Reviewerly/exHarmony huggingface dataset:
AHP4HPA: An AHP-based Autoscaling Framework for Kubernetes Clusters at the Network Edge

Autoscaling resources in a power-efficient way is essential to enable Green Computing resource management solutions. The development of dynamic resource provisioning techniques could lead to the minimization of power consumption and simultaneously guarantee high quality of service (QoS) inline with the workload demand. In this work, we introduce AHP4HPA, an autoscaling framework for Kubernetes Clusters, which is aligned with the Kubernetes architecture and state-of-the-art practices. We define resource profiles, namely a mapping between the QoS and the computing resources, to maximize the performance. Furthermore, Analytic Hierarchy Process (AHP) is exploited to dictate the scaling decision of the resources under various Key Performance Indicators (KPIs) toward power optimization of the allocated resources. To guarantee maximum performance of the deployed image classification application, an ARIMA model is dedicated to providing predictions regarding the incoming workload traffic. The framework is evaluated against a realistic dataset in a small-scale testbed. Numerical results indicate at least a 9% reduction of the average energy consumption when compared to other state of the art techniques.
IL-GAN: Rare Sample Generation via Incremental Learning in GANs

Industry 4.0 imposes strict requirements on the fifth generation of wireless systems (5G), such as high reliability, high availability, and low latency. Guaranteeing such requirements implies that system failures should occur with an extremely low probability. However, some applications (e.g., training a reinforcement learning algorithm to operate in highly reliable systems or rare event simulations) require access to a broad range of observed failures and extreme values, preferably in a short time. In this paper, we propose IL-GAN, an alternative training framework for generative adversarial networks (GANs), which leverages incremental learning (IL) to enable the generation to learn the tail behavior of the distribution using only a few samples. We validate the proposed IL-GAN with data from 5G simulations on a factory automation scenario and real measurements gathered from various video streaming platforms. Our evaluations show that, compared to the state-of-the-art, our solution can significantly improve the learning and generation performance, not only for the tail distribution but also for the rest of the distribution.
Wi-Fi Standard Technology Identification Employing Dynamic Mode Decomposition Modeling

A growing research agenda to identify coexisting wireless technologies is focusing mainly on matching significant advances in wireless technologies with the ever-increasing spectrum demand for the Internet of Things (IoT), including activity in the license-free Industrial, Scientific, and Medical (ISM) bands. This paper presents a novel algorithm based on dynamic mode decomposition (DMD) modeling for detecting and differentiating various IEEE 802.11x Wi-Fi standard signals using collected timeseries raw power measurements. Wi-Fi signals were collected across 802.11n, 802.11ac, and 802.11ax standards operating in the 5GHz ISM band under individual (no sharing) and multiple coexisted technologies. The proposed algorithm identified the time domain signature of a signal by capturing embedded periodicity features transmitted within the signal. A major advantage of the technique is its minimal requirements-no data preprocessing, no channel estimation, no time/frequency synchronization, and no need for long observation time intervals. Method accuracy for targeted signals was evaluated and is reported herein.
Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Services

In this paper, we study the expanding attack surface of Adversarial Machine Learning (AML) and the potential attacks against Vehicle-to-Microgrid (V2M) services. We present an anticipatory study of a multi-stage gray-box attack that can achieve a comparable result to a white-box attack. Adversaries aim to deceive the targeted Machine Learning (ML) classifier at the network edge to misclassify the incoming energy requests from microgrids. With an inference attack, an adversary can collect real-time data from the communication between smart microgrids and a 5G gNodeB to train a surrogate (i.e., shadow) model of the targeted classifier at the edge. To anticipate the associated impact of an adversary's capability to collect real-time data instances, we study five different cases, each representing different amounts of real-time data instances collected by an adversary. Out of six ML models trained on the complete dataset, K-Nearest Neighbour (K-NN) is selected as the surrogate model, and through simulations, we demonstrate that the multi-stage gray-box attack is able to mislead the ML classifier and cause an Evasion Increase Rate (EIR) up to 73.2% using 40% less data than what a white-box attack needs to achieve a similar EIR.
Physics-informed Cyber-Attack Detection in Wind Farms

