Reviewerly/exHarmony huggingface dataset:
Multibranch Adversarial Regression for Domain Adaptative Hand Pose Estimation

Although hand pose estimation has achieved a great success in recent years, there are still challenges with RGB-based estimation tasks, the most significant of which is the absence of labeled training data. At present, the synthetic dataset has plenty of images with accurate annotation, but the difference from real-world datasets affects generalization. Therefore, a transfer learning strategy, which tries to transfer knowledge from a labeled source domain to an unlabeled target domain, is a frequent solution. Existing methods such as mean-teacher, Cyclegan, and MCD will train models with the help of some easily accessible domains such as synthetic data. However, these methods are not guaranteed to operate well in real-world settings due to the domain shift. In this paper, we design a new unsupervised domain adaptation method named Multi-branch Adversarial Regressors (MarsDA) in hand pose estimation, where it could be better for feature migration. Specifically, we first generate pseudo-labels for unlabeled target domain data. Then, the new adversarial training loss between multiple regression branches we designed for hand pose estimation is introduced to narrow the domain gap. In this way, our model can reduce the noise of pseudo labels caused by the domain gap and improve the accuracy of pseudo labels. We evaluate our method on two publicly available real-world datasets, H3D and STB. Experimental results show that our method outperforms existing methods by a large margin.
Superframe-Based Temporal Proposals for Weakly Supervised Temporal Action Detection

The weakly supervised Temporal Action Detection (TAD) by using the video-level annotations can lighten the burden of labor consumption. However, the current methods for weakly supervised TAD do not take full advantage of the short-term consistency between consecutive frames and the long-term continuity inside an action, resulting in less accurate detecting boundaries of actions in untrimmed videos. In this paper, the SuperFrame-based Temporal Proposal (SFTP) is proposed, in which superframes are formed for representing a series of consecutive frames with high temporal consistency and their features are pooled from the features of frames through the integration function. Then, the temporal proposal is built based on the multiple consecutive superframes and the features of all proposals are generated from a pyramidal feature hierarchy. This hierarchy consists of the designed Structured Outer-Inner Context (SOIC) features formed from superframe features and is able to explicitly characterize the temporal continuity inside a proposal. Furthermore, a novel Scale-Wise Normalization Strategy (SWNS) is proposed to identify proposals, which can effectively detect multiple actions with different duration in one untrimmed video. Extensive experiments are conducted on two public datasets: THUMOS14 and ActivityNet1.2 for performance evaluation. Our experimental results have demonstrated that the proposed approach is able to detect the boundaries of actions more effectively and obtain competitive mAP (mean average precision) compared with other approaches.
Mining Top-K Competitors by Eliminating the K-Least Items from Unstructured Dataset

In today’s merciless business environment, victory is entirely dependent on the potential to create something that is maximum engaging to consumer than the competitiveness. ‘Huge information’ is a popular statement that refers to large amounts of information that can be divided into three categories: organized information, semi-organized information, and unstructured information (see Fig. 1). The volume and variety of significant amounts of information are so vast that it is nearly impossible to collect, process, and store it using normal database administration frameworks and programming methods. As a result, breaking down massive amounts of information necessitates the use of a variety of approaches and instruments. In this work, we attempted to address the issue of entrepreneurial practices, often known as the identification of the k-most competitive products (k-MCP).
Simulating an Ethereum 2.0 Beacon Chain Network

In this contribution, we present a simulator for the Ethereum 2.0 Beacon Chain Network. The purpose of this tool is to run various simulations to verify hypotheses and attack scenarios without the need to actually stake cryptocurrency and fulfilling validator's obligations inside the network. Because its codebase is derived from the official specification, functionality such as the fork choice rule and finality mechanisms are guaran-teed to match the behavior of conforming client implementations. Basic metrics about the live Beacon Chain network were collected and used as an input to the conducted simulations and to verify the obtained simulation results. We find that the results of conducted simulations show great correspondence to the behavior of the live Beacon Chain. We further implement various kinds of misbehavior to verify that misbehaving validators are slashed and deprived of their invested capital. Finally, two attacks are implemented to demonstrate how the simulator can be used to verify and test the applicability of various attack scenarios.
MMNet: RGB-t Semantic Segmentation Network Based on Multi-scale and Adaptively Mutual Enhancement Mechanism

Existing RGB-T semantic segmentation networks adopt the one-way RGB-T data fusion strategy that enhances RGB features with thermal features, resulting in the underutilization of thermal information. To address it, we propose a novel RGB-T fusion network termed MMNet which introduces an adaptively mutual enhancement mechanism to enhance RGB and thermal features by each other in the encoder, as well as hierarchically aggregates multi-resolution RGB-T features in the decoder. Experimental results show that MMNet achieves state-of-the-art 56.8% mIoU, being 1.66× faster than the best previous work.
East Meets West: Sentiment Analysis for Election Prediction

