Reviewerly/exHarmony huggingface dataset:
The impact of intelligent personal assistants on Mandarin second language learners: interaction process, acquisition of listening and speaking ability

Authors:
raw_author_name: Jianrong Zhou
raw_affiliation_string: School of Educational Technology, Beijing Normal University, Beijing, China

Abstract: Intelligent personal assistants (IPAs) are becoming increasingly interactive, convenient and intelligent, and their potential for improving second language skills has been proven by many studies. However, these studies have mainly concentrated on secondary and higher education groups, and little is known about the use of IPAs by second language learners in a sensitive period. Moreover, few studies have analyzed learners’ discourse and behavior. In this study, an intelligent personal assistant called MSLIPA is introduced, and 44 Mandarin second language learners in the second grade of elementary school are recruited and randomly assigned into an experimental group (communicated with MSLIPA) and a control group (communicated with their peers). Both groups completed five Mandarin exchange classes. The researchers explored the impact of IPA on second language learners from two aspects: the interaction process and the acquisition of listening and speaking ability. The results show that learners engage in more rounds of dialog and use more interaction strategies when interacting with IPAs than with their peers; through the LDA analysis of the text data, it is found that the communication topics between the learners and the IPA are rich; conversations with IPAs can significantly improve learners’ listening and speaking ability, but there is no significant difference in the improvement brought about by conversations with peers. These findings indicate that the IPA has potential in Mandarin second language learning in the sensitive period, but longer-term research on technology implementation and strategy application is needed.
Demonstration of Autonomous Driving Control for a Retrofitted Wheel Loader

Authors:
raw_author_name: Tomohito Kawabe
raw_affiliation_string: Hirosaki University, 3 Bunkyo-cho, Hirosaki-city, Aomori 036-8561, Japan
raw_author_name: Masahiro Inagawa
raw_affiliation_string: Hirosaki University, 3 Bunkyo-cho, Hirosaki-city, Aomori 036-8561, Japan
raw_author_name: Toshinobu Takei
raw_affiliation_string: Seikei University, 3-3-1 Kichijoji Kitamachi, Musashino-city, Tokyo 180-8633, Japan
raw_author_name: Hiroto Murayama
raw_affiliation_string: Hirosaki University, 3 Bunkyo-cho, Hirosaki-city, Aomori 036-8561, Japan
raw_author_name: Keiichi Yoshizawa
raw_affiliation_string: Hirosaki University, 3 Bunkyo-cho, Hirosaki-city, Aomori 036-8561, Japan
raw_author_name: Munehiro Ishibashi
raw_affiliation_string: Hirosaki University, 3 Bunkyo-cho, Hirosaki-city, Aomori 036-8561, Japan
raw_author_name: Keiji Nagatani
raw_affiliation_string: The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Abstract: Most research on automating gravel pile transport using wheel loaders has been performed primarily through simulations. Thus, studies should evaluate the usefulness of automatic gravel pile transportation by demonstrating it with an actual wheel loader. This study demonstrates automatic driving control using a retrofitted 3-ton wheel loader for gravel pile transportation. The driving model of a retrofitted wheel loader, in which multiple control systems are interlocked, is considered a simple control model with one input and one output for the pedal and vehicle velocity as well as for the steering wheel and steering angular velocity. In this study, we propose a simple and practical method for constructing a driving model via simple response analysis using an actual machine by constructing a feedforward control model based on control input/output using step responses. In this study, feedforward control was applied to the translation of the vehicle, which has a large dead time. By generating the path following the target point from the vehicle predicted position after the dead time from the driving model, the appropriate control input value calculation considering the dead time was performed. By applying the proposed method to a retrofitted wheel loader in a real environment and evaluating the control performance through control experiments, the effectiveness of the proposed method in practice was demonstrated.
Double-Constraint Fuzzy Clustering Algorithm

Authors:
raw_author_name: Shihong Yue
raw_affiliation_string: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China

Abstract: Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.
Entrepreneurial Orientation and Innovation Performance of Saudi Firms: A Sequential Mediation Model of Absorptive Capacity and Innovation Capability

