User Ranking Research Articles

Involvement of interactive educational platforms in the training of graphic design students using the Figma platform as an example

The goal is to provide a rationale for choosing the optimal prototyping and UI/UX design software for teaching design students. The purpose of writing this paper is to determine the optimal tools for teaching UI/UX design students through interactive educational platforms. The research uses the methods of cross-multiple-iteration expert ranking, personal ranking of students, and comparative cross-analysis of the results of expert and user ranking. The ranking results confirmed the choice of tools such as Figma, Adobe XD, InVision, Sketch, and Marvel. The users’ conclusions agree with the experts’ evaluations. Still, the analysis revealed a significant difference in approaches: the gap in user evaluations is 84.6% versus 32.0% for experts, indicating the influence of user empathy and the importance of UI/UX in product perception. Figma was selected as a tool for prototyping and UI/UX design with a median rating of 57.2%, justifying its use in interactive educational programs for future designers, contributing to the development of skilled digital production specialists. Further research is focused on developing detailed educational courses for design students, as well as studying the empathic component of experience for creating ergonomic interfaces.

Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching.

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor-recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor-recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor-recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.

Dynamic Short-Term Perspective Estimation Based on Formal Concept Analysis

In online shopping, user perspectives transit dynamically from abstract categories to concrete subcategories within a short period. We propose a perspective-estimation system that estimates the dynamic, short-term perspectives of users by inferring a hierarchy of categories based on their actions. The proposed system analyzes the wish list rankings of users and their operational histories to extract the categories emphasized at that moment. It then employs formal concept analysis to infer the hierarchical structure of categories, thereby visualizing the dynamic short-term perspective. In experiments involving 57 participants, the proposed method rates its match with user perspectives on a seven-point scale, achieving an average score of 4.84, outperforming the feature estimation method using latent Dirichlet allocation (LDA), which scored 4.36. The statistical significance was confirmed through the Wilcoxon rank-sum test with a statistic W=4.80 and a p-value of 1.56×10-6. Compared with LDA, the proposed system is statistically significant in terms of the degree of agreement with the perspectives.

The effects of icon design features on user perception and preference: A case study of icons for Covid-19.

The objective of this study is to evaluate users' perceptions and preferences on the design features of the COVID-19 prevention promotion icon from the perspective of users' aesthetic and perceptual needs. In this study, 120 officially published icons from 24 countries and regions were collected from online platforms for ranking tests, and then the top-ranked icons were subjectively rated by the semantic differential method. By evaluating the quality of users' perceptions of multiple semantic dimensions of icons, we extracted the perceptual semantic words that users valued as the main icon design features. Spearmen correlations were applied to derive possible correlations between user rankings and semantic scales, and a Friedman test was also conducted to determine the true differences in user perceptions and preferences for different styles of icons. Factor analysis was conducted to extract six perceptual words that influence the design features of the COVID-19 prevention promotion icon. The methodology adopted in this study facilitated the screening of design features related to icon effectiveness, and the findings show that "Interesting," "Simple," "Familiar, "Recognizable," "Concrete," and "Close(semantic distance)" are the key features that influence users' perception and preference of COVID-19 icon design. The results of this study can be used as the basis for designing and improving publicity icons for preventive measures in COVID-19, and the methods adopted in this study can be applied to evaluate other types of icon design.

Multi-granularity attribute similarity model for user alignment across social platforms under pre-aligned data sparsity

Cross-platform User Alignment (UA) aims to identify accounts belonging to the same individual across multiple social network platforms. This study seeks to enhance the performance of UA tasks while reducing the required sample data. Previous research has focused excessively on model design, lacking optimization throughout the entire process, making it challenging to achieve performance without heavy reliance on labeled data. This paper proposes a semi-supervised Multi-Granularity Attribute Similarity Model (MGASM). First, MGASM optimizes the embedding process through multi-granularity modeling at the levels of characters, words, articles, structures, and labels, and enhances missing data by leveraging adjacent text attributes. Next, MGASM quantifies the correlation between attributes of the same granularity by constructing Multi-Granularity Attribute Cosine Distance Distribution Vectors (MA-CDDVs). These vectors form the basis for a binary classification similarity model trained to calculate similarity scores for user pairs. Additionally, an attribute reappearance score correction (ARSC) mechanism is introduced to further refine the ranking of candidate users. Extensive experiments on the Weibo-Douban and DBLP17-DBLP19 datasets demonstrate that compared to state-of-the-art methods, The hit-precision of the MGASM series has significantly improved by 68.15% and 27.02%, almost reaching 100% precision. The F1 score has increased by 37.6% and 21.4%.

