Reviewerly/exHarmony huggingface dataset:
The effects of NMT as a de facto dictionary on vocabulary learning: a comparison of three look-up conditions

Authors:
raw_author_name: Siowai Lo
raw_affiliation_string: Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macau, China

Abstract: Many language learners have reported using Neural machine translation (NMT) such as Google Translate as a reference tool for writing production or as a dictionary substitute to facilitate reading comprehension. Although increasing evidence has suggested that editing with NMT can help learners make improvement on the vocabulary level, no study has explored how consulting NMT for unknown vocabulary may impact the retention of word meaning compared to more conventional look-up conditions. Using a 3 × 3 quasi-experimental design, this study investigated the extent to which the use of NMT to learn unknown target words during reading comprehension activities yielded vocabulary retention compared to that of an online dictionary and how the findings varied by learners' proficiency levels. The research questions focused on the comparison of the outcomes of three different look-up conditions: (1) NMT word search, (2) NMT sentence search, and (3) online dictionary search. ANOVA results showed that learners who performed online dictionary search and NMT sentence search demonstrated significantly higher levels of word retention than those who carried out NMT single-word search, and the same pattern was observed regardless of their proficiency levels.
Flexible learning spaces as an arena for developing professional digital competence through small group collaboration

Authors:
raw_author_name: Alexandra Lazareva
raw_affiliation_string: Department of Education, University of Agder, Kristiansand, Norway
raw_author_name: Cathrine Edelhard Tømte
raw_affiliation_string: Department of Information Systems &amp; CeDiT, Faculty of Social Sciences, University of Agder, Kristiansand, Norway; SLATE Centre for the Science of Learning and Technology, University of Bergen, Bergen, Norway

Abstract: Flexible technology-rich learning spaces such as Future Classroom Labs (FCL) have a large potential for developing teacher education (TE) students’ professional digital competence through engaging in authentic collaborative learning experiences with digital technology. However, there are limited reports on how students perceive and enact the affordances existing in such learning spaces. This paper reports on how one such FCL can facilitate collaborative learning among TE students. The paper poses two research questions: 1) How does the FCL as a learning space accommodate for collaborative learning? and 2) How do the artefacts promote or limit collaboration among students at the FCL? The data were collected through observations of four sessions of group work at the FCL, each having a different group of 15–16 first-year TE students. The results suggest that the FCL promoted collaborative work in terms of finding and building groups, monitoring, and resource-sharing across the small groups. However, students depended on external guidance from a teacher or student assistant to understand the technical sides of the artefacts, and their educational potential. Therefore, the implication is that in their first visits to FCLs, students need guidance to get started with the distinct tasks, and to master the various devices.
Dunhuang murals image restoration method based on generative adversarial network

Authors:
raw_author_name: Hui Ren
raw_affiliation_string: School of Information and Communication Engineering, Communication University of China, Beijing, 100024, China
raw_author_name: Ke Sun
raw_affiliation_string: School of Information and Communication Engineering, Communication University of China, Beijing, 100024, China
raw_author_name: Fanhua Zhao
raw_affiliation_string: School of Information and Communication Engineering, Communication University of China, Beijing, 100024, China
raw_author_name: Xian Zhu
raw_affiliation_string: Key Laboratory of Acoustic Visual Technology and Intelligent Control System, Ministry of Culture and Tourism, Beijing, 100024, China

Abstract: Abstract Murals are an important part of China’s cultural heritage. After more than a 1000 years of exposure to the sun and wind, most of these ancient murals have become mottled, with damage such as cracking, mold, and even large-scale detachment. It is an urgent work to restore these damaged murals. The technique of digital restoration of mural images refers to the reconstruction of structures and textures to virtually fill in the damaged areas of the image. Existing digital restoration methods have the problems of incomplete restoration and distortion of local details. In this paper, we propose a generative adversarial network model combining a parallel dual convolutional feature extraction depth generator and a ternary heterogeneous joint discriminator. The generator network is designed with the mechanism of parallel extraction of image features by vanilla convolution and dilated convolution, capturing multi-scale features simultaneously, and reasonable parameter settings reduce the loss of image information. A pixel-level discriminator is proposed to identify the pixel-level defects of the captured image, and its joint global discriminator and local discriminator discriminate the generated image at different levels and granularities. In this paper, we create the Dunhuang murals dataset and validate our method on this dataset, and the experimental results show that the method of this paper has an overall improvement in the evaluation metrics of PSNR and SSIM compared with the comparative methods. The restored resultant image is more in line with the subjective vision of human beings, which achieves the effective restoration of mural images.
A multimodal virtual vision platform as a next-generation vision system for a surgical robot

Authors:
raw_author_name: Min Jung Kim
raw_affiliation_string: Department of Surgery, Seoul National University College of Medicine, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
raw_author_name: Ji Won Park
raw_affiliation_string: Department of Surgery, Seoul National University College of Medicine, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea

Abstract: Abstract Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot. Graphical Abstract
Towards an obstacle detection system for robot obstacle negotiation

Authors:
raw_author_name: Han Wang
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Quan Zhang
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Zhenquan Fan
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Gongcheng Wang
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Pengchao Ding
raw_affiliation_string: Harbin Institute of Technology, Harbin, China
raw_author_name: Weidong Wang
raw_affiliation_string: Harbin Institute of Technology, Harbin, China

Abstract: Purpose To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles. Design/methodology/approach The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method. Findings The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage. Originality/value This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.
LCPCWSC: a Web service classification approach based on label confusion and priori correction

Authors:
raw_author_name: Lin Xue
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
raw_author_name: Feng Zhang
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

Abstract: Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.
Color watermarking algorithm combining the quantum discrete cosine transform with the sinusoidal–tent map

Authors:
raw_author_name: Xi Zhou
raw_affiliation_string: Department of Electronic Information Engineering, Nanchang University, Nanchang, China
raw_author_name: Su-Hua Chen
raw_affiliation_string: Department of Electronic Information Engineering, Nanchang University, Nanchang, China
raw_author_name: Li-Hua Gong
raw_affiliation_string: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China

Abstract: To overcome the drawbacks of the existing sinusoidal map and tent map, this paper proposes the design of a sinusoidal–tent (ST) map. The test results indicate that the new chaotic system exhibits more significant advantages in chaos control. Compared with the sinusoidal map and tent map, the proposed sinusoidal–tent map performs better in terms of bifurcation diagram and Lyapunov exponents. The trajectories of the sinusoidal–tent map can occupy all the phase planes over (0,4), while those of the two classic maps only occupy a small phase space, and the Lyapunov exponents of the ST map are all positive within the range of control parameters, higher than those of seed maps. Simultaneously, a novel quantum scrambling operation is devised based on the sinusoidal–tent map to avoid the periodicity of the quantum Arnold scrambling method. Initially, two chaotic sequences are generated to scramble the pixel positions of the watermark image, further enhancing the security of the watermarking algorithm. Subsequently, the host image is processed by the quantum discrete cosine transform, and finally, the scrambled watermark image is inserted into the medium-frequency band of the transformed host image, ensuring the invisibility of the watermarking. According to the simulation results, the quantum watermarking algorithm has excellent invisibility and robustness.
Blockchain-cloud privacy-enhanced distributed industrial data trading based on verifiable credentials

Authors:
raw_author_name: Junli Fang
raw_affiliation_string: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
raw_author_name: Tao Feng
raw_affiliation_string: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
raw_author_name: Xian Guo
raw_affiliation_string: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
raw_author_name: Rong Ma
raw_affiliation_string: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
raw_author_name: Ye Lu
raw_affiliation_string: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

Abstract: Abstract Industrial data trading can considerably enhance the economic and social value of abundant data resources. However, traditional data trading models are plagued by critical flaws in fairness, security, privacy and regulation. To tackle the above issues, we first proposed a distributed industrial data trading architecture based on blockchain and cloud for multiple data owners. Subsequently, we realized implemented distributed identity management by the distributed verifiable credentials scheme that possesses the desirable properties, i.e., selective disclosure, multi-show unlinkability, threshold traceability, and public verifiability. Finally, we presented a fair trading mechanism without trusted third parties based on smart contracts, and we employed blockchain and multi-signature to ensure data integrity during data storage and trading. The security and performance analysis shows that our proposal is feasible for sensitive data trading for multiple data owners and provides a useful exploration for future industrial data trading and management.
Super‐resolution reconstruction based on generative adversarial networks with dual branch half instance normalization

Authors:
raw_author_name: Xiaoxin Guo
raw_affiliation_string: Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
raw_author_name: Haoran Zhang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, China

Abstract: Abstract This paper proposes a super‐resolution reconstruction model, SRPGANto improve the visual quality of images based on generative adversarial networks (GANs) by improving the network structures of the generator and the discriminator. In the generator, a dual branch residual block is designed instead of the residual block, including a branch with an attention mechanism and a branch without an attention mechanism, to extract more differentiated features. Normalization methods are explored to avoid unstable training and bath normalization artifacts and use a half instance normalization layer that is more suitable for underlying visual problems compared with traditional batch normalization. In the discriminator, PatchGAN is applied instead of typical GAN to improve the generation of local texture by discriminating each patch rather than the global image. The experimental results on the public datasets demonstrate that the proposed SRPGAN can achieve excellent quantitative evaluation while improving the visual quality of reconstructed images.
Optimization on Manifolds via Graph Gaussian Processes

Authors:
raw_author_name: Hwanwoo Kim
raw_affiliation_string: Committee on Computational and Applied Mathematics, University of Chicago, Chicago, IL 60637 USA.
raw_author_name: Daniel Sanz-Alonso
raw_affiliation_string: Department of Statistics, University of Chicago, Chicago, IL 60637 USA.
raw_author_name: Ruiyi Yang
raw_affiliation_string: Program of Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544 USA.

