Reviewerly/exHarmony huggingface dataset:
Mutually Guided Dual-Task Network for Scene Text Detection

Scene text detection has been studied extensively. Existing methods detect either words or text lines and use either word-level or line-level annotated data for training. In this paper, we propose a dual-task network that can perform word-level and line-level text detection simultaneously and use training data of both levels of annotation to boost the performance. The dual-task network has two detection heads for word-level and line-level text detection, respectively. Then we propose a mutual guidance scheme for the joint training of the two tasks with two modules: line filtering module utilizes the output feature map of the text line detector to filter out the non-text regions for the word detector, and word enhancing module provides prior positions of words for the text line detector depending on the output feature map of the word detector. Experimental results of word-level and line-level text detection demonstrate the effectiveness of the proposed dual-task network and mutual guidance scheme, and the results of our method are competitive with state-of-the-art methods.
Image Super-Resolution Using Multi-Resolution Attention Network

In recent years, single image super-resolution based on convolution neural network (CNN) has been extensively researched. However, most CNN-based methods only focus on mining features at a single resolution, which will cause the loss of some useful information. Besides, most of them still have difficulty in training and obtaining high-quality images for large scale factors. To address these issues, we propose a multi-resolution attention network (MRAN), which progressively reconstructs images at large scale factors by aggregating features from multiple resolutions. Specially, a multi-resolution residual block (MRRB) is designed as basic block to specialize features at different resolutions and share information across different resolutions, improving the representation ability of features. Simultaneously, we design a resolution-wise attention block (RAB) to evaluate the importance of features from different resolutions, making the use of features more effective and enhancing feature fusion. Experimental results show that our proposed method is superior to the state-of-the-art methods.
Antibunching of photons in a coherent radiation field coupled to a non-degenerate parametric oscillator beyond rotating wave approximation

Under the classical (strong) pump condition, the Hamiltonian involving the signal and the idler modes of a non-degenerate parametric oscillator is exhibited. Without using the usual rotating wave approximation (RWA), the analytical solutions of the field operators are used to investigate the antibunching of photons of the input radiation field coupled to the non-degenerate parametric oscillator. By using the symbolic calculation, the antibunching of photons for both the signal and idler modes are investigated. In particular, the effects of the inclusion of rotating wave approximated terms on the antibunching of photons are clearly indicated. To substantiate the analytical results, the temporal evolution of signal photon and idler photons, and the antibunching effects of the signal and idler modes are investigated numerically by using the QuTip 3.1.0. The exact numerical results obtained by QuTip 3.1.0 matches extremely well with those of the analytical results. The present article and hence the analytical method might be of use for investigating the situations of having ultra-strongly and deep-strongly coupled systems where the possibilities of using RWA is completely ruled out.
Efficient grouping for keypoint detection

The success of deep neural networks in the traditional keypoint detection task encourages researchers to solve new problems and collect more complex datasets. The size of the DeepFashion2 dataset [1] poses a new challenge on the keypoint detection task, as it comprises 13 clothing categories that span a wide range of keypoints (294 in total). The direct prediction of all keypoints leads to huge memory consumption, slow training, and a slow inference time. This paper studies the keypoint grouping approach and how it affects the performance of the CenterNet [2] architecture. We propose a simple and efficient automatic grouping technique with a powerful post-processing method and apply it to the DeepFashion2 fashion landmark task and the MS COCO pose estimation task. This reduces memory consumption and processing time during inference by up to 19% and 30% respectively, and during the training stage by 28% and 26% respectively, without compromising accuracy.
Aspect and Opinion Extraction of Indonesian Lipsticks Product Reviews using Conditional Random Field (CRF)

Aspect and opinion extraction is a key process in several downstream applications, such as market analysis. In this paper, we modeled aspect and opinion extraction of lipsticks product reviews in Indonesia using Conditional Random Field (CRF) method. The dataset contains review text written not only in standard and colloquial Indonesian languages but also standard and colloquial English, labeled by BIO format notation. The experimental results show that this is a challenging task, described by the average F1 score on 10 aspect opinion labels is 44.1% with accuracy of 81.8%. The results on the baseline method, HMM, show lower F1 and accuracy scores. The errors found in the results have mostly occurred when the same words have different meanings. This error has a percentage of 75% of the total result. Second largest error caused by unknown words with the percentage of 14% of the total result.
Performance Comparison of Image Restoration techniques using CNN and their applications

