The rise in cryptoasset valuations and the ease of creating new tokens have spurred an increase in illicit activities within the market. Decentralized exchanges (DEX) facilitate the trading of a vast array of tokens, including those with minimal liquidity, amplifying the risk of fraudulent schemes. Fraudulent practices take various forms, including counterfeit tokens, rug pulls, and pump-and-dump schemes, all lacking functional innovation and relying heavily on aggressive social media marketing. This study contributes to the identification and profiling of deceitful tokens on DEX platforms. Our approach involved compiling on new tokens with an active trading start and attracted competition to buy them in first blocks spanning multiple years from the Ethereum blockchain, tracking all associated purchase and sale transactions. Our analysis revealed that Uniswap V2 predominantly hosts the trading of new tokens, with an alarming discovery that over 98% of tokens minted daily exhibit fraudulent characteristics. Subsequently, a machine learning model was developed to predict the likelihood of a rug pull occurring shortly after trading commencement. Although the dataset labeling methodology and detection problem statement are exploratory, we demonstrate the economic significance of the proposed approach within trading pipeline. The findings highlight the importance of identifying fraudulent activities and emphasize the need for collaboration between decentralized exchanges and regulatory bodies to mitigate financial losses for investors.

Unmanned aerial vehicle (UAV) swarms are increasingly used in critical applications such as aerial mapping, environmental monitoring, and autonomous delivery. However, the reliability of these systems is highly dependent on uninterrupted access to the Global Navigation Satellite Systems (GNSS) signals, which can be disrupted in real-world scenarios due to interference, environmental conditions, or adversarial attacks, causing disorientation, collision risks, and mission failure. This paper proposes SwarmRaft, a blockchain-inspired positioning and consensus framework for maintaining coordination and data integrity in UAV swarms operating under GNSS-denied conditions. SwarmRaft leverages the Raft consensus algorithm to enable distributed drones (nodes) to agree on state updates such as location and heading, even in the absence of GNSS signals for one or more nodes. In our prototype, each node uses GNSS and local sensing, and communicates over WiFi in a simulated swarm. Upon signal loss, consensus is used to reconstruct or verify the position of the failed node based on its last known state and trajectory. Our system demonstrates robustness in maintaining swarm coherence and fault tolerance through a lightweight, scalable communication model. This work provides a practical and secure approach for decentralized drone operation in unpredictable environments.

Decentralized exchanges (DEXs) are crucial to de-centralized finance (DeFi) as they enable trading without intermediaries. However, they face challenges like impermanent loss (IL), where liquidity providers (LPs) see their assets’ value change un-favorably within a liquidity pool compared to outside it. To tackle these issues, we propose dynamic fee mechanisms over traditional fixed-fee structures used in automated market makers (AMM). Our solution includes asymmetric fees via block-adaptive, deal-adaptive, and the "ideal but unattainable" oracle-based fee algorithm, utilizing all data available to arbitrageurs to mitigate IL. We developed a simulation-based framework to compare these fee algorithms systematically. This framework replicates trading on a DEX, considering both informed and uninformed users and a psychological relative loss factor. Results show that adaptive algorithms outperform fixed-fee baselines in reducing IL while maintaining trading activity among uninformed users. Additionally, insights from oracle-based performance underscore the potential of dynamic fee strategies to lower IL, boost LP profitability, and enhance overall market efficiency.

This paper introduces B4B.app, a Web3 influencer marketing protocol that leverages the Internet Computer (ICP) to solve persistent industry challenges such as fragmented data, unreliable ratings, and payment disputes. B4B.app delivers a trustless, cross-chain infrastructure combining influencer discovery, campaign management, and secure escrow payments in one seamless platform. At its core, B4B-ICP acts as a reputation and settlement layer, aggregating performance data from multiple social platforms and ensuring transparent, on-chain record keeping. This paper presents the platform’s architecture, user workflow, and implementation stack, along with key milestones and planned enhancements, including EVM interoperability and AI-driven performance prediction.

