Reviewerly/exHarmony huggingface dataset:
Fully On-chain Cloud Storage DApp on the Internet Computer Protocol

This article explores the feasibility of developing cloud storage dApps on the Internet Computer (IC) blockchain, which can hold full-stack applications entirely on-chain and run at web speed. We analyze cloud storage services’ security, stability, cost, and performance issues and propose a multi-canister system design scheme for building cloud storage dApps on the IC. Our quantitative tests demonstrate that the blockchain-secured data I/O operations on the IC can result in ten times time savings and up to a million times cost savings compared to those on Ethereum; deploying small-scale data storage applications on the IC can even be more resource-efficient than on traditional servers. We also discuss the trade-offs and decision-making considerations in our conclusion.
Incentive-driven Federated Learning in Mobile Edge Networks

Federated Learning (FL) is proposed as a privacy-preserving distributed learning methodology that can better protect the privacy and reduce communication costs. To stimulate sufficient User Equipments (UEs) to participate in FL, proper incentives need to be designed for FL. Existing incentive mechanisms do not jointly consider UE selection and local learning accuracy optimization to reduce the training expenditure. This paper designs a reverse auction-based incentive mechanism for FL to minimize the training expenditure of Base Station (BS). To this end, we first propose a Greedy Winner Determination (GWD) algorithm to select UEs with the minimum bidding prices. Then, we incorporate the Particle Swarm Optimization (PSO)-based local learning accuracy optimization into UE selection to further reduce the training expenditure of BS. In addition, we design a Vickrey Clarke Groves (VCG)-based payment rule to determine the payment to each participating UE. The simulation experiments show that our proposed PSO with Winner Determination (PSOWD) algorithm is superior to other existing methods in different scenarios.
Proof of Location through a Blockchain Agnostic Smart Contract Language

Location-based services are at the heart of many applications that individuals use every day. However, there is often no guarantee of the truthfulness of users’ location data, since this information can be easily spoofed without a proof mechanism. In distributed system applications, preventing users from submitting counterfeit locations becomes even more challenging because of the lack of a central authority that monitors data provenance. In this work, we propose a decentralized architecture based on blockchains and decentralized technologies, offering a transparent solution for Proof of Location (PoL). We specifically address two main challenges, i.e., the issuing process of the PoL and the proof verification. We describe a smart contract based implementation in Reach, a blockchain-agnostic smart contract language, and the tests we conducted on different blockchains, i.e. Ethereum, Polygon, and Algorand, measuring latency and costs due to the payment of fees. Results confirm the viability of the proposal.
The Future of Work: Do We Need Interactive Collaborative Offices? The Most Interesting Survey in a Post-Covid World

Meta Platforms (Facebook) launched a novel paradigm of work called "Horizon Workrooms", an interactive collaborative office facilitated by virtual and augmented reality, which has also brought various fields to deploy phenomenal investment into the Metaverse. Meta focuses on building the "Metaverse", a buzzword that originated from the science fiction novel Snow Crash (1992). The new pattern of work has obtained positive feedback during the COVID-19 pandemic. However, only two years later, People think Metaverse may be just a passing fad as millions give up on it. So, do we need interactive collaborative offices in a Post-Covid World? This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to investigate whether user acceptance of Horizon Workrooms is influenced by users' perceptions and task-technology fit. The study uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to shed light on whether user acceptance is affected by these factors. This study also aims to determine whether the framework of the general scenario in the Metaverse differs from that in the work scenario in communication from the perspective of technology and the ecosystem, and if so, what the technology roadmap in the work scenario should be. The key findings indicate that the priority of the architecture in the work scenario is different from that of the general scenario in the Metaverse. According to the results, nine out of eleven hypotheses are valid. Unexpectedly, we found there is no direct correlation between task-technology fit and users’ behavioural intention, which is contrary to our expectations and previous studies. The study suggests that priority should be given to six technologies when more resources are invested in building up virtual offices in Metaverse: extended reality, computer vision, network, edge computing, content creation, and social acceptability. Additionally, the study suggests that the focus of current technological development does not need to be placed on four technologies, namely avatar, artificial intelligence, blockchain, and virtual economy, for the time being.
Rethinking Incentive in Payment Channel Networks

