an ROC curve. The expected equalities among and between our newly derived partial measures and their existing full measure counterparts are confirmed. These measures may be used with any data set but this paper focuses on imbalanced data with low prevalence. Future work Future work with our proposed measures may: demonstrate their value for imbalanced data with high prevalence, compare them to other measures not based on areas; and combine them with other ROC measures and techniques.
A Massive Analysis of Ethereum Smart Contracts Empirical Study and Code Metrics

In this work, we perform a comprehensive empirical study of smart contracts deployed on the ethereum blockchain. The objective of the analysis is to provide empirical results on smart contracts features, smart contract transactions within the blockchain, the role of the development community, and the source code characteristics. We collected a set of more than 10000 smart contracts source codes and a dataset of meta-data regarding their interaction with the blockchain from etherscan.io. We examined the collected data computing different statistics on naming policies, smart contract ether balance, number of smart contract transactions, functions, and other quantities characterizing the use and purpose of smart contracts. We found that the number of transactions and the balances follow power-law distributions and the software code metrics display, on average, values lower than corresponding metrics in standard software but have high variances. Focusing the attention on the 20 smart contracts with the topmost number of transactions, we found that most of them represent financial smart contracts and some of them have peculiar software development stories behind them. The results show that blockchain software is rapidly changing and evolving and it is no longer devoted only to cryptovalues applications but to general purpose computation.
An effective CNN and Transformer complementary network for medical image segmentation

The Transformer network was originally proposed for natural language processing. Due to its powerful representation ability for long-range dependency, it has been extended for vision tasks in recent years. To fully utilize the advantages of Transformers and Convolutional Neural Networks (CNNs), we propose a CNN and Transformer Complementary Network (CTCNet) for medical image segmentation. We first design two encoders by Swin Transformers and Residual CNNs to produce complementary features in Transformer and CNN domains, respectively. Then we cross-wisely concatenate these complementary features to propose a Cross-domain Fusion Block (CFB) for effectively blending them. In addition, we compute the correlation between features from the CNN and Transformer domains, and apply channel attention to the self-attention features by Transformers for capturing dual attention information. We incorporate cross-domain fusion, feature correlation and dual attention together to propose a Feature Complementary Module (FCM) for improving the representation ability of features. Finally, we design a Swin Transformer decoder to further improve the representation ability of long-range dependencies, and propose to use skip connections between the Transformer decoded features and the complementary features for extracting spatial details, contextual semantics and long-range information. Skip connections are performed in different levels for enhancing multi-scale invariance. Experimental results show that our CTCNet significantly surpasses the state-of-the-art image segmentation models based on CNNs, Transformers, and even Transformer and CNN combined models designed for medical image segmentation. It achieves superior performance on different medical applications, including multi-organ segmentation and cardiac segmentation.
Exploring Plain Vision Transformer Backbones for Object Detection

We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient to use window attention (without shifting) aided with very few cross-window propagation blocks. With plain ViT backbones pre-trained as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the previous leading methods that were all based on hierarchical backbones, reaching up to 61.3 AP $$^\text {box}$$ on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors. Code for ViTDet is available ( https://github.com/facebookresearch/detectron2/tree/main/projects/ViTDet ).
Robust Estimators in High-Dimensions Without the Computational Intractability