Abstract: .This paper integrates manifold learning techniques within a Gaussian process upper confidence bound algorithm to optimize an objective function on a manifold. Our approach is motivated by applications where a full representation of the manifold is not available and querying the objective is expensive. We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective. Query points are sequentially chosen using the posterior distribution of the surrogate model given all previous queries. We establish regret bounds in terms of the number of queries and the size of the point cloud. Several numerical examples complement the theory and illustrate the performance of our method.KeywordsBayesian optimizationmanifold learninggraph Gaussian processesMSC codes60G1562F1505C8590C26
Research on path planning technology of mobile robot in the restaurant scene based on A* and D* algorithm

Authors:
raw_author_name: Jiahao Han
raw_affiliation_string: School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan City, Hubei Province, 430074, China

Abstract: Intelligent robots have found widespread applications in various fields including medical treatment, industrial production, and social services. These robots are specifically designed to fulfill diverse tasks in different environments, the design and control of these robots play a crucial role in ensuring their efficiency and security. In response to the high demand for fast and convenient service, the catering field is a crucial and promising application field for intelligent robots. This article is primarily focused on the robot in the restaurant scene where the robot could automatically deliver food to the customers, thus reducing the burden on human service providers. In this special environment, the delivery robot must possess the capability to identify its surroundings within the restaurant and autonomously calculate an obstacle-free path from the starting point to the desired destination without colliding with either obstacles or individuals. In this article, the restaurant scene is built in the Gazebo, and map scanning and conversion methods are utilized to enable the robots to recognize the environment effectively. Furthermore, A* and D* algorithms are employed in Python to achieve global and local path planning, respectively, thus validating the feasibility of the methods. The combination of these two algorithms demonstrates a successful path-planning application within the context of a restaurant scene.
Mixed Reality: Present and future

Authors:
raw_author_name: Zhenyu Hu
raw_affiliation_string: Faculty of science and engineering, University of Liverpool, Liverpool, L69 3BX UK

Abstract: Mixed Reality (MR) is an emerging technology that can create a visual environment connecting the real world and the virtual environment. With this technology, the way information is presented has changed in a great number of domains. However, for common people, this technology still looks far away from life and seems unintelligible. Therefore, this essay reviews the development and recent research of MR, trying to help people without professional backgrounds understand this emerging technology and offer researchers another perspective on MR. By using the methods of text mining, this essay has reviewed more than twenty papers related to MR on different topics, including the definition, the principles, and the applications. The objective of this essay is to provide a comprehensive understanding of MR, which may promote wider applications and research.
Investigating the Effectiveness of Synchronous E-Learning Tools on Developing EFL Learners' Language Communication Skills Among the Hashemite University Students

Authors:
raw_author_name: Sabri S.Y. Al-Shboul
raw_affiliation_string: The Hashemite University

Abstract: After COVID-19, today’s language classes have become blended with e-learning tools that have become indispensable to the process of learning in many situations. Synchronous e-learning classrooms have been studied for their impact on EFL communication skills, but few studies have been conducted in the Jordanian institutions. The purpose of this study is to investigate the EFL students’ viewpoints on the efficiency of online classrooms in improving their verbal communication abilities. This study consisted of 284 participants who were EFL learners from the Hashemite University. It was conducted during the academic year 2022-2023, and it followed a mixed qualitative approach. Two means of data collection were used: a questionnaire and observations. The results indicated that students have a positive viewpoint toward online learning effectiveness in developing their communication. Nevertheless, students showed that low self-assurance, nervousness over making errors, and poor lexical knowledge are their main concerns when communicating in EFL classrooms.
Unravelling Yu Guangzhong’s Identity Through His Poetic Reflections: A Corpus-Based Investigation

Authors:
raw_author_name: Shengnan Li
raw_affiliation_string: Universiti Putra Malaysia
raw_author_name: Hazlina Abdul Halim
raw_affiliation_string: Universiti Putra Malaysia
raw_author_name: Roslina Mamat
raw_affiliation_string: Universiti Putra Malaysia

Abstract: This study examines Yu Guangzhong’s identity presented in his self-translated poetry collection, The Night Watchman. It employs corpus linguistic methodologies and utilizes the corpus linguistic tool AntConc for analysis. This study highlights Yu Guangzhong’s identity, which is presented in different categories and topics of poems. It is found that Yu Guangzhong’s identity is ever-changing, varying at different times, and occasionally conflicting and opposing. This study may have practical implications for Chinese literature and culture looking to enhance their international influence, and it may also provide academic insights for investigating other literary giants.