Reviewerly/exHarmony huggingface dataset:
Hybrid dilation and attention residual U-Net for medical image segmentation

Medical image segmentation is a typical task in medical image processing and critical foundation in medical image analysis. U-Net is well-liked in medical image segmentation, but it doesn't fully explore useful features of the channel and capitalize on the contextual information. Therefore, we present an improved U-Net with residual connections, adding a plug-and-play, very portable channel attention (CA) block and a hybrid dilated attention convolutional (HDAC) layer to perform medical image segmentation for different tasks accurately and effectively, and call it HDA-ResUNet, in which we fully utilize advantages of U-Net, attention mechanism and dilated convolution. In contrast to the simple copy splicing of U-Net in the skip connection, the channel attention block is inserted into the extracted feature map of the encoding path before decoding operation. Since this block is lightweight, we can apply it to multiple layers in the backbone network to optimize the channel effect of this layer's coding operation. In addition, the convolutional layer at the bottom of the “U"-shaped network is replaced by a hybrid dilated attention convolutional (HDAC) layer to fuse information from different sizes of receptive fields. The proposed HDA-ResUNet is evaluated on four datasets: liver and tumor segmentation (LiTS 2017), lung segmentation (Lung dataset), nuclear segmentation in microscope images (DSB 2018) and neuron structure segmentation (ISBI 2012). The dice global scores of liver and tumor segmentation (LiTS 2017) reach 0.949 and 0.799. The dice coefficients of lung segmentation and nuclear segmentation are 0.9797 and 0.9081 respectively, and the information theoretic score for the last one is 0.9703. The segmentation results are all more accurate than U-Net with fewer parameters, and the problem of slow convergence speed of U-Net on DBS 2018 is solved.
Double Parameters Fractal Sorting Matrix and Its Application in Image Encryption

In the field of frontier research, information security has received a lot of interest, but in the field of information security algorithm, the introduction of decimals makes it impossible to bypass the topic of calculation accuracy. This article creatively proposes the definition and related proofs of double parameters fractal sorting matrix (DPFSM). As a new matrix classification with fractal properties, DPFSM contains self-similar structures in the ordering of both elements and sub-blocks in the matrix. These two self-similar structures are determined by two different parameters. To verify the theory, this paper presents a type of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> DPFSM iterative generation method, as well as the theory, steps, and examples of the iteration. DPFSM is a space position transformation matrix, which has a better periodic law than a single parameter fractal sorting matrix (FSM). The proposal of DPFSM expands the fractal theory and solves the limitation of calculation accuracy on information security. The image encryption algorithm based on DPFSM is proposed, and the security analysis demonstrates the security. DPFSM has good application value in the field of information security.
Design in Action: From Prototyping by Demonstration to Cooperative Prototyping

The dental assistants would be the primary users of the application, and they took part in a series of prototyping sessions to specify how they would like to use computers in their work. They knew that some kind of computer application would soon be introduced at their workplace, and it was important for them to be able to influence the choice of system. In this chapter, the authors discuss how to get started with prototyping that involves users actively and creatively. They give examples illustrating how to obtain close coupling between design activities and experimental evaluation of prototypes in work-like situations. A rich variety of approaches to prototyping has been proposed in recent years. They all provide possibilities for users to gain hands-on experience before final application is built; and yet, the way they are used today, they are not often applied this way. K. Gronbak gives critique of three categories of prototyping: prototypebecomes the system; executable specification; and exploratory approaches.
Shallowing Deep Networks: Layer-Wise Pruning Based on Feature Representations

Recent surge of Convolutional Neural Networks (CNNs) has brought successes among various applications. However, these successes are accompanied by a significant increase in computational cost and the demand for computational resources, which critically hampers the utilization of complex CNNs on devices with limited computational power. In this work, we propose a feature representation based layer-wise pruning method that aims at reducing complex CNNs to more compact ones with equivalent performance. Different from previous parameter pruning methods that conduct connection-wise or filter-wise pruning based on weight information, our method determines redundant parameters by investigating the features learned in the convolutional layers and the pruning process is operated at a layer level. Experiments demonstrate that the proposed method is able to significantly reduce computational cost and the pruned models achieve equivalent or even better performance compared to the original models on various datasets.
Fast AutoAugment

Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment \cite{cubuk2018autoaugment} has been proposed as an algorithm to automatically search for augmentation policies from a dataset and has significantly enhanced performances on many image recognition tasks. However, its search method requires thousands of GPU hours even for a relatively small dataset. In this paper, we propose an algorithm called Fast AutoAugment that finds effective augmentation policies via a more efficient search strategy based on density matching. In comparison to AutoAugment, the proposed algorithm speeds up the search time by orders of magnitude while achieves comparable performances on image recognition tasks with various models and datasets including CIFAR-10, CIFAR-100, SVHN, and ImageNet. Our code is open to the public by the official GitHub\footnote{\url{https://github.com/kakaobrain/fast-autoaugment}} of Kakao Brain.
On the Fairness of Disentangled Representations

Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an unobserved sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than 12600 trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed.
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration. However, limited by factors such as imaging hardware, scanning time, and cost, it is challenging to acquire high-resolution MR images clinically. In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts. By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly. Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain. Then, each subband generative adversarial network (GAN) simultaneously concentrates on super-resolving the corresponding subband image. In generator, multiple residual-in-residual dense blocks are introduced for better feature extraction. In addition, the texture-enhancing module is designed to trade off the weight between global topology and detailed textures. Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs. Comprehensive experiments on the MultiRes_7T and ADNI datasets demonstrate that the proposed model achieves finer structure recovery and outperforms the competing methods quantitatively and qualitatively. Moreover, FP-GANs further show the value by applying the SR results in classification tasks.
Multi-vehicle detection algorithm through combining Harr and HOG features

In order to achieve a better performance of detection and tracking of multi-vehicle targets in complex urban environment, we propose a two-step detection algorithm based on combining the features of Harr and Histogram of Oriented Gradients (HOG). This algorithm makes full use of HOG characteristic advantages for target vehicles, i.e., the good descriptive ability of HOG feature, and the prospect region of interest (ROI) can be extracted using Harr features. Moreover, the extracted HOG features from the ROI target area can be selected through applying the cascade structured AdaBoost classifier features and target area classification. Precise target can be further extracted by using support vector machine (SVM). Experimental results using video collected from real world scenarios are provided, showing that the proposed method possesses higher detecting accuracy and time efficiency than the conventional ones, and it can detect and track the multi-vehicle targets successfully in complex urban environment.
A BERT Baseline for the Natural Questions

This technical note describes a new baseline for the Natural Questions. Our model is based on BERT and reduces the gap between the model F1 scores reported in the original dataset paper and the human upper bound by 30% and 50% relative for the long and short answer tasks respectively. This baseline has been submitted to the official NQ leaderboard at this http URL. Code, preprocessed data and pretrained model are available at this https URL.
Going big: a large-scale study on what big data developers ask

Software developers are increasingly required to write big data code. However, they find big data software development challenging. To help these developers it is necessary to understand big data topics that they are interested in and the difficulty of finding answers for questions in these topics. In this work, we conduct a large-scale study on Stackoverflow to understand the interest and difficulties of big data developers. To conduct the study, we develop a set of big data tags to extract big data posts from Stackoverflow; use topic modeling to group these posts into big data topics; group similar topics into categories to construct a topic hierarchy; analyze popularity and difficulty of topics and their correlations; and discuss implications of our findings for practice, research and education of big data software development and investigate their coincidence with the findings of previous work.
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation

Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single objective can be further improved without hurting the others. However existing approaches to Pareto efficient multi-objective recommendation still lack good theoretical guarantees.
Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-Based Person Re-Identification

