Reviewerly/exHarmony huggingface dataset:
Null subject acquisition in L2 Chinese speakers: a case study on English L1 speakers

Abstract This article investigates the acquisition of null subjects (henceforth NSs) in (radical pro-drop) Chinese by learners whose native language is (non-pro-drop) English – henceforth ELs – working within the ‘topic chain’ information-structural approach. Our main goal is to verify whether a learner’s proficiency level can have an impact on the interpretation and acceptability of embedded NSs when more than one head is proposed for the relevant topic chain in different structural contexts (i.e., complements of bridge and factive verbs). The results of a pilot experimental test suggest that the requirement for a minimal overt link (MOLC) in the relevant chain plays an important role for ELs with a lower proficiency level. Conversely, MOLC restrictions do not affect ELs with a higher proficiency. Furthermore, the results show that ELs can correctly distinguish between complements of bridge and factive verbs already at an HSK 3 level. As for interpretation, results suggest that contextual information cannot supersede structural constraints for ELs. However, since a clash with contextual information seems to not affect acceptability for ELs, contrary to Chinese Native Speakers, we propose that even HSK 5 students have not fully acquired the competence for managing NSs in a radical language such as Chinese.
Construction of Virtual Teaching and Research Section of Circuit Analysis Course in U-learning Era

With the development of AI, 5G and big data technology, learning mode has gradually changed from traditional learning to ubiquitous learning (U-learning), which is a new learning paradigm that has been studied in recent years all over the world. Fully applying ubiquitous computing technology and building an intelligent online learning education platform to provide learners with powerful learning experience are the future development trend of intelligent learning. In U-learning era, the trend of education informatization, teaching reform, and academic cross-integration has promoted the reform of the teaching and research section. At this time, the emergence of the virtual teaching and research section is of great significance for the innovative exploration of the new grassroots teaching organization. Based on this, this paper firstly discussed the basic connotation of the virtual teaching and research section, and pointed out that the virtual teaching and research section was not only a new mechanism of regional cooperative education and a community of teachers’ teaching research, but also a new platform of teaching resources sharing. Then, taking the virtual teaching and research section of circuit analysis course as reference, the structure of the virtual teaching and research section was designed, which included four modules of platform support, basic model, achievement output and smart teaching. At the same time, the application effect of the virtual teaching and research section of circuit analysis course was analyzed. Finally, the construction path of virtual teaching and research section was proposed from the aspects of intelligent platform construction, teachers’ quality improvement, resources co-construction and sharing, and guarantee mechanism formulation, in order to provide reference for deepening education reform and achieving high-quality development of higher education.
Blockchain For Internet of Things: A Consensus Mechanism Analysis

The Internet of Things (IoT) is rapidly expanding, resulting in an exponential growth of IoT devices and an increasing need for secure data storage. Currently, data management and device interaction for IoT devices are centralized, with a central server managing the storage functions. As a novel data storage solution, blockchain technology offers security through its inherent features, such as distributivity, decentralization, immutability, and consensus mechanisms. Consensus mechanisms represents the rule set imposed by the blockchain network on the IoT devices to enhance network security. In this paper, we analyzed three types of consensus mechanisms - Proof of Work, Proof of Stake, and Proof of Authority - to determine which is best suited for use in IoT data storage solution that uses blockchain. Thus, we evaluated the consensus mechanisms on public blockchain test networks using three metrics: transaction latency, average block time, and average query time. Our obtained results show the PoS and PoA consensus mechanisms are more appropriate for IoT, exhibiting lower transaction latency, lower average block time and query time when compared to the PoW mechanism. This suggests that PoS and PoA are promising alternatives for blockchain-based IoT systems that require energy-efficient and low-latency transactions.
Lithology classification in semi-arid area combining multi-source remote sensing images using support vector machine optimized by improved particle swarm algorithm

