Reviewerly/exHarmony huggingface dataset:
The attribute classification model in intelligent question answering system based on domain knowledge graph

Authors:
raw_author_name: Yiming Sun
raw_affiliation_string: Changchun Univ. of Science and Technology (China)
raw_author_name: Dan Zhang
raw_affiliation_string: Changchun Univ. of Science and Technology (China)
raw_author_name: Chunsheng Xu
raw_affiliation_string: Changchun Univ. of Science and Technology (China)

Abstract: The knowledge graph-based question answering system aims to find the connection between the internal knowledge behind the problem and the known knowledge base triplet. This paper proposes a framework for knowledge graph question answering (ZDNN-KGQA), which divides intelligent question answering based on knowledge graphs into four steps: named entity recognition, entity disambiguation, attribute classification, and answer selection. In the named entity recognition step, this paper proposes a BERT-BiLSTM-CRF model that combines an attention mechanism to identify entities in question sentences. In the entity disambiguation step, this paper utilizes the BERT-Softmax model to extract the accurate entities from the candidate entity set. In the attribute classification step, it is converted into a problem of text similarity. This paper proposes the use of BERT-Softmax and Word2vec models to extract attributes intext problems, effectively improving entity recognition performance and question semantic parsing performance. The average F1 value of this system on the test dataset provided by the NLPCC-ICCPOL 2016 KBQA task is 0.8974, which is close to the optimal performance level.
Pengembangan Sistem Informasi Manajemen Kepegawaian pada Yayasan Pondok Pesantren Nurul Huda Sukaraja

Authors:
raw_author_name: Thoha Firdaus
raw_author_name: Widayanti Widayanti

Abstract: Along with advances in information technology, the Nurul Huda Islamic Boarding School Foundation still relies on managing personnel data manually without adopting a personnel management information system. This research aims to design a personnel information system that includes employee data management, including rank, leave applications, attendance, further studies, and monitoring of employee performance. In this research, the methodology used is Extreme, which is used to model process flow. In contrast, Data Flow Diagrams (DFD) are used to model data flow in a system, helping to clearly describe processes, data, and interactions between system components. The system development method used is extreme programming. Then, the system will be tested using the Black Box Testing method. The results show that the system is 100% valid and 100% compatible.
On logistic regression versus support vectors machine using vaccination dataset

Authors:
raw_author_name: Olumide S. Adesina
raw_author_name: Adedayo F. Adedotuun
raw_author_name: Kayode S. Adekeye
raw_author_name: Ogbu F. Imaga

Abstract: The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.
AUGMENTED REALITY ADVERTISEMENTS WITHIN THE REALM OF PHYGITAL MARKETING: CASE STUDIES OF ADVERTISING EXAMPLES

Authors:
raw_author_name: Sinem EYİCE BASEV
raw_affiliation_string: İSTANBUL GELİŞİM ÜNİVERSİTESİ

Abstract: Technology has evolved to be a concept of constant development and innovation, significantly impacting both individual and societal life. It has become a fundamental building block that influences personal inclinations, choices, and decisions, and is already present in many phases of human life. Individuals react to technological advancements they encounter in their daily lives, as they are not just consumers in the current era, but also producers. The responses to technological advancements extend to influencing purchasing activities and shopping behaviors of individuals. This study has been developed to analyze the role and usage of Augmented Reality technology, one of these technological advancements, in the transforming field of marketing and specifically in advertising. The evolution of the marketing concept has brought about new approaches, as is the case in almost every developing field. Among these new approaches, the influence of digitalization is acknowledged. Within these definitions, the concept of phygital marketing is included. The phenomenon of transferring real-life actions to a virtual (digital) environment has given way to the transfer of virtual (digital) environments into the physical, real-life aspect. The term "phygital", a combination of physical and digital, can be summarized as the reflection of digital phenomena, especially in the physical dimension, into everyday life. One of the application areas where the impact of phygital concept in marketing is evident is the use of Augmented Reality technology in advertisements, which defines the scope of this study
Autonomous collision avoidance method based on improved artificial potential field in the traffic separation scheme waters

Authors:
raw_author_name: Wei Liu
raw_affiliation_string: Dalian Maritime Univ. (China)

