MohammadAmin Fazli

Abolfazl Younesi, Mohsen Ansari, MohammadAmin Fazli et al.

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, attention, depthwise convolutions, and NAS, among others. Each type of CNN has its unique structure and characteristics, making it suitable for specific tasks. It's crucial to gain a thorough understanding and perform a comparative analysis of these different CNN types to understand their strengths and weaknesses. Furthermore, studying the performance, limitations, and practical applications of each type of CNN can aid in the development of new and improved architectures in the future. We also dive into the platforms and frameworks that researchers utilize for their research or development from various perspectives. Additionally, we explore the main research fields of CNN like 6D vision, generative models, and meta-learning. This survey paper provides a comprehensive examination and comparison of various CNN architectures, highlighting their architectural differences and emphasizing their respective advantages, disadvantages, applications, challenges, and future trends.

Seyed Moein Ayyoubzadeh, Kourosh Shahnazari, Mohammmadali Keshtparvar et al.

Training large language models (LLMs) requires vast amounts of high-quality data from institutions that face legal, privacy, and strategic constraints. Existing data procurement methods often rely on unverifiable trust or ignore heterogeneous provider costs. We introduce a mechanism-design framework for truthful, trust-minimized data sharing that ensures dominant-strategy incentive compatibility (DSIC), individual rationality, and weak budget balance, while rewarding data based on both quality and learning utility. We formalize a model where providers privately know their data cost and quality, and value arises solely from the data's contribution to model performance. Based on this, we propose the Quality-Weighted Marginal-Incentive Auction (Q-MIA), which ranks providers using a virtual cost metric and uses Myerson-style payments to ensure DSIC and budget feasibility. To support settings with limited liquidity or long-term incentives, we introduce the Marginal Utility Token (MUT), which allocates future rights based on marginal contributions. We unify these in Mixed-MIA, a hybrid mechanism balancing upfront payments and deferred rewards. All mechanisms support verifiable, privacy-preserving implementation. Theoretically and empirically, they outperform volume-based and trust-based baselines, eliciting higher-quality data under budget constraints while remaining robust to misreporting and collusion. This establishes a principled foundation for sustainable and fair data markets for future LLMs.

MohammadAmin Fazli, Ali Owfi, Mohammad Reza Taesiri

Non Fungible Tokens (NFTs) have gained a solid foothold within the crypto community, and substantial amounts of money have been allocated to their trades. In this paper, we studied one of the most prominent marketplaces dedicated to NFT auctions and trades, Foundation. We analyzed the activities on Foundation and identified several intriguing underlying dynamics that occur on this platform. Moreover, We performed social network analysis on a graph that we had created based on transferred NFTs on Foundation, and then described the characteristics of this graph. Lastly, We built a neural network-based similarity model for retrieving and clustering similar NFTs. We also showed that for most NFTs, their performances in auctions were comparable with the auction performance of other NFTs in their cluster.