Reviewerly/exHarmony huggingface dataset:
Demo: EQuaTE: Efficient Quantum Train Engine Design and Demonstration for Dynamic Software Analysis

This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neural network (QNN) falls into local minima (called barren plateaus in QNN). EQuaTE can be realized via dynamic analysis of the undetermined probabilistic qubit states. Furthermore, the proposed EQuaTE is capable of HCI-based visual feedback such that software engineers can recognize barren plateaus via visualization, allowing the modification of QNN based on this information.
Data-Driven Rate Control for RDMA Networks: A Lightweight Online Learning Approach

Link speed in datacenter networks (DCNs) keeps growing rapidly, inducing an increasingly large portion of network flows to become short flows which can be finished within one round-trip time (RTT). This phenomenon makes many existing congestion control schemes ineffective because they iteratively adjust the sending rate based on the latest congestion feedback in multiple rounds. We find that the representative DCQCN scheme for RDMA exhibits substantial performance degradation when there are many short flows, and this is specially true in High Performance Computing (HPC) scenarios where most of Message Passing Interface (MPI) messages are small. In this paper, we propose a data-driven rate control framework which can learn from long-term online data about past rate control decisions via a lightweight online learning technique named Multi-Armed Bandit (MAB) which has a provable performance guarantee. Utilizing the framework, we devise a rate control scheme named Dolce-RC, which dynamically controls the rate increase and reduction by learning from online data. We implement Dolce-RC in commodity smart NICs, and show via testbed experiments and large-scale simulations that compared to DCQCN, Dolce-RC reduces average completion time of MPI messages by up to 68%, while not requiring any modification to switches.
A Data Flow Framework with High Throughput and Low Latency for Permissioned Blockchains

In permissioned blockchains, the bandwidth of consensus nodes is mainly consumed by transaction ordering and block distribution; hence, the allocation of consensus nodes' bandwidth makes a significant difference to the system throughput. Previous research focuses on the consensus layer and attempts to optimize consensus protocols to improve throughput, which, however, neglects the impact of data distribution on the throughput and transfers performance bottlenecks to the network layer. In fact, the overall throughput of permissioned blockchains is co-determined by data production in the consensus layer and data distribution in the network layer. This paper proposes a novel data flow framework composed of Predis and Multi-Zone. The former is a data production strategy for permissioned blockchains that employ leader-based BFT protocols and the latter, its corresponding network topology. Predis enables each consensus node to contribute its idle bandwidth for block content pre-distribution so that a much higher volume of transactions can be confirmed in one consensus round, significantly increasing consensus efficiency. Multi-Zone is a network topology to distribute blocks. It can regulate the bandwidth consumption of consensus nodes at a certain value during data distribution and effectively reduce block propagation latency. To test our framework, we implement Predis based on Hotstuff and PBFT, respectively, and experiments show that Predis significantly improves their throughput by 300% to 800%. Multi-Zone is implemented on BFT-SMaRt and compared with random and star network topologies, and it is shown that Multi-Zone holds excellent scalability and the capability of reducing block propagation latency by at least 50%.
Hwamei: A Learning-Based Synchronization Scheme for Hierarchical Federated Learning

Federated learning (FL) enables collaborative model training among distributed devices without data sharing, but existing FL suffers from poor scalability because of global model synchronization. To address this issue, hierarchical federated learning (HFL) has been recently proposed to let edge servers aggregate models of devices in proximity, while synchronizing via the cloud periodically. However, a critical open challenge about how to design a good synchronization scheme (when devices and edges should be synchronized) is still unsolved. Devices are heterogeneous in computing and communication capability, and their data could be non-IID. No existing work can well synchronize various roles (e.g., devices and edge) in HFL to guarantee high learning efficiency and accuracy. In this paper, we propose a learning-based synchronization scheme for HFL systems. By collecting data such as edge models, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. We build an HFL testbed and conduct experiments using real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experimental results have confirmed the effectiveness of Hwamei.
Computation-Effective Personalized Federated Learning: A Meta Learning Approach