The growth of wind power generation is accelerating worldwide. However, wind turbines need to be carefully controlled to maintain structural stability and optimal power generation. The state of the turbine and power generated is digitized and transmitted over communication networks for physical component monitoring, control, and data management. Motivated by creating power outages, financial loss, and physical wind turbine damage, attackers could craft and inject falsified network traffic to maliciously influence control actions. In this paper, we propose a physics-informed machine learning anomaly detection approach. The mechanism is utilized at each wind turbine, where generated power signals are validated based on laws of physics and incorporated into machine learning algorithm to detect anomalies. We show that the proposed combined physics and machine learning approach improves the accuracy of detecting falsification attacks compared with using machine learning alone.
Optimal Job Scheduling and Bandwidth Augmentation in Hybrid Data Center Networks

Optimizing data transfers is critical for improving job performance in data-parallel frameworks. In the hybrid data center with both wired and wireless links, reconfigurable wireless links can provide additional bandwidth to speed up job execution. However, it requires the scheduler and transceivers to make joint decisions under coupled constraints. In this work, we identify that the joint job scheduling and bandwidth augmentation problem is a complex mixed integer nonlinear problem, which is not solvable by existing optimization methods. To address this bottleneck, we transform it into an equivalent problem based on the coupling of its heuristic bounds, the revised data transfer representation and non-linear constraints decoupling and reformulation, such that the optimal solution can be efficiently acquired by the Branch and Bound method. Based on the proposed method, the performance of job scheduling with and without bandwidth augmentation is studied. Experiments show that the performance gain depends on multiple factors, especially the data size. Compared with existing solutions, our method can averagely reduce the job completion time by up to 10% under the setting of production scenario.
Learning-Based Load-Aware Heterogeneous Vehicular Edge Computing

Vehicular edge computing is an emerging enabler to support vehicular-based computation-intensive tasks. By reason of the time-varying vehicular wireless environments and the stochastic task generation, the dynamically unbalanced task load distribution among resource-constrained edge infrastructures leads to the performance bottleneck and low efficiency of computation resource utilization. We employ an aerial relay station that can establish relay connections between vehicles and nearby heterogeneous edge infrastructures to relieve this situation. The computation offloading strategy design in the multivehicle multi-edge infrastructure scenario that is closely linked to system latency performance will be particularly complicated. To address this issue, a model-free multi-agent reinforcement learning is adopted, and we propose a practical constraint in the problem formulation. Simulation experiments show that the proposed strategy can guarantee load balancing among edge infrastructures.
FLAC: Federated Learning with Autoencoder Compression and Convergence Guarantee

Federated Learning (FL) is considered the key approach for privacy-preserving, distributed machine learning (ML) systems. However, due to the transmission of large ML models from users to the server in each iteration of FL, communication on resource-constrained networks is currently a fundamental bottleneck in FL, restricting the ML model complex-ity and user participation. One of the notable trends to reduce the communication cost of FL systems is gradient compression, in which techniques in the form of sparsification or quantization are utilized. However, these methods are pre-fixed and do not capture the redundant, correlated information across parameters of the ML models, user devices' data, and iterations of FL. Further, these methods do not fully take advantage of the error-correcting capability of the FL process. In this paper, we propose the Federated Learning with Autoencoder Compression (FLAC) approach that utilizes the redundant information and error-correcting capability of FL to compress user devices' models for uplink transmission. FLAC trains an autoencoder to encode and decode users' models at the server in the Training State, and then, sends the autoencoder to user devices for compressing local models for future iterations during the Compression State. To guarantee the convergence of the FL, FLAC dynamically controls the autoencoder error by switching between the Training State and Compression State to adjust its autoencoder and its compression rate based on the error tolerance of the FL system. We theoretically prove that FLAC converges for FL systems with strongly convex ML models and non-i.i.d. data distribution. Our extensive experimental results'over three datasets with different network architectures show that FLAC can achieve compression rates ranging from 83x to 875x while staying near 7 percent of the accuracy of the non-compressed FL systems.
MMSE Signal Detection for MIMO Systems based on Ordinary Differential Equation