There has been an exponential growth of social media in the recent past. Social media has provided the common public a medium to voice their opinions freely. Twitter is amongst the most used social networks, especially when it comes to raising opinions regarding politics. Politics has been a fascinating subject to discuss for the common people for ages, but modern-day Twitter has become the new platform for these political discussions. The data generated from these discussions can be used to analyse many real-life scenarios. In this paper, we used Twitter data to analyse and predict the 2020 US elections. We extracted tweets featuring the two primary candidates of the election: Joe Biden and Donald J. Trump. We have extracted two sets of tweets; 1) tweets by American users and 2) tweets by Indian users; based on geopolitical location. Using python packages, we classified the tweets into five different emotions. We used sentiment analysis (VADER) algorithms to assign polarity to the tweets, which would later help us predict the outcome of the elections. We have also compared the opinions of two countries on the election results. The observations have been displayed using various visualization techniques.
Blockchain based Distributed Oracle in Time Sensitive Scenario

Distributed oracle protocol is proposed to support on-chain transaction execution which depends on off-chain information without a trusted party as source. Although the commit-reveal mechanism based on commitment scheme used in distributed oracle protocol solve the problem of freeloading, it introduces a new one of availability, which is significant when information requester would like to obtain protocol output before expiration. To address the issue, we describe an architecture of distributed oracle protocol in time-sensitive scenario. Then we propose a new protocol which satisfies all requirements defined in the architecture by applying timed commitment scheme. Finally, a prototype program is implemented to evaluate additional cost caused by the core algorithm in new protocol, which shows that the cost is acceptable.
Feature Selection with a Backtracking Search Optimization Algorithm

Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms in feature selection has led to the construction of efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed to perform wrapper-based feature selection, where two modifications of the standard backtracking search algorithm are adopted. The first one concentrates on utilizing a particle ranking operator regarding the current population. The second one focuses on removing the case of using a single particle on the mutation process. Then, the implementation of the above algorithm in feature selection is carried out in terms of two general frameworks, which originally were developed for the particle swarm optimization. The first framework is based on the binary and the second on the set-based particle swarm optimization. The experimental analysis shows that the above variants of the backtracking search algorithm perform equally well on the classification of several datasets.
Formally Verifying Industry Cryptography

Over the past five years, Galois has formally verified several cryptographic systems that are used in demanding industry environments. This article discusses our approach to these verification projects, focusing on the practical engineering challenges that exist when building and deploying proofs in industry.
Using Blockchain technologies to facilitate international trading

International trading is the lifeblood of the modern economy, and, despite its importance, it is still governed by outdated processes and ecosystems generating huge amounts of challenges and causing USD billions of losses per year to the global economy. We have demonstrated through our research some of the most important challenges that international trading is suffering from. We also demonstrated that the global trading ecosystem is yet to benefit from the digital revolution, and due to the sensitive nature and the complexity of the international trading value chain requiring trust, security and consensus, Blockchain seems to be the perfect solution for these challenges. We investigated the features of Blockchain and how they can be utilized to answer the identified challenges and we found out that this technology is, as we anticipated, a potential adequate solution for global trading. Nevertheless, our research revealed that, due to the disruptive nature of Blockchain, such a move can face huge deployment barriers and resistance to change, and needs first to have a global agreement on how the underlying technologies, processes and governance will be run, then be proven in a next step through a proof-of-concept and finally introduced through a well-designed stepwise approach.
Face Mask Detection using Keras, Opencv and Tensorflow by Implementing Mobilenetv2

The pandemic of Corona Virus Disease (COVID-19) is triggering a public health emergency. Wearing a face mask is one of the most important ways to combat the infection. Face mask identification is discussed in the article, and it can be used by authorities to enable COVID-19 avoidance, assessment, prevention, and action plans. In this project, we developed a deep learning model to detect the presence of a mask on a person. We have trained it using Keras and OpenCV. The image classification method: MobilenetV2 is used to speed up the training time and we use approx. 2000 images to train the model.
A Flow-Based Generative Network for Photo-Realistic Virtual Try-on

