Reviewerly/exHarmony huggingface dataset:
Optimization of Hierarchical Graph Layout with a Genetic Algorithm and Sprawl/Clutter Metrics

Graph layouts are useful for visualizing relationships between data entities. Nodes represent each entity, and edges represent the relationships between the nodes. Hierarchical graph visualization is known as an efficient method to represent the overview of large-scale graphs, where some clusters are generated depending on node properties and are drawn as a set of multiple nodes (meta-nodes). However, it is often difficult to determine the layouts of nodes and edges of large-scale graphs, including hierarchical graphs. Although many graph layout methods have been proposed so far, the quality of the layouts by many of such methods including force-directed layout methods strongly depends on the initial positions of the layouts. Therefore, it is often a difficult task to obtain desired layouts. This paper presents a layout optimization method for hierarchical graphs using a genetic algorithm. Specific metrics for hierarchical graph layouts are used to evaluate the generated layout. This method makes users easy to select a favorite layout from several layouts which are optimized based on the evaluation results. First, hierarchical graph layouts are generated by applying an existing method multiple times. Then, they are optimized using a genetic algorithm. Users can select their favorite layouts from several optimized layouts. Consequently, this method does not require adjusting initial positions of the layouts to obtain good layouts. The paper also introduces examples using human relationship graph datasets, including a co-authorship dataset.
Stackelberg Game-Driven Computational Offloading in Edge Computing Scenarios

With the number of users that use mobile devices for frequent transactions increasing rapidly, it is a great challenge to guarantee the credibility of transactions. Blockchain is regarded as a practical technology for such demand, however, the limited computing capacity of each user's device becomes a bottleneck. In this paper, the edge computing pattern is introduced to support complex computing for mobile devices of users by renting computing resource from computing service providers. By considering demands of both the user and the service provider, we propose a two-level game approach based on the Stackelberg Game for multiple users and multiple service providers on computing resources renting and pricing. The simulation results show that the proposed mechanism is feasible and effective.
Similarity-Based Gossip Learning for Generative Adversarial Networks

Generative Adversarial Networks (GANs) have shown remarkable results in tasks such as image generation and data augmentation, but traditional centralized training methods often require a large number of computational resources and high-speed network connections, making it difficult to apply them to large-scale and distributed scenarios. In this paper, we propose a distributed GAN training method based on Gossip Learning, which realizes decentralized communication and collaborative learning among clients. We also use the Meta-Learning framework to improve the model's generalization ability in few-shot settings. We employ similarity-based neighbor selection in the Gossip algorithm to make collaborative learning more efficient. Experiments on various datasets showed that compared to traditional centralized training methods, using Gossip Learning significantly improves the training performance of GAN models and can be scaled to large-scale distributed training.
Exploring the Design of Visualizations of Personal Online Data Based on Users' Mental Models

As data becomes more and more pervasive in our daily lives, supporting people in getting visual insights into their data is an important challenge to address. However, as data visualization literacy is still low, a gap between designers' mental model and users is not uncommon. To best pick up users where they are, we propose to include the data mental models of users into the design process. In this paper, we present our investigations in this direction by incorporating user sketches of their idea about their personal data stored at online services as a basis of our designs. We present our design study on personal data visualization interfaces resulting in a set of user sketches for three types of user groups and in three visualization interfaces. Finally, we reflect on our learnings and identify pitfalls to support other researchers in applying similar approaches.
Principal Components in General Line Coordinates for Visualization and Machine Learning

Understanding and visualizing principal components without losing multi-dimensional information has been a long-standing challenge until now. Here, we describe a novel lossless visual technique that overcomes such difficulties, General Line Coordinates-Principal Components Analysis (GLC-PCA)-an approach that provides much more information about individual cases than other methods, with improved structural readability and comprehensibility for PCA. Experimental case studies and comparisons with other works demonstrate this effect. A software system called MAVERICK implements it.
Clustering and Association Rules Based Missing Data Filling Algorithm

Filling missing data is crucial to preprocessing data. Missing data reduces the accuracy of statistics, causes statistical errors. Traditional methods for dealing with missing data include simple deletion, mean filling, or simply ignoring missing values. However, these methods have limitations in accuracy and computational feasibility. Therefore, it is essential to select a effective method to fill missing data. In this paper, we propose a method based on Fuzzy c-means clustering algorithm, Dynamic division of connected graph algorithm and Association Rules named FDAR to fill missing data. First, we use Fuzzy c-means clustering (FCM) and Dynamic division of connected graph (CABDDCG) algorithm to cluster, and then mine association rules (AR) in each part of cluster. Next, if there are association rules, the association rules are filled; Otherwise, the nearest first <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> values in the same cluster are used to fill. Experiments show FDAR algorithm in filling missing data has high accuracy on six UCI datasets.
A Novel Multi-Criteria Contribution Evaluation Scheme for Federated Learning in Internet of Vehicles

