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Accurate depth information is essential for many computer vision applications. Yet, no available dataset recording method allows for fully dense accurate depth estimation in a large scale dynamic environment. In this paper, we introduce DOC-Depth, a novel, efficient and easy-to-deploy approach for dense depth generation from any LiDAR sensor. After reconstructing consistent dense 3D environment using LiDAR odometry, we address dynamic objects occlusions automatically thanks to DOC, our state-of-the art dynamic object classification method. Additionally, DOC-Depth is fast and scalable, allowing for the creation of unbounded datasets in terms of size and time. We demonstrate the effectiveness of our approach on the KITTI dataset, improving its density from 16.1% to 71.2% and release this new fully dense depth annotation, to facilitate future research in the domain. We also showcase results using various LiDAR sensors and in multiple environments. All software components are publicly available for the research community., Comment: Preprint. Code and dataset available on the project page : https://simondemoreau.github.io/DOC-Depth/

This paper provides a closed-form market equilibrium formula consolidating informational imperfections and investors' beliefs about assets. Based on Merton's incomplete information model, we characterize the equilibrium expected excess returns vector with asymmetric information. We then derive the corresponding market portfolio as the solution to a non-linear system of equations, and analyze the sensitivities of each asset's extra excess returns to its shadow-costs and market weight. We derive the market reference model for excess returns under random shadow-costs. The conditional posterior distribution of excess returns integrates the pick-matrix and pick-vector of views and the vector of shadow-costs into a multivariate distribution with mean vector and covariance matrix dependent on the model and structure of the considered market reference.

The emergence of quantum computing presents a formidable challenge to the security of blockchain systems. Traditional cryptographic algorithms, foundational to digital signatures, message encryption, and hashing functions, become vulnerable to the immense computational power of quantum computers. This paper conducts a thorough risk assessment of transitioning to quantum-resistant blockchains, comprehensively analyzing potential threats targeting vital blockchain components: the network, mining pools, transaction verification mechanisms, smart contracts, and user wallets. By elucidating the intricate challenges and strategic considerations inherent in transitioning to quantum-resistant algorithms, the paper evaluates risks and highlights obstacles in securing blockchain components with quantum-resistant cryptography. It offers a hybrid migration strategy to facilitate a smooth transition from classical to quantum-resistant cryptography. The analysis extends to prominent blockchains such as Bitcoin, Ethereum, Ripple, Litecoin, and Zcash, assessing vulnerable components, potential impacts, and associated STRIDE threats, thereby identifying areas susceptible to quantum attacks. Beyond analysis, the paper provides actionable guidance for designing secure and resilient blockchain ecosystems in the quantum computing era. Recognizing the looming threat of quantum computers, this research advocates for a proactive transition to quantum-resistant blockchain networks. It proposes a tailored security blueprint that strategically fortifies each component against the evolving landscape of quantum-induced cyber threats. Emphasizing the critical need for blockchain stakeholders to adopt proactive measures and implement quantum-resistant solutions, the paper underscores the importance of embracing these insights to navigate the complexities of the quantum era with resilience and confidence.

The proliferation of Internet services has led to an increasing need to protect private data. User authentication serves as a crucial mechanism to ensure data security. Although robust authentication forms the cornerstone of remote service security, it can still leave users vulnerable to credential disclosure, device-theft attacks, session hijacking, and inadequate adaptive security measures. Risk-based Authentication (RBA) emerges as a potential solution, offering a multi-level authentication approach that enhances user experience without compromising security. In this paper, we propose a Federated Risk-based Authentication (F-RBA) framework that leverages Federated Learning to ensure privacy-centric training, keeping user data local while distributing learning across devices. Whereas traditional approaches rely on centralized storage, F-RBA introduces a distributed architecture where risk assessment occurs locally on users' devices. The framework's core innovation lies in its similarity-based feature engineering approach, which addresses the heterogeneous data challenges inherent in federated settings, a significant advancement for distributed authentication. By facilitating real-time risk evaluation across devices while maintaining unified user profiles, F-RBA achieves a balance between data protection, security, and scalability. Through its federated approach, F-RBA addresses the cold-start challenge in risk model creation, enabling swift adaptation to new users without compromising security. Empirical evaluation using a real-world multi-user dataset demonstrates the framework's effectiveness, achieving a superior true positive rate for detecting suspicious logins compared to conventional unsupervised anomaly detection models. This research introduces a new paradigm for privacy-focused RBA in distributed digital environments, facilitating advancements in federated security systems.

