Reviewerly/exHarmony huggingface dataset:
The Impact Of Quantum Computing In Evolutionary Algorithms

Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the “state of the art” of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates.
CL-SGD: Efficient Communication by Clustering and Local-SGD for Distributed Machine Learning

Training a deep neural network model requires frequent communications between machines, and heavy communication traffic limits the scalability of the distributed ma-chine learning training. Some works try to reduce the communication traffic by transmitting the clustered gradients. Howe-er, the granularity of gradient clustering in these works is relatively coarse, which may decrease the accuracy and stability of the model. Moreover, our experiments reveal that a type of gradient have a certain degree of correlation, which means that we should cluster the gradients by a fine-grained way. In this article, we propose Cluster and Local Stochastic Gradient De-scent (CL-SGD) scheme, which combines the type-by-type gradient clustering method with local training scheme under the master-slave node architecture. CL-SGD has two key designs: first, fully taking account of the differences from each type of gradient, we propose the type-by-type gradient clustering method, which separately clusters each type of gradient meanwhile combining the local training scheme, to significantly reduce the communication traffic. Second, we use master-slave node architecture to reduce the model accuracy loss caused by clustering gradient. Experiment results show that CL-SGD achieves 1500x compression ratio and reduces training time by up to 51 % than the BSP, Local-SGD, STL-SGD, ClusterGrad.
Data-Efficient Adaptive Global Pruning for Convolutional Neural Networks in Edge Computing

Deep convolutional neural networks are hindered from empowering resource-constrained devices due to their demanding computational and storage resources. Structured pruning effectively removes the redundant components from neural networks and obtains compact models. Previous pruning methods usually evaluate the importance of filters from a layer-wise perspective, which is deprived of global guidance. We propose an adaptive pruning algorithm based on relevance scores to evaluate the contribution of each channel by calculating its relevance score from the back-propagation of the neural network's output. Our method identifies and removes channels with low contribution from a global perspective. Unlike previous methods that manually set the pruning rate for each pruning iteration, our method adaptively adjusts the pruning rate. In addition, our method performs satisfactorily with limited data for one-shot pruning in the absence of fine-tuning. The ability to obtain compact models through one-shot pruning with limited data is ideally suited for edge computing scenarios. We validate the effectiveness of our method with multiple combinations of convolutional neural networks and datasets. Our approach outperforms existing pruning methods in scenarios with limited data.
Two Proposed Solutions for Mitigating Blurred Output of Autoencoder

An autoencoder is a neural network that generates data highly similar to the input data for output. Although an autoencoder theoretically produces output almost identical to the input upon completion of learning, it actually generates blurred outputs for complex face images due to the omission of detailed information during the compression process and the use of MSE loss during learning. This paper addresses these issues by mapping detailed information from the frequency domain onto the latent space, adding to the existing latent vector, and learning using a mixed loss of MS-SSIM (Multi Scale Structural Similarity Index Measure) loss and l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> loss instead of MSE loss. As a result, the 100 x l1, 100 x l2 loss, SSIM, MS-SSIM between input and output are 12, 3.1, 0.53, and 0.575 respectively, leading to the production of images of higher quality than the standard autoencoder.
Noise-Tolerant Self-Embedded LSTM for Seismic Event Classification

The modeling of temporal data has significant implications for a wide range of applications, such as the classification of volcano seismic events. Accurately identifying such events at the earliest stages allows for proactive measures to be taken to support communities located nearby volcanoes. However, the presence of noise in the original signals significantly diminishes the performance of event classifiers, requiring the implementation of preprocessing methods to remove noises before adjusting models, which is a time-consuming process that typically needs specialized knowledge and appropriate tools. In this work, we first developed a deep neural network capable of achieving superior results in predicting seismic events for the Llaima volcano in Chile. Secondly, we devised a new memory cell that is more robust to noise. Experiments indicate that our trainable memory cell can be applied to scenarios with different noise influences.
Orchestrating Blockchain with Decentralized Federated Learning in Edge Networks

Decentralized federated learning across edge networks can leverage blockchain with consensus mechanisms for training information exchange among participants over costly and distrustful wide-area networks. However, it is non-trivial to optimally operate the blockchain to support decentralized federated learning due to the complex cost structure of blockchain operations, the balance between blockchain overhead and model convergence, and the dynamics and uncertainties of edge network environments. To overcome these challenges, we formulate a non-linear time-varying integer program that jointly places blockchain nodes and determines the number of training iterations to minimize the long-term blockchain computation and communication cost. We then design an online polynomial-time approximation algorithm that decomposes the problem and solves the subproblems alternately on the fly using only estimated inputs. We rigorously prove the sublinear regret of our approach. We further implement our approach with a prototype system, and conduct extensive trace-driven experiments to validate the superiority of our approach over other alternatives.
Efficient Distributed Swarm Learning for Edge Computing