Many Image applications depend on the image processing or image visualizations. Noisy image is a major concern due to the increase in pixels per inch(ppi), Nowadays, ppi are increasing making images clearer and pleasing, but also increasing the possibilities of noise addition and pixel corruption. However, image denoising methods requires a lot of computational effort and are degraded with noise which makes images visually unpleasing. Therefore, some denoising methods are required to lower the noise levels without losing the image features including textures, contrasts, edges and structures. As of now many methods have been proposed to decrease noise, including spatial domain, transform domain and convolutional based methods on image denoising problem. In this paper we will be focusing on the CNN based state-of-art denoiser methods and perform a comparative study on the various methods. After studying about all the popular image denoising techniques we are using it as a base network and use it for fingerprint enhancement . The efficiency and accuracy of model is it learns only dependent over the image dataset descriptions which was demonstrated by experiments ,also performed visual and metric comparisons of application using the trained model.
(e)Textile New Materialities

This paper explores the intersections within bodily materialism and future textiles by inquiring into embodied practices and materiality in care. By placing the body as a site of research, it centres around concepts of bodily care and the body as an ecosystem, one that is always in flux and considers the fluidity of bodies and bodily fluids, such as urine, discharge, breath and sweat, as fluids with potential to design with. It looks at how bodies are acted upon by outside forces, and explore more-than-human relations as co-creators in co-habiting the space of the body and that around it. To illustrate this, the paper introduces a series of design research artefacts that take a variety of approaches to exploring the materiality of care in the everyday. First, an eTextile toolkit that aims to create bodily awareness through hands-on engagement with textile crafting technology, then a biotextile harvesting toolkit that involves the raw material of the intimate body that explores DIYbio in the context of the home, and lastly a set of wearable living material-based explorations that recognize biomimicry and symbiotic relationships in designing for chronic stress. In embracing notions of bodily materialism, this paper explores the bodily abject, i.e. fluids and the more-than-human as crucial to engendering new modes of knowing in intimate and personal care through textile-based materials. The paper engages critically with textile design research and practice by placing material that embraces care as ambivalent at the forefront and thus challenging traditional approaches to health and care and, importantly, the design of future textiles.
Pulse Compression Radar Ship Detection Method Based on Improved SSD

Radar ship target detection plays an important role in ship navigation and collision avoidance. The ship target detection for synthetic aperture radar has been relatively mature, while pulse compression radar has relatively poor imaging effect and more noise interference, so there are few related studies. Aiming at the problem of poor detection of small target ships in pulse compression radar images, an improved SSD-based radar ship target detection method is proposed in this paper. Based on the Single Shot MultiBox Detector, we improved the network model. Resnet50 is used to replace the backbone network to effectively extract the feature information of small targets. Feature fusion module which contains deconvolution and group convolution is added to combine low-level features and deep features. Spatial attention module is used to suppress background and increase detection accuracy. Experimental results demonstrate that the proposed model performs better than the benchmark model.
A Semantic Tableau Method for Argument Construction

A semantic tableau method, called an argumentation tableau, that enables the derivation of arguments, is proposed. First, the derivation of arguments for standard propositional and predicate logic is addressed. Next, an extension that enables reasoning with defeasible rules is presented. Finally, reasoning by cases using an argumentation tableau is discussed.
Learning the Relevant Substructures for Tasks on Graph Data

Focusing on graph-structured prediction tasks, we demonstrate the ability of neural networks to provide both strong predictive performance and easy interpretability, two proper-ties often at odds in modern deep architectures. We formulate the latter by the ability to extract the relevant substructures for a given task, inspired by biology and chemistry applications. To do so, we utilize the Local Relational Pooling (LRP) model, which is recently introduced with motivations from substructure counting. In this work, we demonstrate that LRP models can be used on challenging graph classification tasks to provide both state-of-the-art performance and interpretability, through the detection of the relevant substructures used by the network to make its decisions. Besides their broad applications (biology, chemistry, fraud detection, etc.), these models also raise new theoretical questions related to compressed sensing and to computational thresholds on random graphs.
Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search

