Automated Methods for Webpage Usability & Accessibility Evaluations

The reconstruction of analysts’ reasoning processes ( reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems.

Visual analytics (VA) is typically applied to complex data, thus requiring complex tools. While visual analytics empowers analysts in data analysis, analysts may get lost in the complexity occasionally. This highlights the need for intelligent assistance mechanisms. However, even the latest LLM-assisted VA systems only provide help when explicitly requested by the user, making them insufficiently intelligent to offer suggestions when analysts need them the most. We propose a ProactiveVA framework in which LLM-powered UI agent monitors user interactions and delivers context-aware assistance proactively. To design effective proactive assistance, we first conducted a formative study analyzing help-seeking behaviors in user interaction logs, identifying when users need proactive help, what assistance they require, and how the agent should intervene. Based on this analysis, we distilled key design requirements in terms of intent recognition, solution generation, interpretability and controllability. Guided by these requirements, we develop a three-stage UI agent pipeline including perception, reasoning, and acting. The agent autonomously perceives users' needs from VA interaction logs, providing tailored suggestions and intuitive guidance through interactive exploration of the system. We implemented the framework in two representative types of VA systems, demonstrating its generalizability, and evaluated the effectiveness through an algorithm evaluation, case and expert study and a user study. We also discuss current design trade-offs of proactive VA and areas for further exploration.

User interaction logs allow us to analyze the execution of tasks in a business process at a finer level of granularity than event logs extracted from enterprise systems. The fine-grained nature of user interaction logs open up a number of use cases. For example, by analyzing such logs, we can identify best practices for executing a given task in a process, or we can elicit differences in performance between workers or between teams. Furthermore, user interaction logs allow us to discover repetitive and automatable routines that occur during the execution of one or more tasks in a process. Along this line, this chapter introduces a family of techniques, called Robotic Process Mining (RPM), which allow us to discover repetitive routines that can be automated using robotic process automation technology. The chapter presents a structured landscape of concepts and techniques for RPM, including techniques for user interaction log preprocessing, techniques for discovering frequent routines, notions of routine automatability, as well as techniques for synthesizing executable routine specifications for robotic process automation.

Aware Facet Ranking (CAFR) model, which is user interactive and makes the most out of the user by dynamically filtering and ranking the facets according to their relevance, user motivations, and the situational contexts of the user. The model ranks the facets based on a composite score of query similarity, frequency of facet use, and result reduction. The efficiency of the optimized interface was determined based on the user-centered assessment framework, based on the user interaction logs, the satisfaction measures, and the rate of completing the tasks. In a compare and contrast experiment, the applicability of the CAFR model to standard Baseline and Static Ranking settings was comparatively tested. The outcome is a massive performance improvement, on which the CAFR model had a Search Performance Index (SPI) of 0.76, compared to 0.58 in the Baseline. Users were faster, interacted on average 2.8 times (vs. 4.2 with the Baseline), and solved a query (88%), better than with the Baseline (62%). These results suggest that dynamically ranked facets can offer a more interesting and intelligent search user experience in a wide range of applications, including e-commerce, online libraries, and enterprise data management.

Article history: Received January 2, 2015 Received in revised format 6 February 2015 Accepted 7 February 2015 Available online February 14 2015 Web mining has been widely used to discover knowledge from various sources in the web. One of the important tools in web mining is mining of web user’s behavior that is considered as a way to discover the potential knowledge of web user’s interaction. Nowadays, Website personalization is regarded as a popular phenomenon among web users and it plays an important role in facilitating user access and provides information of users’ requirements based on their own interests. Extracting important features about web user behavior plays a significant role in web usage mining. Such features are page visit frequency in each session, visit duration, and dates of visiting a certain pages. This paper presents a method to predict user’s interest and to propose a list of pages based on their interests by identifying user’s behavior based on fuzzy techniques called fuzzy clustering method. Due to the user’s different interests and use of one or more interest at a time, user’s interest may belong to several clusters and fuzzy clustering provide a possible overlap. Using the resulted cluster helps extract fuzzy rules. This helps detecting user’s movement pattern and using neural network a list of suggested pages to the users is provided. Growing Science Ltd. All rights reserved. 5 © 201

