Additional Overhead Research Articles

NDNOTA: NDN One-Time Authentication

Named Data Networking (NDN) stands out as a prominent architectural framework for the future Internet, aiming to address deficiencies present in IP networks, specifically in the domain of security. Although NDN packets containing requested content are signed with the publisher’s signature which establishes data provenance for content, the NDN domain still requires more holistic frameworks that address consumers’ identity verification while accessing protected contents or services using producer/publisher-preapproved authentication servers. In response, this paper introduces the NDN One-Time Authentication (NDNOTA) framework, designed to authenticate NDN online services, applications, and data in real time. NDNOTA comprises three fundamental elements: the consumer, producer, and authentication server. Employing a variety of security measures such as single sign-on (SSO), token credentials, certified asymmetric keys, and signed NDN packets, NDNOTA aims to reinforce the security of NDN-based interactions. To assess the effectiveness of the proposed framework, we validate and evaluate its impact on the three core elements in terms of time performance. For example, when accessing authenticated content through the entire NDNOTA process, consumers experience an additional time overhead of 70 milliseconds, making the total process take 83 milliseconds. In contrast, accessing normal content that does not require authentication does not incur this delay. The additional NDNOTA delay is mitigated once the authentication token is generated and stored, resulting in a comparable time frame to unauthenticated content requests. Additionally, obtaining private content through the authentication process requires 10 messages, whereas acquiring public data only requires two messages.

PW-NeRF: Progressive wavelet-mask guided neural radiance fields view synthesis

Neural Radiance Fields (NeRF) can achieve state-of-the-art new view results when given a sufficient number of training views. However, NeRF's rendering is based on minimizing photometric consistency loss, and during the optimization process, it can suffer from overfitting due to factors such as lighting and texture, resulting in poor geometric and color reconstruction. The less data there is in the overlapping areas of the images, the greater the impact on the results, such as insufficient data caused by occlusion relationships. In this paper, we observed through experiments that introducing low-frequency images into NeRF during training can quickly obtain approximate geometric structures, which can guide NeRF to achieve more stable view synthesis results. Therefore, we propose a progressive wavelet mask to assist in the training of neural radiance fields. By first constructing a gaussian pyramid for the training images and then applying wavelet decomposition to them, we can obtain a series of low-frequency region masks to guide the neural radiance field to focus on learning low-frequency pixel regions and gradually introduce high-frequency lighting and texture changes. Our experiments on the LLFF and Blender datasets show that using a progressive wavelet mask in NeRF training can achieve more realistic generation effects with almost no additional computational overhead. At the same time, we also tested our method in sparse scenes, where it still performs well in avoiding overfitting.

Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System.

The data incest problem causes inter-estimate correlation during data fusion processes, which yields inconsistent data fusion results. Especially in the multi-sensor multi-vehicle (MSMV) system, the data incest problem is serious due to multiple relative position estimations, which not only lead to pessimistic estimation but also cause additional computational overhead. In order to address the data incest problem, we propose a new data fusion method termed the interval split covariance intersection filter (ISCIF). The general consistency of the ISCIF is proven, serving as supplementary proof for the split covariance intersection filter (SCIF). Moreover, a decentralized MSMV localization system including absolute and relative positioning stages is designed. In the absolute positioning stage, each vehicle uses the ISCIF algorithm to update its own position based on absolute measurements. In the relative position stage, the interval constraint propagation (ICP) method is implemented to preprocess multiple relative position estimates and initially prepare input data for ISCIF. Then, the proposed ISCIF algorithm is employed to realize relative positioning. In addition, comparative simulations demonstrate that the proposed method can achieve both accurate and consistent results compared with the state-of-the-art methods.

Confidential Container Groups

The experiments presented here demonstrate that Parma, the architecture that drives confidential containers on Azure container instances, adds less than one percent additional performance overhead beyond that added by the underlying TEE. Importantly, Parma ensures a security invariant over all reachable states of the container group rooted in the attestation report. This allows external third parties to communicate securely with containers, enabling a wide range of containerized workflows that require confidential access to secure data. Companies obtain the advantages of running their most confidential workflows in the cloud without having to compromise on their security requirements. Tenants gain flexibility, efficiency, and reliability; CSPs get more business; and users can trust that their data is private, confidential, and secure.

