Reviewerly/exHarmony huggingface dataset:
Augmented reality as a tool for teaching physics to students with special needs

Authors:
raw_author_name: G Pavlendova
raw_affiliation_string: Department of Biophysics, Faculty of Natural Sciences, University of Ss. Cyril and Methodius, Nam. J Herdu 2, 91701 Trnava, Slovakia; Slovak University of Technology, Bratislava, Slovakia
raw_author_name: M Kosak Babuder
raw_affiliation_string: University of Ljubljana, Faculty of Education, Kardeljeva ploščad 16, 1000, Ljubljana, Slovenia
raw_author_name: K Javornik
raw_affiliation_string: University of Ljubljana, Faculty of Education, Kardeljeva ploščad 16, 1000, Ljubljana, Slovenia
raw_author_name: J Rihter
raw_affiliation_string: University of Ljubljana, Faculty of Education, Kardeljeva ploščad 16, 1000, Ljubljana, Slovenia

Abstract: Abstract Nowadays, teaching physics is becoming more and more challenging, particularly in regard to reduced number of physics lessons, lack of physics teachers and/or of the experimental equipment in the laboratories. This is becoming even more visible when students with special needs (SEN) are concerned. Consequently, physics rates among the most unpopular subjects in schools. We present a project ARphymedes plus („Augmented Reality made for student with special needs“), specifically designed to improve teaching of physics for students with special needs by applying Augmented Reality (AR). Within this project, we connect physics content with stories related to distinguished physicists, as well as to supporting experiments. For each phenomenon or physical law, examples in the form of videos, animations or 3D animated models are presented using AR. AR can be visualised in the book using tablets or smartphones, which are a daily part of students’ lives.
Q‐learning based adaptive Kalman filtering for partial model‐free dynamic systems

Authors:
raw_author_name: Xiaoli Luan
raw_affiliation_string: Key Laboratory for Advanced Process Control of Light Industry (Ministry of Education), Institute of Automation Jiangnan University Wuxi China
raw_author_name: Feng Ding
raw_affiliation_string: Key Laboratory for Advanced Process Control of Light Industry (Ministry of Education), Institute of Automation Jiangnan University Wuxi China
raw_author_name: Fei Liu
raw_affiliation_string: Key Laboratory for Advanced Process Control of Light Industry (Ministry of Education), Institute of Automation Jiangnan University Wuxi China

Abstract: Summary In this article, we propose an adaptive Kalman filtering based on Q‐learning for partial model‐free dynamic systems. First, a cost function is defined to iteratively update the prior state value when the model parameters are unknown. Then, the observations in a period of time are utilized to improve the accuracy and updating speed of the prior state estimation by means of the multi‐innovation least squares. Next, considering that the weight matrix in the cost function will change due to external noise noise and model mismatch, the innovation‐based adaptive estimation algorithm is presented to adjust the weight matrix by using the covariance of the information sequence. Finally, the proposed algorithms are applied to estimate the water level of a quadruple water tank system.
Paraconsistent logics as a Way of Expressing Objective Contradictions in Science

Authors:
raw_author_name: Boris Leonidovich Iashin

Abstract: The article focuses on the problems associated with the need to express in the language of science in a consistent way the movement and various kinds of changes taking place in the objective world by developing logics that would be tolerant of logical contradictions. The paper presents a brief history of the creation of paraconsistent (paraconsistent) logics, in which logical contradictions turned out to be quite permissible. It is noted that the priority in their development belongs to Russian scientists N. A. Vasiliev and I. E. Orlov, as well as Polish philosophers and logicians Y. Lukasevich and S. Yaskovsky; that since the seventies of the last century, the development of paraconsistent logics has assumed an international character; that interest in these logics is not abating at the present time. The possibilities of using paraconsistent logics presented in the works of foreign and domestic philosophers and logicians for the formalization of dialectics are discussed. The positive role of these logics is shown in solving many paradoxes in the foundations of classical propositional calculation and predicate logic, as well as in order to limit undesirable consequences when using IT technologies related to the processing of inconsistent or contradictory information. It is concluded that the use of paranoherent logics in the construction of models of individual fragments of dialectics contributes to its development as logic and as ontology, and indicates that the apparatus of non-classical logics as a whole is a very powerful means of studying and explaining many problems of theoretical cognition.
Digital Safeguards

Authors:
raw_author_name: Muhammad Tayyab
raw_affiliation_string: Taylor's University, Malaysia
raw_author_name: Khizar Hameed
raw_affiliation_string: University of Tasmania, Australia
raw_author_name: Noor Zaman Jhanjhi
raw_affiliation_string: Taylor's University, Malaysia
raw_author_name: Faizan Qamar
raw_affiliation_string: Universiti Kebangsaan Malaysia, Malaysia

