Reviewerly/exHarmony huggingface dataset:
Comparative Analysis to Predict Breast Cancer using Machine Learning Algorithms: A Survey

Breast Cancer is the second most dangerous cancer in the world. Most of the women die due to breast cancer not only in India but everywhere in the world. In 2011, USA stated that one in eight women suffered from cancer. Breast cancer develops due to the abnormal cell division in the breast itself which results in the formation of either benign or malignant cancer. So, it is very important to predict breast cancer at an early stage and by providing proper treatment, many lives can be saved. This paper aims to give a comparative study by applying different machine learning algorithms such as Support Vector Machine, K-Nearest Neighbour, Naïve Bayes, Decision Tree, K-means and Artificial Neural Networks on Wisconsin Diagnostic dataset to predict breast cancer at an early stage.
Classification of breast cancer mammogram images using convolution neural network

Summary Medical imaging systems have broadly used in the diagnosis and identification of breast cancer. It is essential to recognize breast tumor as soon as possible. Mammography is a widely utilized method for the identification of breast cancer. The identification of cancer is trailed by the segmentation of the cancer area in an image of the mammogram. Numerous researches have been made on the diagnosing and identification of breast cancer utilizing different classification and image processing methods. In this work, we proposed the Convolutional Neural Network (CNN) classifier for diagnosing breast cancer utilizing MIAS (Mammographic Image Analysis Society)‐dataset. CNN established as an efficient class of methods for image recognition problems. CNN is a deep learning system that extricates the feature of an image and utilizes those features for classification of the image. Because deep learning methods are utilized for high task objective Computer Vision, Medical Diagnosis, Image processing, and so on. Wiener filter is utilized to expel the noise and background of the image and the K‐means clustering technique was utilized for the segmentation. After segmentation, the features are extracted and classified utilizing CNN classifier. The performance of this proposed method was analyzed and compared with the conventional techniques based on accuracy, sensitivity, and specificity result parameters. From the comparison result, it is seen that the CNN classifier performed better compared with different techniques with 0.5‐4% additional accuracy and 3‐13% specificity.
Risk Assessment of Tunnel Construction Based on Improved Cloud Model

Objective and scientific risk assessment is of great significance for tunnel construction. The intrinsic link between risk level and influencing factors are poorly understood, and our ability to assess the risks of tunnel construction remains limited. In this study, we improved the normal cloud model to combine fuzziness and randomness into risk assessment to make the result more accurate. First, we constructed the risk index system for tunnel construction; second, we calculated the weight of the risk index by using the AHP method. Then we determined the improved cloud model eigenvalue through investigation and analysis and put the risk index data into a forward cloud generator to obtain the fuzzy comprehensive evaluation matrix. Following this, we combined weights with the matrix to receive the membership degree of different risk levels. Finally, the final risk level was obtained by analyzing the purposely designed membership radar chart. We applied this assessment model to evaluate the construction risk of the Tiger Mountain tunnel in section ZK3+280–ZK3+300. The study shows that the improved cloud model delivers a more scientific and reasonable risk assessment by using the fuzzy random theory, which introduces a new concept for risk assessment in tunnel construction.
Automatic Poetry Generation from Prosaic Text

In the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language.In particular, language models based on neural networks have improved the state of the art with regard to predictive language modeling, while topic models are successful at capturing clear-cut, semantic dimensions.In this paper, we explore how these approaches can be adapted and combined to model the linguistic and literary aspects needed for poetry generation.The system is exclusively trained on standard, non-poetic text, and its output is constrained in order to confer a poetic character to the generated verse.The framework is applied to the generation of poems in both English and French, and is equally evaluated for both languages.Even though it only uses standard, non-poetic text as input, the system yields state of the art results for poetry generation.
Digital pathology for second opinion consultation and donor assessment during organ procurement: Review of the literature and guidance for deployment in transplant practice

Telepathology has been an important application for second opinion consultation ever since the introduction of digital pathology. However, little is known regarding teleconsultation for second opinion in transplantation. There is also limited literature on telepathology during organ donor procurement, typically utilized when general pathologists on-call request back-up to help assess donor biopsies for organ suitability or to diagnose newly discovered tumors with urgent time constraints. In this review, we searched Pubmed/Embase and websites of transplant organizations to collect and analyze published evidence on teleconsultation for donor evaluation and organ procurement. Of 2725 records retrieved using the key terms 'telepathology', 'second opinion' and 'transplantation', 26 suitable studies were included. Most records were from North America and included validation studies of telepathology being used for remote frozen section interpretation of donor biopsies with whole slide imaging. The data from these published studies supports the transition towards digital teleconsultation in transplant settings where consultations among pathologists are still handled by pathologists being called on site, via telephone and/or email.
Improving Contextual Language Models for Response Retrieval in Multi-Turn Conversation