Federated learning (FL) methods face major challenges including communication bottleneck, data heterogeneity and security concerns in edge IoT scenarios. In this paper, inspired by the success of biological intelligence (BI) of gregarious organisms, we propose a novel edge learning approach for swarm IoT, called communication-efficient and Byzantine-robust distributed swarm learning (CB-DSL), through a holistic integration of AI-enabled stochastic gradient descent and BI-enabled particle swarm optimization. To deal with non-independent and identically distributed (non-i.i.d.) data issues and Byzantine attacks, a very small amount of global data samples are introduced in CB-DSL and shared among IoT workers, which not only alleviates the local data heterogeneity effectively but also enables to fully utilize the exploration-exploitation mechanism of swarm intelligence. Further, we provide convergence analysis to theoretically demonstrate that the proposed CB-DSL is superior to the standard FL with better convergence behavior. In addition, to measure the effectiveness of the introduction of the globally shared dataset, we also evaluate the model divergence by deriving its upper bound. Numerical results verify that the proposed CB-DSL outperforms the existing benchmarks in terms of faster convergence speed, higher convergent accuracy, lower communication cost, and better robustness against non-i.i.d. data and Byzantine attacks <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> Our code can be found at:https://github.com/fuanxiyin/CB-DSL.git..
Adaptive ProductAE: SNR-Aware Adaptive Decoding of Neural Product Codes

As a pioneering and deep-learning driven neural channel code, Product Autoencoder (ProductAE) shows significant superiority over Turbo Autoencoder (TurboAE) and other classical codes. However, received noisy codewords with different noise levels have different decoding difficulties for the neural decoder of ProductAE. The existing neural decoder processes all noisy codewords equally without discrimination, and neglects the prior knowledge of signal-to-noise ratio (SNR), thus leading to high decoding complexity. In this paper, we propose a new approach to speed up the decoding process of ProductAE. Intuitively, noisy codewords with different SNR can be recovered by decoders of different complexity, and our proposed novel pipeline, Adaptive ProductAE, an SNR-aware adaptive decoding strategy, adopts deep decoders to different SNR. Specifically, adaptive ProductAE combines an encoding module, an additional classification module, and two independent decoding branches in a unified framework. After receiving noisy codewords, it first judges the decoding difficulty of each signal and assigns them to different branches to get the decoded messages. Furthermore, we introduce a novel training strategy with a gap loss to maintain the classification and decoding performance. It can thus switch to a simpler branch of decoding networks automatically when it comes to signals with a higher SNR, and the overall computational cost can be reduced. Experiments show that our adaptive ProductAE saves up to 30.4% FLOPs for a moderate-length code of parameters (225, 100) and 25.5% FLOPs for a code of parameters (441, 196) in a higher SNR range.
Semantic-Layer: A High-Level Semantic Ontology For Data-Driven Approaches

Recent years have seen a breakthrough in object detection in computer vision thanks to the development of deep learning algorithms. Although neural approaches appear to perform on par with or even better than human judgment for several benchmarks, issues remain for identifying random concepts in random fields. In this paper, we propose and construct a high-level semantic ontology called the “semantic-layer”, which can be used as an inference engine as well as in training for data-driven models, such as, computer vision, image retrieval, and NLP (Natural language processing) systems. In addition, the proposed layer is also destined to query generation systems, which aim to modify or enhance the users' queries before being transmitted to the server of the research engines.
Spatio-Temporal Action Verification of Basketball Travelling Dribble Violation Using Mediapipe-YOLO-LSTM Framework

