CHATGPT: A CRITICAL EVALUATION

Abstract: TESOL JournalEarly View e810 MEDIA REVIEW CHATGPT: A CRITICAL EVALUATION https://chat.openai.com İbrahim Halil Topal, Corresponding Author İbrahim Halil Topal [email protected] orcid.org/0000-0003-4220-3706 Gazi UniversitySearch for more papers by this author İbrahim Halil Topal, Corresponding Author İbrahim Halil Topal [email protected] orcid.org/0000-0003-4220-3706 Gazi UniversitySearch for more papers by this author First published: 17 February 2024 https://doi.org/10.1002/tesj.810Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat REFERENCES Aliakbari, M., Khany, R., & Adibpour, M. (2020). EFL teachers' reflective practice, job satisfaction, and school context variables: Exploring possible relationships. TESOL Journal, 11(1), e00461. https://doi.org/10.1002/tesj.461 10.1002/tesj.461 Web of Science®Google Scholar Chen, X., Zou, D., Xie, H., & Cheng, G. (2021). Twenty years of personalized language learning. Educational Technology and Society, 24(1), 205–222. https://doi.org/10.30191/ETS.202101_24(1).0016 10.30191/ETS.202101_24(1).0016 Web of Science®Google Scholar Hamlin, M. D. (2022). Developing self-directed learning skills for lifelong learning. In P. Hughes & J. Yarbrough (Eds.), Self-directed learning and the academic evolution from pedagogy to andragogy (pp. 209–234). Hershey, PA: IGI Global. 10.4018/978-1-7998-7661-8.ch012 Google Scholar Jin, Y., Dewaele, J. M., & MacIntyre, P. D. (2021). Reducing anxiety in the foreign language classroom: A positive psychology approach. System, 101, 102604. https://doi.org/10.1016/j.system.2021.102604 10.1016/j.system.2021.102604 Web of Science®Google Scholar Kohnke, L., & Moorhouse, B. L. (2022). "There's an app for that!" Writing technology reviews for academic journals. RELC Journal, 53(1), 261–265. https://doi.org/10.1177/0033688220945419 10.1177/0033688220945419 Web of Science®Google Scholar York, J. (2023). Engaging with the world: Applying connected learning in a university language learning context. Foreign Language Annals, 56, 334–361. https://doi.org/10.1111/flan.12691 10.1111/flan.12691 Web of Science®Google Scholar Early ViewOnline Version of Record before inclusion in an issuee810 ReferencesRelatedInformation


FDI and the Triangle “Growth, Inequality and Poverty” in North Africa

Abstract: The economic literature has shown that the effect of FDI on poverty through these instruments has undergone radical changes which reflect, on the one hand, the growing progress, made by the academic community, in the very complex analysis of interactions with income growth and inequality; and the level of political interest in the topic of poverty reduction.


The impact of servant leadership on team innovative performance: Mediating role of employee innovative behavior

Abstract: This study is aimed at exploring the impacts of servant leadership on team innovation performance, examining the role of employee innovative behavior as a mediator and team innovation atmosphere as a moderator. By conducting a questionnaire survey involving 647 employees from the hotel industry and utilizing structural equation modelling for data analysis. Additionally, the study shows that servant leadership significantly improves employees' innovative behavior, which in turn acts as a mediator between servant leadership and team innovation performance. The team innovation climate was identified as a positive moderator reinforcing the link between service-oriented leadership and employee innovation behavior, as well as employee innovation behavior and team innovation performance. This study provides an incisive view of the beneficial impact of service-oriented leadership on team innovation performance, emphasizing the mediating and moderating role played by employee innovation behaviour and team innovation atmosphere. Based on hotel management, the pursuit of timely and effective responses within an intensely competitive market environment is critical to safeguarding the hotel's competitive standing. To address this challenge, the study offers the following recommendations: the foremost emphasis should be on bolstering employees' innovation capabilities. Second, hotel managers are advised to meticulously select, train, and cultivate leaders with a strong service orientation. Thirdly, enterprise leaders should continually elevate their personal knowledge to provide superior guidance to subordinates.


The Role of Blockchain Technology in Securing Supply Chain Information Systems

Abstract: This research aims to analyze the role of blockchain technology in securing supply chain information systems. The research method used involves literature study and qualitative analysis of blockchain implementation in the context of existing supply chain information systems. The research results show that blockchain technology can play an important role in strengthening the security and integrity of supply chain information systems. By using characteristics such as decentralization, transparency, and reliability, blockchain can help fight security threats such as cyberattacks, counterfeiting, and data manipulation. Through the use of blockchain, information can be secured, verified and safely accessed by stakeholders in the supply chain. Apart from that, blockchain can also increase efficiency and accountability in supply chain information systems. In conclusion, blockchain technology has significant potential in securing supply chain information systems and can contribute to increasing trust between stakeholders.


Utilization of Reading Bags as a Strategy to Improve Reading Habits for Secondary Schools Learners

Abstract: The main purpose of this chapter was to investigate the utilization of reading bags to inculcate the love for reading among secondary school learners living in impoverished social settings. A quantitative descriptive cross-sectional design was used to measure the shift in reading strategies after the use of the reading bags strategy to inculcate the love for reading. A total of 155 learners in schools who have interest in the presentations from grade to grade on reading are identified from the Capricorn district in Limpopo province, enlisted for the reading project. The survey with 185 questionnaires on metacognitive reading strategies was applied to collect data. Data were collected in two phases and analysis through using descriptive statistics using the Statistical Package for Social Sciences, version 17. Learners reported a high percentage of 72.3% for global reading strategies, 80.2% for problem-solving strategies and 76.4% for support reading strategies. This study revealed the positive effect of using reading bags to enhance learners' reading habits and develop useful strategies in academic reading.


HIFINet: Examination-Diagnosis-Treatment Hierarchical Feedback Interaction Network for Medication Recommendation

Abstract: Abstract Medication combination recommendation is critical in clinic, since accurately predicting therapeutic drug can provide essential decision support to physicians. However, current approaches do not consider the multilevel structure of electronic health record (EHR) data or the hierarchical dependencies between multiple visits, leading to suboptimal recommendations. To address these limitations, we propose a novel hierarchical feedback interaction network (HIFINet) to utilize an examination-diagnosis-treatment hierarchical network for modeling the inherent multilevel structure of EHR data. The feedback long short-term memory network called FeLSTM, which is the basic unit of our hierarchical network, performs hierarchical interactions and leverages change information as feedback to propagate forward among different levels. Additionally, HIFINet contains four modules. First, an embedding module is designed to learn the health information representation of patients. Second, a three-layer time-series learning module is employed to capture temporal dependencies within each sequence. Next, a differential feedback interaction module is developed to capture the difference features between visits. Finally, an attention fusion module is used to learn a comprehensive representation of the patient’s health information and to recommend next multiple treatment medications. HIFINet is compared with state-of-the-art approaches on a real-world dataset. The results indicate that HIFINet outperforms other approaches, offering more accurate recommendations.


MGSGA: Multi-grained and Semantic-Guided Alignment for Text-Video Retrieval

Abstract: Abstract In the text-video retrieval task, the objective is to calculate the similarity between a text and a video, and rank the relevant candidates higher. Most existing methods only consider the text-video semantic alignment in the global view. But using mean-pooling to obtain global semantics and simply aligning text and video in the global view may lead to semantic bias. In addition, some methods utilize offline object detectors or sentence parsers to obtain entity-level information in text and video and achieve local alignment. However, inaccurate detection introduces possible errors and such approaches prevent models from being trained end-to-end for retrieval. To overcome these limitations, we propose multi-grained and semantic-guided alignment for text-video retrieval in this paper, which can achieve fine-grained alignment based on video frames and text words, local alignment based on semantic centers, and global alignment. Specially, we explore summary semantics of text and video to guide the local alignment based on semantic centers for we believe that the importance of each semantic center is determined by summary semantics. We evaluate our approach on four benchmark datasets of MSRVTT, MSVD, ActivityNet Captions, and DiDeMo, achieving better performance than most existing methods.


Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Disiase Classification

Abstract: Dengue hemorrhagic fever (DHF) is a condition brought on by infection with the dengue virus. DHF is a severe illness with hemorrhagic clinical signs that can result in shock and death. One of the four viral serotypes of the genus Flavivirus is responsible for DHF. DHF symptoms include fever, joint pain, red skin patches, and others that are similar to those of other illnesses. So that there are no errors in illness prediction, strong accuracy and accuracy are required when classifying DHF patients or not. The Fuzzy K-Nearest Neighbor (FKNN) method is used in this study to classify dengue sickness in order to obtain the best classification outcomes. In this investigation, k was searched for eight times, with values of 3,5,7,9,11,13,15, and 20. Each K's accuracy statistics are 75.15, 75.16, 77.58%, 79.51%, 85.01%, 78.14%, and 75.20 percent. K = 13, which has an accuracy score of 85.01%, yields the highest accuracy.


Project Tutok 3Rs (Reading, Writing, Arithmetic) in Maybancal Elementary School

Abstract: The purpose of this study was to investigate and determine the effectiveness of Project Tutok 3Rs (Reading, Writing, Arithmetic) in improving the performance of grade 4 learners and teacher’s perception in Maybancal Elementary School and SPED Center. This study used quantitative types of research to determine the performance of the learners after the implementation of intervention and tested the significant difference before and after the implementation. This is also a 1ualitative type of research for this determined the perception of the learners after the implementation of intervention and had tested the significant difference on the perception before and after the implementation. The respondents of the study were the Grade 4 learners in Maybancal Elementary School and Sped Center. The study was conducted during the Third Quarter of the school year 2021-2022. Tracking the findings, based from the result on Mean and Paired Sample T-test, the study found that Project Tutok 3Rs increased the performance of the learners where the mean percentage of 82% increased to 90%. It was concluded that in Maybancal Elementary School, Project Tutok 3Rs has a great impact in the improvement of learner’s performances in the school.


ENHANCING BREAST LUMP DIAGNOSTICS: A COMPARATIVE STUDY OF HISTOPATHOLOGICAL AND CYTOLOGY

Abstract: Introduction: The increasing incidence of breast lumps cases represents a signicant health challenge, necessitating . The integration of multiple diagnostic modalities, including histopathological and cytological techniques, is imperative. Correlating histopathological ndings with cytological evaluation can signicantly enhance the accuracy of preoperative diagnosis and contribute to the development of evidence-based guidelines for integrating cytological evaluation into the diagnostic algorithm for breast lumps. In a prospective observational study at a Methods: tertiary care teaching hospital, female patients aged 18 years and above with palpable breast lumps were enrolled according to the inclusion criteria. Data on demographics and clinical history were collected. Histopathological and cytological evaluations were conducted, assessing concordance, with statistical analysis revealing sensitivity, specicity, PPV, and NPV of cytological evaluation. Ethical considerations, including informed consent and condentiality, were obtained. In the cytological evaluation Results: of 290 breast lump cases, benign cases constituted the majority, accounting for 68%, followed by malignant cases at 27.5%. Out of 290 patients, 75 underwent histopathological examination. Among these, 30 cases were diagnosed as benign, while 45 were identied as malignant. The sensitivity, specicity, positive predictive value, negative predictive value, and accuracy of the present study were 80.12%, 95.62%, 97.2%, 70.12%, and 84.52% respectively. Among cases, 84% exhibit concordance, while 16% show discordance, suggesting a strong association between the two diagnostic methods in the study. The Conclusion: demographic characteristics, cytological and histopathological ndings, along with the correlation analysis, provide a comprehensive understanding of the diagnostic process of breast lump cases. High concordance and favorable statistics validate diagnostic test reliability, addressing discordance improves breast lump diagnosis accuracy.


Low Communication-Cost PSI Protocol for Unbalanced Two-Party Private Sets

Abstract: Two-party private set intersection (PSI) plays a pivotal role in secure two-party computation protocols. The communication cost in a PSI protocol is normally influenced by the sizes of the participating parties. However, for parties with unbalanced sets, the communication costs of existing protocols mainly depend on the size of the larger set, leading to high communication cost. In this paper, we propose a low communication-cost PSI protocol designed specifically for unbalanced two-party private sets, aiming to enhance the efficiency of communication. For each item in the smaller set, the receiver queries whether it belongs to the larger set, such that the communication cost depends solely on the smaller set. The queries are implemented by private information retrieval which is constructed with trapdoor hash function. Our investigation indicates that in each instance of invoking the trapdoor hash function, the receiver is required to transmit both a hash key and an encoding key to the sender, thus incurring significant communication cost. In order to address this concern, we propose the utilization of a seed hash key, a seed encoding key, and a Latin square. By employing these components, the sender can autonomously generate all the necessary hash keys and encoding keys, obviating the multiple transmissions of such keys. The proposed protocol is provably secure against a semihonest adversary under the Decisional Diffie–Hellman assumption. Through implementation demonstration, we showcase that when the sizes of the two sets are <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><msup><mn>2</mn><mn>8</mn></msup></math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mn>2</mn><mn>14</mn></msup></math> , the communication cost of our protocol is only 3.3% of the state-of-the-art protocol and under 100 Kbps bandwidth, we achieve 1.46x speedup compared to the state-of-the-art protocol. Our source code is available on GitHub: https://github.com/TAN-OpenLab/Unbanlanced-PSI.


Balancing Motivation and Burnout in The Learning of Foreign Languages

Abstract: Being motivated and experiencing burnout during study is a common occurrence, and there appears to be a number of dimensions in which they are related. Despite having the most sophisticated educational frameworks, students always rely on their own motivation to meet their learning goals and avoid burnout at the same time. However, motivation is the driving force behind learning and can act as a mediator in reducing burnout in students. Hence, this study investigates the balancing motivation and burnout in the learning of foreign languages. A quantitative method is used to explore the relation of motivation and burnout in the learning of foreign languages. The data was collected using a five-point Likert scale survey adapted from (Pintrich & DeGroot, 1990; Campos, et al., 2011). There are four sections in the survey. Section A are items on the demographic profile, Section B are 24 items on motivational factors and Section C are the 16 items on burnout factors. 125 undergraduate students studying French at a public university in Malaysia were selected using purposive sampling to answer the survey. The study found that task beliefs and extrinsic motivation drive learners more than intrinsic motivation. Though students are motivated, they are also found to be burned out. This demonstrates the connection between motivation and burnout. The data evaluated using SPSS further supports this, demonstrating a moderate positive correlation between motivation and burnout. The results indicated a significant pedagogical outcome and issued invaluable aspects and future research exploring the correlation between motivation and burnout in the learning of foreign languages is recommended.


The Future of Quick Service Restaurants

Abstract: Abstract Grant Powell MBCS recently spoke to Alana Abbitt, Vice President of Product Development at Miso Robotics, to learn how AI driven robotic technology is helping to revolutionise the fast food industry.