User profiling is a critical component of adaptive risk-based authentication, yet it raises significant privacy concerns, particularly when handling sensitive data. Profiling involves collecting and aggregating various user features, potentially creating quasi-identifiers that can reveal identities and compromise privacy. Even anonymized profiling methods remain vulnerable to re-identification attacks through these quasi-identifiers. This paper introduces a novel privacy-enhanced adaptive authentication protocol that leverages Oblivious Pseudorandom Functions (OPRF), anonymous tokens, and Differential Privacy (DP) to provide robust privacy guarantees. Our proposed approach dynamically adjusts authentication requirements based on real-time risk assessments, enhancing security while safeguarding user privacy. By integrating privacy considerations into the core of adaptive risk-based adaptive authentication, this approach addresses a gap often overlooked in traditional models. Advanced cryptographic techniques ensure confidentiality, integrity, and unlinkability of user data, while differential privacy mechanisms minimize the impact of individual data points on overall analysis. Formal security and privacy proofs demonstrate the protocol's resilience against various threats and its ability to provide strong privacy guarantees. Additionally, a comprehensive performance evaluation reveals that the computational and communication overheads are manageable, making the protocol practical for real-world deployment. By adhering to data protection regulations such as GDPR and CCPA, our protocol not only enhances security but also fosters user trust and compliance with legal standards.

Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model's performance, and the results show a buy/sell signal accuracy of over 92%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market., Comment: 12 pages, 8 figures, and 6 tables

Nighttime camera-based depth estimation is a highly challenging task, especially for autonomous driving applications, where accurate depth perception is essential for ensuring safe navigation. We aim to improve the reliability of perception systems at night time, where models trained on daytime data often fail in the absence of precise but costly LiDAR sensors. In this work, we introduce Light Enhanced Depth (LED), a novel cost-effective approach that significantly improves depth estimation in low-light environments by harnessing a pattern projected by high definition headlights available in modern vehicles. LED leads to significant performance boosts across multiple depth-estimation architectures (encoder-decoder, Adabins, DepthFormer) both on synthetic and real datasets. Furthermore, increased performances beyond illuminated areas reveal a holistic enhancement in scene understanding. Finally, we release the Nighttime Synthetic Drive Dataset, a new synthetic and photo-realistic nighttime dataset, which comprises 49,990 comprehensively annotated images., Comment: Preprint. Code and dataset available on the project page : https://simondemoreau.github.io/LED/

Maximal Extractable Value (MEV) represents a pivotal challenge within the Ethereum ecosystem; it impacts the fairness, security, and efficiency of both Layer 1 (L1) and Layer 2 (L2) networks. MEV arises when miners or validators manipulate transaction ordering to extract additional value, often at the expense of other network participants. This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust. Given the growing complexity of blockchain applications, particularly with the increase of Decentralized Finance (DeFi) protocols, addressing MEV is crucial. This paper presents a comprehensive survey of MEV mitigation techniques as applied to both Ethereums L1 and various L2 solutions. We provide a novel categorization of mitigation strategies; we also describe the challenges, ranging from transaction sequencing and cryptographic methods to reconfiguring decentralized applications (DApps) to reduce front-running opportunities. We investigate their effectiveness, implementation challenges, and impact on network performance. By synthesizing current research, real-world applications, and emerging trends, this paper aims to provide a detailed roadmap for researchers, developers, and policymakers to understand and combat MEV in an evolving blockchain landscape., Comment: 19 pages

The rapid growth of the stock market has attracted many investors due to its potential for significant profits. However, predicting stock prices accurately is difficult because financial markets are complex and constantly changing. This is especially true for the cryptocurrency market, which is known for its extreme volatility, making it challenging for traders and investors to make wise and profitable decisions. This study introduces a machine learning approach to predict cryptocurrency prices. Specifically, we make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We demonstrate our approach through an analysis focusing on the closing prices of Bitcoin cryptocurrency. We evaluate the model's performance through various simulations, showing promising results that suggest its usefulness in aiding/guiding cryptocurrency traders and investors in dynamic market conditions., Comment: 9 pages, 3 figures, 4 tables, submitted to the 43rd IEEE International Performance Computing and Communications Conference (IPCCC 2024)

