Reviewerly/exHarmony huggingface dataset:
Detection of Hate Speech in COVID-19–Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Background The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. Objective This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. Methods We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. Results The analysis of hate speech in Twitter data in the Arab region identified that the number of non–hate tweets greatly exceeded the number of hate tweets, where the percentage of hate tweets among COVID-19 related tweets was 3.2% (11,743/547,554). The analysis also revealed that the majority of hate tweets (8385/11,743, 71.4%) contained a low level of hate based on the score provided by the CNN. This study identified Saudi Arabia as the Arab country from which the most COVID-19 hate tweets originated during the pandemic. Furthermore, we showed that the largest number of hate tweets appeared during the time period of March 1-30, 2020, representing 51.9% of all hate tweets (6095/11,743). Contrary to what was anticipated, in the Arab region, it was found that the spread of COVID-19–related hate speech on Twitter was weakly related with the dissemination of the pandemic based on the Pearson correlation coefficient (r=0.1982, P=.50). The study also identified the commonly discussed topics in hate tweets during the pandemic. Analysis of the 7 extracted topics showed that 6 of the 7 identified topics were related to hate speech against China and Iran. Arab users also discussed topics related to political conflicts in the Arab region during the COVID-19 pandemic. Conclusions The COVID-19 pandemic poses serious public health challenges to nations worldwide. During the COVID-19 pandemic, frequent use of social media can contribute to the spread of hate speech. Hate speech on the web can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which increases the threat associated with being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19–related tweets in the Arab region.
Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge

Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. While numerous methods for detecting, segmenting and tracking of medical instruments based on endoscopic video images have been proposed in the literature, key limitations remain to be addressed: Firstly, robustness, that is, the reliable performance of state-of-the-art methods when run on challenging images (e.g. in the presence of blood, smoke or motion artifacts). Secondly, generalization; algorithms trained for a specific intervention in a specific hospital should generalize to other interventions or institutions. In an effort to promote solutions for these limitations, we organized the Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an international benchmarking competition with a specific focus on the robustness and generalization capabilities of algorithms. For the first time in the field of endoscopic image processing, our challenge included a task on binary segmentation and also addressed multi-instance detection and segmentation. The challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures from three different types of surgery. The validation of the competing methods for the three tasks (binary segmentation, multi-instance detection and multi-instance segmentation) was performed in three different stages with an increasing domain gap between the training and the test data. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap. While the average detection and segmentation quality of the best-performing algorithms is high, future research should concentrate on detection and segmentation of small, crossing, moving and transparent instrument(s) (parts).
Affective and Contextual Embedding for Sarcasm Detection

Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets.Despite its usefulness, sarcasm detection remains a challenging task, due to a lack of any vocal intonation or facial gestures in textual data.To date, most of the approaches to addressing the problem have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec.However, these models inherit limitations that render them inadequate for the task of sarcasm detection.In this paper, we propose two novel deep neural network models for sarcasm detection, namely ACE 1 and ACE 2. Given as input a text passage, the models predict whether it is sarcastic (or not).Our models extend the architecture of BERT by incorporating both affective and contextual features.To the best of our knowledge, this is the first attempt to directly alter BERT's architecture and train it from scratch to build a sarcasm classifier.Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.
VinVL: Making Visual Representations Matter in Vision-Language Models

This paper presents a detailed study of improving visual representations for vision language (VL)tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger,better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model OSCAR[21],and utilize an improved approach OSCAR+ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public.
Obstacle detection of rail transit based on deep learning

Obstacle detection plays an important role in train automatic operation. To overcome the low accuracy and poor real-time performance of traditional detection methods, and better detect medium and long distances obstacles, the Improved-YOLOv4 network based on deep learning was proposed. The D-CSPDarknet was designed as feature extraction network. A combination of path aggregation and feature pyramid networks were used in feature fusion network, and a spatial pyramid pooling network was set up at each fusion layer. A method of dividing the ROI using a mask was proposed to improve the accuracy of the model while the processing speed can reach 0.004 s. Data augmentation, transfer learning and phased training strategies were used to improve model performance. Based on the data collected in the real operating environment of the train, Improved-YOLOv4 obtained the mAP of 93% on NVIDIA Jetson AGX, which is more suitable for the obstacle detection of rail transit.
Reinforcement learning for robot research: A comprehensive review and open issues

