Reviewerly/exHarmony huggingface dataset:
Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation

Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolutional Neural Networks are limited by the lack of ability to spatially transform the inputs. In this article, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. This framework first estimates global flow fields between sources and targets. Then, corresponding local source feature patches are sampled with content-aware local attention coefficients. We show that our framework can spatially transform the inputs in an efficient manner. Meanwhile, we further model the temporal consistency for the person image animation task to generate coherent videos. The experiment results of both image generation and animation tasks demonstrate the superiority of our model. Besides, additional results of novel view synthesis and face image animation show that our model is applicable to other tasks requiring spatial transformation. The source code of our project is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RenYurui/Global-Flow-Local-Attention</uri> .
Technical Sentiment Analysis. Measuring Advantages and Drawbacks of New Products Using Social Media

The paper contributes to the literature on sentiment analysis by introducing a new knowledge-based lexicon. The lexicon, based on fundamental research and systematic practice in Engineering Design, describes the Advantages or Drawbacks (Disadvantages) of products as an effect of the interaction between artifacts and users. The paper extracts data from Twitter that report consumer conversations after the launch of new products in the videogame industry. It compares the results of a traditional sentiment analysis with the results filtered using the lexicon. We observe a drop in the number of positive tweets but a sharp increase in the informativeness of consumers’ opinions. Comments filtered using the lexicon offer a much more useful basis for understanding customers and designing new products. The paper develops several areas of potential applicability of the methodology.
Attribute-Aware Pedestrian Detection in a Crowd

Pedestrian detection is an initial step to perform outdoor scene analysis, which plays an essential role in many real-world applications. Although having enjoyed the merits of deep learning frameworks from the generic object detectors, pedestrian detection is still a very challenging task due to heavy occlusions, and highly crowded group. Generally, the conventional detectors are unable to differentiate individuals from each other effectively under such a dense environment. To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion. Besides the typical semantic features, center position, target's scale, and offset, we introduce a pedestrian-oriented attribute feature to encode the high-level semantic differences among the crowd. Moreover, a novel attribute-feature-based Non-Maximum Suppression (NMS) is proposed to distinguish the person from a highly overlapped group by adaptively rejecting the false-positive results in a very crowd settings. Furthermore, an enhanced ground truth target is designed to alleviate the difficulties caused by the attribute configuration, and to ease the class imbalance issue during training. Finally, we evaluate our proposed attribute-aware pedestrian detector on three benchmark datasets including CityPerson, CrowdHuman, and EuroCityPerson, and achieves the state-of-the-art results.
A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning

Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten. However, comparison of individual methods is nevertheless performed in isolation from the real world by monitoring accumulated benchmark test set performance. The closed world assumption remains predominant, i.e. models are evaluated on data that is guaranteed to originate from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown and corrupted instances. In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era. Hence, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Finally, the established synergies are supported empirically, showing joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, while exhibiting robust open world application.
Hyperbolic Hopfield neural networks for image classification in content-based image retrieval

Neural networks play a significant role in data classification. Complex-valued Hopfield Neural Network (CHNN) is mostly used in various fields including the image classification. Though CHNN has proven its credibility in the classification task, it has a few issues. Activation function of complex-valued neuron maps to a unit circle in the complex plane affecting the resolution factor, flexibility and compatibility to changes, during adaptation in retrieval systems. The proposed work demonstrates Content-Based Image Retrieval System (CBIR) with Hyperbolic Hopfield Neural Networks (HHNN), an analogue of CHNN for classifying images. Activation function of the Hyperbolic neuron is not cyclic in hyperbolic plane. The images are mathematically represented and indexed using the six basic features. The proposed HHNN classifier is trained, tested and evaluated through extensive experiments considering individual features and four combined features for indexing. The obtained results prove that HHNN guides retrieval process, enhances system performance and minimizes the cost of implementing Neural Network Classifier-based image retrieval system.
The Natural Stories corpus: a reading-time corpus of English texts containing rare syntactic constructions

It is now a common practice to compare models of human language processing by comparing how well they predict behavioral and neural measures of processing difficulty, such as reading times, on corpora of rich naturalistic linguistic materials. However, many of these corpora, which are based on naturally-occurring text, do not contain many of the low-frequency syntactic constructions that are often required to distinguish between processing theories. Here we describe a new corpus consisting of English texts edited to contain many low-frequency syntactic constructions while still sounding fluent to native speakers. The corpus is annotated with hand-corrected Penn Treebank-style parse trees and includes self-paced reading time data and aligned audio recordings. We give an overview of the content of the corpus, review recent work using the corpus, and release the data.
PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models

Pivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled data from the source and target domains, but neglect to incorporate massive unlabeled corpora that are not necessarily drawn from these domains. To alleviate this, we propose PERL: A representation learning model that extends contextualized word embedding models such as BERT (Devlin et al., 2019 ) with pivot-based fine-tuning. PERL outperforms strong baselines across 22 sentiment classification domain adaptation setups, improves in-domain model performance, yields effective reduced-size models, and increases model stability. 1
Learning Gated Non-Local Residual for Single-Image Rain Streak Removal