The development of multi-source remote sensing technologies is helpful for geologists to obtain more comprehensive and complete lithological maps. In recent years, establishing automatic classification models based on Machine Learning (ML) algorithms has become an important approach to identify various lithologies supported by remote sensing data. Aiming at the specific geological and geographical conditions in a semi-arid area, Duolun County, Inner Mongolia Autonomous Region, China, this paper integrated GaoFen-2 (GF-2), Sentinel-2A, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and GaoFen-3 (GF-3) remote sensing data, and used Support Vector Machine (SVM) classifier on the basis of Particle Swarm Optimization (PSO) to carry out the lithology classification. Firstly, on the basis of removing the interference of vegetation information from the acquired remote sensing data, a 63-dimensional candidate feature sequence was constructed by extracting spectral, backscattering, polarization and texture features. Secondly, an improved PSO algorithm with the Inertia Factor changing with the S-curve Decreasing function (SDIF-PSO) was proposed, and on this basis, a feature selection and lithology classification algorithm using SVM classifier based on two-layer SDIF-PSO was designed. Finally, the iterative optimization process of multiple optimization algorithms for SVM model parameters and the lithology classification accuracy before and after feature selection were compared. The experimental results showed that the proposed SDIF-PSO algorithm had the best optimization capability, with the highest cross-validation accuracy of 90.90%, which was improved by 3.85% than that of Grid-Search Optimization (GSO) algorithm, and 0.15% than that of the improved PSO algorithm with the Inertia Factor changing with the Linear Decreasing function (LDIF-PSO) and the improved PSO algorithm with the Inertia Factor Decreasing with the Concave function (CDIF-PSO). The dimension of the best feature combination was reduced to 35 through feature selection, and the convergence cross-validation accuracy reaches 92.14%, which was improved by 1.24% than that of all 63-dimensional candidate features in the same optimization process using SDIF-PSO algorithm.
Design and Application Research of Embedded Voice Teaching System Based on Cloud Computing

Deep changes are occurring in the components and forms of education as a result of the ongoing integration and development of emerging technologies like cloud computing, mobile computing, and artificial intelligence with teaching and learning, and the digital transformation of education is consistently being pushed to new heights. Simultaneously, China’s higher education has concurrently reached the stage of popularization. The digitalization of higher education is related to the development quality and value proposition of higher education and determines whether it can adapt to the needs of quality diversification, lifelong learning, training personalization, and governance modernization in the popularization stage. As a result, the current and future phases of China’s higher education reform call for accelerating the pace of higher education’s digital transformation and guiding the high-quality growth of higher education with digital innovation. The application potential of intelligent learning systems in higher education is becoming more and more clear in this context. In view of this, this work draws from previous research and experiences to build and implement an embedded voice teaching system based on cloud computing and a deep learning model to meet the development needs of the current digital transformation of higher education. On the one hand, the new system can well compensate for the flaws and shortcomings of the current teaching means in universities and realize the accompanying ubiquitous learning by relying on the powerful storage and computing capacity of the cloud computing platform. On the other hand, this study designs a set of voice recognition methods integrating <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>HMM</mtext> <mo>+</mo> <mtext>LSTM</mtext> </math> to enhance the embedded voice system’s recognition performance, ultimately allowing for the voice recognition feature to be implemented in the pedagogical system. When it comes to processing audio signals, the hybrid model makes use of both the HMM’s robust time processing capability and the deep neural network’s robust characterization capability and generalization performance. As a result, the voice recognition rate, anti-interference performance, and noise robustness can all be significantly improved. Finally, the embedded voice system is put through its paces in an experimental setting to gauge its performance and functionality. The results of the tests demonstrate that the created hybrid model has high recognition accuracy and good noise immunity, which will be utilized as a foundation for the design and development of the final system. Meanwhile, the new system’s functional modules have achieved the expected results with good stability and reliability. Trial results gathered through interviews and questionnaires demonstrate that the new system significantly enhances the intelligence and adaptability of college teaching methods and is conducive to promoting the improvement of college students’ cultural literacy and innovation ability.
Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis Of Covid-19

Ubiquitous environments are not fixed in time. Entities are constantly evolving; they are dynamic. Ubiquitous applications therefore have a strong need to adapt during their execution and react to the context changes, and developing ubiquitous applications is still complex. The use of the separation of needs and model-driven engineering present the promising solutions adopted in this approach to resolve this complexity. The authors thought that the best way to improve efficiency was to make these models intelligent. That's why they decided to propose an architecture combining machine learning with the domain of modeling. In this article, a novel tool is proposed for the design of ubiquitous applications, associated with a graphical modeling editor with a drag-drop palette, which will allow to instantiate in a graphical way in order to obtain platform independent model, which will be transformed into platform specific model using Acceleo language. The validity of the proposed framework has been demonstrated via a case study of COVID-19.
Origin-Oriented Shuffled Frog Leaping Vehicle Routing Multiobjective Optimization Algorithm

Shuffled frog leaping algorithm is a biological swarm intelligent optimization algorithm and improved into capacity-limited vehicle routing problem. However, the optimization performance is limited with improvement strategies in major of the improvement algorithm. A novel framework of algorithm is proposed to solve capacity-limited vehicle routing problem, including three modules such as origin oriented shuffled frog leaping algorithm strategy, origin oriented shuffled frog leaping vehicle routing multiobjective optimization algorithm strategy, and output module. The frog individuals gather near the origin with the maximum probability and in the area circle, with the frog leaping radius or frog-oriented radius, as the neighborhood. The negative value of the maximum entropy and the shortest total path length of the vehicle are selected as the fitness. The performance test shows that it overcomes the defect of slow convergence compared with other five algorithms. It performs well to solve vehicle routing problems.
Analysis of very large voxel datasets