A Bitcoin address is a unique identifier for participants in cryptocurrency transactions. The Bitcoin network supports various address formats, including Legacy, Script, Seg-Wit, and Taproot, each distinguished by its specific functionalities and associated transaction fees. Although best privacy practices advise using each address only once for both input and output, our findings indicate that users frequently reuse addresses and may even derive different address types from the same private key. This study investigates the phenomenon of address reuse, provides detailed usage statistics, and introduces KeyLinker– a novel method for grouping addresses by user based on this reuse pattern. Unlike traditional heuristics such as one-time change (OTC) and common spending (CS), KeyLinker is not reliant on heuristics; it offers a dependable solution grounded in the practical impossibility of private key collisions. Through KeyLinker, we identified reliable reuse information for over 260,000 addresses, offering more data than the strict version of OTC and around hundred times less than the information from CS. The method is particularly valuable for AML/KYC compliance in centralized exchanges (CEX) and decentralized exchanges (DEX), providing a deductive and error-free approach to address grouping.

The evolution of blockchain technology, from its origins as a decentralized ledger for cryptocurrencies to its broader applications in areas like decentralized finance (DeFi), has significantly transformed financial ecosystems while introducing new challenges such as Maximum Extractable Value (MEV). This paper explores MEV on the Polygon blockchain, with a particular focus on Atomic Arbitrage (AA) transactions. We establish criteria for identifying AA transactions and analyze key factors such as searcher behavior, bidding dynamics, and token usage. Utilizing a dataset spanning 22 months and covering 23 million blocks, we examine MEV dynamics with a focus on Spam-based and Auction-based backrunning strategies. Our findings reveal that while Spam-based transactions are more prevalent, Auction-based transactions demonstrate greater profitability. Through detailed examples and analysis, we investigate the interactions between network architecture, transaction sequencing, and MEV extraction, offering comprehensive insights into the evolution and challenges of MEV in decentralized ecosystems. These results emphasize the need for robust transaction ordering mechanisms and highlight the implications of emerging MEV strategies for blockchain networks.

In this paper, we analyze Shared Send Mixers (SSMs) within Cardano's Extended Unspent Transaction Output (EUTXO) model, presenting significant contributions to the understanding of UTXO-based blockchains. Firstly, we define the EUTXO SSM untangling problem and propose an algorithm to address it, along with providing an open-source implementation of the solution. Utilizing real transaction examples, we formulate and solve this problem in a systematic manner, shedding light on the intricacies of coin circulation within the Cardano blockchain. Through our analysis, we reveal insights into the usage of SSMs, including statistics on their frequency and effectiveness in untangling transactions. With this method, our findings show that 11% of transactions—as per the method—seem to involve SSMs, 83% of which enable unique untangling. Moreover, we discuss the potential application of our algorithm in enhancing address clustering results of transaction-level heuristics. Overall, our work contributes to a deeper understanding of transactional dynamics within UTXO cryptocurrencies, particularly within the context of Cardano's EUTXO model.

The article introduces a proof-of-concept (PoC) that demonstrates the management of Internet of Things (IoT) devices' infrastructure via smart contracts, facilitating their interaction with the blockchain through gasless transactions. The focus is empowering IoT devices to autonomously sign transactions using their verified private keys, eliminating the necessity for external wallets and enabling blockchain interaction using Biconomy without incurring gas fees. In this PoC, managers can validate IoT devices, permitting them to transmit transactions securely without being able to manipulate measurements or risking losing crypto assets in case of hardware malfunctions. This innovative method ensures that devices with minimal funds can access sensor data and communicate with a smart contract on the blockchain to update information utilizing account abstraction. Detailed workflow and simulation results are provided to showcase the practicality and advantages of this approach in scenarios demanding seamless automated blockchain engagement through IoT devices. The PoC code is openly accessible on GitHub, enhancing the transparency and accessibility of our research outcomes.

Decentralized finance (DeFi) is a rapidly evolving blockchain technology that offers a new perspective on financial services through Web3 applications. DeFi offers developers the flexibility to create financial services using smart contracts, leading to a lack of standardized protocols and challenges in applying traditional finance models for risk assessment, especially in the early stages of adoption. The Maker protocol is a prominent DeFi platform known for its diverse functionalities, including loan services. This study focuses on analyzing the risk associated with Maker's loan portfolio by developing a risk model based on multiple Brownian motions and passage levels, with Brownian motions representing different collateral types and passage levels representing users' collateralization ratios. Through numerical experiments using artificial and real data, we evaluate the model's effectiveness in assessing risk within the loan portfolio. While our findings demonstrate the model's potential for assessing risk within a single DeFi project, it is important to acknowledge that the model's assumptions may not be fully applicable to real-world data. This research underscores the importance of developing project-specific risk assessment models for individual DeFi projects and encourages further exploration of other DeFi protocols.

