Reviewerly/exHarmony huggingface dataset:
Towards Boosting Federated Learning Convergence: A Computation Offloading &amp; Clustering Approach

With an innovative door opened for a new era of Machine Learning, Federated Learning (FL) is now revolutionizing Artificial Intelligence. It exploits both decentralized data and decentralized computation to preserve user privacy. Albeit its popularity and being the most widely used framework nowadays, FL becomes a sub-optimal solution when the convergence of the global model occurs at a slow pace, which exacerbates the communication bottlenecks. To address this challenge, we propose in this paper CISCO-FL, a Clustered FL with Intelligent Selection and Computation Offloading. First, we partition the clients into different groups, where sub-aggregations of the clients models are performed at each cluster before the global aggregation. Second, we study the computing resources of the clients, and we embed in the proposed approach an intelligent offloading model, where the clients with high computational resources can assist and optimize the model of those struggling with limited resources. As such, both communication cost and computation resources are reduced and optimized. Finally, thorough experimental results are presented to support our findings and validate our model.
Federated Learning Based Task Orchestration Scheme Using Intelligent Vehicular Edge Networks

Vehicular Edge Computing (VEC) is gradually evolving into one of the most prevalent paradigms for vehicular computation. This is due to its ability for effectively handling the tasks of varied complexity. VEC based task orchestration has therefore emerged as an exciting research domain. A large number of task orchestration schemes have been proposed that exploit its technical capabilities. However, identifying the most appropriate vehicles for such edges still remain a challenge. In this work, we propose an intelligence based Task Orchestration Scheme integrated with Vehicular Cloud Edge Networks that uses Federated learning (FL) for forming vehicular edges. FL is a privacy preserving technique with no data being shared centrally. This scheme uses characteristics of vehicles such as computational capacity and their starting as well as ending point for creating the edges. Obtained results depict the improved performance of this scheme as compared to conventional schemes.
Deep Joint Source-Channel and Encryption Coding: Secure Semantic Communications

Deep learning driven joint source-channel coding (JSCC) for wireless image or video transmission, also called DeepJSCC, has been a topic of interest recently with very promising results. The idea is to map similar source samples to nearby points in the channel input space such that, despite the noise introduced by the channel, the input can be recovered with minimal distortion. However, the inherent correlation between the source sample and channel input makes DeepJSCC vulnerable to eavesdropping attacks. In this paper, we propose the first DeepJSCC scheme for wireless image transmission that is secure against eavesdroppers, called DeepJSCEC. The proposed solution not only preserves the results demonstrated by DeepJSCC, it also provides security against chosen-plaintext attacks from the eavesdropper, without the need to make assumptions about the eavesdropper's channel condition or its intended use of the intercepted signal.
Deep Separate Source-channel Coding for Semantic-aware Image Transmission

This paper proposes a deep separate source-channel coding (DSSCC) scheme for the semantic-aware image transmission, where image is lossily compressed and transmitted to receiver for recovery and processing certain semantic tasks. To improve the compression efficiency, the forward adaption (FA) method is incorporated into the DSSCC scheme to capture the density information of compressed features as side information. For a typical application of image classification task, we derive a novel rate-distortion optimization problem by analyzing the Bayesian model of the FA-based DSSCC framework. Then, a variational autoencoder approach is proposed to effectively compress image for semantic-aware transmission by minimizing the proposed rate-distortion problem. Simulation results reveal that the proposed FA-based DSSCC scheme achieves better image recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.
AI Chatbot Technology to Predict Disease: A Systematic Literature Review