Cryptocurrencies and their underlying blockchains suffer from the scalability issue: they do not support efficient transaction processing. Payment channel networks were designed and implemented to alleviate the scalability issue. However, payment channel network is far from solving the scalability issue due to its routing fee, inefficient rebalancing, and limited accessibility. We propose the Practical Payment Channel Network. The Practical Payment Channel Network aims to minimize the routing fee, improve the rebalancing efficiency, and make payment channel network accessible to the general public.
Analyzing the Effect of Taproot on Bitcoin Deanonymization

Bitcoin was released in 2008 as an electronic peer-to-peer payment system. The aim was to enable financial transactions in an anonymous manner between participating parties. However, Bitcoin is considered a pseudonymous network rather than anonymous, as the identity of the address owner is unknown, but every transaction is permanently stored on the Bitcoin blockchain and can be tracked by anyone. The structure of the blockchain, its transactions, and the Bitcoin network make it possible to deanonymize pseudonyms through various methods such as flow analysis, heuristics, and network traffic observations. Once a connection is established between pseudonyms used in the Bitcoin network and the real world, all previous transactions can be attributed to that identity. In 2021, Taproot was introduced to further increase privacy within the Bitcoin Network by introducing a new address format which will allow transactions to be more indistinguishable from one another. In this paper, we analyze current methods for deanonymizing Bitcoin transactions to understand which parts of the Bitcoin protocol they exploit. In addition, we look at the changes introduced by Taproot and determine the extent to which these changes affect the methods and what assumptions must be made for these methods to remain applicable.
Implementation and preliminary evaluation of an auditable confidentiality mechanism for DeFi

A well-known use of the blockchain technology is Decentralized Finance (DeFi). DeFi makes financial information accessible to the public but raises potential privacy and security issues. In this study, we implemented a DeFi protocol that protects privacy, which is based on the Mystiko.Network protocol. As a proxy between the user and DeFi platforms, the Mystiko.Network protocol offers an auditable confidentiality mechanism for blockchain transactions. Via the new system, users may submit anonymous DeFi transactions and get income back into a shielded tokens pool. Moreover, we implemented a rollup approach to handle anonymous DeFi transactions in groups. The evaluation results suggest that the protocol is both practical and affordable, in fact it is able to save around 90% of the cost for DeFi transactions.
A Blockchain-driven Architecture for Usage Control in Solid

Decentralization initiatives like Solid and Digi.me enable data owners to control who has access to their data and to stimulate innovation by creating both application and data markets. Once data owners share their data with others, though, it is no longer possible for them to control how their data are used. To address this issue, we propose a usage control architecture to monitor compliance with usage control policies. To this end, our solution relies on blockchain and trusted execution environments. We demonstrate the potential of the architecture by describing the various workflows needed to realize a motivating use case scenario for data markets. Additionally, we discuss the merits of the approach from privacy, security, integrateability, and affordability perspectives.
Network Traffic in the Metaverse: The Case of Social VR

The Metaverse connects our physical reality with virtual worlds. Social VR platforms facilitate the creation of such virtual worlds, enabling activities such as interactive teaching, conferences, and community gatherings. These activities can be performed in mixed mode, with some participants physically present in the same location. In this paper, we evaluate the feasibility of such mixed-mode events by studying three leading social VR platforms. We uncover the network traffic patterns generated by these platforms, which affect the user experience when multiple users share the same network. We explore the traffic patterns to show that model loading creates a significant overhead and impacts user quality of experience. When the number of simultaneously connected users increases, some operations lead to network congestion that degrades or even interrupts service for most users. From these observations, we derive concrete action points to develop social VR platforms that enable mixed-mode events over the same network.
Diffusion-Based Sampling for Deep Active Learning

The remarkable performance of deep neural networks depends on the availability of massive labeled training data. To alleviate the load of data annotation with labels, deep active learning aims to sample a minimal set of training points to be labelled which yields maximal model accuracy. We propose an efficient sampling criterion to sample data for annotation, which automatically shifts from an exploration type of sampling to a class-decision-boundary refinement. Our criterion relies on a process of diffusing the existing label information over a graph constructed from the hidden representation of the data. This graph representation captures the intrinsic geometry of the approximated labeling function. We analyze our sampling criterion and its exploration - refinement transition in light of the eigen-spectrum of the diffusion operator. Additionally, we provide a comprehensive sample complexity analysis that captures the two phases of exploration and refinement. The diffusion-based sampling criterion is shown to be advantageous over state-of-the-art criteria for deep active learning on synthetic and real benchmark data.
Quantization of Bandlimited Graph Signals

