Reviewerly/exHarmony huggingface dataset:
Adaptive Recurrent Frame Prediction with Learnable Motion Vectors

The utilization of dedicated ray tracing graphics cards has revolutionized the production of stunning visual effects in real-time rendering. However, the demand for high frame rates and high resolutions remains a challenge. The pixel warping approach is a crucial technique for increasing frame rate and resolution by exploiting the spatio-temporal coherence. To this end, existing super-resolution and frame prediction methods rely heavily on motion vectors from rendering engine pipelines to track object movements. This work builds upon state-of-the-art heuristic approaches by exploring a novel adaptive recurrent frame prediction framework that integrates learnable motion vectors. Our framework supports the prediction of transparency, particles, and texture animations, with improved motion vectors that capture shading, reflections, and occlusions, in addition to geometry movements. In addition, we introduce a feature streaming neural network, dubbed FSNet, that allows for the adaptive prediction of one or multiple sequential frames. Extensive experiments against state-of-the-art methods demonstrate that FSNet can operate at lower latency with significant visual enhancements and can upscale frame rates by at least two times. This approach offers a flexible pipeline to improve the rendering frame rates of various graphics applications and devices.
Input-Dependent Uncorrelated Weighting for Monte Carlo Denoising

Image-space denoising techniques have been widely employed in Monte Carlo rendering, typically blending neighboring pixel estimates using a denoising kernel. It is widely recognized that a kernel should be adapted to characteristics of the input pixel estimates in order to ensure robustness to diverse image features and amount of noise. Denoising with such an input-dependent kernel, however, can introduce a bias that makes the denoised estimate even less accurate than the noisy input estimate. Consequently, it has been considered essential to balance the bias introduced by denoising and the reduction of noise. We propose a new framework to define an input-dependent kernel that departs from the existing approaches based on error estimation or supervised learning. Rather than seeking an optimal bias-noise balance as in those existing approaches, we propose to constrain the amount of bias introduced by denoising. Such a constraint is made possible by the concept of uncorrelated statistics, which has never been applied for denoising. By designing an input-dependent kernel with uncorrelated weights against the input pixel estimates, our denoising kernel can reduce data-dependent noise with a negligible amount of bias in most cases. We demonstrate the effectiveness of our method for various scenes.
Compact Neural Graphics Primitives with Learned Hash Probing

Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid. However, existing feature grids either come with a large memory footprint (dense or factorized grids, trees, and hash tables) or slow performance (index learning and vector quantization). In this paper, we show that a hash table with learned probes has neither disadvantage, resulting in a favorable combination of size and speed. Inference is faster than unprobed hash tables at equal quality while training is only 1.2–2.6 × slower, significantly outperforming prior index learning approaches. We arrive at this formulation by casting all feature grids into a common framework: they each correspond to a lookup function that indexes into a table of feature vectors. In this framework, the lookup functions of existing data structures can be combined by simple arithmetic combinations of their indices, resulting in Pareto optimal compression and speed.
UVDoc: Neural Grid-based Document Unwarping

Restoring the original, flat appearance of a printed document from casual photographs of bent and wrinkled pages is a common everyday problem. In this paper we propose a novel method for grid-based single-image document unwarping. Our method performs geometric distortion correction via a fully convolutional deep neural network that learns to predict the 3D grid mesh of the document and the corresponding 2D unwarping grid in a dual-task fashion, implicitly encoding the coupling between the shape of a 3D piece of paper and its 2D image. In order to allow unwarping models to train on data that is more realistic in appearance than the commonly used synthetic Doc3D dataset, we create and publish our own dataset, called UVDoc, which combines pseudo-photorealistic document images with physically accurate 3D shape and unwarping function annotations. Our dataset is labeled with all the information necessary to train our unwarping network, without having to engineer separate loss functions that can deal with the lack of ground-truth typically found in document in the wild datasets. We perform an in-depth evaluation that demonstrates that with the inclusion of our novel pseudo-photorealistic dataset, our relatively small network architecture achieves state-of-the-art results on the DocUNet benchmark. We show that the pseudo-photorealistic nature of our UVDoc dataset allows for new and better evaluation methods, such as lighting-corrected MS-SSIM. We provide a novel benchmark dataset that facilitates such evaluations, and propose a metric that quantifies line straightness after unwarping. Our code, results and UVDoc dataset are available at https://github.com/tanguymagne/UVDoc.
Analysis of the Possibility of Using Machine Learning to Solve Problems of Object Classification with a Lack of a Priori Information on the Example of Observing Living Organisms