Motivated by emerging technologies for energy ef-ficient analog computing and continuous-time processing, this paper proposes continuous-time minimum mean squared error estimation for multiple-input multiple-output (MIMO) systems based on an ordinary differential equation. Mean squared error (MSE) is a principal detection performance measure of estimation methods for MIMO systems. We derive an analytical MSE formula that indicates the MSE at any time. The MSE of the proposed method depends on a regularization parameter which affects the convergence property of the MSE. Furthermore, we extend the proposed method by using a time-dependent regularization parameter to achieve better convergence performance. Numerical experiments indicated excellent agreement with the theoretical values and improvement in the convergence performance owing to the use of the time-dependent parameter.
FedAegis: Edge-Based Byzantine-Robust Federated Learning for Heterogeneous Data

This paper studies how an edge-based federated learning algorithm called FedAegis can be designed to be ro-bust under both heterogeneous data distributions and Byzantine adversaries. The divergence of local data distributions leads to suboptimal results for the training process of federated learning, and the Byzantine adversaries aim to prevent the training process from converging in a distributed learning system. In this paper, we show that an edge-based hierarchical federated learning architecture can help tackle this dilemma by utilizing edge nodes geographically close to clusters of local devices. By combining a distributionally robust global loss function with a local Byzantine-robust aggregation rule, FedAegis can defend against remote Byzantine adversaries who cannot manipulate local devices' connections to edge nodes, meanwhile accounting for global data heterogeneity across benign local devices. Experiments with the MNIST, FMNIST and CIFAR-IO datasets show that our proposed algorithm can achieve convergence and high accuracy under heterogeneous data and various attack scenarios, while state-of-the-art defenses and robustness mechanisms are non-converging or have reduced average and/or worst-case accuracy.
Object-Based Resolution Selection for Efficient Edge-Assisted Multi-Task Video Analytics

Camera-based monitoring is becoming increasingly popular, as multi-objective detection tasks can be enabled by video analytics over captured frames. Yet, video frames have to be delivered to computation-capable edge nodes for further processing, because the amount of required resources exceeds the capacity of built-in hardware of video cameras. In this paper, observing that video resolution directly determines the subsequent bandwidth and computing resource consumption, as well as the analytic accuracy, we propose an edge-assisted object-based resolution configuration algorithm to achieve efficient multi-task video analytics. The proposed algorithm harnesses the diversity of neural networks used for detecting different objects in one frame, which brings about two-fold possibility for bandwidth saving. On one hand, background information cannot be indiscriminately transmitted, as is unlikely to contribute to improving the analytics accuracy. On the other hand, fine-grained resolution selection allows object-level optimal resolution that minimizes the transmitted data volume under accuracy and latency constraints. Simulation results demonstrate that the proposed method can effectively reduce up to 50% of the transmitted data volume, compared to existing benchmarks.
Locally Random Sampling for Practical Privacy Protection in Federated Learning

Federated learning (FL) is an emerging solution for machine learning model training in edge/fog computing systems. Unlike traditional systems that collect and train models on clouds, FL allows multiple edge/fog nodes to train a global model collaboratively without revealing their local data to clouds. Compared with traditional systems, it is inherited with better privacy protection ability. Although the basic privacy protection is inherited in FL, the privacy leakage from shard models is still unsolved. Existing solutions attempt to enhance the privacy of shared model parameters by adding differential privacy (DP) noise. However, these solutions all suffer from accuracy loss and convergence problems owing to the injected noise. In this paper, we propose a novel federated learning protocol to solve the above problem. The model trained on a carefully selected sampling subset can achieve the same level privacy protection as DP while preserving the model accuracy. Experimentally, we proved that our protocol achieves better model accuracy in the same privacy guarantee compared with noise injecting DP methods.
An Energy-efficient Computing Offloading Framework for Blockchain-enabled Video Streaming Systems