Suspicious behaviour detection in multilayer social networks using PF-KMA and SS-GAE techniques

Multilayer Social networks are an important part of human life to interact on different networks at the same time. Due to the openness of such networks, they become a platform for spammers to spread malicious behaviors. Hence, there is an urgent need for effective detection of malicious behaviors; thereby, enabling the networks to take mitigation actions to decrease the possibility to reward such activities. Detection of suspicious behaviors in previous works is challenging due to the problems of community detection, the large amount of feature corruption, and memory requirements. Thus, to deal with such problems, in this paper, an efficient clustering-based detection of malicious users in multilayer social networks is proposed. Initially, the input dataset is pre-processed and used for Exponential Distribution based Erdős–Rényi based graph construction. From the graph structure, two types of data, such as user representations and graph features are extracted for graph encoding using the Soft Sign activated Graph Auto Encoder model. Then, the decoding is done to predict the information diffusion level, thereby, ranking the users using the Laplace Regularization technique. Then, the ranked users are clustered into different groups using the Pareto Front based K-Means Clustering Algorithm technique. Finally, the experimental results were analyzed to demonstrate the efficacy of the proposed model to detect malicious users in multilayer social networks.

Gamification Techniques and Contribution Filtering in Crowdsourcing Micro-Task Applications

The rapid expansion of the internet has led to a growing worldwide user base, with Brazil alone having approximately 83% of households connected to the network, equating to around 61.8 million households. Crowdsourcing, a production model that harnesses collective wisdom for problem-solving, has gained prominence in this digital landscape. Challenges in crowdsourcing include improving people's motivation and engagement and verifying the quality of a high number of contributions. In our research, we investigated the hypothesis that using gamification techniques, including recognition badges, feedback mechanisms, and user rankings, improves users' engagement and motivation in crowdsourcing micro-tasks applications. This paper presents ConTask, a crowdsourcing micro-task application, and two studies conducted to investigate the impact of using gamification techniques and contribution filtering as motivational factors in crowdsourcing. The first was a case study comparing two versions of ConTask: the original version and a gamified one. The second was an experimental study to evaluate the developed contribution filtering mechanism. Findings suggest that the use of gamification and contribution filtering can improve user participation in crowdsourcing micro-task applications.

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

Monitoring Perkembangan Program Bank Sampah Menggunakan Tableau Public di RW 05 Kelurahan Cipinang Besar Selatan

This community service focuses on managing the Waste Bank in RW 05, Cipinang Besar Selatan Subdistrict with the aim of overcoming the lack of public interest in the Waste Bank program. Through a mentoring approach using Tableau Public, this activity aims to increase public understanding of the program. By using qualitative analysis, the results of the waste movement dashboard visualization are expected to provide an understanding of the influence of the Waste Bank on increasing waste volume. The research results show that data visualization using Tableau Desktop successfully achieved service objectives, providing significant insight into the effectiveness and development potential of the Waste Bank program. Features such as Total Waste per Month, Top Customer Rank, Top District, Top Types of Waste, and RT Participation provide an in-depth understanding of program dynamics. Data visualization has succeeded in increasing public interest, effectively reducing environmental pollution, and providing transparency of contributions, positive comparisons, personal understanding of environmental impacts, and motivation through growth and achievement. Therefore, the use of Tableau data visualization in the Waste Bank program is not only a data management tool, but also a driver of active participation and public awareness of the importance of protecting the environment. The Waste Bank program supported by data visualization has great potential to achieve sustainability goals and reduce negative impacts on the environment.

Outage Analysis of NOMA Systems With User Ranking Based on ISP and Data Rate Requirement

Outage Analysis of NOMA Systems With User Ranking Based on ISP and Data Rate Requirement

Identification and ranking of customers with energy theft using hypothesis tests

Identification and ranking of customers with energy theft using hypothesis tests

Analysis of Teachers’ Discourse in the Digital Platform Mode of College English Teaching Based on the Information Diffusion Model

Abstract This paper utilizes the information diffusion mechanism of the information extension model, which can act on the influence of students in the process of university English teaching to the information extension model to the diffusion ability that English information has. It examines the level of student activity and English information coverage in university English teaching and discusses the use of the information extension model in English teaching. According to the objectives of university English teaching, the functional requirements of the university English teaching platform are analyzed, the university English teaching platform based on the information diffusion model is designed through big data technology, and the English teaching platform combined with the information extension model is analyzed in an example study. The results show that on the model simulation analysis, the performance of this paper’s model is better than the User Rank model and UI-LR model when mining less than 30 information extensions. In the analysis of teachers’ discourse, the speech rate of teachers in the university English teaching platform is mainly concentrated in the range of 140-149 syllables/minute and 150-159 syllables/minute, and only one teacher’s speech rate exceeds 170-179 syllables/minute. This study has the potential to enhance students’ understanding of teachers’ discourse and establish a solid platform for communication between teachers and learners.