The paper addresses the problem that large voxel models of detailed scenes in urban environments do not fit inside the (physical or virtual) memory of standard, or even high-end, workstations. It presents an investigation into out-of-core processing options and cloud solutions, while looking in particular for combinations of both. As a result, an octree-based datastructure is proposed. It is implemented in an already available key–value data store system. This solution has the advantage that existing in-core software can be quite easily converted into an out-of-core version. The resulting programs can still be executed on standard workstations with reasonable performance, while the expectation is that they can also be implemented in high-performance distributed multi-processor environments.
How satisfactory are empathetic care and robotic assistance in telemedicine services?

ABSTRACTABSTRACTTelemedicine healthcare and assistive robotic technologies are gaining popularity as support systems for traditional healthcare practices. However, there are concerns regarding the use of these digital services due to their lack of empathy and intimacy. This paper investigates the impact of physicians' empathetic care and telepresence robot assistance on patients' intimate experience and satisfaction. We employ a multi-method design to achieve our research objective. The first study utilizes a 2 (digital empathetic care: high vs. low) × 2 (telepresence robot assistance: present vs. absent) × 2 (severity of illness: high vs. low) between-subject experimental design involving 287 participants who have used robotic assistance and telemedicine services. The findings from this study confirm that the telepresence robotic assistance and empathetic care by physicians positively impact intimate experience and patient satisfaction significantly more for patients with low severity of illness, thus confirming the significant moderated-moderated mediation effect. The second study employs field data (n = 4629 reviews) to further validate the telepresence robots' usability in establishing intimate patient experiences in digital settings. The study recommends to healthcare practitioners that empathetic care and robotic assistance are essential factors for building intimate experience and patient satisfaction in telehealthcare services.抽象的远程医疗保健和辅助机器人技术作为传统医疗保健实践的支持系统越来越受欢迎。然而,由于缺乏同理心和亲密感,人们对使用这些数字服务感到担忧。本文研究了医生的同理心护理和远程呈现机器人辅助对患者亲密体验和满意度的影响。我们采用多方法设计来实现我们的研究目标。第一项研究采用 2(数字同理心护理:高与低)X 2(远程呈现机器人辅助:存在与不存在)X 2(疾病严重程度:高与低)受试者间实验设计,涉及 287 名有经验的参与者 使用机器人协助和远程医疗服务。这项研究的结果证实,远程呈现机器人协助和医生的同理心护理对疾病严重程度较低的患者的亲密体验和患者满意度产生了积极影响,从而证实了显着的调节-调节中介效应。第二项研究使用现场数据(n = 4629 条评论)进一步验证远程呈现机器人在数字环境中建立亲密患者体验的可用性。该研究向医疗从业者建议,同理心护理和机器人协助是在远程医疗服务中建立亲密体验和患者满意度的重要因素。KEYWORDS: Telemedicine servicehealthcare managementtelepresence robotsempathyintimacypatient satisfactionexperience关键词: 远程医疗服务健康管理远程呈现机器人共情亲密患者满意度经验 AcknowledgementProf. Jay Kandampully, Professor of Service Management, The Ohio State University, USA.Disclosure statementNo potential conflict of interest was reported by the author(s).
Biomedical named entity recognition based on fusion multi-features embedding

With the exponential increase in the volume of biomedical literature, text mining tasks are becoming increasingly important in the medical domain. Named entities are the primary identification tasks in text mining, prerequisites and critical parts for building medical domain knowledge graphs, medical question and answer systems, medical text classification.The study goal is to recognize biomedical entities effectively by fusing multi-feature embedding. Multiple features provide more comprehensive information so that better predictions can be obtained.Firstly, three different kinds of features are generated, including deep contextual word-level features, local char-level features, and part-of-speech features at the word representation layer. The word representation vectors are inputs into BiLSTM as features to obtain the dependency information. Finally, the CRF algorithm is used to learn the features of the state sequences to obtain the global optimal tagging sequences.The experimental results showed that the model outperformed other state-of-the-art methods for all-around performance in six datasets among eight of four biomedical entity types.The proposed method has a positive effect on the prediction results. It comprehensively considers the relevant factors of named entity recognition because the semantic information is enhanced by fusing multi-features embedding.
STFTrack: Spatio-Temporal-Focused Siamese Network for Infrared UAV Tracking