\bfA \bfb \bfs \bft \bfr \bfa \bfc \bft .We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an \varepsi -fraction of the samples.Such questions have a rich history spanning statistics, machine learning, and theoretical computer science.Even in the most basic settings, the only known approaches are either computationally inefficient or lose dimensiondependent factors in their error guarantees.This raises the following question: Is high-dimensional agnostic distribution learning even possible, algorithmically?In this work, we obtain the first computationally efficient algorithms with dimension-independent error guarantees for agnostically learning several fundamental classes of high-dimensional distributions: (1) a single Gaussian, (2) a product distribution on the hypercube, (3) mixtures of two product distributions (under a natural balancedness condition), and (4) mixtures of spherical Gaussians.Our algorithms achieve error that is independent of the dimension, and in many cases scales nearly linearly with the fraction of adversarially corrupted samples.Moreover, we develop a general recipe for detecting and correcting corruptions in high-dimensions that may be applicable to many other problems.\bfK \bfe \bfy \bfw \bfo \bfr \bfd \bfs .robust learning, high-dimensions, Gaussian distribution, mixture models, product distributions \bfA \bfM \bfS \bfs \bfu \bfb \bfj \bfe \bfc \bft \bfc \bfl \bfa \bfs \bfs \bfi fi\bfc \bfa \bft \bfi \bfo \bfn \bfs .68Q25, 68Q32
A small-world topology enhances the echo state property and signal propagation in reservoir computing

Cortical neural connectivity has been shown to exhibit a small-world (SW) network topology. However, the role of the topology in neural information processing remains unclear. In this study, we investigated the learning performance of an echo state network (ESN) that includes the SW topology as a reservoir. To elucidate the potential of the SW topology, we limited the numbers of the input and output nodes in the ESN and spatially segregated the output nodes from the input nodes. We tested the ESNs in two benchmark tasks: memory capacity and nonlinear time-series prediction. The SW-ESN exhibited the best learning performance when the spectral radius of the weight matrix was large and when the input and output nodes were segregated. That is, the SW topology provided the ESN with a stable echo state property over a broad range of the weight matrix and efficiently propagated input signals to the output nodes. This result is the same as that of the ESN using a real human cortical connectivity. Thus, the results suggest that the SW topology is essential for maintaining the echo state property, which is the appropriate neural dynamics between input and output brain regions.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-Aware Attention Using Latent Entity Typing

Classifying semantic relations between entity pairs in sentences is an important task in natural language processing (NLP). Most previous models applied to relation classification rely on high-level lexical and syntactic features obtained by NLP tools such as WordNet, the dependency parser, part-of-speech (POS) tagger, and named entity recognizers (NER). In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information related to the entity, which may be the most crucial feature for relation classification. To address these issues, we propose a novel end-to-end recurrent neural model that incorporates an entity-aware attention mechanism with a latent entity typing (LET) method. Our model not only effectively utilizes entities and their latent types as features, but also builds word representations by applying self-attention based on symmetrical similarity of a sentence itself. Moreover, the model is interpretable by visualizing applied attention mechanisms. Experimental results obtained with the SemEval-2010 Task 8 dataset, which is one of the most popular relation classification tasks, demonstrate that our model outperforms existing state-of-the-art models without any high-level features.
Multinomial Distribution Learning for Effective Neural Architecture Search

Architectures obtained by Neural Architecture Search (NAS) have achieved highly competitive performance in various computer vision tasks. However, the prohibitive computation demand of forward-backward propagation in deep neural networks and searching algorithms makes it difficult to apply NAS in practice. In this paper, we propose a Multinomial Distribution Learning for extremely effective NAS, which considers the search space as a joint multinomial distribution, i.e., the operation between two nodes is sampled from this distribution, and the optimal network structure is obtained by the operations with the most likely probability in this distribution. Therefore, NAS can be transformed to a multinomial distribution learning problem, i.e., the distribution is optimized to have a high expectation of the performance. Besides, a hypothesis that the performance ranking is consistent in every training epoch is proposed and demonstrated to further accelerate the learning process. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness of our method. On CIFAR-10, the structure searched by our method achieves 2.55% test error, while being 6.0× (only 4 GPU hours on GTX1080Ti) faster compared with state-of-the-art NAS algorithms. On ImageNet, our model achieves 74% top1 accuracy under MobileNet settings (MobileNet V1/V2), while being 1.2× faster with measured GPU latency. Test code with pre-trained models are available at https: //github.com/tanglang96/MDENAS.