Reviewerly/exHarmony huggingface dataset:
Channel and spatial attention based deep object co-segmentation

Object co-segmentation is a challenging task, which aims to segment common objects in multiple images at the same time. Generally, common information of the same object needs to be found to solve this problem. For various scenarios, common objects in different images only have the same semantic information. In this paper, we propose a deep object co-segmentation method based on channel and spatial attention, which combines the attention mechanism with a deep neural network to enhance the common semantic information. Siamese encoder and decoder structure are used for this task. Firstly, the encoder network is employed to extract low-level and high-level features of image pairs. Secondly, we introduce an improved attention mechanism in the channel and spatial domain to enhance the multi-level semantic features of common objects. Then, the decoder module accepts the enhanced feature maps and generates the masks of both images. Finally, we evaluate our approach on the commonly used datasets for the co-segmentation task. And the experimental results show that our approach achieves competitive performance.
An Effective and Improved CNN-ELM Classifier for Handwritten Digits Recognition and Classification

Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.
Error-Bounded Graph Anomaly Loss for GNNs

Graph neural networks (GNNs) have been widely used to learn node representations from graph data in an unsupervised way for downstream tasks. However, when applied to detect anomalies (e.g., outliers, unexpected density), they deliver unsatisfactory performance as existing loss functions fail. For example, any loss based on random walk (RW) algorithms would no longer work because the assumption that anomalous nodes were close with each other could not hold. Moreover, the nature of class imbalance in anomaly detection tasks brings great challenges to reduce the prediction error. In this work, we propose a novel loss function to train GNNs for anomaly-detectable node representations. It evaluates node similarity using global grouping patterns discovered from graph mining algorithms. It can automatically adjust margins for minority classes based on data distribution. Theoretically, we prove that the prediction error is bounded given the proposed loss function. We empirically investigate the GNN effectiveness of different loss variants based on different algorithms. Experiments on two real-world datasets show that they perform significantly better than RW-based loss for graph anomaly detection.
Sentiment Analysis of Restaurant Reviews using Combined CNN-LSTM

The combination of machine learning approach and natural language processing is applied to analyze the sentiment of text for particular sentences. In this particular area lots of work done in recent times. Restaurant business was always a popular business in Bangladesh. These business is now Leaning towards online delivery services and the overall quality of restaurants are now judged by reviews of customers. One try to understand the quality of a restaurant by the reviews from other customers. These opinions of customers organizing in structured way and to understand perception of customers reviews and reactions is the main motto of our work. Collecting data was the first thing we have done for deploying this piece of work. Then making a dataset which we harvested from websites and tried to deploy with deep learning technique. In this piece of research, a combined CNN-LSTM architecture used in our dataset and got an accuracy of 94.22%. Also used some other performance metrics to evaluate our model.
Towards Hate Speech Detection at Large via Deep Generative Modeling

Hate speech detection is a critical problem in social media, being often accused for enabling the spread of hatred and igniting violence. Hate speech detection requires overwhelming computing resources for online monitoring as well as thousands of human experts for daily screening of suspected posts or tweets. Recently, deep learning (DL)-based solutions have been proposed for hate speech detection, using modest-sized datasets of few thousands of sequences. While these methods perform well on the specific datasets, their ability to generalize to new hate speech sequences is limited. Being a data-driven approach, it is known that DL surpasses other methods whenever scale-up in trainset size and diversity is achieved. Therefore, we first present a dataset of 1 million hate and nonhate sequences, produced by a deep generative model. We further utilize the generated data to train a well-studied DL detector, demonstrating significant performance improvements across five hate speech datasets.
Sea-Surface Floating Small Target Detection by Multifeature Detector Based on Isolation Forest

In this article, a multifeature detector based on isolation forest (iForest) algorithm is developed to detect floating small targets in sea clutter. The conventional multifeature detector can only process three features or less. The proposed detector aims to break the limitation of feature dimensions' number of the existed feature-based detectors and to improve the detection performance. It transforms the detection of floating small target into an anomaly detection problem in a high-dimensional feature space, breaking the limitation of the number of features. First, a modified isolation forest is constructed from multiple features extracted from sea clutter. Meanwhile, the relative Doppler coefficient of variation is proposed and added into the feature library. Then, taking the average path length as detection statistic, the detection threshold is obtained by Monte-Carlo technique at the given false alarm probability. Finally, the final decision is made by comparing the path length calculated from the cell under test of radar returns with the detection threshold. Detection performances are evaluated based on twenty measured IPIX radar datasets. The experiment results show that the multifeature detector based on isolation forest can obtain a significant performance improvement and has lower computation cost compared with the existed detectors.
Lightweight Image Super-Resolution by Multi-Scale Aggregation

