Reviewerly/exHarmony huggingface dataset:
An On-Board Equipment and Blockchain-Based Automobile Insurance and Maintenance Platform

This study provides a forward-looking usage-based automobile blockchain platform on the Internet of Vehicles network, describing the possible stakeholders, services, and their interaction modes. Most existing UBI (Usage-Based Insurance) products use the drivers’ driving distance, driving time, or driving area as premium calculating standard. If driving data are recorded on the blockchain to take usage-based automobile insurance, it could bring more additional services such as automatic claiming, improvement of traffic adjudication’s quality, and preventive maintenance. The feasibility of this platform will discuss the configuration of the blockchain and related management or privacy issues to explain how this research can improve the existing limitation of usage-based automobile insurance.
Iterative Methods for Computing Eigenvectors of Nonlinear Operators

In this chapter we are examining several iterative methods for solving nonlinear eigenvalue problems. These arise in variational image processing, graph partition and classification, nonlinear physics, and more. The canonical eigenproblem we solve is T(u) = λu, where \(T:\mathbb {R}^n\to \mathbb {R}^n\) is some bounded nonlinear operator. Other variations of eigenvalue problems are also discussed. We present a progression of five algorithms, coauthored in recent years by the author and colleagues. Each algorithm attempts to solve a unique problem or to improve the theoretical foundations. The algorithms can be understood as nonlinear PDEs which converge to an eigenfunction in the continuous time domain. This allows a unique view and understanding of the discrete iterative process. Finally, it is shown how to evaluate numerically the results, along with some examples and insights related to priors of nonlinear denoisers, both classical algorithms and ones based on deep networks.
Knowledge Transfer via Decomposing Essential Information in Convolutional Neural Networks

Knowledge distillation (KD) from a "teacher" neural network and transfer of the knowledge to a small student network is done to improve the performance of the student network. This method is one of the most popular techniques to lighten convolutional neural networks (CNNs). Many KD algorithms have been proposed recently, but they still cannot properly distill essential knowledge of the teacher network, and the transfer tends to depend on the spatial shape of the teacher's feature map. To solve these problems, we propose a method to transfer knowledge independently of the spatial shape of the teacher's feature map, which is major information obtained by decomposing the feature map through singular value decomposition (SVD). In addition, we present a multitask learning method that enables the student to learn the teacher's knowledge effectively by adaptively adjusting the teacher's constraints to the student's learning speed. Experimental results show that the proposed method performs 2.37% better on the CIFAR100 data set and 2.89% better on the TinyImageNet data set than the state-of-the-art method. The source code is publicly available at https://github.com/sseung0703/KD_methods_with_TF.
PD-DARTS: Progressive Discretization Differentiable Architecture Search

Architecture design is a crucial step for neural-network-based methods, and it requires years of experience and extensive work. Encouragingly, with recently proposed neural architecture search (NAS), the architecture design process could be automated. In particular, differentiable architecture search (DARTS) reduces the time cost of search to a couple of GPU days. However, due to the inconsistency between the architecture search and evaluation of DARTS, its performance has yet to be improved. We propose two strategies to narrow the search/evaluation gap: firstly, rectify the operation with the highest confidence; secondly, prune the operation with the lowest confidence iteratively. Experiments show that our method achieves 2.46%/2.48% (test error, Strategy 1 or 2) on CIFAR-10 and 16.48%/16.15% (test error, Strategy 1 or 2) on CIFAR-100 at a low cost of 11 or 8 (Strategy 1 or 2) GPU hours, and outperforms state-of-the-art algorithms.
The Autonomy Evolution in Unmanned Aerial Vehicle: Theory, Challenges and Techniques