Basketball is a very dynamic game where the movements the players make can be fast and subtle, yet fine-grained. As such the difference between an event occurring and not occurring can be small and occasionally unnoticeable, and the sequence of frames of which the dribble is done matters tremendously in order to determine whether a violation takes place. As such, a spatio-temporal action verification model is needed to assist referees in making decisions, as it is almost instantaneous in nature. The challenge is to achieve balance between speed and accuracy, since the violation detection has to be fast enough for real-time use. Mediapipe Pose Estimation is chosen for its lightweight nature and capability for human joint feature extraction, and YOLO is trained to detect and extract basketball coordinates and person in order to pinpoint a region of interest for Mediapipe Pose Estimation in the case of failure to detect a player. The raw dataset consists of videos of basketball players dribbling properly and committing a travelling violation. Long Short- Term Memory (LSTM) is used to model the temporal dynamics of the movements the players make, in the form of a binary classification as to whether the violation occurs. Fine-tuning is done on various hyperparameters of the LSTM model, such as the batch size, layers, number of units, optimizer, dropout rate, loss function, etc. Stacked LSTM architecture is found to be the most suitable model, at an accuracy of 0.8690. The resulting pipeline manages to run at least 10 FPS, with enough overhead to theoretically achieve a higher framerate, after various methods of optimizations.
MAIVS: Machine Learning Based Adaptive UHD 360-Degree Immersive Video Streaming

The 360-degree video transmission offers an immersive experience to viewers and is an integral part of several applications such as Metaverse. Ultra-High Definition (UHD) or greater resolutions for such content requires a substantially higher bitrate for transmission even when encoded using the latest codecs. In this work, we propose a machine learning based adaptive UHD 360° immersive video streaming solution, MAIVS, that reduces the data rate requirement to stream the high resolution 360-degree immersive videos. We divide the videos spatially into motion constrained tiles (MCTS), encode (using HEVC), and package them into mp4 containers at different quality levels. We train a Deep Neural Network (DNN) model for each segment of the video to upscale (at client) it to a higher resolution. We use the DASH (dynamic adaptive streaming over HTTP) framework for streaming the video tiles and the model parameters in a progressive manner. The tiles directly in the viewers Field of View (FoV) are streamed at the highest possible quality while a lower resolution is used for the other tiles. We use video quality parameter (PSNR), buffer conditions, and available network bandwidth, as feedback to train the Deep Q-network (DQN) and selectively pack the bitrate quality segments accordingly. Overall, by using reinforcement learning in our proposed MAIVS framework, we improve the client-side PSNR while reducing the bitrate requirement for streaming high resolution (UHD and higher) 360° videos over the internet.
Be Careful of Your Neighbors: Injected Sub-Prefix Hijacking Invisible to Public Monitors

Prefix hijackings have always been a significant security issue in BGP and have continued to occur in recent years. Detecting prefix hijackings is a vital part of defending against them. Most detection approaches mainly rely on the feed from the monitors of public route collector infrastructures. We propose an injected sub-prefix hijacking that utilizes the BGP communities attribute and AS path poisoning to control the propagation of invalid sub-prefix routes. This attack only pollutes neighboring ASes, thus guaranteeing the invisibility to monitors. Then the attacker can stealthily hijack traffic passing through the polluted ASes. Through extensive simulations, we show that this attack has an enormous impact and propose the crucial indicator affecting the attacker's capability. Finally, we demonstrate that existing defenses are difficult to handle this attack and then propose several defense strategies against it.
Real-Time Phishing Detection Based on URL Multi-Perspective Features: Aiming at the Real Web Environment

Phishing deceives users' trust through subtle URL and HTML disguises, stealing sensitive data or spreading malicious viruses. Phishing detection from URLs has been the focus of research in recent years, which balances the performance and time compared to list-based and content-based approaches. The approaches using neural networks to extract semantic information from URLs to detect phishing websites can avoid feature engineering. However, the features' plausibility cannot be verified. Heuristic features designed artificially can reflect URL differences more reasonably, but the current features lack diversity and have poor generalization in the real web environment. In this paper, we propose a phishing detection model combining heuristic features and machine learning, which extracts features from URL components and linguistics perspectives, leading to lightweight and feature diversity. Three datasets with significant differences are used to verify the model's generalizability. Eventually, the average accuracy of the three datasets reaches 98.68%, and the average precision, recall, and F1-score are all above 98%, which shows good generalizability and outperforms the baselines.
Fuzzing Logical Bugs in eBPF Verifier with Bound-Violation Indicator

eBPF is widely used in Microsoft, Google, and Facebook because it is able to extend kernel without modifying the kernel source code. Nevertheless, vulnerabilities in kernel with eBPF will affect the stability and security of information system. Fuzzing has proven to be an effective approach for finding kernel bugs since it requires minimal knowledge about the target. However, two main challenges exist in discovering eBPF logical bugs: generating input that satisfies all eBPF instruction semantic requirements, and detecting the eBPF logical bug states. We remove highly semantically demanding and unnecessary instructions by analyzing the impact of the instructions to obtain a higher verification pass rate to address the first challenge. We also develop a bound-violation indicator to address the second challenge based on our analysis of eBPF logical bug patterns. We manually introduce 10 recently fixed logical bugs in eBPF for evaluation, and the experimental results show that we can effectively find 9 of them, while Syzkaller fails on all of them. In addition, 4 new bugs have been fixed for upstream Linux based on our work, and 3 functional issues have been reported.
VISTA: Video Transmission over A Semantic Communication Approach

