Reviewerly/exHarmony huggingface dataset:
Particle Filter Estimation Method of Parameters Time-varying Discrete Dynamic Bayesian Network with application to UGV Decision-making

Unmanned ground vehicles(UGV) autonomous control technology contains lots of methods, which autonomous decision-making is the most important one. The reliability of its strategy determines the result of UGV missions. To improve the environmental adaptability of traditional methods such as Bayesian network and dynamic Bayesian network, this paper proposes a parameter estimation method of particle filter based on the parameter time-varying discrete dynamic Bayesian network (PTVDDBN) for the situation where the parameters change smoothly with time, and applies it to UGV decision-making tasks. First, formally describe the general model of the discrete dynamic Bayesian network with time-varying parameters under the condition of invariable structure. Secondly, a novel method of parameter estimation and inference decision framework of PTVDDBN is put forward. Thirdly, a parameter estimation method of PTVDDBN based on particle filter is proposed. Finally, with UGV cooperative decision-making in battlefield as the application background, time-varying parameter estimation and PTVDDBN model inference and decision-making experiments were carried out. By comparing static Bayesian network and dynamic Bayesian network models, the analysis showed that the PTVDDBN model algorithm can estimate the time-varying parameters more accurately, and the decision-making inference is more reliable.
Synthesizing and Recovering Dynamic Patterns Using the Causal Reasoning Model

A new method using the causal reasoning ConvNet mode is proposed for synthesizing and recovering dynamic patterns. In the model which defines the probability distribution, a multi-layer filter is used to capture the spatiotemporal information. The problem of synthesizing and recovering pattern design is to capture the optimal synthesis or recovery based on the training model and the solution to maximize the objective function. In the algorithm, the synthetic video generated by the ConvNet of opponent training is matched with the observed (recovered) video frame, and the multi-layer parameters of the filter are updated. The maximum likelihood algorithm is designed to get the synthesis (recovery) of the real video patterns. The DynTex database is used to evaluate the effectiveness of this method and the results are compared with typical synthesis methods.
Improved heterogeneous aggregation signature scheme

The heterogeneous aggregate signcryption scheme can authenticate the signatures of multiple messages, thereby ensuring the authenticity of the messages. And this technology also solves the communication problem under different cryptosystems. However, most of the current schemes have hidden dangers of insecurity, and the signcryption may be forged, resulting in the leakage of the message ciphertext. In this article, a heterogeneous polymerization signcryption scheme is analyzed, and the forgery process of the scheme is explained, and finally an improvement scheme is given. Our scheme meets the unforgeability, and the final performance analysis proves that this scheme is more efficient than other similar schemes.
Gender and Firm Performance around the World: The Roles of Finance, Technology, and Labor

Using the World Bank Enterprise Survey (WBES) data from 2006-2017 on 130,000 firms in 130 countries across the globe, we document that women-led firms tend to underperform men-led firms. The main contribution of this work is to shed light on three mechanisms that help explain the underperformance: finance, technology, and labor. First, women-led firms are investing less in fixed assets and obtaining less credit from banks. Second, women-led firms are less likely to use information technology to manage their businesses. Third, women-led firms tend to employ more skilled and educated workers, and are more likely to provide workers with permanent contracts, hence incurring higher labor costs. Interestingly, in our data, women top managers do not perceive themselves facing more business obstacles than their male counterparts do.
The MovieWall: A New Interface for Browsing Large Video Collections

Streaming services offer access to huge amounts of movie and video collections, resulting in the need for intuitive interaction designs. Yet, most current interfaces are focused on targeted search, neglecting support for interactive data exploration and prioritizing speed over experience. We present the MovieWall, a new interface that complements such designs by enabling users to randomly browse large movie collections. A pilot study proved the feasibility of our approach. We confirmed this observation with a detailed evaluation of an improved design, which received overwhelmingly positive subjective feedback; 80% of the subjects enjoyed using the application and even more stated that they would use it again. The study also gave insight into concrete characteristics of the implementation, such as the benefit of a clustered visualization.
An Effective Super-Resolution Reconstruction Method for Geometrically Deformed Image Sequences

Despite of the technology advancements, remote sensing images usually suffer from a poor spatial resolution. To resolve this issue, a lot of research efforts have been devoted to developing resolution enhancement methods which retrieve a high-resolution image out of its low-resolution degraded versions. In this paper, we consider a nonlocal total variation (NLTV) based super-resolution method which handles low-resolution images with geometric deformations. In particular, we apply the framework of alternating direction method of multipliers (ADMM) to deduce an effective algorithm, which involves soft thresholding and gradient descent. Effectiveness and robustness to noise of the proposed method are verified by various numerical experiments.
Data Visualization for Transgender Voice Training