Enhancing Spatiotemporal Resampling with a Novel MIS Weight

AbstractIn real‐time rendering, optimizing the sampling of large‐scale candidates is crucial. The spatiotemporal reservoir resampling (ReSTIR) method provides an effective approach for handling large candidate samples, while the Generalized Resampled Importance Sampling (GRIS) theory provides a general framework for resampling algorithms. However, we have observed that when using the generalized multiple importance sampling (MIS) weight in previous work during spatiotemporal reuse, variances gradually amplify in the candidate domain when there are significant differences. To address this issue, we propose a new MIS weight suitable for resampling that blends samples from different sampling domains, ensuring convergence of results as the proportion of non‐canonical samples increases. Additionally, we apply this weight to temporal resampling to reduce noise caused by scene changes or jitter. Our method effectively reduces energy loss in the biased version of ReSTIR DI while incurring no additional overhead, and it also suppresses artifacts caused by a high proportion of temporal samples. As a result, our approach leads to lower variance in the sampling results.

ESVFL: Efficient and secure verifiable federated learning with privacy-preserving

Federated learning has been widely applied as a distributed machine learning method in various fields, allowing a global model to be trained by sharing local gradients instead of raw data. However, direct sharing of local gradients still carries the risk of privacy data leakage, and the malicious server might falsify aggregated result to disrupt model updates. To address these issues, a lot of privacy-preserving and verifiable federated learning schemes have been proposed. However, existing schemes suffer from significant computation overhead in either encryption or verification. In this paper, we present ESVFL, an efficient and secure verifiable federated learning scheme with privacy-preserving. This scheme can simultaneously achieve low computation overhead for encryption and verification on the user side. We design an efficient privacy-preserving method to encrypt the users’ local gradients. Using this method, the computation and communication overheads of encryption on the user side is independent of the number of users. Users can efficiently verify the correctness of aggregated results returned by the cloud servers using cross-verification. During the verification process, there is no interaction among users and no additional computation is required. Furthermore, we also construct an efficient method to address the issue of user dropout. When some users drop out, online users do not incur any additional computation and communication overheads, while guaranteeing the correctness of the aggregated result of online users’ encrypted gradients. The security analysis and the performance evaluation demonstrate that ESVFL is secure and can achieve efficient encryption and verification.

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs.

A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust

Internet applications rely on Secure Socket Layer (SSL)/Transport Security Layer (TSL) certifications to establish secure communication. However, the centralized nature of certificate authorities (CAs) poses a risk, as malicious third parties could exploit the CA to issue fake certificates to malicious web servers, potentially compromising the privacy and integrity of user data. In this paper, we demonstrate how the utilization of decentralized certificate verification with blockchain technology can effectively address and mitigate such attacks. We present a decentralized public key infrastructure (PKI) based on a distributed trust model, e.g., Web of Trust (WoT) and blockchain technologies, to overcome vulnerabilities like single points of failure and to prevent tampering with existing certificates. In addition, our infrastructure establishes a trusted key-ring network that decouples the authentication process from CAs in order to enhance secure certificate issuance and accelerate the revocation process. Furthermore, as a proof of concept, we present the implementation of our proposed system in the Ethereum blockchain, confirming that the proposed framework meets the five identified requirements. Our experimental results demonstrate the effectiveness of our proposed system in practice, albeit with additional overhead compared to conventional PKIs.

Velocity Based Performance Analysis of GreedLea Routing Protocol in Internet of Vehicle (IoV)

Intelligent routing protocols for IoV have also been made possible by the convergence of IoT and machine learning algorithm. In order to make informed routing decisions, these intelligent routing protocols examine data gathered from IoT devices like vehicle sensors and traffic monitoring systems using machine learning algorithms. Moreover, as the number of vehicles increases and road networks become more complex, traditional routing protocols for ad hoc networks are being replaced by more advanced and efficient protocols. The purpose of this study is to concentrate on these unique qualities of IoVs network scenario. A combined routing method has been developed to construct periodic connectivity and find routes on-demand in order to save route data as graphs. The simulation's findings show that GreedLea routing protocol outperforms GPSR and AODV routing protocols in terms of delay and packet delivery ratio (PDR). The results demonstrate that the average AODV latency is significantly higher when there are fewer vehicles on the network. This is due to the fact that connections are frequently lost at higher speeds and lower densities, and re-establishing new channels takes a lot of time. As the number of vehicles rises, efficiency improves and the wait gets shorter. The average latency, yet, keeps increasing as vehicle density increases due to the additional overheads related with routing.

