Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence in Teaching Reform of New Economics

research-article Application of Artificial Intelligence in Teaching Reform of New Economics Share on Authors: Shifeng Wu Hebei University of Economics and Business, China Hebei University of Economics and Business, ChinaSearch about this author , Xiaohong Li Hebei University of Economics and Business, China Hebei University of Economics and Business, ChinaSearch about this author , Shuo Zhu Hebei University of Economics and Business, China Hebei University of Economics and Business, ChinaSearch about this author , Weining Shen Hebei University of Economics and Business, China Hebei University of Economics and Business, ChinaSearch about this author , Jingnan Wu Hebei University of Economics and Business, China Hebei University of Economics and Business, ChinaSearch about this author , Yujie Sun Hebei University of Economics and Business, China Hebei University of Economics and Business, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 572–577https://doi.org/10.1145/3495018.3495121Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Testing and Targeting a Mobile Application for Creatives

Will another social media application that aims to connect creators have a chance at success in our crowded digital landscape? We are putting that question to the test, by working on our idea of helping anyone find specific workspaces or spots that help them do their craft. Within a mobile application, letting users quickly find and post such spaces in their community while being able to connect with other users behind those posts, is what our ideas (and subsequent research) led us to. From target market and user experience interviews we have conducted for nearly a year, we believe that the need for such a mobile application among creatives justifies the work that must go into creating it.
Unusual Expression Detection in Multiple Scenarios Based on One-Class SVM

Human facial expressions are often abnormal when they have a sudden illness. Time will be saved for taking emergency measures if we can catch the anomalies in time. But there is not enough abnormal data for the model to learn. It is difficult to evaluate unknown anomalies even if a certain amount of abnormal data is obtained. One-Class Support Vector Machine(One-Class SVM) can solve this problem by calculating the boundary of normal/abnormal data without relying on any independent abnormal data. This paper analyzed the anomalies of facial expression changes which are presented in multi-scene videos recorded by camera. The whole process is divided into three stages: face region segmentation, feature extraction and anomaly classification. We used multi-task convolutional neural network (MTCNN) to detect and align faces, and used FaceNet to extract facial features. In the process of anomaly classification, the performance of One-Class SVM under different kernels of Linear, Polynomial, RBF, and Sigmoid was recorded.
Dynamic Parameter Adaptation Based on Using Interval Type-2 Fuzzy Logic in Bio-inspired Optimization Methods

In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA). Each of these methods is enhanced with a methodology for parameter adaptation using interval type-2 fuzzy logic, where based on some metrics about the algorithm, like the percentage of iterations elapsed or the diversity of the population, we aim at controlling their behavior and therefore control their abilities to perform a global or a local search. To test these methods two benchmark control problems were used in which a fuzzy controller is optimized to minimize the error in the simulation with nonlinear complex plants.
Managing Seven Dimensions of ICT4D Projects to Address Project Challenges

A large number of ICT for development (ICT4D) projects experience a variety of challenges, especially when conducting field research with disadvantaged communities in developing nations. Using cluster analysis, this chapter identifies the six most common factors associated with a majority of ICT4D project challenges, and depicts the inter-relationship between these factors and over 100 distinct challenges reported by existing literature. In addition, based on the secondary analysis of 380 research artifacts in the ICT4D literature, this chapter proposes ways to manage the scope, time, costs, quality, human resources, communication, and risks for addressing ICT4D project challenges. Findings inform researchers of best practices for conducting ICT4D research with disadvantaged communities in developing nations.
Flow pattern identification of oil-water two-phase flow based on multi-feature convolutional neural network

In order to extract the multivariate characteristics of oil-water two-phase flow accurately and improve the accuracy of flow pattern identification, in this paper, we combine complex network and multivariate fluid feature to propose a multi-feature convolutional neural network. Firstly, we construct a 1D-CNN model for network input, and discuss the influence of each parameter setting on classification performance. Then the original signal (X), the energy signal (ES), the power spectrum signal (P) and the fused signal (M) are selected as the input of the 1D-CNN model respectively. The experimental results show that when we select fusion feature information as network input, the performance of two-phase flow pattern classification is the best. Finally, through multiple sets of experiments to verity the recognition performance of fusion feature information, it can reach 97.6% accuracy on the existing test set. Therefore, the 1D-CNN method based on deep learning theory provides a new idea for flow pattern identification.
Classification of Erythemato-squamous diseases using Artificial Neural Network and Genetic algorithm