The access time of using the portal in certain blocks in a residential area can be a problem for some residents. Another problem that arises is if the officer holding the portal key is not in place. The purpose of this study is to create a system to regulate access rights to a particular block within a residential area so that the opening and closing of the portal can be done at any time by residents in the intended area. There are several blocks of this system, namely the NodeMCU controller block, ESP32CAM, Android applications, and web applications that are built using the PHP and MySQL programming languages. NodeMCU is used as the main controller to manage servo motors, send and receive data to and from the server, receive input related to open and close portals from the android application. The web application is used to register users, view the portal usage log, and verify the login process of the application. This system has been running well based on the results of tests that have been carried out, where the registration process, login, opening and closing portals, log usage is in accordance with the objectives to be achieved. Index Terms”Portal; NodeMCU; ESP32CAM; Android; Web Application

Poisoning and shilling attacks remain a serious threat to recommender systems, especially as attackers increasingly mimic plausible profile statistics. This paper proposes an architecture-independent behavioral detection layer that models user interactions as short-window phase-dynamic trajectories rather than static aggregates. Interaction logs are transformed into temporal signals, reconstructed in phase space by delay embedding, and summarized by a compact 15-dimensional portrait combining recurrence-based, entropy-based, spectral, and stabilizing statistical descriptors. In a controlled targeted injection protocol evaluated over 10 independent runs, the statistical baseline achieved PR-AUC = 0.723 ± 0.037 and TPR@1%FPR = 0.029 ± 0.006, the dynamic block achieved PR-AUC = 0.831 ± 0.011 and TPR@1%FPR = 0.220 ± 0.050, and the full portrait achieved PR-AUC = 0.872 ± 0.017 and TPR@1%FPR = 0.291 ± 0.043. Sensitivity analysis showed that recurrence-only descriptors were parameter-sensitive, whereas the extended dynamic block formed a stable high-performance region across a broad range of embedding settings. An IQR-normalized aggregated risk score further demonstrated clear post-window regime separation during injection periods. The results indicate that poisoning attacks primarily deform the temporal organization of behavior rather than only first-order statistics. The proposed phase-dynamic portrait is therefore best interpreted as a complementary behavioral risk-scoring layer for auditing, filtering, and monitoring rather than as a standalone defense.

The modern business system increasingly integrates artificial intelligence to improve decision quality, operational efficiency, and the human experience. The rapid growth of digital platforms, customer data, and real-time services creates an environment in which traditional rule-based systems fail to respond adaptively. The business organizations therefore require intelligent systems that align technological efficiency with human needs, trust, and satisfaction. Despite widespread adoption, many AI-driven business systems focus primarily on automation and cost reduction, which has resulted in fragmented user experiences, reduced transparency, and limited human engagement. The lack of alignment between AI outputs and human expectations reduces customer satisfaction and employee acceptance. This gap highlights the need for a structured AI-enabled business framework that prioritizes human-centered outcomes while maintaining measurable business performance. This study proposes an AI-integrated modern business system that combines predictive analytics, natural language processing, and adaptive decision support. The system architecture includes data ingestion layers, an AI reasoning module, and a human-interaction layer that emphasizes explainability. A mixed-method evaluation approach has been adopted that combines quantitative performance analysis with user-experience assessment. The model has been validated using a simulated retail and service dataset consisting of 50,000 transactions and 12,000 user interaction logs. The proposed system has achieved a 28.6% improvement in customer satisfaction scores and a 21.4% reduction in service response time when compared with conventional business intelligence systems. Decision accuracy has improved from 76.2% to 89.7%, while employee task efficiency has increased by 18.9%. The explainable AI module has improved user trust ratings from 3.1 to 4.2 on a five-point scale. These results indicate that AI that has been aligned with human-centric design significantly enhances both business performance and human experience.

Most of usability evaluation methods for the Web domain have several limitations such as: the concept of usability is only partially supported; usability evaluations are mainly performed when the Web application has been completed; there is a lack of guidelines on how to properly integrate usability into Web development. This paper addresses these issues through the presentation of a Web Usability Evaluation Process (WUEP) for integrating usability evaluations at different stages of model-driven Web development processes. A case study was performed in order to analyze the feasibility of the approach by applying WUEP to evaluate the usability of a real Web application that was developed by using a specific model-driven development process in the industrial domain. The results suggest that WUEP provides good insights into the performance of usability evaluations. The usability problems are corrected at the model and model-transformation levels, thereby improving the usability of the final Web application.KeywordsWeb UsabilityEvaluation ProcessWeb MetricsModel-Driven Web Development

Usability problems are barriers of the adoption of e-government services. A combination of methods was used to evaluate the usability of any e-government service. A questionnaire that followed U.S. Department of Health and Human Services (HHS) web usability guidelines and ISO9241-151 web usability standard was developed. Greece has one central e-deliberation service. Four experts examined 125 usability points. The results showed that 23% usability points had major usability problems, 14% had minor usability problems and 63% had no usability problems. Usability problems were grouped in accordance with Nielsen's Heuristics. If designers follow this combination of methods approach at the stage of design of any e-government service, the usability will increase.

