Reviewerly/exHarmony huggingface dataset:
Pemodelan Design Thinking dalam Perancangan UI dan UX Aplikasi Jasa Potong Rumput CleaningKuu

A grass cutter is a tool used to cut grass or plants. At present, data and information related to lawn mower rental services are still conventional, namely obtained through word of mouth and print media such as newspapers, pamphlets, and magazines. Because the media used is limited, consumers have problems finding information about the grass cutting service assistance business. The problems experienced by consumers are the lack of various information, such as the address of the rental place, the unavailability of the process for renting the lawn mower service online, and the absence of official prices for lawn mowing services listed in newspapers and flyers so that the price can be manipulated according to the wishes of the service provider. To solve these problems, a car-based lawn mower application was designed that can make it easier for users and more efficient by using design thinking and modeling. The stages of the Design Thinking process start with the empathize, define, ideate, prototype, and test stages. After designing the user interface, testing was then carried out using the System Usability Scale (SUS) approach, and a score of 81 was obtained, which was in grade B, so it can be concluded that the design of the user interface application for mowing services is feasible to be developed into a mobile-based application.
Implementasi Augmented Reality Metode Marker dalam Pengenalan Cryptocurrency

The variety of investments in modern times is not only limited to gold, stocks, bonds and others. Along with the times, a new type of investment instrument called Cryptocurrency has emerged. The virtual currency Bitcoin became the first Cryptocurrency to be introduced in the online market and turned out to have an impact on the world. In contrast, in this digital era there are still many people who still do not know Cryptocurrency. Augmented Reality-based Cryptocurrency introduction application where we can get to know Cryptocurrency better with a 3-Dimensional animation display where this technology combines the real world and the virtual world. The results of testing using a questionnaire of 50 participants using this application can be concluded that, before this application was introduced there were only 10% of participants who knew Cryptocurrency well and very well. After this application was introduced and used there were 76% of participants who could very well recognize cryptocurrency and 20% with participants knowing Cryptocurrency well. Therefore, the application of Augmented Reality Implementation of Marker Method in Cryptocurrency Introduction can answer the existing problems, namely with this application people can learn and know Cryptocurrency well.
A Study on the Selection Factors of Containers Inland Carriers Under the Korean Safe Rates System

As the container freight rate is fixed with the implementation of the safe freight rates system for trucks in 2020, the freight factor can no longer function as a differentiated factor for shippers. As a result, domestic container inland transportation companies have become the main competitive factors for corporate’s characteristics and service-related factors, excluding cost factors in order to attract shippers. This study aims to suggest implications for the competitive strategy of shipping companies by deriving the priority of factors in selecting inland container transport truck companies for domestic shippers under circumstances excluding freight factors. Based on previous studies, four factors, i.e. company characteristics, service quality, service diversity, and additional service were pairwise-compared to determine relative priorities of alternative choices including four sub-criteria of each using AHP(Hierarchy Analysis Process). As a result, the overall ranking of main criteria of selection carrier is service quality factors, service diversity factors, additional service factors, and company characteristics factors in the order, and the priority of quality factors was revealed to be quite high compared to other factors. In detailed sub-factors, accident response capabilities, responsiveness of order, delivery on time, flexible dispatch time of vehicle, and information exchange and sharing were drawn in the order, confirming that quality, the essence of transport service, is of importance. Inland container carriers should strengthen their efforts to improve quality factors such as accident response capabilities, responsiveness of order, and delivery on time to enhance their competitiveness.
The Impacts of Innovation and Facilitating Condition of Logistics 4.0 Technology on Logistics Safety Participation and Safety Performance

Purpose : The purpose of this study is to empirically analyze the impact of innovation and facilitating condition of logistics 4.0 technology on logistics safety participation and safety performance for logistics companies. Research design, data and methodology : Based on previous studies related to logistics 4.0 and logistics safety, measurements for each variable were derived, and a survey was conducted on employees of logistics companies. Also exploratory factor analysis, reliability analysis, correlation analysis, and regression analysis were conducted. Results : The innovation and facilitating condition of logistics 4.0 technology had a significant impact on logistics safety participation, and the facilitating condition of logistics 4.0 technology had a meaningful impact on safety performance, but innovation did not. In addition, logistics safety participation had a significant effect on safety performance. Conclusions : Logistics companies need to induce participation in logistics safety by actively introducing and utilizing logistics 4.0 technology, thereby prevent the occurrence of safety accidents.
A Study on International Research Trend of E-Commerce Logistics Using Abstract Text Data

