Reviewerly/exHarmony huggingface dataset:
Two-Model Synergetic Learning Systems Optimization with Maxwell's Demon Technique

A two-model Synergetic Learning Systems (2MSLS) is a special case of the synergetic learning systems. In the 2MSLS, two models (subsystems) are contained: the reduction model and the evolution model, which are governed by neural partial differential equations. The information “particles” diffusion process is described by evolution model, hence the evolution model is equivalent to the generative model. And the particles condensate process is described by reduction model, which is equivalent to the inference model. In order to optimize 2MSLS, we propose a novel optimization scheme, named Maxwell's demon technique (MDT). The MDT is applied to decrease system entropy, and it is a integrated technique which includes Bayesian pseudoinverse learners, pseudoinverse learning algorithm for autoencoders, probabilistic principle component analysis. Theoretical analysis shows that our proposed 2MSLS has stronger interpretability with statistical physics image. And the MDT has a great efficiency in optimization compared with Monte Carlo Markov Chain sampling methods. This work is of significance in that it presents a clear view of interpretable deep neural networks with statistical physics perspective, and paves the road to physical artificial intelligence.
Autonomous Learning of Fuzzy Recurrent Neural Networks for Human Activity Recognition

In this article, an Autonomous Fuzzy Echo State Network (AFESN) algorithm is being proposed. This algorithm combines Autonomous Learning Multi-Model Systems and Echo State Network (ESN) to perform Human Activity Recognition on the Heterogeneity Human Activity Recognition HHAR dataset. We incorporate the structural information of the dataset into the ESN by using the Takagi-Sugeno (TS) models/rules concept. The process of obtaining the structural information in the dataset was fully automated by the unimodal density based membership function, which then acts as the antecedent part. Subsequently, a consequent part composed by ESN was used to replace the affine models commonly used in TS models. The final model predictions are inferred by the fuzzy-weighed mechanism in a new fuzzy rule composed of multiple ESNs. Since our algorithm is fully automatic and does not require any prior knowledge, its use in online learning scenarios is adequately applicable. Lastly, the effectiveness of the algorithm is tested using the HHAR dataset and an accuracy of 42.9% was obtained. Compared with the performance of recent Autonomous Learning Multi-Model Systems (19.6%), our algorithm has gained a significant improvement.
De-Biasing Methods in Neural Networks: A Survey

Bias is a common problem in both human cognition and machine learning tasks. However, machines struggle more than humans with bias reduction, mainly because most algorithms rely on the assumption that the training data represent a full population in the real world. This assumption does not hold in most cases, i.e., collecting unbiased or representative data is challenging due to various factors such as sampling errors, human prejudices, and cultural differences. Therefore, bias mitigation has become an essential task in artifical intelligence. In recent years, significant progress has been made in improving the effectivess of debiasing, especially in computer vision. This paper provides a comprehensive review of de-biasing methods for neural networks trained on image data. Firstly, we present a formal defination of the bias mitigation problem and discuss some relevant topics. Moreover, we categorize the existing methods into three main types, namely, data-level methods that aim to balance or augment the training data, model-level methods that aim to modify or regularize the learning algorithms, and adversarial and ensembling approaches that use additional models to capture the biases present in data and to guide the training of the primary model. Finally, we conclude this survey and suggest potential research directions for the future.
DEFL: A Novel Blockchain Fully-Orchestrated Federated Learning Framework

During the last few years, federated Learning (FL), an AI paradigm shift, has become an active research field, coming to allow the collaborative training of machine/deep learning models among different stakeholders without compromising privacy and security restrictions. Although researchers are attempting to support more FL-based architectures, federated learning systems (FLSs) are still facing challenges from various aspects such as privacy, consistency, and efficiency, especially in critical use cases where no stakeholder can be trusted for FL aggregation and validation. In this paper, we propose a novel Blockchain fully-orchestrated federated learning framework, we named DeFL, adapted for low or zero-trust configurations. We provide a thorough description of the proposed framework, its workflow and the different design components.
Leveraging High-Order Similar Nodes and Structural Correlation for Attention-Based Graph Embedding