Federated learning has gained widespread attention because of its protection of data privacy. It faces two key challenges, one is network bottleneck and stragglers due to performance differences among clients, and the other is performance degradation due to data heterogeneity. Per-FedAvg is a variant of FedAvg that utilizes model-agnostic meta-learning to achieve personalization. However, Per-FedAvg is computationally demanding, which can potentially cause severe straggler effects. In this work, we propose a strategy which allows resource-constrained clients to use the local update of FedAvg as an approximation to the local update of Per-FedAvg. Theoretical results show that the same convergence rate can be achieved when a fraction of the clients use the local update of FedAvg as the approximate update.
CONFPILOT: A Pilot for Faster Configuration by Learning from Device Manuals

The command line interface (CLI) is widely used to configure and manage network devices. However, as heterogeneous devices are introduced into the network, the CLI-based method is becoming time-consuming and inefficient because much effort is required to learn proprietary configuration languages of different vendors or consult online documents. In this work, we present CONFPILOT, an assistant system that can accelerate configuration by automatically converting natural language intents into commands. Our solution is based on a retrieval-augmented generation framework that features a unified parser that parses device manuals into a searchable configuration library, a vendor-agnostic retriever that finds the most relevant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> syntaxes through a two-stage coarse-to-fine process, and a reliable generator that predicts syntactically correct commands via a pointer-generator network and syntax-guided decoding. In a nutshell, CONFPILOT frees engineers from most time-consuming efforts by learning directly from device manuals to generate configuration commands. Our evaluation and user study show, CONFPILOT can speed up the configuration process by 60x compared to manual lookup while maintaining acceptable exact match accuracy. Furthermore, CONFPILOT can quickly adapt to new vendors and devices with little human effort and time cost.
Digital Twin-Assisted Resource Demand Prediction for Multicast Short Video Streaming

In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collecting real-time user status, including channel condition, location, watching duration, and preference. A reinforcement learning-empowered K-means++ algorithm is developed to cluster users based on the collected user status in UDTs. We then analyze users' watching duration and preferences in each multicast group to obtain the swiping probability distribution and recommended videos, respectively. The obtained information is utilized to predict radio and computing resource demand of each multicast group. Initial simulation results demonstrate that the proposed scheme can accurately predict resource demand.
Cross-Domain Data Extraction and Knowledge Graph Construction for Dispute Analysis

This study aims to establish a comprehensive knowledge graph that spans domains and networks, with a specific focus on legal cases and their applications. The proposed methodology enables efficient collection and storage of large volumes of structured, semi-structured, and unstructured data related to cases from various sources including organizations, the government, and the internet. To analyze the relationships between roles in cases, a multimodal model is proposed to process and collect data for domain-specific knowledge graphs. Furthermore, to support social governance and public safety, a knowledge-driven intelligent recommendation algorithm is proposed in the form of question-answering, providing multiple strategies such as causal analysis, similar case matching and pre-disaster response. This work contributes to the field of artificial intelligence and natural language processing, with potential applications in legal and governmental domains, as well as in disaster response and prevention.
DUO: Stealthy Adversarial Example Attack on Video Retrieval Systems via Frame-Pixel Search

Massive videos are released every day particularly through video-focused social media apps such as TikTok. This trend has fostered the quick emergence of video retrieval systems, which provide cloud-based services to retrieve similar videos using machine learning techniques. Adversarial example (AE) attacks have been shown to be effective on such systems by perturbing an unaltered video subtly to induce false retrieval results. Such AE attacks can be easily detected because the adversarial perturbations are all over pixels and frames. In this paper, we propose DUO, a stealthy targeted black-box AE attack which uses DUal search Over frame-pixel to generate sparse perturbations and improve stealthiness. DUO is motivated by two observations: only “key frames” in a video decide model predictions, and different pixels and frames contribute far differently to AEs. We implement DUO into a sequential attack pipeline consisting of two components (i.e., SparseTransfer and SparseQuery) built upon such intuitions. In particular, DUO uses SparseTransfer to generate initial perturbations and then SparseQuery to further rectify them. Extensive evaluations on two popular datasets confirm the higher efficacy and stealthiness of DUO over existing AE attacks on video retrieval systems. In particular, we show that DUO achieves higher precision while significantly reducing adversarial perturbations by more than ×100 than the state-of-the-art AE attack.
Poster: Task Difficulty Adjustment in the Energy-Recycling Consensus Mechanism