Authors:
raw_author_name: Wassim J. Aloulou
raw_affiliation_string: Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

Abstract: This study examines the relationship between entrepreneurial orientation (EO) and innovation performance (IP) using a sequential mediation model of absorptive capacity (AC) and innovation capability (ICAP). We conduct a literature review, develop and test hypotheses using structural equation modelling (SEM) and data collected from 225 Saudi small and medium enterprises (SMEs) operating in several sectors in Saudi Arabia. The findings reveal statistically significant relationships between the EO dimensions (proactive innovativeness and risk-taking) and AC and ICAP. However, no direct relationship is found between the EO dimensions and IP. Therefore, AC mediates the relationship between the EO dimensions and ICAP. In addition, ICAP mediates the relationship between the EO dimensions and AC to IP, while AC does not mediate the relationship between the EO dimensions and IP. To the best of our knowledge, limited research has simultaneously explored the impact of EO, AC, ICAP, and IP in the Saudi context. Our study adds to the existing body of knowledge in the fields of entrepreneurship, innovation, and strategic management by providing empirical evidence specific to the Saudi context. It also displays the significance of managers turning strategic EO dimensions into innovation outcomes through AC and ICAP as a contribution to managerial practices.
Anomaly Detection Methods in Autonomous Robotic Missions

Authors:
raw_author_name: Shivoh Chirayil Nandakumar
raw_affiliation_string: School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh EH14 4AS, UK
raw_author_name: Mustafa Suphi Erden
raw_affiliation_string: School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh EH14 4AS, UK
raw_author_name: David Flynn
raw_affiliation_string: James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
raw_author_name: Theodore Lim
raw_affiliation_string: School of Engineering and Physical Sciences, Heriot Watt University, Edinburgh EH14 4AS, UK

Abstract: Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.
A Light Multi-View Stereo Method with Patch-Uncertainty Awareness

Authors:
raw_author_name: Zhen Liu
raw_affiliation_string: College of Science, Zhejiang University of Technology, Hangzhou 310023, China
raw_author_name: Tao Xie
raw_affiliation_string: College of Science, Zhejiang University of Technology, Hangzhou 310023, China
raw_author_name: Shilong Li
raw_affiliation_string: College of Science, Zhejiang University of Technology, Hangzhou 310023, China
raw_author_name: Chao Wu
raw_affiliation_string: College of Science, Zhejiang University of Technology, Hangzhou 310023, China
raw_author_name: Jiali Zhou
raw_affiliation_string: College of Science, Zhejiang University of Technology, Hangzhou 310023, China

Abstract: Multi-view stereo methods utilize image sequences from different views to generate a 3D point cloud model of the scene. However, existing approaches often overlook coarse-stage features, impacting the final reconstruction accuracy. Moreover, using a fixed range for all the pixels during inverse depth sampling can adversely affect depth estimation. To address these challenges, we present a novel learning-based multi-view stereo method incorporating attention mechanisms and an adaptive depth sampling strategy. Firstly, we propose a lightweight, coarse-feature-enhanced feature pyramid network in the feature extraction stage, augmented by a coarse-feature-enhanced module. This module integrates features with channel and spatial attention, enriching the contextual features that are crucial for the initial depth estimation. Secondly, we introduce a novel patch-uncertainty-based depth sampling strategy for depth refinement, dynamically configuring depth sampling ranges within the GRU-based optimization process. Furthermore, we incorporate an edge detection operator to extract edge features from the reference image's feature map. These edge features are additionally integrated into the iterative cost volume construction, enhancing the reconstruction accuracy. Lastly, our method is rigorously evaluated on the DTU and Tanks and Temples benchmark datasets, revealing its low GPU memory consumption and competitive reconstruction quality compared to other learning-based MVS methods.
Diverse Metrics for Robust LBS Privacy: Distance, Semantics, and Temporal Factors

