Reviewerly/exHarmony huggingface dataset:
Acoustics of Breath Noises in Human Speech: Descriptive and Three-Dimensional Modeling Approaches

Purpose: Breathing is ubiquitous in speech production, crucial for structuring speech, and a potential diagnostic indicator for respiratory diseases. However, the acoustic characteristics of speech breathing remain underresearched. This work aims to characterize the spectral properties of human inhalation noises in a large speaker sample and explore their potential similarities with speech sounds. Speech sounds are mostly realized with egressive airflow. To account for this, we investigated the effect of airflow direction (inhalation vs. exhalation) on acoustic properties of certain vocal tract (VT) configurations. Method: To characterize human inhalation, we describe spectra of breath noises produced by human speakers from two data sets comprising 34 female and 100 male participants. To investigate the effect of airflow direction, three-dimensional–printed VT models of a male and a female speaker with static VT configurations of four vowels and four fricatives were used. An airstream was directed through these VT configurations in both directions, and their spectral consequences were analyzed. Results: For human inhalations, we found spectra with a decreasing slope and several weak peaks below 3 kHz. These peaks show moderate (female) to strong (male) overlap with resonances found for participants inhaling with a VT configuration of a central vowel. Results for the VT models suggest that airflow direction is crucial for spectral properties of sibilants, /ç/, and /i:/, but not the other sounds we investigated. Inhalation noise is most similar to /ə/ where airflow direction does not play a role. Conclusions: Inhalation is realized on ingressive airflow, and inhalation noises have specific resonance properties that are most similar to /ə/ but occur without phonation. Airflow direction does not play a role in this specific VT configuration, but subglottal resonances may do. For future work, we suggest investigating the articulation of speech breathing and link it to current work on pause postures. Supplemental Material: https://doi.org/10.23641/asha.24520585
JS-04 Effects of augmented reality control method on task performance and subjective experience during order picking

This study examined the effects of head-mounted display-based augmented reality control method on task performance and subjective experience during order picking. Twenty-four male participants performed a manual order picking task, which consisted of two subtasks: 1) picking up a 2.5kg bin from a shelf, and 2) placing the bin onto another shelf. Four augmented reality control methods for proceeding through the order picking instructions on a head-mounted display-based augmented reality system were employed: 1) pushing a physical button on a portable keyboard, 2) clicking on a virtual 3D box appearing near the participant, 3) using voice to say “Next”, and 4) gazing at a virtual 3D box appearing at a specific location. Each participant performed 10 repetitions of picking and placing. Physical button showed a significantly shorter task completion time compared to virtual button and gaze control. Also, physical button and voice control demonstrated significantly lower subjective workload scores compared to gaze control, and, signficantly higher subjective usability scores compared to virtual button and gaze control.
Impact of Trust Adjustment Factor on Artificial Intelligence Driven Collaborative Filtering Recommendation Algorithm

Trust on artificial intelligence (AI) is a major concern in the contemporary computing paradigms. Studies show that AI systems may outsmart humans, leading to an ultimate extinction of mankind. Therefore, the behavior of these systems must be controlled to avert potential use by bad actors. Recommender systems, which are variant of AI products, learn shoppers past data and predict items that shoppers may prefer. This helps in identifying items that may be recommended to the active user. Studies indicate that classical recommender systems allow untrustworthy data, tempting unscrupulous dealers to misdirect the learning process. This action potentially defrauds buyers. Our study introduces trust adjustment factor into the AI learning pipeline. We conducted experiments to test the difference in robustness of the trust-enhanced collaborative filtering recommendation algorithm against the classical counterpart. Prediction shift and hit ratios for the two sets of algorithms were measured when subjected to various forms of profile injection attacks. We found that the trust-enhanced variant of the algorithm significantly outperforms classical collaborative filtering recommendation in terms of robustness by up to 52% when measured by prediction shift and by up to 18% when measured by hit ratio. Confirmed by t-test, results suggest that embedding trust adjustment factor into recommender systems improves its robustness.
Quantum-improved phase estimation with a displacement-assisted SU(1,1) interferometer