Local-Global Multi-Modal Distillation for Weakly-Supervised Temporal Video Grounding

This paper for the first time leverages multi-modal videos for weakly-supervised temporal video grounding. As labeling the video moment is labor-intensive and subjective, the weakly-supervised approaches have gained increasing attention in recent years. However, these approaches could inherently compromise performance due to inadequate supervision. Therefore, to tackle this challenge, we for the first time pay attention to exploiting complementary information extracted from multi-modal videos (e.g., RGB frames, optical flows), where richer supervision is naturally introduced in the weaklysupervised context. Our motivation is that by integrating different modalities of the videos, the model is learned from synergic supervision and thereby can attain superior generalization capability. However, addressing multiple modalities† would also inevitably introduce additional computational overhead, and might become inapplicable if a particular modality is inaccessible. To solve this issue, we adopt a novel route: building a multi-modal distillation algorithm to capitalize on the multi-modal knowledge as supervision for model training, while still being able to work with only the single modal input during inference. As such, we can utilize the benefits brought by the supplementary nature of multiple modalities, without compromising the applicability in practical scenarios. Specifically, we first propose a cross-modal mutual learning framework and train a sophisticated teacher model to learn collaboratively from the multi-modal videos. Then we identify two sorts of knowledge from the teacher model, i.e., temporal boundaries and semantic activation map. And we devise a local-global distillation algorithm to transfer this knowledge to a student model of single-modal input at both local and global levels. Extensive experiments on large-scale datasets demonstrate that our method achieves state-of-the-art performance with/without multi-modal inputs.

Hierarchical Framework for Predicting Entropies in Bottom-Up Coarse-Grained Models.

The thermodynamic entropy of coarse-grained (CG) models stands as one of the most important properties for quantifying the missing information during the CG process and for establishing transferable (or extendible) CG interactions. However, performing additional CG simulations on top of model construction often leads to significant additional computational overhead. In this work, we propose a simple hierarchical framework for predicting the thermodynamic entropies of various molecular CG systems. Our approach employs a decomposition of the CG interactions, enabling the estimation of the CG partition function and thermodynamic properties a priori. Starting from the ideal gas description, we leverage classical perturbation theory to systematically incorporate simple yet essential interactions, ranging from the hard sphere model to the generalized van der Waals model. Additionally, we propose an alternative approach based on multiparticle correlation functions, allowing for systematic improvements through higher-order correlations. Numerical applications to molecular liquids validate the high fidelity of our approach, and our computational protocols demonstrate that a reduced model with simple energetics can reasonably estimate the thermodynamic entropy of CG models without performing any CG simulations. Overall, our findings present a systematic framework for estimating not only the entropy but also other thermodynamic properties of CG models, relying solely on information from the reference system.

Detecting abnormal behaviors in smart contracts using opcode sequences

With the fast growth of blockchain technology, blockchain as a decentralized distributed ledger technology has become more widely used and is gradually changing our way of life. But it also raises more and more security issues. As there are more and more smart contracts on the blockchain, and smart contracts cannot be changed once they are added to the blockchain, there is an opportunity for hackers to attack smart contracts. If not handled properly, it will cause serious economic losses to users. In this paper, we introduce a unique method for identifying abnormal behaviors of smart contract vulnerabilities using opcode sequences. We aim to identify the control flow paths triggered by transactions to capture the abnormal behaviors of smart contracts. The control flow paths are the traces on which the transaction is executed. Using Geth instrumentation, we collect the opcode sequences executed on the traces to represent the control flow paths. It should be noted that the process of detecting abnormal behaviors introduces some additional time overhead. However, our experimental results show that this method achieves high abnormal detection accuracy with minimal overhead. This suggests that our proposed method is effective in identifying potential security issues in smart contracts without significantly impacting the overall execution time.

