Reviewerly/exHarmony huggingface dataset:
The Semantic Scholar Academic Graph (S2AG)

The Semantic Scholar Academic Graph, or S2AG (pronounced ”stag”), is a large, open, heterogeneous knowledge graph of scholarly works, authors, and citations that powers the Semantic Scholar discovery service. S2AG currently contains over 205M publications, 121M authors, and nearly 2.5B citation edges. Semantic Scholar integrates metadata from Crossref, PubMed, Unpaywall, and other sources. In addition, through partnerships with academic publishers and through web-crawling, we source and process the full-text of nearly 60M full-text publications in order extract and classify the document structure, including references, citation contexts, figures, tables, and more. S2AG is available via an open API as well as via downloadable monthly snapshots. In this talk, we will describe the S2AG resource as well as the Semantic Scholar Open Research Corpus, or S2ORC (pronounced ”stork”), a general purpose, multi-domain corpus for NLP and text mining research.
Towards Model checking approach for Smart contract validation in the EIP-1559 Ethereum

Smart contracts' vulnerabilities are widely dependent on developed code. Due to blockchain immutability, once this code is deployed it cannot be reversed. In this context, formal verification techniques are widely used to check smart contracts' correctness with respect to a given specification. In light of this, we introduce a model checking-based approach for solidity smart contracts and their blockchain environment using the nuXmv model checker. Our model-based approach considers the transaction pricing mechanism set by the Ethereum proposal EIP-1559 [1] and consists of three main steps, respectively, representing a smart contract with its blockchain behavior into an Extended Finite State Machine (EFSM) meanwhile, providing an over-approximation of the contract gas usage following the EIP-1559 proposal, then, encoding the modeled EFSM into nuXmv input language and, finally, specifying safety and liveness properties that will be checked against the model. Our contributions reason about modelling and checking gas usage consumption which makes it failure-aware. We illustrate this approach through a voting case study.
Adversarially regularized joint structured clustering network

Deep clustering has achieved great success as its powerful ability to learn effective representation. Especially, graph network clustering has attracted more and more attention. Considering the great success of Graph Autoencoder (GAE) in encoding the graph structure and Deep Autoencoder (DAE) in extracting valuable representations from the data itself, in this paper, we construct an Adversarially regularized Joint Structured Clustering Network(AJSCN) by integrating GAE and DAE. The framework links GAE and DAE together by transferring the representation learned by DAE to the corresponding layer of GAE to alleviate the over-smoothing problem. Furthermore, the latent representation learned by GAE is enforced to match a prior distribution via an adversarial training scheme to avoid the free of any structure of latent space. We design a joint supervision mechanism to improve the clustering performance consisting of self-supervision and mutual supervision. The self-supervision is to learn more compact representations, and mutual-supervision makes different representations more consistent. Experiment results demonstrate the superiority of the proposed model against the state-of-the-art algorithms and achieve significant improvement on six benchmark datasets.
TSEV-GAN: Generative Adversarial Networks with Target-aware Style Encoding and Verification for facial makeup transfer

Generative Adversarial Networks (GANs) have brought great progress in image-to-image translation. The problem that we focus on is how to accurately extract and transfer the makeup style from a reference facial image to a target face. We propose a GAN-based generative model with Target-aware makeup Style Encoding and Verification, which is referred to as TSEV-GAN. This design is due to the following two insights: (a) When directly encoding the reference image, the encoder may focus on regions which are not necessarily important or desirable. To precisely capture the style, we encode the difference map between the reference and corresponding de-makeup images, and then inject the obtained style code into a generator. (b) A generic real-fake discriminator cannot ensure the correctness of the rendered makeup pattern. In view of this, we impose style representation learning on a conditional discriminator. By identifying style consistency between the reference and synthesized images, the generator is induced to precisely replicate the desirable makeup. We perform extensive experiments on the existing makeup benchmarks to verify the effectiveness of our improvement strategies in transferring a variety of makeup styles. Moreover, the proposed model is able to outperform other existing state-of-the-art makeup transfer methods in terms of makeup similarity and irrelevant content preservation.
Knowledge points navigation based on three-way concept lattice for autonomous learning

In view of the ability of the three-way concept lattices to describe the correlations and hierarchical relationships among the knowledge points, this paper proposes a knowledge point navigation approach based on the three-way concept lattices for autonomous learning. First, this paper constructs the formal context of the exercises-knowledge points according to the corresponding relationship between the exercises and knowledge points; then, the three-way concept lattice generation algorithms are used to obtain the AE-concept lattice and OE-concept lattice; finally, this paper provides students with an effective autonomous learning path and knowledge points navigation Hasse diagram to achieve efficient guidance of autonomous learning.
A Comparative Analysis of Machine Learning-Based Website Phishing Detection Using URL Information