Graph Attention Network (GAT) is a popular method for graph embedding as they effectively leverage attention mechanisms to aggregate first-order neighborhood node information. However, they have limitations in their ability to consider high-order neighborhood nodes and structural information. Directly incorporating high-order neighborhood node information into traditional GAT may result in over-smoothing. To address this challenge, in this paper, we propose a joint attention graph embedding model based on similar networks and structural correlation. secondly, we introduce the concept of structural correlation to the original graph attentional method, and consider not only node content feature but also joint node topological structure feature when calculating the attention score. Finally, the graph attention mechanism module performs feature extraction to generate embedding representations of the nodes. The proposed algorithm achieved an accuracy of 85.7%, 74.30%, and 84.10% on the Cora, Citeseer, and Pubmed datasets respectively in node classification tasks. This represents an improvement of 2.70%, 3.94%, and 2.60% respectively compared to the traditional graph attention model. It can be seen that the proposed method can obtain a better representation of node embedding.
Modeling and Assessment of Machine Learning Models for Solar Radiation Forecast

Solar radiation significantly impacts the energy received from the sun in a specific area, crucial for planning non-conventional renewable energy power plants like solar photovoltaic or solar thermal systems. Variability in this resource, influenced by climate and geography, poses challenges for solar integration planning. Numerical models estimate solar resource but lack realtime and future responses. Machine Learning (ML) offers heuristic predictive tools, using extensive datasets and algorithms for quantifying and forecasting solar radiation. A proposed ML model incorporates geolocation and links primary resource with climate data from diverse Colombian cities. It consists of three stages: clustering, estimation, and response, utilizing ML predictors selected by criteria and literature review. Model response is validated using statistical methods, providing accurate solar resource predictions.
Contrastive Subspace Clustering with Dissimilarity Regularization

Subspace Clustering (SC) aims at clustering the data according to its underlying subspaces. However, existing subspace clustering methods fail to make full use of available information. Inspired by advances that contrastive learning has achieved in unsupervised representation learning, we propose a method, namely contrastive subspace clustering with dissimilarity regularization (CSCDR), incorporating contrastive learning into subspace clustering. Specifically, we first construct another view of data through augmentation. Then, the deep subspace clustering networks (DSC-Nets) are used to learn the self-expressive coefficients of data and its augmentation, which further interact with each other by contrastive learning. Moreover, a dissimilarity matrix is formulated based on the latent feature of data to further calibrate the noisy connections in the self-expressive coefficient. And we also utilize the relevant similarity matrix contributing to the multi-positive contrasitve learning, which ameliorates the cluster-collapse problem of representation. Experimental results on several data sets demonstrate the efficacy of the proposed method.
Workload Placement with Bounded Slowdown in Disaggregated Datacenters

Disaggregated Data Center (DDC) is a modern datacenter architecture that decouples hardware resources from monolithic servers into pools of resources that can be dynamically composed to match diverse workload requirements. While disaggregation improves resource utilization, it could negatively impact workload slowdown due to the latency of accessing disaggregated resources over the datacenter network. To this end, we consider CPU and memory disaggregation and conduct measurements to experimentally profile several popular datacenter workloads in order to characterize the impact of disaggregation on workload execution slowdown. We then develop a workload placement algorithm, called Iterative Rounding-based Placement (IRoP), that given a set of workloads, determines where to place each workload (i.e., on which CPU) and how much local and remote memory allocate to it. The key insight in designing IRoP is that the impact of remote memory latency on slowdown can be substantially masked by assigning workloads to higher-performing CPUs, albeit at the cost of higher energy consumption. As such, IRoP aims to find a workload placement that minimizes the DDC energy consumption while respecting a bounded slowdown for each workload. We provide extensive simulation results to demonstrate the flexibility of IRoP in providing a wide range of trade-offs between energy consumption and workload slowdown. We also compare IRoP with several existing baselines. Our results indicate that IRoP can reduce energy consumption and slowdown in the considered scenarios by up to 8% and 12%, respectively.
Look at my Network: An Insight into the ISP Backbone Traffic

High-speed ISP networks provide several challenges that prevent the creation of long-term datasets for giving insight into the traffic. Currently, there are no publicly available long-term datasets capturing the entirety of high-speed ISP networks. Such networks are traditionally monitored using IP Flows, which provide enough high-level information about the situation in the network and support various use cases, such as the detection of outages or security threats. Even with this type of aggregation long-term datasets are very unpractical due to their size. The other problem is that flow monitoring comes with significant aggregation and common traffic statistics are brief and lack useful details and require further processing. This paper addresses these problems and presents a new long-term aggregated dataset, a detailed analysis of public network traffic measured on the ISP backbone, and a monitoring architecture composed of open-source tools capable of using an existing flow exporter infrastructure. Such insight into traffic helps to design and develop hardware optimizations, tuning the performance of monitoring systems, and adapting security detection algorithms.
An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance.
An End-to-End Training Method to Ensemble Bi-Linear Models for Knowledge Graph Completion