Abstract: In order to realize the autonomous navigation of ships in the complex waters under the traffic separation system (TSS), taking the Chengshanjiao TSS waters as an example, an improved autonomous collision avoidance algorithm is proposed based on the artificial potential field (APF) method. By establishing five kinds of potential fields and their force functions, including the gravitational function of the goal point, the repulsion function of the target ships (TS), the gravitational function of the centerline of the traffic lane, the velocity function of the ship, and the boundary repulsion function of the navigation lane, finally the resultant force of them is calculated to guide the navigation direction of the ship, during the navigation process in this TSS water. Multiple groups of comparative experiments demonstrated that under the improved algorithm, the ship could avoid collisions with TS in four typical scenarios, according to the requirements of the 1972 International Regulations for Preventing Collisions at Sea (COLREGS) and good seamanship practice, simultaneously the ship could proceed in the navigable lane and keep safe distance with the boundaries and separation zones. The method proposed in this paper is expected to provide basic theoretical basis and technical support for the safe navigation of autonomous ships in TSS waters.
A study on the correlation between the English score of collegeentrance Examination and the score of the first CET-4

Authors:
raw_author_name: Zixuan Xu

Abstract: English is the most widely used language in the world. Nowadays, the world is showing a trend of globalization. As a bridge connecting us with the world, the importance of English is self-evident. The study of high school is to let us master the ability to use English communication and to strengthen our ability to use English further when we enter the university. The college entrance examination is a standard reference examination for high school students in China after completing the basic learning stage. Its purpose is to comprehensively check whether the students who have completed the high school courses have met the requirements stipulated in the syllabus. After years of practice, it has become one of the most authoritative examinations in China. [1] In recent years, many colleges and universities have paid great attention to the CET-4 test, and most have linked the CET-4 test with a bachelor’s degree. Students cannot get the CET-4 certificate in universities, and the schools will not award bachelor’s degrees to the students. China’s College entrance examination and CET-4, as two of the most objective level examinations in China, have strong reliability and validity in the field of English learning and have a certain continuity over the years, which can objectively and honestly reflect the English learning level of students from high school to college. [2] The English score of the college entrance examination can also reflect the English level of first-year college students when they first enter school. While CET-4 represents a student’s English level at the university level.
Scalable measurement error mitigation via iterative bayesian unfolding

Authors:
raw_author_name: Bibek Pokharel
raw_affiliation_string: Center for Quantum Information Science & Technology, University of Southern California, Los Angeles, California 90089, USA; Department of Physics and Astronomy, University of Southern California, Los Angeles, California, 90089, USA; IBM Quantum, IBM Research – Almaden, San Jose, California 95120, USA
raw_author_name: Siddarth Srinivasan
raw_affiliation_string: Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA
raw_author_name: Gregory Quiroz
raw_affiliation_string: Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland 20723, USA; William H. Miller III Department of Physics & Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA
raw_author_name: Byron Boots
raw_affiliation_string: Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, USA

Abstract: Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead.
Magic behaviors in non-Markovian environment

Authors:
raw_author_name: Zhiming Huang
raw_affiliation_string: School of Economics and Management, Wuyi University, Jiangmen 529020, P. R. China
raw_author_name: Xiangfu Zou
raw_affiliation_string: School of Mathematics and Computational Science, Wuyi University, Jiangmen 529020, P. R. China
raw_author_name: Zhenbang Rong
raw_affiliation_string: Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, P. R. China
raw_author_name: Zhimin He
raw_affiliation_string: School of Electronic and Information Engineering, Foshan University, Foshan 528000, P. R. China
raw_author_name: Lianghui Zhao
raw_affiliation_string: School of Economics and Management, Wuyi University, Jiangmen 529020, P. R. China
raw_author_name: Yiyong Ye
raw_affiliation_string: School of Economics and Management, Wuyi University, Jiangmen 529020, P. R. China
raw_author_name: Haozhen Situ
raw_affiliation_string: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, P. R. China