An increasing number of energy-recycling consensus mechanisms are being employed to address the drawback of proof of work (PoW) wasting computation and energy. For instance, the computing power wasted in solving difficult but meaningless PoW puzzles is used to conduct practical federated learning tasks and train deep learning models. However, there remains a neglected issue of task difficulty adjustment. To address this problem, we propose a method for measuring task difficulty and an algorithm for adjustment to achieve controlled minting and stable transaction processing capacity for cryptocurrency based on energy-recycling consensus mechanisms. Our research evaluates the effectiveness of this algorithm and highlights the potential benefits of this approach.
Differentially Private Two-Party Top-$k$ Frequent Item Mining

Various data analysis tasks take frequent item mining as an essential part of them. Thus, it is crucial to design practical privacy-preserving frequent item mining methods such that private data analysis can be enabled. For two-party data analysis tasks, each party possesses its portion of the data and is reluctant to share the data with the other. Although secure computation can enable two-party frequent item mining, the output of exact top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> items may still leave the adversary a chance to infer the sensitive information. Differential privacy has been utilized in various data analysis tasks to protect participating individuals, but how to provide differential privacy for two-party frequent item mining has not been addressed. To prevent each party's data from being leaked to the other while achieving differential privacy for releasing the output, we study the problem of differentially private two-party frequent item mining in this paper. We have proposed a practical protocol that can efficiently select the frequent items of the union of two confidential databases in a differentially private way but does not need to combine all the elements. We have conducted extensive experiments to evaluate the proposed method on real-world datasets, and the results show that it can achieve satisfactory utility with affordable overheads.
Adaptively Secure Random Beacons for Ungrindable Blockchains

We describe and analyze a simple protocol for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> parties that implements a randomness beacon: a sequence of high entropy values, continuously emitted at regular intervals, with sub-linear communication per value. The algorithm can tolerate a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-\epsilon)/2$</tex> fraction of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> players to be controlled by an adaptive adversary that may deviate arbitrarily from the protocol. The randomness mechanism relies on verifiable random functions (VRF), modeled as random functions, and effectively stretches an initial <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> -bit seed to an arbitrarily long public sequence so that (i) with overwhelming probability in k-the security parameter-each beacon value has high min-entropy conditioned on the full history of the algorithm, and (ii) the total work and communication required per value is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(k)$</tex> cryptographic operations. The protocol can be directly applied to provide a qualitative improvement in the security of several proof-of-stake blockchain algorithms, rendering them safe from “grinding” attacks.
Two-Stage Coded Distributed Learning: A Dynamic Partial Gradient Coding Perspective

Distributed learning has been widely adopted to train a global model from local data. However, its performance can be severely affected by stragglers. Recently, some research has been dedicated to resolving the straggler problem by adopting gradient coding, the essence of gradient coding is to solve the straggler problem by adding data redundancy. However, the large amount of data redundancy as well as computation and communication overhead that it brings is still hard to be resolved. Besides, the complexity of the encoding and decoding will increase linearly with the number of the local workers. To this end, in this paper, we design a lightweight coding method in the computing phase and seek to ensure fair transmission in the communication phase. Specifically, to tolerate stragglers in computing phase, we propose a two-stage dynamic coding scheme, part of the workers start computing the partial gradients from the data partitions assigned in the first stage, and the remaining workers for computation in the second stage is decided based on which workers have finished in the first stage. To further tolerate stragglers in the communication phase, a perturbed Lyapunov function is designed to maximize admission data balancing fairness as well as the throughput. The experimental result verifies the derived properties and demonstrates that our proposed solution can achieve a better performance for practical network parameters and benchmark data in terms of accuracy and resource utilization in the distributed learning system.
EdgeOrcher: Predictive Function Orchestration for Serverless-Based Edge Native Applications

Serverless computing is becoming prevalent to develop resource-demanding and delay-sensitive edge native applications across the edge and cloud. The unique pricing mechanism of serverless computing brings new opportunities to reduce the cost of edge native applications, by orchestrating function fusion and placement across the edge and cloud. However, function fusion potentially increases the latency of the serverless workflow. To navigate this performance-cost tradeoff, we present an online predictive function orchestration framework which leverages predictions to dynamically optimize the function fusion and placement. Preliminary evaluation results verify the efficacy of the proposed framework.
Work Condition Monitoring for Knife-Edge Switches on Lightweight Edge Devices