As an important branch of current dialogue systems, retrieval-based chatbots leverage information retrieval to select proper predefined responses. Various promising architectures have been designed for boosting response retrieval, however, few researches exploit the effectiveness of the pre-trained contextual language models. In this paper, we propose two approaches to adapt contextual language models in dialogue response selection task. In detail, the Speaker Segmentation approach is designed to discriminate different speakers to fully utilize speaker characteristics. Besides, we propose the Dialogue Augmentation approach, i.e., cutting off real conversations at different time points, to enlarge the training corpora. Compared with previous works which use utterance-level representations, our augmented contextual language models are able to obtain top-hole contextual dialogue representations for deeper semantic understanding. Evaluation on three large-scale datasets has demonstrated that our proposed approaches yield better performance than existing models.
Unsupervised Single Image Super-Resolution Network (USISResNet) for Real-World Data Using Generative Adversarial Network

Current state-of-the-art Single Image Super-Resolution (SISR) techniques rely largely on supervised learning where Low-Resolution (LR) images are synthetically generated with known degradation (e.g., bicubic downsampling). The deep learning models trained with such synthetic dataset generalize poorly on the real-world or natural data where the degradation characteristics cannot be fully modelled. As an implication, the super-resolved images obtained for real LR images do not produce optimal Super-Resolution (SR) images. We propose a new SR approach to mitigate such an issue using unsupervised learning in Generative Adversarial Network (GAN) framework - USISResNet. In an attempt to provide high quality SR image for perceptual inspection, we also introduce a new loss function based on the Mean Opinion Score (MOS). The effectiveness of the proposed architecture is validated with extensive experiments on NTIRE-2020 Real-world SR Challenge validation (Track-1) set along with testing datasets (Track-1 and Track-2). We demonstrate the generalizable nature of proposed network by evaluating real-world images as against other state-of-the-art methods which employ synthetically downsampled LR images. The proposed network has further been evaluated on NTIRE 2020 Real-world SR Challenge dataset where the approach has achieved reliable accuracy.
Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images

Differentiation between benign and malignant breast cancer cases in X-ray images can be difficult due to their similar features. In recent studies, the transfer learning technique has been used to classify benign and malignant breast cancer by fine-tuning various pre-trained networks such as AlexNet, visual geometry group (VGG), GoogLeNet, and residual network (ResNet) on breast cancer datasets. However, these pre-trained networks have been trained on large benchmark datasets such as ImageNet, which do not contain labeled images related to breast cancers which lead to poor performance. In this research, we introduce a novel technique based on the concept of transfer learning, called double-shot transfer learning (DSTL). DSTL is used to improve the overall accuracy and performance of the pre-trained networks for breast cancer classification. DSTL updates the learnable parameters (weights and biases) of any pre-trained network by fine-tuning them on a large dataset that is similar to the target dataset. Then, the updated networks are fine-tuned with the target dataset. Moreover, the number of X-ray images is enlarged by a combination of augmentation methods including different variations of rotation, brightness, flipping, and contrast to reduce overfitting and produce robust results. The proposed approach has demonstrated a significant improvement in classification accuracy and performance of the pre-trained networks, making them more suitable for medical imaging.
Multi-Scale Fusion Subspace Clustering Using Similarity Constraint

Classical subspace clustering methods often assume that the raw form data lie in a union of the low-dimension linear subspace. This assumption is too strict in practice, which largely limits the generalization of subspace clustering. To tackle this issue, deep subspace clustering (DSC) networks based on deep autoencoder (DAE) have been proposed, which non-linearly map the raw form data into a latent space well-adapted to subspace clustering. However, existing DSC models ignore the important multi-scale information embedded in DAE, thus abandon the much more useful deep features, leading their suboptimal clustering results. In this paper, we propose the Multi-Scale Fusion Subspace Clustering Using Similarity Constraint (SC-MSFSC) network, which learns a more discriminative self-expression coefficient matrix by a novel multi-scale fusion module. More importantly, it introduces a similarity constraint module to guide the fused self-expression coefficient matrix in training. Specifically, the multi-scale fusion module is framed to generate the self-expression coefficient matrix of each convolutional layer in DAE and then fuses them with the convolutional kernel. In addition, the similarity constraint module is to supervise the fused self-expression coefficient matrix by the designed similarity matrix. Extensive experimental results on four benchmark datasets demonstrate the superiority of our new model against state-of-the-art methods.
Pixel Consensus Voting for Panoptic Segmentation