The research areas in the field of UAVs have increased considerably during the last years, the research in this field is driven by the specific needs of each organization that conduct the research, There are two main research areas, the first is the operational and it is conducted by the governmental institutions and the universities, and the second is the technological and it is conducted mainly by the companies, This paper discusses the current technological research topics in the field of UAVs, focusing on the fuzzy-logic based methods which are employed in many control problems to increase the level of autonomy, the fuzzy-logic is considered as a promising hot subject which contains many active research topics and multiple potential tools for solving complex control problems to extend the UAV capabilities to perform different functions like Optimal path planning, Collision avoidance, Trajectory motion and path following autonomously without the need of the human pilot with the minimum human supervision, the paper illustrates the different levels, functions and challenges of autonomy and a comparative analysis is conducted to analyze four potential directions which are considered to be promising areas for fuzzy-logic based approaches. It also highlights the two main areas of AI research in the field of UAV autonomous flight, 1) the imitation of the human pilot and 2) the high-level applications like image evaluation, and how to tackle some of the problems in these areas with aid of fuzzy-logic based machine learning algorithms.
Moving Object Detection in Video Streaming Using Improved DNN Algorithm

An efficient approach for MOD (Moving Object Detection) has been implemented in research work. In this proposed work, collect the video samples such as *.avi format. After that, it developed the frame differencing to divide the video into frame format or extraction. It has converted the RGB color image into three different mechanisms like red, green, and blue channels. It has identified the noise level in the uploading video. It has developed the Gaussian filtration method to remove the noise in the uploaded image frames. It has developed the SURF algorithm to extract the features in the key-points format. In research work using improved classification algorithm to detect the moving objects. The experimental result analysis using the MATLAB simulation tool used and proposed work parameters have achieved the Accuracy value 99 per cent, Precision value 0.96, Recall Value 0.875, Specificity value 0.99, FPR value 0.0067, FNR value 0.124, F1-measure value 0.972, and compared with the existing parameters.
Passing the Data Baton: A Retrospective Analysis on Data Science Work and Workers

Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work.
Hierarchical clustering based Intelligent Information Retrieval approach

In this digitalera, there is an enormous increase in the digital documents due to the use of the internet. Despite the hype, in the processing of digital documents, the problem of polysemy interprets more than one meaning for a single word. Hence the polysemy related queries will adversely affect the relevant result retrieval process. The paper addresses the polysemyissue on information retrieval paradigm and used clustering as a tool for improving the accuracy and appropriateness in the information retrieval process. This paper proposed a novel approach that generates a coherent group for the query topic. The proposed model is a hybrid of the k-means algorithm and the hierarchical approach named as HK clustering model to utilize the efficiency of both the strategies and overcome the drawbacks of each other.
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 20 May 2019Accepted: 08 June 2020Published online: 07 October 2020Keywordsbagging, bootstrap, randomized algorithms, random forestsAMS Subject Headings62F40, 65B05, 68W20, 60G25Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
Design and Development of OCR Integrated Automated Writing Machine

The changes in the recent industrial trends have given rise to a technological evolution, which is leading to the development of Industry 4.0 with highly automated industries through human-machine interaction. As the process gets more complex and cumbersome, automation becomes more vital for the growth and efficiency of a system. Automated machines are more accurate, versatile, timely and reduces the probability of error significantly. In recent years, numerous systems were proposed to operate as a writing machine that can give output in predefined fonts. The system proposed in this paper deals with recognizing the text in the document and then giving the output in the user's font. Using this method, the final production cost can be reduced to approximately 73%. Additionally, the weight of the entire system is made relatively lower than the other commercially available writing machines.
3D Human Pose Estimation with Adversarial Learning

In recent years, due to the deep learning and the development of computer vision, great progress has been made in the 3D human pose estimation from RGB images. However, due to the lack of depth information in RGB images, this task still faces great challenges. In this paper, we propose a method of adversarial learning to estimate the 3D pose of the human body. Our framework consists of two parts, a pose generator and a discriminator. Using the 3D pose descriptor, we designed for adversarial learning can effectively increase the accuracy and visual effect of 3D pose estimation results. We performed ablation experiments on the public dataset, which is a good improvement compared to our baseline.
Mispronunciation Detection and Diagnosis for Mandarin Accented English Speech