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal.
Efficient uniform approximation using Random Vector Functional Link networks

A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. As only the outer weights of such an architecture need to be learned, the learning process boils down to a linear optimization task, allowing one to sidestep the pitfalls of nonconvex optimization problems. In this paper, we prove that an RVFL with ReLU activation functions can approximate Lipschitz continuous functions provided its hidden layer is exponentially wide in the input dimension. Although it has been established before that such approximation can be achieved in L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> sense, we prove it for L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> approximation error and Gaussian inner weights. To the best of our knowledge, our result is the first of this kind. We give a non-asymptotic lower bound for the number of hidden layer nodes, depending on, among other things, the Lipschitz constant of the target function, the desired accuracy, and the input dimension. Our method of proof is rooted in probability theory and harmonic analysis.
Data Imputation with an Autoencoder and MAGIC

Missing data is a common problem in many applications. Imputing missing values is a challenging task, as the imputations need to be accurate and robust to avoid introducing bias in downstream analysis. In this paper, we propose an ensemble method that combines the strengths of a manifold learning-based imputation method called MAGIC and an autoencoder deep learning model. We call our method Deep MAGIC. Deep MAGIC is trained on a linear combination of the mean squared error of the original data and the mean squared error of the MAGIC-imputed data. Experimental results on three benchmark datasets show that Deep MAGIC outperforms several state-of-the-art imputation methods, demonstrating its effectiveness and robustness in handling large amounts of missing data.
Quasi-analytic directional wavelet packets: applications to image processing

Recently, a versatile library of quasi-analytic complex-valued wavelet packets (WPs) which originate from splines of arbitrary orders, was designed [1]. The real parts of the quasi-analytic WPs (qWPs) are the regular spline-based orthonormal WPs. The imaginary parts, which are slightly modified Hilbert transforms of the real parts, are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Both regular and complementary WPs are well localized in time domain and their DFT spectra provide a variety of refined splits of the frequency domain. The waveforms can have arbitrary number of vanishing moments.Tensor products of 1D quasi-analytic WPs (qWPs) provide a diversity of 2D waveforms oriented in multiple directions. The designed computational scheme enables us to get fast and easy implementation of the qWP transforms. The shapes of real and imaginary parts of the qWPs can be regarded as directional cosine waves with different frequencies modulated by localized low-frequency signals. For example, the set of the fourth-level WPs comprises waveforms which are oriented in 314 different directions and are oscillating with 256 different frequencies. Various combinations of qWPs form multiple frames in the 2D signal space.The combination of the exceptional properties of the designed qWPs, such as unlimited directionality and oscillating structure of the waveforms, vanishing moments and refined frequency resolution, make them a powerful tool for image processing applications. The algorithms based on the qWPs proved to be competitive with the best existing methods in solving such classical image processing problems as denoising, inpainting and deblurring. The qWP algorithms are especially efficient for capturing edges and fine texture and oscillating patterns even in severely degraded images.Due to the above properties and next to unlimited diversity of testing waveforms, the qWPs have strong capabilities for extraction characteristic features from signals and images, which are utilized in the image classification algorithms in conjunction with Support Vector Machines and Convolutional Neural Networks.
Symmetric Perceptron with Random Labels

The symmetric binary perceptron (SBP) is a random constraint satisfaction problem (CSP) and a single-layer neural network; it exhibits intriguing features, most notably a sharp phase transition regarding the existence of satisfying solutions. In this paper, we propose two novel generalizations of the SBP by incorporating random labels. Our proposals admit a natural machine learning interpretation: any satisfying solution to the random CSP is a minimizer of a certain empirical risk. We establish that the expected number of solutions for both models undergoes a sharp phase transition and calculate the location of this transition, which corresponds to the annealed capacity in statistical physics. We then establish a universality result: the location of this transition does not depend on the underlying distribution. We conjecture that both models in fact exhibit an even stronger phase transition akin to the SBP and give rigorous evidence towards this conjecture through the second moment method.
Sampling Informative Positives Pairs in Contrastive Learning