Applying the learning mechanism of natural living beings to endow intelligent robots with humanoid perception and decision-making wisdom becomes an important force to promote the revolution of science and technology in robot domains. Advances in reinforcement learning (RL) over the past decades have led robotics to be highly automated and intelligent, which ensures safety operation instead of manual work and implementation of more intelligence for many challenging tasks. As an important branch of machine learning, RL can realize sequential decision-making under uncertainties through end-to-end learning and has made a series of significant breakthroughs in robot applications. In this review article, we cover RL algorithms from theoretical background to advanced learning policies in different domains, which accelerate to solving practical problems in robotics. The challenges, open issues, and our thoughts on future research directions of RL are also presented to discover new research areas with the objective to motivate new interest.
A Deep Learning Model for Earthquake Parameters Observation in IoT System-Based Earthquake Early Warning

Earthquake early-warning system (EEWS) is inevitable for saving human lives. The fast determination of the Earthquake’s (EQ’s) magnitude and its location is significant in disaster management and EQ risk mitigation. These parameters can be conveyed over the Internet-of-Things (IoT) network to alleviate an EQ disaster. In this article, a deep learning model based on integrating autoencoder (AE) and convolutional neural network (CNN) for a swift pinpointing of EQ magnitude and location after 3 s from the onset of the P-wave is proposed. Thus, we name it 3 s AE and CNN (3S-AE-CNN). The employed data set is observed by three stations from the Japanese Hi-net seismic network. We have trained our model on 12200 events (109.80 thousand 3-s-three-component seismic windows). The model facilitates the extraction of waveforms’ significant features leading to robust estimation of the EQ parameters. The proposed model predicts the magnitude and location of EQ with errors in magnitude, latitude, and longitude that reach 0.000028, 0.0000033, and 0.0001, respectively. The EQ’s parameters calculated by the proposed 3S-AE-CNN model are swiftly sent to a centralized IoT system that in turn directs the involved entity to take suitable action. The obtained results of the 3S-AE-CNN are compared to the conventional manual solution method, which represents the optimum solution mean. The 3S-AE-CNN shows an enhanced performance for the magnitude and location determination as compared with the benchmark method, which proves its effectiveness for EEWS.
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding. However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5D as they cover only partial underlying shapes, which poses a fundamental challenge to 3D perception. To tackle the challenge, we present a novel LiDAR-based 3D object detection model, dubbed Behind the Curtain Detector (BtcDet), which learns the object shape priors and estimates the complete object shapes that are partially occluded (curtained) in point clouds. BtcDet first identifies the regions that are affected by occlusion and signal miss. In these regions, our model predicts the probability of occupancy that indicates if a region contains object shapes and integrates this probability map with detection features and generates high-quality 3D proposals. Finally, the occupancy estimation is integrated into the proposal refinement module to generate accurate bounding boxes. Extensive experiments on the KITTI Dataset and the Waymo Open Dataset demonstrate the effectiveness of BtcDet. Particularly for the 3D detection of both cars and cyclists on the KITTI benchmark, BtcDet surpasses all of the published state-of-the-art methods by remarkable margins. Code is released.
Barrier-Certified Adaptive Reinforcement Learning With Applications to Brushbot Navigation

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates which constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex and nonstationary.
Efficient and Effective Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) optimally fuses multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, overcomplicated optimization and limitedly improved clustering performance. In this paper, we propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. We carefully develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed EE-IMVC in terms of clustering accuracy, running time, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.
Relational Pooling for Graph Representations

This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Pooling (RP), draws from the theory of finite partial exchangeability to provide a framework with maximal representation power for graphs. RP can work with existing graph representation models and, somewhat counterintuitively, can make them even more powerful than the original WL isomorphism test. Additionally, RP allows architectures like Recurrent Neural Networks and Convolutional Neural Networks to be used in a theoretically sound approach for graph classification. We demonstrate improved performance of RP-based graph representations over state-of-the-art methods on a number of tasks.
Feature Learning Viewpoint of Adaboost and a New Algorithm

The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the mysteries of this phenomena is a very fascinating fundamental theoretical problem. Many studies are devoted to explaining it from statistical view and margin theory. In this paper, we illustrate it from feature learning viewpoint, and propose the AdaBoost+SVM algorithm, which can explain the resistant to overfitting of AdaBoost directly and easily to understand. Firstly, we adopt the AdaBoost algorithm to learn the base classifiers. Then, instead of directly weighted combination the base classifiers, we regard them as features and input them to SVM classifier. With this, the new coefficient and bias can be obtained, which can be used to construct the final classifier. We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.
CrossRec: Supporting software developers by recommending third-party libraries

When creating a new software system, or when evolving an existing one, developers do not reinvent the wheel but, rather, seek available libraries that suit their purpose. In such a context, open source software repositories contain rich resources that can provide developers with helpful advice to support their tasks. However, the heterogeneity of resources and the dependencies among them are the main obstacles to the effective mining and exploitation of the available data. In this sense, advanced techniques and tools are needed to mine the metadata to bring in meaningful recommendations. In this paper, we present CrossRec, a recommender system to assist open source software developers in selecting suitable third-party libraries. CrossRec exploits a collaborative filtering technique to recommend libraries to developers by relying on the set of dependencies, which are currently included in the project being developed. We perform an empirical evaluation to compare the proposed approach with three state-of-the-art baselines, i.e., LibRec, LibFinder, and LibCUP on three considerably large datasets. The experimental results show that CrossRec overcomes the limitation of the baselines by recommending also libraries with a specific version. More importantly, it outperforms LibRec and LibCUP with respect to various quality metrics.
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation

Open-domain dialogue generation has gained increasing attention in Natural Language Processing.Its evaluation requires a holistic means.Human ratings are deemed as the gold standard.As human evaluation is inefficient and costly, an automated substitute is highly desirable.In this paper, we propose holistic evaluation metrics that capture different aspects of open-domain dialogues.Our metrics consist of (1) GPT-2 based context coherence between sentences in a dialogue, (2) GPT-2 based fluency in phrasing, (3) n-gram based diversity in responses to augmented queries, and (4) textual-entailment-inference based logical self-consistency.The empirical validity of our metrics is demonstrated by strong correlations with human judgments.We open source the code and relevant materials.
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.
An integrated decision-making method for product design scheme evaluation based on cloud model and EEG data

Selecting the optimal design scheme is a vital task in the product design area. It not only improves the performance of the product, but also leads to the greatest satisfaction of customers. However, existing methods express qualitative evaluation information roughly, and none of them has taken the implicit psychological states of customers into consideration. Therefore, an integrated decision-making method for product design scheme evaluation is proposed. This method applies the cloud model to facilitate the evaluation process of experts and uses the EEG data to reveal the psychological states of customers. Benefit from the probability theory and fuzzy set theory, the cloud model deals with the fuzziness and randomness simultaneously. It can decrease the cognitive discrepancy of experts and allow the information distortion to be neutralized to a great extent. Since the experts are not the final users of products, the evaluation results from experts cannot truly reflect the psychological states of customers when they use the product. An experiment is designed to collect the EEG data which can reveal the implicit psychological states of customers. The recorded data are segmented based on the operation process and tagged with the self-reported psychological states. Subsequently, the wavelet packet decomposition is applied and the sample entropy of each EEG frequency band is extracted as the feature. Taking advantage of the random forest classifier, the psychological states of customers can be classified with the average accuracy of 90.76%. This study can lead to a practical system for automatic assessment of psychological states in future applications. The evaluation process of elevator design schemes is conducted as a case study to illustrate the feasibility of the proposed method.
Capacity-Achieving Private Information Retrieval Codes From MDS-Coded Databases With Minimum Message Size

We consider constructing capacity-achieving linear codes with minimum message size for private information retrieval (PIR) from N non-colluding databases, where each message is coded using maximum distance separable (MDS) codes, such that it can be recovered from accessing the contents of any T databases. It is shown that the minimum message size (sometimes also referred to as the sub-packetization factor) is significantly, in fact exponentially, lower than previously believed. More precisely, when K > T/ gcd(N, T) where K is the total number of messages in the system and gcd(·, ·) means the greatest common divisor, we establish, by providing both novel code constructions and a matching converse, the minimum message size as lcm(N - T, T), where lcm(·, ·) means the least common multiple. On the other hand, when K is small, we show that it is in fact possible to design codes with a message size even smaller than lcm(N - T, T).