Artificial Intelligence (AI) and chatbot technology have emerged as promising solutions to improve healthcare services. AI chatbots can mimic human-like interactions and assist with tasks such as triaging patients and providing medical advice. This review focuses on the use of AI chatbots in predicting diseases, aiming to explore their effectiveness and potential for early intervention and treatment. The purpose of this systematic literature review is to analyze studies related to AI chatbot technology in disease prediction. A systematic literature review was conducted, analyzing a total of 24 selected journals based on predefined inclusion and exclusion criteria. The review protocol involved examining studies published from various years, with a particular emphasis on articles from 2020. The findings indicate that AI chatbots have the potential to play a significant role in predicting diseases and assisting healthcare professionals in making informed decisions. AI chatbot technology shows tremendous potential in disease prediction. By utilizing machine learning algorithms and techniques, chatbots can enhance the accuracy and quickness of disease diagnosis. Continued research and development efforts are necessary to refine AI chatbots' capabilities and revolutionize healthcare delivery for improved patient outcomes and disease management.
Personalized Decentralized Federated Learning with Knowledge Distillation

Personalization in federated learning (FL) functions as a coordinator for clients with high variance in data or behavior.Ensuring the convergence of these clients' models relies on how closely users collaborate with those with similar patterns or preferences.However, it is generally challenging to quantify similarity under limited knowledge about other users' models given to users in a decentralized network.To cope with this issue, we propose a personalized and fully decentralized FL algorithm, leveraging knowledge distillation techniques to empower each device so as to discern statistical distances between local models.Each client device can enhance its performance without sharing local data by estimating the similarity between two intermediate outputs from feeding local samples as in knowledge distillation.Our empirical studies demonstrate that the proposed algorithm improves the test accuracy of clients in fewer iterations under highly non-independent and identically distributed (non-i.i.d.) data distributions and is beneficial to agents with small datasets, even without the need for a central server.
Latency of spatial audio plugins: a comparative study

The use of spatial audio plugins (SAPs) with Ambisonics processing and binaural rendering has become widespread in the last decade, thanks to their increased accessibility and usability. SAPs are particularly relevant in scenarios involving real-time music playing with headphones, such as networked music performance and individual recreational music-making using backing tracks. However, a crucial issue that has been largely overlooked thus far is the measurement of the processing latency introduced by currently available SAPs. Identifying which SAPs are the fastest is essential to enable designers, musicians, and researchers to create time-sensitive applications involving 3D audio. To bridge this gap, we compared nine systems formed by different SAPs that enable 3D audio management. We measured the latency of each system throughout the third-order Ambisonics plugins pipeline: encoding, room simulation, sound scene rotation, and binaural decoding. In particular, the measurements were performed utilizing different buffer sizes. Results showed that to achieve a minimization of the latency, it is necessary to use a combination of different SAPs from different systems. Based on our measurements, we propose two spatial audio systems that mix different SAPs. Considering a sampling rate of 48 kHz, a Dell Alienware x15 R2 laptop running the Windows 10 operating system, and an RME Fireface UFX sound card, the two systems achieved an overall latency of 0.33 ms and 0.94 ms respectively.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on endto-end performance.However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks.This limitation is a major obstacle that hinders their practical deployment.In this paper, we attempt to quantify the uncertainty of an important category of DL-based channel estimators.An efficient statistical method is proposed to make blind predictions for the mean squared error of the DL-estimated channel solely based on received pilots, without knowledge of the ground-truth channel, the prior distribution of the channel, or the noise statistics.The complexity of the blind performance prediction is low and scales only linearly with the number of antennas.Simulation results for ultra-massive multiple-input multiple-output (UM-MIMO) channel estimation with a mixture of far-field and near-field paths are provided to verify the accuracy and efficiency of the proposed method.
Joint Edge Association and Aggregation Frequency for Energy-Efficient Hierarchical Federated Learning by Deep Reinforcement Learning

Hierarchical Federated Learning (HFL) has been proposed to achieve larger-scale model training and more efficient communications compared to conventional Federated Learning (FL). However, both inappropriate edge association strategy and aggregation frequency may consume massive energy in users with poor channel conditions or degrade the HFL convergence performance due to Non Independent and Identical Distribution (NIID) data, which is challenging to energy-limited users. Motivated by this, a dynamically joint edge association and aggregation frequency optimization problem is proposed from the perspective of minimizing long-term energy consumption. By incorporating the communication model and convergence analysis, the problem can be formulated to strike a balance between HFL convergence rate and energy consumed by all users within one global communication round. Then, a Deep Reinforcement Learning (DRL) agent is designed to approximate the optimal solution. Simulation results verify the convergence analysis and the proposed DRL-assisted joint strategy can consume the least energy while reaching the required target model accuracy compared to other benchmarks.
Enhancing Decision Making with Semantic-Based Hybrid Recommender System

