Reviewerly/exHarmony huggingface dataset:
An Improved SVM with Earth Mover’s Distance Regularization and Its Application in Pattern Recognition

A support vector machine (SVM) aims to achieve an optimal hyperplane with a maximum interclass margin and has been widely utilized in pattern recognition. Traditionally, a SVM mainly considers the separability of boundary points (i.e., support vectors), while the underlying data structure information is commonly ignored. In this paper, an improved support vector machine with earth mover’s distance (EMD-SVM) is proposed. It can be regarded as an improved generalization of the standard SVM, and can automatically learn the distribution between the classes. To validate its performance, we discuss the necessity of the structural information of EMD-SVM in the linear and nonlinear cases, respectively. Experimental validation was designed and conducted in different application fields, which have shown its superior and robust performance.
Advancements in the Sine Cosine Algorithm

Abstract In the last few decades, the development and advancement of meta-heuristic algorithms have become the focus of the research community as these algorithms face various challenges like, balance between exploration and exploitation, tuning of parameters, getting trapped in local optima, and very slow convergence rate. Sine cosine algorithm (SCA) also faces similar kinds of challenges and sometimes fails to perform effectively in finding the global optimal solution. Sine and cosine are trigonometric operators with a 90 $$^\circ $$ phase shift from each other. The range of sine and cosine functions lies in the range $$[-1,1]$$ . Sine and cosine functions in the position update equation of SCA help solutions to perform search procedure. However, in some situations, SCA promotes similar solutions in the search space, which results in the loss of diversity in the population, and the search process is susceptible to trapping in the region of local optimum [1]. Motivated by these challenges, SCA has been modified to improve its capability and efficiency in several ways. Several strategies have been employed to alter the basic version of SCA [2], aiming to enhance its effectiveness and optimization capabilities. In this chapter, we will discuss about these modifications and strategies, which have been incorporated into the sine cosine algorithm (SCA) in past few years. Apart from this, we will briefly describe the applications of the modified versions of SCA.
Introduction

Abstract Decision-making is a difficult task, and it requires careful analysis of the underlying problem at hand. The presence of various alternative solutions makes the decision-making problem even more difficult as all the available solutions are not optimal. Since resources, time, and money are limited, or even sometimes scarce, the quest for optimal choices is of paramount importance for the welfare of the mankind. Optimization is a mathematical tool and an indispensable part of the decision-making process which assists in finding optimal (or near optimal) solutions from the set of available solutions.
Two-Dimensional Positioning with Machine Learning in Virtual and Real Environments

In this paper, a ball-on-plate control system driven only by a neural network agent is presented. Apart from reinforcement learning, no other control solution or support was applied. The implemented device, driven by two servo motors, learned by itself through thousands of iterations how to keep the ball in the center of the resistive sensor. We compared the real-world performance of agents trained in both a real-world and in a virtual environment. We also examined the efficacy of a virtually pre-trained agent fine-tuned in the real environment. The obtained results were evaluated and compared to see which approach makes a good basis for the implementation of a control task implemented purely with a neural network.
Stacked Siamese Generative Adversarial Nets: A Novel Way to Enlarge Image Dataset

Deep neural networks often need to be trained with a large number of samples in a dataset. When the training samples in a dataset are not enough, the performance of the model will degrade. The Generative Adversarial Network (GAN) is considered to be effective at generating samples, and thus, at expanding the datasets. Consequently, in this paper, we proposed a novel method, called the Stacked Siamese Generative Adversarial Network (SSGAN), for generating large-scale images with high quality. The SSGAN is made of a Color Mean Segmentation Encoder (CMS-Encoder) and several Siamese Generative Adversarial Networks (SGAN). The CMS-Encoder extracts features from images using a clustering-based method. Therefore, the CMS-Encoder does not need to be trained and its output has a high interpretability of human visuals. The proposed Siamese Generative Adversarial Network (SGAN) controls the category of generated samples while guaranteeing diversity by introducing a supervisor to the WGAN. The SSGAN progressively learns features in the feature pyramid. We compare the Fréchet Inception Distance (FID) of generated samples of the SSGAN with previous works on four datasets. The result shows that our method outperforms the previous works. In addition, we trained the SSGAN on the CelebA dataset, which consists of cropped images with a size of 128 × 128. The good visual effect further proves the outstanding performance of our method in generating large-scale images.
Multidimensional Domain Knowledge Framework for Poet Profiling

Authorship profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text’s stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution.
Complex Dynamical Sampling Mechanism for the Random Pulse Circulation Model and Its Application