Blockchain and edge computing have been widely applied in video streaming systems. However, previous works lack a joint consideration of video redundancy and full utilization of edge resources (bandwidth resources, CPU frequency), resulting in suboptimal performance of video streaming systems. In this paper, we propose a computing offloading framework for blockchain-enabled video streaming systems to fully exploit edge resources and reduce energy consumption. Specifically, we formulate computing offloading, resource allocation, and adaptive compression as a joint optimization problem. We transform and decompose the original non-convex problem and propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the decomposed problem in a distributed manner. Simulation results demonstrate that our scheme can effectively reduce energy consumption and fully utilize the bandwidth and computational resources.
Aesop: White-Box Best-First Proof Search for Lean

We present Aesop, a proof search tactic for the Lean 4 interactive theorem prover. Aesop performs a tree-based search over a user-specified set of proof rules. It supports safe and unsafe rules and uses a best-first search strategy with customisable prioritisation. Aesop also allows users to register custom normalisation rules and integrates Lean's simplifier to support equational reasoning. Many details of Aesop's search procedure are designed to make it a white-box proof automation tactic, meaning that users should be able to easily predict how their rules will be applied, and thus how powerful and fast their Aesop invocations will be.
Compiling Higher-Order Specifications to SMT Solvers: How to Deal with Rejection Constructively

Modern verification tools frequently rely on compiling high-level specifications to SMT queries. However, the high-level specification language is usually more expressive than the available solvers and therefore some syntactically valid specifications must be rejected by the tool. In such cases, the challenge is to provide a comprehensible error message to the user that relates the original syntactic form of the specification to the semantic reason it has been rejected.
Encoding Dependently-Typed Constructions into Simple Type Theory

In this article, we show how one can formalise in type theory mathematical objects, for which dependent types are usually deemed unavoidable, using only simple types. We outline a method to encode most of the terms of Lean's dependent type theory into the simple type theory of Isabelle/HOL. Taking advantage of Isabelle's automation, we illustrate our method with the formalisation in Isabelle/HOL of a mathematical notion developed in the 1980s: strict omega-categories.
Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: A DES Instance

Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers.
Predictive and Adaptive Deep Coding for Wireless Image Transmission in Semantic Communication

Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.
Elements of Quantitative Rewriting

We introduce a general theory of quantitative and metric rewriting systems, namely systems with a rewriting relation enriched over quantales modelling abstract quantities. We develop theories of abstract and term-based systems, refining cornerstone results of rewriting theory (such as Newman’s Lemma, Church-Rosser Theorem, and critical pair-like lemmas) to a metric and quantitative setting. To avoid distance trivialisation and lack of confluence issues, we introduce non-expansive, linear term rewriting systems, and then generalise the latter to the novel class of graded term rewriting systems. These systems make quantitative rewriting modal and context-sensitive, this way endowing rewriting with coeffectful behaviours.
CAFI-AR

Freehand interaction enhances user experience, allowing one to use bare hands to manipulate virtual objects in AR. Yet, it remains challenging to accurately and efficiently detect contacts between real hand and virtual object, due to the imprecise captured/estimated hand geometry. This paper presents CAFI-AR, a new approach for Contact-Aware Freehand Interaction with virtual AR objects, enabling us to automatically detect hand-object contacts in real-time with low latency. Specifically, we formulate a compact deep architecture to efficiently learn to predict hand action and contact moment from sequences of captured RGB images relative to the 3D virtual object. To train the architecture for detecting contacts on AR objects, we build a new dataset with 4,008 frame sequences, each with annotated hand-object interaction information. Further, we integrate CAFI-AR into our prototyping AR system and develop various interactive scenarios, demonstrating fine-grained contact-aware interactions on a rich variety of virtual AR objects, which cannot be achieved by existing AR interaction approaches. Lastly, we also evaluate CAFI-AR, quantitatively and qualitatively, through two user studies to demonstrate its effectiveness in terms of accurately detecting the hand-object contacts and promoting fluid freehand interactions