Frame Selection Using Spatiotemporal Dynamics and Key Features as Input Pre-processing for Video Super-Resolution Models

This paper presents a novel approach to video super-resolution (VSR) by focusing on the selection of input frames, a process critical to VSR. VSR methods typically rely on deep learning techniques, those that are able to learn features from a large dataset of low-resolution (LR) and corresponding high-resolution (HR) videos and generate high-quality HR frames from any new LR input frames using the learned features. However, these methods often use as input the immediate neighbouring frames to a given target frame without considering the importance and dynamics of the frames across the temporal dimension of a video. This work aims to address the limitations of the conventional sliding-window mechanisms by developing input frame selection algorithms. By dynamically selecting the most representative neighbouring frames based on content-aware selection measures, our proposed algorithms enable VSR models to extract more informative and accurate features that are better aligned with the target frame, leading to improved performance and higher-quality HR frames. Through an empirical study, we demonstrate that the proposed dynamic content-aware selection mechanism improves super-resolution results without any additional architectural overhead, offering a counter-intuitive yet effective alternative to the long-established trend of increasing architectural complexity to improve VSR results.

Reduced On-chip Storage of Seeds for Built-in Test Generation

Logic built-in self-test ( LBIST ) approaches use an on-chip logic block for test generation and thus enable in-field testing. Recent reports of silent data corruption underline the importance of in-field testing. In a class of storage-based LBIST approaches, compressed tests are stored on-chip and decompressed by an on-chip decompression logic. The on-chip storage requirements may become a bottleneck when the number of compressed tests is large. In this case, using each compressed test for applying several different tests allows the storage requirements to be reduced. However, producing different tests from each compressed test has a hardware overhead. This article suggests a new on-chip storage scheme for compressed tests that eliminates the additional hardware overhead. Under the new storage scheme, a set of N B -bit compressed tests targeting a set of faults F 0 is translated into a sequence S of N ⋅ B bits. Every B consecutive bits of S are considered as a compressed test. The sequence S thus yields close to N ⋅ B compressed tests, magnifying the test data stored in S almost B times. Taking advantage of the extra tests, the article describes a software procedure that is applied offline to reduce S without losing fault coverage of F 0 . Experimental results for benchmark circuits demonstrate significant reductions in the storage requirements of S and significant increases in the fault coverage of a second set of faults, F 1 .

Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety

Rapid advances in technology and shifting tastes among motorists have reworked the contemporary automobile production sector. Driving is now much safer and more convenient than ever before thanks to a plethora of new technology and apps. Millions of people are hurt every year despite the fact that automobiles are networked and have several sensors and radars for collision avoidance. Each year, many of them are injured in car accidents and need emergency care, and sadly, the fatality rate is growing. Vehicle and pedestrian collisions are still a serious problem, making it imperative to advance methods that prevent them. This paper refines our previous efficient VANET-based pedestrian safety system based on two-way communication between smart cars and the cell phones of vulnerable road users. We implemented the scheme using C and NS3 to simulate different traffic scenarios. Our objective is to measure the additional overhead to protect vulnerable road users. We prove that our proposed scheme adds just a little amount of additional overhead and successfully satisfies the stringent criteria of safety applications.

On the Use of the Jacobian-Free Newton Krylov Method to Generate One-Group Discontinuity and Super Homogenization Factors for Full-Core Neutron Diffusion Simulations

Coupled Monte Carlo (MC) and thermal-hydraulic analysis is valuable as a design or reference tool but can be slow, especially when implemented in a Picard iteration. Previous work has developed a novel prediction block to achieve convergence with fewer MC simulations. The prediction works in two stages: (1) a surrogate-like model predicts macroscopic cross sections on the fly and (2) a reduced-order neutronic model predicts the flux response to the updated cross sections. The main challenge with the prediction block is that the reduced-order neutronic model cannot reproduce the spatial flux distribution with high fidelity. This paper investigates the well-established Jacobian-free Newton Krylov (JFNK) method to preserve equivalency between a homogeneous (nodal diffusion) solution and a high-fidelity transport (MC) solution. Instead of performing multiple computationally consuming MC simulations, the nonlinear iterative approach iterates on correction parameters, e.g., assembly discontinuity factors (ADFs) or super homogenization (SPH) factors, using unexpensive nodal solutions. The JFNK approach does not require additional overhead from the MC solver to generate flux tallies. Further, the approach iterates on diffusion solutions produced directly from a desired code, thus ensuring that the parameters are compatible with that code. The approach is applied to correct a nodal diffusion solution for a realistic three-dimensional pressurized water reactor core. The results obtained in the paper show that the method is very successful in reproducing the heterogeneous solution (up to 2.5% difference in assembly flux for SPH and 0.3% for ADFs) without needing to modify the source code of the nodal diffusion solver. In addition, the results show that ADFs yield the best agreement and are also stable (i.e., weakly varying) when thermal-hydraulic fields are perturbed.