The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware. This paper introduces a fast and robust method that ensures both of these goals by obtaining state-of-the-art performance for an image-to-video search scenario. Hence, we present critical enhancements to well-known indexing and visual representation techniques by promoting faster, better and moderate retrieval performance. We also boost the superiority of our method for some visual challenges by exploiting individual decisions of local and global descriptors at query time. For instance, local content descriptors represent copied/duplicated scenes with large geometric deformations such as scale, orientation and affine transformation. In contrast, the use of global content descriptors is more practical for near-duplicate and semantic searches. Experiments are conducted on a large-scale Stanford I2V dataset. The experimental results show that our method is useful in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. The proposed method is superior and achieves state-of-the-art performance based on the mean average precision (MAP) score of this dataset. Lastly, we report additional MAP scores after updating the ground annotations unveiled by retrieval results of the proposed method, and it shows that the actual performance.
A Survey on Detection Approaches of Shilling Attacks in SAN

In social Aware Network (SAN), the major exercises focus on investigating the behaviour and properties of the consumer. This examination of consumer trademark helps in the plan of profoundly productive and appropriate conventions. Specifically, the peddling assault presents a serious level of weakness into the recommender frameworks. Proposal System is an uncommon kind of data sifting framework adjusted by online merchants to give suggestions to their consumer dependent on their necessities. Collective sifting is quite possibly the most generally utilized suggestion frameworks; lamentably, it is inclined to peddling/profile infusion assaults. Such assaults modify the suggestion interaction to advance or downgrade a specific item. Throughout the long term, numerous assault models and recognition strategies have been created to moderate the issue. This paper means to be an extensive review of the shilling attack models and discovery calculations. Likewise, this research work momentarily examines the late works in the advancement of strong calculations that reduce the effect of pushing assaults. This paper likewise presents potential gain and disadvantage of every one of the inspected papers with its various sorts of identification approaches and execution measurements.
Iterative Soft-Input Soft-Output Bit-Level Reed-Solomon Decoder Based on Information Set Decoding

In this paper, a bit-level decoder is presented for soft-input soft-output iterative decoding of Reed-Solomon (RS) codes.The main aim for the development of the proposed algorithm is to reduce the complexity of the decoding process, while yielding a relatively good error correction performance, for the efficient use of RS codes.The decoder utilises information set decoding techniques to reduce the computational complexity cost by lowering the iterative convergence rate during the decoding process.As opposed to most iterative bit-level soft-decision decoders for RS codes, the proposed algorithm is also able to avoid the use of belief propagation in the iterative decoding of the soft bit information, which also contributes to the reduction in the computational complexity cost of the decoding process.The performance of the proposed decoder is investigated when applied to short RS codes.The error correction simulations show the proposed algorithm is able to yield a similar performance to that of the Adaptive Belief Propagation (ABP) algorithm, while being a less complex decoder.
Adaptive Word Embedding Module for Semantic Reasoning in Large-scale Detection

In recent years, convolutional neural networks have achieved rapid development in the field of object detection. However, due to the imbalance of data, high costs in labor and uneven level of data labeling, the overall performance of the previous detection network has dropped sharply when dataset extended to the large-scale with hundreds and thousands of categories. The embedding of external semantic knowledge can bring new additional information to reduce the cost of annotation and improve the performance of large-scale detection. In this paper, the Adaptive Word Embedding Module is proposed to extract the adaptive semantic knowledge graph under the constraint of the semantic consistency. Our method endows the ability to infer global semantic of detection networks without other attribute or relationship annotations. Compared with Faster RCNN, the algorithm on the MSCOCO dataset was significantly improved by 4.1%, and the mAP value has reached 32.8%. On the VG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1000</sub> dataset, it increased by 0.9% to 6.7% compared with Faster RCNN. Adaptive Word Embedding Module is lightweight, general-purpose and can be plugged into diverse detection networks. Code will be made available.
Super-resolution of compressed images using enhanced attention network