Video transmission over ultra-reliable and low-latency communication (URLLC) is a promising trend to support various multimedia services. However, in view of the rapid surge in video content demand along with a stringent resolution requirement, there is an unprecedented burden on wireless networks with limited yet precious bandwidth resources. In this paper, we propose a VIdeo transmission framework over Semantic communicaTion Approach (VISTA), where semantics rather than all bits of a video should be transmitted with the aim of reducing bandwidth consumption while keeping a high visual perception. Specifically, the semantic segmentation module in VISTA is first developed to classify and encode the dynamic and static segments in source video separately. Next, the semantic location graphs (SLGs) are built to describe semantics and location relations among the detected dynamic objects. Through the joint source-channel coding (JSCC) module which is adaptive to different channel conditions, the encoded semantic features and SLGs are transmitted over wireless channel. Finally, the video is recovered at the receiver end based on the distorted semantic features and SLGs with the assistance of frame interpolation module. Simulation results demonstrate that VISTA outperforms two benchmarks in terms of required bandwidth reduction and robustness against channel noise, further satisfying the requirements on URLLC.
Joint Quality Evaluation, Model Splitting and Resource Provisioning for Split Edge Learning

Edge learning (EL) is an end-edge collaborative learning paradigm that enables numerous edge devices to participate in model training and data analysis, opening countless opportunities to enable edge intelligence. As is a promising EL approach, split edge learning (SPEL) alleviates the computation and communication overhead on resource-constrained devices via offloading part of the machine learning (ML) model to the edge server for cooperative training. Nevertheless, due to the system and statistical heterogeneity of the edge environment, naively using existing SPEL methods brings significantly time-consuming and accuracy degradation. Specifically, system heterogeneity causes intolerable time costs in each training round, while statistical heterogeneity further results in weight divergence and more training rounds to achieve global convergence. Motivated by this issue, this paper designs an efficient SPEL scheme to minimize the total time cost of participating devices. Specifically, we propose a novel SPEL framework and formulate the edge learning cost minimization (ELCM) problem that involves jointly optimizing model splitting and resource provisioning. We design OL-MG, i.e., OnLine Model Splitting and Resource Provisioning Game scheme, to solve the ELCM problem. In OL-MG, we first transform and decompose the original ELCM into two subproblems based on data quality evaluation. Second, we determine the optimal resource provisioning of Sub-problem1 with a given model splitting decision, based on which optimal model splitting of Sub-problem2 is modeled as a potential game. Then, we propose a decentralized algorithm to find a Nash equilibrium (NE) solution for the ELCM problem. Experimental results from both hardware prototype and simulation demonstrate that OL-MG outperforms the state-of-the-art methods, achieving up to 3.1x training cost savings and 40% accuracy improvement.
Narrowband Interference Detection via Deep Learning

Due to the increased usage of spectrum caused by the exponential growth of wireless devices, detecting and avoiding interference has become an increasingly relevant problem to ensure uninterrupted wireless communications. In this paper, we focus our interest on detecting narrowband interference caused by signals that, despite occupying a small portion of the spectrum only, can cause significant harm to wireless systems. For example, in the case of interference with pilots and other signals that are used to equalize the effect of the channel or attain synchronization. Due to the small sizes of these signals, detection can be difficult due to their low energy footprint, while greatly impacting (or denying completely in some cases) network communications. We present a novel narrowband interference detection solution that utilizes convolutional neural networks (CNNs) to detect and locate these signals with high accuracy. To demonstrate the effectiveness of our solution, we have built a prototype that has been tested and validated on a real-world over-the-air large-scale wireless testbed. Our experimental results show that our solution is capable of detecting narrowband jamming attacks with an accuracy of up to 99%. Moreover, it is also able to detect multiple attacks affecting several frequencies at the same time even in the case of previously unseen attack patterns. Not only can our solution achieve a detection accuracy between 92% and 99%, but it does so by only adding an inference latency of 0.093ms.