This work presents a gated non-local deep residual learning framework for image deraining. It can avoid the over-deraining or under-deraining caused by the global residual learning in existing deraining networks, since the learned soft gate in our method adaptively adjusts the amount of global residual to be passed for generating the final derained result. To generate feature maps for global residual prediction, we develop a non-local guided attention module (NLAM), which first obtains non-local features by exploiting spatial inter-dependencies among all the feature positions of local features produced by convolutional neural network (CNN), and then leverages the attention mechanism to merge the local and non-local features based on their complementary relation. Moreover, we develop a channel-wise gated prediction module to learn a soft gate on the global residual by explicitly modelling channel inter-dependencies of the feature maps obtained from NLAM. Experiments on four deraining benchmark datasets and real-world rainy images show that our network has a quantitative and qualitative improvement over state-of-the-arts.
A non-convex semi-supervised approach to opinion spam detection by ramp-one class SVM

Nowadays, e-commerce has become a part of our daily life in such a way that people's decision for buying products or choosing services highly depends on comments, reviews, and rates, which are posted on related businesses' website and other social media. Because of the importance and prevalence of these sources of information, fraudsters are tempted to use fraudulently opinion sharing platforms in order to promote or to discredit some target products or services. Although a wide range of approaches have been proposed to address this problem and to help distinguish between the deceptive or fraudulent opinions from the trustful ones, this is still a challenging problem. Lack of well-defined deceptive data samples or insufficiency of spam review instances in the training sets cause supervised techniques facing an imbalanced classes problem. Furthermore, even in the golden normal opinion datasets, there is a possibility of the presence of some abnormal records or outliers. To deal with these two issues, we propose a robust and non-convex semi-supervised algorithm called "Ramp One-Class SVM". In the proposed method, one-class SVM is adopted to handle the lack of labeled data for the deceptive opinions and by taking the advantages of non-convex properties of the Ramp loss function, we eliminate the effects of outliers and non-review opinions. The performance of the proposed method is evaluated by an artificial dataset and two real datasets including Ott and Yelp crowdsourced datasets. The results show the superiority of our method by achieving an accuracy of 92.13% and 74.37% for Ott and Yelp crowdsourced datasets, respectively. The obtained results also reveal the effectiveness of the proposed model in terms of precision, recall, generalization power, and robustness to the outliers.
Online teaching quality evaluation model based on support vector machine and decision tree

At present, online education evaluation models are insufficient when dealing with small-scale evaluation data sets. In order to discriminate the learner’s learning state, this paper further studies online teaching machine learning methods, and introduces adaptive learning rate and momentum terms to improve the gradient descent method of BP neural network to improve the convergence rate of the model. Moreover, this study proposes a deep neural network model to deal with complex high-dimensional large-scale data set problems. In the process of supervised prediction, this study uses support vector regression as a predictor for supervised prediction, and this study maps complex non-linear relationships into high-dimensional space to achieve a linear relationship similar to low-dimensional space. In addition, in this study, small-scale teaching quality evaluation data sets and large-scale data sets are input into the model to perform experiments. Finally, the model proposed in this study is compared with other shallow models. The results show that the model proposed in this research is effective and advantageous in evaluating teaching quality in universities and processing large-scale data sets.
EHSO: Evolutionary Hybrid Sampling in overlapping scenarios for imbalanced learning

Imbalanced learning is a challenging task for conventional algorithms. Sampling techniques address this problem by synthesizing minority class samples or selecting part of majority class samples to provide a balanced data. A large number of related researches emerged in the past decades. Recent studies show that the samples in overlapping area play a more important role in improving the classification performance for imbalanced data. However, how to eliminate majority class samples in overlapping area efficiently while avoiding classification performance deterioration caused by loss of original distribution is still an open problem. This paper proposes to deal with the overlapped samples with an Evolutionary Hybrid Sampling technique (EHSO). The main purpose of EHSO is to make the decision boundary more visible through removing useless majority class samples and to avoid the possible unexpected data introduced by synthetic new minority samples. EHSO applies evolutionary algorithm to find the optimal compromise between classification performance and the replicate ratio of random oversampling. Numerical experiments on all the binary-class imbalanced datasets (100 datasets) of KEEL repository have demonstrated its superiority compared with other well-known sampling methods.
Duplicate Question Detection With Deep Learning in Stack Overflow