Contrastive Learning is a paradigm for learning representation functions that recover useful similarity structure in a dataset based on samples of positive (similar) and negative (dissimilar) instances. The quality of the learned representations depends crucially on the degree to which the strategies for sampling positive and negative instances reflect useful structure in the data. Typically, positive instances are sampled by randomly perturbing an anchor point using some form of data augmentation. However, not all randomly sampled positive instances are equally effective. In this paper, we analyze strategies for sampling more effective positive instances. We consider a setting where class structure in the observed data derives from analogous structure in an unobserved latent space. We propose active sampling approaches for positive instances and investigate their role in effectively learning representation functions which recover the class structure in the underlying latent space.
The More You Know: Energy Labelling Enables More Sustainable Cryptocurrency Investments

The energy consumption of popular cryptocurrencies varies greatly: cryptocurrencies based on proof-of-work (e.g. Bitcoin) consume much more electricity than their counterparts that use alternative consensus mechanisms, such as proof-of-stake (e.g. Ethereum). Nevertheless, proof-of-work cryptocurrencies dominate the market. We investigate whether energy labelling, i.e., displaying electricity consumption information on centralized exchanges, influences consumers’ product preferences. We conduct a control/treatment study: during this study, participants with an interest in cryptocurrencies (N = 200) are presented with a fictitious cryptocurrency exchange user interface. The treatment group is shown a user interface that displays energy labels, while the control group receives no information related to electricity consumption. Participants then declare how likely they are to acquire particular cryptocurrencies. We measure the treatment effect and find a significant negative correlation (p = 0.002) between being exposed to energy labels and expressing a strong preference for energy-ineﬃcient cryptocurrencies. Based on this finding, we reflect on the sustainability issues of cryptocurrencies and discuss how energy labelling on centralized exchanges can be applied to nudge investors away from energy-ineﬃcient cryptocurrencies. This indicates that regulators would be well advised to consider energy labelling to address the adverse climate impacts of cryptoassets.
On the Impact of Sample Size in Reconstructing Graph Signals

Reconstructing a signal on a graph from observations on a subset of the vertices is a fundamental problem in the field of graph signal processing. It is often assumed that adding additional observations to an observation set will reduce the expected reconstruction error. We show that under the setting of noisy observation and least-squares reconstruction this is not always the case, characterising the behaviour both theoretically and experimentally.
First-Order Algorithms for Optimization over Graph Laplacians

When solving an optimization problem over the set of graph Laplacian matrices, one must deal with a large number of constraints as well as the large objective variable size. In this paper we explore first-order methods for optimization over graph Laplacian matrices. These methods include two popular methods for constrained optimization: the mirror descent algorithm and the Frank-Wolfe (conditional gradient) algorithm. We derive efficiently implementable formulations of these algorithms over graph Laplacians, and use existing theory to show their iteration complexity in various regimes. Experiments demonstrate the efficiency of these methods over alternatives like interior point methods.
Fast Dual-Graph Regularized Background Foreground Separation

Foreground-background separation is a crucial task in various applications such as computer vision, robotics, and surveillance. Robust Principal Component Analysis (RPCA) is a popular method for this task, which considers the static background as the low-rank component and the moving objects in the foreground as the sparse component. To enhance the performance of RPCA, graph regularization is typically used to incorporate the sophisticated geometry of the background and temporal correlation. However, handling the graph Laplacians can be challenging due to the substantial number of data points. In this study, we propose a novel dual-graph regularized foreground-background separation model based on Sobolev smoothness. Our model is solved using a fast numerical algorithm based on the matrix CUR decomposition. Experimental results on real datasets demonstrate that our proposed algorithm achieves state-of-the-art computational efficiency.
Largest Angle Path Distance for Multi-Manifold Clustering

We propose a novel, angle-based path metric for the multi-manifold clustering problem. This metric, which we call the largest-angle path distance (LAPD), is computed as a bottleneck path distance in a graph constructed on d-simplices of data points. When data is sampled from a collection of d-dimensional manifolds which may intersect, the method can cluster the manifolds with high accuracy and automatically detect how many manifolds are present. By leveraging fast approximation schemes for bottleneck distance, this method exhibits quasi-linear computational complexity in the number of data points. In addition to being highly scalable, the method outperforms existing algorithms in numerous numerical experiments on intersecting manifolds, and exhibits robustness with respect to noise and curvature in the data.