The study of ensemble learning in knowledge graph embedding (KGE) shows that combining multiple individual KGE models can perform better on knowledge graph completion. However, existing KGE ensemble methods ignore the creation of model diversity because these methods independently train individual models, which are short of training interaction. To create rich model diversity, we propose a novel training method for ensemble bilinear models (EBM) for the problem of knowledge graph completion. EBM uses a weighted loss to allow individual KGE models to interact during training. In this way, the relations in the knowledge graph can be automatically modeled by the most appropriate model from the KGE individual ones. The experiments on knowledge graph completion show that EBM has richer diversity and performs better than the single bilinear model and the ensemble methods without interaction.
Adaptive Entanglement Routing for Quantum Networks with Cutoff

Quantum networks, with applications like Quantum Key Distribution (QKD), are gaining significant attention.However, their implementation faces challenges due to low entanglement generation success rates and quantum decoherence.Recent quantum technology advancements have extended entanglement memory lifetimes to one minute, termed cutoff, opening new opportunities for entanglement routing.We propose the Adaptive Entanglement Routing (AER) algorithm, which optimizes resource utilization to improve the success probability of serving entanglement and ultimately reduce the time needed for entanglement establishment.AER includes two phases: 1) determine redundant paths based on load and 2) utilize shared entanglements for entanglement swapping.Moreover, we design the highest-success-path (HSP) algorithm to maximize the success probability of entanglement routing with limited quantum memory.These innovative routing algorithms significantly reduce entanglement request failures, resulting in up to 70% reduction in average waiting times.
Analysis and Recognition of Confidence Level Based on Eye Gaze and Head Movement Towards Human-Robot Co-Learning

Understanding internal conditions of humans, such as emotion, concentration, confidence level, and so on, is a challenging topic for human-robot interaction, even recently. In this research, we focus on estimating confidence levels from physiological sensor data, eye gaze and head movement, aiming to offer support for self-study with robots which give learners hints. Previous research recognized confidence levels after obtaining data, which was considered as “offline recognition”. To augment the method from offline to real-time recognition, this paper compared participants' physiological data before/after hearing hints. Additionally, we tried to classify their confidence levels by machine learning. Due to differences in their physiological data, distribution of gaze points when coming up answers tends to be more clustered than that of considering answers. The performance of machine learning was around 60% of accuracy in estimating confidence levels. The result suggested that we need to focus on individual differences, modify experimental designs, and improve quality of data for future works.
NIS2 Impact on Electronic Communications Networks Providers

Five years after the deadline for the transposition of the first EU-wide legislation on cybersecurity, known as Directive (EU) 2016/1148 or NIS, ended, the Directive (EU) 2022/2555, otherwise NIS2, entered into force, addressing the limitations and challenges of NIS [1], [2]. NIS2 expands the scope of the previous rules by adding eleven more sectors, introducing new cybersecurity risk and incident management requirements and strengthening the reporting requirements. In terms of categories of operators and providers, NIS2 eliminates this distinction and classifies the entities based on whether they are essential or important, which also determines their supervisory regime. Security and reporting requirements take a risk management approach with a minimum list of basic security elements that need to be applied [3].
Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-Resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning

Lately, differential privacy (DP) has been introduced in cooperative multiagent reinforcement learning (CMARL) to safe-guard the agents' privacy against adversarial inference during knowledge sharing. Nevertheless, we argue that the noise introduced by DP mechanisms may inadvertently give rise to a novel poisoning threat, specifically in the context of private knowledge sharing during CMARL, which remains unexplored in the literature. To address this shortcoming, we present an adaptive, privacy-exploiting, and evasion-resilient localized poisoning attack (PeLPA) that capitalizes on the inherent DP-noise to circumvent anomaly detection systems and hinder the optimal convergence of the CMARL model. We rigorously evaluate our proposed PeLPA attack in diverse environments, encompassing both non-adversarial and multiple-adversarial contexts. Our findings reveal that, in a medium-scale environment, the PeLPA attack with attacker ratios of 20% and 40% can lead to an increase in average steps to goal by 50.69% and 64.41%, respectively. Furthermore, under similar conditions, PeLPA can result in a 1.4x and 1.6x computational time increase in optimal reward attainment and a 1.18x and 1.38x slower convergence for attacker ratios of 20% and 40%, respectively.
Orthogonal Universal Kernel Non-Negative Matrix Factorization with Total Variation Regularization

