Reviewerly/exHarmony huggingface dataset:
The effects of tones and time pressure on the user experience of voice input on mobile devices for Mandarin Chinese: an experimental study

Authors:
raw_author_name: Dongyue Liu
raw_affiliation_string: School of Economics and Management, Beihang University, Beijing, People’s Republic of China
raw_author_name: Zhe Chen
raw_affiliation_string: Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing, People's Republic of China; School of Economics and Management, Beihang University, Beijing, People’s Republic of China
raw_author_name: Wenbo Zhu
raw_affiliation_string: School of Economics and Management, Beihang University, Beijing, People’s Republic of China
raw_author_name: Guozhu Jia
raw_affiliation_string: School of Economics and Management, Beihang University, Beijing, People’s Republic of China
raw_author_name: Ronggang Zhou
raw_affiliation_string: Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing, People's Republic of China; School of Economics and Management, Beihang University, Beijing, People’s Republic of China

Abstract: This study experimented with mobile devices to investigate the effects of tones and time pressure on the voice input experience for Mandarin Chinese. Voice input for Mandarin Chinese is gaining popularity, and its implementation on mobile devices has recently developed very rapidly. Previous studies have focused mainly on technical considerations for designing a voice input system and have not investigated the user experience of voice input. Two experiments were conducted to test single-character and two-character phrases. Four measurements, the number of errors, user satisfaction, mental workload, and skin conductance, were gathered from sixty-four participants for each experiment. The results indicated that Mandarin tones had a significant influence on the number of errors and user satisfaction, and time pressure significantly influenced all measurements. Specifically, the falling-rising tone produced the highest number of errors, and the rising tone led to the lowest satisfaction. A high level of time pressure resulted in low satisfaction, a high mental workload, and high skin conductance. The results indicated that (a) Mandarin tones significantly affected error rates and user satisfaction, and (b) time pressure had a significant effect on user experience. The findings have design implications for voice input systems in terms of user experience.
Automatic label curation from large-scale text corpus

Authors:
raw_author_name: Sandhya Avasthi
raw_affiliation_string: CSE, ABES Engineering College, C-12/D Ashirwad Building, Kaushambi, Ghaziabad, Uttar Pradesh, 201010, INDIA
raw_author_name: Ritu Chauhan
raw_affiliation_string: Amity Institute of Biotechnology, Amity University System, Noida, Noida, Uttar Pradesh, 201303, INDIA

Abstract: Abstract The topic modeling technique extracts themes based on their probabilistic measurements from any large-scale text collection. Even though topic modeling pulls out the most important phrases that describe latent themes in text collections, a suitable label has yet to be found. Interpreting the topics extracted from a text corpus and identifying a suitable label automatically reduces the cognitive load for the analyst. Extractive methods are used typically to select a label from a given candidate set, based on probability metrics for each candidate set. Some of the existing approaches use phrases, words, and images to generate labels using frequency counts of different words in the text. The paper proposes a method to generate labels automatically to represent each topic based on a labeling strategy to filter candidate labels and then apply sequence-to-sequence labelers. The objective of the method is to get a meaningful label for the result of the Latent Dirichlet Allocation algorithm. The BERTScore metric is used to evaluate the effectiveness of the proposed method. The proposed method generates good interpretative labels as compared to baseline models for topic words or terms automatically. The comparison with the label generated through ChatGPT API shows the quality of the generated label with the experiment performed on Four Datasets NIPS, Kindle, PUBMED, and CORD-19.
A Multi-Attention Feature Distillation Neural Network for Lightweight Single Image Super-Resolution

Authors:
raw_author_name: Hong Yang
raw_affiliation_string: College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
raw_author_name: Linbo Qing
raw_affiliation_string: College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
raw_author_name: Xiaohai He
raw_affiliation_string: College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
raw_author_name: Honggang Chen
raw_affiliation_string: College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China; Yunnan Key Laboratory of Software Engineering, Yunnan University, Kunming 650600, China