Abstract: Magic as an essential resource in fault-tolerant quantum computation may shed insight into quantum information processing of open quantum system. In this work, we study the dynamics of magic for atom system immersed in non-Markovian reservoir. It is found that magic decay process can be delayed and adjusted by the strength of classical driving field and non-Markovian memory effect. In addition, it is shown that the magic state is less susceptible to decay the process of system-environment coupling than stabilizer state, and for two atoms coupling to two independent reservoirs, magic can be generated. The results may be beneficial for theory and experiment of quantum computation and information in open quantum system.
A Proposal of Naturalistic Software Development Method

Authors:
raw_author_name: Lizbeth Alejandra Hernández-González
raw_author_name: Ulises Juárez-Martínez
raw_author_name: Jezreel Mejía
raw_author_name: Alberto Aguilar-Laserre

Abstract: Naturalistic programming purports to include natural language elements in programming languages to increase software expressiveness. Even though natural language is inherently ambiguous, it is richer and thus more expressive than any artificial language. Currently, the Naturalistic Programming Paradigm (NPP) is supported by its conceptual model and three general-purpose naturalistic programming languages that can generate executable binary code. Nevertheless, to date, no research efforts have been concentrated on applying the NPP within a software development process. To address this gap, in this article, we propose a naturalistic software development method to test the advantages of the NPP. The method focuses on the analysis and design stages of the software development process and seeks to contribute to closing the gap between the problem and the solution domains. We also present an example of an implementation using Cal-4700, a naturalistic programming language, showing the differences in expressiveness of programming with a traditional programming language, like Python.&amp;nbsp;
Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

Authors:
raw_author_name: None L V Narasimha Prasad
raw_author_name: None Manisha Guduri
raw_author_name: None Ganapati Panda

Abstract: Due to the recent developments in communications technology, cognitive computations have been used in smart healthcare techniques that can combine massive medical data, Artificial intelligence, federated learning, Bio-inspired computation, and the Internet of Medical Things. It has helped in knowledge sharing and scaling ability between patients, doctors, and clinics for effective treatment of patients. Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results. Since the subject’s speech can be remotely recorded and submitted for further examination, it offers a quick, economical, dependable, and non-invasive prospective alternative detection approach. However, the two main requirements of this are higher accuracy and lower computational complexity and, in many cases, these two requirements do not correlate with each other. This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy. A Cascaded Perceptual functional link artificial neural network (PFLANN) is used to capture the non-linearity in the data for better classification performance with low computational complexity. The proposed model is being tested for multiple respiratory diseases and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.
Optimasi Centroid Awal Algoritma K-Medoids Menggunakan Particle Swarm Optimization Untuk Segmentasi Customer

Authors:
raw_author_name: Danang Bagus Wijaya
raw_author_name: Edi Noersasongko
raw_author_name: Purwanto Purwanto

Abstract: Customer segmentation is an important strategy in a company, it affects good customer relationships which will result in increased profits. Grouping customers in data mining can use several algorithms, but K-Medoids is the right choice because it can reduce noise and outlier sensitivity. However, the selection of cluster centers is still random and has an effect on the results of clustering, so it is necessary to improve the k-medoids algorithm so that the resulting cluster value can be optimal. Particle Swarm Optimization is an optimization algorithm that is often used and has been proven to improve the results of a clustering. In this case, optimization using Particle Swarm Optimization (PSO) in the selection of the initial cluster center needs to be applied to the k-medoids algorithm so that the results of the cluster can be optimal. The results of the study showed the Davies-Bouldin Index (DBI) value for K-Medoids K 2 = 0.379, K 3 = 0.283, and K 4 = 0.593, while the DBI value PSO + K-Medoids K 2 = 0.088, K 3 = 0.226, and K4 = 0.363. The DBI value shows that PSO optimization on K-Medoids to determine the initial centroid is proven to improve the results of clustering than standard K-Medoids.
Software security: Threats, solutions and challenges

Authors:
raw_author_name: Himani Mittal

Abstract: Software security is of great concern as computers have entered almost all walks of life and people at large have become dependent on technology for not only entertainment and communication but for performing tasks involving money and a lot of stake. Software security not only involves securing the software but also user data and communication media. This paper states the several types of security threats that exist since the time networking has evolved, namely, malware, Trojans, viruses, denial of service attacks, and many more. This paper reviews several measures to address these threats. It includes logging, anti-malware, network security methods, and encryption methods. It has been identified that a lot of work has been done to deal with security threats, and it is not only limited to the protection of software but also extends to the protection of data and networks. The existing methods make extensive use of artificial intelligence, and it is identified that there is a need to develop a model that is able to identify known as well as unknown threats. There is a huge scope for research in this area.
Inclusive Virtual Reality Learning: Review and 'Best-Fit' Framework for Universal Learning