Decision making can be a complex and challenging task, particularly in the context of an overwhelming amount of information available on the web. Recommender systems have emerged as a solution to aid decision making by suggesting relevant items based on users' preferences and needs. However, traditional content-based recommender systems have been criticized for their lack of diversity in recommendations. On the other hand, collaborative filtering suffers from scalability issues due to the high computational complexity of similarity calculations for large datasets. To overcome these limitations, we propose a hybrid recommender system that leverages Linked Open Data and Ant Colony Optimization. Our approach utilizes semantic databases to improve the precision of recommendations and ACO algorithm to optimize the selection of diverse items. Experimental results demonstrate that our proposed system outperforms traditional recommendation systems in terms of decision aid and provides users with personalized and diverse recommendations. This research can contribute to the development of decision aid applications and improve the quality of decision making.
Pushing Alias Resolution to the Limit

In this paper, we show that utilizing multiple protocols offers a unique opportunity to improve IP alias resolution and dual-stack inference substantially. Our key observation is that prevalent protocols, e.g., SSH and BGP, reply to unsolicited requests with a set of values that can be combined to form a unique device identifier. More importantly, this is possible by just completing the TCP hand-shake. Our empirical study shows that utilizing readily available scans and our active measurements can double the discovered IPv4 alias sets and more than 30× the dual-stack sets compared to the state-of-the-art techniques. We provide insights into our method's accuracy and performance compared to popular techniques.
An LLM-based Framework for Fingerprinting Internet-connected Devices

In this paper we propose the use of large language models (LLMs) for characterizing, clustering, and fingerprinting raw text obtained from network measurements. To this end, We first train a transformer-based masked language model, namely RoBERTa, on a dataset containing hundreds of millions of banners obtained from Internet-wide scans. We further fine-tune this model using a contrastive loss function (driven by domain knowledge) to produce temporally stable numerical representations (embeddings) that can be used out-of-the-box for downstream learning tasks. Our embeddings are robust, resilient to small random changes in the content of a banner, and maintain proximity between embeddings of similar hardware/software products. We further cluster HTTP banners using a density-based approach (HDBSCAN), and examine the obtained clusters to generate text-based fingerprints for the purpose of labeling raw scan data. We compare our fingerprints to Recog, an existing database of manually curated fingerprints, and show that we can identify new IoT devices and server products that were not previously captured by Recog. Our proposed methodology poses an important direction for future research by utilizing state-of-the-art language models to automatically analyze, interpret, and label the large amounts of data generated by Internet scans.
Cutting multi-control quantum gates with ZX calculus

Circuit cutting, the decomposition of a quantum circuit into independent partitions, has become a promising avenue towards experiments with larger quantum circuits in the noisy-intermediate scale quantum (NISQ) era. While previous work focused on cutting qubit wires or two-qubit gates, in this work we introduce a method for cutting multi-controlled Z gates. We construct a decomposition and prove the upper bound <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mn>6</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>2</mml:mn><mml:mi>K</mml:mi></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> on the associated sampling overhead, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> is the number of cuts in the circuit. This bound is independent of the number of control qubits but can be further reduced to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi class="MJX-tex-caligraphic" mathvariant="script">O</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mn>4.5</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>2</mml:mn><mml:mi>K</mml:mi></mml:mrow></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> for the special case of CCZ gates. Furthermore, we evaluate our proposal on IBM hardware and experimentally show noise resilience due to the strong reduction of CNOT gates in the cut circuits.
Blockchain-Based Malicious Behaviour Management Scheme for Smart Grids