This paper introduces a hybrid model using artificial neural network (ANN) and genetic algorithm (GA) to develop an efficient classification technique for classification of different categories of Erythemato-squamous diseases. Neural network has been extensively used in many applications like classification, regression, web mining, system identification and pattern recognition. Weight optimization in neural network has been a matter of concern for researchers in the field of soft computing. In this paper the weights of ANN are optimized with GA. The proposed hybrid model is applied on the Erythemato-squamous dataset taken from UCI machine learning repository. The dataset contains six different categories: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris of Erythemato-squamous diseases. The main aim of this paper is to determine the type of Eryhemato-Squamous disease using the hybrid model. The performance of the hybrid model is evaluated using statistical measures like accuracy, specificity and sensitivity. The accuracy of the proposed model is found to be 99.34% on test dataset. The experimental result shows the effectiveness of the hybrid model in classification of Erythematosquamous diseases.
KnitKit

In this work, we introduce KnitKit, a flexible and customizable system for the computational design and production of functional, multi-material, and three-dimensional knitted textiles. Our system greatly simplifies the knitting of 3D objects with complex, varying patterns that use multiple yarns and stitch patterns by separating the high-level design specification in terms of geometry, stitch patterns, materials or colors from the low-level, machine-specific knitting instruction generation. Starting from a triangular 3D mesh and a 2D texture that specifies knitting patterns on top of the geometry, our system generates the required machine instructions in three major steps. First, the input is processed and the KnitNet data structure is generated. This graph structure serves as an abstract interface between the high-level geometric and knitting configuration and the low-level, machine-specific knitting instructions. Second, a graph rewriting procedure is applied on the KnitNet that produces a sequence of abstract machine actions. Finally, the low-level machine instructions are generated by adapting those abstract actions to a specific machine context. We showcase the potential of this computational approach by designing and fabricating a variety of objects with complex geometries, multiple yarns, and multiple stitch patterns.
Local Search

This chapter introduces several local search methods and their R implementations, namely hill climbing (pure and steepest ascent and stochastic variants), simulated annealing, and tabu search. Then, the chapter demonstrates how local search methods can be compared. Finally, it shows how modern optimization methods (including local search ones) can be tuned in terms of their internal parameters by using an iterated racing.
Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.
A Depth Estimation Method for Ground Moving Platforms via Detecting Region of Interest

Depth estimation is an essential part of decentralized coordinated control of multiple moving platforms, and many studies on depth reconstruction use machine learning methods to obtain depth information directly. However, the obtained target depth value has high uncertainty, which will lead to errors. This paper proposes a depth estimation algorithm for ground moving platforms, which can quickly estimate the relative position of its neighbor. The depth estimation algorithm consists of two parts. The detection part uses a deep convolutional neural network to extract the region of interest (ROI) while the depth recovery part estimates the depth value of the points obtaining from the feature extractor, which only processes the features in ROI. Then we feed 3D points into a depth optimizer to remove the outliers. Finally, the experiment results are presented to verify the effectiveness of our depth estimation algorithm.
The Effect of the application of the Cooperative Learning Model Type Snowball Throwing Against an increase in the activity of learning to the learners on subjects Science in grade VII Inclusive SMPN 23 Padang