The rapid popularity of UAVs has encouraged the development of Anti-UAV technology. Infrared-detector-based visual tracking for UAVs provides an encouraging solution for Anti-UAVs. However, it still faces the problem of tracking instability caused by environmental thermal crossover and similar distractors. To address these issues, we propose a spatio-temporal-focused Siamese network for infrared UAV tracking, called STFTrack. This method employs a two-level target focusing strategy from global to local. First, a feature pyramid-based Siamese backbone is constructed to enhance the feature expression of infrared UAVs through cross-scale feature fusion. By combining template and motion features, we guide prior anchor boxes towards the suspicious region to enable adaptive search region selection, thus effectively suppressing background interference and generating high-quality candidates. Furthermore, we propose an instance-discriminative RCNN based on metric learning to focus on the target UAV among candidates. By measuring calculating the feature distance between the candidates and the template, it assists in discriminating the optimal target from the candidates, thus improving the discrimination of the proposed method to infrared UAV. Extensive experiments on the Anti-UAV dataset demonstrate that the proposed method achieves outstanding performance for infrared tracking, with 91.2% precision, 66.6% success rate, and 67.7% average overlap accuracy, and it exceeded the baseline algorithm by 2.3%, 2.7%, and 3.5%, respectively. The attribute-based evaluation demonstrates that the proposed method achieves robust tracking effects on challenging scenes such as fast motion, thermal crossover, and similar distractors. Evaluation on the LSOTB-TIR dataset shows that the proposed method reaches a precision of 77.2% and a success rate of 63.4%, outperforming other advanced trackers.
A Knowledge Enhanced Hierarchical Fusion Network for CTR Prediction under Account Search Scenario in WeChat

Click-through rate (CTR) estimation plays as a pivotal function module in various online services. Previous studies mainly apply CTR models to the field of recommendation or online advertisement. Indeed, CTR is also critical in information retrieval, since the CTR probability can serve as a valuable feature for a query-document pair. In this paper, we study the CTR task under account search scenario in WeChat, where users search official accounts or mini programs corresponding to an organization. Despite the large number of CTR models, directly applying them to our task is inappropriate since the account retrieval task has a number of specific characteristics. E.g., different from traditional user-centric CTR models, in our task, CTR prediction is query-centric and does not model user information. In addition, queries and accounts are short texts, and heavily rely on prior knowledge and semantic understanding. These characteristics require us to specially design a CTR model for the task. To this end, we propose a novel CTR prediction model named Knowledge eNhanced hIerarchical Fusion nEtwork (KNIFE). Specifically, to tackle the prior information problem, we mine the knowledge graph of accounts as side information; to enhance the representations of queries, we construct a bipartite graph for queries and accounts. In addition, a hierarchical network structure is proposed to fuse the representations of different information in a fine-grained manner. Finally, the representations of queries and accounts are obtained from this hierarchical network and fed into the CTR model together with other features for prediction. We conduct extensive experiments against 12 existing models across two industrial datasets. Both offline and online A/B test results indicate the effectiveness of KNIFE.
Chatbots in libraries: A systematic literature review

Chatbots have experienced significant growth over the past decade, with a proliferation of new applications across various domains. Previous studies also demonstrate the trend of new technologies, especially artificial intelligence, being adopted in libraries. The purpose of this study is to determine the current research priorities and findings in the field of chatbots in libraries. A systematic literature review was performed utilising the PRISMA checklist and the databases Scopus and Web of Science, identifying 5734 records. Upon conducting the first screening, abstract screening, full-text assessment, and quality assessments guided by the CASP appraisal checklist, 19 papers were deemed suitable for inclusion in the review. The results of the review indicate that the majority of the existing studies were empirical in nature (primarily adopting qualitative methods) and technology reviews with a focus on reviewing the implementation and maintenance, design, evaluation, characteristics, and application of chatbots. The chatbots of interest were mainly text-based and guided chatbots, with closed-source tools with access portals mostly built on library web pages or integrated with social software. The research findings primarily concerned the development models and necessary tools and technologies, the application of chatbots in libraries. Our systematic review also suggests that studies on chatbots in libraries are still in the early stages.
SoarGraph: Numerical Reasoning over Financial Table-Text Data via Semantic-Oriented Hierarchical Graphs

Towards the intelligent understanding of table-text data in the finance domain, previous research explores numerical reasoning over table-text content with Question Answering (QA) tasks. A general framework is to extract supporting evidence from the table and text and then perform numerical reasoning over extracted evidence for inferring the answer. However, existing models are vulnerable to missing supporting evidence, which limits their performance. In this work, we propose a novel Semantic-Oriented Hierarchical Graph (SoarGraph) that models the semantic relationships and dependencies among the different elements (e.g., question, table cells, text paragraphs, quantities, and dates) using hierarchical graphs to facilitate supporting evidence extraction and enhance numerical reasoning capability. We conduct our experiments on two popular benchmarks, FinQA and TAT-QA datasets, and the results show that our SoarGraph significantly outperforms all strong baselines, demonstrating remarkable effectiveness.