Image-based virtual try-on systems aim at transferring the try-on clothes onto a target person. Despite making considerable progress recently, such systems are still highly challenging for real-world applications because of occlusion and drastic spatial deformation. To address the issues, we propose a novel Flow-based Virtual Try-on Network (FVTN). It consists of three modules. Firstly, the Parsing Alignment Module (PAM) aligns the source clothing to the target person at the semantic level by predicting a semantic parsing map. Secondly, the Flow Estimation Module (FEM) learns a robust clothing deformation model by estimating multi-scale dense flow fields in an unsupervised fashion. Thirdly, the Fusion and Rendering Module (FRM) synthesizes the final try-on image by effectively integrating the warped clothing features and human body features. Extensive experiments on a public fashion dataset demonstrate that our FVTN qualitatively and quantitatively outperforms the state-of-the-art approaches. The source code and trained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gxl-groups/FVNT</uri> .
Entity disambiguation method based on Graph Attention Networks

Entity disambiguation based on entity-link is a technique which constructs the mappings between entity reference items appearing in the short text and target entity in knowledge base respectively. In this paper we propose an entity disambiguation method based on Graph Attention Networks for semi-structured knowledge base data. First, a global Knowledge Graph is constructed from the semi-structured knowledge base, and the entity reference items are embedded by Bert pre-trained model meanwhile. Next, Graph Attention Networks which leverages masked self-attention layers is applyed on candidate entity nodes of global Knowledge Graph to fetch a vector of node level. Furtherly, we compute similarity scores rank between the entity reference items and the candidate entity to complete the task of entity disambiguation. The experimental results on CCKS2019 dataset achieve state-of-the-art.
Using a Smartphone-Based Chatbot in EFL Learners' Oral Tasks

Large class sizes significantly limit EFL (English as a foreign language) learners' speaking opportunities in class, and it is also quite difficult for them to have a regular language partner if they want to practice English after class. Microsoft Xiaoying, a chatbot available on smart phones, offers EFL learners a virtual partner for them to conduct conversational practice with and engage in language learning anytime and anywhere. This study evaluates 50 EFL learners' accuracy, errors, and improvements in oral tasks during 28 days of training employing this chatbot, which located their grammar and pronunciation errors. The subjects' grammar and pronunciation accuracy increased, and their oral performance was significantly improved. In addition, the chatbot was positively perceived by most participants. This study suggests that this chatbot could be one useful learning tool for after-class oral practice and that students might significantly improve their grammar and pronunciation through conversational practice with this chatbot.
“Why tag me?”: Detecting motivations of comment tagging in Instagram

Tagging a friend in a comment is one of the main mechanisms to lead user interaction in social media. This paper investigates the current practice of user tagging in Instagram by collecting large-scale data that includes 9K uploaded posts and associated 4M comments shared by 3M users. Our analysis reveals that 54.8% of the comment contains user tagging, meaning that user tagging is widely used in Instagram. By analyzing the comment texts, we observe that the comments with user tagging tend to have more social and fewer negative words than those without user tagging, suggesting that user tagging is often used for friendly conversations. Based on lessons learned, we propose a learning-based model to classify the motivation of user tagging into one of the following categories: information-, relationship-, and discussion-oriented motivation. The proposed model can achieve a high f1-score of 83.72% in identifying the motivations for user tagging, which can provide considerable insights into user responses. We then apply our classification model to the user tagging comments in our dataset, and find that 44.08%, 47.74%, and 8.18% of comments are information-, relationship-, and discussion-oriented comments, respectively, which reveals that user tagging is frequently used to socialize with other friends.
Experimental Evaluation of QFT Adders on IBM QX Hardware

Quantum Fourier transform (QFT) offers a versatile way to perform modular and non-modular quantum arithmetics and provide compact quantum circuits. QFT is the quantum implementation of the classical discrete Fourier transform (DFT) over the vector of amplitudes of a quantum state (wavefunction). In this paper, the authors experimentally evaluate the performance of QFT adders on various IBM Quantum Experience (QX) hardware against Quantum Information Science Kit (Qiskit) Simulator. The authors have constructed quantum circuits to represent one-qubit and two-qubit QFT adders using Qiskit and run on IBM QASM Simulator and eight IBM real quantum devices: YorkTown, Santiago, Athens, Quito, Belem, Lima, Manila and Melbourne. Experimental analysis of one-qubit QFT adder shows that IBM Lima and IBM Athens provide better accuracy results of 80.2% and 80.125%, respectively, in comparison to other five-qubit and fifteen-qubit quantum hardware. Experimental results of two-qubit QFT adder demonstrate that IBM Athens and IBM Manila provide better accuracy results of 25.575% and 17.52%, respectively, in comparison to other five-qubit and fifteen-qubit quantum hardware.
Analysis of uniform resource locator using boosting algorithms for forensic purpose