Social and political factors structure how data are collected, analyzed, and visually presented. Visualization researchers are increasingly discussing these issues, and calling for feminist approaches that allow not only for the interrogation of unspoken assumptions and power imbalances in how visualizations are designed and deployed, but also for the creation of new systems by and for marginalized groups. This paper describes the motivation, ideation, and prototyping of a novel visualization tool to support voice training for transgender people. We centered our design around user needs and lived experiences, in order to support self-determination of gender expression. In so doing, we make novel inroads into how to visualize speech data, and open possibilities for future work in the design of visualizations that prioritize user agency within social and political contexts.
Performance Analysis of an Efficient Ensemble Meta Learning Framework for Streaming Data Analytics

Streaming data is the continuous arrival of data with rapid speed and unbounded in size. Apart from the huge volume and rapid speed of streaming data, it also holds the issue of Concept Drift which leads to drastic drop in prediction accuracy of the model. Therefore, Drift Detection is the major challenge in streaming data which has been attracted by many academicians, industrialists and researchers to provide a novel solution. This paper focusses on a novel solution for the Concept Drift in streaming data, comprises of two phases. In the first phase of the research work, a memory efficient Lasso Regression Hoeffding Tree (LASH Tree) is introduced, as an efficient base learner, with proven experimental studies, and this has been well accepted by the research forum and published in a refereed journal. In the second phase of the proposed research work, an Efficient Ensemble Meta Learning Framework is designed and developed including LASH Tree as base learner, a novel solution to identify and to be adaptable with Concept Drift in streaming data. The experiments are conducted using Solar Power Plant Data Set and it is proved that the proposed framework using LASH Tree consumes less memory and less execution time when compared with the existing ensemble CART and ensemble VFDT algorithms.
Estimating the Number of Fork Projects of Bitcoin Based on a Birth-Death-Immigration Process

Since the first cryptocurrency, BitcoinBitcoin, was invented in 2008, there are 105 Bitcoin fork projects in total. The number of them is still raising now. Whether it will keep increasing and what the increasing ratio is, are important and interesting questions. However there is no model to answer these question. Thus, this chapter tries to propose a population model, using a birth-death-immigration process, to estimate the number of Bitcoin fork projects.
Multi-Experts with Context Awareness Correlation Filters for Visual Tracking

Correlation filters based trackers have achieved competitive performance in tracking community due to their high computational efficiency in Fourier domain. However, a large number of recent research focuses on improving the representative power of the target, which is still difficult to distinguish between target and background. At the same time through fusion and the use of deep features, the computational burden will be increased. In this paper, a multi experts with context aware correlation filters framework is proposed. Under this framework, target and contextual information are extracted to construct experts, and a most reliable one is selected as the result of current frame. Through this refining strategy, tracking drift can be alleviated. Extensive experiments are conducted on three benchmarks demonstrate that the proposed trackers performs favorably against state-of-the-art approaches.
Persian Language Phone Recognition Based on Robust Extraction of Acoustic Landmarks

Acoustic landmarks are defined as more informative parts of the speech signal and are proofed to be beneficial in designing more robust speech recognition systems. This work aims to present a Persian phone recognition system based on acoustic landmarks to achieve a quality phone recognition system. For this, appropriate acoustic landmarks for the Persian language was selected and trained to an artificial neural network. Then to boost the performance of our landmark recognition system, the model's structure and the training method were modified. The goal of these modifications is to filter variations of acoustic landmarks as much as possible. For this, we utilized neural network structures to map landmarks to their corresponding gold ones nonlinearly. These gold landmarks are the ones that could be recognized without any error in our landmark recognition system. The experiments were implemented on a Persian database named Farsdat. The best landmark recognition model is a five-hidden layer feedforward neural network with 21.74 phone error rate. We also attained 0.56 percent PER improvement using our best variation filtering method.
Invisible water obstacle recognition and obstacle avoidance algorithm based on vehicle dynamics