With the rapid development of smart factories, it is necessary to provide lightweight and real-time working condition monitoring for remote facility sensors to reduce labor costs and ensure safe operation. In the high-voltage power grid scenario, it is crucial to detect the working state of the electric knife-edge switch, i.e., the switch's rotation angle in 3D space, which controls the on-off state of the high-voltage lines. However, existing solutions are mainly in a contact way, which is easy to cause accidents in high risk scenarios such as high-voltage transmission lines. In this paper, we propose EdgeMonitor, a contactless monitoring system for knife-edge switches, which can provide robust and real-time working state detection based on lightweight edge devices. EdgeMonitor includes a lightweight edge device, a monocular camera, and an inertial sensor. Specifically, we propose a lightweight two-stage strategy first to extract the switch's feature information, then perform continuous state tracking for the switch's rotation angle in 3D space. Experiment results show that EdgeMonitor achieves the knife-edge switches detection error within 2.5° with a latency of less than 50ms.
Detecting Flash Loan Based Attacks in Ethereum

Decentralized Finance (DeFi) ecosystem has grown rapidly in the past few years. In the DeFi ecosystem, flash loan is a novel type of uncollateralized loan with nearly negligible lending costs. Malicious attackers can easily borrow a large number of crypto assets, and utilize them to disrupt the price of crypto assets to make a profit. Many flash loan based price manipulation attacks have been reported recently, and caused immense economic losses, e.g., 30 million USD in a single attack. In this paper, we conduct an empirical study on real-world flash loan based attacks in the past two years and present three attack patterns for price manipulation attacks. Then, we propose an approach, LeiShen, to automatically detect price manipulation attacks with asset transfers. We evaluate LeiShen on the first 14,500,000 blocks in Ethereum, and detect 180 attacks with a precision of 78.9%. Among our newly-found attacks, the severest attack has caused a total loss of more than 6.1 million USD.
Edge-Assisted Adaptive Configuration for Serverless-Based Video Analytics

The growth of video volumes and increased DNN capabilities have led to a growing desire for video analytics, which demands intensive computation resources. Traditional resource provisioning strategies, such as configuring a cluster per peak utilization, lead to low resource efficiency. Serverless computing is a promising way to avoid wasteful resource provisioning since video analytics regularly encounters bursty input workloads and finegrained video content dynamics. For serverless-based video analytics, the application configuration (frame rate, detection model, and computation resources) will impact several metrics, such as computation cost and analytics accuracy. In this paper, we investigate the joint configuration adjustment problem for video knobs and computation resources provided by the serverless platform. We propose an algorithm that can efficiently adapt configurations for video streams to address two key challenges in serverless-based video analytics systems, including the complex relationships between the configurations and the key performance metrics, and the dynamically best configuration. Our algorithm is developed based on Markov approximation to minimize the computation cost within an accuracy constraint. We have developed a prototype over AWS Lambda and conducted extensive experiments with real-world video streams. The results show that our algorithm can greatly reduce the computation cost under the constraint of target accuracy.
Poster: Two-Phase KalmanNet Based Collaborative Detection Framework

The collaborative detection problem has been widely used in many applications. Existing works typically exploit a Kalman Filter or its variant to estimate target state with an impractical assumption that the state space and environment are fully known. To address this issue, we propose a novel multi-agent reinforcement learning (MARL) based collaborative detection framework. The key is (1) a Two-Phase Kalman Neural Network (TPKN) to estimate target state and (2) a reinforcement learning (RL) model by taking the target estimation state as input and generating an action to track targets. Our initial evaluation with 4 pursuer agents and 4 targets demonstrates that our framework outperforms the state-of-the-art by a much higher tracking ability and lower localization error. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Weixiong Rao and Feng Ye are joint corresponding authors of the paper.
Demo: Non-Fungible Tokens in Asset-Backed Securitization

This paper demonstrates the use of blockchain technology in asset-backed securitization (ABS) and presents Quicktoken, a blockchain platform for ABS. Financial institutions can use Quicktoken to assign a correspondence between initial assets and securities, deploy smart contracts for securities issuance, and store the correspondence between assets and non fungible tokens (NFTs) on the blockchain. Investors can buy, sell, and get dividends upon securities redeem via an Android application, while all transactions are secured by the blockchain. The paper highlights the advantages of blockchain usage in ABS, such as a diversification without a loss of auditability.