The fast multi-pulse spectrum is a spectrum acquisition method that obtains an average pulse amplitude in a dynamic window, which improves the energy resolution by sharpening peaks in the acquired spectra, but produces the counting loss. Owing to the counting loss problem, a counting rate multiplication method based on uniform sampling, also called the pulse circulation method, is presented in this paper. Based on the theory of mathematical statistics and uniform sampling, this method adopted a dynamic sample pool to update the pulse amplitude sample in real time. Random numbers from the uniform distribution were sampled from the sample pool, and the sampled results were stored in the random pulse circulator so that the pulse amplitude information used for spectrum generation was uniformly expanded. In the experiment section, the obtained spectrum was analyzed to verify the multiplication effect of the pulse circulation method on the counting rate and the compensation effect of the fast multi-pulse spectrum algorithm on the counting rate loss. The results indicated that the characteristic peaks of each element in the X-ray spectrogram obtained by the pulse circulation method could realize counting rate multiplication uniformly, and the multiplication ratio of every element was approximately equal. This is of great significance for obtaining an accurate X-ray fluorescence spectrum.
Evaluating the usability of a cancer registry system using Cognitive Walkthrough, and assessing user agreement with its problems

Good design of cancer registry systems makes them easy to use, while poor design of their user interfaces leads to user dissatisfaction and resistance. The objective of this study was to evaluate the usability of a cancer registry system using Cognitive Walkthrough (CW) and to assess users' agreement with its usability problems.CW was used to evaluate the registry system. We developed a checklist to help evaluators speed up the evaluation process, a problems form to collect the usability issues identified by the evaluators, and a problems severity form to determine the severity of problems by the evaluators. The problems were classified into two categories according to the CW questions and the system tasks. The agreement of the users with the system problems was examined by an online questionnaire. Users' agreement with the problems was then analyzed using the Interclass Correlation Coefficient in the SPSS 22 (Statistical Package for Social Science).In this study, 114 problems were identified. In the categorization of problems based on the CW questions, 41% (n = 47) of the problems concerned the issue of "users do not know what to do at each stage of working with the system", 24% (n = 27) were classified as "users cannot link what they intend to do with system controls", and 22% (n = 25) were related to "user's lack of understanding of the system processes". Based on user tasks, about 36% (n = 41) of the problems were related to "removing patient duplication" and 33% (n = 38) were related to "registration of patient identification information". User agreement with the problems was high (CI 95% = 0.9 (0.96, 0.98)).System problems often originate from user ignorance about what to do at each stage of using the system. Also, half of the system problems concern a mismatch between what users want to do and the system controls, or a lack of understanding about what the system does at different stages. Therefore, to avoid user confusion, designers should use clues and guides on the screen for users, design controls consistent with the user model of thinking, and provide appropriate feedback after each user action to help users understand what the system is doing. The high agreement of users with the problems showed that in the absence of users system designers can use CW to identify the problems that users face in the real environment.
Periodic Behavioral Routine Discovery Based on Implicit Spatial Correlations for Smart Home

As the degree of elders’ social activity and self-care ability depreciates, the potential risk for elderly people who live independently increases. The development of assistive services such as smart homes could likely provide them with a safer living environment. These systems collect sensor data to monitor residents’ daily activities and provide assistance services accordingly. In order to do so, a smart home must understand its residents’ daily activities and identify their periodic behavioral daily routine accordingly. However, existing solutions mainly focus on the temporal feature of daily activities and require prior labeling of where sensors are geographically deployed. In this study, we extract implicit spatial information from hidden correlations between sensors deployed in the environment and present a concept of virtual locations that establishes an abstract spatial representation of the physical living space so that prior labeling of the actual location of the sensors is not required. To demonstrate the viability of this concept, an unsupervised periodic behavioral routine discovery method that does not require any predefined location-specific sensor data for a smart home environment is proposed. The experimental results show that with the help of virtual location, the proposed method achieves high accuracy in activity discovery and significantly reduces the computation time required to complete the task relative to a system without virtual location. Furthermore, the result of simulated anomaly detection also shows that the periodic behavioral routine discovery system is more tolerant to differences in the way routines are performed.
Economic feasibility studies in Keude Seumot irrigation project

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Zakia, D. Febrianti, M. Safriani, I. Y. Salena; Economic feasibility studies in Keude Seumot irrigation project. AIP Conference Proceedings 30 January 2023; 2613 (1): 030001. https://doi.org/10.1063/5.0120376 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Global fraud prevention leveraging artificial and machine learning technologies

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation G. Jaculine Priya, S. Saradha; Global fraud prevention leveraging artificial and machine learning technologies. AIP Conference Proceedings 30 January 2023; 2523 (1): 020174. https://doi.org/10.1063/5.0109860 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A novel framework for inspection management system using cloud computing

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Viswanathan Chakravarthi, Arumugam Santhana Santhanavelu, Karthikeyan Palaniappan, Vijayalakshmi Kuppan; A novel framework for inspection management system using cloud computing. AIP Conference Proceedings 30 January 2023; 2523 (1): 020150. https://doi.org/10.1063/5.0114458 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Email notifier using named entity extraction