Abstract: In recent years, remarkable performance improvements have been produced by deep convolutional neural networks (CNN) for single image super-resolution (SISR). Nevertheless, a high proportion of CNN-based SISR models are with quite a few network parameters and high computational complexity for deep or wide architectures. How to more fully utilize deep features to make a balance between model complexity and reconstruction performance is one of the main challenges in this field. To address this problem, on the basis of the well-known information multi-distillation model, a multi-attention feature distillation network termed as MAFDN is developed for lightweight and accurate SISR. Specifically, an effective multi-attention feature distillation block (MAFDB) is designed and used as the basic feature extraction unit in MAFDN. With the help of multi-attention layers including pixel attention, spatial attention, and channel attention, MAFDB uses multiple information distillation branches to learn more discriminative and representative features. Furthermore, MAFDB introduces the depthwise over-parameterized convolutional layer (DO-Conv)-based residual block (OPCRB) to enhance its ability without incurring any parameter and computation increase in the inference stage. The results on commonly used datasets demonstrate that our MAFDN outperforms existing representative lightweight SISR models when taking both reconstruction performance and model complexity into consideration. For example, for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>×</mo></math> 4 SR on Set5, MAFDN (597K/33.79G) obtains 0.21 dB/0.0037 and 0.10 dB/0.0015 PSNR/SSIM gains over the attention-based SR model AFAN (692K/50.90G) and the feature distillation-based SR model DDistill-SR (675K/32.83G), respectively.
Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm

Authors:
raw_author_name: Siti Nurhaliza
raw_affiliation_string: Universitas Muhammadiyah Prof. DR. HAMKA
raw_author_name: Firman Noor Hasan
raw_affiliation_string: Universitas Muhammadiyah Prof. DR. HAMKA

Abstract: The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.
On constrained Kaczmarz algorithm with momentum for image reconstruction

Authors:
raw_author_name: Zigen Ouyang
raw_affiliation_string: School of Mathematics and Physics University of South China Hengyang China

Abstract: Summary The Kaczmarz algorithm is widely used in image reconstruction. In this paper, we propose an enhanced version of the Kaczmarz algorithm by integrating Nesterov momentum into the Kaczmarz algorithm, denoted as the Kaczmarz algorithm with momentum. Our innovation offers a novel approach to accelerate the convergence rate of the Kaczmarz algorithm significantly. Based on greedy rules for selecting work row, we prove that the Kaczmarz method with momentum can achieve global linear convergence rates. Furthermore, we extend a constrained version of the Kaczmarz algorithm with Nesterov momentum for improved image reconstruction and prove its convergence. Numerical experiments show that the proposed algorithm outperforms the original Kaczmarz algorithm in terms of efficiency for image reconstruction.
Quantum tomography for arbitrary single-photon polarization-path states

Authors:
raw_author_name: Bertúlio de Lima Bernardo
raw_affiliation_string: Departamento de Física, Universidade Federal da Paraíba, 58051-900 João Pessoa, Paraíba, Brazil

Abstract: Quantum state tomography (QST), the process through which the density matrix of a quantum system is characterized from measurements of specific observables, is a fundamental pillar in the fields of quantum information and computation. In this work, we propose a simple QST method to reconstruct the density matrix of two qubits encoded in the polarization and path degrees of freedom of a single photon, which can be realized with a single linear-optical setup. We demonstrate that the density matrix can be fully described in terms of the Stokes parameters related to the two possible paths of the photon, together with a quantum version of the two-point Stokes parameters introduced here. Our findings put forward photonic circuits for the investigation of the dynamics of open quantum systems.
Factors influencing the adoption of robotic technologies in academic libraries: A systematic literature review (SLR)

Authors:
raw_author_name: Khurram Shahzad
raw_affiliation_string: Government College University Lahore, Pakistan
raw_author_name: Shakeel Ahmad Khan
raw_affiliation_string: The Islamia University of Bahawalpur Pakistan, Pakistan
raw_author_name: Abid Iqbal
raw_affiliation_string: Prince Sultan University, Saudi Arabia