Ultra-high-definition display technology is widely used in broadcasting, but there is a huge contradiction between its ultra-high-resolution content and short storage. Super-Resolution (SR) can effectively alleviate this contradiction. Recently, State-of-the-art image SR approaches leveraging Deep Convolutional Neural Networks (DCNNs) have demonstrated high-quality reconstruction performance. However, most of them suffer from large model parameters, which restricts their practical application. Besides, image SR for large scaling factors (e.g., ×8) is a tricky issue when the parameters diminish. To remedy these issues, we propose the Lightweight Multi-scale Aggregation Network (LMAN) for the image SR, which works well for both small and large scaling factors with limited parameters. Specifically, we propose a Group-wise Multi-scale Block (GMB) in which a group convolution is exploited for extracting and fusing multi-scale features before a channel attention layer to obtain discriminative features. Additionally, we present a novel Hierarchical Spatial Attention (HSA) mechanism to jointly and adaptively fuse local and global hierarchical features for high-resolution image reconstruction. Extensive experiments illustrate that our LMAN achieves superior performance against state-of-the-art methods with similar parameters and in particular for large scaling factors such as 4× and 8×.
CauseNet

Causal knowledge is seen as one of the key ingredients to advance artificial intelligence. Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant efforts required for validation. Notwithstanding this challenge, we compile CauseNet, a large-scale knowledge base of claimed causal relations between causal concepts. By extraction from different semi- and unstructured web sources, we collect more than 11 million causal relations with an estimated extraction precision of 83% and construct the first large-scale and open-domain causality graph. We analyze the graph to gain insights about causal beliefs expressed on the web and we demonstrate its benefits in basic causal question answering. Future work may use the graph for causal reasoning, computational argumentation, multi-hop question answering, and more.
Potential Flow Generator With <i>L</i> <sub>2</sub> Optimal Transport Regularity for Generative Models

We propose a potential flow generator with L2 optimal transport regularity, which can be easily integrated into a wide range of generative models, including different versions of generative adversarial networks (GANs) and normalizing flow models. With only a slight augmentation to the original generator loss functions, our generator not only tries to transport the input distribution to the target one but also aims to find the one with minimum L2 transport cost. We show the effectiveness of our method in several 2-D problems and illustrate the concept of "proximity" due to the L2 optimal transport regularity. Subsequently, we demonstrate the effectiveness of the potential flow generator in image translation tasks with unpaired training data from the MNIST data set and the CelebA data set with a comparison against vanilla Wasserstein GAN with gradient penalty (WGAN-GP) and CycleGAN.
Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics

Infographic is a data visualization technique which combines graphic and textual descriptions in an aesthetic and effective manner. Creating infographics is a difficult and time-consuming process which often requires significant attempts and adjustments even for experienced designers, not to mention novice users with limited design expertise. Recently, a few approaches have been proposed to automate the creation process by applying predefined blueprints to user information. However, predefined blueprints are often hard to create, hence limited in volume and diversity. In contrast, good infogrpahics have been created by professionals and accumulated on the Internet rapidly. These online examples often represent a wide variety of design styles, and serve as exemplars or inspiration to people who like to create their own infographics. Based on these observations, we propose to generate infographics by automatically imitating examples. We present a two-stage approach, namely retrieve-then-adapt. In the retrieval stage, we index online examples by their visual elements. For a given user information, we transform it to a concrete query by sampling from a learned distribution about visual elements, and then find appropriate examples in our example library based on the similarity between example indexes and the query. For a retrieved example, we generate an initial drafts by replacing its content with user information. However, in many cases, user information cannot be perfectly fitted to retrieved examples. Therefore, we further introduce an adaption stage. Specifically, we propose a MCMC-like approach and leverage recursive neural networks to help adjust the initial draft and improve its visual appearance iteratively, until a satisfactory result is obtained. We implement our approach on widely-used proportion-related infographics, and demonstrate its effectiveness by sample results and expert reviews.
Identifying Impacts of Protocol and Internet Development on the Bitcoin Network