Kernel-based non-negative matrix factorization (KNMF) is a promising data analysis method for nonlinear feature extraction. It has achieved better performance in pattern classification. However, KNMF algorithms suffer from problems including kernel function restriction, inaccurate pre-image learning, and sensitivity to noise. This paper proposes a novel framework of orthogonal universal KNMF with total variation regularization (OUKNMF-TV) to address these issues. The model contains three objective functions subject to TV and orthogonal constraints. The optimization problems of the OUKNMF-TV model are solved using the gradient descent method, and its updated rules are the same in the case of using different kernel functions. Convergence of the OUKNMF-TV algorithm is guaranteed using auxiliary function techniques. The experiments on non-noisy and contaminated datasets are implemented, and the results confirm the effectiveness and robustness of the proposed algorithm.
A Token-Prioritization Strategy for Handling Data Imbalance in Network-Change Ticket Classification

Changes are an integral part of the day-to-day operation of large telecommunications networks as they allow to keep pace with technological advancements, meet growing network demands, ensure scalability, enhance security, improve service quality, and meet customer expectations. Changing configurations, installing devices, and migrating traffic are some examples of these changes. These changes are documented by opening tickets through a ticket management system. Automation in the ticket management system is now becoming highly desirable to manage the large number of submitted tickets. An automated ticket management system supports the management of a ticket by automating several parts of a ticket's lifecycle. In this context, ticket classification problem consists in assigning an appropriate label to a ticket to be utilized in the later stages of the ticket management cycle. In this paper, we use a collection of network-change tickets from a real network operator to solve a ticket classification problem. We observe that the network-change ticket dataset is highly skewed in the number of tickets for different possible classes. We address this challenge of classification in a highly imbalanced dataset by proposing two token-prioritization strategies along with other components. We compare three variations of our proposed approach with three methods from the literature and show that the variations of the proposed approach outperform existing methods by up to 7% in terms of F1 score.
Deep Learning-based Resolution Enhancement in SAR Image for Automotive Radar Sensors

Forming high-resolution synthetic aperture radar (SAR) images requires large amounts of sampled data, which increases computation time and complexity. Therefore, in this paper, we propose a method to enhance the resolution of SAR images for automotive radar sensors using a generative adversarial network (GAN). The proposed GAN is an unsupervised image-to-image translation GAN based on a variational autoencoder and can form high-resolution SAR images from a small amount of sampled data. The SAR images formed by the proposed method are compared in terms of peak signal-to-noise ratio and structural similarity index measure for performance evaluation, and they are increased by 2.75% and 4.43%, respectively, compared to the existing low-resolution SAR images.
Enhancing Sensor-Based Human Activity Recognition using Efficient Channel Attention

Sensor-based human activity recognition (S-HAR) has found applications in various domains such as medical care, sports player tracking, and health monitoring using smart wearable devices. Among the different types of sensors used in S-HAR, inertial sensors have gained popularity. Researchers have made significant progress in S- HAR systems over the past decade by employing machine learning (ML) techniques. However, the effectiveness of ML approaches was limited because they relied on manually designed feature extraction methods. To overcome this limitation, researchers have developed deep learning (DL) methodologies that automate the feature extraction process from sensor data. This study aims to investigate the effectiveness of an attention mechanism in improving the identification capabilities of DL models. We introduced an efficient method called efficient channel attention (ECA) to enhance the performance of advanced DL models in HAR. To evaluate the progress, we utilized the UCI-HAR dataset, a publicly available benchmark dataset that records six distinct human behaviors in daily living. Five baseline DL models, namely CNN, LSTM, BiLSTM, GRU, and BiGRU, were employed. The ECA mechanism was then incorporated into these models. The experimental findings demonstrate that the proposed ECA mechanism has the potential to improve the recognition capabilities of S- HAR.