Leaderboard Application as A Ranking Media for Internet Users

The technology of utilizing hotspot networks has developed quite rapidly. In its development, internet technology uses a more flexible Mikrotik hotspot because it provides convenience for administrators and users. The object of this study is the hotspot network of Hayam Wuruk University (UHW) Perbanas. The goal is to develop a leaderboard design as a medium for monitoring internet use through the UHW Perbanas hotspot. Its application is through the integration of mikrotik with the web service API as a ranking of internet users against three categories of activities, namely downloads, uploads and internet usage times on each day and month. Each of these categories has 20 users. The test method uses a black box. Hasil testing states that the system is successfully operating, so that it can be implemented in the context of decision making by the management of UHW Perbanas.

Personalized Medical Terminology Learning Game: Guess the Term.

Objective: Wordbot, a chatbot designed for gamified education, transforms the process of memorizing complex medical terminology into an engaging and enjoyable activity for medical students. Taking inspiration from the "guessing words" game, Wordbot aims to improve medical students' learning outcomes by making the vocabulary memorization process more memorable. Materials and Methods: Wordbot, which can be implemented on the LINE platform, was created for this research, specifically to improve medical terminology learning. Wordbot incorporated mobile devices and personal computer-compatible flashcard games with features such as user ranking and personalization to enhance motivation and optimize learning outcomes. In the experimental research setting, half of a total of 48 nursing students were randomly assigned to use Wordbot for 4 months, and the other half were assigned to a control group relying on self-study without the help of Wordbot. Both groups received pretest and post-test to assess their respective learning of medical terminology. In this study, a statistical t-test was used to analyze the results between the two groups. In addition, user usability testing was conducted to evaluate the usability of Wordbot and gather feedback on user experience. Results: The results of this study have demonstrated that Wordbot is effective in facilitating students learning of medical terminology. Students experienced a significant improvement in their knowledge of medical terminology. An average user usability test score of 83.25 indicated that users' satisfaction with Wordbot is high. Conclusion: Incorporating gamification and personalization elements in Wordbot can significantly improve the overall enjoyment of the learning process. By participating in diverse interactive activities, users can effectively enhance their proficiency in spelling, recognition, and speaking. Wordbot utilizes sophisticated algorithms to generate customized questions based on identified mistakes, which facilitate error identification and correction. The robust findings of this study overwhelmingly support Wordbot's role as a convenient and easily accessible tool for learning medical terminology. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Chang Gung Medical Foundation (Protocol code: 202201586B0, date of approval: 8 November 2022). We obtained informed consent from all of our study participants regarding their willingness to participate in this study.

User Real-Time Influence Ranking Algorithm of Social Networks Considering Interactivity and Topicality.

At present, the existing influence evaluation algorithms often ignore network structure attributes, user interests and the time-varying propagation characteristics of influence. To address these issues, this work comprehensively discusses users' own influence, weighted indicators, users' interaction influence and the similarity between user interests and topics, thus proposing a dynamic user influence ranking algorithm called UWUSRank. First, we determine the user's own basic influence based on their activity, authentication information and blog response. This improves the problem of poor objectivity of initial value on user influence evaluation when using PageRank to calculate user influence. Next, this paper mines users' interaction influence by introducing the propagation network properties of Weibo (a Twitter-like service in China) information and scientifically quantifies the contribution value of followers' influence to the users they follow according to different interaction influences, thereby solving the drawback of equal value transfer of followers' influence. Additionally, we analyze the relevance of users' personalized interest preferences and topic content and realize real-time monitoring of users' influence at various time periods during the process of public opinion dissemination. Finally, we conduct experiments by extracting real Weibo topic data to verify the effectiveness of introducing each attribute of users' own influence, interaction timeliness and interest similarity. Compared to TwitterRank, PageRank and FansRank, the results show that the UWUSRank algorithm improves the rationality of user ranking by 9.3%, 14.2%, and 16.7%, respectively, which proves the practicality of the UWUSRank algorithm. This approach can serve as a guide for research on user mining, information transmission methods, and public opinion tracking in social network-related areas.