The field of Artificial Intelligence (AI) is rapidly evolving, creating a demand for sophisticated models that rely on substantial data and computational resources for training. However, the high costs associated with training these models have limited accessibility, leading to concerns about transparency, biases, and hidden agendas within AI systems. As AI becomes more integrated into governmental services and the pursuit of Artificial General Intelligence (AGI) advances, the necessity for transparent and reliable AI models becomes increasingly critical. Decentralized Federated Learning (DFL) offers decentralized approaches to model training while safeguarding data privacy and ensuring resilience against adversarial participants. Nonetheless, the guarantees provided are not absolute, and even open-weight AI models do not qualify as truly open source. This paper suggests using blockchain technology, smart contracts, and publicly verifiable secret sharing in DFL environments to bolster trust, cooperation, and transparency in model training processes. Our numerical experiments illustrate that the overhead required to offer robust assurances to all peers regarding the correctness of the training process is relatively small. By incorporating these tools, participants can trust that trained models adhere to specified procedures, addressing accountability issues within AI systems and promoting the development of more ethical and dependable applications of AI.

Cryptocurrency has transformed finance and investment, with platforms like Uniswap facilitating billions of dollars in trades. However, malicious smart contracts and scam tokens have led to significant financial losses for decentralized finance (DeFi) users. Code analysis alone cannot detect rug pulls using social engineering tactics. To address this issue, machine learning algorithms can leverage the vast amount of transactional data stored on the blockchain, particularly time series data, to identify scam tokens. This study aims to determine the optimal timeframe for detecting rug pulls and highlights the importance of token volume and transaction count features. The findings suggest that shorter timeframes are sufficient for detecting rug pull tokens since most incidents occur soon after token creation. This research offers new insights into scam token classification and prevention and contributes to a broader understanding of this field.

Decentralized Finance (DeFi) has revolutionized the financial landscape, with protocols like Uniswap offering innovative automated market-making mechanisms. This article explores the development of a backtesting framework specifically tailored for Concentrated Liquidity Market Makers (CLMMs). The focus is on leveraging the liquidity distribution approximated using a parametric model to estimate the rewards within liquidity pools. The article details the design, implementation, and insights derived from this novel approach to backtesting within the context of Uniswap V3. The developed backtester was successfully utilized to assess reward levels across several pools using historical data from 2023 (pools Uniswap V3 for pairs of altcoins, stablecoins, and USDC/ETH with different fee levels). Moreover, the error in modeling the level of rewards for the period under review for each pool was less than 1%. This demonstrated the effectiveness of the backtester in quantifying liquidity pool rewards and its potential in estimating revenues of Liquidity Provider (LP) as part of the pool rewards, which is the focus of our next research. The backtester serves as a tool to simulate trading strategies and liquidity provision scenarios, providing a quantitative assessment of potential returns for LPs. By incorporating statistical tools to mirror CLMM pool liquidity dynamics, this framework can be further leveraged for strategy enhancement and risk evaluation for LPs operating within decentralized exchanges.

Rarity meters are incorporated by industry and discursive by academia. Rarity, as an intuitive term, attracted numerous researchers to present their own view of it. While there is existing literature on comparing rarity meters, it requires access to NFT collection data, which can be challenging for researchers without a background in blockchain technology. This has created a demand for an easily accessible rarity meter benchmark. In this paper, we introduce the Rating over all Rarities (ROAR) benchmark, which includes data from one hundred popular NFT collections from the Ethereum blockchain, implemented a weighted correlation-based performance measurement function, as well as four state-of-the-art rarity meters (Rarity.tools, Kramer, OpenRarity, and NFTGo), along with a new rarity meter called ROAR. Our experiments show that the ROAR rarity meter, an ensemble of the other four meters, outperforms its competitors, with Rarity.tools and Kramer as runner-ups. The ROAR benchmark is a tool for examination and testing of rarity meter ideas, and we challenge readers to develop models that can outperform the ROAR rarity meter.