Optimizing Business Processes through Information System Integration A Case Study in Banda Aceh

Abstract: This research aims to optimize business processes through information system integration using a case study in Banda Aceh. This research uses a qualitative approach with a single case study research method. Data was collected through in-depth interviews, direct observation and documentation. Research respondents consist of managers and employees involved in the business processes to be integrated. Data analysis was carried out using content analysis. The research results show that information system integration significantly increases the efficiency and effectiveness of business processes in Banda Aceh. Information system integration enables faster data processing, better monitoring of business processes, and more accurate decision making. Additionally, information system integration also enables better collaboration between different departments and stakeholders. Based on these findings, it can be concluded that information system integration plays an important role in optimizing business processes in Banda Aceh. By integrating information systems, companies can increase operational efficiency, reduce costs, and increase customer satisfaction. However, the implementation of information systems integration also faces challenges, such as technical difficulties and changes in organizational culture. Therefore, strong commitment and support from company management is needed to achieve optimal results.


Effects of blockchain technology (BT) on the university librarians and libraries: a systematic literature review (SLR)

Abstract: Purpose The objectives of the study were to identify the effects of blockchain technology (BT) on the university librarians, the impact of BT on the university library services and to reveal the challenges to adopt BT in the university libraries. Design/methodology/approach A systematic literature review was applied to address the objectives of the study. Around 25 studies published in peer-reviewed journals were selected to conduct the study. Findings The findings of the study revealed that blockchain technology (BT) has positive effects on the university librarians as it assists them in digital resources management, provision of integrated library services, effective records management and continued professional development. The study also displayed that BT has a positive impact on the university libraries through effective information management, user privacy, collaboration, technological innovation and access control. Results also revealed that technical issues, financial constraints, security problems, skill issues and sociocultural issues created challenges to adopt BT in the university libraries. Originality/value The study has offered theoretical implications for future investigators through the provision of innovative literature on the prospectus and challenges associated with blockchain in the context of librarianship. The study has also provided practical implications for management bodies by offering recommendations for the successful adoption of blockchain in the university libraries. Additionally, a framework has been developed to adopt BT successfully in the university libraries for the delivery of smart library services to library patrons.


Unmasking overestimation: a re-evaluation of deep anomaly detection in spacecraft telemetry

Abstract: Abstract As the volume of telemetry data generated by satellites and other complex systems continues to grow, there is a pressing need for more efficient and accurate anomaly detection methods. Current techniques often rely on human analysis and preset criteria, presenting several challenges including the necessity for expert interpretation and continual updates to match the dynamic mission environment. This paper critically examines the use of deep anomaly detection (DAD) methods in addressing these challenges, evaluating their efficacy on real-world spacecraft telemetry data. It exposes limitations in current DAD research, highlighting the tendency for performance results to be overestimated and suggesting that simpler methods can sometimes outperform more complex DAD algorithms. By comparing established metrics for anomaly detection with newly proposed ones, this paper aims to improve the evaluation of DAD algorithms. It underscores the importance of using less accuracy-inflating metrics and offers a comprehensive comparison of DAD methods on popular benchmark datasets and real-life satellite telemetry data. Among the DAD methods examined, the LSTM algorithm demonstrates considerable promise. However, the paper also reveals the potential limitations of this approach, particularly in complex systems that lack a single, clear predictive failure channel. The paper concludes with a series of recommendations for future research, including the adoption of best practices, the need for high-quality, pre-split datasets, and the investigation of other prediction error methods. Through these insights, this paper contributes to the improved understanding and application of DAD methods, ultimately enhancing the reliability and effectiveness of anomaly detection in real-world scenarios.


The Future of Marketing: The Transformative Power of Artificial Intelligence

Abstract: This research explores the profound impact of Artificial Intelligence (AI) on the marketing landscape, examining its evolution from early applications in data analytics to contemporary uses. AI’s role in transforming marketing strategies is pivotal, encompassing personalized customer experiences, predictive analytics, and efficiency improvements. The study highlights AI’s ability to process vast data sets rapidly, reshaping customer engagement and market analysis. Focusing on the multifaceted integration of AI into marketing, the research emphasizes its contribution to customer personalization, fostering brand loyalty, and boosting conversion rates. Predictive analytics, another cornerstone, enables businesses to craft strategies aligned with future market dynamics proactively. Despite its advantages, ethical considerations surrounding data privacy and consumer consent are pivotal, requiring responsible and transparent AI use. The study is a comprehensive resource for academic researchers and industry professionals, providing insights into historical development, current applications, and ethical implications. It offers organizations a clear roadmap for leveraging AI effectively in marketing operations amid the growing reliance on digital platforms and expanding data availability. Anticipating future developments, the research discusses the convergence of AI with augmented reality, virtual reality, predictive customer journeys, and the Internet of Things. Balancing technological advancements with ethical considerations remains crucial, emphasizing the need for ongoing adaptation in this dynamic landscape. The synergy between AI and marketing propels businesses into a new era of precision, personalization, and strategic insight, redefining customer connections. In this transformative journey, embracing change, innovation, and ethical practices becomes paramount for sustained success.


Zero Trust and Data Engineering

Abstract: Abstract Pankaj Gupta, a data analytics engineer from Discover Financial Services, puts zero trust under the microscope and explores why it's necessary, how it works and what the concept might mean for the future of security.


Cybersecurity Challenges In The Era Of Digital Transformation A Comprehensive Analysis Of Information Systems

Abstract: Increased digitalization in business transformation has provided significant benefits, but has also created new challenges for information security. This research aims to analyze the cyber security challenges faced in the era of digital transformation, with a focus on Information Systems. The research method used is a comprehensive analysis of literature and case studies to gain an in-depth understanding of these issues. In this research, we found that there are a number of significant challenges related to cyber security in implementing digital transformation, including malware attacks, phishing, insufficient network security, and privacy violations. Causal analysis concluded that insufficient training and security awareness, as well as weak design in information systems, were the main risk factors. The implication of this research is the need for proactive steps in implementing strong security practices in information systems, including adequate training for employees, careful security monitoring, and early warning of security threats. In addition, the importance of collaboration between organizations, governments and related entities in facing increasingly global threats was also highlighted.


Politicization of Hate and Weaponization of Twitter/X in a Polarized Digital Space in Nigeria

Abstract: Hate speech on social media platforms during electioneering campaign has been institutionalized. The campaign hate-filled rhetoric continues unabated. Some have attributed this phenomenon to access to social media. This study therefore investigates the prevalence of hate speech on Twitter in Nigeria. Twitter API was used to generate data that was later content analyzed. Anchored on the technological determinism theory, the study revealed political periods saw the prevalence of hate speech and political, ethnic, religion and regional-based hate speech are the dominant themes on hate discourse on Twitter. Some regions in Nigeria weaponized Twitter to denigrate others while others used it to promote their agitation but in the process also resort to hate speech. The study argued that Twitter is just a technology in the hand of hate-filled people who used it to promote their bigotry and bile. This study recommends the development of a legal framework for the regulation of hate speech on social media. Also, the utterance of politicians must be guarded, and the electoral acts must be fully implemented.


Impact of age and comorbid heart failure on the utility of smart voice-assistant devices

Abstract: Abstract Aims The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyze the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and Results Subgroup analysis (HF vs Caregivers and younger vs older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, median age was 51 (34-65) years and 21 (40%) were HF patients. The HF subgroup showed a significant lower percentage of agreement compared with caregivers (95% vs 99%, p=0.03), and both the HF and older subgroup tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use comparing with caregivers and younger individuals. Conclusions This subgroup analysis highlights important differences in the performance of a voice-assistant based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Trial registration number: NCT04508972.


Beyond Quantum Threats: Bridging Fully Homomorphic Encryption Schemes

Abstract: In the advent of quantum computing, fully homomorphic encryption (FHE), whose security is based on the hardness of lattice problems, gains significant importance due to its quantum-resistant nature. This work provides a comprehensive survey of recent advancements in bridges between FHE schemes, with an emphasis on practical applications.


VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles

Abstract: In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.


Comparison and improvement of some attention mechanisms in the video field

Abstract: This paper compares the effects of SE-Net and the improved CBAM attention mechanism, and proposes a CTSA (Channel and Temporal and Spatial Attention) attention mechanism that adds temporal attention for temporal features, and further proposes a TSA (Time-domain and Spatial Attention) attention mechanism that focuses on time-domain on the basis of comparing the effects of multiple attention mechanisms. The experimental results show that in the field of behaviour recognition, the TSA attention mechanism designed in this paper can achieve better recognition results when using a lighter weight network structure.


The Use of Digital and Disruptive Technologies in the Artificial Intelligence Era

Abstract: This study aims to analyze the emergent and disruptive digital technologies in an era of artificial intelligence (IA). It departs from the assumption that the emerging technologies create both opportunities and challenges with an impact on individuals, organizations, institutions, and society at large in terms of bias, surveillance, hacking, etc. The method employed is the critical analysis based on the recent developments reported in scientific literature. It is concluded that control over the ethical disruption of digital technologies and more specifically artificial strategy must require a digital conversation and leadership about acceptable ethical behaviors under the assumption that digital transformation cannot be interrupted and must be guided by humans.


PQC and QKD Are Both Required to Enable a Quantum-Safe Future

Abstract: Quantum Key Distribution (QKD) and Post Quantum Cryptography (PQC) are often incorrectly assumed to be competitors of one another. In reality, they are complementary technologies aimed at solving different problems, with each having strengths and weaknesses that the other does not have. In this article, we will briefly summarise the current state of QKD and PQC, highlighting the advantages, disadvantages, and limitations of each technology stack. Finally, we explain our take on how both these technologies can be combined to provide better solutions that are better than any solution that uses solely PQC or QKD.


An Inductive Heterogeneous Graph Recommendation Model for High-Scoring Items Applied to Business Intelligence

Abstract: Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution and direction of data and assists them in making correct decisions. The core of this feature is visual analysis charts, which need to be pre-created and integrated into the dashboard by the chart makers, so there are situations where user needs cannot be accurately grasped. At the same time, there may be omissions in the work of users, and a method is needed to remind them. Introducing recommendation models into data visualization is a good solution; therefore, this paper proposes a recommendation model suitable for this type of scenario, which recommends high-scoring items (charts) to users. This model consists of a inductive heterogeneous graph recommendation algorithm with user preferences and a slow-acting collaborative filtering method. The experimental results in two datasets showed an improvement of 0.020/0.045, 0.083/0.019 and 0.076/0.023 in Hit, F-score, and NDCG compared to baselines, which proves that it is more suitable for data visualization requirements and other similar scenarios that require inductive recommendations based on user preferences.


Adaptive variational ground state preparation for spin-1 models on qubit-based architectures

Abstract: We apply the adaptive variational quantum imaginary time evolution (AVQITE) method to prepare ground states of one-dimensional spin $S=1$ models. We compare different spin-to-qubit encodings (standard binary, Gray, unary, and multiplet) with regard to the performance and quantum resource cost of the algorithm. Using state-vector simulations, we study two well-known spin-1 models: the Blume-Capel model of transverse-field Ising spins with single-ion anisotropy, and the XXZ model with single-ion anisotropy. We consider system sizes of up to 20 qubits, which corresponds to spin-1 chains up to length 10. We determine the dependence of the number of cnot gates in the AVQITE state preparation circuit on the encoding, the initial state, and the choice of operator pool in the adaptive method. Independent of the choice of encoding, we find that the cnot gate count scales cubically with the number of spins for the Blume-Capel model and quartically for the anisotropic XXZ model. However, the multiplet and Gray encodings present smaller prefactors in the scaling relations. These results provide useful insights for the implementation of AVQITE on quantum hardware.


Unlabeled learning algorithms and operations: overview and future trends in defense sector

Abstract: Abstract In the defense sector, artificial intelligence (AI) and machine learning (ML) have been used to analyse and decipher massive volumes of data, namely for target recognition, surveillance, threat detection and cybersecurity, autonomous vehicles and drones guidance, and language translation. However, there are key points that have been identified as barriers or challenges, especially related to data curation. For this reason, and also due to the need for quick response, the defense sector is looking for AI technologies capable of successfully processing and extracting results from huge amounts of unlabelled or very poorly labelled data. This paper presents an in-depth review of AI/ML algorithms for unsupervised or poorly supervised data, and machine learning operations (MLOps) techniques that are suitable for the defense industry. The algorithms are divided according to their nature, meaning that they either focus on techniques, or on applications. Techniques can belong to the supervision spectrum, or focus on explainability. Applications are either focused on text processing or computer vision. MLOps techniques, tools and practices are then discussed, revealing approaches and reporting experiences with the objective of declaring how to make the operationalization of ML integrated systems more efficient. Despite many contributions from several researchers and industry, further efforts are required to construct substantially robust and reliable models and supporting infrastructures for AI systems, which are reliable and suitable for the defense sector. This review brings up-to-date information regarding AI algorithms and MLOps that will be helpful for future research in the field.


Genesis and Prospecting Potential of the Da’anhe Skarn Au Deposit in the Central of the Lesser Xing’an Range, NE China: Evidence from Skarn Mineralogy, Fluid Inclusions and H-O Isotopes

Abstract: Skarn Au deposits exist in the circum-pacific metallogenic belt. Interestingly, the Da’anhe Au deposit is the only independent skarn gold deposit in the Lesser Xing’an Range. To determine the metallogenic mechanism and prospecting potential of the Da’anhe deposit, we performed skarn mineralogy, fluid inclusion (FI) and H-O isotope analyses. The results show the following: (1) The Da’anhe deposit is a calcareous reduced skarn Au deposit that formed between an Early Jurassic gabbroic diorite and the Permian Tumenling Formation marble. Its metallogenic process includes five stages: the early skarn stage (Stage I1), late skarn stage (Stage I2), early quartz-sulfide stage (Stage II1), late quartz-sulfide stage (Stage II2) and quartz-carbonate stage (Stage II3). Gold precipitated in Stage II1 and Stage II2. (2) The initial ore-forming fluid was derived from magmatic water and featured a high temperature and intermediate to high salinity. After boiling and mixing, the fluid eventually changed to a low-temperature and low-salinity reducing fluid dominated by meteoric water. (3) The formation depth of the Au orebodies was 2.27-3.11 km, and the orebodies were later lifted to the surface (<500 m). The potential for finding skarn Au deposits in the study area is limited. (4) The distinctive nature of the ore-related magma (i.e., source, reducing conditions and high water content) was key to the formation of the Da’anhe skarn gold deposit.


PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition

Abstract: Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its efficacy. In this study, we propose augmentations to the sequence labeling approach by employing a pipeline model bifurcated into sequence labeling and text classification tasks. Departing from annotating specific entity categories, we amalgamated types into main and sub-categories for a unified treatment. These categories were subsequently embedded as identifiers in the recognition text for the text categorization task. Our choice of resolution involved BERT+BiLSTM+CRF for sequence labeling and the BERT model for text classification. Experiments were conducted across three nested NER datasets: GENIA, CMeEE, and GermEval 2014, featuring annotations varying from four to two levels. Before model training, we conducted separate statistical analyses on nested entities within the medical dataset CMeEE and the everyday life dataset GermEval 2014. Our research unveiled a consistent dominance of a particular entity category within nested entities across both datasets. This observation suggests the potential utility of labeling primary and subsidiary entities for effective category recognition. Model performance was evaluated based on F1 scores, considering correct recognition only when both the complete entity name and category were identified. Results showcased substantial performance enhancement after our proposed modifications compared to the original method. Additionally, our improved model exhibited strong competitiveness against existing models. F1 scores on the GENIA, CMeEE, and GermEval 2014 datasets reached 79.21, 66.71, and 87.81, respectively. Our research highlights that, while preserving the original method’s simplicity and implementation ease, our enhanced model achieves heightened performance and competitive prowess compared to other methodologies.


Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

Abstract: This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.


A text extraction framework of financial report in traditional format with OpenCV

Abstract: Due to intensified off-balance sheet disclosure by regulatory authorities, financial reports now contain a substantial amount of information beyond the financial statements. Consequently, the length of footnotes in financial reports exceeds that of the financial statements. This poses a novel challenge for regulators and users of financial reports in efficiently managing this information. Financial reports, with their clear structure, encompass abundant structured information applicable to information extraction, automatic summarization, and information retrieval. Extracting headings and paragraph content from financial reports enables the acquisition of the annual report text’s framework. This paper focuses on extracting the structural framework of annual report texts and introduces an OpenCV-based method for text framework extraction using computer vision. The proposed method employs morphological image dilation to distinguish headings from the main body of the text. Moreover, this paper combines the proposed method with a traditional, rule-based extraction method that exploits the characteristic features of numbers and symbols at the beginning of headings. This combination results in an optimized framework extraction method, producing a more concise text framework.


Verifiable quantum protocol for dynamic secure multiparty summation based on homomorphic encryption

Abstract: Abstract The research of quantum secure multiparty computation is a subject of great importance in modern cryptography. In this study, we construct a veriﬁable quantum protocol for dynamic secure multiparty summation based on the cyclic property of d-level MUBs. Our protocol can realize dynamic parameter update in the aspect of members and secret inputs, improving the practicality of the protocol. Moreover, a veriﬁcation mechanism for result checking by applying ElGamal homomorphic encryption is given, and further enables the detectability of cheating behaviors, making our protocol safer. The security analysis proves the proposed protocol not only can resist a range of typical attacks from outside and inside, but also is secure against dishonest revoked participant attack which has been neglected in previous dynamic quantum summation protocols. From a theoretical perspective, compared with existing summation protocols, the protocol provides better practicability, higher privacy protection, and higher efﬁciency.&#xD;


Image semantic segmentation model based on CBAMUNet

Abstract: Mainstream image semantic segmentation networks can only extract local features of an image. Their receptive field is limited to the range of convolutional kernel size, which impacts the distinguishability of extracted features by the encoder. This issue leads to inaccurate segmentation, loss of small targets, and other related problems. This paper presents a solution to the aforementioned challenges by proposing the use of a CBAMUNet image semantic segmentation model. The model combines CBAM attention module and UNet En-decoder network. Firstly, the UNet network has been enhanced to eliminate the need for cropping of input images to reduce feature loss. Secondly, the CBAM module has been integrated into its skip connection to enable the UNet network to extract more efficient features and enhance the segmentation effect. And the model uses a new loss function. Data augmentation is also used to improve the generalization ability of the model. Through the comparison experiments with UNet, it is found that CBAMUNet is able to significantly improve the segmentation effect of images, and increase the pixel precision(PA) and the mean intersection over union(mIoU) of the semantic segmentation of images.


Digital Marketing in the Age of Cyber Threats: A Comprehensive Guide to Cybersecurity Practices

Abstract: As digital marketing strategies become increasingly integrated into business models, unique vulnerabilities to cyber threats make cybersecurity essential. This descriptive study provides a detailed analysis of cybersecurity for digital marketing, which is rapidly evolving with technological advancements and consumer behavior transitions. The integration of tools like Search Engine Optimization, social media, and online advertising and their susceptibility to cyber risks are investigated. Critical cybersecurity threats in digital marketing, including phishing attacks, malware and ransomware, data breaches, and Distributed Denial of Service attacks, are explored, emphasizing their potential impact on business operations, customer trust, and brand reputation. The study further explores best practices in cybersecurity tailored to the digital marketing area, advocating for regular software updates, comprehensive employee training, stringent data encryption protocols, strong password policies, multi-factor authentication, and periodic security audits, highlighting the importance of data backups and adherence to data protection laws in maintaining legal and ethical standards. The role of Artificial Intelligence and Machine Learning is investigated, emphasizing how these technologies enhance cybersecurity measures through proactive threat detection and efficient incident management. Additionally, the study examines the rising consumer concerns and awareness regarding data privacy and security in digital marketing, reflecting how these concerns influence business practices and the increasing demand for transparency and data control among consumers. Necessitating continuous vigilance and adaptation to protect against ever-evolving cyber threats, effective cybersecurity is indispensable in digital marketing for protection against cyber threats and as a vital element in building and maintaining consumer trust and loyalty.


Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

Abstract: &nbsp;For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project&#39;s development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.


Using a fuzzy cognitive map to assess interventions to reduce antimicrobial resistance in a Swedish One Health system context under potential climate change conditions

Abstract: Abstract Introduction: Antimicrobial resistance (AMR) is a growing One Health crisis that can be impacted by other challenges of sustainable development, such as climate change, but few interventions have been assessed with a systems-wide lens. The objectives of this study were to use a previously defined fuzzy cognitive map (FCM) of the Swedish One Health system to: 1) identify areas in the system to target interventions; and 2) test the potential ability and viability of interventions to reduce AMR under a changing climate. Methods: The FCM, based on participatory modelling workshops and literature scan, was used to assess the sustainability of eight interventions under potential climate change conditions. Network metrics were calculated to describe the system structure and identify highly impactful nodes. Results: The network metrics identified high-leverage nodes including alternative productions systems and good farming practices. None of the scenarios evaluated were able to adequately reduce AMR within the system. Conclusions: Overall, fuzzy cognitive mapping provides an innovative way to analyse the AMR system, identify high-leverage interventions, and examine potential impact of interventions using a broader systems lens.


The development of higher-order thinking skill-based immune system learning tools

Abstract: Learning tools can assist teachers in facilitating achievement during the learning process. At that time, the learning tools utilized still relied upon the pre-existing 2013 curriculum, lacking the incorporation of higher-order thinking Skills (HOTS) indicators for critical thinking skills, particularly in the context of immune system material. Therefore, research was conducted to create a learning tool immunity that was based on HOTS. The objectives of this study were as follows: 1) To analyze the requirement of HOTS-based biology learning tools in the immune system material for class XI in SMA/MA; 2) To design the product development for HOTS-based biology learning tools in class XI immune system material in SMA/MA; 3) To validating HOTS-based biology learning tool products in class XI immune system material in SMA/MA. This study employed the ADDIE (Analysis, Design, Development, Implementation, Evaluation) a model for instructional design. However, this research was conducted only up to the development stage of the ADDIE model. The sample comprised five experts and three practitioners, namely biology learning experts, lecturers’ teachers, and language experts. The purposive sampling technique was employed in this research. The instruments used included product validation sheets and interview sheets. Data collection methods involved distributing product validation sheets and conducting interviews with validators. This research produces a HOTS-based immunity learning tool product which generally falls into the very valid category with a percentage of 94.56%. It means that the development product is valid for use in biology learning at the SMA/MA level.


Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax

Abstract: Learning Electronic Health Records (EHRs) representation is a preeminent yet under-discovered research topic. It benefits various clinical decision support applications, e.g., medication outcome prediction or patient similarity search. Current approaches focus on task-specific label supervision on vectorized sequential EHR, which is not applicable to large-scale unsupervised scenarios. Recently, contrastive learning has shown great success in self-supervised representation learning problems. However, complex temporality often degrades the performance. We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems. Unlike the state-of-the-art, we do not change the graph structure to construct augmented views. Instead, we use Kernel Subspace Augmentation to embed nodes into two geometrically different manifold views. The entire framework is trained by contrasting nodes and graph representations on those two manifold views through the commonly used contrastive objectives. Empirically, using publicly available benchmark EHR datasets, our approach yields performance on clinical downstream tasks that exceeds the state-of-the-art. Theoretically, the variation in distance metrics naturally creates different views as data augmentation without changing graph structures. Practically, our method is non-ad hoc and confirms superior performance on commonly used graph benchmark datasets.


Obstacle Avoidance Path Planning of a 4-DOF Weapon Arm Based on Improved RRT (RRT-H) Algorithm

Abstract: To improve soldiers’ combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> smart, and information <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><msup><mtext>RRT</mtext><mi>∗</mi></msup></math> , the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm’s motion costs.


Robust Testing of AI Language Model Resiliency with Novel Adversarial Prompts

Abstract: In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this research are innovative adversarial prompts designed to rigorously test the content moderation capabilities of these AI systems. This study introduces new adversarial tests and the Response Quality Score (RQS), a metric specifically developed to assess the nuances of AI responses. Additionally, the research spotlights FreedomGPT, an AI tool engineered to optimize the alignment between user intent and AI interpretation. The empirical results from this investigation are pivotal for assessing AI models’ current robustness and security. They highlight the necessity for ongoing development and meticulous testing to bolster AI defenses against various adversarial challenges. Notably, this study also delves into the ethical and societal implications of employing advanced “jailbreak” techniques in AI testing. The findings are significant for understanding AI vulnerabilities and formulating strategies to enhance AI technologies’ reliability and ethical soundness, paving the way for safer and more secure AI applications.


Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases

Abstract: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations.AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment.Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance.This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.


Learning noisy transition matrix using a neural network

Abstract: In the field of deep learning, it is crucial to know the accurate distribution of dataset. However, to obtain a high quality of dataset using traditional methods is prone to be both costly and inefficient. Using deep learning methods to estimate the noisy transition matrix provides a feasible way, as the result of its essential function of learning to denote the relationship between clean labels and noisy labels and building a statistically consistent classifier. The major difficulty of learning the noisy transition matrix stems from the unavailability of the distribution of clean data and noisy data. In this paper, we propose a practical and convenient method to study a combination of augumentation and a novel loss function, only leveraging the already known clean labels to aid in learning the noisy transition matrix in the whole dataset. Finally, Through the experiment, the result demonstrates a superior performance and generalization capabilities of the proposed method.


A comprehensive analysis of gesture recognition systems: Advancements, challenges, and future direct

Abstract: Gesture recognition emerges as a potent avenue for human-computer interaction, harnessing mathematical algorithms to interpret gestures. It promises to surpass text-based or graphical interfaces, enabling touchless device control through simple gestures. Our review of 7 papers encompassing various fields and methods underscores its diverse applications. Challenges persist, such as distinguishing genuine user intent from accidental actions amid environmental interference. Creating a universal EMG pattern recognition model demands intricate individual pre-training. Sensor-based gesture recognition grapples with real-world dynamics, necessitating adaptable models that discern user intent from non-intent actions. Addressing these gaps holds the key. Adaptable models and personalized approaches can enhance robustness and accuracy across applications, surmounting challenges in the gesture interaction technology realm.


Investigation related to database design for letter of credit

Abstract: The "Letter of Credit" is a fundamental payment mechanism in global trade, and its efficiency has significantly declined due to the impact of the COVID-19 pandemic. This document delves deeply into the development and execution of a comprehensive database for managing Letters of Credit (L/Cs). It is designed with a real-world case study derived from the International Settlement Department of the Bank of China. The central goal of this database is to enhance the efficient management and simplification of the complex procedures inherent in L/C transactions. The development process follows a systematic approach, commencing with the establishment of three fundamental assumptions to delineate the scope of the study. Subsequently, the database design progresses through stages involving Entity-Relationship Diagram (ERD) development, relational model construction, and normalization procedures. These steps collectively culminate in the creation of an intelligently structured database comprising thirteen distinct tables. In conclusion, this paper not only delves into the design and implementation of the L/C database but also showcases its practicality through real-world scenarios. The database, fueled by meticulous design and thoughtful implementation, is poised to revolutionize the management of L/C operations, offering enhanced efficiency and accuracy in international trade transactions.


Suspicious Activity Tracking

Abstract: Abstract: Our offering includes a deep neural network model that can identify firearms in photographs and a machine learning and computer vision pipeline that can detect abandoned luggage in order to identify potential gun-based crime and circumstances involving abandoned luggage in surveillance film. Unusual behavior the technique of identifying undesired human activity in locations and circumstances is called detection. To do this, footage is converted into frames, and the processed frames are then used to analyze the people's sports. YOLOv3 is used to find a niche, in dubious sports like lock breaking and bag snatching, among others. Our gadget has a superb processing pace in addition to appropriate accuracy of detection. It is harder for computers to detect things in videos compared to images because of issues like blurriness or things getting blocked. They propose a solution called Shot Video Object Detector, which is a faster kind of detector for videos. It works by combining information from nearby frames to make better guesses about where objects are. Unlike other methods, Shot Video Object Detector does this by figuring out how things move between frames and then using that info to combine features. It also creates new features by borrowing information directly from neighboring frames using a special structure. Automated surveillance in public areas plays a crucial role in upholding law and order and proactively identifying potential risks to the public. Not only does the procedure automatically identify and detect known crooks, but it also tracks people's and things' movements and uses machine learning algorithms to alert the authorities to any questionable activity


Didactic Use of the Publisher Processor to Enhance Meaningful Learning in Peruvian Secondary School Students

Abstract: The coronavirus pandemic has changed the course of education from face-to-face to remote. In response to this context, we investigated the positive effect of the didactic use of the Publisher processor to enhance meaningful learning in the competence "explain the physical world based on scientific knowledge about matter and energy, biodiversity, earth and universe" of the science and technology area in fifth grade students of secondary education of a Public Educational Institution of Chiclayo (Peru). For this purpose, a pre-experimental design with pre- and post-test was used with a sample of 102 students selected in a non-probabilistic way. A program based on activities incorporated in learning experiences proposed by the Ministry of Education (Peru) was applied. As a result, 72% of the students increased their academic performance after the application of the program, improving up to five (5) points compared to the initial test. Thus, the development of didactic material using the Publisher processor strengthens learning, which highlights the importance of information technologies in the achievement of active learning and the need to use them in the processes of mediation, reinforcement and teacher feedback in virtual and/or face-to-face scenarios.


Research on the intersection of natural language processing and deep learning

Abstract: In the past ten years, Natural Language Processing (NLP) has made many surprising progress due to the rapid development of Deep Learning(DL) and further explored the possibility of future development. This article briefly introduces the NLP field, the basic structure of DL, and the impact of the combination of DL and NLP on the NLP field. Finally, it reviews the limitations of the two under the constraints of current science and technology and looks forward to the possibility and direction of their future development. Appropriately applying DL to NLP can indeed bring great progress to the core areas and applications of NLP. Still, at the same time, the development of NLP will also be limited by the shortcomings of DL. It is necessary to continuously optimize the DL model while taking advantage of the advantages of DL. The most critical factor to promote the development of DL and NLP in the future.


Intelligent Navigation System based on Big Data Traffic System

Abstract: This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.


Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network

Abstract: Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can precisely recognize the human central issues, really work on the exactness of human posture assessment, and can adjust to the intricate scenes with thick individuals and impediment. Finally, the difficulties and possible future trends are described, and the development of the field is presented.