The growing popularity of the internet help to expand e-commerce business, but such activities have security challenges caused by cybercriminals defrauding and stealing personal and financial information through website phishing. Phishing attacks are becoming increasingly complex and challenging to detect. An anti-phishing machine learning technique can help to identify a legitimate website from a phishing website by extracting various features from various sources like URL, page content, search engine, etc. This paper represents a comparative analysis of machine learning (ML) based website phishing detection and discusses some ML approaches for identifying phishing. We have compared five machine learning techniques-Decision tree, Random Forest, KNeighbors, Gaussian Naïve Byes, and XGBoost. Essential features which contribute much to the accuracy of the results have been chosen. The results show that the random forest algorithm outperforms the other proposed algorithms with an accuracy of 97.0 %.
Subjective QoE Evaluation of User-Centered Adaptive Streaming of Dynamic Point Clouds

Technological advances in head-mounted displays and novel real-time 3D acquisition and reconstruction solutions have fostered the development of 6 Degrees of Freedom (6DoF) teleimmersive systems for social VR applications. Point clouds have emerged as a popular format for such applications, owing to their simplicity and versatility; yet, dense point cloud contents are too large to deliver directly over bandwidth-limited networks. In this context, user-adaptive delivery mechanisms are a promising solution to exploit the increased range of motion offered by 6DoF VR applications to yield gains in perceived quality of 3D point cloud user representations, while reducing their bandwidth requirements. In this paper, we perform a user study in VR to quantify the gains adaptive tile selection strategies can bring with respect to non-adaptive solutions. In particular, we define an auxiliary utility function, we employ established methods from the literature and newly-proposed schemes for distributing the bit budget across the tiles, and we evaluate them together with non-adaptive streaming baselines through subjective QoE assessment. Results confirm that considerable gains can be obtained with user-adaptive streaming, achieving bit rate gains of up to 65% with respect to a non-adaptive approach to deliver comparable quality. Our analysis provides useful insights for the design and development of social VR applications.
Modeling of Energy Consumption and Streaming Video QoE using a Crowdsourcing Dataset

In the past decade, we have witnessed an enormous growth in the demand for online video services. Recent studies estimate that nowadays, more than 1% of the global greenhouse gas emissions can be attributed to the production and use of devices performing online video tasks. As such, research on the true power consumption of devices and their energy efficiency during video streaming is highly important for a sustainable use of this technology. At the same time, over-the-top providers strive to offer high-quality streaming experiences to satisfy user expectations. Here, energy consumption and QoE partly depend on the same system parameters. Hence, a joint view is needed for their evaluation. In this paper, we perform a first analysis of both end-user power efficiency and Quality of Experience of a video streaming service. We take a crowdsourced dataset comprising 447,000 streaming events from YouTube and estimate both the power consumption and perceived quality. The power consumption is modeled based on previous work which we extended towards predicting the power usage of different devices and codecs. The user-perceived QoE is estimated using a standardized model. Our results indicate that an intelligent choice of streaming parameters can optimize both the QoE and the power efficiency of the end user device. Further, the paper discusses limitations of the approach and identifies directions for future research.
Optimizing Deep Learning Model for Software Cost Estimation Using Hybrid Meta-Heuristic Algorithmic Approach

Effective software cost estimation significantly contributes to decision-making. The rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. The constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. The limitation of the COCOMO models is that the values of these coefficients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. Therefore, for accurate estimation, it is necessary to fine-tune the coefficients. The research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing flat technologies, they also have some shortcomings, such as large training delays, over-fitting, and under-fitting. Deep learning models usually require fine-tuning to a large number of parameters. The meta-heuristic algorithm supports finding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. The hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BAT algorithm (BA). This technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. This work will perform a two-fold assessment of algorithms: (i) comparing the efficacy of ACO, BA, and HACO-BA in optimizing COCOMO II coefficients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. The experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. The process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.
Dense Face Network: A Dense Face Detector Based on Global Context and Visual Attention Mechanism

Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.
Frequency domain regularization for iterative adversarial attacks

Adversarial examples have attracted more and more attentions with the prosperity of convolutional neural networks. The transferability of adversarial examples is an important property that makes black-box attacks possible in real-world applications. On the other side, many adversarial defense methods have been proposed to improve the robustness, leading to the requirement for more transferable adversarial examples. Inspired by the regularization term for network parameters at training process, we treat adversarial attacks as training process of inputs and propose regularization constraint for inputs to prevent adversarial examples from overfitting the white-box networks and enhance the transferability. Specifically, we find a universal attribute that the outputs of convolutional neural networks have consistency to the low frequencies of inputs, and based on this, we construct a frequency domain regularization to inputs for iterative attacks. In this way, our method is compatible with existing iterative attack methods and can learn more transferable adversarial examples. Extensive experiments on ImageNet validate the superiority of our method, and compared with several attacks, we achieve attack success rate improvements of 8.0% and 11.5% on average to normal models and defense methods respectively.
BPMS: Blockchain-based Privacy-preserving Multi-keyword Search in Multi-owner Setting