Aiming at the problem that UGV can easily fail to identify invisible water obstacles on unstructured roads due to image recognition method, this paper proposes an algorithm for recognizing and avoiding invisible water obstacles in the field based on vehicle dynamics. First, the vehicle model and tire model are established, the road adhesion coefficient identification framework is built, and the identification algorithm is designed. Secondly, a three-dimensional information grid obstacle map containing road adhesion coefficient information is established to define the original planned path coordinates, and the mapping processing method on the grid map and the search direction of modified path are defined according to different obstacle shapes in the field. Finally, an obstacle avoidance algorithm is designed and evaluated. By comparing the recognition value and setting the threshold value, it can judge whether to drive to the edge of the obstacle. If the point is not suitable for walking, the feasible grid point is searched again; otherwise, the grid point is kept. The simulation results show that this method can be used to plan a safe obstacle avoidance path for unmanned ground vehicle in the field under the rainy bog obstacle scenario.
Scaling Agile at Enterprise to Enable and Accelerate the Digital Transformation

One of the great mistakes some companies or business leaders do when thinking about their digital transformation is to start by technology. Frequently, it is common to hear from companies that they would like to do a project using artificial intelligence (AI), or they would like to have a blockchain or even use internet of things (IoT). There is nothing wrong on thinking and knowing about technologies and their potential. However, the digital transformation is broader than it. So, the question that comes immediately is: How to start? What should organizations think about when planning a digital journey? There are several frameworks to help to start.
A Survey on Different Search Techniques Over Encrypted Data in Cloud

Cloud computing technology allows us to outsource our data to the cloud. The main advantage of this technology is its storage capacity. To maintain privacy, everything should be encrypted before outsourcing into the cloud and only authorized users can retrieve their data. But this process is complicated in the case of encrypted data, because traditional data storage system works only on plain data. Therefore dedicated information retrieval systems were developed to handle the encrypted data. Different search methods over cloud data have been proposed for efficient information retrieval. The main objective of this paper is to provide an overview of some of the existing searching techniques on the encrypted data and make a comparative study.
Deep learning approaches in face analysis

Although face analysis algorithms have changed over the decades, almost in every face-related algorithm it is still usually the case that the order of the problem solving algorithm is the same. The analysis task is relatively easy on frontal and clear faces. However, when it comes to in-the-wild objects with spontaneous expressions, it is a challenging issue due to the changes in illumination, pose variation, expression intensity, subtle deformations, occlusion, etc. The recent success in deep neural networks makes it inevitable to ignore the technique in face analysis to automatically learn the discriminative representations of the face. In a deep network, the input data is run through several hidden layers that decompose the features of the input. The features are then classified using a function to retrieve the class probabilities to predict the output class. This chapter will investigate the deep learning approaches used to detect and pre-process the face, estimate its attributes, classify the expression and recognize the face.
Parallel Reconstruction for High Resolution Multi-frame Solar Speckle Images

The high resolution reconstruction of solar speckle image is an important research issue in the field of astronomical observation. Due to the influence of atmospheric turbulence, the astronomical images obtained by ground-based optical telescope will be blurred or degraded seriously, which needs to be reconstructed by image restoration method. Most of the existing regularization methods deal with a single frame image. The more image features are, the better the reconstruction effect is. However, the poor quality of the reconstructed image is caused by the insufficient features of a single solar speckle image. In this paper, we combined the complementary relationship between multi-frame speckle images to establish a multi-frame blind restoration model suitable for solar speckle image reconstruction, used genetic algorithm to select regularization parameters and realized multi-frame block reconstruction in parallel. The experimental results show that the proposed method can restore the solar speckle image well, and the reconstruction speed is fast, which can meet the requirements of astronomical observation.
Data Acquisition System Based on Hybrid Filtering Algorithm

The gearbox performance test generates a large amount of data, and how to collect these data accurately is directly related to the final performance evaluation of the gearbox. In the production of actual industrial, complex environmental factors will lead to negligence errors in the acquisition of these data, which will affect the evaluation results. To solve the problem, this paper analyzes a variety of negligence error processing methods, and proposes a hybrid filtering algorithm based on distribution graph method and limiting filtering method. The hybrid filtering algorithm uses the distribution graph method to conduct the first filtering, and then uses the limiting filtering method to complete the second filtering, and finally the program can obtain data without negligence error. Based on the hybrid filtering algorithm and Labview software development platform, this paper designs a data acquisition system dedicated to gearbox performance testing. The system can effectively suppress the occurrence of negligence errors and achieve the requirements for the reliability of test data.
Multi-Angle Movie Reviews Analysis Based on Multi Model

Abstract In this study, movie reviews are used as data sets to extract related phrases, topics, and sentiment scores from the text. Based on users’ information, users’ behavior preferences and their influences are analysed, and text semantic information is mined from multiple perspectives. A variety of data processing and machine learning methods including text segmentation, Apriori association rule mining algorithm, sentiment analysis, linear fitting, TFIDF algorithm, PCA dimensionality reduction, and LDA topic model is used in the research. At the same time, due to the coarse granularity of the topic extraction in the LDA algorithm, it is not suitable for short text, this paper proposes a new topic model based on improved k-means and TextRank and gets good results on this dataset. This paper uses multiple data mining models to analyse film reviews and presents an empirical study of the efficacy of machine learning techniques in text semantic mining.
Confident sequence learning: A sequence class-label noise filtering technique to improve scene digit recognition