The solution is considered by learning on examples of object classification problems described by a set of data with a large number of degrees of freedom - color images, multidimensional processes with a wide spectrum, etc. The minimum sizes of the training set necessary for the classification of such objects are estimated depending on the available a priori information about the properties of the classes being separated.
Quantum Computing Technologies to Accelerate 6G and CONASENSE Integration Beyond 2030

This article underscores the pivotal role of quantum computing and related technologies in advancing Communication, Navigation, Sensing, and Services Integration (CONASENSE) in the 6G realm. The study provides a methodical journey through the chronological development of computing paradigms, starting from foundational computational frameworks like Turing Machines and Von Neumann's architecture, culminating in a sophisticated understanding of Quantum Turing Machines. Tracing computing's evolution from Turing Machines to Quantum Turing Machines, it acknowledges contributions from scientists like Bohr, Einstein, Feynman, and Benioff to quantum theory. The convergence of Quantum Computing and AI offers solutions to complex, NP-hard challenges, heralding a new era of optimization. This includes Quantum Computing, Quantum Algorithms, Quantum Machine Learning (QML), Quantum Communications, Quantum Sensing & Metrology, Quantum Simulation, and Quantum Imaging within the emerging CONASENSE and 6G paradigms, projecting their sustained relevance and impact beyond 2030.
Optimal Deep Learning Model for UAVs: A Case Study

Unmanned Autonomous Vehicles (UAVs), particularly autonomous flying drones, have garnered significant attention in the field of Artificial Intelligence. Advancements in electronic technology, marked by smaller, cheaper, and more efficient components, have led to substantial progress in UAV research. These versatile devices find applications in diverse fields, ranging from flood monitoring and algae spread detection in water bodies to forest trail identification. In this work, we focus on autonomous flying drones, conducting a comprehensive case study to assess the efficiency, robustness, and accuracy of UAVs. Our research is substantiated through a series of experiments, providing a detailed analysis of the software and hardware architecture employed in the study. We present the implementation algorithms and compare three state-of-the-art approaches: TrailNet, InceptionResnet, and MobileNet, in terms of accuracy, robustness, power consumption, and inference time. Our study reveals that MobileNet outperforms the other algorithms, offering superior results with minimal computational requirements and power consumption. Additionally, we address the challenges encountered during our research and discuss potential avenues for enhancing safety features and overall performance in future work.
GPT for the Metaverse in Smart Cities

The metaverse is a virtual space that blends elements of augmented reality, virtual reality, and many other technologies, offering a tailored and immersive experience where individuals can communicate with each other and digital objects. Though the metaverse incorporates various advanced technologies, there is still room for enhancement in terms of interactivity and in achieving realism of virtual environments. Generative Pre-trained Transformers can help address these issues. GPTs advanced NLP algorithms that generate dynamic and realistic content in real-time, allowing the creation of interactive non-playable characters, improving NLP for chatbots and voice assistants, and generating realistic virtual environments in the metaverse. The integration of GPT and metaverse is essential to make it a more dynamic, realistic, and engaging workspace. This review paper explores the opportunity of integrating GPTs with the metaverse applications and highlights prospective challenges associated with this integration.
Federated Learning with Packet Losses

This paper tackles the problem of training Federated Learning (FL) algorithms over real-world wireless networks with packet losses. Lossy communication channels between the orchestrating server and the clients affect the convergence of FL training as well as the quality of the learned model. Although many previous works investigated how to mitigate the adverse effects of packet losses, this paper demonstrates that FL algorithms over asymmetric lossy channels can still learn the optimal model, the same model that would have been trained in a lossless scenario by classic FL algorithms like FedAvg. Convergence to the optimum only requires slight changes to FedAvg: i) while FedAvg computes a new global model by averaging the received clients' models, our algorithm, UPGA-PL, updates the global model by a pseudo-gradient step; ii) UPGA-PL accounts for the potentially heterogeneous packet losses experienced by the clients to unbias the pseudo-gradient step. Still, UPGA-PL maintains the same computational and communication complexity as FedAvg. In our experiments, UPGA-PL not only outperforms existing state-of-the-art solutions for lossy channels (by more than 5 percentage points on test accuracy) but also matches FedAvg's performance in lossless scenarios after less than 150 communication rounds.
A Security Comparison between AES-128 and AES-256 FPGA implementations against DPA attacks

As the AES is the standard symmetric cipher selected by NIST, is the best-known and the most widely used block cipher. Consequently, security threats are constantly rising and increasingly powerful. With the addition of the upcoming scenario of quantum computing, these threats have become a front-line concern in the crypto-community. Although is claimed that using larger key sizes in symmetric key algorithms for implementing quantum-resistant implementations is enough to counteract brute force attacks, this paper shows that both AES-128 and AES-256 are vulnerable to Power Analysis attacks. This paper presents a security comparison against Differential Power Analysis (DPA) attacks over both AES 128-256. Through experimental attacks in FPGA AES implementations, results show that although AES-256 reaches a greater level of security than AES-128, is still vulnerable to this kind of attack. Specifically, we have obtained 75% of the bytes needed to find the original key for AES-128 while only 28.125% for AES-256 by performing the same attack.
Metric Indexing for the Earth Mover's Distance

The Earth Mover's Distance (EMD) has become a popular choice for applications in similarity search, particularly in applications such as few-shot image classification where it is observed to match human perceptions of image differences better than other distance measures such as the Euclidean distance. Currently, in domains such as few-shot image classification, it is common to use exhaustive search during query time which is inefficient in applications using distances with high computational complexity such as the EMD. Since the space in these applications is not guaranteed to be a vector space, existing techniques such as product quantization cannot be applied. In this paper, we study the application of metric space indexing structures towards the reduction of the number of EMD computations needed during query time. We conduct experiments on several image datasets in order to identify the advantages and disadvantages of different metric space data structures. These experiments have not been performed in the context of the EMD before and demonstrate that the VP-tree is more robust to increases in dataset complexity in this domain than comparable metric indexing data structures. Furthermore, we combine these data structures with deep feature extraction to develop a method for efficient deep image retrieval in metric spaces. Taking inspiration from distance stretching methods in the previous literature, we develop a novel approximate nearest neighbor algorithm for k-NN search that can greatly reduce the number of distance computations needed for retrieval without significantly changing k-NN accuracy.
Pluggable Integrity Layer for Property Registration

Property registration plays a crucial role in property transfers; hence, it is necessary to ensure the transparency and integrity of the property registration records. In this paper, we present our successful deployment of a Hyperledger Fabric [1] based integrity layer for property registration in the state of Karnataka, India. Unlike traditional approaches that build the entire system on the blockchain, we propose an integrity layer approach that leverages the benefits of blockchain while addressing its limitations in terms of speed and complexity. Our solution aims to enhance the existing property registration system by integrating a pluggable layer that can be adopted by any state, considering the diverse state laws governing land (property) in India. We comprehensively address various practical scenarios in property records, including the registration of attorneys and encumbrances on properties. To facilitate user interaction with the blockchain, we have developed a Java card as a hardware wallet, which could be of independent interest.
The Greed Trap: Uncovering Intrinsic Ethereum Honeypots Through Symbolic Execution

Smart contracts are computer programs that run on blockchain networks, enabling secure, transparent, and decentralized transactions. However, the security of smart contracts has always been a critical issue in the blockchain community. One major concern in recent years is the proliferation of honeypots - malicious contracts that deceive users into depositing funds, only to discover that they cannot withdraw their money and have lost their original deposit. In this research, we present a novel classification of honeypots and introduce a new type of contract that allows for the development of a future-proof method for detecting honeypots based on the contract owner and cash flow. We implement this method in a detection tool called HoneyVader, which uses symbolic execution to identify real-world honeypot contracts. Our tool analyzes over 2 million contracts deployed on the Ethereum network, detecting 139 honeypots. By using HoneyVader, we are able to uncover previously unknown zero-day honeypots and new techniques used by attackers, in addition to the ones identified in previous works.
DLT-Based Personal Data Access Control with Key-Redistribution

Data management services present a challenge in terms of trust, as service managers can access the data on their servers easily. Decentralized data services and smart contracts can solve problems related to the presence of centralized trusted authorities, but in turn they can introduce other issues related to compliance with data protection and regulations (e.g., GDPR). Historically, encryption has been used to address some of these concerns, but it restricts data sharing. To facilitate encrypted decentralized file storage while enabling data sharing, we propose a Key-Redistribution Proxy Re-Encryption (KeRePRE) system. KeRePRE is a decentralized and encrypted data-service, where authorization servers are part of a threshold proxy re-encryption scheme. A key-redistribution mechanism (that extends the Um-bral scheme) allows for the addition and removal of managers in a decentralized and trustless manner. Additionally, we offer a proof of concept implementation, where data access control is based on an access control list, implemented as a smart contract in a DLT, and can be read-only accessed by the authorization servers.
SuperChain: Decentralized and Trustful Supervised Learning over Blockchain