$C^*$-algebra-valued kernels could pave the way for the next generation of kernel machines. To further our fundamental understanding of learning with $C^*$-algebraic kernels, we propose a new class of positive definite kernels based on the spectral truncation. We focus on kernels whose inputs and outputs are vectors or functions and generalize typical kernels by introducing the noncommutativity of the products appearing in the kernels. The noncommutativity induces interactions along the data function domain. We show that it is a governing factor leading to performance enhancement: we can balance the representation power and the model complexity. We also propose a deep learning perspective to increase the representation capacity of spectral truncation kernels. The flexibility of the proposed class of kernels allows us to go beyond previous commutative kernels, addressing two of the foremost issues regarding learning in vector-valued RKHSs, namely the choice of the kernel and the computational cost.

Real-time Internet of Things (IoT) applications require real-time support to handle the ever-growing demand for computing resources to process IoT workloads. Fog Computing provides high availability of such resources in a distributed manner. However, these resources must be efficiently managed to distribute unpredictable traffic demands among heterogeneous Fog resources. This paper proposes a fully distributed load-balancing solution with Multi-Agent Reinforcement Learning (MARL) that intelligently distributes IoT workloads to optimize the waiting time while providing fair resource utilization in the Fog network. These agents use transfer learning for life-long self-adaptation to dynamic changes in the environment. By leveraging distributed decision-making, MARL agents effectively minimize the waiting time compared to a single centralized agent solution and other baselines, enhancing end-to-end execution delay. Besides performance gain, a fully distributed solution allows for a global-scale implementation where agents can work independently in small collaboration regions, leveraging nearby local resources. Furthermore, we analyze the impact of a realistic frequency to observe the state of the environment, unlike the unrealistic common assumption in the literature of having observations readily available in real-time for every required action. The findings highlight the trade-off between realism and performance using an interval-based Gossip-based multi-casting protocol against assuming real-time observation availability for every generated workload., Comment: Submitted to IEEE IoTJ with 13 pages, 11 figures, and 3 tables

The full realization of smart city technology is dependent on the secure and honest collaboration between IoT applications and edge-computing. In particular, resource constrained IoT devices may rely on fog-computing to alleviate the computing load of IoT tasks. Mutual authentication is needed between IoT and fog to preserve IoT data security, and monetization of fog services is needed to promote the fog service ecosystem. However, there is no guarantee that fog nodes will always respond to IoT requests correctly, either intentionally or accidentally. In the public decentralized IoT-fog environment, it is crucial to enforce integrity among fog nodes. In this paper, we propose a blockchain-based system that 1) streamlines the mutual authentication service monetization between IoT and fog, 2) verifies the integrity of fog nodes via service audits, and 3) discourages malicious activity and promotes honesty among fog nodes through incentives and penalties., Comment: 14 pages, 10 figures TSC-2024-05-0329 submitted

Wearable devices and medical sensors revolutionize health monitoring, raising concerns about data privacy in ML for healthcare. This tutorial explores FL and BC integration, offering a secure and privacy-preserving approach to healthcare analytics. FL enables decentralized model training on local devices at healthcare institutions, keeping patient data localized. This facilitates collaborative model development without compromising privacy. However, FL introduces vulnerabilities. BC, with its tamper-proof ledger and smart contracts, provides a robust framework for secure collaborative learning in FL. After presenting a taxonomy for the various types of data used in ML in medical applications, and a concise review of ML techniques for healthcare use cases, this tutorial explores three integration architectures for balancing decentralization, scalability, and reliability in healthcare data. Furthermore, it investigates how BCFL enhances data security and collaboration in disease prediction, medical image analysis, patient monitoring, and drug discovery. By providing a tutorial on FL, blockchain, and their integration, along with a review of BCFL applications, this paper serves as a valuable resource for researchers and practitioners seeking to leverage these technologies for secure and privacy-preserving healthcare ML. It aims to accelerate advancements in secure and collaborative healthcare analytics, ultimately improving patient outcomes.