Abstract: The rapid integration of internet and technological advancements over the past two decades has reshaped the global landscape, leading to transformative changes in various sectors.The emergence of Industry 4.0, conceptualized in 2011, has particularly revolutionized manufacturing and logistics by advocating for systematic digitalization technology integration to enhance efficiency and reduce costs. However, this technological evolution, while fostering efficiency and process optimization, also introduces vulnerabilities to cyber threats. The logistics sector, heavily reliant on interconnected systems like internet of things, faces potential risks from cyberattacks that target sensitive data across the interconnected supply chain. This chapter aims to address the intricate relationship between digitalization and the logistics sector, emphasizing the crucial role of digital safeguards in navigating cyber dangers. Furthermore, the chapter delves into the significance of visibility in supply-chain operations and explores technologies and practices for enhancing supply-chain visibility.
Logistics Industry in the Context of the Blockchain Technology

Authors:
raw_author_name: Imdad Ali Shah
raw_affiliation_string: School of Computing Science, Taylor's University, Malaysia
raw_author_name: Areeba Laraib
raw_affiliation_string: Mehran University of Engineering and Technology, Pakistan

Abstract: Blockchain technology has garnered considerable interest from academics and businesses. One of the primary reasons to keep track of customer satisfaction and adoption is that customers don't use the product or service enough. Identifying the factors that influence the use and adoption of blockchain technologies will help solve the adoption problems effectively. Many significant firms, like Google, Amazon, and others, will embrace blockchain technology. It will indeed be found in the future and revolutionise logistics and transportation. Even though the advantages of blockchain technology have been well studied in the financial sector, using blockchain technology can help decrease significant logistical challenges such as border delays, product damage, errors, and multiple data entry.
A Codebook Compression Method for Vector Quantization Algorithm

Authors:
raw_author_name: None Abul Hasnat
raw_affiliation_string: Government College of Engineering and Textile Technology, Berhampore, West Bengal, India
raw_author_name: None Dibyendu Barman
raw_affiliation_string: Government College of Engineering and Textile Technology, Berhampore, West Bengal, India
raw_author_name: None Santanu Halder
raw_affiliation_string: Government College of Engineering and Leather Technology, Kolkata, West Bengal, India
raw_author_name: None Debotosh Bhattacharjee
raw_affiliation_string: Jadavpur University, Kolkata, West Bengal, India

Abstract: This study introduces a novel approach to enhance the compression ratio of the vector quantization (VQ) algorithm by specifically targeting the compression of its codebook. The VQ algorithm typically generates an index matrix and a codebook to represent compressed images. The proposed method focuses on reducing the size of the codebook, which comprises N codewords, each with elements quantized into four levels. Each 8-bit element in a codeword is compressed to 2-bits, and the encoded codeword is accompanied by the minimum value and a threshold value in the codebook. Experimental results on benchmark color images, such as baboon, airplane, Lena, and others, demonstrate a significant reduction of 62.50% in the size of the VQ codebook.
The Internet of Things (IoT) Is Revolutionizing Inventory Management

Authors:
raw_author_name: Imdad Ali Shah
raw_affiliation_string: School of Computing Science, Taylor's University, Malaysia
raw_author_name: Sarfraz Nawaz Brohi
raw_affiliation_string: University of the West of England, UK

Abstract: Every part of the supply chain has been affected by the rise of Industry 4.0 technologies. Many businesses have been trying out this modern technology to see if it can help them make more money. IoT devices make it simple for companies to keep track of an item's specific location, which helps inventory management (IM). The time it takes to find inventory is shortened as a result. The abundance of real-time data makes it possible to offer insightful information that supports strategic and tactical business decisions. Developing a supply chain into a fully integrated supply chain has several benefits. The differences between fourth-generation technology and earlier generations make it seem like traditional ways of restocking inventory don't adapt well enough to new technologies and can't handle IoT systems. Thanks to technological advancements, supply chains are undergoing rapid transformation.
Bag-Based Feature-Class Correlation Analysis for Multi-Instance Learning Application

Authors:
raw_author_name: Noor Azah Samsudin
raw_affiliation_string: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
raw_author_name: Aida Mustapha
raw_affiliation_string: Faculty of Applied Science and Technology, Universiti Tun Hussein Onn Malaysia (Pagoh Campus), KM1, Jalan Panchor,84600 Muar, Johor, Malaysia