Federated Learning (FL) has shown potential for various Internet of Vehicle (IoV) applications. In FL, models are transmitted from local devices to a server for aggregation, ensuring privacy and security. However, the frequent data exchange and limited communication resources in IoV lead to network congestion and latency, limiting the number of FL participants. Additionally, due to vehicles' high-speed mobility and data heterogeneity, random participant selection may affect FL performance. To address these issues and improve the quality of participants, we propose a client selection mechanism for FL in IoV composed of two parts: the best-effort training mechanism and the multi-criteria contribution evaluation method. The best-effort mechanism enables vehicles with high dynamics to participate in FL by utilizing partial local data while satisfying time constraints. Furthermore, considering local data size, historical model performance, and time constraints, the proposed multi-criteria contribution evaluation scheme selects high-quality vehicles and improves FL performance. The simulation results demonstrate that the proposed mechanism can improve the efficiency of the FL training process and the model performance compared to existing client selection strategies under various mobility constraints. Therefore, the proposed scheme can improve the efficiency of federated learning in vehicular networks.
Dynamic Heterogeneous Network Link Prediction Based on Incremental Node Embedding

In a dynamic heterogeneous network, the number of nodes in the network will continue to increase over time, which brings great challenges to the link prediction task. To address this, we propose a dynamic heterogeneous network link prediction method based on incremental embedding named INE-DHNLP. This method divides the network into multiple subgraphs in term of the type of edges, and performs temporal random walks in different subgraphs to generate random walk sequences with temporal information. Then, the node embedding vectors are incrementally updated for nodes in each subgraph, and use attention mechanisms to aggregate node embedding vectors in different subgraphs. In order to deal with the challenge of node increment affecting link prediction, we designed the dynamically expandable decoder (DED) using incremental learning technology to predict the future link situation of the network. Experimental results on two real datasets show that this method has better comprehensive performance compared to other baseline methods.
CloudETC: a Privacy-Preserving Encrypted Traffic Classification Platform for QoS in Wi-Fi

Traffic classification (TC) is essential to many Quality of Service (QoS) provisioning frameworks. Most modern network traffic is encrypted with Transport Layer Security (TLS) protocol, and usually, TC is carried out by analysis of the TLS parameter server name indication (SNI). This parameter contains information about the host a client tries to connect to. However, the TLS workgroup has protected SNI in the TLS Encrypted Client Hello (ECH) amendment, and only complex and continually updated TC algorithms can achieve sufficient classification accuracy in scenarios with ECH. Yet, many home and office Wi-Fi access points (APs) are short on computational resources, and the owners rarely upgrade their software. This demo addresses this challenge with the Traffic-Classification-as-a-Service concept. The idea is to send several first packets of the flows to a cloud server that classifies them and suggests an appropriate QoS policy to the AP. Notably, we preserve privacy by sending only the first packets that do not contain users' payload. The demo confirms that this concept may double the bitrate for YouTube traffic in a highly loaded network.
Remote Controlling Mobile Robots with Adaptive Video Streaming and Augmented Reality

New generations of mobile robots enable to access challenging and dangerous environments such as oil platforms. To remotely control the mobile robot, the camera sensor data needs to be transmitted reliably and presented in an user- friendly way to the operator. Augmented Reality (AR) technology enables to show the camera data in a convenient way to the operator. Since the network and computing capabilities on the mobile robot are limited, the transmission of all camera streams can lead to a low frame rate. The lower frame rate results in a degraded user experience and can lead to accidents. We present a system, which adapts the request of camera images per stream to the field of view of the user. Additionally, the system adapts dynamically to changing network connection qualities. We show that with our adaptive system the Quality of Experience (QoE) of the remote operator of the robot increases noticeably, resulting in a user-friendly and efficient control of the mobile robot.
Work-in-Progress: An Empirical Study of the Joint Effects of WiFi Coverage and Channel Congestion on Quality of Experience in 5 GHz Band