This paper presents a Mispronunciation Detection and Diagnosis (MDD) system based on a range of Automatic Speech Recognition (ASR) models and feature types. The goals of this research are to assess the ability of speech recognition systems to detect and diagnose the common pronunciation errors seen in non-native speakers (L2) of English and to assess the contribution of the information offered by Electromagnetic Articulography (EMA) data in improving the performance of such MDD systems. To evaluate the ability of the ASR systems to detect and diagnose pronunciation errors, the recognized sequence of phonemes generated by the ASR models were aligned with human-labeled phonetic transcripts as well as with the original phonetic prompts. This three-way alignment determined the MDD related metrics of the ASR system. System architectures included GMM-HMM, DNN, and RNN based ASR engines for the MDD system. Articulatory features derived from the Electromagnetic Articulography corpus of Mandarin-Accented English (EMA-MAE) were utilized along with acoustic features to compare the performance of MDD systems. The best performing system using a combination of acoustic and articulatory features had an accuracy of 82.4%, diagnostic accuracy of 75.8% and a false rejection rate of 17.2%.
Continuous Planning and Forecasting Framework (CPFF) for Agile Project Management

Despite the many problems the COVID-19 pandemic creates in the economies worldwide, recent research in academia tries to find new ways to support enterprises and companies counteracting the crisis. This study discusses the question of how an agile project budgeting resp. The Continuous Planning and Forecasting Framework (CPFF) for agile project management can be used to support teams working in an "agilefall" (the in-between state of traditional and agile) environment to reach a certain level of certainty under uncertain conditions. This contribution refines the first drafted Continuous Forecasting Framework, presented on the Software Engineering 2020, incorporating feedback from academia and practitioners using agile methods. For readers who have never worked in a real agile environment, it is often difficult to grasp why getting off the strict path following an iterative beyond budgeting approach could increase certainty. Therefore, the authors depict the framework by applying it to specific problems within traditional project boundaries focusing on elements that could help teams overcoming the "agilefall"-budgeting trap.
A Hardware Implementation on Customizable Embedded DSP Core for Colorectal Tumor Classification with Endoscopic Video toward Real-Time Computer-Aided Diagnosais System

In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.
Instance Segmentation Method of User Interface Component of Games

On the game screen, the UI interface provides key information for game play. A vision deep learning network exploits pure pixel information in the screen. Apart from this, if we separately extract the information provided by the UI interface and use it as an additional input value, we can enhance the learning efficiency of deep learning networks. To this end, by effectively segmenting UI interface components such as buttons, image icons, and gauge bars on the game screen, we should be able to separately analyze only the relevant images. In this paper, we propose a methodology that segments UI components in a game by using synthetic game images created on a game engine. We developed a tool that approximately detected the UI areas of an image in games on the game screen and generated a large amount of synthetic labeling data through this. By training this data on a Pix2Pix, we applied UI segmentation. The network trained in this way can segment the UI areas of the target game regardless of the position of the corresponding UI components. Our methodology can help analyze the game screen without applying data augmentation to the game screen. It can also help vision researchers who should extract semantic information from game image data.
Lightweight Multiple Perspective Fusion with Information Enriching for BERT-Based Answer Selection

Answer selection (AS), as one of the hottest topics in the field of natural language processing, has developed rapidly with outstanding performances reported, especially with the emergency of pretrained model (e.g., BERT). However, the current BERT based AS methods applied BERT only by fine-tuning or stacking other modules such as CNN and RNN, but ignored to exploit the discrimination embedded inside the BERT. In this paper, we proposed a novel method LMPF-IE, i.e., Lightweight Multiple Perspective Fusion with Information Enriching. The method can mine and fuse the multi-layer discrimination inside different layers of BERT and can use Question Category and Name Entity Recognition to enrich the information which can help BERT better understand the relationship between questions and answers. We test the proposed BERT layer-wised attention model in 5 benchmark datasets of answer selection task. The experimental results clearly verify better performances than the baseline models can be achieved by our method.
Knowledge Exchange and Knowing: The Self, Art Practice and the Digital

The chapter considers knowledge socialism in relation to the production of the self in the digital age. It explores the extent to which the URL versions of ourselves are portraits. We ask to what degree a lack of interpersonal engagement has resulted in a diminished awareness and ownership of the processes through which we acquire knowledge. Art practice as institutional critique is explored as a means to test strategies to regain consciousness of the self and one’s environment. Aspects of knowledge exchange are outlined from the perspectives of Bourdieu, Dewey, and Peters alongside the Black Quantum Futurists, Cassie Thornton, Eve and Franko Mattes, the Ideal Spaces group, Lynn Hershmann Leeson and Helen Hester. This chapter explores the issues and actions of the digital self in relation to art practice as a method of learning and increased consciousness.
Data Augmentation with Reinforcement Learning for Document-Level Event Coreference Resolution