The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the generalized Hough transform. Pixels cast discretized, probabilistic votes for the likely regions that contain instance centroids. At the detected peaks that emerge in the voting heatmap, backprojection is applied to collect pixels and produce instance masks. Unlike a sliding window detector that densely enumerates object proposals, our method detects instances as a result of the consensus among pixel-wise votes. We implement vote aggregation and backprojection using native operators of a convolutional neural network. The discretization of centroid voting reduces the training of instance segmentation to pixel labeling, analogous and complementary to FCN-style semantic segmentation, leading to an efficient and unified architecture that jointly models things and stuff. We demonstrate the effectiveness of our pipeline on COCO and Cityscapes Panoptic Segmentation and obtain competitive results. Code will be open-sourced.
PS-RCNN: Detecting Secondary Human Instances in a Crowd via Primary Object Suppression

Detecting human bodies in highly crowded scenes is a challenging problem. Two main reasons result in such a problem: 1). weak visual cues of heavily occluded instances can hardly provide sufficient information for accurate detection; 2). heavily occluded instances are easier to be suppressed by Non-Maximum-Suppression (NMS). To address these two issues, we introduce a variant of two-stage detectors called PS-RCNN. PS-RCNN first detects slightly/none occluded objects by an R-CNN [1] module (referred as P-RCNN), and then suppress the detected instances by human-shaped masks so that the features of heavily occluded instances can stand out. After that, PS-RCNN utilizes another R-CNN module specialized in heavily occluded human detection (referred as S-RCNN) to detect the rest missed objects by P-RCNN. Final results are the ensemble of the outputs from these two RCNNs. Moreover, we introduce a High Resolution RoI Align (HRRA) module to retain as much of fine-grained features of visible parts of the heavily occluded humans as possible. Our PS-RCNN significantly improves recall and AP by 4.49% and 2.92% respectively on CrowdHuman [2], compared to the baseline. Similar improvements on Widerperson [3] are also achieved by the PS-RCNN.
A Rule-Based Approach to Embedding Techniques for Text Document Classification

With the growth of online information and sudden expansion in the number of electronic documents provided on websites and in electronic libraries, there is difficulty in categorizing text documents. Therefore, a rule-based approach is a solution to this problem; the purpose of this study is to classify documents by using a rule-based. This paper deals with the rule-based approach with the embedding technique for a document to vector (doc2vec) files. An experiment was performed on two data sets Reuters-21578 and the 20 Newsgroups to classify the top ten categories of these data sets by using a document to vector rule-based (D2vecRule). Finally, this method provided us a good classification result according to the F-measures and implementation time metrics. In conclusion, it was observed that our algorithm document to vector rule-based (D2vecRule) was good when compared with other algorithms such as JRip, One R, and ZeroR applied to the same Reuters-21578 dataset.
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction

Image super-resolution is one of the important computer vision techniques aiming to reconstruct high-resolution images from corresponding low-resolution ones. Most recently, deep learning based approaches have been demonstrated for image super-resolution. However, as the deep networks go deeper, they become more difficult to train and more difficult to restore the finer texture details, especially under real-world settings. In this paper, we propose a Residual Channel Attention-Generative Adversarial Network (RCA-GAN) to solve these problems. Specifically, a novel residual channel attention block is proposed to form RCA-GAN, which consists of a set of residual blocks with shortcut connections, and a channel attention mechanism to model the interdependence and interaction of the feature representations among different channels. Besides, a generative adversarial network (GAN) is employed to further produce realistic and highly detailed results. Benefiting from these improvements, the proposed RCA-GAN yields consistently better visual quality with more detailed and natural textures than baseline models; and achieves comparable or better performance compared with the state-of-the-art methods for real-world image super-resolution.
Survival Regression with Accelerated Failure Time Model in XGBoost