Authors:
raw_author_name: Yongjun Li
raw_affiliation_string: School of Cyberspace Security, Information Engineering University, Zhengzhou 450002, China; Software College, Zhongyuan University of Technology, Zhengzhou 450007, China
raw_author_name: Jinlong Fei
raw_affiliation_string: School of Cyberspace Security, Information Engineering University, Zhengzhou 450002, China
raw_author_name: Wei Wu
raw_affiliation_string: School of Cyberspace Security, Information Engineering University, Zhengzhou 450002, China

Abstract: Addressing inherent limitations in distinguishing metrics relying solely on Euclidean distance, especially within the context of geo-indistinguishability (Geo-I) as a protection mechanism for location-based service (LBS) privacy, this paper introduces an innovative and comprehensive metric. Our proposed metric not only incorporates geographical information but also integrates semantic, temporal, and query data, serving as a powerful tool to foster semantic diversity, ensure high servifice similarity, and promote spatial dispersion. We extensively evaluate our technique by constructing a comprehensive metric for Dongcheng District, Beijing, using road network data obtained through the OSMNX package and semantic and temporal information acquired through Gaode Map. This holistic approach proves highly effective in mitigating adversarial attacks based on background knowledge. Compared with existing methods, our proposed protection mechanism showcases a minimum 50% reduction in service quality and an increase of at least 0.3 times in adversarial attack error using a real-world dataset from Geolife. The simulation results underscore the efficacy of our protection mechanism in significantly enhancing user privacy compared to existing methodologies in the LBS location privacy-protection framework. This adjustment more fully reflects the authors' preference while maintaining clarity about the role of Geo-I as a protection mechanism within the broader framework of LBS location privacy protection.
Implementation of JPEG XS entropy encoding and decoding on FPGA

Authors:
raw_author_name: Qinghua Song
raw_affiliation_string: School of Information Engineering, China University of Geosciences, Beijing, 100083, China
raw_author_name: Jialin He
raw_affiliation_string: School of Information Engineering, China University of Geosciences, Beijing, 100083, China
raw_author_name: Yihan Wang
raw_affiliation_string: School of Information Engineering, China University of Geosciences, Beijing, 100083, China
raw_author_name: Kai Nie
raw_affiliation_string: School of Information Engineering, China University of Geosciences, Beijing, 100083, China
raw_author_name: Gang Du
raw_affiliation_string: School of Information Engineering, China University of Geosciences, Beijing, 100083, China

Abstract: Abstract JPEG XS is the latest international standard for shallow compression fields launched by the International Organization for Standardization (ISO). The coding standard was officially released in 2019. The JPEG XS standard can be encoded and decoded on different devices, but there is no research on the implementation of JPEG XS entropy codec on FPGAs. This paper briefly introduces JPEG XS encoding, proposes a modular design scheme of encoder and decoder on FPGA for the entropy encoding and decoding part, and parallelizes the algorithm in JPEG XS coding standard according to the characteristics of FPGA parallelization processing, mainly including low-latency optimization design, storage space optimization design. The optimized scheme in this paper scheme enables encoding speeds of up to 4 coefficients/clock and decoding speeds of up to 2 coefficients/clock, with a 75% reduction in encoding and decoding time. The maximum clock frequency of the entropy encoder is about 222.6 MHz, and the maximum clock frequency of the entropy decoder is about 127 MHz. The design and implementation of the FPGA-based JPEG XS entropy encoding and decoding algorithm is of great significance and provides ideas for the subsequent implementation and optimization of the entire JPEG XS standard on FPGAs. This work is the first in the world to propose the design and implementation of an algorithm that can implement the JPEG XS entropy encoding and decoding process on FPGA. It creates the possibility for the effective application of JPEG XS standard in more media.
Global Digital Compact: A Mechanism for the Governance of Online Discriminatory and Misleading Content Generation

Authors:
raw_author_name: Zhi Li
raw_affiliation_string: School of Media and Law, NingboTech University, Ningbo, China
raw_author_name: Wenyi Zhang
raw_affiliation_string: School of Humanities, University of Chinese Academy of Sciences, Beijing, China
raw_author_name: Ran Gao
raw_affiliation_string: School of Journalism and Communication, Nankai University, Tianjin, China