The research was distributed by the low activity of learning to learners in an inclusive classroom while learning the IPA take place where student activity still centered on teachers so that students not actively in learning. This research aims to find out how big the influence of cooperative learning model type Snowball Throwing against learning activities learners in learning the IPA in the VII inclusive classroom junior high school 23 Padang. This type of research is the Quasy alphabets experiment using Control-Group design Only. This research was carried out in class VII2 as class experiments with the number of learners 32 people (with 3 learners ABK) and VII3 class as the class of the control to the number of learners 32 people (with 2 of HER students). The instruments used in the research in the form of the now learners learning activities that contains items of the statement drawn up in accordance with a predetermined indicators. Data analysis technique used is comparing the difference of two average learners with learning activities begins conducting a test of its homogeneity and normality, so that in the end done with test t (t-test).The average value of learning activities students class experiments is higher compared to the average value of learning activities learners class control. The average value of experimental class 76.41 and average value of kotrol class 70.08. Hypothesis test results data study activity learners class Sample thitung 2.67 and ttabel 1.67 its mean thitung &gt; ttabel means then the hypothesis is accepted. This means that applying cooperative learning model type Snowball Throwing a positive effect against an increase in the activity of learning to the learners.
Introduction

This chapter provides a global overview of the topic treated in this book. In particular, we shall firstly contextualize the work in the wide panorama of aerial robotics, and more precisely, of aerial physical interaction. Afterword, we will focus our attention to the topic of this work, i.e., tethered aerial vehicles. We shall cover the scientific and practical motivations that brought us analyzing these kind of systems, listing the several objectives and challenges at which we aim in this work. We finally provide a reader’s guide describing in a detailed way the organization and content of the book.
Vulnerability Assessment of Blockchain Using Confrontation Deduction

blockchain technology is one of the strategic emerging information technologies. It integrates a variety of technologies such as cryptography, peer-to-peer network, consensus algorithm and smart contract, and has been gradually applied in various fields. The contradiction between the rapid development of blockchain industry and the incompleteness of evaluation system and technical means has become increasingly prominent. The vulnerability of blockchain system is an important part of blockchain evaluation system. It is of great significance for the development of blockchain technology and large-scale application to fully study the vulnerability of blockchain system and conduct quantitative evaluation or real-time monitoring. This paper systematically analyzes the vulnerability of blockchain, and puts forward the technical indicators to evaluate the vulnerability of blockchain. Based on the vulnerability technical index of blockchain consensus algorithm and industrial blockchain consensus algorithm PBFT, a consensus algorithm confrontation deduction model is designed, and the vulnerability index of blockchain system using PBFT is experimentally simulated and evaluated.
Linear Codes

There is a lack of structure in the block codes we have considered in the first few chapters. Either we chose the code entirely at random, as in the proof of Theorem 1.12, or we built the code using the greedy algorithm, as in the proof of the Gilbert–Varshamov bound, Theorem 3.7. In this chapter, we introduce some algebraic structure to the block codes by restricting our attention to linear codes, codes whose codewords are the vectors of a subspace of a vector space over a finite field. Linear codes have the immediate advantage of being fast to encode. We shall also consider a decoding algorithm for this broad class of block codes. We shall prove the Griesmer bound, a bound which applies only to linear codes and show how certain linear codes can be used to construct combinatorial designs.
Computer and Network Forensics

This chapter discusses what constitutes digital evidence, the collection and analysis of digital evidence, the chain of custody, the writing of the report, and the possible appearance in court as an expert witness. There is an in-depth discussion of the digital evidence acquisition rule of thumb and the candidates for evidence extraction. On preserving of evidence, extra care must be taken in preserving digital evidence since digital evidence is very fluid, in that it can disappear or change so fast. The chapter discusses the various techniques to preserve evidence and what needs to be done if evidence is to be moved. Emphasis is given on the importance of careful analysis of digital evidence noting that this process is the most difficult and most opinionated. It is also the most important, most time-consuming, and painstakingly slow and should be thorough so that it can support or reject a fact based on identified patterns of activities, file signature anomalies, unusual behaviors, file transfers, and several other trends in the evidence. Final issues discussed in this chapter include the process of report writing and presentation and also the ethical implications and responsibilities of both the investigator and the lawyer.
Introduction

Nowadays, objective image quality assessment (IQA) plays an important role for performance evaluation of image/video processing systems. Over the past few years, a variety of IQA methods have been introduced and they can be divided into three categories: full-reference IQA, reduced-reference IQA and no-reference IQA. All of these methods are clarified in detail in this book. In this chapter, the overall structure of the book is explained briefly and a summary of each of the following chapters is also provided.
Secret Communication Techniques

