Reviewerly/exHarmony huggingface dataset:
Network Calculus With Flow Prolongation – A Feedforward FIFO Analysis Enabled by ML

The derivation of upper bounds on data flows’ worst-case traversal times is an important task in many application areas. For accurate bounds, model simplifications should be avoided even in large networks. Network Calculus (NC) provides a modeling framework and different analyses for delay bounding. We investigate the analysis of feedforward networks where all queues implement First-In First-Out (FIFO) service. Correctly considering the effect of data flows onto each other under FIFO is already a challenging task. Yet, the fastest available NC FIFO analysis (called LUDB) suffers from limitations resulting in unnecessarily loose bounds. A feature called Flow Prolongation (FP) has been shown to improve delay bound accuracy significantly. Unfortunately, FP needs to be executed within this NC FIFO analysis very often and each time it creates an exponentially growing set of alternative networks with prolongations. FP therefore does not scale and has been out of reach for the exhaustive analysis of large networks. We introduce DeepFP, an approach to make FP scale by predicting prolongations using machine learning. In our evaluation, we show that DeepFP can improve results in FIFO networks considerably. Compared to the aforementioned LUDB analysis, DeepFP reduces delay bounds by <inline-formula><tex-math notation="LaTeX">$12.1 \;\%$</tex-math></inline-formula> on average at negligible additional computational cost.
Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models

Recent research has shown that it is possible to perform online learning of nonlinear dynamical systems. Furthermore, the results suggest that combining approximate Gaussian-process (GP) regression with model-based estimators, such as Kalman filters and particle filters (PFs), leads to efficient learners under the GP-state-space model (GP-SSM) framework. Here, we analyze how learning of GP-SSMs can be done when there are constraints on the system to be learned. Our analysis is based on a recently developed online PF-based learning method, where the GP-SSM is expressed as a basis-function expansion. We show that the method by adaptation of the basis functions can satisfy several constraints, such as symmetry, antisymmetry, Neumann boundary conditions, and linear operator constraints. A Monte-Carlo simulation study indicates reduced estimation errors with more than 50%.
Uncertainty Quantification for the Extended and the Deterministic-Gain Kalman Filters

This paper is aimed at characterizing the mean square error and probabilistic uncertainty of a popular class of filtering algorithms in nonlinear systems. The state estimation error of the extended Kalman filter and the deterministic-gain Kalman filter are analyzed. We allow a vector state, but assume scalar measurements. A set of conditions for the mean square error to be upper-bounded is derived. Furthermore, the probabilistic bounds for the estimation error are computed via both the moment-based approach and the stochastic comparison analysis approach. The latter provides a formal means determining uncertainty bounds, such as statistical confidence regions.
Power-Efficient Double-Cyclic Low-Precision Training for Convolutional Neural Networks

Owing to the rapid development of deep learning, there has been a remarkable growth in the field of computer vision, including image classification. However, because recent deep learning models require many parameters and calculations, it is essential to reduce power consumption through weight reduction for practical use in embedded platforms, such as mobile devices. In particular, recent attempts to train deep learning models on edge/mobiles have been increasing to obtain customized models with user environments and to solve privacy issues. However, because batteries and hardware resources are limited in the edge/mobile environment, the need for low-precision training has increased. In this study, we propose a power-efficient double-cyclic low-precision training method that uses two different precision cycles for weights and activations during training. The results of verifying the proposed method in various ResNet models indicate an average accuracy improvement of 0.25% compared with the existing low-precision training method and an approximately 25% power reduction effect. Consequently, a 92.8% reduction in hardware resources is achieved with negligible performance degradation compared to full-precision training.
A Democratically Anonymous and Trusted Architecture for CTI Sharing using Blockchain

Cyber Threat Intelligence (CTI) sharing has become a significant issue with the increasing number of cyberattacks.In CTI sharing, one entity (e.g., an organisation or a user) intends to share specific threat information to another entity that might otherwise be unavailable to another entity.However, this process needs to address many challenges, including privacy, trust, and accountability.In this paper, we propose a novel blockchain-based architecture that facilitates the secure dissemination of CTI data.The motivation for this study is to provide a solution that can efficiently address privacy, trust, and accountability when sharing CTI among organisations as well as maintaining an intelligencebased informed decisions.We discuss the current problems within the domain of CTI sharing using blockchain, and our proposal leverages the salient properties of the blockchain, e.g., decentralised, cryptographic keys, immutability, etc., to address those issues.We discuss the detailed design of the proposed architecture.We demonstrate that our approach offers a more effective and efficient way of CTI sharing that has the potential to overcome the trust barriers, data privacy, and accountability issues inherent in this domain.
High-Fidelity Model Extraction Attacks via Remote Power Monitors