Innovations are taking up new roles in all fields. It still has a crucial role in Internet technology, as the ease with which the Internet is available everywhere and accessible from any device has resulted in a slew of cyber-attacks., A prevalent scenario during and before a pandemic is phishing, which is accomplished by smartly altering the URL as a legitimate one and then redirecting the user to other sites and extracting personal information. The benchmark URL datasets used for the study considering an equal balance between phishing/ malicious URLs and benign/ legitimate URLs. URLs are parsed in this procedure to extract valuable elements that aid in the identification of URL phishing. Our research emphasized using different machine learning boosting algorithms such as Extreme Gradient Boosting, Light Gradient Boosting, Adaptive Boosting, and Gradient Boosting and have achieved an accuracy of more than 98% for most of the algorithms considered.
Effectiveness of the Use of Golden Ratio in Identifying Similar Faces Using Ensemble Learning

Face Recognition System (FRS) is a significant area for communicating non verbally in day-to-day life. It is well understood that the face is a unique and vital part of the human body in identifying a person. Therefore, it can be used to identify faces for commercial and law enforcement applications. The aim of this study is to explore the relationship of facial proportion with respect to golden ratio for identifying a person. The goal of this paper is to investigate the application of divine proportions among human faces to extract features for classification and recognition using an ensemble classifier model. These feature extraction techniques can be used in image analysis, the advantages of which can be used for commercial purposes and criminal investigation processes.
Optimizing 3D Virtual Fitting Mirror Using Augmented Reality Technology under Internet Plus

At present, with the ups and downs of the &#x201C;Internet Plus&#x201D; era, all things realize intelligent interconnection. Internet technologies such as big data and artificial intelligence have exploded, playing an important role as a bridge in the era of &#x201C;Internet Plus&#x201D; and bringing a profound revolution to human life. In order to solve the personalized and diversified dressing needs of consumers in the new retail mode, enhance the reality of 3D clothing model, and make up for the shortcomings of the user experience in the clothing e-commerce industry, this paper studies the 3D fitting mirror based on Kinect, and proposes the use of Random Sample Consensus algorithm to optimize the virtual fitting mirror and improve the user&#x0027;s fitting experience.
Linguistic characterization of answer passages for fact-seeking question answering

The performance of the passage ranking process for Question Answering (QA) systems has seen significant improvements, especially with recent advances in deep learning and distributed textual representation mechanisms. As the effectiveness of rankers and comprehension systems has improved, our understanding of what constitutes specific answer-containing passages has been shadowed under the complex inner mechanics of deep learning constructs. In this paper, rather than making attempts at developing a yet new deep learning structure for answer passage ranking, we step back on several of the core linguistic characteristics of questions and relevant passages to gain a more comprehensive understanding of answer-containing text snippets in relation to fact-seeking natural language questions. We have experimented with two large benchmark data sets, QUASAR-T and TriviaQA, and extracted several linguistic features from the questions and passages. Our statistical and Bayesian analyses show that answer-containing and answer-free passages have significantly different distributions of linguistic features and that a Naïve Bayes model can effectively capture, model, and explain feature relationships to distinguish between answer-containing and answer-free passages.
A new type of Chinese speech synthesis technology and system research

In this paper, we propose a method of Chinese speech synthesis. In order to achieve the purpose of synthesizing fluent and natural speech, two processing methods, rule-based and statistics-based, are used in the developed system. Firstly, a module design method with the function of text language recognition is introduced in this paper. This module can classify and recognize the text of Chinese, English and special symbols, and deal with the recognition problem that the input text contains Chinese, English and special symbols. Secondly, the Chinese speech synthesis methods used in the system are explained. In the prosody control module, we use a prosody structure prediction model combining neural network and decision tree; when concatenating speech, we propose two smoothing method. One is smoothing algorithm based on spectrum analysis and the other is smoothing algorithm based on dual-threshold. Experiments prove that the synthesis effect of our system is great, and the synthesized speech is clear and natural. Due to the moderate amount of system data and high code efficiency, it is suitable for application systems in mobile Android.
Optimal solution of PM synchronous motor obtained by gravitational search algorithm

This paper presents a novel approach to the efficiency improvement of permanent magnet synchronous motor using gravitational search algorithm as an optimization tool. The gravitational search algorithm (GSA) is a recently developed meta-heuristic optimization algorithm, which so far has proven to be quite suitable for solving power engineering optimization problems. The aim of this research work is to implement this novel optimization algorithm for the efficiency improvement of permanent magnet synchronous motor, where the objective function in the optimization process is the efficiency of the investigated motor. Comparative analysis of the initial and a number of optimized solutions of the motor model is performed.