This chapter introduces the reader to cryptography, steganography, watermarking, and quality parameters of image steganography techniques. Cryptography is a technique for secret communication. Steganography is a technique for secret and unnoticeable communication. The watermarking techniques hide watermarks inside the digital media. There are four types of steganography techniques: (1) image steganography, (2) audio steganography, (3) video steganography, and (4) text steganography. The quality of image steganographic algorithms can be measured by three parameters like (1) hiding capacity, (2) distortion measure, and (3) security check.
MonkeyPosekit: Automated Markerless 2D Pose Estimation of Monkey

Video-based bone recognition is becoming a crucial tool for both clinical and neuroscientific research of fine and complicated movements. But it is very time- consuming and there is lack of accuracy to extract specific aspects of behaviors, such as hand shaking and other fine motor skills, especially for automated analysis in non-human primate studies. The OpenMonkeyStudio as a 3D toolbox is available to estimate the pose of an unmarked monkey. However, there is still a lack of the 2D method, since most of laboratories are using a single front camera to obtain 2D videos due to financial constraints. Here, we build a bone- recognition auxiliary tool called MonkeyPosekit, which is based on deep learning for automatically capturing the stream information from 2D videos without the need for external hardware assistance. The MonkeyPosekit is able to identify the monkey’s activity space to track 13 bone joint points for the behavioral testing. Futhermore, we propose a novel approach of data augmentation called CageAUG to overcome the occlusion issues in this study. Equipped with the CageAUG augmentation, the accuracy has reached 98.8% in Open Monkey dataset by using the High-Resolution network.
475K Dataset

A lot of progress has been made in recent times on optical character recognition (OCR) like OpenCV, Tesseract OCR, Amazon Web Services Textract, Google Cloud Platform OCR, Microsoft Azure OCR to name a few, which are some platforms/libraries to convert image to text and image datasets like National Institute of Standards and Technology (NIST) and Google Street View House Number are available for printed text, handwritten text, and text from natural scenes. However, there is limited data available for printed text with lower case, upper case, digits, and special characters with varied formatting (bold, italic, underline, and strikethrough). In this paper, we are presenting dataset “475K Chars” that has 400+ synthesized fonts via computer with various combinations of text formatting like bold, italic, etc. Each image in the presented dataset is unique, and we have presented more than 475,000 images in our dataset. The dataset is open source and can be used to train deep learning neural networks, and images in this dataset have different text formatting with text format label for each character. Most of the characters have 16 different combinations of images of a font. To the best of our knowledge, this is the first attempt to present such a wide dataset publicly for scientific community. This dataset is publicly available and has been moderately tested for accuracy.
Web-Scale Discovery Service in Academic Library Environment

Web-Scale Discovery service is the new buzzword of library service in the current environment. It refers to a group of products that index a wide number of resources in various formats and allows users to search for content in the physical collection, print and electronic journals collections, and other resources from a single search box. It has the capacity to connect the researchers more easily with the vast information repository of the library including both locally and hosted content i.e. physical holdings, digital collection, and local institutional repositories. This article defines the concept of Web-Scale Discovery Service and its features. The author tries to illustrate the evaluation process for implementing Web-Scale Discovery Service. He also outlines some of important steps involved while execution.
Design of A Differential Wheeled Trolley Based on ROS Robot Operating System

From the perspective of the motion control of the mobile robot, this article realizes the development of a mobile robot based on the ROS Melodic version of the operating system under the Ubuntu 18.04 system, which can finally realize the functions of map construction and autonomous navigation. The mobile robot adopts a differential wheel structure, with Raspberry Pi as the host computer control board and STM32 as the bottom driver board to build a hardware platform. Raspberry Pi communicates with STM32 to obtain current speed information, heading angle information, and realize path planning, autonomous navigation and obstacle avoidance functions. The surrounding environment information is collected by lidar, SLAM map creation is carried out, and global path planning and local path planning are realized through the navigation function package.