Searchable encryption&#x00A0;(SE) has emerged as a cryptographic primitive that allows data users to search on encrypted data. Most existing SE schemes usually delegate search operations to an intermediary such as a cloud server, which would inevitably result in single-point failure, privacy leakage, and even untrustworthy results. Several blockchain-based SE schemes have been proposed to alleviate these issues; however, they suffer from some issues, such as the support for multi-keyword multi-owner model, query privacy and data storage availability. In this paper, we propose BPMS, blockchain-based privacy-preserving multi-keyword search in multi-owner setting, which supports searching over encrypted data in trustworthy, private and efficient manners. The attribute Bloom filter has been introduced into our BPMS to build indexes, which protects query privacy and improves index generation performance. To guarantee data storage availability, our BPMS leverages the advantages of IPFS&#x00A0;(InterPlanetary File System) to store large scale of encrypted data. Security proof and comparative analysis in theory indicate that our BPMS is more secure and efficient. A series of experiments conducted on a real-world dataset further demonstrate that our BPMS is feasible in practice.
Convergent binocular vision algorithm for guiding machining robot under extended imaging dynamic range

The machining quality of large and complex components highly depends on the positioning accuracy of the vision guided robot. In order to solve the problems of limited imaging field-of-view and dynamic range of existing parallel binocular cameras, a convergent binocular vision algorithm under extended imaging dynamic range is proposed to guide the machining robot for precise positioning. Specifically, synthesize high dynamic range images combined with sigmoid function model, and chromaticity space color correction algorithm introduced for adaptive correction of image chromaticity information. The pose between the convergent binocular camera and the robot is calibrated to correct the three-dimensional coordinates of the target workpiece CAD model. The experimental results indicate that the high dynamic images synthesized by the proposed algorithm have clear surface details and balanced colors. This work further enhances the positioning accuracy of the machining robot to the target workpiece which is placed in arbitrary pose within the field-of-view.
Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder

The spread of data on the web has increased in the last twenty years. One of the reasons is the appearance of social media. The data on social sites describe many real-life events in our daily lives. In the period of the COVID-19 pandemic, a lot of people and media organizations were writing and documenting their health status and the latest news about the coronavirus on social media. Using these tweets (sentiments) about the coronavirus and analyzing them in a computational model can help decision makers in measuring public opinion and yielding remarkable findings. In this research article, we introduce a deep learning sentiment analysis model based on Universal Sentence Encoder. The dataset used in this research was collected from Twitter, and it was classified as positive, neutral, and negative. The sentence embedding model determines the meaning of word sequences instead of individual words. The model divides the dataset into training and testing and depends on the sentence similarity in detecting sentiment class. The obtained accuracy results reached 78.062%, and this result outperforms many traditional ML classifiers based on TF-IDF applied on the same dataset and another model based on the CNN classifier.
On the Relative Importance of Visual and Spatial Audio Rendering on VR Immersion

A study was performed using a virtual environment to investigate the relative importance of spatial audio fidelity and video resolution on perceived audio-visual quality and immersion. Subjects wore a head-mounted display and headphones and were presented with a virtual environment featuring music and speech stimuli using three levels each of spatial audio quality and video resolution. Spatial audio was rendered monaurally, binaurally with head-tracking, and binaurally with head-tracking and room acoustic rendering. Video was rendered at resolutions of 0.5 megapixels per eye, 1.5 megapixels per eye, and 2.5 megapixels per eye. Results showed that both video resolution and spatial audio rendering had a statistically significant effect on both immersion and audio-visual quality. Most strikingly, the results showed that under the conditions that were tested in the experiment, the addition of room acoustic rendering to head-tracked binaural audio had the same improvement on immersion as increasing the video resolution five-fold, from 0.5 megapixels per eye to 2.5 megapixels per eye.
Analysis of Multimedia Combination-Assisted English Teaching Mode Based on Computer Platform

English is an important international language. Whether it is international economic or international political communication, it is inseparable from English. English teaching is mainly to impart the basic knowledge of English as well as the knowledge of audiovisual knowledge. However, the traditional English teaching mode often ignores the expression of English emotion and the background of English. The traditional English teaching mode often uses textbooks and other forms to impart knowledge, which can no longer meet the English teaching in today’s era. Computer-aided systems have become an important technology in the field of teaching today. Multimedia technology can also show English knowledge to students in the form of video or audio. This will increase students’ interest in learning English. This research will use computer platform and multimedia technology to build a new English teaching platform. It also uses the ConvLSTM algorithm and the CF algorithm to implement the active recommendation function of English knowledge. The research results show that multimedia technology can achieve the purpose of collecting video and audio information in the process of English teaching. The CF algorithm has a specific high similarity index in recommending English knowledge. The ConvLSTM method can also better predict the characteristics of English grammar and English emotion in the English teaching process. This can also show that the multimedia-assisted English teaching mode has a certain use value for different English teaching scenarios.
Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image

Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relative depth datasets that are much easier to collect. However, existing relative depth estimation models often fail to recover accurate 3D scene shapes due to the unknown depth shift caused by training with the relative depth data. We tackle this problem here and attempt to estimate accurate scene shapes by training on large-scale relative depth data, and estimating the depth shift. To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes. As the two modules are trained separately, we do not need strictly paired training data. In addition, we propose an image-level normalized regression loss and a normal-based geometry loss to improve training with relative depth annotation. We test our depth model on nine unseen datasets and achieve state-of-the-art performance on zero-shot evaluation. Code is available at: https://github.com/aim-uofa/depth/.