Abstract: The study aimed to identify the factors influencing the adoption of robotic technologies in academic libraries, investigate the impact of robots on library services, and reveal challenges to adopt robotic applications in academic libraries. A systematic literature review was applied to address the study’s objectives. The 16 most relevant seminal studies published in peer-reviewed journals were selected to conduct the study. Findings displayed that the factors of innovation, advancements in artificial intelligence, library security, and personalized services influenced positively the adoption of robotic technologies in academic libraries. The study also illustrated that robots had a significant positive impact on academic libraries by performing a variety of roles, solving space issues, and providing entertainment to library patrons. Results also revealed that loss of critical thinking, technical issues, lack of organizational planning, financial hurdles, fear of job loss, shortage of skilled manpower, and ethical considerations caused barriers to the successful implementation of robotic applications in academic libraries. The study has added valuable literature to the existing body of knowledge. It is the first systematic literature review on the factors influencing the adoption of robotic technologies in academic libraries. It has provided practical solutions in light of evidence-based data for the effective adoption of robotic technologies in academic libraries.
Athena – The NSF AI Institute for Edge Computing

Authors:
raw_author_name: Yiran Chen
raw_affiliation_string: Department of Electrical and Computer Engineering Duke University North Carolina USA
raw_author_name: Suman Banerjee
raw_affiliation_string: Department of Computer Sciences University of Wisconsin Wisconsin USA
raw_author_name: Shaundra Daily
raw_affiliation_string: Department of Electrical and Computer Engineering Duke University North Carolina USA
raw_author_name: Jeffery Krolik
raw_affiliation_string: Department of Electrical and Computer Engineering Duke University North Carolina USA
raw_author_name: Hai (Helen) Li
raw_affiliation_string: Department of Electrical and Computer Engineering Duke University North Carolina USA
raw_author_name: Daniel Limbrick
raw_affiliation_string: Electrical and Computer Engineering Department North Carolina A&amp;T University North Carolina USA
raw_author_name: Miroslav Pajic
raw_affiliation_string: Department of Electrical and Computer Engineering Duke University North Carolina USA
raw_author_name: Lin Zhong
raw_affiliation_string: Department of Computer Science Yale University Connecticut USA

Abstract: Abstract The National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber‐physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI‐driven next‐generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use‐inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.
A secure and efficient electronic medical record data sharing scheme based on blockchain and proxy re-encryption

Authors:
raw_author_name: Haibo Xie
raw_affiliation_string: School of Computer and Information, Anqing Normal University, Anqing, 246133, China
raw_author_name: Wenming Wang
raw_affiliation_string: School of Computer and Information, Anqing Normal University, Anqing, 246133, China
raw_author_name: Haiping Huang
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

Abstract: Abstract With the rapid development of the Internet of Medical Things (IoMT) and the increasing concern for personal health, sharing Electronic Medical Record (EMR) data is widely recognized as a crucial method for enhancing the quality of care and reducing healthcare expenses. EMRs are often shared to ensure accurate diagnosis, predict prognosis, and provide health advice. However, the process of sharing EMRs always raises significant concerns about potential security issues and breaches of privacy. Previous research has demonstrated that centralized cloud-based EMR systems are at high risk, e.g., single points of failure, denial of service (DoS) attacks, and insider attacks. With this motivation, we propose an EMR sharing scheme based on a consortium blockchain that is designed to prioritize both security and privacy. The interplanetary file system (IPFS) is used to store the encrypted EMR while the returned hash addresses are recorded on the blockchain. Then, the user can authorize other users to decrypt the EMR ciphertext via the proxy re-encryption algorithm, ensuring that only authorized personnel may access the files. Moreover, the scheme attains personalized access control and guarantees privacy protection by employing attribute-based access control. The safety analysis shows that the designed scheme meets the expected design goals. Security analysis and performance evaluation show that the scheme outperforms the comparison schemes in terms of computation and communication costs.
Classification of Rice Growth Phases Using the K-Nearest Neighbor Algorithm in the Irrigation area of Seulimeum Sub District, Aceh, Indonesia