This paper shows the first side-channel attack on neural network (NN) IPs through a remote power monitor. We demonstrate that a remote monitor implemented with time-to-digital converters can be exploited to steal the weights from a hardware implementation of NN inference. Such an attack alleviates the need to have physical access to the target device and thus expands the attack vector to multi-tenant cloud FPGA platforms. Our results quantify the effectiveness of the attack on an FPGA implementation of NN inference and compare it to an attack with physical access. We demonstrate that it is indeed possible to extract the weights using DPA with 25000 traces if the SNR is sufficient. The paper, therefore, motivates secure virtualization-to protect the confidentiality of high-valued NN model IPs in multi-tenant execution environments, platform developers need to employ strong countermeasures against physical side-channel attacks.
Kalman Filtering for Discrete-Time Linear Systems with Infinite-Dimensional Observations

Estimating the finite-dimensional state of dynamic systems using modern sensors such as cameras, lidar, and radar involves processing increasingly high-dimensional observations. In this paper, we exploit concepts from the theory of infinite-dimensional systems to examine state estimation in the continuum limit of infinite-dimensional observations. Specifically, we investigate state estimation in discrete-time linear systems with finite-dimensional states and infinite-dimensional observations corrupted by additive noise. In contrast to previous derivations of the Kalman filter for infinite-dimensional observations, we are able to derive an explicit solution for the optimal Kalman gain by modeling the infinite-dimensional observation noise as a stationary Gaussian Process. We demonstrate the utility of our Kalman filter in a simulation of a linearized system derived from the pinhole camera model.
An Architecture of Sparse Length Sum Accelerator in AxDIMM

In this paper, we have implemented high-efficient near-memory sparse-length sum hardware accelerator which is parallelized over each channel or rank to support Meta's deep learning recommendation model (DLRM). In addition, we described high-level architecture and efforts to enable on the conventional x86 server system. From our suggested near-memory accelerator, we got 1.94 × performance gain on two rank system which are physically multiplied.
Video Sampled Frame Category Aggregation and Consistent Representation for Cross-Modal Retrieval

Many current video and text cross-modal retrieval research works focus on narrowing the semantic gap between video and text, but ignore the semantic difference between different sampled frames in the same video and the correlation of feature distribution of objects contained in different sampled frames in the same video, as a result, the features of the sampled frames in the final learned video cannot well represent the semantic features of the whole video. To overcome the shortcomings of existing studies, we first use a pre-trained video frame classification-aggregation network to make the object categories contained in different sampled frames in the same video be more close to the important object categories contained in the whole video, so as to promote the feature distribution of different sampled frames in the same video to be consistent, and increase the relevance of object features in different frames. Then we propose a video internal frame aggregation loss module to solve the problem of inconsistent feature distribution between different frame features encoded by video encoder in the same video and the aggregation feature of the sampled frame, thus enhancing the ability of video sampled frame aggregation feature representation. Experiments conducted on three common datasets MSVD, MSR-VTT and DiDeMo demonstrate the validity of the proposed approach.
Interactive lighting editing system for single indoor low-light scene images with corresponding depth maps

We propose a novel interactive lighting editing system for lighting a single indoor RGB image based on spherical harmonic lighting. It allows users to intuitively edit illumination and relight the complicated low-light indoor scene. Our method not only achieves plausible global relighting but also enhances the local details of the complicated scene according to the spatially-varying spherical harmonic lighting, which only requires a single RGB image along with a corresponding depth map. To this end, we first present a joint optimization algorithm, which is based on the geometric optimization of the depth map and intrinsic image decomposition avoiding texture-copy, for refining the depth map and obtaining the shading map. Then we propose a lighting estimation method based on spherical harmonic lighting, which not only achieves the global illumination estimation of the scene, but also further enhances local details of the complicated scene. Finally, we use a simple and intuitive interactive method to edit the environment lighting map to adjust lighting and relight the scene. Through extensive experimental results, we demonstrate that our proposed approach is simple and intuitive for relighting the low-light indoor scene, and achieve state-of-the-art results.
Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting

The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise. In this paper, we explore a methodology for tailoring a model to on-site noises through on-device domain adaptation, while accounting for the edge computing-associated costs. We show that accuracy improvements by up to 18 % can be obtained by specialising on difficult, previously unseen noise types, on embedded devices with a power budget in the Watt range, with a storage requirement of 1.1 GB. We also demonstrate an accuracy improvement of 1.43% on an ultra-low power platform consuming few-10mW, requiring only 1.47 MB of memory for the adaptation stage, at a one-time energy cost of 5.81 J.
Processing Health Insurance Claims Based on Blockchain Technology