Improving transaction throughput is an important challenge for Bitcoin. However, shortening the block generation interval or increasing the block size to improve throughput makes it sharing blocks within the network slower and increases the number of orphan blocks. Consequently, the security of the blockchain is sacrificed. To mitigate this, it is necessary to reduce the block propagation delay. Because of the contribution of new Bitcoin protocols and the improvements of the Internet, the block propagation delay in the Bitcoin network has been shortened in recent years. In this study, we identify impacts of compact block relay—an up-to-date Bitcoin protocol—and Internet improvement on the block propagation delay and fork rate in the Bitcoin network from 2015 to 2019. Existing measurement studies could not identify them but our simulation enables it. The experimental results reveal that compact block relay contributes to shortening the block propagation delay more than Internet improvements. The block propagation delay is reduced by 64.5% for the 50th percentile and 63.7% for the 90th percentile due to Internet improvements, and by 90.1% for the 50th percentile and by 87.6% for the 90th percentile due to compact block relay.
Tree Space Prototypes

Ensembles of decision trees perform well on many problems, but are not interpretable. In contrast to existing approaches in interpretability that focus on explaining relationships between features and predictions, we propose an alternative approach to interpret tree ensemble classifiers by surfacing representative points for each class -- prototypes. We introduce a new distance for Gradient Boosted Tree models, and propose new, adaptive prototype selection methods with theoretical guarantees, with the flexibility to choose a different number of prototypes in each class. We demonstrate our methods on random forests and gradient boosted trees, showing that the prototypes can perform as well as or even better than the original tree ensemble when used as a nearest-prototype classifier. In a user study, humans were better at predicting the output of a tree ensemble classifier when using prototypes than when using Shapley values, a popular feature attribution method. Hence, prototypes present a viable alternative to feature-based explanations for tree ensembles.
Metaphors in moderation

Volunteer content moderators are essential to the social media ecosystem through the roles they play in managing and supporting online social spaces. Recent work has described moderation primarily as a functional process of actions that moderators take, such as making rules, removing content, and banning users. However, the nuanced ways in which volunteer moderators envision their roles within their communities remain understudied. Informed by insights gained from 79 interviews with volunteer moderators from three platforms, we present a conceptual map of the territory of social roles in volunteer moderation, which identifies five categories with 22 metaphorical variants that reveal moderators’ implicit values and the heuristics that help them make decisions. These metaphors more clearly enunciate the roles volunteer moderators play in the broader social media content moderation apparatus and can drive purposeful engagement with volunteer moderators to better support the ways they guide and shape their communities.
ZoomWalls

We focus on the problem of simulating the haptic infrastructure of a virtual environment (i.e. walls, doors). Our approach relies on multiple ZoomWalls---autonomous robotic encounter-type haptic wall-shaped props---that coordinate to provide haptic feedback for room-scale virtual reality. Based on a user's movement through the physical space, ZoomWall props are coordinated through a predict-and-dispatch architecture to provide just-in-time haptic feedback for objects the user is about to touch. To refine our system, we conducted simulation studies of different prediction algorithms, which helped us to refine our algorithmic approach to realize the physical ZoomWall prototype. Finally, we evaluated our system through a user experience study, which showed that participants found that ZoomWalls increased their sense of presence in the VR environment. ZoomWalls represents an instance of autonomous mobile reusable props, which we view as an important design direction for haptics in VR.
Subjective and Objective Quality Assessment of Compressed Screen Content Videos