Purpose : This study analyzed the trends of international e-commerce logistics research and compared the results of Korean research. Research design, data and methodology : The analysis method used a research trend method that utilizes abstract text data based on SLNA. The analysis period was from 2000 to 2019, and a total of 20 years of international e-commerce logistics academic literature was collected. Results : The results of the analysis showed that international e-commerce logistics research has been actively conducted since 2016. The research trends were mainly focused on measuring the quality of logistics services and logistics operation strategies of e-commerce companies. In particular, the proportion of questionnaire analysis studies targeting customers who use e-commerce companies was high. The results of comparing the research trends of Korean and international e-commerce logistics showed that there was no significant difference in the time when the research was conducted and the research trends. Conclusions : E-commerce is a market with flexibility and innovation in the introduction of technology, and it will require challenging research and an inclusive attitude.
A Case Study of Real-Time Art Education in a Virtual Space: Focusing on Theater Classes using Zoom and Padlet

This study aimed at examining real-time non-face-to-face distance art education cases and to draw implications for art education in schools in the untact era. Due to the COVID-19 outbreak, art education in schools has made it difficult to take advantage of the existing advantage of an aesthetic experience through face-to-face meetings. Researchers who had been practicing art education in schools through drama/theater were also asked to switch to non-face-to-face classes. To this end, the researchers developed and applied a real-time non-face-to-face remote theater class program using Zoom and Padlet for first-year middle school students from September to October 2020. We obtained the following results of this case study by interpreting the collected data such as survey, observation data, and artifacts etc.&#x0D; First, we tried to form a rapport with students who had few opportunities to meet each other due to Corona 19 through a strategy of establishing relationships even on the screen. Second, we provided students with an opportunity to collaborate, thereby providing an experience of joint achievement. The content of the program was to share experiences about what they have experienced, write a play script based on this, and then put on a performance together. As for the method of the program, a relationship was formed using various theatrical techniques, joint writing was performed using Padlets, and a Zoom-based reading play was performed. In this case, despite the unusual situation of ‘untact’, through the aesthetic activity of ‘art education’ in the limited space ‘in school’, the possibility of art education in schools in the untact era was realized and a vision was presented.
The COVID-19 Pandemic and ESG Reporting Priorities: Far Beyond Business as Usual

This chapter assesses the impact of Covid-19 on African economies and the implications for ESG reporting. In the process, we discuss the incidence of COVID-19, the measures that have been or are being undertaken to contain and eliminate it in the continent. We also make recommendations for broader measures as well as ESG-specific actions necessary for the purposes of containment and elimination.
Measuring the value of online databases: a case study to validate an ROI model

Purpose The return on investment (ROI) model is a tool used to measure the financial benefits and costs of an investment, in this case, the investment in digital library resources. By applying this model to the AAU digital library resources, the study seeks to determine whether these resources are providing sufficient value for the investment made in them. Design/methodology/approach The proposed ROI model has two distinct phases and utilizes two different sets of data to calculate the return on investment for a database subscription. In Phase I, the ROI is calculated based on the total number of downloads of full-text articles from the database during the academic year 2019–2020. This information is used to determine the financial returns of the database subscription costs. In Phase II, the ROI is calculated by examining the citations drawn from the Scopus database on a sample of 30 funded research projects for the College of Engineering during the year 2019. These data are used to determine the impact of the database subscription on research output and its contribution to the success of the College of Engineering's research projects. The two phases of the proposed ROI model aim to provide a comprehensive understanding of the value of the database subscription and its impact on both financial returns and research output. Findings The findings of the study indicated different results between Phase 1 and Phase 2 of the study. The positive ROI in Phase 1 suggests that the investment in online databases has a good return for the AAU, as they are gaining almost a dollar for every dollar spent. However, the negative ROI in Phase 2 is concerning. It suggests that the investment in the IEEE database is not generating a positive return for the AAU and may even be costing the institution money. Overall, these findings highlight the importance of measuring ROI in academic libraries, particularly in Arab countries where resources may be limited. By understanding the impact of library investments on institutional outcomes, libraries can make informed decisions about where to allocate their resources and how to optimize their services to best serve their communities. Research limitations/implications The findings of the current study were based on data collected from a specific sample, therefore, the findings may not be generalized to other academic libraries. A similar study with larger and more diverse samples can help to validate and extend the results of this study. Originality/value The findings of the study provide evidence that the proposed ROI model can be effectively applied in Arab countries and academic libraries in the Arab world, this could encourage more institutions in the region to adopt this model for evaluating their investments and projects. The study may also guide how to adapt the model to the specific cultural and organizational contexts of Arab countries.
Enabling Qualified Anonymity for Enhanced User Privacy in the Digital Era

