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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Proceedings of the 2026 Fourth International Conference on Distributed Computing and High Performance Computing (DCHPC)

IEEE, 2026.

On behalf of the Organizing Committee, it is my great pleasure to extend a warm
welcome to all participants of the Fourth International IEEE Conference on
Distributed Computing and High-Performance Computing (DCHPC 2026), held
in Tehran from May 10–11, 2026. This conference is jointly organized by the
School of Computer Science at the Institute for Research in Fundamental
Sciences (IPM) and the Informatics Society of Iran (DCS scientific group).
Our objective is to provide an interactive platform for researchers and professionals in distributed and
high-performance computing to exchange ideas and share recent advances. Similar to its predecessors,
DCHPC 2026 has received technical support from the IEEE Iran Section. The organizing committee has
dedicated significant efforts to ensure the success of this event.
The primary aim of the DCHPC conference series is to bring together researchers from industry,
academia, and other institutions to share and discuss their latest scientific findings in high-performance
computing, distributed systems, and related applications. This conference brings together over one
hundred participants in the roles of authors, scientific committee members, reviewers, and keynote
speakers, creating a valuable international gathering. For this edition, we received 95 submissions from
200 authors across different countries, including Iran, Malaysia, Russia, Italy, Spain, Iraq, Hungary,
Canada, the Netherlands, Jordan, Vietnam, and Taiwan.
All papers underwent a rigorous peer-review process by 50 expert committee members and
approximately 20 sub-reviewers. Each paper received at least three reviews. Following this process, 24
papers were selected for oral presentation and publication in IEEE Xplore.
We are honored to host three distinguished keynote speakers and four workshop presenters. Furthermore,
we have organized two discussion panels covering topics such as divisible load theory and graph theory,
fostering deeper engagement with specialized subjects.
I would like to express my sincere appreciation to all conference participants, with special thanks to the
keynote speakers and workshop presenters for their contributions. I also thank the committee members,
sub-reviewers, and session chairs for their invaluable assistance in reviewing papers, making decisions,
and managing the parallel sessions. My gratitude also goes to the authors for their pivotal contributions
to the conference, and congratulations to the authors of the accepted papers.
Moreover, I extend my deepest gratitude to the sponsors and the organizing committee, including the
general chair, program co-chairs, executive chair, and publication chair. I am also thankful to my
colleagues, students, and friends who generously contributed to organizing this event.
We look forward to two days of inspiring presentations, fruitful discussions, and meaningful
connections.
Yours sincerely,
Shamsollah Ghanbari
Conference Chair

 

Chapters
Challenges in Building and Visualizing Isolines on Tile-Based Data Quickly and Accurately
Onyekwelu U. F., Rodriges Zalipynis R. A., , in: Proceedings of the 2026 Fourth International Conference on Distributed Computing and High Performance Computing (DCHPC).: IEEE, 2026. P. 1–18.
Modern web-based geospatial applications increasingly demand client-side processing of large scientific datasets. Web Tensor DBMSs (WT-DBMS) such as WebArrayDB enable browsers to receive and process raw tensor data through tile-based delivery mechanisms (such as WMTS with GeoTIFF tiles), shifting computational load from servers to clients. However, generating isolines from tile-based data presents fundamental challenges in ...
Added: September 2, 2026
Mapping Forest Fire Risks Using Deep Learning: A Case Study in An Giang Province, Vietnam
Suliman M., Rodriges Zalipynis R. A., , in: Proceedings of the 2026 Fourth International Conference on Distributed Computing and High Performance Computing (DCHPC).: IEEE, 2026. P. 1–14.
With the escalating frequency and intensity of global wildfires, there is an urgent need for predictive models that balance accuracy with operational practicality. Building upon the TFDeepNN wildfire prediction framework of Truong et al. [1], this study develops a deep learning framework for wildfire risk assessment in Vietnam’s An Giang province, focusing on early detection ...
Added: September 2, 2026
Research target: Computer Science
Language: English
DOI
Text on another site
Keywords: computer science
Proceedings of the 2026 Fourth International Conference on Distributed Computing and High Performance Computing (DCHPC)
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