Reviewerly/exHarmony huggingface dataset:
Word Embedding-based Text Processing for Comprehensive Summarization and Distinct Information Extraction

In this paper, we propose two automated text processing frameworks specifically designed to analyze online reviews. The objective of the first framework is to summarize the reviews dataset by extracting essential sentence. This is performed by converting sentences into numerical vectors and clustering them using a community detection algorithm based on their similarity levels. Afterwards, a correlation score is measured for each sentence to determine its importance level in each cluster and assign it as a tag for that community. The second framework is based on a question-answering neural network model trained to extract answers to multiple different questions. The collected answers are effectively clustered to find multiple distinct answers to a single question that might be asked by a customer. The proposed frameworks are shown to be more comprehensive than existing reviews processing solutions.
Research on The Comment Text Classification based on Transfer Learning

In the traditional review text classification method, in order to realize the high accuracy of classification model, there are two basic premises: (1) training data and test data must be distributed independently and uniformly; (2) there must be enough training data to learn a good classification model. However, in many cases, these two premises are not true. If a classification model already exists and classifies data from a domain well, then a classification task for a related domain exists, but only data from the source domain, then it may violate this assumption. The comment text classification method based on transfer learning refers to applying the classification knowledge learned in the source domain to the new classification task in the relevant field by using the transfer learning method in the process of classifying the comment text. Therefore, after constructing the isomorphic feature space of source domain and target domain, the TrAdaBoost migration learning framework was used to train the classification model. This model allows users to leverage old data with a small amount of new markup data to build a high-quality classification model for new data. Experimental results show that the model can effectively transfer classification knowledge from source domain to target domain.
Recommending E-Commerce Products on Cold Start and Long Tail Using Transaction Data

Recommender method plays a critical role in the discovery and display of interesting items from near-infinite inventories to potential users. Yet the recommender systems are being challenged by two problems. One is “how to address new users” and the other one is “how to amaze users”. The problem is known as a cold-start recommendation. In this paper, how the products are handled in the e-commerce retailers getting sold out soon, and products remain stagnant for a long duration isdiscussed. To overcome this problem, the selling of products to customers of the same kind based is tracked on the previous purchase by using clustering and classification algorithms. To recommend combo products to users and general recommendation based on user point of interest and product transaction details. This way the retailers are getting helped to gain some profit which is not sold for a long period.
ReSIde: Reusable service identification from software families

The clone-and-own approach becomes a common practice to quickly develop Software Product Variants (SPVs) that meet variability in user requirements. However, managing the reuse and maintenance of the cloned codes is a very hard task. Therefore, we aim to analyze SPVs to identify cloned codes and package them using a modern systematic reuse approach like Service-Oriented Architecture (SOA). The objective is to benefit from all the advantages of SOA when creating new SPVs. The development based on services in SOA supports the software reuse and maintenance better than the development based on individual classes in monolithic object-oriented software. Existing service identification approaches identify services based on the analysis of a single software product. These approaches are not able to analyze multiple SPVs to identify reusable services of cloned codes. Identifying services by analyzing several SPVs allows to increase the reusability of identified services. In this paper, we propose ReSIde (Reusable Service Identification): an automated approach that identifies reusable services from a set of object-oriented SPVs. This is based on analyzing the commonality and the variability between SPVs to identify the implementation of reusable functionalities corresponding to cloned codes that can be packaged as reusable services. To validate ReSIde, we have applied it on three product families of different sizes. The results show that the services identified based on the analysis of multiple product variants using ReSIde are more reusable than services identified based on the analysis of singular ones.
Topic-sensitive neural headline generation

Neural models are being widely applied for text summarization, including headline generation, and are typically trained using a set of document-headline pairs. In a large document set, documents can usually be grouped into various topics, and documents within a certain topic may exhibit specific summarization patterns. Most existing neural models, however, have not taken the topic information of documents into consideration. This paper categorizes documents into multiple topics, since documents within the same topic have similar content and share similar summarization patterns. By taking advantage of document topic information, this study proposes a topic-sensitive neural headline generation model (TopicNHG). It is evaluated on a real-world dataset, large scale Chinese short text summarization dataset. Experimental results show that it outperforms several baseline systems on each topic and achieves comparable performance with the state-of-the-art system. This indicates that TopicNHG can generate more accurate headlines guided by document topics.
Toward a Controllable Disentanglement Network