Abstract: Multi-instance Learning (MIL) is widely applied in image classification. In MIL, an image is presented as a bag. A bag consists of multi-instance which is known as patches. Irrelevant features of the image presented to the classifier affects the classification performance. Feature selection is one of the essential phases to select relevant. However, limited studies discuss the feature selection phase in MIL. Correlation between feature-class (FC) relationship is one important criterion to analyse features’ relevance. However, it cannot be performed directly in MIL. To address this gap, this study proposed the MultiBag-FCCorr feature selection technique. It consists of three steps: transformation, evaluation and fusion. The bags of feature information are acquired from summarization from different statistical central tendency measures of trimmed mean, mode and median. In feature evaluation step, extended point biserial correlation has been used to measure FC correlation and then the FC score has been analysed. The selected features are validated via two prominent classifiers (Support Vector Machine (SVM) and K-Nearest Neighbour (KNN)) on benchmark MI image datasets: UCSB Breast Cancer, Tiger, Elephant and Fox datasets. The selected features of UCSB Breast Cancer dataset were reduced to 92% number of features from the proposed technique giving the best result of average accuracy with 86.8.% using SVM and 84.5% using KNN. The average accuracy improved 6.3% using SVM and 16.4% using KNN compared without implementing the proposed feature selection. The results proved that the selected feature set improved the performance of MI image classification.
Optimized design of smart toilet spray bar flow channel based on CFD

Authors:
raw_author_name: Xuelong Yang
raw_affiliation_string: College of Metrology and Measurement Engineering, China Jiliang University.

Abstract: Abstract Currently, there are significant issues with the intelligent toilets available on the market, such as a large swing of the spray bar when spraying water column and eccentricity. This paper presents four optimization schemes for the intelligent toilet spray bar channel by analysing and redesigning a specific model. Computational Fluid Dynamics (CFD) technology was employed to analyse the water flow conditions inside the spray bar of the intelligent toilet. The calculation results indicate that for the same water inlet flow rate, the optimal structure in optimization scheme 1 is the block with a length (L) of 1.5 mm. In optimization scheme 2, the optimal structure is the block located at position (3). In optimization scheme 3, the optimal structure is a 15° rotation, which exhibits the least swirls and pressure loss while maintaining the prototype structure. When the prototype structure is modified, the optimal structure in optimization scheme 4 is the excision bend pipe. This structure exhibits the lowest swirl number and pressure loss among all the optimization schemes. The determined structure offers valuable insights for enhancing the flow characteristics of the improved product.
The Effect of the Director's Authentic Leadership that MZ Generation Early Childhood teachers acknowledge on the Constructive Voice Behavior of MZ Generation Early Childhood Teachers: Examining the Parallel Mediating Effect of Organizational commitment

Authors:
raw_author_name: Seonyoung Shin
raw_author_name: Jin-Young Chae

Abstract: The aim of this study was to examine how the MZ generation (born between 1980 and 2005) early childhood teachers perceive preschool directors' authentic leadership and how their perceptions influenced their organizational commitment and constructive voice behaviors. To this end, a survey was conducted on 242 MZ generation early childhood teachers currently employed in kindergartens and daycare centers across South Korea. Data analysis, performed using SPSS version 26.0, involved calculating frequencies, percentages, means, and standard deviations. Additionally, the PROCESS macro for SPSS version 3.5, specifically model 4, was utilized to confirm parallel mediation effects. Bootstrap analysis was employed to validate the statistical significance of the mediating effects. The findings of this study were as follows. First, the level of the director's authentic leadership recognized by MZ generation early childhood teachers, constructive voice behavior, and organizational commitment of MZ generation early childhood teachers were all reported as ranging from moderate to high. Second, parallel partial mediating effects were found through emotional and normative commitment in verifying the mediating effect of organizational commitment in the relationship between the director's authentic leadership recognized by MZ generation early childhood teachers and the constructive voice behavior of MZ generation early childhood teachers. These results suggest that the director's authentic leadership influences the constructive voice behavior of MZ generation early childhood teachers, and emotional and normative immersion can play a mediating role in this process to promote constructive voice behavior.
Determination of Elongated Aggregates through Computer Vision-Based Technique

Authors:
raw_author_name: Prabal Singh
raw_affiliation_string: Assistant Professor, Dept. of Civil and Infrastructure Engineering, Indian Institute of Technology Jodhpur, Rajasthan, India. ORCID: .; B.Tech Student, Dept. of Civil and Infrastructure Engineering, Indian Institute of Technology Jodhpur, Rajasthan, India.