In recent years, to save bandwidth and storage space, images are usually compressed to reduce data volume, which leads to the loss of image details and affects the super-resolution (SR) performance. SR of compressed images is a key technique for addressing this problem. We propose the compressed-image super-resolution using enhanced attention network (CISREAN). First, the task is divided into two subtasks: image decompression and SR. Each subprocess introduces an enhanced residual module (ERM) with an attention mechanism. The ERM consists of several wide-activation residual blocks (WARBs) and an attention unit called the cascading residual attention (CRA) block. A WARB achieves better reconstruction than traditional image processing with the same computational complexity, and the CRA extracts more useful information in feature mapping even with fewer channels. This makes the ERM light and effective. Next, initial features are selected for wider inception and less blocking by overlapping extractions from the compressed image during decompression by convolutional layers. After completing both subprocesses, an end-to-end network is trained; it reduces compression artifacts, performing SR simultaneously. Extensive experiments on JPEG images with different quality factors show that CISREAN provides state-of-the-art performance based on objective metrics and subjective visual quality.
Blockchain based Double Spending Prevention for Invoice Financing

Invoice financing has been third largest financing market and is still growing continuously during last few years. To meet up immediate liquidity requirements most of the companies often use Invoice Financing as a key approach. By way of Invoice Financing, companies can encash the amount from financial institutes against an unpaid invoice for their immediate needs instead of waiting for due payments from their clients. Major challenges faced in invoice financing are double spending due to lack of communication between financial institutes and information security. Implementation of Blockchain for Invoice Financing can be a major breakthrough to avoid such challenges. In this Paper, we are presenting Hyperledger Fabric based Blockchain scheme to prevent fraudulent tryouts of double spending which will also provide security to the invoice information in order to facilitate immutability. Results show that, Blockchain can be used as an effective way to provide security, transparency, decentralization and avoid double spending.
An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks

With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of a person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the time and effort which is generally required for changing the skin tone using existing applications (e.g., Photoshop) by professionals or novice. To establish the efficacy of this method we have compared our result with that of some popular photo editor and also with the result of some existing benchmark method related to human attribute manipulation. Rigorous experiments on different datasets show the effectiveness of this method in terms of synthesizing perceptually convincing outputs.
Automatic Generation of Structured Abstracts from Research Papers by using Deep Learning

In recent years, the volume of research papers has become enormous. Therefore, it is difficult for researchers to select their required papers. To lighten this problem, a method of describing abstracts called "Structured Abstract" is used in the fields of physiology and medicine. This paper proposes a method that extracts sentences matching with each heading of Structured Abstract. Specifically, SciBERT and BioBERT, which are specialized in the science and technology fields, are used in the method. The effectiveness of the proposed method is evaluated by comparing automatically generated abstracts and human-written abstracts.
Sexist hate speech: Topical organization of intolerant discourse

The article is aimed at determining the topical structure of sexist hate speech as form of intolerant discourse. Sexist hate speech is viewed as a type of gender-based hate speech that is influenced by the same social, political and legal, cultural and ethical factors as sexual orientation-based or gender identity-based hate speech. The article proposes a topical structure of hate speech in general and provides the examples of verbalization of sexist hate speech topics and subtopics in the Belarusian online discourse.
Multivariate Data Model Prediction Analysis Using Backpropagation Neural Network Method

Indonesia has a relatively large population, making health an important necessity. Census data has been widely available in accordance with problems in health. With the available data, researchers have a great opportunity to conduct research. Research on predictions in health is nothing new. By making predictions, it can help in making decisions quickly and accurately decisions that can be determined by the health sector or other fields. Prediction methods have been developed, one of which is an artificial neural network (ANN). In the ANN method, there is a Backpropagation Neural Network (BPNN) model. In general, the BPNN method is implemented in one input data model. Therefore, it requires an analytical study that uses a lot of data models to be implemented into the BPNN method. In this study, five data models were used with the number of variables and the amount of data that were different. Processed with the BPNN method and outputs the accuracy and execution time of all data models. From experiments and analyzes conducted on 5 kinds of data models, data model 4 has the number of variables 19 and the number of data 392 yields accuracy of 98.718% and a relatively slow execution time of 27.798 seconds. The highest execution time is found in the data model 3 with the number of variables 13 and the amount of data of 589 yields an accuracy of 95,385% and execution time of 45,442 seconds.