Stack Overflow is a popular Community-based Question Answer (CQA) website focused on software programming and has attracted more and more users in recent years. However, duplicate questions frequently appear in Stack Overflow and they are manually marked by the users with high reputation. Automatic duplicate question detection alleviates labor and effort for users with high reputation. Although existing approaches extract textual features to automatically detect duplicate questions, these approaches are limited since semantic information could be lost. To tackle this problem, we explore the use of powerful deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), to detect duplicate questions in Stack Overflow. In addition, we use Word2Vec to obtain the vector representations of words. They can fully capture semantic information at document-level and word-level respectively. Therefore, we construct three deep learning approaches WV-CNN, WV-RNN and WV-LSTM, which are based on Word2Vec, CNN, RNN and LSTM, to detect duplicate questions in Stack Overflow. Evaluation results show that WV-CNN and WV-LSTM have made significant improvements over four baseline approaches (i.e., DupPredictor, Dupe, DupPredictorRep-T, and DupeRep) and three deep learning approaches (i.e., DQ-CNN, DQ-RNN, and DQ-LSTM) in terms of recall-rate@5, recall-rate@10 and recall-rate@20. Furthermore, the experimental results indicate that our approaches WV-CNN, WV-RNN, and WV-LSTM outperform four machine learning approaches based on Support Vector Machine, Logic Regression, Random Forest and eXtreme Gradient Boosting in terms of recall-rate@5, recall-rate@10 and recall-rate@20.
Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks

Abstract The number of both service providers operating in the cloud market and customers consuming cloud-based services is constantly increasing, proving that the cloud computing paradigm has successfully delivered its potential. Nevertheless, the unceasing growth of the cloud market is posing hard challenges on its participants. On the provider side, the capability of orchestrating resources in order to maximise profits without failing customers’ expectations is a matter of concern. On the customer side, the efficient resource selection from a plethora of similar services advertised by a multitude of providers is an open question. In such a multi-cloud landscape, several research initiatives advocate the employment of software frameworks (namely, cloud resource orchestration frameworks - CROFs) capable of orchestrating the heterogeneous resources offered by a multitude of cloud providers in a way that best suits the customer’s need. The objective of this paper is to provide the reader with a systematic review and comparison of the most relevant CROFs found in the literature, as well as to highlight the multi-cloud computing open issues that need to be addressed by the research community in the near future.
A many-objective feature selection for multi-label classification

Feature selection is an important task in machine learning. As multi-label classification tasks appear in various fields, researchers have investigated multi-label feature selection algorithms to reduce data dimensions. Most of the existing wrapper multi-label feature selection algorithms use multi-objective method to obtain the selected features. However, there are multiple criteria to measure the quality of multi-label classification results. In view of this, this study presents a many-objective optimization based multi-label feature selection algorithm (MMFS). To improve the diversity and convergence of NSGA III, we propose an improved NSGA III algorithm with two archives. In this algorithm, new crossover and mutation operators for feature selection are designed to improve the exploration capability, and the influence of the selection threshold θ on feature scale and multi-label classification performance in real number coding is studied. Finally, we conduct experiments on 11 multi-label datasets. The experiments demonstrate that MMFS can balance multiple objectives, remove irrelevant and redundant features, and obtain satisfactory classification results.
Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting.High-magnification (×400) immunofluorescence images of kidney biopsies performed from the year 2001 to 2018 were collected. The report, adopted at the Division of Nephrology of the AOU Policlinico di Modena, describes the specimen in terms of "appearance," "distribution," "location," and "intensity" of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and κ- and λ-light chains. The report was used as ground truth for the training of the convolutional neural networks.In total, 12,259 immunofluorescence images of 2542 subjects undergoing kidney biopsy were collected. The test set analysis showed accuracy values between 0.79 ("irregular capillary wall" feature) and 0.94 ("fine granular" feature). The agreement test of the results obtained by the convolutional neural networks with respect to the ground truth showed similar values to three pathologists of our center. Convolutional neural networks were 117 times faster than human evaluators in analyzing 180 test images. A web platform, where it is possible to upload digitized images of immunofluorescence specimens, is available to evaluate the potential of our approach.The data showed that the accuracy of convolutional neural networks is comparable with that of pathologists experienced in the field.
Toward the automatic classification of Self-Affirmed Refactoring

The concept of Self-Affirmed Refactoring (SAR) was introduced to explore how developers document their refactoring activities in commit messages, i.e., developers explicit documentation of refactoring operations intentionally introduced during a code change. In our previous study, we have manually identified refactoring patterns and defined three main common quality improvement categories including internal quality attributes, external quality attributes, and code smells, by only considering refactoring-related commits. However, this approach heavily depends on the manual inspection of commit messages. In this paper, we propose a two-step approach to first identify whether a commit describes developer-related refactoring events, then to classify it according to the refactoring common quality improvement categories. Specifically, we combine the N-Gram TF–IDF feature selection with binary and multiclass classifiers to build a new model to automate the classification of refactorings based on their quality improvement categories. We challenge our model using a total of 2,867 commit messages extracted from well engineered open-source Java projects. Our findings show that (1) our model is able to accurately classify SAR commits, outperforming the pattern-based and random classifier approaches, and allowing the discovery of 40 more relevant SAR patterns, and (2) our model reaches an F-measure of up to 90% even with a relatively small training dataset.
CRT-BIoV: A Cognitive Radio Technique for Blockchain-Enabled Internet of Vehicles

Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism.