Reviewerly/exHarmony huggingface dataset:
Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.
Do Feature Attribution Methods Correctly Attribute Features?

Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no consensus on the definition of "attribution", leading to many competing methods with little systematic evaluation, complicated in particular by the lack of ground truth attribution. To address this, we propose a dataset modification procedure to induce such ground truth. Using this procedure, we evaluate three common methods: saliency maps, rationales, and attentions. We identify several deficiencies and add new perspectives to the growing body of evidence questioning the correctness and reliability of these methods applied on datasets in the wild. We further discuss possible avenues for remedy and recommend new attribution methods to be tested against ground truth before deployment. The code and appendix are available at https://yilunzhou.github.io/feature-attribution-evaluation/.
Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer

With the development of blockchain technologies, many Ponzi schemes disguise themselves under the veil of smart contracts. The Ponzi scheme contracts cause serious financial losses, which has a bad effect on the blockchain. Existing Ponzi scheme contract detection studies have mainly focused on extracting hand-crafted features and training a machine learning classifier to detect Ponzi scheme contracts. However, the hand-crafted features cannot capture the structural and semantic feature of the source code. Therefore, in this study, we propose a Ponzi scheme contract detection method called MTCformer (Multi-channel Text Convolutional Neural Networks and Transofrmer). In order to reserve the structural information of the source code, the MTCformer first converts the Abstract Syntax Tree (AST) of the smart contract code to the specially formatted code token sequence via the Structure-Based Traversal (SBT) method. Then, the MTCformer uses multi-channel TextCNN (Text Convolutional Neural Networks) to learn local structural and semantic features from the code token sequence. Next, the MTCformer employs the Transformer to capture the long-range dependencies of code tokens. Finally, a fully connected neural network with a cost-sensitive loss function in the MTCformer is used for classification. The experimental results show that the MTCformer is superior to the state-of-the-art methods and its variants in Ponzi scheme contract detection.
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence.While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging.To this end, in this paper, we propose to address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method designed to learn dynamic aspect-oriented semantics for ABSA.Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Reweighting Adapter (DRA).Note that the DRA can pay close attention to a small region of the sentences at each step and re-weigh the vitally important words for better aspect-aware sentiment understanding.Finally, experimental results on three benchmark datasets demonstrate the effectiveness and the rationality of our proposed model and provide good interpretable insights for future semantic modeling.
Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving

Cooperative computing is promising to enhance the performance and safety of autonomous vehicles benefiting from the increase in the amount, diversity as well as scope of data resources. However, effective and privacy-preserving utilization of multi-modal and multi-source data remains an open challenge during the construction of cooperative mechanisms. Recently, Transformers have demonstrated their potential in the unified representation of multi-modal features, which provides a new perspective for effective representation and fusion of diverse inputs of intelligent vehicles. Federated learning proposes a distributed learning scheme and is hopeful to achieve privacy-secure sharing of data resources among different vehicles. Towards privacy-preserving computing and cooperation in autonomous driving, this paper reviews recent progress of Transformers, federated learning as well as cooperative perception, and proposes a hierarchical structure of Transformers for intelligent vehicles which is comprised of Vehicular Transformers, Federated Vehicular Transformers and the Federation of Vehicular Transformers to exploit their potential in privacy-preserving collaboration.
Parallel Tracking and Verifying

Visual object tracking has played a crucial role in computer vision with many applications. Being intensively studied in recent decades, visual tracking has witnessed great advances in either speed (e.g., with correlation filters) or accuracy (e.g., with deep features). Real-time and high accuracy tracking algorithms, nevertheless, remain scarce. In this paper, we study the problem from a new perspective and present a novel parallel tracking and verifying (PTAV) framework, by taking advantage of the ubiquity of multi-thread techniques and borrowing ideas from the success of parallel tracking and mapping in visual SLAM. The proposed PTAV framework typically consists of two components, a (base) tracker T and a verifier V, working in parallel on two separate threads. The tracker T aims at providing a super real-time tracking inference and is expected to perform well most of the time; by contrast, the verifier V validates the tracking results and corrects T when needed. The key innovation is that V does not work on every frame but only upon the requests from T ; on the other end, T may adjust the tracking according to the feedback from V. With such collaboration, PTAV enjoys both high efficiencies provided by T and strong discriminative power by V. Meanwhile, in order to adapt V to object appearance changes, we maintain a dynamic target template pool for adaptive verification, resulting in further improvement. In our extensive experiments on OTB2015, TC128, UAV20L, and VOT2016, PTAV achieves top tracking accuracy among all real-time trackers, and in fact, even outperforms many deep learning-based algorithms. Moreover, as a general framework, PTAV is very flexible with great potentials for future improvement and generalization.
Multiscale Molecular Visualization

We provide a high-level survey of multiscale molecular visualization techniques, with a focus on application-domain questions, challenges, and tasks. We provide a general introduction to molecular visualization basics and describe a number of domain-specific tasks that drive this work. These tasks, in turn, serve as the general structure of the following survey. First, we discuss methods that support the visual analysis of molecular dynamics simulations. We discuss, in particular, visual abstraction and temporal aggregation. In the second part, we survey multiscale approaches that support the design, analysis, and manipulation of DNA nanostructures and related concepts for abstraction, scale transition, scale-dependent modeling, and navigation of the resulting abstraction spaces. In the third part of the survey, we showcase approaches that support interactive exploration within large structural biology assemblies up to the size of bacterial cells. We describe fundamental rendering techniques as well as approaches for element instantiation, visibility management, visual guidance, camera control, and support of depth perception. We close the survey with a brief listing of important tools that implement many of the discussed approaches and a conclusion that provides some research challenges in the field.
A Disocclusion Inpainting Framework for Depth-Based View Synthesis

This paper proposes a disocclusion inpainting framework for depth-based view synthesis. It consists of four modules: foreground extraction, motion compensation, improved background reconstruction, and inpainting. The foreground extraction module detects the foreground objects and removes them from both depth map and rendered video; the motion compensation module guarantees the background reconstruction model to suit for moving camera scenarios; the improved background reconstruction module constructs a stable background video by exploiting the temporal correlation information in both 2D video and its corresponding depth map; and the constructed background video and inpainting module are used to eliminate the holes in the synthesized view. The analysis and experiment indicate that the proposed framework has good generality, scalability and effectiveness, which means most of the existing background reconstruction methods and image inpainting methods can be employed or extended as the modules in our framework. Our comparison results have demonstrated that the proposed framework achieves better synthesized quality, temporal consistency, and has lower running time compared to the other methods.
Nuclear norm based adapted occlusion dictionary learning for face recognition with occlusion and illumination changes

In this paper, we propose a robust framework named Nuclear Norm based adapted Occlusion Dictionary Learning (NNAODL) for face recognition with illumination changes and occlusions. Specifically, we first introduce a nuclear norm based error model to characterize the occlusion and corrupted region in the query image. Secondly, we innovatively integrate the error image with training samples to construct the dictionary, thus can both accurately reconstruct the corrupted region and non-corruption region in query images. Moreover, we use two-dimensional structure for representation and adapted sample weights to preserve more structural information. Above advantages are integrated by a unified objective function, and an effective algorithm is proposed to solve our model. Compared with existing sparse representation methods, our model can better represent the noisy samples and reduce the influence of occlusion and pixel errors. Experiments on multiple public datasets show that the NNAODL model can achieve better results than classical methods under occlusion and illumination changes.
Continuous-variable measurement-device-independent quantum key distribution using modulated squeezed states and optical amplifiers

The measurement-device-independent quantum key distribution (MDI-QKD) protocol is immune to imperfect measurement devices. However, its practicality and performance when implemented in continuous-variable (CV) mode are still not satisfactory. We propose a CV-MDI-QKD protocol based on modulated squeezed states, and analyze its security against two-mode Gaussian attack. The results show that the protocol can achieve a higher secret key rate and transmission distance than previous coherent-state and squeezed-state protocols. A method to compensate the imperfection of a practical homodyne detector using phase-sensitive optical amplifiers is also presented. By setting an appropriate optical amplification gain, realistic detectors with ordinary quantum efficiency can be employed. In addition, we discuss the impact of finite-size effects on the secret key rate of the protocol. The methods presented may aid in the practical application of the CV-MDI-QKD protocol.
Toward an Privacy Protection Based on Access Control Model in Hybrid Cloud for Healthcare Systems

The Electronic Patient Health Recorded systems have been designed and implemented to provide the ability of collaboration among healthcare providers. Such collaborations raise the need for an authorization system to implement network security and protect sensitive data of each patient. Without a proper security protocol, this confidential information could be exploited by malicious attackers. This paper presents a novel access control system called “Access Control Model in Hybrid Cloud for Healthcare Systems” will help us to deal with any security vulnerabilities within the system. The proposed system is based upon two levels of access control: (1) protecting shared data containing basic information of patients among hospitals in the systems and (2) protecting private’s patient data that can be accessed by only the treating doctor. The model is implemented in a real-case application to demonstrate its effectiveness in managing the different level of security and privacy concerns.
Towards Content Transfer through Grounded Text Generation

Shrimai Prabhumoye, Chris Quirk, Michel Galley. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
15. Polynomials over finite fields: an index approach

The degree of a polynomial is an important parameter in the study of numerous problems on polynomials over finite fields. Recently, a new notion of the index of a polynomial over a finite field has been introduced to study the distribution of permutation polynomials over finite fields. This parameter also turns out to be very useful in studying bounds for the size of value sets, character sum bounds, among others. In this paper, we survey this new index approach and report some recent results on polynomials over finite fields.
A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems

The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.
Optimizing CNN model inference on CPUs