The emergence of quantum computing poses a formidable security challenge to network protocols traditionally safeguarded by classical cryptographic algorithms. This paper provides an exhaustive analysis of vulnerabilities introduced by quantum computing in a diverse array of widely utilized security protocols across the layers of the TCP/IP model, including TLS, IPsec, SSH, PGP, and more. Our investigation focuses on precisely identifying vulnerabilities susceptible to exploitation by quantum adversaries at various migration stages for each protocol while also assessing the associated risks and consequences for secure communication. We delve deep into the impact of quantum computing on each protocol, emphasizing potential threats posed by quantum attacks and scrutinizing the effectiveness of post-quantum cryptographic solutions. Through carefully evaluating vulnerabilities and risks that network protocols face in the post-quantum era, this study provides invaluable insights to guide the development of appropriate countermeasures. Our findings contribute to a broader comprehension of quantum computing's influence on network security and offer practical guidance for protocol designers, implementers, and policymakers in addressing the challenges stemming from the advancement of quantum computing. This comprehensive study is a crucial step toward fortifying the security of networked environments in the quantum age.

Ethereum smart contracts are highly powerful, immutable, and able to retain massive amounts of tokens. However, smart contracts keep attracting attackers to benefit from smart contract flaws and Ethereum unexpected behavior. Thus, methodologies and tools have been proposed to help implement secure smart contracts and to evaluate the security of smart contracts already deployed. Most related surveys focus on tools without discussing the logic behind them. in addition, they assess the tools based on papers rather than testing the tools and collecting community feedback. Other surveys lack guidelines on how to use tools specific to smart contract functionalities. This paper presents a literature review combined with an experimental report that aims to assist developers in developing secure smarts, with a novel emphasis on the challenges and vulnerabilities introduced by NFT fractionalization by addressing the unique risks of dividing NFT ownership into tradeable units called fractions. It provides a list of frequent vulnerabilities and corresponding mitigation solutions. In addition, it evaluates the community most widely used tools by executing and testing them on sample smart contracts. Finally, a comprehensive guide on implementing secure smart contracts is presented.

The proximal gradient method is a generic technique introduced to tackle the non-smoothness in optimization problems, wherein the objective function is expressed as the sum of a differentiable convex part and a non-differentiable regularization term. Such problems with tensor format are of interest in many fields of applied mathematics such as image and video processing. Our goal in this paper is to address the solution of such problems with a more general form of the regularization term. An adapted iterative proximal gradient method is introduced for this purpose. Due to the slowness of the proposed algorithm, we use new tensor extrapolation methods to enhance its convergence. Numerical experiments on color image deblurring are conducted to illustrate the efficiency of our approach., Comment: 54 figures

Fog computing emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things (IoT). Load balancing (LB) plays a crucial role in Fog computing environments to optimize the overall system performance. It requires efficient resource allocation to improve resource utilization, minimize latency, and enhance the quality of service for end-users. In this work, we improve the performance of privacy-aware Reinforcement Learning (RL) agents that optimize the execution delay of IoT applications by minimizing the waiting delay. To maintain privacy, these agents optimize the waiting delay by minimizing the change in the number of queued requests in the whole system, i.e., without explicitly observing the actual number of requests that are queued in each Fog node nor observing the compute resource capabilities of those nodes. Besides improving the performance of these agents, we propose in this paper a lifelong learning framework for these agents, where lightweight inference models are used during deployment to minimize action delay and only retrained in case of significant environmental changes. To improve the performance, minimize the training cost, and adapt the agents to those changes, we explore the application of Transfer Learning (TL). TL transfers the knowledge acquired from a source domain and applies it to a target domain, enabling the reuse of learned policies and experiences. TL can be also used to pre-train the agent in simulation before fine-tuning it in the real environment; this significantly reduces failure probability compared to learning from scratch in the real environment. To our knowledge, there are no existing efforts in the literature that use TL to address lifelong learning for RL-based Fog LB; this is one of the main obstacles in deploying RL LB solutions in Fog systems., Comment: 13 pages and 9 figures