By performing two local displacement operations (LDOs) inside an SU(1,1) interferometer, called as the displacement-assisted SU(1,1) [DSU(1,1)], both the phase sensitivity based on homodyne detection and quantum Fisher information (QFI) with and without photon losses are investigated in this paper. In this DSU(1,1) interferometer, we focus our attention on the extent to which the introduced LDO affects the phase sensitivity and the QFI, even in the realistic scenario. Our analyses show that the estimation performance of DSU(1,1) interferometer is always better than that of SU(1,1) interferometer without the LDO, especially for the phase precision of the former in the ideal scenario closer to the Heisenberg limit via the increase of the LDO strength. Different from the latter, the robustness of the former can be also enhanced markedly by regulating and controlling the LDO. Our findings would open an useful view for quantum-improved phase estimation of optical interferometers.
Illustrating the significance of explainable artificial intelligence (XAI)

Artificial intelligence (AI) is turning out to be an indispensable paradigm for businesses and individuals. AI is automating and accelerating a specific set of everyday problems such as classification, regression, clustering, detection, recognition, translation, etc. AI can classify whether an incoming e-mail is spam or real, recognize a person&apos;s face in an image, understand a speech and convert it into text, create an appropriate caption for a scene, etc. The scope of AI is fast expanding. Industry verticals are keenly exploring and experimenting with different things. Business processes are being automated and optimized through the smart leverage of all kinds of noteworthy advancements happening in the AI space. Increasingly AI takes the center stage in business operations across the globe. There is a dazzling array of integrated platforms, frameworks, toolsets, libraries, and case studies and hence the adoption of AI algorithms and models is picking dramatically in the recent past. However, there are a few critical challenges to be surmounted before the widespread usage of AI models in mission-critical domains such as healthcare, security, retailing, supply chain, and infrastructure management. That is, business executives and IT experts insist on trustworthy and transparent decision-making by AI models.This chapter is to explain the brewing challenges in the AI field and how they can be surmounted through competent technology solutions. Especially how the fast-emerging explainable AI (XAI) is a set of methods and software libraries that allow human users to comprehend and trust the results created by AI models. XAI is to describe an AI model and how it arrived at a particular decision. XAI is to explain the AI model&apos;s implications and potential biases. It helps in understanding model accuracy and fairness. XAI turns out to be a crucial cog for mission-critical enterprises to embark on the AI paradigm with all the clarity and confidence. With the maturity of the XAI concept, the AI adoption happens in a responsible manner across industry verticals.
KLASIFIKASI KESEHATAN PADA TANAMAN PADI MENGGUNAKAN CITRA UNMANED AERIAL VEHICLE (UAV) DENGAN METODE CONVOLUTIONAL NEURAL NETWORKS (CNN)

Indonesia is a country with a majority of the population making rice the main food. With an increasing population, of course, it is necessary to maintain the quality of rice to reduce the risk of crop failure. In 2019, it was stated that nearly 40% of the world's crop was lost due to disease and pest infestation. Unmanned Aerial Vehicle (UAV) is a technology that has been widely used for the observation and mapping of rice plants. The UAV's small size allows it to maneuver more, making shooting land easier and faster. These diseases and pest attacks can be detected by looking at the plant parts. The easiest part to detect is on the leaves because the signs of the disease can be seen clearly. However, it is not easy to recognize these diseases, it requires experts to identify diseases through a more accurate UAV image. Convolutional Neural Network (CNN) is a deep learning method that is often used in digital image recognition. This is because CNN is trying to imitate the image recognition method in the human visual cortex. The CNN method in this study was used to classify healthy rice plants and diseased rice plants through UAV imagery
Advanced permanent magnet generator topologies using multimaterial shape optimization and 3D printing

A vast majority of utility-scale wind turbine generators in the United States depend on foreign-sourced rare-earth permanent magnets that are vulnerable to supply chain uncertainties. Many small wind original equipment manufacturers are motivated to pursue continuous improvements to the generator design to lower the material and production costs and improve performance by lowering cogging torque and increasing efficiency. Traditional design and manufacturing offer limited opportunities. In this work, we demonstrate advanced design approaches for a 15-kW baseline wind turbine generator by making use of recent progress in three-dimensional (3D) printing of polymer-bonded magnets and electrical and structural steel. We explore three methods of magnet parametrization using Bézier curves resulting in symmetric, asymmetric and multimaterial magnet designs. We employ a multiphysics approach combining parametric computer-aided-design modeling, finite-element analysis, and targeted sampling to identify novel designs with more opportunities for reducing rare-earth material, improving efficiency and minimizing cogging torque. The results show that asymmetric-pole design and multimaterial-pole designs offer a greater opportunity to minimize rare-earth magnet materials by up to 35% with similar performance as the baseline generator, suggesting newer opportunities with design freedom beyond traditional limits of symmetry and as allowed by 3D printing.
Algorithm efficiency and hybrid applications of quantum computing