This paper presents a privacy-enhancing identity management platform designed to address the challenges associated with online identity verification and privacy protection. INCOGNITO offers a comprehensive solution by leveraging concepts such as Qualified Anonymity and cryptographic credentials, along with technologies including blockchain, Tor Network, and software stacks like Idemix. By employing these mechanisms, INCOGNITO aims to enable users to securely acquire and manage their identity attributes, while preserving their privacy and ensuring compliance with both regulatory bodies and Service Providers’ requirements. The platform facilitates the issuance and verification of cryptographic credentials, granting users access to online services based on fine-grained subsets of their identity attributes. Furthermore, the effectiveness and feasibility of the platform are demonstrated through two pilot projects focused on online multimedia content sharing and identifying bots or fake users in online social networks. These pilots showcase the practical applicability of INCOGNITO in solving identity-related challenges while safeguarding user privacy and security.
Supplemental Material: Geoscience academic hiring networks reinforce historic patterns of inequity

&lt;p&gt;Item S1: Network nodelist, a spreadsheet that includes all nodes included in the network. Item S2: Network edgelist, a spreadsheet that maps every connection between nodes in the network. &lt;/p&gt;
A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency.
English Translation Intelligent System Based on Artificial Intelligence and Data Fusion Technology

As people communicate more and more around the world, the accuracy and timeliness of language communication are becoming more and more important. To solve multilingual, 24h, anytime, anywhere translation and communication services, these anytime, anywhere, short-term, multilingual translation services require the help of computers, big data, artificial intelligence, virtual agents, language intelligent judgment and other technologies. This paper aims to study the design method of intelligent English translation system based on artificial intelligence and data fusion technology. This paper proposes a new method for machine translation based on artificial intelligence and data fusion technology. Using the concept categories of HNC theory as semantic information to fuse with the neural network on the source language encoder side, two different fusion methods of horizontally mixing HNC concept category information and vertical splicing HNC concept category information are proposed, and verification experiments are carried out. The results show that compared with the attention mechanism-based neural machine translation baseline model RNNSearch, the method of integrating concept networks can improve the model by 1.48 BLEU values and reduce 1.76 TER values. Case analysis and other aspects have better performance. The research of neural machine translation integrating concept network is a beneficial attempt to combine rationalism and empiricism in the field of machine translation, which enriches the model architecture of neural machine translation and provides new ideas for the research of semantic machine translation. Experiments have shown that the current mainstream translation system in the market has a BLEU value of 6.62 lower than that of general texts for professional text translation, and 42% of the errors are in terminology translation. The lower response speed is lower than 0.05s.
Performance Analysis of Enhancement Methods on Fetal Ultrasound Images

Ultrasound imaging is widely used in medical diagnosis because it is non-invasive and free from ionizing radiation. However, ultrasound images have low contrast and contain speckle noise, making diagnosis difficult. Therefore, speckle noise reduction and image contrast enhancement are important prerequisites in ultrasound image processing. Many methods can be used in the ultrasound image pre-processing stage. In this paper, fetal ultrasound images were enhanced in contrast and sharpness using four enhancement methods, namely histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), unsharp masking (UM), and maximum local variation-based unsharp masking (MLVUM). These methods were applied to ultrasound images in two ways. Those are without filtering them and by first filtering them using a speckle reducing anisotropic diffusion (SRAD) filter. A comparative analysis was carried out on the performance of the four enhancement methods using the absolute mean brightness error (AMBE), average local contrast (ALC), and average gradient (AG) parameters. The results show that UM and MLVUM work better in increasing the contrast of fetal ultrasound images than HE and CLAHE. Applying the HE, CLAHE, UM, and MLVUM methods without filtering produces ultrasound images with better sharpness and contrast than enhanced images involving filtering but causing speckle noise amplification.
A neural network model for adversarial defense based on deep learning