Most previous models on event coreference resolution largely depend on hand-crafted features and annotated corpora. To address above issues, this paper introduces a neural model to resolve document-level event coreference in raw texts by both employing various neural components to better represent event semantics and integrating data augmentation with reinforcement learning to largely expand the dataset and effectively improve its quality. Experimentation on three KBP datasets shows that our proposed neural model significantly outperforms several strong state-of-the-art baselines.
Application of Deep Learning in Handwritten Mathematical Expressions Recognition

Handwritten mathematical expression (HME) is a research highlight in pattern recognition area since it has many practical application scenarios such as scientific documents digitalization and online education. However, it can be very challenging due to its complicated two-dimensional structure and great variety among different individuals. Inspired by the excellent performance of deep learning in various recognition problems, some researchers in this field began to use deep learning method to solve the problem of handwriting recognition in order to reach the prediction accuracy that traditional resolutions cannot achieve. In this survey paper, we aim to review the solutions of deep learning (encoder-decoder, GRU, attention mechanism and so on) to various problems of handwritten mathematical expression recognition. In particular, some important methods and issues will be described in depth. Moreover, we will try to discuss the future application of deep learning to HME recognition. All these together will help us have a clear picture about how deep learning methods are applied to in this research area.
A Novel Scheme of Schnorr Multi-signatures for Multiple Messages with Key Aggregation

A digital signature is essential in verifying the reliability of people and data over networks, such as through web server certificates, authentication, and blockchain technologies. In blockchain, multi-signature signature schemes have recently attracted attention for reducing the amount of data in transactions. While such schemes support only a single message, Interactive Aggregate Signatures (IAS), an extended Schnorr multi-signature scheme, supports some messages under the plain public key model. However, there are three problems with this scheme in certain use cases. We propose a key aggregatable IAS scheme called KAIAS. In contrast to the previous works, KAIAS solves these problems which means that KAIAS (1) includes a verification algorithm using only a single aggregated public key, (2) dynamically signature aggregation, and (3) requires signers to sign only their own messages. Recently, the Schnorr multi-signature scheme has been discussed mainly from its advantages of reducing the size of the signatures in the implementation of Bitcoin. Thus, we also propose a practical application of KAIAS that takes advantage of its feature to aggregate both signatures and public keys with low computational complexity of signing.
Learning Embeddings Based on Global Structural Similarity in Heterogeneous Networks

With different types of nodes and edges, heterogeneous networks have higher levels of structural diversity than homogeneous networks. This article proposes an unsupervised representation learning model, named gs2vec, to address structural diversity of a node being connected to other types of nodes via different types of edges in heterogeneous networks. The model measures a node's structural roles based on its numbers of neighboring nodes of different types. It also attempts to measure such structural roles beyond the immediate neighborhood of each node by incorporating structural roles of other nodes k-hop away. Experiments based on synthetic and empirical datasets show that gs2vec outperforms state-of-the-art network representation learning models in heterogeneous network analysis tasks such as node classification and node clustering.
Biofeedback as a cognitive research technique for enhancing learning process

Abstract The new technologies of measuring the biological indicators and the progress of neuron-communications help to better connect the emotional, intellectual and physiological parameters of the body and brain activity. The emerging research techniques include the biofeedback which enables to define audience’s attention, students’ engagement, and offers a more thorough understanding of the learning process. The paper presents the results of the experiments related to the assessment of distance and classroom learning and to the measurement of the consequences for the perceptive, cognitive and volition features of students after both types of work – with physical participation of a teacher and with autonomous studying material through computer. The CMS (Current Mental State) technique in the form of a hardware-software biofeedback complex was used to obtain an unbiased evaluation of the students’ emotional and mental state and well-being. The result of this quantitative research was a set of parameters displaying the current mental state of the participants and showing the students’ reaction to the learning material and environment. The practical significance of the CMS study is to deepen the understanding of the perceptive and cognitive processes through the different forms of educational activity.