Journal of Cognitive Human-Computer Interaction

Mobile application usability is often discussed after deployment through user reviews or task testing, but many visible design problems can be measured earlier from the interface itself. This paper presents a feature-based framework for quantifying mobile interface visibility, usability, and accessibility risk from screen-level design properties. The study defines a Mobile Interface Visibility–Usability Quality score using observable measures such as primary-action salience, visual density, tap-target adequacy, label completeness, contrast proxy, navigation depth, whitespace, and clutter. The analysis uses a structured extract following public Rico and UICrit-style mobile UI data, where screenshots, hierarchy information, and designer critique concepts support data-driven assessment. The results show that usability quality is not determined by a single visual property. Screens with strong contrast may still be difficult to use if feature discoverability is weak, and screens with many functions may remain usable when hierarchy and labels are clear. The paper contributes a measurement protocol, design risk taxonomy, empirical score analysis, and practical remediation loop for mobile app teams seeking objective evidence before user-facing release.

When people are immersed in a visually demanding task, the attentional resources required to monitor the environment for cues that should trigger a remembered intention are frequently captured by the primary task, causing prospective memory failures that range from the inconvenient to the safety-critical. This problem is pervasive in modern work environments in which digital interfaces compete continuously for visual attention, yet the overwhelming majority of reminder and notification systems rely on the same visual channel that is already congested. This paper reports a controlled user study examining whether carefully designed haptic and spatial audio cues can compensate for this visual saturation and restore prospective memory performance without substantially increasing cognitive burden. Thirty-two participants completed a counterbalanced within-subjects protocol in which they performed primary cognitive tasks—document editing on a virtual desktop and navigating in a driving simulation—while managing a set of time-critical intentions delivered through four reminder conditions: visual-only, haptic-only, spatial audio only, and the combined haptic-plus-audio channel. The study measures prospective memory hit rate, task-switching errors, cue response latency, and multidimensional subjective workload across both scenarios and all four conditions. Results consistently favour the combined modality, which produces substantially fewer memory failures and lower reported workload than any single channel, while individual differences in baseline workload predict the magnitude of benefit from non-visual cueing. These findings carry direct implications for the design of ambient notification systems in high-demand professional and safety-critical environments.

Wireless human-computer interaction increasingly depends on distributed sensing, yet adaptive computer interfaces are still commonly modelled from isolated evidence streams. This paper presents an interaction-centric wireless multimodal fusion model for recognizing cognitive state during computer-based task execution. The model integrates wearable physiology, ocular behaviour, compact neurophysiological summaries, and direct interaction evidence obtained from the task interface, then adjusts each sensing channel through a reliability term that reflects wireless degradation. The experimental workflow follows a public stress-resilience human-computer interaction protocol involving synchronized task phases and computer interaction logs. The analysis shows that interaction variables such as task error, response latency, and click activity are among the strongest indicators of cognitive state and complement physiological information in a meaningful way. The results support the design of adaptive computer interfaces that respond not only to what the user is doing on the screen, but also to how reliably the supporting wireless sensing infrastructure is functioning.

The rapid evolution of digital technologies has transformed how societies manage sensitive information and authenticate identity in critical systems. Within the domain of cybersecurity and artificial intelligence (AI), the integration of computer vision and Internet of Things (IoT) technologies has emerged as a promising approach to improving real-time data verification and process automation. This systematic literature review examines how computer vision and IoT technologies can be jointly leveraged to enhance voter verification accuracy in electoral systems. Following the PRISMA 2020 guidelines, the review systematically searched four academic databases, identifying 351 initial studies. After rigorous screening based on predefined inclusion and exclusion criteria, 15 studies were selected for comprehensive analysis. The findings reveal three major themes: (1) emerging technical architectures combining biometric authentication with blockchain-based verification, (2) performance outcomes demonstrating high accuracy rates (97–100%) in controlled environments, and (3) persistent challenges in scalability, real-world deployment, and security against sophisticated AI-enabled attacks such as deepfakes. While the PRISMA process was conducted in full, the limited scope of the project, compressed timeline, and restricted access to paywalled articles likely influenced the depth and completeness of the synthesis. Nevertheless, the review provides structured insight into current implementation approaches, technical methods, and research gaps, with particular relevance to contexts like Uzbekistan where recent OSCE ODIHR election observation reports have documented systemic weaknesses in voter verification and turnout reporting.

The interaction logs which researchers collected during their information-architecture evaluation process contain detailed proof which shows how users select between successful and unsuccessful navigation routes. The predictive signal displays its initial appearance during task execution yet users exhibit different navigation patterns depending on their current task and interface they are using. The researchers of this study developed an early navigation failure prediction system which uses public interaction data to create task-specific prefix classification models. The study analyzes data from an open dataset which includes 180 participants completing 1800 tasks across six testing conditions that evaluate tree testing and highfidelity prototype navigation. A prefix-structural encoder works together with a regularized task-conditioned logistic model which predicts success based on the first k navigation actions. The researchers assessed model performance through participant-specific validation using three different machine learning techniques which included random forest, extra trees, and gradient boosting. The optimal configuration achieved 0.7833 accuracy, 0.7513 balanced accuracy, 0.8350 F1-score, and 0.7949 ROC–AUC performance at k = 3. The horizon analysis demonstration shows that predictive signals become accessible after users complete their first three actions. The ablation study proves that task conditioning functions as an essential component. The study results demonstrate that early trace analytics enable quick identification of navigation failures in information-architecture research while providing a useful method for customized assessment during usability testing.