Authors:
raw_author_name: H Basri
raw_affiliation_string: Soil Science Department, Universitas Syiah Kuala, Jalan Tgk. Hasan Krueng Kalee 3, Darussalam-Banda Aceh 23111, Indonesia
raw_author_name: S Sugianto
raw_affiliation_string: Soil Science Department, Universitas Syiah Kuala, Jalan Tgk. Hasan Krueng Kalee 3, Darussalam-Banda Aceh 23111, Indonesia

Abstract: Abstract Rice ( Oryza sativa L.) is the main food commodity for most of Indonesia’s population. The existence of irrigation areas dramatically affects the water demand in each phase of rice growth. Due to a lack of remote sensing knowledge, classifying the rice growth phase is done manually. This study aims to classify rice growth phases using the K-Nearest Neighbor Algorithm in the irrigation area of the Seulimuem Sub-District. This research uses Google Earth satellite imagery in November 2021 through three stages: multi-resolution segmentation, guided classification, and accuracy testing. The results showed that the paddy and non-paddy fields are 1,425.56 ha and 6,581.70 ha, respectively. The classification results of rice plant growth phases consist of 4 phases, namely the inundation phase, the vegetative phase, the generative phase, and the fallow phase. Based on the accuracy assessment results in November 2021, the overall accuracy ranged from 90.65% to 92.92%, with a kappa index value of 0.85 to 0.87, categorizing it as “almost perfect.”
Utilizing modern computer architectures to solve mathematical optimization problems: A survey

Authors:
raw_author_name: David E. Bernal Neira
raw_affiliation_string: Davidson School of Chemical Engineering, Purdue University, West Lafayette IN, United States of America; Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center, Moffett Field, CA, United States of America; Research Institute for Advanced Computer Science, Universities Space Research Association, Mountain View, CA, United States of America
raw_author_name: Carl D. Laird
raw_affiliation_string: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States of America
raw_author_name: Laurens R. Lueg
raw_affiliation_string: Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States of America
raw_author_name: Stuart M. Harwood
raw_affiliation_string: ExxonMobil Technology and Engineering Company - Research, Clinton, NJ, United States of America
raw_author_name: Dimitar Trenev
raw_affiliation_string: ExxonMobil Technology and Engineering Company - Research, Clinton, NJ, United States of America
raw_author_name: Davide Venturelli
raw_affiliation_string: Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center, Moffett Field, CA, United States of America; Research Institute for Advanced Computer Science, Universities Space Research Association, Mountain View, CA, United States of America

Abstract: Numerical algorithms to solve mathematical optimization problems efficiently are essential to applications in many areas of engineering and computational science. To solve optimization problems of ever-increasing scale and complexity, we need methods that exploit emerging hardware systems. However, the complexities of specific architectures and their impact on performance can be challenging. This article provides an overview of emerging hardware architectures and how they are used to solve mathematical optimization problems. We focus on parallel high-performance computing architectures, which are well-established yet challenging to employ for optimization, as well as digital quantum computing, which has recently gained attention due to its potential for transformative computational performance. Furthermore, we highlight several other emerging hardware architectures that may become relevant for mathematical optimization. We intend for this review to encourage the optimization and process engineering communities to increasingly consider both hardware and software developments in the pursuit of superior computational performance.
Fast random opposition-based learning aquila optimization algorithm

Authors:
raw_author_name: S. Gopi
raw_affiliation_string: Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.
raw_author_name: Prabhujit Mohapatra
raw_affiliation_string: Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, 632 014, Tamil Nadu, India.

Abstract: Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been a continuous effort to develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting strategy of Aquila birds in nature. However, in complex optimization problems, the AO has shown a sluggish convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, in this study, a new mechanism named Fast Random Opposition-Based Learning (FROBL) is combined with the AO algorithm to improve the optimization process. The proposed approach is called the FROBLAO algorithm. To validate the performance of the FROBLAO algorithm, the CEC 2005, CEC 2019, and CEC 2020 test functions, along with six real-life engineering optimization problems, are tested. Moreover, statistical analyses such as the Wilcoxon rank-sum test, the t-test, and the Friedman test are performed to analyze the significant difference between the proposed algorithm FROBLAO and other algorithms. The results demonstrate that FROBLAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.