by jsendak | Dec 30, 2023 | Computer Science
In the era of 5G networks, privacy concerns are becoming increasingly important for end users. The focus has turned to the handling of sensitive metadata transmitted from mobile devices to base stations during network registration. Previous generation cellular networks, such as 3G and 4G, lacked encryption during this transmission, leaving it vulnerable to interception by malicious actors. This paper delves into this issue, exploring the implications of such vulnerability and introducing a new approach in 5G networks that aims to address this problem. The technical details of the encryption schemes used to secure this sensitive information are discussed, along with any limitations of the new approach. Join us as we explore this pressing issue and the solutions being implemented to safeguard user privacy in the age of 5G.
Abstract:As 5G networks become more mainstream, privacy has come to the forefront of end users. More scrutiny has been shown to previous generation cellular technologies such as 3G and 4G on how they handle sensitive metadata transmitted from an end user mobile device to base stations during registration with a cellular network. These generation cellular networks do not enforce any encryption on this information transmitted during this process, giving malicious actors an easy way to intercept the information. Such an interception can allow an adversary to locate end users with shocking accuracy. This paper investigates this problem in great detail and discusses how a newly introduced approach in 5G networks is helping combat this problem. The paper discusses the implications of this vulnerability and the technical details of the new approach, including the encryption schemes used to secure this sensitive information. Finally, the paper will discuss any limitations to this new approach.
Read the original article
by jsendak | Dec 30, 2023 | Computer Science
Improving Session-Based Recommendation in E-commerce with FAPAT
Session-based recommendation in e-commerce aims to accurately predict the next item an anonymous user will purchase using their browsing and purchase history. However, existing methods that rely on global or local transition graphs to supplement session data can introduce noisy correlations and obscure user intent. In this article, we introduce the Frequent Attribute Pattern Augmented Transformer (FAPAT) approach, which effectively characterizes user intents by constructing attribute transition graphs and matching attribute patterns.
FAPAT leverages frequent and compact attribute patterns as memory to enhance session representations. This is achieved through a gate and a transformer block that fuse the entire session information. To validate the effectiveness of FAPAT, we conducted extensive experiments on two public benchmarks and analyzed 100 million industrial data across three domains.
The results showcase the superiority of FAPAT over state-of-the-art methods, with an average improvement of 4.5% across various evaluation metrics including Hits, NDCG, and MRR. Furthermore, FAPAT not only improves next-item prediction accuracy but also demonstrates its capabilities to capture user intents by accurately predicting items’ attributes and offering period-item recommendations.
Abstract:The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history. However, constructing global or local transition graphs to supplement session data can lead to noisy correlations and user intent vanishing. In this work, we propose the Frequent Attribute Pattern Augmented Transformer (FAPAT) that characterizes user intents by building attribute transition graphs and matching attribute patterns. Specifically, the frequent and compact attribute patterns are served as memory to augment session representations, followed by a gate and a transformer block to fuse the whole session information. Through extensive experiments on two public benchmarks and 100 million industrial data in three domains, we demonstrate that FAPAT consistently outperforms state-of-the-art methods by an average of 4.5% across various evaluation metrics (Hits, NDCG, MRR). Besides evaluating the next-item prediction, we estimate the models’ capabilities to capture user intents via predicting items’ attributes and period-item recommendations.
Read the original article
by jsendak | Dec 30, 2023 | Computer Science
Introducing INFAMOUS-NeRF: Enhancing Implicit Face Modeling and Rendering
In a breakthrough development, researchers have introduced INFAMOUS-NeRF, a cutting-edge morphable face model that combines hypernetworks with NeRF technology. This innovative approach significantly improves the representation power of implicit face modeling, particularly in scenarios involving multiple training subjects. Unlike previous methods, INFAMOUS-NeRF achieves a perfect balance between representation power and editability by learning semantically-aligned latent spaces without the need for a large pretrained model. Furthermore, INFAMOUS-NeRF incorporates a unique constraint that enhances NeRF rendering along the facial boundary, resulting in more realistic surface color prediction and improved rendering near the surface. To further enhance NeRF training, this groundbreaking model introduces a loss-guided adaptive sampling method, effectively reducing sampling redundancy. Through rigorous quantitative and qualitative analysis, it has been demonstrated that INFAMOUS-NeRF outperforms existing face modeling techniques in both controlled and in-the-wild settings. The researchers have announced that the code and models for INFAMOUS-NeRF will be made publicly available upon publication, allowing further exploration and applications of this groundbreaking technology.
Abstract:We propose INFAMOUS-NeRF, an implicit morphable face model that introduces hypernetworks to NeRF to improve the representation power in the presence of many training subjects. At the same time, INFAMOUS-NeRF resolves the classic hypernetwork tradeoff of representation power and editability by learning semantically-aligned latent spaces despite the subject-specific models, all without requiring a large pretrained model. INFAMOUS-NeRF further introduces a novel constraint to improve NeRF rendering along the face boundary. Our constraint can leverage photometric surface rendering and multi-view supervision to guide surface color prediction and improve rendering near the surface. Finally, we introduce a novel, loss-guided adaptive sampling method for more effective NeRF training by reducing the sampling redundancy. We show quantitatively and qualitatively that our method achieves higher representation power than prior face modeling methods in both controlled and in-the-wild settings. Code and models will be released upon publication.
Read the original article
by jsendak | Dec 30, 2023 | Computer Science
A Multi-Layer Blockchain Architecture for Cross-Border Trading of CBDCs
In an era where central bank digital currencies (CBDCs) are gaining traction, a new multi-layer blockchain architecture has been introduced to facilitate cross-border trading. This innovative system ensures the security and integrity of transactions while also promoting interoperability with domestic CBDC implementations.