With the development of science and technology, it is difficult for traditional computers to solve cutting-edge problems due to the lack of computing power, and the importance of quantum computers is increasing day by day. This article starts with the simple principle of quantum computing, introduces the most advanced quantum computing instruments and quantum computing algorithms, and points out the application prospects in medicine, chemistry and other fields. This paper explains the basic principles of quantum computing algorithms, their efficiency over traditional algorithms, and focuses on the Shor algorithm and its variations. In terms of applications, the quantum computer Zu Chongzhi and its contribution to the sampling problem of quantum random circuits are introduced. This paper makes a certain analysis of the limitations of quantum computing, and gives the future development goals in a targeted manner. Besides, we summarize popular quantum computing algorithms and applications, and make contributions to the promotion and development of quantum computing. Overall, these results shed light on guiding further exploration of how to improve the computational efficiency of quantum computers.
Design and Development of Data Processing Applications for Make-Up and Wardrobe Service Orders in Excel Wedding Organizer

Data processing of make-up and wardrobe orders on the Excel wedding organizer in its current operations is still done manually, so the admin who wants to process the data still has to record manually on paper, besides that reporting to the CEO also still uses recording in a book. Based on these conditions, this study aims to design and create an information system for data processing of Android-based make-up and wardrobe orders in Excel wedding organizer. The stages of system development in making this information system use the prototyping model method which consists of planning stages, analysis stages, design stages, and implementation stages. Using the Delphi programming language in its manufacture and HeidiSQL as the database. The result of the research is an Android-based make-up and wardrobe data processing information system that assists the admin in processing order data.
Maintenance of Plan Libraries for Case-Based Planning: Offline and Online Policies

Case-based planning is an approach to planning where previous planning experience provides guidance to solving new problems. Such a guidance can be extremely useful, or even necessary, when the new problem is very hard to solve, or the stored previous experience is highly valuable, because, e.g., it was provided or validated by human experts, and the system should try to reuse it as much as possible. To do so, a case-based planning system stores in a library previous planning experience in the form of already encountered problems and their solutions. The quality of such a plan library critically influences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it is also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the library was originally designed for. Moreover, like in general case-based reasoning, the library needs to be maintained at a manageable size, otherwise the computational cost of querying it grows excessively, making the entire approach ineffective. In this paper, we formally define the problem of maintaining a library of cases, discuss which criteria should drive the maintenance, study the computational complexity of the maintenance problem, and propose offline techniques to reduce an oversized library that optimize different criteria. Moreover, we introduce a complementary online approach that attempts to limit the growth of the library, and we consider the combination of offline and online techniques to ensure the best performance of the case-based planner. Finally, we experimentally show the practical effectiveness of the offline and online methods for reducing the library.
Grid Disaster Information Fusion and Integrated Prediction Technology

Abstract In terms of earthquake disaster loss modeling research, the main international research lies in disaster loss risk assessment, which studies the risk of damage outcomes and their impacts with an indicator system model.
An Attempt to Carry Out Blended Teaching Based on National Primary and Secondary School Platform

Zhanggongshan No. 3 Primary School currently has 25 classes with nearly 1200 teachers and students. The school attaches great importance to the construction of a smart school, establishes various rules, regulations, incentives and evaluation mechanisms for informatization, and has a special person responsible for the guarantee of informatization technology.
Information Technology Empowers Literacy-Oriented Interdisciplinary Inquiry and Practice

Hongling Experimental Primary School in Futian District, Shenzhen, follows the educational philosophy of “cherishing each student”. To realize this goal, the school starts with the curriculum development (that is to use the curriculum as a lever to leverage the whole school reform), innovates the teaching organization and evaluation mechanism, and adopts project-based learning, so that “inquiry” has become the most important learning method, which fully stimulates students' independent learning initiative and ensures that students can achieve learning goals such as conceptual understanding, necessary character formation, and key ability acquisition.
“Teaching According to Aptitude” Smart Education Solution Under the Background of “Double Reduction”

Under the background of “double reduction”, iFLYTEK effectively combines the general technology of artificial intelligence and data in the education process, converts various scenarios of information of the education process into big data that can support education services in the future, builds a new model of people-oriented digital learning, and forms two smart education theme solutions of “teaching according to aptitude” and “smart double reduction”.
Teaching Resource Construction and Application Solution Based on Knowledge Graph