Improving Big Data Recommendation System Performance using NLP techniques with multi attributes

Abstract: Due to the wide availability of big data, institutions and companies are currently concentrating on developing highly effective recommender systems for their users. Traditional recommender systems use standard information like user, item, and ratings. However, this data may not be sufficient for precise results. To enhance accuracy, it is recommended to include additional information such as textual data in the recommendation system. When dealing with large textual data, employing Natural Language Processing (NLP) techniques is essential for effective data analysis. Hence, this paper proposed a novel big data recommender system that enhances collaborative filtering (CF) results by leveraging NLP techniques and dealing with multiple attributes. The study constructs two big data recommendation system models by using a machine learning algorithm. In both models, the Alternating Least Squares (ALS) algorithm within the Apache Spark big data tool was utilized. The first model did not incorporate NLP techniques, while the second model considered the novel NLP techniques by taking into account the user's review comments. A dataset of more than 3 million ratings and reviews was gathered from the Amazon website, amounting to a size of 3.1 GB. The results demonstrated significant improvement after incorporating the suggested NLP-based techniques with multiple attributes.


Metadata design for the first electronic learner corpus of Romanian

Abstract: This paper introduces an ongoing work of collecting, annotating and documenting the first digital Romanian Learner Corpus (LECOR), focusing on its metadata. We shortly describe the institutional context of the project, the current state of the art in the field, the objectives in terms of structure, dimensions and annotations and what work has already been done at this stage of the project. Then we present the modular structure of the metadata scheme and a detailed account of all the metadata fields and their possible values, from general metadata concerning the whole corpus (Section 3.1), to metadata organised around the student/learner (Section 3.2) and text/composition (Section 3.3). We will also give some examples of how metadata has been dealt with in various researches (including based on LECOR corpus).


Abstract: There have been few, if any, open data and information management policies openly published from natural science collections. This paper contextualises the rationale for publishing the Open Information and Exceptions Policy of the Natural History Museum, London and provides the policy itself. The policy outlines how the Natural History Museum puts the principle of 'open by default' into practice; and includes sections on purpose and scope, relationship to relevant legislation (which always takes precedence over the policy), the categories of possible exceptions to open information release, what happens when exceptions are declared, relations to UK government information security classifications and definition of terms.


Application of Discrete and Fast Fourier Transforms to Increase the Speed of Multiscale Image Analysis

Abstract: The paper compares the accuracy of reconstruction using different wavelets, and the authors also use fast and discrete Fourier transforms together to calculate the forward and inverse continuous wavelet transform in the frequency domain. Due to the use of calculations in the frequency domain, it becomes possible to perform decomposition, reconstruction, image filtering, and other transformations with high performance and precision. For multiscale signal analysis, a wavelet with a rectangular amplitude-frequency response has been constructed, which allows for an increase in the accuracy of decomposition and reconstruction compared to the Mallat algorithm presented in Matlab computer mathematics. At the same time, the time of multiscale analysis is reduced several times compared to the Mallat algorithm.


Correction: Gerber, M.; Pillay, N. Automated Design of the Deep Neural Network Pipeline. Appl. Sci. 2022, 12, 12215

Abstract: The authors would like to make the following corrections to this published paper [...]


FACTORS EFFECTING JOB SATISFACTION FOR ONLINE EDUCATION AMONG LECTURERS IN BANGLADESH

Abstract: The global pandemic caused by Covid-19 infectious disease during the year 2020 has pierced the life of humankind in almost all countries and all members of the society. This pandemic issue limits one to cope with a changing work environment and it induces both structural and functional consequences on the education system. Thus, the online education system emerged and repositioned itself as one of the most effective mediums of education system recently. This study examined decision factors such as happiness, quality of work-life commitment and attitude toward online education and its influence on job satisfaction of academic staffs in Bangladesh. A quantitative research approach has been taken using a random sampling method to collect data from 293 lecturers working in colleges and universities in the metropolitan city of Dhaka. For the study, Exploratory Factor Analysis and Structural Equation Modelling techniques were performed. The findings helped in filling the satisfaction-behavioural gap in understanding attitude measures of academic staffs toward conducting courses in online mode.


Factors and Conditions of Practical Training for Future Choreographer-Teachers

Abstract: The article addresses the task emphasized in the Federal State Educational Standards for Higher Education in all areas of choreographer training, as well as in the professional standard “Teacher of Additional Education for Children and Adults” (Order of the Ministry of Labor and Social Protection of the Russian Federation, October 22, 2021), namely, the preparation of future specialists in choreography for teaching activities. The goal of the article is to draw attention to the factors that determine this process since pedagogical tasks are present in all types of professional activities for future choreographers. The study was conducted based on comparative, critical analysis, synthesis, generalization, and interpretation methods. Examination of regulatory documents, psychological-pedagogical scientific literature, and practice allowed us to identify factors that have the greatest influence on the practical training of future choreographers and categorize them based on their sources of impact as external and internal. External factors include: cultural-educational environment, interaction with teachers, and student community. Internal factors include: passion for the profession, creativity, self-development, self-education, and self-discipline. Based on these factors, conditions were identified and justified that can significantly improve this process. Such conditions include: enriching the pedagogical component in teaching special disciplines to choreography students; involving students in practice-oriented activities during internships; and using new technologies in teaching disciplines. In conclusion, it is stated that practical training for teaching activities of choreography students represents a process of complex factors influenced by the specifics of choreographic education and implemented through the theoretically substantiated pedagogical conditions discussed in the article.


Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling

Abstract:
Cloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.
 Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service.


Artificial Intelligence as a Harbinger of Significant Changes in Education

Abstract: The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge.


Development of A Curriculum Type Preference Scale for Preschool Education Vocational University

Abstract: Exploring students’ preferences for curriculum types is beneficial for further designing curriculum structures based on the differentiated needs of students, thereby improving curriculum and education quality. To make up for the lack of an effective measurement instrument for investigating the curriculum type preferences of Chinese vocational university students majoring in preschool education, a 5-point Likert scale is designed and developed in this study. The development procedure consists of three phases: (1) the scale generation phase; (2) the scale evaluation phase, including expert evaluation and target population pretesting; and (3) the scale testing phase, including first testing, second testing, and third testing. The results show that the initial scale consisted of three constructs (theory curriculum, application curriculum, and internship curriculum) and 21 items in total. By removing one item from the initial scale, 20 items in the final scale have been proven to have acceptable validity and reliability. The conclusions suggest that the curriculum type preference scale can be applied in practice, and the scale development procedure and methods in this study can serve as a theoretical reference for scale developers.


QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

Abstract:
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics.
 Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.


A GCC Artificial Food Additives Management based Mobile Application Development

Abstract: Artificial food additives pose significant health risks to Gulf Cooperation Council (GCC) citizens despite regional authorities' extensive medical, legislative, and technological efforts. Literature highlights the detrimental impacts of these additives, including malnutrition, digestive disorders, respiratory problems, skin issues, hives, nausea, diarrhea, shortness of breath, allergic reactions, high blood pressure, and tumors. The research project at hand aims at becoming the first official and comprehensive mobile application of its own in the GCC region that manages the calculation and demonstration of an up-to-date health and legal knowledge base of the impacts of artificial additives, enhances the awareness, automatically recognizes the artificial additives, and provides alternative solutions, for both android and IOS mobile platforms. This research project introduces "Weqaya," a pioneering mobile application designed to manage, educate, and raise awareness about the effects of artificial additives. Weqaya provides real-time health and legal information, identifies additives, and suggests alternative solutions for Android and iOS platforms. The project employs an Agile-based SDLC model to explore, develop, and evaluate the food additive phenomena in Weqaya. The application's usability evaluation scores a promising 95.21%, indicating its potential utility for GCC health ministries, dietitians, academics, researchers, and food producers in enhancing knowledge and promoting non-artificial food options.