Cross-domain resources optimization for hybrid edge computing networks: federated DRL approach

Due to the dynamic nature of service requests and the uneven distribution of services in the Internet of Vehicles (IoV), Multi-access Edge Computing (MEC) networks with pre-installed servers are often susceptible to insufficient computing power at certain times or in certain areas. In addition, Vehicular Users (VUs) need to share their observations for centralized neural network training, resulting in additional communication overhead. In this paper, we present a hybrid MEC server architecture, where fixed RoadSide Units (RSUs) and Mobile Edge Servers (MESs) cooperate to provide computation offloading services to VUs. We propose a distributed federated learning and Deep Reinforcement Learning (DRL) based algorithm, namely Federated Dueling Double Deep Q-Network (FD3QN), with the objective of minimizing the weighted sum of service latency and energy consumption. Horizontal federated learning is incorporated into the Dueling Double Deep Q-Network (D3QN) to allocate cross-domain resources after the offload decision process. A client-server framework with federated aggregation is used to maintain the global model. The proposed FD3QN algorithm can jointly optimize power, sub-band, and computational resources. Simulation results show that the proposed algorithm outperforms baselines in terms of system cost and exhibits better robustness in uncertain IoV environments.

Incorporating non-linear effects in fast semi-analytical thermal modelling of powder bed fusion

The usefulness of semi-analytical thermal models for predicting the connection between process, microstructure and properties in powder bed fusion has been well illustrated in recent years. Such an approach provides predictions that are orders of magnitude more computationally efficient than numerical approaches. However, the opportunity to make predictions that span several orders of magnitude in space and time comes at the cost of significant simplifications, limiting fully quantitative predictions without empirical calibration. This approach relies on the solution to a linear problem meaning that first order non-linear effects induced by e.g. the temperature dependence of material properties and surface boundary conditions, are not incorporated. Here, we revisit these limitations and highlight ways that temperature-varying material properties and radiative heat loss from the melt pool can be systematically accounted for without prior calibration. These corrections, made with an eye to minimizing additional computational overhead, bring the technique’s predictive capability much closer to that of higher fidelity thermal simulations without a significant computational cost. Quantitative comparisons to experiments are used to illustrate the important impact of including such corrections.

Rethinking Position Embedding Methods in the Transformer Architecture

In the transformer architecture, as self-attention reads entire image patches at once, the context of the sequence between patches is omitted. Therefore, the position embedding method is employed to assist the self-attention layers in computing the ordering information of tokens. While many papers simply add the position vector to the corresponding token vector rather than concatenating them, few papers offer a thorough explanation and comparison beyond dimension reduction. However, the addition method is not meaningful because token vectors and position vectors are different physical quantities that cannot be directly combined through addition. Hence, we investigate the disparity in learnable absolute position information between the two embedding methods (concatenation and addition) and compare their performance on models. Experiments demonstrate that the concatenation method can learn more spatial information (such as horizontal, vertical, and angle) than the addition method. Furthermore, it reduces the attention distance in the final few layers. Moreover, the concatenation method exhibits greater robustness and leads to a performance gain of 0.1–0.5% for existing models without additional computation overhead.

Adversarial training of multi-scale channel attention network for enhanced robustness in bearing fault diagnosis

For bearing fault diagnosis problems in extremely noisy environments, this paper proposes an innovative universal adversarial training method. This method dynamically introduces noise into the training data, adaptively optimizing the model’s robustness. It applies to any neural network without incurring additional computational overhead in the reasoning process. Additionally, we introduce the multi-scale channel attention network (MSCAN). This network employs stacked convolutional kernels of varying sizes to extract features at different scales from the input signal. The incorporation of the channel attention mechanism allocates distinct weights to features of different scales, further enhancing the network’s representational capacity. Moreover, an automated machine learning-based automated tuning approach is employed to optimize the model training process, aiding in improving inference accuracy. Compared to existing designs, MSCAN exhibits exceptional accuracy. Through adversarial training, it maintains a 99.44% accuracy rate on the Case Western Reserve University dataset under strong −3 dB noise conditions. On the Paderborn University dataset at 0 dB, this adversarial training significantly improves the testing accuracy of various models by an average of 36.42%.