The architecture comprises a permissioned layer-2 that leverages the public consensus of the underlying network to guarantee transaction security. This layer acts as a bridge between various domestic CBDCs, enabling seamless cross-border transactions. Additionally, multiple Layer-3s, equipped with Automated Market Makers (AMMs), create a competitive environment focused on achieving the lowest costs.
To evaluate the practical implications of this system, simulations are conducted using historical foreign exchange rates. These simulations consider Project Mariana as a benchmark and analyze trading costs. Surprisingly, the study reveals that even with liquidity fragmentation, a multi-layer and multi-AMM setup proves to be more cost-efficient than relying on a single AMM.
This groundbreaking research offers valuable insights into the potential benefits of using a multi-layer blockchain architecture for cross-border trading of CBDCs. By combining robust security measures, interoperability, and cost-effective arrangements, this system could revolutionize international financial transactions.
Abstract:This paper proposes a novel multi-layer blockchain architecture for the cross-border trading of CBDCs. The permissioned layer-2, by relying on the public consensus of the underlying network, assures the security and integrity of the transactions and ensures interoperability with domestic CBDCs implementations. Multiple Layer-3s operate various Automated Market Makers (AMMs) and compete with each other for the lowest costs. To provide insights into the practical implications of the system, simulations of trading costs are conducted based on historical FX rates, with Project Mariana as a benchmark. The study shows that, even with liquidity fragmentation, a multi-layer and multi-AMM setup is more cost-efficient than a single AMM.
Read the original article
by jsendak | Dec 30, 2023 | Computer Science
Autodifferentiable Voronoi Tessellation: A Breakthrough in Computational Geometry
In the field of computational geometry, the Voronoi tessellation is an essential technique with a wide range of applications across various scientific disciplines. This method involves dividing a given space into distinct regions based on the proximity to a set of specified points. However, the Voronoi tessellation has long posed a challenge in optimization tasks due to its non-differentiable nature.
Fortunately, a breakthrough has now been achieved with the development of an autodifferentiable method for the 2D Voronoi tessellation. This innovative approach allows for the construction of the tessellation while enabling the computation of gradients using the backpropagation algorithm. As a result, the construction process becomes end-to-end differentiable, opening up new possibilities for seamless integration into larger computational pipelines.
In this article, we delve into the implementation details of this groundbreaking method for autodifferentiation of the Voronoi tessellation. We explore how gradients can be seamlessly passed through the construction process, providing a fully differentiable framework for extracting Voronoi geometrical parameters. This represents a significant advancement in computational geometry, as prior methods lacked the ability to obtain such parameters in a differentiable manner.
Furthermore, we showcase several important applications made possible by this autodifferentiable realization of the Voronoi tessellation. From optimization tasks in computer science to pattern recognition in image analysis, the versatility of this method is truly remarkable. By integrating autodifferentiation into the Voronoi tessellation, researchers and practitioners have gained a powerful tool that enhances their ability to solve complex problems in diverse scientific domains.
Abstract:Voronoi tessellation, also known as Voronoi diagram, is an important computational geometry technique that has applications in various scientific disciplines. It involves dividing a given space into regions based on the proximity to a set of points. Autodifferentiation is a powerful tool for solving optimization tasks. Autodifferentiation assumes constructing a computational graph that allows to compute gradients using backpropagation algorithm. However, often the Voronoi tessellation remains the only non-differentiable part of a pipeline, prohibiting end-to-end differentiation. We present the method for autodifferentiation of the 2D Voronoi tessellation. The method allows one to construct the Voronoi tessellation and pass gradients, making the construction end-to-end differentiable. We provide the implementation details and present several important applications. To the best of our knowledge this is the first autodifferentiable realization of the Voronoi tessellation providing full set of Voronoi geometrical parameters in a differentiable way.
Read the original article
by jsendak | Dec 30, 2023 | Computer Science
Understanding the Interplay between Interpretability, Fairness, and Privacy in Machine Learning
In the realm of high-stakes decision-making, machine learning techniques are being increasingly employed. From college admissions to loan attribution and recidivism prediction, these models have the power to shape lives. As a result, it is imperative that the models used can be understood by human users, do not perpetuate discrimination or bias, and maintain the privacy of sensitive information. While interpretability, fairness, and privacy have all been extensively studied as individual concepts, their interplay with one another has largely been neglected. In this Systematization of Knowledge paper, we delve into the literature surrounding the interactions between these three factors, exploring the synergies and tensions that arise. Through our findings, we uncover fundamental conflicts and demonstrate the challenges that arise when trying to balance these requirements while preserving utility. However, we also provide insights into possible methods for reconciling these concerns, emphasizing that with careful design, it is possible to successfully navigate the complex landscape of responsible machine learning.
Abstract:Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias, and do not leak sensitive information regarding their training data. Indeed, interpretability, fairness and privacy are key requirements for the development of responsible machine learning, and all three have been studied extensively during the last decade. However, they were mainly considered in isolation, while in practice they interplay with each other, either positively or negatively. In this Systematization of Knowledge (SoK) paper, we survey the literature on the interactions between these three desiderata. More precisely, for each pairwise interaction, we summarize the identified synergies and tensions. These findings highlight several fundamental theoretical and empirical conflicts, while also demonstrating that jointly considering these different requirements is challenging when one aims at preserving a high level of utility. To solve this issue, we also discuss possible conciliation mechanisms, showing that a careful design can enable to successfully handle these different concerns in practice.
Read the original article