An address in the Bitcoin blockchain serves as an identifier for spending cryptocurrency. The blockchain itself does not contain information about the actual users who control the assets. Users have the ability to create multiple addresses, leading to the challenge of grouping addresses, also known as clustering, in order to analyze Bitcoin users. The grouping problem is a crucial initial step in the analysis of Bitcoin users. The grouping solution involves using heuristics based on usage patterns, with a focus on Common Spending (CS) and One-Time Change (OTC) in the current research. Additionally, anonymization techniques such as Shared Send Mixers (SSM) are considered in this paper as they prevent or at least complicate analysis. It is possible to untangle SSM, and based on the number of untanglings per transaction and their size, the transaction can be classified into a certain complexity class. Both heuristics and untangling were applied to the Bitcoin transaction history in our study. Our findings revealed that OTC misuse may occur in 19 to 26 percent of cases, depending on the specific algorithm used. Furthermore, CS and OTC were found to generate 13 to 0.8 percent of new cases when applied to subtransactions of separable SSM. Additionally, we demonstrated that SSM address grouping respects untangling complexity classes. In the future, we plan to adapt our workflow to other Bitcoin-like blockchains and modify our untangling algorithm to cover more SSM transactions.

The demo introduces a Proof of Concept (PoC) that integrates IoT devices with blockchain technology to create a secure transaction environment. The primary goal is to enable IoT devices to sign transactions on the blockchain independently, using their authenticated private keys. This approach is innovative and ensures scalability, efficiency, and real-time responsiveness. It accommodates a higher transaction volume, streamlines processes, improves efficiency, and eliminates delays associated with human interaction. The PoC code is publicly accessible on GitHub.

The paper discusses the challenges open-source software (OSS) developers face in securing funding for their projects. It proposes the creation of a demo for an escrow smart contract, modeled after Initial Coin Offerings (ICO), to provide funding for OSS projects. The smart contract incorporates multi-signature, escrow, and ICO logic to give donors control over the project’s status in a decentralized autonomous organization. The workflow involves developers creating proposals and roadmaps, with guest users supporting the project to become donors. Donors receive special tokens that allow them to vote on the project’s development process. The demo aims to provide a structured and transparent process for funding and decision-making within the project, addressing the challenges OSS developers face.

Renewable energy projects, particularly wind and solar farms, have garnered significant attention as a potential solution to global energy challenges. Despite the energy production obstacles, the steady availability of fossil fuels continues to compete due to established distribution systems, as societies increasingly rely on electricity. Hydrogen fuel emerges as a promising avenue for energy production, storage, and distribution, involving converting surplus renewable electricity into hydrogen through electrolysis, storing it, and distributing it. Our novel approach leverages blockchain technology to enhance the efficiency and traceability of hydrogen fuel production, offering a unique synergy of transparency, security, and decentralized governance. We showcase its viability and effectiveness using the ERC-1155 token standard to tokenize renewable resources and convert them into hydrogen fuel. Within our tokenized fuel blockchain architecture, we simulate the forecasted growth in hydrogen production and vehicle demand, highlighting our approach's efficiency, traceability, and transparency. This integration showcases the potential for a sustainable hydrogen fuel ecosystem. The gas consumption data analysis indicates that the daily gas consumption remains below 194 million Gas for refilling 3245 vehicles (including the cost of one-time contract deployment), demonstrating the feasibility and efficiency of our approach.

In the demo, we present B4B.World–an influencer marketing platform with reputation-based rewards. The platform allows influencers making payable advertising campaigns and earning rewards proportionally to the speed, quality, and quantity of ads. To get access to the active community of influencers, brands buy a monthly subscription and make campaigns by crypto or project-based NFTs. B4B. World prototype runs on Aurora EVM of Near blockchain testnet since November 2022. By February 2023, over 880 volunteers posted more than 4600 Twitter adds for 12 campaigns.

In the demo, we present NFT SMASH-a game to test your NFT rarity sense. A rarity meter aims to be an immanent value estimator for NFTs: the rarer the NFT, the more expensive it is now compared to other NFTs within the same collection. While working with rarity meters, we designed and developed a game to examine how intuitive are their results. The BAYC collection data mining, rarity computation with the Kramer algorithm, and a web game application are essential parts of the demo design. The game is a five-round binary choice of picture followed by the game run summary.

This demo addresses the challenges of creating influencer marketing campaigns, including difficulties in measuring campaign performance and payment risks. Blockchain technology can provide a solution by offering reliable influencer profile generation, transparent reputation models, and template escrow payments. However, communication and processing of payments across ecosystems remain a challenge for Web3 projects. The paper presents B4B.World, an influencer marketing platform that utilizes cross-chain smart contract calls and supports payments in multiple blockchains. The platform offers reputation-based rewards for influencers and allows advertisers to make campaigns using crypto assets or project-based NFTs. B4B.World is the winner both The Illuminate/22 Hack by Moonbeam and BNB Chain Hackathon 23 in Georgia and utilizes the Axelar platform for cross-chain interaction.