Video-based person re-identification (reID) aims at matching the same person across video clips. It is a challenging task due to the existence of redundancy among frames, newly revealed appearance, occlusion, and motion blurs. In this paper, we propose an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation. In order to determine the contribution/importance of a spatial-temporal feature node, we propose to learn the attention from a global view with convolutional operations. Specifically, we stack its relations, \ieno, pairwise correlations with respect to a representative set of reference feature nodes (S-RFNs) that represents global video information, together with the feature itself to infer the attention. Moreover, to exploit the semantics of different levels, we propose to learn multi-granularity attentions based on the relations captured at different granularities. Extensive ablation studies demonstrate the effectiveness of our attentive feature aggregation module MG-RAFA. Our framework achieves the state-of-the-art performance on three benchmark datasets.
An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.
Blockchain and artificial intelligence technology in e-Health

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.
A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Cryptocurrency is a new sort of asset that has emerged as a result of the advancement of financial technology and it has created a big opportunity for researches. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Around the world, there are hundreds of cryptocurrencies that are used. This paper proposes three types of recurrent neural network (RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending on the mean absolute percentage error (MAPE). Results obtained from these models show that the gated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can be considered the best algorithm. GRU presents the most accurate prediction for LTC with MAPE percentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTM algorithm presents the lowest prediction result compared with the other two algorithms as the MAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, the prediction models in this paper represent accurate results close to the actual prices of cryptocurrencies. The importance of having these models is that they can have significant economic ramifications by helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan for future work, a recommendation is made to investigate other factors that might affect the prices of cryptocurrency market such as social media, tweets, and trading volume.
Generalized Semantic Preserving Hashing for Cross-Modal Retrieval

Cross-modal retrieval is gaining importance due to the availability of large amounts of multimedia data. Hashing-based techniques provide an attractive solution to this problem when the data size is large. For cross-modal retrieval, data from the two modalities may be associated with a single label or multiple labels, and in addition, may or may not have a one-to-one correspondence. This work proposes a simple hashing framework which has the capability to work with different scenarios while effectively capturing the semantic relationship between the data items. The work proceeds in two stages in which the first stage learns the optimum hash codes by factorizing an affinity matrix, constructed using the label information. In the second stage, ridge regression and kernel logistic regression is used to learn the hash functions for mapping the input data to the bit domain. We also propose a novel iterative solution for cases where the training data is very large, or when the whole training data is not available at once. Extensive experiments on single label data set like Wiki and multi-label datasets like MirFlickr, NUS-WIDE, Pascal, and LabelMe, and comparisons with the state-of-the-art, shows the usefulness of the proposed approach.
Flow-based Kernel Prior with Application to Blind Super-Resolution

Kernel estimation is generally one of the key problems for blind image super-resolution (SR). Recently, Double-DIP proposes to model the kernel via a network architecture prior, while KernelGAN employs the deep linear network and several regularization losses to constrain the kernel space. However, they fail to fully exploit the general SR kernel assumption that anisotropic Gaussian kernels are sufficient for image SR. To address this issue, this paper proposes a normalizing flow-based kernel prior (FKP) for kernel modeling. By learning an invertible mapping between the anisotropic Gaussian kernel distribution and a tractable latent distribution, FKP can be easily used to replace the kernel modeling modules of Double-DIP and KernelGAN. Specifically, FKP optimizes the kernel in the latent space rather than the network parameter space, which allows it to generate reasonable kernel initialization, traverse the learned kernel manifold and improve the optimization stability. Extensive experiments on synthetic and real-world images demonstrate that the proposed FKP can significantly improve the kernel estimation accuracy with less parameters, runtime and memory usage, leading to state-of-the-art blind SR results.
AI-synthesized faces are indistinguishable from real faces and more trustworthy

Artificial intelligence (AI)-synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable-and more trustworthy-than real faces.
Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset)

Sentiment analysis or Opinion Mining or Emotion Artificial Intelligence is an on-going field which refers to the use of Natural Language Processing, analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic Natural Language Processing techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.
Rethinking Complex Neural Network Architectures for Document Classification

Ashutosh Adhikari, Achyudh Ram, Raphael Tang, Jimmy Lin. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.