Principle of explicability: regulatory challenges on artificial intelligence

Abstract: This essay aims to examine topic related to artificial intelligence, explaining how it works and specially studying aspects of regulatory framework. The paper approaches the principle of explicability of AI decisions, performing an analysis on the braziilian parliamentary discussion and brazilian data protection act. The hermeneutic method was adopted as well as literature review.


PENGEMBANGAN SUMBER BELAJAR BERBASIS WEB PADA MATA PELAJARAN PAI DI SMA NEGERI 1 TENJOLAYA BOGOR

Abstract:
This research is based on the development of technology and information that has developed greatly towards the world of education. Utilization of the web as a learning medium in accordance with the learning styles of students who are heading towards the digital era, web-based learning is a learning activity that utilizes website media that can be accessed through the internet network. internet network access provided by the school then the web-based learning media can be used by the teacher as one of the learning media where the teacher still uses learning in every learning. This research focuses on the problem of How to Develop WEB-Based Learning Media in Islamic Religious Education Subjects at SMAN 1 Tenjolaya Bogor.This research studies: 1) Producing web-based learning media on Islamic Religious Education subjects; 2) Describe the results of student responses to the use of web-based learning media in Islamic Religious Education subjects
 Related to this research is research and development (Research & Development) or R&D related to products, instructional media development, using ADDIE models which include Analysis, Design, Development, Implementation, and Evaluation. Research data obtained through observation, questionnaire, and documentation. Furthermore, data analysis using the results score from the validation of media experts, material experts and student responses to web-based learning media.
 The results of the research show: 1) the development by the researcher carries out student character analysis, media analysis, and data analysis of media and material experts, then designs the media with a web site application that contains a blog, media development on materials, desired products, validation, tests try to do 28 students of class X, implement the developed media then given to the teacher to be used for students then the researcher gives a questionnaire to students about perceptions of the developed media, then a further evaluation of the media based on the assessment of students and the media developed. 2) Inviting students about Learning Media by providing a questionnaire of 28 students using web-based learning media that are close to God, the media developed "feasible" is used as a learning medium in class X SMA1 Tenjolaya Bogor


Performance Analysis and Comparison of Machine Learning Algorithms for Breast Cancer Dataset

Abstract: Breast cancer is one of the most commonly found disease in women. In this research Wisconsin breast cancer data is considered. Data preprocessing and feature selection is done on this dataset. Later machine learning algorithms were applied on this dataset for classification of the disease. In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on this dataset. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python. From the results it can be observed that Decision Tree is giving an accuracy of 90.7%.


Abstract: Economic Management and Big Data Application, pp. 892-900 (2024) No AccessSimulation Stock Investment System Based on Vue and PythonJiayi CuiJiayi CuiComputer Science School, University of New South Wales,2052 NSW, Australiahttps://doi.org/10.1142/9789811270277_0079Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Stocks have become a common way to manage money and invest. However, it is too risky for initial users to invest directly. In the era of artificial intelligence, computer technology can be combined with many fields. This paper summarizes and designs a stock investment simulation system which combines the front and back end. The front end uses the VUE framework, and the back end builds a database. The back end uses Python to write the server to help novices quickly get familiar with the form of stock investment methods. This system also adds a risk assessment method to evaluate the risk coefficient of the stock, so that beginners can better adapt to real stock investment. Keywords: Stock investment simulatorArtificial intelligenceRisk predictionEuclidean DistanceVuepython FiguresReferencesRelatedDetails Recommended Economic Management and Big Data ApplicationMetrics History KeywordsStock investment simulatorArtificial intelligenceRisk predictionEuclidean DistanceVuepythonPDF download


Pemanfaatan Media YouTube dan Blog Pembelajaran Materi Statistika dan Hubungannya dengan Kemampuan Pemecahan Masalah Matematis dan Prestasi Belajar

Abstract: This research aims to examine the use of learning media that is good, effective and appropriate to the needs and characteristics of students through YouTube media and Learning Blogs and its relationship with mathematical problemsolving abilities and learning achievement. The method used was quantitative quasi-experiment with correlation tests on mathematical problem solving abilities and learning achievement for classes VIII C and VIII D at SMP Tarakanita 2 Jakarta with 58 respondents. The Cronbach's Alpha test on the YouTube media test instruments (0.83), Blog (0.82), Problem Solving (Pretest 0.92 and Posttest 0.86), and Achievement (0.90) shows high validity and reliability. Based on non-parametric hypothesis testing using the Spearman Test method, it is known that there is a correlation between the use of YouTube media and Problem Solving (R-count 0.37), YouTube and Achievement (R-count 0.41), Blogs and Problem Solving (R-count 0. .42), and Blog with Achievement (R-count .39). The non-parametric Mann Whitney test in the control class and experimental class regarding achievement was stated to be identical with the same mean and variance so that there was no difference in the increase in NGain in the two classes.


Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study

Abstract: With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations for potential recognition. It takes the Han Suyin International Translation Contest as a case study, which is a large-scale and influential translation contest in China, with a history of over 30 years. The experimental results show that the BERT-assist system is a reliable second rater for massive translations in terms of translation quality, as it can effectively sift out high-quality translations with a reliability of r = 0.9 or higher. Thus, the automated translation scoring system based on BERT can satisfactorily predict the ranking of translations according to translation quality and sift out high-quality translations potentially shortlisted for prizes.


Implementasi Media Digital Myon terhadap Kemampuan Literasi, Minat Baca dan Hasil Belajar Bahasa Inggris Kelas IV SD XYZ Jakarta

Abstract: Literacy skills are crucial for students in the 21st century, as a student's ability to self-process learning greatly depends on these literacy skills. However, some studies indicate a decline in literacy activities among them. With the rapid development of the digital media and the high use of gadgets among schoolchildren, it is expected that these gadgets can enhance their literacy skills, reading interests, and English learning outcomes. This research aims to determine the effect of using the myON digital media on the students' literacy skills, reading interests, and English learning outcomes. A pre-experimental design was employed to observe the differences before and after the use of the myON digital media, followed by a mixed-method design with an explanatory sequential approach. The research population consisted of 48 Grade IV students. The research instruments included literacy skills worksheets to observe the differences between the pretest and posttest, an N-Gain test was conducted, with the analysis showing an increase classified as low (g ≤ 0.3). A questionnaire was used to assess the impacts of the myON media on their reading interests and English learning outcomes. A simple linear regression analysis was applied, and the results have indicated a significant value > 0.05 for both variables, stating that there has been no significant influence from using myON on the students' reading interests and English learning outcomes.


Abstract: Economic Management and Big Data Application, pp. 832-842 (2024) No AccessBlockchain-based Trust Mechanism for Online Shared MobilityHaonan Yao and Nan RenHaonan YaoSchool of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China and Nan RenSchool of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, Chinahttps://doi.org/10.1142/9789811270277_0073Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: In order to solve the trust problem in China’s online shared mobility, blockchain technology is used to build a trust mechanism to guide and encourage users’ honest transactions through smart contracts. Then, the agent model is built by AnyLogic software to simulate the behavior of users participating in the transaction. The results show that the willingness of users to perform the contract can be effectively improved under the incentive of the trust mechanism. Finally, some suggestions are put forward to ensure that the trust mechanism can be effectively applied. Keywords: Online Shared MobilityBlockchainSmart Contract FiguresReferencesRelatedDetails Recommended Economic Management and Big Data ApplicationMetrics History KeywordsOnline Shared MobilityBlockchainSmart ContractPDF download