Poor User Experience Research Articles

The Art of Dialogue: Synergistic Effects of Language Adaptation and Emotional Intelligence in AI Systems

Artificial intelligence (AI) systems are increasingly applied in areas such as voice assistants, customer service, and healthcare. However, current AI systems often lack semantic understanding and emotional expression capabilities when interacting with humans, leading to poor user experiences. To enable AI systems to converse more naturally with humans, it is necessary to synergistically optimize two key capabilities: language adaptation and emotional intelligence. This paper reviews the latest research progress in AI dialogue systems regarding language adaptation and emotional intelligence. In terms of language adaptation, this paper summarizes technologies such as personalized dialogue, knowledge-enhanced dialogue, and multi-turn contextual dialogue. In terms of emotional intelligence, this paper outlines work in areas such as emotion recognition, emotional dialogue generation, and empathetic dialogue. Additionally, this paper discusses strategies for synergistic optimization of language adaptation and emotional intelligence, as well as challenges in dialogue system evaluation. Finally, this paper looks ahead to future directions for AI dialogue systems.

Emotional dialogue generation model of electronic commerce intelligent customer service based on topic expansion

Current electronic commerce (e-commerce) customer service systems often rely on retrieval-based methods, resulting in impersonal and emotionally flat responses. These systems struggle to handle diverse expressions and emotional nuances in user queries, leading to poor user experience. In this paper, we applied artificial intelligence techniques, notably the Gated Recurrent Unit model, to generate emotionally resonant dialogues in e-commerce customer service systems. We integrate intent recognition, emotion recognition, and topic expansion for accurate intent identification and nuanced responses. The model is trained on a customer service dataset from Jing Dong, a leading Chinese e-commerce platform. Through extensive experiments, we conducted comparative analyses against benchmark model Gated Recurrent Unit neural network. The results demonstrate that our model significantly outperforms existing approaches, achieving fluency, rationality, and emotionality scores of 62%, 58% and 61% respectively. These findings indicate that our approach generates responses with greater consistency to user intent and richer emotional content. The significance of these results lies in their potential to enhance user experience in e-commerce customer service interactions by providing more personalized and emotionally resonant automated responses.

Monte Carlo Workflow Unification for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo (MC) transport codes are a cornerstone of nuclear reactor analysis frameworks, providing reference solutions and multigroup cross sections, and even as core components in multiphysics couplings. These applications can be seen in toolkits from the Nuclear Energy Advanced Modeling and Simulation program and the U.S. Nuclear Regulatory Commission’s BlueCRAB (Comprehensive Reactor Analysis Bundle). Contrary to their ubiquitousness in reactor physics modeling and simulation, popular MC codes, such as MCNP, Serpent, and KENO, are still reliant on antiquated textual input formats. These input languages use a plethora of keywords with terse syntax to specify all facets of a model, including its geometry, materials, physics settings, and tally options. This poses a steep learning curve and a poor user experience. Being tied to unique text-based input formats also significantly complicates programmatic input generation or modification that may be desired and/or required within a multiphysics framework. This work demonstrates how the development of programmatic interfaces for MC codes can support model unification and translation activities. Building on the Python application program interface (API) development of MCNPy, similar capabilities are being implemented for Serpent that are able to support model translation. In future work, the Serpent API capabilities will be made more robust and the work will be further expanded to include translations with KENO.

Effect of combined treatments of hydrolysis and succinylation on the solubility and emulsion stability of rennet casein and micellar casein

Casein is the most abundant protein in milk with good emulsifying properties and bioavailability. However, the tight micellar structure of casein results in poor solubility. In the case of soft solid materials such as processed cheese, imitation cheese, yoghurt and protein-based oil-in-water emulsions, poor solubility directly affects the homogeneity and stability of the texture structure of such products, leading to a poor user experience. In this study, two protein modification techniques, hydrolysis and succinylation, were combined to improve the solubility of casein and the stability of its emulsions. The individual and combined effects of enzymatic hydrolysis and succinylation modification approaches on the stability of rennet casein (RC) and micellar casein (MC) emulsions were further explored. After double-treatment of casein with enzymatic hydrolysis and succinylation, the solubility of RC and MC was up to about 95 %, which was superior to that of single-treatment. Fourier transform infrared spectroscopy showed that the characteristic wave signals of the double-treated samples were located between the two single-treated samples, and that there may be an opposite effect between the two modifications. After 21 days of storage, the emulsions prepared from double-treated caseins still remained stable. The salt ionic stability and freeze-thaw stability were significantly improved, and the physical stability of MC was increased by nearly three times. The results explained the effects of enzymatic hydrolysis and succinylation on the functional properties of casein, provided a reference for the development of food systems based on oil-in-water emulsions, and offered a new idea for the wide application of succinylated casein.