Based on the teaching resource construction project in key areas of the Ministry of Education, we use new technologies such as artificial intelligence, big data, and blockchain, explore new paths, new mechanisms and new models for the construction and application of professional resources based on knowledge graph, in order to provide resource construction and application solutions for the training of professionals in emerging fields and key core fields.
LEARNM: Augmented Reality Mobile Application in Learning the Concepts of Basic Biological Structures

This study endeavors to investigate the effectiveness of LearnM, an innovative augmented reality mobile application, in elevating the comprehension and assimilation of molecular structures among Grade 12 Science, Technology, Engineering, and Mathematics (STEM) students. LearnM showcases an immersive and interactive platform that empowers students to visualize and manipulate intricate three-dimensional models of molecular structures through the convenience of their smartphones. By integrating cutting-edge augmented reality technology, LearnM aims to transcend the traditional boundaries of textbook-based learning, fostering an engaging and dynamic learning experience. The research design employed in this investigation entails a quasi-experimental study with a pretest-posttest control group design. A cohort of 40 Grade 12 STEM students has been selected, with participants randomly assigned to either the experimental or control group. The experimental group will utilize the LearnM application, while the control group will receive conventional instruction. Pretest and posttest assessments will be administered to both groups, enabling a comprehensive analysis of the application’s impact on learning outcomes. The significance of this research lies in its potential to augment the body of knowledge within the realm of mobile learning and augmented reality, with a specific emphasis on molecular biology education. The integration of technology in education has become increasingly vital in the digital age, and this study endeavors to shed light on the effectiveness of such applications in enhancing students’ understanding of complex scientific concepts. Keywords: Augmented Reality, Mobile Application, Molecular Biology, STEM Education.
A Survey on Blockchain Based Solution for Agriculture Supply Chain Management

Abstract: Agriculture, a practice that spans thousands of years, has always been at the heart of human civilization. Throughout history, agriculture has continuously adapted to meet the needs of growing populations and ensure food quality, traceability, and efficiency. Recent advancements in blockchain technology have brought about significant positive changes in the agricultural sector. Blockchain has found a valuable role in agriculture, enhancing transparency, traceability, and trust throughout the supply chain. This paper provides an in-depth survey of how blockchain has impacted various aspects of agriculture, discusses the challenges it faces, and considers the promising future it holds for the industry. As blockchain adoption in agriculture continues to evolve, it promises to revolutionize the way we produce and distribute food, ensuring a more efficient, secure, and sustainable supply chain.
Cyber Bullying

Abstract: Cyberbullying involves the use of cell phones or social networking platforms like Facebook and Twitter to harass, threaten, or intimidate individuals, adversely impacting their mental health. Our project focuses on the detection of such harmful commentsusing a machine learning algorithm, categorizing them into two severity levels: "low" and "high." Given the ubiquitous nature of social media, especially in the post-pandemic era, it has become an integral part of our lives for communication across all age groups. While it facilitates easy communication, social media also presents challenges, such as the escalation of hateful comments beyond the realm of healthy discourse. Recognizing the potential harm caused by these comments, our solution aims to identify and classify them based on severity using machine learning, enabling appropriate intervention.
Bitcoin Transaction Analysis System

The lack of a user identification system and the existence of a variety of ways to obfuscate a transaction on the Bitcoin network is of great interest to attackers and can be used by them to conduct illegal activities. There is a need to develop new methods of cash identification in the Bitcoin network. The purpose of this work is to develop a method of transaction verification in the Bitcoin network to improve the efficiency of the process of identification of illegally obtained funds and their sources. The work solves the following tasks: the development of a method for transaction verification in the Bitcoin network and the development of a decision support system, which includes the proposed method. The article describes each of the stages of the method: collection, aggregation, processing and analysis of information. The information analysis stage proposes a clustering method that takes into account an extended set of empirical rules (heuristics) of transaction analysis, as well as information about Bitcoin network address owners. The scientific novelty lies in increasing the efficiency of the identification process of illegally obtained funds and their sources through a comprehensive analysis of transactions, including the extended collection of information and its subsequent aggregation in the multi-model database of the decision support system. In contrast to existing methods, the reliability of the identification of Bitcoin network subjects is increased through the use of intelligent methods of data analysis. The results of this work will provide an opportunity to develop new and improve existing transaction analysis tools in future research, which will allow more effective identification of funds in the Bitcoin network associated with illegal activities.