Blockchain is predicted to have a substantial influence in the healthcare business. Because This field of study is still relatively new, yet it is growing rapidly, health informatics academics and practitioners are always fighting to stay up with it. This study presents the results of a comprehensive evaluation of current research into blockchain technology’s application in healthcare A blockchain that connects clinics, physicians, laboratory vendors, and security network providers might provide a steady supply of health data for better case acceptance and approval. For beginning, maintaining, and cancelling various sorts of agreements, the security business significantly relies on many procedures between executing parties. The time it takes to process and pay claims, as well as security, are two important considerations when it comes to executing claims. Our research aims to create a blockchain-based health insurance claims administration system that will assist insurers in creating a secure, transparent network. It is simple to use for everyone, and it is safe and secure since only trustworthy persons will be added to the system.
Distributed Filtering with Value of Information Censoring

This work presents a distributed estimation algorithm that efficiently uses the available communication resources. The approach is based on Bayesian filtering that is distributed across a network by using the logarithmic opinion pool operator. Communication efficiency is achieved by having only agents with high Value of Information (VoI) share their estimates, and the algorithm provides a tunable trade-off between communication resources and estimation error. Under linear-Gaussian models the algorithm takes the form of a censored distributed Information filter, which guarantees the consistency of agent estimates. Importantly, consistent estimates are shown to play a crucial role in enabling the large reductions in communication usage provided by the VoI censoring approach. We verify the performance of the proposed method via complex simulations in a dynamic network topology and by experimental validation over a real ad-hoc wireless communication network. The results show the validity of using the proposed method to drastically reduce the communication costs of distributed estimation tasks.
Rebop: Reputation-Based Incentives in Committee-Based Blockchains

Blockchains based on proof-of-work suffer from serious drawbacks, such as high computational overhead, long confirmation time, and forks. Committee-based blockchains provide an alternative that tackles these problems. These blockchains use a committee to approve a block at each height. However, rewarding the committee for their work is challenging. The reward mechanism must be fair and robust to attacks. In this paper, we study leader-based reward mechanisms in committee-based blockchains in the presence of rational, colluding, and Byzantine committee members. First, we study the incentives of committee members to deviate and show that an existing reward mechanism is susceptible to attacks from both colluding and Byzantine members. We then propose a reputation-based leader selection mechanism that provides sufficient incentives to coerce rational members to abide by the protocol, and significantly limits the possible gains of collusion. Additionally, our approach reduces the ability of Byzantine members to perform targeted attacks.
BPOMP: A Bilevel Path Optimization Formulation for Motion Planning

Balancing computation efficiency and success rate is challenging for path optimization. To obtain a collision-free path, each waypoint along the path should be collision-free, and the waypoints should be dense. As a consequence, the solutions are computationally expensive to obtain. This paper introduces a bilevel path optimization formulation for motion planning (BPOMP). Different from standard formulations that only consider the collision on each waypoint, BPOMP additionally considers the collision that may happen in the middle by computing the closest position to the obstacle along the continuous path. Intuitively, if the closest position is out of collision, the entire path should be collision-free. The problem is formulated as a bilevel optimization and then relaxed to canonical nonlinear programming (NLP), which can be solved classically. Comparison results in both simulations and experiments are provided to show the effeteness of the proposed formulation. Videos are available at https://changhaowang.github.io/BPOMP/BPOMP.html.
Evaluation of the Effectiveness of Community Activities Restriction in Containing the Spread of COVID-19 in West Java, Indonesia Using Time-Series Clustering

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities and regencies in West Java. We performed time-series clustering on daily positive case data for COVID-19 in 27 cities and regencies in West Java Province, Indonesia for this study. The k-medoids clustering algorithm was used for clustering, with shape-based lock step measures, specifically, the cross correlation-based distance. We used daily new infected cases data for COVID-19 in 27 cities and regencies in West Java Province during the worst situation. We used data from 1 July 2021 to 31 September 2021 and from 1 January 2022 to 31 May 2022, during the Emergency Community Activity Restriction period (PPKM). According to our findings, the optimal number of clusters that could be formed from the data we had was 4 clusters for the first period and 2 clusters for the second period, with silhouette value of 0.2633 and 0.6363, respectively. For the first period, we discovered that PPKM was successful in clusters 1 and 2, namely in 25 cities/districts in West Java, except for Bogor and Depok, while for the second period, we found PPKM to be effective in reducing the number of COVID-19 cases throughout cities and regencies in West Java. This shows there is an improvement from the implementation of PPKM in the first period. We also found that the cluster that was formed was not only influenced by the effectiveness of the PPKM, but also by geography. The closer a city is to a hotspot region for the spread of COVID-19, the earlier the increase in the number of new COVID-19 cases will occur.
Zero-Knowledge for Homomorphic Key-Value Commitments with Applications to Privacy-Preserving Ledgers