Dynamic web pages are widely used by web applications to provide a better user experience and to attract more web users. The web applications use client-side and server-side scripts to provide dynamic behavior to the web pages. Cross-Site Scripting (XSS) attack uses malicious scripts and links injected into the trusted webpages to steal sensitive data from the victims. In this work, the experimental results obtained using machine learning algorithms (Random Forest Classifier, MLP Classifier, Gen ELM Classifier, Naïve Bayes, Support Vector Machine, and K Neighbours Classifier) for the prediction of XSS attack and phishing websites attacks are implemented. This is done using the features based on normal and malicious URLs and JavaScript. Random Forest Classifier gave better results than MLP Classifier, Gen ELM Classifier, Naïve Bayes, Support Vector Machine, and K Neighbours Classifier based on the features extracted from URL and JavaScript code. All the algorithms gave comparatively better results with discretized attributes, but a noticeable difference in performance was seen only in the case of SVM. A detailed literature survey is presented and proposes a new approach to detect phishing websites by features extraction and machine learning algorithm.

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

ABSTRACTUser Interface (UI) and User Experience (UX) are two crucial factors in the success of information systems, especially web-based applications and mobile applications. Design elements in web or mobile applications can be made more atrractive, by applying several elements in human-computer interaction theory based on user segmentation, product knowledge, consumer behavior, latest marketing theories, both in terms of visuals, after-effects, display sets, content, multimedia, lay-out menus on the web or mobile applications. In the recent state development of tourism in Bali, tourism transactions and promotions are already based on E-Tourism using web media and mobile applications. Making it easier for tourists to access information and services related to ticketing, transportation, accommodation and even travel packages. This study uses the User Interface (UI) and User Experience (UX) methods to view usability and web-quality from a web-based E-Tourism websites that promotes tourism in Bali.Keywords: User Interface, User Experience, E-Tourism, Informastion System, Bali Tourism.ABSTRAKUser Interface (UI) dan User Experience (UX) adalah dua faktor krusial dalam keberhasilan sistem informasi, terutama aplikasi berbasis web dan aplikasi mobile. Elemen desain dalam web dapat dibuat lebih menarik, dengan menerapkan beberapa elemen dalam teori interaksi manusia-komputer berdasarkan segmentasi user, pengetahuan produk, perilaku konsumen, teori pemasaran terbaru, baik dari segi visual, after-effect, display set, konten, multimedia, lay-out menu di web atau aplikasi mobile. Dalam perkembangan pariwisata di Bali saat ini, transaksi dan promosi pariwisata sudah berbasis E-Tourism dengan menggunakan media web dan aplikasi mobile. Sehingga mempermudah wisatawan dalam mengakses informasi dan layanan terkait dengan ticketing, transportasi, akomodasi bahkan paket perjalanan wisata. Penelitian ini menggunakan metode User Interface (UI) dan User Experience (UX) untuk melihat usability dan web-quality dari website layanan E-Tourism yang berbasis web yangmempromosikan pariwisata di Bali.Kata kunci: User Interface, User Experience, E-Tourism, Sistem Informasi, Pariwisata Bali.

Batch evaluation with test collections of documents, search topics, and relevance judgments has been the bedrock of IR evaluation since its adoption by Salton for his experiments on vector space systems. Such test collections have limitations: they contain no user interaction data; there is typically only one query per topic; they have limited size due to the cost of constructing them. In the last 15-20 years, it has become evident that having a log of user interactions and a large space of queries is invaluable for building effective retrieval systems, but such data is generally only available to search engine companies. Thus there is a gap between what academics can study using static test collections and what industrial researchers can study using dynamic user data.

This paper focus to narrative review the adaptive user interface using model-driven approach to overcome the usability problem. Several UI designs suffered from the usability problem that might cause some applications hardly to deliver the function intended to the user. Therefore, the adaptive user interface design was introduced to acknowledge the usability problem that caused by the traditional UI which cannot satisfy context-of-use (user, device, and environment). In this paper, we describe model-driven approach of adaptive UI using modelling language to create the models of adaptive UI. Model-Driven Engineering (MDE) can raise the level of abstraction in software application, so it will be the basis for devising adaptive UIs because it has the possibility to apply numerous types of adaptions to the various levels of abstraction. The evaluation of usability has its own requirement that different from the usability evaluation of other system application. At the end of this paper we will conclude our finding that the adaptive UI could minimize the usability problem in web application using model-driven approach.