This article addresses two crucial problems of learning disentangled image representations, namely, controlling the degree of disentanglement during image editing, and balancing the disentanglement strength and the reconstruction quality. To encourage disentanglement, we devise distance covariance-based decorrelation regularization. Further, for the reconstruction step, our model leverages a soft target representation combined with the latent image code. By exploring the real-valued space of the soft target representation, we are able to synthesize novel images with the designated properties. To improve the perceptual quality of images generated by autoencoder (AE)-based models, we extend the encoder-decoder architecture with the generative adversarial network (GAN) by collapsing the AE decoder and the GAN generator into one. We also design a classification-based protocol to quantitatively evaluate the disentanglement strength of our model. The experimental results showcase the benefits of the proposed model.
Co-Utile Peer-to-Peer Decentralized Computing

Outsourcing computation allows wielding huge computational power. Even though cloud computing is the most usual type of outsourcing, resorting to idle edge devices for decentralized computation is an increasingly attractive alternative. We tackle the problem of making peer honesty and thus computation correctness self-enforcing in decentralized computing with untrusted peers. To do so, we leverage the co-utility property, which characterizes a situation in which honest co-operation is the best rational option to take even for purely selfish agents; in particular, if a protocol is co-utile, it is self-enforcing. Reputation is a powerful incentive that can make a P2P protocol co-utile. We present a co-utile P2P decentralized computing protocol that builds on a decentralized reputation calculation, which is itself co-utile and therefore self-enforcing. In this protocol, peers are given a computational task including code and data and they are incentivized to compute it correctly. Based also on co-utile reputation, we then present a protocol for federated learning, whereby peers compute on their local private data and have no incentive to randomly attack or poison the model. Our experiments show the viability of our co-utile approach to obtain correct results in both decentralized computation and federated learning.
Construction of reversible self-dual codes

We study a construction method of binary reversible self-dual codes in this paper. Reversible codes have good properties in applications, and it is interesting to note that a class of reversible codes is closely connected to BCH codes and LCD codes. We first characterize binary reversible self-dual codes. Using these characteristics of reversible self-dual codes, we find an explicit method for constructing all the binary reversible self-dual codes up to equivalence. Furthermore, using this construction, we obtain nine optimal reversible self-dual codes of length 70 which are all inequivalent, and these codes are all new with respect to binary self-dual codes; they all have the same parameter [70,35,12] and their automorphism groups have the same order two.
Data design and analysis based on cloud computing and improved K-Means algorithm

The IoT and Artificial intelligence, the amount of information generated on the Web site is increasing. The rise of the Hadoop distributed cloud computing platform (HDCCP) makes it possible to use multiple computing nodes for parallel computing to solve the performance problems of traditional serial algorithms. The purpose of this paper is to study data design based on cloud computing and improved k-means algorithm (KMA). This paper deeply researches Hadoop distributed cloud computing platform and clustering algorithm and other related technologies, and designs and implements a cluster analysis system (CAS) based on HP. And through an in-depth analysis of the problems existing in the KMA, an improved scheme based on the HDP is designed. The experimental environment was conFig.d with the cluster analysis system implemented. Finally, the improved KMPA was tested experimentally from four directions: convergence speed, acceleration ratio, initialization sampling rate, and accuracy rate. We can see the experimental results that the CAS based on the HDCCP designed in this paper can provide efficient and configurable cluster analysis services. In this paper, the correct rate is 90.7%.
Do English noun phrases tend to minimize dependency distance?

Many studies have shown that, owing to the constraint of working memory capacity, language users prefer shorter dependency distances. However, these studies, which are all based on dependency distances in sentences or texts, leave the question unanswered: does noun phrase structure also demonstrate the tendency of dependency distance minimization? To answer this question, the article, based on a self-annotated English corpus, was carried out to probe the dependency distance in noun phrases between heads and their pre- and post- modifiers. The results show that the dependency distance between pre-modifiers and heads is much shorter than that between post-modifiers and heads. This finding suggests that noun phrases also show the tendency of dependency distance minimization, although they present a much longer mean dependency distance than sentences.
An Approach for Objective Quality Assessment of Image Inpainting Results