Commitments to key-value maps (or, authenticated dictionaries) are an important building block in cryptographic applications, including cryptocurrencies and distributed file systems. In this work we study short commitments to key-value maps with two additional properties: double-hiding (both keys and values should be hidden) and homomorphism (we should be able to combine two commitments to obtain one that is the “sum” of their key-value openings). Furthermore, we require these commitments to be short and to support efficient transparent zero-knowledge arguments (i.e., without a trusted setup). As our main contribution, we show how to construct commitments with the properties above as well as efficient zero-knowledge arguments over them. We additionally discuss a range of practical optimizations that can be carried out depending on the application domain. Finally, we formally describe a specific application of commitments to key-value maps to scalable anonymous ledgers. We show how to extend QuisQuis (Fauzi et al. ASIACRYPT 2019). This results in an efficient, confidential multi-type system with a state whose size is independent of the number of transactions.
Watermarkable Public Key Encryption with Efficient Extraction Under Standard Assumptions

The current state of the art in watermarked public-key encryption schemes under standard cryptographic assumptions suggests that extracting the embedded message requires either linear time in the number of marked keys or the a-priori knowledge of the marked key employed in the decoder. We present the first scheme that obviates these restrictions in the secret-key marking model, i.e., the setting where extraction is performed using a private extraction key. Our construction offers constant time extraction complexity with constant size keys and ciphertexts and is secure under standard assumptions, namely the Decisional Composite Residuosity Assumption [Eurocrypt’99] and the Decisional Diffie Hellman in prime order subgroups of square higher order residues.
Approximate Distance-Comparison-Preserving Symmetric Encryption

We introduce distance-comparison-preserving symmetric encryption (DCPE), a new type of property-preserving encryption that preserves relative distance between plaintext vectors. DCPE is naturally suited for nearest-neighbor search on encrypted data. To boost security, we divert from prior work on Property Preserving Encryption (PPE) and ask for approximate comparison, which is natural given the prevalence of approximate nearest neighbor (ANN) search. We study what security approximate DCPE can provide and how to construct it. Based on a relation we prove between approximate DCP and approximate distance-preserving functions, we design our core approximate DCPE scheme for Euclidean distance we call Scale-And-Perturb ( $$\mathsf {SAP}$$ ). The encryption algorithm of our core scheme processes plaintexts on-the-fly. To further enhance security, we also introduce two preprocessing techniques: (1) normalizing the plaintext distribution, and (2) shuffling, wherein the component-wise encrypted dataset is randomly permuted. We prove that $$\mathsf {SAP}$$ achieves a suitable indistinguishability-based security notion we call real-or-replaced indistinguishability ( $$\mathsf {RoR}$$ ). In particular, our $$\mathsf {RoR}$$ result implies that our scheme prevents a form of membership inference attack. Moreover, we show for i.i.d. multivariate normal plaintexts, we get security against approximate frequency-finding attacks, the main line of attacks against property-preserving encryption. This follows from a one-wayness $$(\mathsf {OW})$$ analysis. Finally, carefully combining our $$\mathsf {OW}$$ and $$\mathsf {RoR}$$ results, we are able characterize bit-security of $$\mathsf {SAP}$$ . Overall, we find that our DCPE scheme not only has superior bit-security to Order Preserving Encryption (OPE) but resists relevant attacks that even ideal order-revealing encryption (Boneh et al., EUROCRYPT 2015) does not.
Credential Transparency System

A major component of the entire digital identity ecosystem are verifiable credentials. However, for users to have complete control and privacy of their digital credentials, they need to be able to store and manage these credentials and associated cryptographic key material on their devices. This approach has severe usability challenges including portability across devises. A more practical solution is for the users to trust a more reliable and available service to manage credentials on their behalf, such as in the case of Single Sign-On (SSO) systems and identity hubs. But the obvious downside of this design is the immense trust that the users need to place on these service providers. In this work, we introduce and formalize a credential transparency system (CTS) framework that adds strong transparency guarantees to a credential management system while preserving privacy and usability features of the system. CTS ensures that if a service provider presents any credential to an honest verifier on behalf of a user, and the user’s device tries to audit all the shows presented on the user’s behalf, the service provider will not be able to drop or modify any show information without getting caught. We define CTS to be a general framework that is compatible with a wide range of credential management systems including SSO and anonymous credential systems. We also provide a CTS instantiation and prove its security formally.