Abstract: The traditional measurement technique for finding elongated aggregates involves sieve analysis followed by passing each particle through an elongation gauge. In this study, machine learning has been used as a computer vision-based methodology to determine the elongation index of coarse aggregate particles. Images of aggregates were captured, and an algorithm was developed for contour detection of each particle. The particle size and the number of elongated particles were then computed in an automated form. The laboratory test results were compared with the results obtained from a machine learning-based algorithm. The results so obtained on the segregation of particles in various sizes give significant accuracy with a deviation of ±1 number of particles. The method so developed can be used by researchers and field engineers for the determination of the physical parameters of aggregates.
Natural Language Processing and Deep Learning Approaches for Multiclass Document Classifier

Authors:
raw_author_name: None Shruti A. Gadewar
raw_affiliation_string: ME Aspirant, Department of Computer Science and Engineering, Babasaheb Naik College of Engineering, Pusad, Maharashtra, India

Abstract: With the recent growth of the internet, the volume of data has also increased. A large section of the internet is full of documents, which may contain data, big data, formatted and unformatted data, structured data, and unstructured data. The increase in the amount of this unstructured data results in making it difficult to manage data. As it is difficult to classify the increasing volume of data for various purposes manually, automated classification is required. This paper overviews different approaches to Natural Language Processing and Deep Learning for content-based classification.
Measuring the Influence of Artificial Intelligence (AI) on Online Purchase Decisions-In Case of Indian Consumers

Authors:
raw_author_name: None Dr. G Manikandan
raw_affiliation_string: Professor, Department of Information Technology, Kings Engineering College, Tamil Nadu, India

Abstract: The industry's ostensible technological sophistication contributes to the highly dynamic ecommerce environment. When new technology is made available, many of these companies openly adopt it to stay competitive market. Internet shop owners have embraced a variety of technologies, including artificial intelligence. Technology is rapidly evolving. Artificial intelligence significantly facilitates the conversion of interest into purchase intentions. The majority of the information gathered by e-commerce companies is about prospective customers or prospects. AI can be used to interact with warm leads or cold leads who have indicated interest in a brand or product. Furthermore, AI has been demonstrated to be a highly constructive technique of retargeting customers. Artificial intelligence advancements have increased consumer satisfaction even further, making it even more critical in today's climate. This paper will investigate the factors that influence artificial intelligence's practical implacability in order to better understand how it affects consumers' online purchase plans. This paper explores the various variables influencing consumers' purchase intentions for e-retailing using a technology-based model as the foundation. This study has developed a model that shows how business organisations can incorporate artificial intelligence into retailing in order to comprehend consumer requirements and encourage technology adoption. This research has looked more closely at consciousness, subjective norms, and faith as constructs that heighten the tenacity of artificial intelligence.
Leakage detection based on variational mode decomposition and long short-term memory neural network

Authors:
raw_author_name: Yan Xu
raw_affiliation_string: Xinjiang Research Center of Water Resources and Ecological Hydraulic Engineering in Cold and Arid Regions, Urumqi 830000, Xinjiang, China

Abstract: Abstract In the process of long-term continuous operation, fluid transportation pipelines are prone to leakage accidents. Therefore, this study investigates the detection of small-sized leaks with a leakage aperture of 13 mm in pipes with a diameter of 100 mm. The experimental investigation is conducted under the following operating conditions: volume flow of 25-80 m 3 /h, pressure of 100-200 kPa. The variations in volume flow and pressure signals during leak occurrences are analysed. To mitigate the interference caused by noise, the variational mode decomposition (VMD) method is introduced. The VMD effectively reduces noise interference in the signals. Furthermore, the denoised signals are utilized to establish a long short-term memory neural network (LSTM). The LSTM model achieves a high accuracy rate of 91.67% for the entire dataset.
ON THE TITLE AND LEXICAL FEATURES OF THE WINTER OLYMPICS NEWS REPORT IN CHINA DAILY

Authors:
raw_author_name: Xiaowei Yuan

Abstract: With the successful hosting of the 2022 Winter Olympics, people all over the world have turned more attention to Chinese winter sports. China Daily is major media to know the news about it. This article made an analysis of the title and lexical features of the Winter Olympics through the news from China Daily.
Adaptive novelty detection with false discovery rate guarantee

Authors:
raw_author_name: Lihua Lei
raw_affiliation_string: Graduate School of Business, Stanford University
raw_author_name: David Mary
raw_affiliation_string: Laboratoire Lagrange, Observatoire de la Côte d’Azur, Université Côte d’Azur & CNRS
raw_author_name: Etienne Roquain
raw_affiliation_string: LPSM, Sorbonne Université, Université de Paris & CNRS

Abstract: This paper studies the semisupervised novelty detection problem where a set of "typical" measurements is available to the researcher. Motivated by recent advances in multiple testing and conformal inference, we propose AdaDetect, a flexible method that is able to wrap around any probabilistic classification algorithm and control the false discovery rate (FDR) on detected novelties in finite samples without any distributional assumption other than exchangeability. In contrast to classical FDR-controlling procedures that are often committed to a pre-specified p-value function, AdaDetect learns the transformation in a data-adaptive manner to focus the power on the directions that distinguish between inliers and outliers. Inspired by the multiple testing literature, we further propose variants of AdaDetect that are adaptive to the proportion of nulls while maintaining the finite-sample FDR control. The methods are illustrated on synthetic datasets and real-world datasets, including an application in astrophysics.