Machine learning and Blockchain are two of the most noticeable technologies during the last years. The first one is based on artificial intelligence and big data, and the second one has significantly revolutionized different scopes from fintech to entertainment to healthcare by adding transparency, immutability, and a layer of security into their ecosystem. Both technologies are data-driven, allowing researchers to take an enhanced interest to study their integration for secure, immutable, and efficient data sharing and analysis. We are interested in the converging of these two technologies and we propose a decentralized and efficient framework for the training of machine learning (ML) algorithms over Blockchain called SuperChain. Mainly, we consider the training of supervised learning algorithms such as Regression, k-nearest neighbors (KNN) and decision trees. Experimental results demonstrate that the proposed framework is reliable and trustworthy and improve the efficiency of supervised learning models.
Mining User Behavior in Decentralized Applications for Blockchain Trust and Security Analytics

This paper presents a cutting-edge data mining approach to investigate user trust in decentralized applications (dApps) on public blockchains. Our innovative data analysis methodology enables a comprehensive exploration of dApp user behaviors, uncovering crucial insights. Contrary to popular belief, large-scale hacking incidents on centralized exchanges (CEX) have a limited impact on decentralized exchange (DEX) migration and inactive user rates. Additionally, our results reveal a notable trend of increased user outflow in older dApp protocols compared to their newer counterparts and emphasize the significant role of government regulations on DEX user inactivity. This research advances the understanding of dApp scalability and user trust, laying the groundwork for future inquiries in this rapidly evolving field.
Towards a Framework for Analyzing the Suitability of DLTs for Integration in the IoT

The Internet of Things (IoT) is marked by the presence of a versatile network of devices employing various forms of communication and data types. Due to their distributed nature and the generation of extensive data, IoT nodes often encounter obstacles such as hardware and storage limitations, security vulnerabilities, and lack of interoperability. To mitigate these challenges, researchers have proposed the integration of Distributed Ledger Technologies (DLTs), such as blockchain or Directed Acyclic Graph (DAG). To this end, researchers have begun analyzing the applicability of DLTs for IoT applications on a case-specific basis. However, DLTs differ with respect to the consensus protocols implied. Hence, there is a need for developing a framework defining categories for evaluating the integration of different DLT consensus protocols for IoT applications. This research-in-progress paper should be seen as a first approach to a framework that can evaluate the suitability of different DLTs for IoT applications.
Design Requirements for Secured and Cost-Efficient Blockchain-Based Data Exchange Frameworks in Healthcare

In recent years cyber security attacks have become a growing concern in healthcare as it involves sensitive data. In 2021 a significant raise in the ransomware attacks has been observed in the medical sector, resulting in disruption in monitoring patients, cancelled appointments, and diverted ambulances. These incidents lead to delays in offering medication, which can potentially trigger life-threatening risks for patients. To address these alarming circumstances Blockchain is considered as the most promising technology to combat against this emergent threat because of its decentralised, immutable, and transparent nature. Therefore, this paper presents a comprehensive investigation on Blockchain applications for healthcare, including different cyber security attacks on medical data, its different types and features, the consensus algorithms, and its integration with other technologies in the healthcare sector. Additionally, a review on the existing Blockchain-based secured and cost-efficient frameworks is presented that highlights their appropriateness in exchanging medical data. In order to overcome the lacking in existing frameworks a new design has been proposed addressing several features including security, cost-efficiency, privacy, transparency, scalability, interoperability integrating several technologies like Blockchain, Edge-Fog-Cloud architecture, IoT, and AI. The aim of this work is to explore the promising potential of Blockchain for the exchange of efficient medical data. Furthermore, this work analyses the existing Blockchain-based paradigms for secure and cost-efficient healthcare data sharing, and it proposes possible directions that are required to be addressed for the seamless medical data exchange along with implementing those to improve the proposed framework.
Artwork NFTs for Online Trading and Transaction Cancellation

Blockchain, a revolutionary technology that has attracted a great deal of interest in recent years, is transforming various industries and redefining the way we think about data, transactions and trust. It began as a secure environment for online commerce and has extended its scope to other areas such as healthcare systems, e-voting, supply chain, digital assets and telecommunications. Its key features are decentralization (no need for a Trusted Third Party (TTP)), immutability, transparency and the implementation of smart contracts. These <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">are</sup> used to automate and execute agreements between blockchain users. They are also used to create unique digital assets (such as artworks, real estate, collectibles, etc.) by representing them as Non-Fungible Tokens (NFTs). In this paper, we present a smart contracts application that allows the creation of an NFT, that represents an artwork, with the possibility of trading this NFT on a marketplace, selling it and transferring the ownership from the seller to the buyer, as well as cancelling transactions and reimbursing buyers with paid amounts