Construction and Ranking of Usability Indicators for Medical Websites Based on Website User Experience

In the era of digitalization, medical websites have rapidly expanded their healthcare market share due to their convenience. However, with this user-base expansion, issues with poor user experience have surfaced. To address this, we developed and ranked usability indicators for medical websites, aiming to improve their design and development from a user experience perspective, thereby improving user satisfaction and the website’s usability. Initially, we reviewed the relevant literature and summarized 30 usability indicators. Subsequently, we formed a Delphi panel of 20 experts and preliminarily identified 24 usability indicators through the Delphi survey method. Using data from 300 valid user surveys, we applied the Exploratory Factor Analysis (EFA) method to categorize these 24 indicators into four groups. Finally, we assessed the relative importance and priorities of these indicators using the Analytic Hierarchy Process (AHP) method. The results showed that, in terms of criterion layer weight priorities, Trust and Security (0.5494), Basic Performance (0.2710), and Features and Technology (0.1355) exhibited higher proportions. For the solution layer, Property Protection (0.1894), Credibility (0.1852), Privacy Protection (0.1194), Effectiveness (0.0932), and Findability (0.0579) exhibited higher weight proportions. The findings of this study will assist in future usability assessments and enhancements of medical websites. By optimizing the usability, we can both advance the digitalization of medical websites and improve the usability of medical websites, and enhance the service experience and satisfaction of your users.

Usability Evaluation of Ticket Purchasing Applications, Case Study Public Railways in Indonesia

This study aims to evaluate the usability of a mobile-based application used to facilitate passengers in purchasing train tickets, the Access by KAI application. As an application widely used by the public, the usability evaluation is essential. The evaluation was conducted by measuring usability in three aspects: effectiveness, efficiency, and user satisfaction. The measurement involved 10 respondents who actively use the application. They performed the experiment at the usability laboratory. The level of effectiveness and efficiency was measured using task analysis, analyzing the final results of each respondent after performing five predetermined tasks. The results show that the effectiveness and efficiency of the application are good. However, the measurement of user satisfaction aspect using the SUS (System Usability Scale) method indicates less favorable results. This obviously results in poor user experience (UX) for users. Therefore, the practical implication of this study is the need for improvements to the application, both in the user interface (UI) and existing business processes, to enhance the user experience.

UWB-enabled Sensing for Fast and Effortless Blood Pressure Monitoring

Blood Pressure (BP) is a critical vital sign to assess cardiovascular health. However, existing cuff-based and wearable-based BP measurement methods require direct contact between the user's skin and the device, resulting in poor user experience and limited engagement for regular daily monitoring of BP. In this paper, we propose a contactless approach using Ultra-WideBand (UWB) signals for regular daily BP monitoring. To remove components of the received signals that are not related to the pulse waves, we propose two methods that utilize peak detection and principal component analysis to identify aliased and deformed parts. Furthermore, to extract BP-related features and improve the accuracy of BP prediction, particularly for hypertensive users, we construct a deep learning model that extracts features of pulse waves at different scales and identifies the different effects of features on BP. We build the corresponding BP monitoring system named RF-BP and conduct extensive experiments on both a public dataset and a self-built dataset. The experimental results show that RF-BP can accurately predict the BP of users and provide alerts for users with hypertension. Over the self-built dataset, the mean absolute error (MAE) and standard deviation (SD) for SBP are 6.5 mmHg and 6.1 mmHg, and the MAE and SD for DBP are 4.7 mmHg and 4.9 mmHg.

Distributed realtime rendering in decentralized network for mobile web augmented reality

Low-latency, real-time rendering of 3D objects is critical for mobile web-based augmented reality (MWAR) applications. While cloud-based or edge-based server rendering offloading can reduce the latency for mobile devices, a high volume of user requests in user aggregation scenarios can overload servers and transmission channels. It can result in a poor user experience due to resource consumption and high latency. This paper presents a decentralized and collaborative real-time rendering offloading network architecture (CRCDnet) to address this issue. CRCDnet makes contributions in the following three areas: (1) In the user aggregation scenarios, mobile devices that perform the same services are used as service nodes to perform the offloading of rendering computing and a collaborative rendering computing network is established. (2) For data exchange in decentralized networks, a data sharing middle layer based on blockchain key indexes separates sensitive service data and ensures a secure, reliable exchange mechanism and efficient data exchange. (3) A data request and computing offloading scheduling approach is proposed for the collaborative rendering computing network to optimize the rendering computation delay.