This paper demonstrates the use of blockchain technology in asset-backed securitization (ABS) and presents Quicktoken, a blockchain platform for ABS. Financial institutions can use Quicktoken to assign a correspondence between initial assets and securities, deploy smart contracts for securities issuance, and store the correspondence between assets and non fungible tokens (NFTs) on the blockchain. Investors can buy, sell, and get dividends upon securities redeem via an Android application, while all transactions are secured by the blockchain. The paper highlights the advantages of blockchain usage in ABS, such as a diversification without a loss of auditability.

This paper discusses a blockchain-based system that uses a quantitative tokenized approach for the production and distribution of hydrogen fuel. Currently, the production of hydrogen fuel depends on the supply of water and electricity from external entities. Our decentralized application utilizes a smart contract deployed on a blockchain, enabling the seamless conversion of elements into fuel and the production and distribution process. The paper presents the application's workflow and demonstrates its potential to revolutionize the hydrogen fuel industry. This paper describes the application workflow. The code is available on GitHub.

Decentralized autonomous organizations (DAOs) offer transparency and integrity in decision-making, but their decentralized structure presents challenges during times of crisis. Our paper implements and demonstrates a solution to this issue by introducing a quasi DAO (qDAO) that allows for centralized decision-making when necessary. By modifying smart contracts and introducing new rules, we strike a balance between decentralization and centralized management, offering potential opportunities for improved crisis management in DAOs. We present a demo of our qDAO implementation on the local Ethereum testnet, along with a web application for user input. Our approach expands the possibilities of DAO administrator control while maintaining the integrity and auditability of DAO operations.

In the demo, we present Kramer– an online rarity meter for the Karnaia NFT collection. Meter takes NFTs’ traits, sets, and editions and fits their combination for trading data. Kramer provides the score and the rank of the desired bird and full statistics of all birds.

Blockchain-based solutions can significantly decrease potential losses from counterfeit products in the majority of existing supply chains. In this paper, we present an architecture of a supply-chain management solution for the plastic pipes market and demonstrate its' feasibility of industrial scenarios. We emulate such a system for plastic pipes factory based on the Exonum blockchain framework and show the robustness and reliability of the proposed system.

The existing approaches to intrinsic dimension estimation usually are not reliable when the data are nonlinearly embedded in the high dimensional space. In this work, we show that the explicit accounting to geometric properties of unknown support leads to the polynomial correction to the standard maximum likelihood estimate of intrinsic dimension for flat manifolds. The proposed algorithm (GeoMLE) realizes the correction by regression of standard MLEs based on distances to nearest neighbors for different sizes of neighborhoods. Moreover, the proposed approach also efficiently handles the case of nonuniform sampling of the manifold. We perform a series of experiments on various synthetic and real-world datasets. The results show that our algorithm achieves state-of-the-art performance, while also being robust to noise in the data and competitive computationally.

Blockchain technology has a lot of applications including but not limited to cryptotokens. For example, permissioned blockchains for state registries can include no money logic inside. But the creation of cryptotoken is a typical example of blockchain application and is a popular model to measure performance. In this paper, we described how to construct an account-based cryptotoken using Exonum, an open-source framework for creating blockchain applications, and by the means of performance tests shown that the proposed solution meets real-life throughput requirements for many applications. The proposed approach can be used to create cryptocurrencies in other open-source frameworks focused on permissioned blockchain applications.

The paper outlines some aspects of developing an information system for e-Health database administrators (DBA). This system is equipped with advanced database performance monitoring and prediction features and based on different captured metrics as CPUs, RAMs, HDDs, and other values as well as historical events from host cluster/nodes. The data are aggregated, processed and transformed to provide the developers, production and host DBA teams with timely, proper, and valuable information about existing issues. The graphical representation of critical parameters to the working instance is provided. The approaches to forecasting database performance troubles related to the degradation of its characteristics due to different cumulative effects are discussed.

Smart Grids is an emerging technology promising significant changes in the economy and the social sphere. One among many challenges in their development and distribution is security. Considering recent hackers attacks on energy grids and taking into account the distributed structure of these systems the use of traditional means of computer protection and the search for a crime figure becomes more difficult or impossible. In this article, we introduce some application areas of smart grid forensic science, discuss the opportunities, and outline the open issues in the topic. We summarized challenges for forensic in Smart Grids in connection with a Blockchain and proposed a decentralized transaction platform based on Blockchain tailored to the energy sector with all the latest technology such as advanced metering infrastructure, distributed generation, etc.