Survival regression is used to estimate the relation between time-to-event and feature variables, and is important in application domains such as medicine, marketing, risk management, and sales management. Nonlinear tree based machine learning algorithms as implemented in libraries such as XGBoost, scikit-learn, LightGBM, and CatBoost are often more accurate in practice than linear models. However, existing state-of-the-art implementations of tree-based models have offered limited support for survival regression. In this work, we implement loss functions for learning accelerated failure time (AFT) models in XGBoost, to increase the support for survival modeling for different kinds of label censoring. We demonstrate with real and simulated experiments the effectiveness of AFT in XGBoost with respect to a number of baselines, in two respects: generalization performance and training speed. Furthermore, we take advantage of the support for NVIDIA GPUs in XGBoost to achieve substantial speedup over multi-core CPUs. To our knowledge, our work is the first implementation of AFT that uses the processing power of NVIDIA GPUs. Starting from the 1.2.0 release, the XGBoost package natively supports the AFT model. The addition of AFT in XGBoost has had significant impact in the open source community, and a few statistics packages now use the XGBoost AFT model. Supplementary materials for this article are available online.
Operationalizing the Legal Principle of Data Minimization for Personalization

Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed (`data minimisation')". To date, the legal and computational definitions of 'purpose limitation' and 'data minimization' remain largely unclear. In particular, the interpretation of these principles is an open issue for information access systems that optimize for user experience through personalization and do not strictly require personal data collection for the delivery of basic service.
Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network

Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.
Blockchain and institutional complexity: an extended institutional approach

Abstract From a modern institutional economics viewpoint, blockchain is an institutional technology that minimizes transaction costs and greatly reduces intermediation. Through an analysis of blockchain, I demonstrate the possibilities of extended institutional approach – a new generation of complexity-focused methodologies and theories of institutional analysis that complement and expand the standard institutional paradigm. By using the theory of transaction value, I argue blockchain technologies not only will lead to a significant reduction in transaction costs but will also reorient intermediaries toward improving the quality of transactions and expanding the offer of additional transaction services. The theory of institutional assemblages indicates it is impossible to form a homogeneous system of blockchain-based institutions associated exclusively with the principles of decentralization, transparency, and openness. Blockchain-based institutions will be of a hybrid and conflicting nature, combining elements of opposing institutional logics – regulatory and algorithmic law, Ricardian and smart contracts, private and public systems, and uncontrollability and arbitration.
Jointly Learning Commonality and Specificity Dictionaries for Person Re-Identification

Despite advances in person re-identification (re-ID), it is still far from meeting the needs of real-world applications due to tremendous visual ambiguity in person appearance across cameras. To overcome this problem, we propose a person re-ID method by decomposing a pedestrian's appearance feature into different components. This method assumes that each pedestrian image is composed of person-shared components that reflect the similarities of different pedestrians and person-specific components that reflect unique identity information. Based on this assumption, we propose to reduce the ambiguity in visual features by removing person-shared components from pedestrian visual features. To this end, we develop a framework for learning a pair of commonality and specificity dictionaries, while introducing a distance constraint to force the particularities of the same person over the specificity dictionary to have the same coding coefficients and the coding coefficients of different pedestrians to have weak correlation. Furthermore, considering the similarity of the commonality dictionary and the sparsity of the specificity dictionary, low-rank and sparse regularization terms are introduced into the dictionary learning framework to improve their representation ability and discriminative ability. Extensive experimental results show that the proposed algorithm outperforms or is competitive with the state-of-the-art methods.
LD-MAN: Layout-Driven Multimodal Attention Network for Online News Sentiment Recognition

The prevailing use of both images and text to express opinions on the web leads to the need for multimodal sentiment recognition. Some commonly used social media data containing short text and few images, such as tweets and product reviews, have been well studied. However, it is still challenging to predict the readers’ sentiment after reading online news articles, since news articles often have more complicated structures, e.g., longer text and more images. To address this problem, we propose a layout-driven multimodal attention network (LD-MAN) to recognize news sentiment in an end-to-end manner. Rather than modeling text and images individually, LD-MAN uses the layout of online news to align images with the corresponding text. Specifically, it exploits a set of distance-based coefficients to model the image locations and measure the contextual relationship between images and text. LD-MAN then learns the affective representations of the articles from the aligned text and images using a multimodal attention mechanism. Considering the lack of relevant datasets in this field, we collect two multimodal online news datasets, containing a total of 14,566 articles with 56,260 images and 251,202 words. Experimental results demonstrate that the proposed method performs favorably compared with state-of-the-art approaches. We will release all the codes, models and datasets to the community.