Printed 1d Perovskite Photodetector for Indoor/Outdoor Non‐Contact and Real‐Time Sports Training Monitoring

AbstractAs people increased emphasis on health problems, various wearable electronic devices are developed for sport‐related activity monitoring. However, these reported sensors must be tightly attached on the body to record the photonic, electronic even chemical changes during exercise. Poor user experience hinders the rapid application of wearable sensors. Here, an all‐printed perovskite photodetector for achieving non‐contact sports motion monitoring is developed. 1D MAPbBr3 arrays are printed with uniform orientation and strict crystallization via the droplet‐manipulation printing strategy. Under the guidance of microarrays on the template, the perovskite‐loaded droplet can be self‐shaped into the linear confined liquid space for the next crystallization. 1D perovskite photodetectors with high responsivity (R, MAX: 198 A W−1) and detectivity (D*, MAX: 6.64 × 1013 Jones) can be utilized to detect changes in the ambient light intensity under the body during the push‐up movement, achieving non‐contact real‐time monitoring of motions. The average accuracy of printed photodetectors to classify the collected push‐up signals reaches 97.40%. This strategy provides a reference for further improving the sensing performance of wearable sensors, which also extends the application of sports monitoring.

Multilevel data simplification methods for flood process visualizations based on visual perceptions

ABSTRACT Visualizations of flood processes require a balance between scientific authenticity and realistic expressions. As the flood area increases, the amounts of time-series data that are produced by flood models often exceed the capacity of computer rendering, leading to poor user experiences and low visualization effectiveness. Therefore, this study carried out visual perception experiments and proposed a multilevel simplification and scheduling method for flood process data based on visual perceptions. This method first simplifies flood data by flood feature point selection according to conclusions related to visual perception, then triangulated irregular networks are built, and finally, flood data scheduling and rendering are achieved through multiple levels of detail combined with visual ranges. An experimental analysis using a 3D visualization prototype system shows that the proposed method can improve the efficiency of visual expressions while preserving flood morphology features.

OPTIMIZING NODE.JS APPLICATION PERFORMANCE THROUGH MAIN THREAD OFFLOADING

Node.js is a JavaScript runtime environment that allows you to run JavaScript code outside of a web browser. It works by using a single thread to handle multiple requests using a callback-based event loop. This means that Node.js operates in an event-driven, non-blocking I/O model. One potential weak point of Node.js is that it is designed to be a single-threaded runtime environment, which means that it may not be the best choice for applications that require a high degree of parallelism or multi-threading. While Node.js can handle multiple I/O operations simultaneously through its event-driven architecture, it can struggle with CPU-bound tasks that require heavy computation. This academic article investigates the concept of "offloading" the main thread in Node.js, which refers to the process of delegating heavy computational tasks to worker threads to improve the overall performance and responsiveness Node. js application. The article provides a comprehensive analysis of different techniques for offloading the main thread, such as using worker threads, child processes, and cluster modules. It also explores the potential trade-offs and performance implications of each technique, and provides recommendations for selecting the most suitable approach for different use cases. The article highlights the importance of optimizing the use of CPU resources and avoiding blocking the event loop, which can lead to degraded performance and poor user experience. It also emphasizes the need for careful monitoring and profiling of Node.js applications to identify performance bottlenecks and optimize the use of available resources. Overall, this article provides valuable insights and practical recommendations for developers and system architects who want to improve the performance and scalability of their Node.js applications by unloading the main thread.

Barriers to using eHealth/mHealth platforms and perceived beneficial eHealth/mHealth platform features among informal carers of persons living with dementia: a qualitative study

BackgroundNew technologies have brought about a new age of technology-enabled aids that can equip informal carers with the relevant resources for better care. These include but are not limited to facilitating access to healthcare providers, knowledge of caring for persons living with dementia, and sources of support for carers’ well-being. This qualitative study explores barriers to using eHealth/mHealth platforms and perceived beneficial eHealth/mHealth platform features among informal carers of persons living with dementia.MethodsAn exploratory qualitative study design was employed. Semi-structured interviews were conducted among 29 informal carers of persons living with dementia in Singapore recruited via convenience and snowball sampling. The interviews were audio-recorded and transcribed verbatim. Thematic analysis was used to analyse the data.ResultsThe participants in this study identified several barriers to using eHealth/mHealth platforms, including personal preference, apprehension, poor user experience and lack of skills. On the other hand, knowledge of dementia, caring for persons living with dementia and self-care, a list of resources, social support, location monitoring and alert systems, and the ability to manage appointments and transactions were valuable features for eHealth/mHealth platforms.ConclusionsDespite the underutilisation of eHealth/mHealth platforms, carers expressed a keen interest in using them if they are functional and capable of reducing their care burden. The findings from this study can contribute to developing content and features for eHealth/mHealth interventions aimed at lightening carers’ burden in their day-to-day caring routine.