Bitcoin is digital assets infrastructure powering the first worldwide decentralized cryptocurrency of the same name. All history of Bitcoins owning and transferring (addresses and transactions) is available as a public ledger called blockchain. But real-world owners of addresses are not known in general. That's why Bitcoin is called pseudo-anonymous. However, some addresses can be grouped by their ownership using behavior patterns and publicly available information from off-chain sources. Blockchain-based common behavior pattern analysis (common spending and one-time change heuristics) is widely used for Bitcoin clustering as votes for addresses association, while offchain information (tags) is mostly used to verify results. In this paper, we propose to use off-chain information as votes for address separation and to consider it together with blockchain information during the clustering model construction step. Both blockchain and off-chain information are not reliable, and our approach aims to filter out errors in input data. The results of the study show the feasibility of a proposed approached for Bitcoin address clustering. It can be useful for the users to avoid insecure Bitcoin usage patterns and for the investigators to conduct a more advanced de-anonymizing analysis.

An appearance-based robot self-localization problem is considered in the machine learning framework. The appearance space is composed of all possible images, which can be captured by a robot's visual system under all robot localizations. Using recent manifold learning and deep learning techniques, we propose a new geometrically motivated solution based on training data consisting of a finite set of images captured in known locations of the robot. The solution includes estimation of the robot localization mapping from the appearance space to the robot localization space, as well as estimation of the inverse mapping for modeling visual image features. The latter allows solving the robot localization problem as the Kalman filtering problem.

In many Data Analysis tasks, one deals with data that are presented in high-dimensional spaces. In practice original high-dimensional data are transformed into lower-dimensional representations (features) preserving certain subject-driven data properties such as distances or geodesic distances, angles, etc. Preserving as much as possible available information contained in the original high-dimensional data is also an important and desirable property of the representation. The real-world high-dimensional data typically lie on or near a certain unknown low-dimensional manifold (Data manifold) embedded in an ambient high-dimensional `observation' space, so in this article we assume this Manifold assumption to be fulfilled. An exact isometric manifold embedding in a low-dimensional space is possible in certain special cases only, so we consider the problem of constructing a `locally isometric and conformal' embedding, which preserves distances and angles between close points. We propose a new geometrically motivated locally isometric and conformal representation method, which employs Tangent Manifold Learning technique consisting in sample-based estimation of tangent spaces to the unknown Data manifold. In numerical experiments, the proposed method compares favourably with popular Manifold Learning methods in terms of isometric and conformal embedding properties as well as of accuracy of Data manifold reconstruction from the sample.

A new geometrically motivated method is proposed for solving the non-linear regression task consisting in constructing a predictive function which estimates an unknown smooth mapping f from q-dimensional inputs to m-dimensional outputs based on a given 'input-output' training pairs. The unknown mapping f determines q-dimensional Regression manifold M(f) consisting of all the (q+m)-dimensional 'input-output' vectors. The manifold is covered by a single chart, the training data set determines a manifold-valued sample from this manifold. Modern Manifold Learning technique is used for constructing the certain estimator M* of the Regression manifold from the sample which accurately approximates the Regression manifold. The proposed method called Manifold Learning Regression (MLR) finds the predictive function fMLR to ensure an equality M(fMLR) = M*. The MLR estimates also the m×q Jacobian matrix of the mapping f.

Images can be represented as vectors in a high-dimensional Image space with components specifying light intensities at image pixels. To avoid the ‘curse of dimensionality’, the original high-dimensional image data are transformed into their lower-dimensional features preserving certain subject-driven data properties. These properties can include ‘information-preserving’ when using the constructed low-dimensional features instead of original high-dimensional vectors, as well preserving the distances and angles between the original high-dimensional image vectors. Under the commonly used Manifold assumption that the high-dimensional image data lie on or near a certain unknown low-dimensional Image manifold embedded in an ambient high-dimensional ‘observation’ space, a constructing of the lower-dimensional features consists in constructing an Embedding mapping from the Image manifold to Feature space, which, in turn, determines a low-dimensional parameterization of the Image manifold. We propose a new geometrically motivated Embedding method which constructs a low-dimensional parameterization of the Image manifold and provides the information-preserving property as well as the locally isometric and conformal properties.