The smart grid optimises energy transmission efficiency and provides practical solutions for energy saving and life convenience. Along with a decentralised, transparent and fair trading model, the smart grid attracts many users to participate. In recent years, many researchers have contributed to the development of smart grids in terms of network and information security so that the security, reliability and stability of smart grid systems can be guaranteed. However, our investigation reveals various malicious behaviours during smart grid transactions and operations, such as electricity theft, erroneous data injection, and distributed denial of service (DDoS). These malicious behaviours threaten the interests of honest suppliers and consumers. While the existing literature has employed machine learning and other methods to detect and defend against malicious behaviour, these defence mechanisms do not impose any penalties on the attackers. This paper proposes a management scheme that can handle different types of malicious behaviour in the smart grid. The scheme uses a consortium blockchain combined with the best–worst multi-criteria decision method (BWM) to accurately quantify and manage malicious behaviour. Smart contracts are used to implement a penalty mechanism that applies appropriate penalties to different malicious users. Through a detailed description of the proposed algorithm, logic model and data structure, we show the principles and workflow of this scheme for dealing with malicious behaviour. We analysed the system’s security attributes and tested the system’s performance. The results indicate that the system meets the security attributes of confidentiality and integrity. The performance results are similar to the benchmark results, demonstrating the feasibility and stability of the system.
Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions

Recent years have seen a substantial increase in interest in deepfakes, a fast-developing field at the nexus of artificial intelligence and multimedia. These artificial media creations, made possible by deep learning algorithms, allow for the manipulation and creation of digital content that is extremely realistic and challenging to identify from authentic content. Deepfakes can be used for entertainment, education, and research; however, they pose a range of significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud. This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake research, as well as associated challenges and future research trends. By synthesizing existing knowledge and research, this survey aims to facilitate further advancements in deepfake detection and mitigation strategies, ultimately fostering a safer and more trustworthy digital environment.
Explainable Approaches for Forecasting Building Electricity Consumption

Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes.
Path Planning Algorithm for Dual-Arm Robot Based on Depth Deterministic Gradient Strategy Algorithm

In recent years, the utilization of dual-arm robots has gained substantial prominence across various industries owing to their collaborative operational capabilities. In order to achieve collision avoidance and facilitate cooperative task completion, efficient path planning plays a pivotal role. The high dimensionality associated with collaborative task execution in dual-arm robots renders existing path planning methods ineffective for conducting efficient exploration. This paper introduces a multi-agent path planning reinforcement learning algorithm that integrates an experience replay strategy, a shortest-path constraint, and the policy gradient method. To foster collaboration and avoid competition between the robot arms, the proposed approach incorporates a mechanism known as “reward cooperation, punishment competition” during the training process. Our algorithm demonstrates strong performance in the control of dual-arm robots and exhibits the potential to mitigate the challenge of reward sparsity encountered during the training process. The effectiveness of the proposed algorithm is validated through simulations and experiments, comparing the results with existing methods and showcasing its superiority in dual-arm robot path planning.
A CBAM‐GAN‐based method for super‐resolution reconstruction of remote sensing image

Abstract As satellite imagery technology advances, remote sensing plays an increasingly prominent role in modern society. Nevertheless, the limitations of existing imaging sensors and complex atmospheric conditions constrain the quality of raw remote sensing data, posing challenges for interpretation and noise reduction. Super‐resolution technology focuses on enhancing low‐quality, low‐resolution remote sensing images. In this study, we introduce a method that utilizes a high‐order degradation model to generate low‐resolution remote sensing images. We employ a Generative Adversarial Network with a Convolutional Block Attention Module (CBAM‐GAN) to enhance these images, reducing noise interference and improving texture and feature display. Our approach outperforms other methods on the UCMerced‐LandUse, WHU‐RS19, and AID datasets. Specifically, it raises SSIM index scores to 0.9443, 0.8928, and 0.8633, respectively, exceeding baselines by 1.31%, 0.19%, and 1.30%. The MOS index also improves to 3.98, 3.96, and 3.83, respectively, representing a 2.31%, 8.20%, and 2.96% gain over the baseline. Our reconstruction produces superior results, demonstrating the effectiveness of our proposed method.