Deep learning has achieved great success in many fields, such as image classification and target detection. Adding small disturbance which is hard to be detected by the human eyes to original images can make the neural network output error results with high confidence. An image after adding small disturbance is an adversarial example. The existence of adversarial examples brings a huge security problem to deep learning. In order to effectively defend against adversarial examples attacks, an adversarial example defense method based on image reconstruction is proposed by analyzing the existing adversarial examples attack methods and defense methods. Our data set is based on ImageNet 1k data set, and some filtering and expansion are carried out. Four attack modes, FGSM, BIM, DeepFool and C&W are selected to test the defense method. Based on the EDSR network, multi-scale feature fusion module and subspace attention module are added. By capturing the global correlation information of the image, the disturbance can be removed, while the image texture details can be better preserved, and the defense performance can be improved. The experimental results show that the proposed method has good defense effect.
Underwater image color correction and adaptive contrast algorithm improvement based on fusion algorithm

Due to the characteristics of water and the particles in water, the underwater images will have problems of color deviation, low contrast and uneven brightness. Therefore, a statistical approach based on variance is proposed in this paper to correct color-biased images and then equalize the image brightness using the Gamma algorithm. Secondly, a local adaptive contrast enhancement algorithm improved by contrast code images and a restricted contrast adaptive histogram equalization algorithm are used to improve the contrast of underwater images. Finally, two different contrast enhanced images after color correction are fused by a multi-scale fusion algorithm to obtain high-quality underwater images. The results of the comparison experiments with some existing underwater image enhancement algorithms show that our algorithm can effectively improve the quality of underwater images.
Video description method with fusion of instance-aware temporal features

There are still challenges in the field of video understanding today, especially how to use natural language to describe the visual content in videos. Existing video encoder-decoder models struggle to extract deep semantic information and effectively understand the complex contextual semantics in a video sequence. Furthermore, different visual elements in the video contribute differently to the generation of video text descriptions. In this paper, we propose a video description method that fuses instance-aware temporal features. We extract local features of instances on the temporal sequence to enhance perception of temporal instances. We also employ spatial attention to perform weighted fusion of temporal features. Finally, we use bidirectional long short-term memory networks to encode the contextual semantic information of the video sequence, thereby helping to generate higher quality descriptive text. Experimental results on two public datasets demonstrate that our method achieves good performance on various evaluation metrics.
Non-interactive GrabCut image segmentation method

The GrabCut image segmentation algorithm based on the principle of graph theory has been extensively used in the field of computer vision. However, the shortcoming is that it requires human-computer interaction to complete the ROI region selection to solve the segmentation task of the foreground image. Therefore, it cannot meet the requirements of fully intelligent image processing. In order to eliminate human-computer interaction and realize smart region selection, this paper proposes a ROI smart region generating and fine-tuning method to improve the GrabCut method, so as to realize intelligent image segmentation. The experimental results show that our method is compatible with both single-target and multi-target foreground image segmentation solutions.
Human posture recognition based on lightweight OpenPose model

To address the problem of slow inference in the original OpenPose pose estimation model and lower the computing power of the model, this paper first uses MobilenetV3 as backbone to make a lightweight improvement for OpenPose's network, followed by using label fusion correction to further improve the accuracy of the model. These steps make a real-time pose recognition system built on embedded devices on robots possible. The performance of the improved model is verified on the COCO dataset, and the results show that the accuracy of the improved model is not much different from the original OpenPose model, but the detection speed is improved by a factor of 4. Finally, a pose recognition model was trained on the self-built dataset using the skeleton map output from the improved model and validated on the test set, and the experiments indicated that the accuracy of the pose recognition model was 92.5%, which was real-time and suitable for various application scenarios.