Reading digits from natural images is a challenging computer vision task central to a variety of emerging applications. However, the increased scalability and complexity of datasets or complex applications bring about inevitable label noise. Because the label noise in the scene digit recognition dataset is sequence-like, most existing methods cannot deal with label noise in scene digit recognition. We propose a novel sequence class-label noise filter called Confident Sequence Learning. Confident Sequence Learning consists of two critical parts: the sequence-like confidence segmentation algorithm and the Confident Learning method. The sequence-like confidence segmentation algorithms slice the sequence-like labels and the sequence-like predicted probabilities, reorganize them in the form of the independent stochastic process and the white noise process. The Confident Learning method estimates the joint distribution between observed labels and latent labels using the segmented labels and probabilities. The TRDG dataset and SVHN dataset experiments showed that the confident sequence learning could find label errors with high accuracy and significantly improve the VGG-Attn and the TPS-ResNet-Attn model’s performance in the presence of synthetic sequence class-label noise.
Learning 3DMM Deformation Coefficients for Action Unit Detection

Facial Action Units (AUs) correspond to the deformation/contraction of individual or combinations of facial muscles. As such, each AU affects just a small portion of the face, with deformations that are asymmetric in many cases. Analysing AUs in 3D is particularly relevant for the potential applications it can enable. In this paper, we propose a solution for AUs detection by developing on a newly defined 3D Morphable Model (3DMM) of the face. Differently from most of the 3DMMs existing in the literature, that mainly model global variations of the face and show limitations in adapting to local and asymmetric deformations, the proposed solution is specifically devised to cope with such difficult morphings. During a learning phase, the deformation coefficients are learned that enable the 3DMM to deform to 3D target scans showing neutral and facial expression of a same individual, thus decoupling expression from identity deformations. Then, such deformation coefficients are used to train an AU classifier. We experimented the proposed approach in a difficult cross-dataset experiment, where the 3DMM is constructed on one dataset (BU-3DFE) and tested on a different one (Bosphorus). Results evidence that effective AU detection is obtained by SVM learning of deformation coefficients from a small training set.
A Contemporary Pictorial Password Technique based on Character Set and Direction

Passwords have always been a medium of authentication which is widely used and implemented for accessing control over devices or an account. With COVID- 19 pandemic, the entire world stays at home and doing the kind of work from home jobs where the bank transactions, data transfers and all happening through online modes. Situations like these where offline mode has taken a back seat, the online attacks, and security breaches have drastically increased. People can't move and have to serve from their homes but feel protected as they deal and transact through passwords only. But, if these passwords are not fully protected, then it can be exposed to others such kind of situations is risky and breaks user's trust. To avoid such repetitive circumstances user has to be more cautious while keeping passwords. In this article, a secure and easy password generating technique has been proposed based on an attractive Graphical User Interface. This technique is efficient in terms of privacy, security, and memorability.
Fuzzy Multi-Attribute Decision Making (FMADM) Application on Decision Support Systems (SPK) to Diagnose a Type of Disease

Fuzzy logic is widely applied to daily life with various methods. One method is fuzzy multi-attribute decision making (FMADM). FMADM is able to select the best alternative from a number of alternatives. In FMADM there is a supporting method so that the results obtained are accurate and optimal, namely the classic MADM method. One method in classic MADM is the Simple Additive Weighting (SAW) method. The SAW method is precisely used to minimize diagnostic errors, but if a decision support system is made, the SAW method still requires a further development method, one of which is the FMADM method with its development. The purposes of this study are to describe the steps of SAW method and the development of FDM in theory, implement SAW method and the development of FDM to diagnose a type of disease and implement it in a decision support system using GUI matlab. The completion step of those two methods is through two stages, the first one will go through FMADM stage with SAW, which is weighted sum, then the output will be used as input to the FDM method based on total integral values. The result of this study is proven by patient experienced initial symptoms of high fever at a temperature of 39.5° C - 40° C, very much spots appear in rumple leed test (&gt; 50 petheciae), bleeding gums, rarely got nausea and headache, as well as diarrhea. Accuracy for the decision support system using MAPE was obtained 93% so that the decision support system with FMADM method to diagnose the disease was feasible to use.