The popularity of Convolutional Neural Network (CNN) models and the ubiquity of CPUs imply that better performance of CNN model inference on CPUs can deliver significant gain to a large number of users. To improve the performance of CNN inference on CPUs, current approaches like MXNet and Intel OpenVINO usually treat the model as a graph and use the high-performance libraries such as Intel MKL-DNN to implement the operations of the graph. While achieving reasonable performance on individual operations from the off-the-shelf libraries, this solution makes it inflexible to conduct optimizations at the graph level, as the local operation-level optimizations are predefined. Therefore, it is restrictive and misses the opportunity to optimize the end-to-end inference pipeline as a whole. This paper presents \emph{NeoCPU}, a comprehensive approach of CNN model inference on CPUs that employs a full-stack and systematic scheme of optimizations. \emph{NeoCPU} optimizes the operations as templates without relying on third-parties libraries, which enables further improvement of the performance via operation- and graph-level joint optimization. Experiments show that \emph{NeoCPU} achieves up to 3.45$\times$ lower latency for CNN model inference than the current state-of-the-art implementations on various kinds of popular CPUs.
Incremental Reinforcement Learning in Continuous Spaces via Policy Relaxation and Importance Weighting

In this paper, a systematic incremental learning method is presented for reinforcement learning in continuous spaces where the learning environment is dynamic. The goal is to adjust the previously learned policy in the original environment to a new one incrementally whenever the environment changes. To improve the adaptability to the ever-changing environment, we propose a two-step solution incorporated with the incremental learning procedure: policy relaxation and importance weighting. First, the behavior policy is relaxed to a random one in the initial learning episodes to encourage a proper exploration in the new environment. It alleviates the conflict between the new information and the existing knowledge for a better adaptation in the long term. Second, it is observed that episodes receiving higher returns are more in line with the new environment, and hence contain more new information. During parameter updating, we assign higher importance weights to the learning episodes that contain more new information, thus encouraging the previous optimal policy to be faster adapted to a new one that fits in the new environment. Empirical studies on continuous controlling tasks with varying configurations verify that the proposed method achieves a significantly faster adaptation to various dynamic environments than the baselines.
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single-Image Super-Resolution

With exploiting contextual information over large image regions in an efficient way, the deep convolutional neural network has shown an impressive performance for single-image super-resolution (SR). In this paper, we propose a new deep convolutional network by cascading multiple well-designed inception-residual blocks within the deep Laplacian pyramid framework to progressively restore the missing high-frequency details in the low-resolution images. By optimizing our network structure, the trainable depth of our proposed network gains a significant improvement, which in turn improves super-resolving accuracy. However, the saturation and degradation of training accuracy remains a critical problem. With regard to this, we propose an effective two-stage training strategy, in which we first use the images downsampled from the ground-truth high-resolution (HR) images to pretrain the inception-residual blocks on each pyramid level with an extremely high learning rate enabled by gradient clipping, and then the original ground-truth HR images are used to fine-tune all the pretrained inception-residual blocks for obtaining our final SR models. Furthermore, we present a new loss function operating in both image space and local rank space to optimize our network for exploiting the contextual information among different output components. Extensive experiments on benchmark data sets validate that the proposed method outperforms the existing state-of-the-art SR methods in terms of the objective evaluation as well as the visual quality.
PDFS: Practical Data Feed Service for Smart Contracts

Smart contracts allow untrusting parties to arrange agreements encoded as code deployed on a blockchain platform. To release their potential, it is necessary to connect the contracts with the outside world, such that they can understand and use information from other infrastructures. However, there are many challenges associated with realizing such a system, and despite the existence of many proposals, no solution is secure, provides easily-parsable data, introduces small overheads, and is easy to deploy. In this paper, we propose Practical Data Feed Service (PDFS), a system that combines the advantages of the previous schemes and introduces new functionalities. PDFS extends content providers by including new features for data transparency and consistency validations. This combination provides multiple benefits like content which is easy to parse and efficient authenticity verification without breaking natural trust chains. PDFS keeps content providers auditable and mitigates their malicious activities (like data modification or censorship) and allows them to create a new business model. We show how PDFS is integrated with content providers, report on a PDFS implementation and present results from conducted experimental evaluations.
3D shape measurement of translucent objects based on Fourier single-pixel imaging in projector-camera system

3D shape measurement by structured light is a popular technique for recovering object surfaces. However, structured light technique assumes that scene points are directly illuminated by the light source(s). Consequently, global illumination effects, such as subsurface scattering in translucent objects, may cause measurement errors in recovered 3D shapes. In this research, we propose a 3D shape measurement method of translucent objects based on Fourier single-pixel imaging (FSI) technique. The 3D shapes of the translucent objects are reconstructed through stereo matching of direct illumination light, which is separated from the surface. Experimental results show that the proposed method can separate the direct illumination light and the subsurface scattering light. The feasibility and accuracy of the method are analyzed, and the qualitative and quantitative results of the method are provided.