Abstract: With the continuous development of artificial intelligence (AI), algorithmic discrimination and discriminatory and misleading content (DMC) generated by AI have given rise to many negative effects in cyberspace, such as racial and gender discrimination, misinformation, and so on. The growing concern in society over AI governance urgently necessitates the establishment of an effective mechanism to supervise and govern AI-generated DMC. In this article, the discriminatory and misleading contents of AIGC (Artificial Intelligence Generated Content) were extracted according to Text Classification Model and then classified by Naive Bayesian algorithm. The results showed that under the Global Digital Compact (GDC), countries differed in their degrees of discrimination related to race, gender, religion, and age. The racial discrimination accounted for the highest proportion in the United States, with a score of 0.15; that in Britain and France took up a share of 0.06 and 0.07, respectively; and merely 0.03 in Germany. Discriminatory content of racial discrimination (M1) and gender discrimination (M2) in science and technology industry was relatively low, accounting for 0.05 and 0.08, respectively. Analyzing data within the Global Digital Compact (GDC) illuminates the disparities and trends in DMC generation across various countries, cities, industries, and individual users. This analysis provides valuable references for subsequent research and problem-solving initiatives under the compact. Furthermore, GDC plays a pivotal role in addressing issues related to AI-generated DMC, contributing significantly to the creation of a secure, reliable, and equitable cyberspace.
Investigating the Effectiveness of Augmented Reality Technology in Science Education in Terms of Environmental Literacy, Self-Regulation, and Motivation to Learn Science

Authors:
raw_author_name: Faruk Arici
raw_affiliation_string: Department of Primary Education, Faculty of Education, Bayburt University, Bayburt, Turkey

Abstract: Augmented Reality (AR) is a technology that provides users with an engaging learning experience by placing interactive virtual 3D models in natural environments. It is known that the use of AR technology in science education has many educational benefits. This study investigated the impact of AR technology on the development of environmental literacy, self-regulation skills, and motivation for science learning, which are increasingly important skills. Acquiring these high-level skills is essential for supporting students’ development in preventing environmental problems, addressing the motivation decrease seen primarily due to distance learning, and acquiring the self-regulation skills that have gained importance due to distance learning. The study was conducted with a quasi-experimental design on fifth-grade students, with one experimental (34) and one control (36) group. The findings were analyzed through MANOVA and content analysis. The results showed that AR technology supports environmental sensitivity, positive behavior, and the development of environmental knowledge and skills. In addition, AR technology has significant contributions in terms of self-regulation. Finally, it is recommended to use AR technology in science education to develop these variables and positively affect participants’ motivation for science learning.
Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Authors:
raw_author_name: Xun Yang
raw_affiliation_string: China Merchants Chongqing Communications Research and Design Institute Co., Ltd., China; Research and Development Center of Transport Industry of Self-Driving Technology, China
raw_author_name: Yong Li
raw_affiliation_string: College of Computer Science, China; Key Laboratory of Dependable Service Computing in Cyber Physical Society, China
raw_author_name: Mengjun Wu
raw_affiliation_string: China Merchants Chongqing Communications Research and Design Institute Co., Ltd., China; Research and Development Center of Transport Industry of Self-Driving Technology, China
raw_author_name: Qing Ye
raw_affiliation_string: China Merchants Chongqing Communications Research and Design Institute Co., Ltd., China; Research and Development Center of Transport Industry of Self-Driving Technology, China

Abstract: Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mi>o</mml:mi><mml:mi>U</mml:mi></mml:mrow></mml:math> loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.
Smart hotels but not necessarily smart decisions: the smartness paradox

Authors:
raw_author_name: IpKin Anthony Wong
raw_author_name: Ya Xiao
raw_author_name: Zhiwei (CJ) Lin
raw_author_name: Danni Sun
raw_author_name: Jingwen (Daisy) Huang
raw_author_name: Matthew Liu

Abstract: Purpose This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service. Design/methodology/approach This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments. Findings Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance. Research limitations/implications This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination. Originality/value The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.