How Python brings efficiency to chatbots: enhancing user experience with magic filters in Aiogram

User experience is of immense importance, especially when it comes to chatbots. Continuous and intuitive interaction with a chatbot can significantly elevate customer satisfaction and loyalty. Conversely, a poor user experience can lead to disappointment and abandonment, ultimately harming the reputation and effectiveness of the chatbot. To ensure a positive user experience, chatbot developers need to focus on designing conversations that are natural, context-aware, and efficient. Chatbots have become a key element of customer service in the digital environment, and this article explores how the Python programming language, particularly through the aiogram library, allows the implementation of magic filters and how they can alter the functionality of any chatbot. The article discusses issues and limitations to consider, as well as the potential and possibilities of using magic filters in aiogram for future developments. Magic filters enhance user interaction, recognize entities, analyze moods, and determine the language of messages. The research aims to explore the possibilities of using Python and magic filters to enhance chatbots using aiogram. The goal is to create a guide on using magic filters and their effective implementation. It's essential to consider the complexity of implementing magic filters, the need for quality data, and understanding linguistic and cultural nuances. These aspects can pose challenges for developers. Recommendations include understanding the target audience, testing, and maintaining a balance between automation and human intervention. Developing chatbots using Python and aiogram with magic filters opens up broad possibilities for improving interaction and functionality. Understanding limitations and implementing best practices are key to success. The implementation of magic filters in chatbots brings numerous benefits for businesses and users. Personalized interaction, time and cost savings, improved scalability, and data-driven statistics collection are key advantages that can significantly enhance chatbot functionality and user satisfaction.

An Exploration of Customer Experience Design Innovation in Tourism and Hospitality Industry Driven by Emerging Technologies

Abstract With the rapid development of tourism hotels, there are many problems, such as uneven design quality and poor user experience. In response to the above issues, this paper proposes a digital design for tourism hotels that integrates VR technology. Using the network crawling technology in the network LDA model, the text data on customer experience design in the tourism hotel industry is obtained, and in order to avoid the problem of duplicate values and null values in the data, data processing is carried out to ensure the precision of the research results. Based on the principle of evaluation system construction, we determine the evaluation index system for tourism hotel customer experience design that allows the integration of VR technology and constructs the LDA-AHP model for hotel customer experience design. Based on the acquired research data, the customer experience design of the tourism hotel industry is analyzed by examples. The results indicate that the comprehensive evaluation score for tourism hotel user experience based on the LDA-AHP model is 3.3075, which suggests that the design of tourism hotel user experience based on the LDA-AHP model is at the 3-crown level. This study provides theoretical references for the design of tourist hotel customer experiences, which can help the tourist hotel industry improve and develop rapidly.

Out-of-scope intent detection with intent-invariant data augmentation

In practical dialogue systems, it is crucial to avoid undesired responses and poor user experiences by detecting Out-Of-Scope (OOS) intents from user utterances. Currently, to detect OOS intents, limited-supervised methods are more potential due to using limited true OOS data to better model the OOS data distribution. However, existing limited-supervised methods often yield low-quality OOS augmentations, i.e., noisy OOS and long-distant OOS, due to the use of the retrieval-based data augmentation mechanism, which will damage the OOS intent detection performance. To tackle this problem, in this paper, we propose a novel OOS intent detection method by leveraging intent-invariant data augmentation, called InInOOS, which can generate high-quality pseudo-OOS utterances with invariant OOS intents but various slot values and different expressions to further enhance the OOS intent detection performance. Specifically, we first generate pseudo-OOS candidates using our pre-trained intent-invariant utterance generation model, and then the most beneficial candidates are selected to train an OOS intent detection model. Extensive experiments on two public datasets show that our method performs better than state-of-the-art baselines.

Movie recommendation system using taymon optimized deep learning network

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets.