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation K. Anand, M. Vijay Anand, R. Saravanan; Email notifier using named entity extraction. AIP Conference Proceedings 30 January 2023; 2523 (1): 020137. https://doi.org/10.1063/5.0109838 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Social media impact on cyber crime during Covid-19

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Bhagyalaxmi Sangle, Apoorva Kulkarni; Social media impact on cyber crime during Covid-19. AIP Conference Proceedings 30 January 2023; 2523 (1): 020120. https://doi.org/10.1063/5.0112571 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Anatomical pattern identification using geographic adaptive components classification

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation R. Mohana Priya, R. Karthikeyan, Varun J. P., Roshin Chandra, Anjas M. P.; Anatomical pattern identification using geographic adaptive components classification. AIP Conference Proceedings 30 January 2023; 2523 (1): 020162. https://doi.org/10.1063/5.0112277 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Rating prediction and email query processing

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Saravanan Raju, M. Vijay Anand, N. Bharathiraja, K. Gunasekaran; Rating prediction and email query processing. AIP Conference Proceedings 30 January 2023; 2523 (1): 020099. https://doi.org/10.1063/5.0111105 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Study of various nano particle additives and its impact in CI engine

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation G. Antony Casmir Jayaseelan, M. Thiruchitrambalam, Judeson Joseph, Bince Mathew, Subramani; Study of various nano particle additives and its impact in CI engine. AIP Conference Proceedings 30 January 2023; 2523 (1): 020048. https://doi.org/10.1063/5.0111031 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A blockchain based virtual machine detection system app market

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Karthikeyan Palaniappan, V. Anantha Krishna, M. Ramasubramanian, T. K. S. Rathish Babu; A blockchain based virtual machine detection system app market. AIP Conference Proceedings 30 January 2023; 2523 (1): 020013. https://doi.org/10.1063/5.0110254 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Modern technologies for technical follow-up of documents

The relevance of the study of modern document research technologies is determined by the growing pace of innovative and technical progress, which produces high-tech achievements that are used not only for rational purposes, but also for the purpose of high-quality falsification of documents to achieve illegitimate and selfish goals. The purpose of this article was to study the latest methods of document forgery and existing technologies for their detection with the aim of assessing the level of development of modern expert research in this field. In order to achieve the above-mentioned result, a complex methodological approach was used in the study of this topic, which consists in the consistent application of methods of scientific knowledge, analysis of the acquired knowledge, their systematization and generalization with the aim of building a system model of the modern process of technical documentation research, as well as the application of comparison and evaluation methods in order to identify weaknesses in the use of existing technologies, the method of scientific forecasting of development prospects in this field. As a result of the study, the general theoretical, methodological and procedural provisions governing the document examination process were highlighted, the known and most used methods of falsification of documents were defined, the methodology for establishing the characteristic features of various types of forgeries and the technical means used for their detection were studied, and the available in this field were outlined problems and prospects for overcoming them. The knowledge gained in this way logically contributes to the formation of fundamental information baggage, which should be used in expert work to achieve the most accurate and justified results. The conducted research has a high applied value, as it focuses on the most optimal and effective modern examination technologies in the field of document circulation, highlighting their characteristic features and advantages, emphasizing the growing need to use the latest technological advances in the examination of documents
Template Matching for Marathi Handwritten Compound Character Recognition

In the research of handwritten character recognition one difficult task is to recognize the handwritten compound characters of Marathi. This work focuses on an analysis of Template Matching approach that can be used to recognize Marathi handwritten compound characters. The template matching provides a more efficient matching, while training samples are fewer. In this paper, we assess the performance of the template matching algorithm based on correlation, normalized cross-correlation, the sum of squared differences.
A Quantitative Model of the Multisubject Quality Responsibility of Construction Projects Based on an IPSO

In order to solve the problem of the quantitative division of multisubject quality responsibility in construction project quality disputes, this article proposes a quantitative model of multisubject quality responsibility division in construction projects based on an improved particle swarm optimization (IPSO). First, this article proposes a set of classification guidelines for quality risk behaviors based on the theory of organizational behavior. Through these, the interconnections between different types of risk behaviors and quality defects were explored. Following this, this article explored potential laws among 84 practical judicial cases from China using the IPSO. The category coefficients of the three types of quality risk behaviors, namely, technical defects, management violations, and irregularities, were obtained in this analysis. This article also deduced the mathematical expression of the division of engineering quality responsibility using fuzzy mathematical theory and established a multisubject quality responsibility quantitative model. It was then simulated and applied in four practical judicial cases. The simulation results revealed that the multisubject quality responsibility quantitative model based on quality risk behavior has good applicability.
Ditto: End-to-End Application Cloning for Networked Cloud Services

The lack of representative, publicly-available cloud services has been a recurring problem in the architecture and systems communities. While open-source benchmarks exist, they do not capture the full complexity of cloud services. Application cloning is a promising way to address this, however, prior work is limited to CPU-/cache-centric, single-node services, operating at user level.