Indoor navigation remains challenging for students with visual impairments because GPS is unavailable indoors and building layouts are often complex. This paper presents a wearable marker-assisted navigation system integrating QR code localization, SSD MobileNet V3 obstacle detection, TFmini-S LiDAR ranging, A*-based dynamic route planning, and audio feedback on a Raspberry Pi 5. The main contribution is an analytical framework relating marker spacing to predicted localization uncertainty and defining a latency budget for obstacle warnings. A confidence-weighted sensor-fusion method is developed analytically but was not implemented in the evaluated prototype, in which the QR code, camera, and LiDAR channels operated independently. The proposed fusion method and the simulated multi-floor planning extension require further experimental validation. Controlled tests produced a mean positioning error below 1.2 m, a LiDAR ranging MAE of 8.3 cm, and an object-detection throughput of 6–9 FPS. A pilot field evaluation covered nine routes totalling 901 m across two buildings and included one participant with self-reported vision loss of approximately 95%. All route trials were completed, although some required researcher assistance. The system remains a proof of concept and has not yet been evaluated against a baseline or with a sufficiently large target-user sample.
Aibol Tileukhan, G. Bekmanova, Valentina Franzoni et al.· Computers· 0 citations
Generative AI tools are now widely used in undergraduate programming, yet most evidence about how students use them comes from self-report rather than from observed behaviour. This study examined the sequential structure of students’ ChatGPT (GPT- 4o, OpenAI)-supported programming work and the cognitive complexity of their queries. Screen recordings of 363 second-year students completing an individual Python 3.12 data-visualisation assignment were coded into 3985 activity episodes and analysed using descriptive statistics, lag-1 sequential analysis, and cognitive network analysis; because recordings capture actions rather than cognition, the coded categories are treated as behavioural indicators interpreted within, rather than as measurements of, the Self-Regulated Learning framework. Programming activities accounted for 63% of coded actions and ChatGPT interactions for 20%. Behaviour was organised around a troubleshooting cycle, the strongest association being between submitting error messages and reviewing ChatGPT responses (PCM → RF, Yule’s Q = 0.84, a descriptive association measure, rather than a transition probability, whose stability across students was not tested). ChatGPT use was concentrated in activities indicative of monitoring and control and was largely absent from planning and reflection. High-achieving students produced a higher proportion of deep-level submissions (32.2% vs. 19.2%) and a lower proportion of surface-level submissions (23.2% vs. 38.4%) than low-achieving students; because submissions are nested within students, this difference is reported as a property of the observed distribution rather than as an inferential finding. Comparisons computed at the level of the student were tested inferentially and reached significance with small effect sizes; comparisons computed at the level of coded actions or query submissions are reported throughout as observed properties of the aggregate distributions rather than as inferentially established differences. These episode-level findings support instructional scaffolding that structures query formulation and reflection.
A. Omarbekova, M. Miłosz, G. Bekmanova et al.· Education sciences· 0 citations
This paper systematically examines monitoring tools for containerized applications, microservices, and DevOps environments, and provides an experimental evaluation across two deployment scenarios. The study was conducted across two infrastructures: on-premises (Docker Swarm, Kubernetes) and cloud-based (Google Kubernetes Engine). A test application was deployed, and load and stress tests were performed using Apache JMeter and k6 with virtual user counts ranging from 100 to 1,000. Metrics including CPU, memory, I/O, and network utilization were collected using Prometheus, Grafana, cAdvisor, and Docker stats. Based on exploration and evaluation, 69 unique monitoring-related tools were identified and grouped into four non-mutually exclusive categories: container-based, cloud-based, microservices-oriented, and DevOps-focused (18, 25, 13, and 38 assignments, respectively; 94 assignments in total because some tools belong to multiple categories). A classification is presented according to visualization capabilities, supported metrics, and quality attributes, including performance, security, interoperability, and usability. Experimental results demonstrate that both monitoring stacks captured variations in processor utilization under load at the resolution configured by their default sampling intervals (15–30 s for Prometheus + cAdvisor and 60 s for Google Cloud Monitoring), with the 1,000-virtual-user stress bursts visible as sharp CPU peaks in both stacks. The contribution of this work is a practical guideline for selecting monitoring tools, developed on the basis of a reference experimental environment.
A. Omarbekova, Lazzat Kussepova, A. Zulkhazhav et al.· Frontiers of Computer Scienc...· 0 citations
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