Authors:
raw_author_name: Raphael Zender

Abstract: The rise of Virtual Reality (VR) in educational contexts has highlighted the need to design Virtual Reality Learning Applications (VRLAs) that prioritize inclusivity, accommodating a spectrum of learner needs. Despite the surging interest, there is a noticeable gap in research that delves into the specifics of creating VRLAs that are rooted in inclusive educational theory. This research sought to extract insights and recommendations for the development of VRLAs tailored for diverse student populations. The intention was to scrutinize research focused on the inclusive design elements of VRLAs, leading to the establishment of preliminary Inclusion Guidelines for VR Learning (IGVRL). Adopting the "best-fit" framework synthesis technique, the research anchored its findings in the Universal Design for Learning (UDL) framework. UDL was developed to mold learning experiences to meet the requirements of heterogenous learners. Using UDL as a coding framework, a comprehensive literature review was undertaken, adhering to the SPIDER search strategy. The review of literature revealed distinct design recommendations that facilitate inclusive learning within VRLAs. Information was systematically categorized based on UDL's nine classifications and subsequently distilled into the preliminary IGVRL. It's pertinent to note that these guidelines, while offering a foundational perspective, necessitate further in-depth evaluations for validation. The analytical process brought to the fore several themes that UDL did not adequately encompass, such as the nuances of embodied learning, the focus on VR contents and their immersive properties, and the pivotal role of collaboration and cooperation in VRLAs. These insights underscore the further need for research in these areas. Although some facets of VR accessibility were discussed, a deeper exploration into this domain was identified as crucial, reiterating the importance of accessibility in underpinning inclusive education. The research underscores the potential of VRLAs in promoting inclusivity within educational settings and introduces the preliminary IGVRL for VRLA design, specifically targeting K-12 contexts. This paper emphasizes the continuum of research required to refine and validate these guidelines, ensuring their applicability and efficacy in diverse educational scenarios.
An Improved Variational-Based Model for Denoising and Segmentation of Vector-Valued Images

Authors:
raw_author_name: None Nurhuda Ismail
raw_author_name: None Abdul Kadir Jumaat

Abstract: Preserving important features such as edges is one of the main concerns in models for denoising and segmenting vector-valued (colour) images. The Rudin-Osher-Fatemi (ROF) model is a well-known variational-based image denoising model that is capable of reducing image noise while preserving image edges. However, the ROF model is not formulated for denoising colour images and is less effective in preserving corners and weak edges. On the other hand, a variational-based selective segmentation model for colour images called the selective distance segmentation (DSS2) model has recently been proposed, which can effectively partition or extract a specific object in an image. However, the DSS2 model has problems in segmenting colour images with noise, which may result in poor segmentation. Therefore, in this research, we first modify the ROF model to denoise vector-valued images by including the edge detector and extending the formulation into a vector-valued framework. Second, we reformulate the DSS2 model by incorporating the modified ROF model as a new fitting term in the DSS2 model. Peak signal-to-noise ratio (PSNR) is used to measure the image quality, while Jaccard and Dice similarity index are used to evaluate the segmentation quality. The comparison between our proposed model and existing model shows that our model is more effective as indicated by higher PSNR, Jaccard and Dice similarity index values.
Simulating quantum backﬂow on a quantum computer

Authors:
raw_author_name: Arseni Goussev
raw_author_name: Jaewoo Joo

Abstract: Abstract Quantum backﬂow is a counterintuitive eﬀect in which the probability density of a free particle moves in the direction opposite to the particle’s momentum. If the particle is electrically charged, then the eﬀect can be viewed as the contrast between the direction of electric current and that of the momentum. To date, there has been no direct experimental observation of quantum backﬂow. However, the eﬀect has been simulated numerically (using classical computers) and optically (using classical light). In this study, we present the ﬁrst simulation of quantum backﬂow using a real quantum computer.&amp;#xD;