Who Will Purchase This Item Next? Reverse Next Period Recommendation in Grocery Shopping

Recommender systems have become an essential instrument to connect people to the items that they need. Online grocery shopping is one scenario where this is very clear. So-called user-centered recommendations take a user as input and suggest items based on the user’s preferences. Such user-centered recommendations have received significant attention and uptake. Instead, we focus on an item-centered recommendation task, again in the grocery-shopping scenario. In the reverse next-period recommendation ( RNPR ) task, we are given an item and have to identify potential users who would like to consume it in the next period. We consider three sub-tasks of the overall reverse next-period recommendation ( RNPR ) task, (i) Expl- RNPR , (ii) Rep- RNPR , and (iii) Mixed- RNPR , where we consider different types of target users, i.e., (i) explore users, who are new to a given item, (ii) repeat users, who previously purchased a given item, and (iii) both explore users and repeat users. To address the Expl- RNPR task, we propose a habit-interest fusion model that employs frequency information to capture the repetition-exploration habits of users and that uses pre-trained item embeddings to model the user’s interests. For the Mixed- RNPR task, we propose a repetition-exploration user ranking algorithm to decouple the repetition and exploration task and investigate the tradeoff between targeting different types of users for a given item. Furthermore, to reduce the computational cost at inference, we analyze the repetition behavior from both user and item perspectives and then introduce a repetition-based candidate filtering method for each sub-task. We conduct experiments on two public grocery-shopping datasets. Our experimental results not only demonstrate the difference between repetition and exploration, but also the effectiveness of the proposed methods.

Boosting the Learning for Ranking Patterns

Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches.

Kano-QFD-based analysis of the influence of user experience on the design of handicraft intangible cultural heritage apps

Mobile internet and digital technology provide new media platforms for the dissemination of handicraft intangible cultural heritage (ICH). Despite extensive research into the effect of user experience (UX) on handicraft ICH applications (apps), there is little quantitative research on the relationship between user needs and app design requirements. This study employed semi-structured interviews, online questionnaire surveys, and expert evaluation to understand and rank user needs and design requirement influencing factors. A quantitative analysis using the Kano-QFD approach revealed the different effects of twelve user needs and twenty-three design requirements on handicraft ICH app design. Following the establishment of a relationship matrix between user needs and design requirements, the app design requirements were ranked according to importance. Furthermore, a concept–category–application UX app design framework was constructed based on an analysis of the importance of design requirements. The concept layer formed the theoretical basis for the UX app design, the category layer represented the different facets of the UX design, and the application layer provided specific suggestions on the importance of app design. The eye-movement experiment and System Usability Scale were used to test the UX effects of a sample handicraft ICH app, verifying that the UX design framework was reliable. The framework provides methodological reference for the UX design of other ICH digital products.

Making the objectively best choice for side-stream resources—Verification of a debiasing method based on cognitive maps and attribute substitution

Multi-criteria decision-making (MCDM) tools are essentially methods to enable a decision maker to achieve a more objective approach to a given decision scenario using quantitative methods. One such complex decision scenario is the underutilization of side-streams in the seafood industry, which is brought about by a combination of complex decision challenges related to processing methods, storage methods, logistics, technical viability, status quo mindset, and the attitude of the decision maker. However, the influence and identification of cognitive biases (e.g., loss aversion bias) in MCDM tools are rarely accounted for and may result in a less objective decision process due to subjective influences, which can influence the valorization and utilization of seafood side-streams in a company. To enable a more objective approach where the influence of these cognitive biases is corrected, in this paper, we propose a debiasing method based on the UN’s 14 SDGs, cognitive mapping (CM), and attribute substitution (AS) as an extension of MCDM tools and the modeling of seafood processing. The results of the case-specific implementation show that the proposed method can identify cognitive biases and correct these by enabling the implementation of relevant debiasing techniques that can aid a decision marker in choosing the best alternative when it comes to decisions on reducing wasted side-streams and increasing the sustainability of their food processing. It was found that the debiasing application provided a correction of the user ranking for the best-evaluated alternative within a side-stream scenario to be in line with the experts’ ranking for the same scenario in terms of environmentally and economically efficient production. This is a novel approach combining existing theories and methods into a single bias identification and debiasing method, which is designed to be generic and can be implemented in other sectors and industries using MCDM tools in their decision process. The approach provides industry and science with a verified and structured method to achieve objectivity through the identification and correction of decision-making biases that also supports a balance between a company’s economic and environmental goals.

Research on Sales Strategy of Electric Vehicles based on Logical Regression and TOPSIS Model

The development of new energy vehicles represented by electric vehicles has a broad market prospect and is an effective way to solve energy and environmental problems. In this paper, outliers are obtained according to the range of scores, variance, population, and economic situation, and they are regarded as missing data or deleted directly, and then the missing outliers are obtained by using the average method. Then, it is used SPSS to count each index of each kind of data, describe each kind of data, get the sample statistics of each index, and finally calculate the normalized scores of the three brands and the satisfaction ranking of the target customers through the TOPSIS evaluation method. Then the data a1~a8 and B1~B17 are discussed, respectively. Firstly, the oversampling method is used to equalize the samples, then the continuous features are discretized, and then the logical regression model is used to eliminate the features to get the final results.