Image Inpainting techniques are generally challenging to evaluate objectively due to the lack of comparative data, as a reference image of the new scene, does not exist.. This paper presents an approach that uses our newly released dataset specifically designed to allow objective evaluation of inpainting techniques. In this work we demonstrate how traditional in-painting techniques can be objectively evaluated and compared together with modern deep learning and adversarial approaches. We further demonstrate how an unsupervised technique compares better than deep learning approaches.
A NEW STABLE INTERNAL STRUCTURE OF THE MANDELBROT SET DURING THE ITERATION PROCESS

In this paper, we focus on the periodic points during the iteration process to understand the convergence characteristics of the Mandelbrot set. A new internal structure of the Mandelbrot set is obtained. In addition, the influences of the number of initial iteration points and computational accuracy on this structure are investigated. The results show that the new internal structure is stable no matter how the computational accuracy and the number of initial iteration points change. However, the boundaries of subsets of the Mandelbrot set are sensitive to the computational accuracy. This finding reveals the true convergence structure of the Mandelbrot set during numerical simulations, which differs from the theoretical convergence point distribution. Thus, it helps us further understand the convergence mechanism of the Mandelbrot set.
Graphic QR Code with the Second Hidden QR Code by Codeword Rearrangement

The development of science and technology has brought about tremendous changes in both the virtual and real worlds. The quick response code (QR code), a kind of two-dimensional barcode, has become an important interface between the two, playing the roles of advertising, marketing, transaction payment, and information authentication. The traditional QR code only has black-and-white information dots. It is difficult to distinguish the content from the appearance and also is easy to be copied. Therefore, for the protection of information and convenient interpretation, how to enhance the security and beauty of the QR code has also been studied by researchers all over the world. Therefore, this study proposes a double-encrypted graphic QR code to modify the previous halftoning technology, to enhance its aesthetics and to perform recognition analysis. The digital halftoning technique is used to hide the second QR code in the cover QR code, and the graphic QR code of the 41 × 41 modules (the sixth version) is obtained. The fourth version QR code has 33 × 33 modules. The codeword data of the fourth version QR code is hidden around the graphic QR’s codeword, which did not affect the central area of the QR code, and its image quality was enhanced. Each module in the graphic QR code is divided into 3 × 3 sub-modules, and the explicit 41 × 41 QR code information is embedded in the middle of the 3 × 3 sub-modules. The 33 × 33 QR codeword information, according to the pseudo-random number generated by the key, is hidden in one of the other eight sub-modules, and the remaining seven sub-modules were used to display the gradation of the cover image. To study the recognition, the graphic QR code is outputted at four different sizes and then all of them are scanned for data recognition. The research results show that the modified double-encrypted QR code has better image quality, and the larger size of the QR code has better recognition rate. In addition to the double hidden information, the graphic QR code generated by this research could achieve anti-counterfeiting function and be aesthetically pleasing. In the future, double-encrypted graphic QR code could also be used to various image value-added applications that require halftone images.
Pedestrian detection based on a hybrid Gaussian model and support vector machine

In order to improve the detection rate of pedestrian, a novel technique that is based on hybrid Gaussian background modeling combined with HOG and SVM is proposed. The random video frame test sample is used to verify the performance of the model. The results show that under the condition of ensuring the detection rate and detection rate, the false detection rate of the hybrid Gauss combined with the HOG + SVM model is only 5.2% in comparison with that of HOG + AdaBoost at 7.1%, which confirms that the model can accurately detect pedestrians in complex scenes in real time.
M2SGD: Learning to Learn Important Weights

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their handcrafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for ResNet20s.We release our codes at https://github.com/Nic5472K/CLVISION2020_CVPR_M2SGD.
Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens

Purpose: Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs. Approach: We proposed a calibration-free adaptive thresholding algorithm, which compensates for staining variability and yields consistent tissue component maps (TCMs) of the nuclei, lumina, and other tissues. We used and compared three machine learning methods for classifying each cancer versus noncancer region of interest (ROI) throughout each WSI: (1) conventional machine learning methods and 14 texture features extracted from TCMs, (2) transfer learning with pretrained AlexNet fine-tuned by TCM ROIs, and (3) transfer learning with pretrained AlexNet fine-tuned with raw image ROIs. Results: The three methods yielded areas under the receiver operating characteristic curve of 0.96, 0.98, and 0.98, respectively, in leave-one-patient-out cross validation using 1.3 million ROIs from 286 mid-gland whole-mount WSIs from 68 patients. Conclusion: Transfer learning with the use of TCMs demonstrated state-of-the-art overall performance and is more stable with respect to sample size across different tissue types. For the tissue types involving Gleason 5 (most aggressive) cancer, it achieved the best performance compared to the other tested methods. This tool can be translated to clinical workflow to assist graphical and quantitative pathology reporting for surgical specimens upon further multicenter validation.
Statistically learning Archean carbonate diagenesis