Caerus: Low-Latency Distributed Transactions for Geo-Replicated Systems

Distributed deterministic database systems achieve high transaction throughput for geographically replicated data. Supporting transactions with ACID guarantees requires deterministic databases to order transactions globally to dictate execution order. In a geographically distributed environment, ordering transactions globally can take multiple wide-area network (WAN) round trips of messaging, which adds significant latency to transaction response times, leading to poor user experiences. To improve the response time of transactions in deterministic databases, we propose an ordering protocol that can include a transaction in the global order in a single WAN round trip to the primary regions of the data items within the transaction's read and write set. The protocol reduces the cost of determining the global order for all transactions by leveraging deterministic merging of partial sequences of transactions per geographic region. We implement the protocol in Caerus, our geo-replicated deterministic database system that serializably commits and replicates transactions after a delay of only a single WAN round trip of messaging. Using popular workload benchmarks over geographically replicated data in Azure, we show that Caerus outperforms state-of-the-art comparison systems to deliver low-latency transaction execution.

Who generates your video ads? The matching effect of short-form video sources and destination types on visit intention

PurposeShort-form videos have gradually become important marketing tools for tourist destinations. However, chaotic sources and homogenized content have led to poor user experiences. Taking Kulangsu and Xi'an City Wall in China as examples, this study explored the influence of matching short-form video sources with destination types on user engagement and visit intention.Design/methodology/approachThis study selected three short-form videos from different sources for each destination on TikTok and conducted an empirical research using a 3 × 2 experimental design to examine the proposed research model.FindingsThe results showed that the matching effect between short-form video sources and destination types will positively affect user engagement and visit intention. (1) the short-form videos with user-generated content (UGC) or professional user-generated content (PUGC) in hedonic destinations can obtain higher user engagement and visit intention; (2) the short-form videos with professionally generated content (PGC) or PUGC in utilitarian destinations can obtain higher user engagement and visit intention and (3) perceived credibility and perceived usefulness played mediating roles in these interactions.Originality/valueThis study considers short-form video sources as antecedent variables influencing user engagement and visit intention and confirms the matching effect between short-form video sources and tourism destination types. The findings will help researchers and marketers better understand the impact of short-form video on destination marketing.

Analysis of route choice based on path characteristics using Geolife GPS trajectories

ABSTRACT Navigation services are essential for daily navigation, providing turn-by-turn instructions to help wayfinders reach their destinations. These services often differ from the heuristics wayfinders use, resulting in a poor user experience. Researchers have attempted to address this issue by developing algorithms that find less complex routes, by integrating prominent locations along the route to make wayfinding easier and to improve a wayfinder’s knowledge about the environment. These approaches, however, have taken a bottom-up approach, involving a limited number of participants navigating in real or virtual environments which may limit generalisability of results. In this study, we took a top-down approach by analysing a large dataset of GPS-based trips in the real world. Using the Geolife dataset, we analysed individual heuristics for route selection in terms of complexity and prominent locations, and found that wayfinders prefer less complex routes, such as routes that require fewer turns or involve simpler intersections. Additionally, we found that wayfinders choose routes with fewer prominent locations, such as routes that bypass well-known landmarks or busy commercial areas. These findings suggest that simplicity and ease of use are prioritized when selecting a route, while overly complex routes or areas with many points of interest are avoided.

A Secure Data-Sharing Scheme for Privacy-Preserving Supporting Node–Edge–Cloud Collaborative Computation

The node–edge–cloud collaborative computation paradigm has introduced new security challenges to data sharing. Existing data-sharing schemes suffer from limitations such as low efficiency and inflexibility and are not easily integrated with the node–edge–cloud environment. Additionally, they do not provide hierarchical access control or dynamic changes to access policies for data privacy preservation, leading to a poor user experience and lower security. To address these issues, we propose a data-sharing scheme using attribute-based encryption (ABE) that supports node–edge–cloud collaborative computation (DS-ABE-CC). Our scheme incorporates access policies into ciphertext, achieving fine-grained access control and data privacy preservation. Firstly, considering node–edge–cloud collaborative computation, it outsources the significant computational overhead of data sharing from the owner and user to the edge nodes and the cloud. Secondly, integrating deeply with the “node–edge–cloud” scenario, the key distribution and agreement between all entities embedded in the encryption and decryption process, with a data privacy-preserving mechanism, improve the efficiency and security. Finally, our scheme supports flexible and dynamic access control policies and realizes hierarchical access control, thereby enhancing the user experience of data sharing. The theoretical analysis confirmed the security of our scheme, while the comparison experiments with other schemes demonstrated the practical feasibility and efficiency of our approach in node–edge–cloud collaborative computation.