With the widespread of application scenarios such as remote office and cloud collaboration, Screen Content Video (SCV) and its processing which show different characteristics from Natural Scene Video (NSV) and its processing, are increasingly attracting researcher's attention. Among these processing techniques, quality evaluation plays an important role in various media processing systems. Despite extensive research on general Image Quality Assessment (IQA) and Video Quality Assessment (VQA), quality assessment of SCVs remains undeveloped. In particular, SCVs always suffer from compression degradations in all kinds of application scenarios. In this article, we first study subjective SCV quality assessment. Specifically, we first construct a Compressed Screen Content Video Quality (CSCVQ) database with 165 distorted SCVs compressed from 11 most common screen application scenarios using the H.264, HEVC and HEVC-SCC formats. Twenty subjects were recruited to participate in the subjective test on the CSCVQ database. Then we study objective SCV quality assessment and propose a SCV quality measure. We observe that localized protruding information such as curves and dots can be well captured by the local relative standard deviation which then can be used to measure the intra-frame quality. Base on this observation, we develop a MutiScale Relative Standard Deviation Similarity (MS-RSDS) model for SCV quality evaluation. In our model, the relative standard deviation similarity between the reference and distorted SCVs is measured from frame differences between two adjacent frames, which can capture the spatiotemporal distortions accurately. A multiscale strategy is also applied to strengthen the original single-scale model. Extensive experiments are performed to compare the proposed model with the most popular and state-of-the-art quality assessment models on the CSCVQ database. Experimental results show that our proposed MS-RSDS model which has relatively low computation complexity, outperforms other IQA/VQA models.
A Neighborhood Undersampling Stacked Ensemble (NUS-SE) in imbalanced classification

Stacked ensemble, which formulates an ensemble by using a meta-learner to combine (stack) the predictions of multiple base classifiers, suffers from the problem of suboptimal performance on imbalanced classification. To improve the classification performance of stacked ensemble on imbalanced datasets, we proposed a method named Neighborhood Undersampling Stacked Ensemble (NUS-SE) in this paper. In general, the NUS-SE can be broken down into two proposed components, an undersampling based stacked ensemble framework (US-SE) component and an undersampling technique component. In the metadata generation step of stacked ensemble, a cross-validation-like procedure (CV-prediction) is commonly used. Unfortunately, incomplete metadata with missing prediction values is generated when undersampling is performed within a stacked ensemble which utilized CV-prediction as the metadata generation procedure. Therefore, in the proposed US-SE component, we replaced the standard CV-prediction procedure with our proposed method coined as Subset and Out-of-Subset (S-OOS) prediction procedure as the metadata generation method. S-OOS prediction procedure will generate metadata without missing prediction values and thus enabling the integration of undersampling within stacked ensemble. By integrating undersampling within stacked ensemble, multiple undersampled-data-subsets are used in the training of US-SE's base learners. While in the undersampling component, we further proposed a novel undersampling technique — Neighborhood Undersampling (NUS) which selects majority instances based on their local neighborhood information. The performance of the NUS-SE is evaluated against those non-resampling based stacked ensemble as baseline methods. The experiment demonstrates that the proposed NUS-SE, which is an undersampling based stacked ensemble, is capable of achieving a better performance when compared to the non-resampling based stacked ensemble.
Granular fuzzy PID controller

In this paper an uncertain dynamical system is investigated in which the coefficients are as a class of fuzzy sets and the fuzzy derivative is considered as the granular derivative. Furthermore, the notions of granular second order derivative of a fuzzy function, fuzzy overshoot, fuzzy rise-time, and fuzzy peak-time are introduced. As a result, designing a class of PID controllers called granular Fuzzy PID (gr-FPID) controller is presented based on fuzzy mathematics. The gr-FPID consists of granular integral, granular derivative with fuzzy coefficients. Moreover, the Particle Swarm Optimization (PSO) algorithm is used to tune gr-FPID fuzzy coefficients. It is demonstrated that the gr-FPID controller can effectively control the temperature in a continuous stirred tank reactor in which the parameters are uncertain.
A density-peak-based clustering algorithm of automatically determining the number of clusters

Clustering is a typical and important method to discover new structures and knowledge from data sets. Most existing clustering methods need to know the number of clusters in advance, which is difficult. Some algorithms claim they do not need to know the number of clusters in advance. Among these algorithms, however, some need to manually determine the cluster centers in a decision graph, which is not easy; some assume that the number of initial cluster centers given is greater than the actual number of classes, but in fact the true number of clusters is not known. In order to tackle this issue, we propose a density-peak-based clustering algorithm of automatically determining the number of clusters. First, we design a density metric by using a continuous function which can well distinguish the densities of different data points. Then, we design a pre-clustering method which can get the initial cluster centers and the corresponding clusters. Furthermore, we propose an automatic clustering method which can automatically determine the final cluster centers and the corresponding clusters. Experiments are conducted on widely used data sets, and the results show the effectiveness of the proposed method.