Geochemical data are often very noisy due to the large natural heterogeneity of the geological materials as well as oversimplification of rock classification. This has serious repercussions on the precision of our knowledge of the deep past as we often rely solely on geochemical proxies to investigate the geological evolution of Archean and Proterozoic environments. Here statistical learning procedures were applied to achieve unbiased classification of Neoarchean stromatolitic dolostone textures on the basis of the distribution of their trace elements and rare earth elements (REE) investigated through laser ablation induced coupled plasma – mass spectrometry (LA-ICP-MS). Multivariate statistical analyses and supervised statistical learning have revealed that different dolomite fabrics, thought as products of aggrading diagenesis and recrystallization, are in fact chemically indistinguishable. The diagenetic processes that cause the re-crystallization of dolomite and the consequent change of textures, is not affecting the distribution of major and trace elements inherited by the depositional environment or during early stages of diagenesis. At the same time the algorithm has revealed that an optically homogeneous microcrystalline dolomite sample may in fact be geochemically inhomogeneous because of processes of ripening and recrystallization occurred at an early stage of marine diagenesis and that have contributed to element mobilization. Statistical learning has succeeded in recognizing chemofacies which not always overlap with dolomite textures and fabrics highlighting the importance of crystallographic and diagenetic studies before any study of carbonates as geochemical proxies.
Two-Layers Particle Swarm Optimizer

Particle Swarm Optimization (PSO) has been recognized as one of the most widely used algorithms. One limitation of PSO is that it suffers from the problem of premature convergence which makes it easily trapped into local optima regions. To enhance its performances, this work presents a new PSO-based two layers optimizer. One layer is dedicated for performing the task of global search and the second layer is used for local search. Each layer consists of three search operations which are exploration, jump out, and convergence. To assess the performances of the proposed optimizer, CEC2010 large-scale optimization benchmark problems were used in this study. The outcomes indicated that the proposed optimizer outperformed other PSO variant algorithms in both convergence rate and average fitness value.
Towards PDE-Based Video Compression with Optimal Masks Prolongated by Optic Flow

Abstract Lossy image compression methods based on partial differential equations have received much attention in recent years. They may yield high-quality results but rely on the computationally expensive task of finding an optimal selection of data. For the possible extension to video compression, this data selection is a crucial issue. In this context, one could either analyse the video sequence as a whole or perform a frame-by-frame optimisation strategy. Both approaches are prohibitive in terms of memory and run time. In this work, we propose to restrict the expensive computation of optimal data to a single frame and to approximate the optimal reconstruction data for the remaining frames by prolongating it by means of an optic flow field. In this way, we achieve a notable decrease in the computational complexity. As a proof-of-concept, we evaluate the proposed approach for multiple sequences with different characteristics. In doing this, we discuss in detail the influence of possible computational setups. We show that the approach preserves a reasonable quality in the reconstruction and is very robust against errors in the flow field.
Inverse Feature Learning: Feature Learning Based on Representation Learning of Error

This paper proposes inverse feature learning (IFL) as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach. The key contribution of this method is to learn the representation of error as high-level features, while current representation learning methods interpret error by loss functions which are obtained as a function of differences between the true labels and the predicted ones. One advantage of this error representation is that the learned features for each class can be obtained independently of learned features for other classes; therefore, IFL can learn simultaneously meaning that it can learn new classes' features without retraining. Error representation learning can also help with generalization and reduce the chance of over-fitting by adding a set of impactful features to the original data set which capture the relationships between each instance and different classes through an error generation and analysis process. This method can be particularly effective in data sets, where the instances of each class have diverse feature representations or the ones with imbalanced classes. The experimental results show that the proposed IFL results in better performance compared to the state-of-the-art classification techniques for several popular data sets. We hope this paper can open a new path to utilize the proposed perspective of error representation learning in different feature learning domains.