Ever-increasing multimedia application usage brings stricter network requirements every day for wireless networks. WiFi is the commonly used wireless technology for in-home and public networks. Existing literature focuses mainly on video streaming QoE over WiFi against channel congestion. None of the studies investigates the joint effects of coverage and congestion issues on the QoE of different multimedia applications with different network requirements and adoption rates. In this study, first time in the literature, QoE of video conferencing, YouTube video streaming, cloud gaming, and VR streaming applications are investigated under different congestion and coverage scenarios in the 5 GHz WiFi band, taking into account the different adoption rates of the applications. The results show that high congestion can prevent a single user from streaming a 4K video, even when the user is in a medium coverage region. On the other hand, the QoE of cloud gaming, and especially VR streaming, are more to the congestion and coverage issues. For better user experience in multimedia applications with more demands, e.g., VR streaming, the new 6 GHz WiFi band could be a solution in near future.
Work-in-Progress: Security of Public-Key Schemes in the Quantum Computing Era – A Literature Review

Quantum computers, although still in the theoretical stage, conceptually have the power to break many current cryptosystems, including public-key schemes., for example, RSA (Rivest, Shamir., Adleman), elliptic-curve cryptography (ECC), and ElGamal. NIST specialists and national governments believe that existing cryptography algorithms will become vulnerable to brute force analysis by the end of the decade and agree that measures should be taken before then to ensure the secrecy of current and future algorithms. In this context, information security, including confidentiality and integrity, must be considered. This paper provides a systematic literature review on whether existing public-key schemes have the necessary complexity and solution space to resist a quantum computer. The discussion will focus on a quantum computer's negative “real-world” impact and examine existing post-quantum algorithms, then provide analysis across two selected dimensions. Finally, we summarise the latest trends and gaps in post-quantum cryptography and provide the security analysis of NIST's last round finalists.
Ensemble Learning to Enhance Continuous User Authentication For Real World Environments

Modern digital applications/systems need robust cybersecurity solutions. Traditional authentication methods like passwords, fingerprints, authorization cards, etc. authenticate the user at the beginning of the session but there is no validation during the session, which makes the system vulnerable. Continuous authentication is the solution to this challenge. In continuous authentication, keystroke data is used to extract the behavior patterns of the user. The data are then applied to train the machine learning (ML) classification algorithms to identify the unique behavioral patterns of each user and classify them accordingly. Thus, the performance of the ML classification algorithm is key in continuous user authentication, and it requires diverse and comprehensive data to be effective in the production environment. In many cases, the ML algorithm is trained on the datasets collected in a controlled lab environment and the model fails or does not perform as expected in the production environment. For example, China's facial recognition system recognized the face on a bus ad as a jaywalker because the model was not trained on real-world data. To overcome this problem, this study uses the real-world data of 48 of a financial organization's employees to compare the prediction accuracy and prediction delay (the time required to make predictions) of advanced ML algorithms, including Light GBM, XGboost, TabNet, Neural Network, and 1D CNN. Among all the individual models, LightGBM performed best with an accuracy of 23.58% and a delay of 34.4 sec. However, some ML models were better at predicting particular sets of users than others, hence ensemble learning was used to combine the prediction ability of all the models, which increased cumulative accuracy to 24.03% with a delay of 43.51sec. These results suggest that the boosting algorithm is effective at classifying users. Additionally, the prediction performance can be improved using ensemble learning techniques. Moreover, high-end infrastructure should be used to reduce the prediction delay of ML algorithms.
An End-to-End Auto-Encoder Algorithm for Hardware-Impaired Transceivers Based on Meta and Joint Learning

The application of Deep learning (DL) in wireless communications has achieved remarkable success. However, there are still marked challenges impeding its use in the physical layer, largely due to the random nature of the wireless channel. This work investigates an end-to-end transceiver based on an unsupervised auto-encoder. We aim to detect a wireless signal suffering from transceiver hardware impairments as it passes through the fading channel. Furthermore, we examine the ability of this receiver to detect signals corrupted with non-Gaussian noise. We use meta and joint learning techniques to train the auto-encoder transceiver with random unknown channels and HWIs to simulate real channel conditions. We compare the results with maximum likelihood detection (MLD) in terms of bit error rate (BER). Our results demonstrate that using meta-learning to train the auto-encoder enhances system performance by reducing the BER of the received signal.
Feature Set Optimization for Machine Learning Traffic Classification in Mobile Networks

The development of new technologies leads to creation of new types of mobile traffic as well as to a significant increase of traffic volume for the existing traffic types. Existing methods of traffic processing (classification, prioritization and routing) may not adapt to such changes quickly, to address new speed and accuracy requirements. This may lead to a deterioration in the quality of service. In this paper the efficiency of machine learning-based algorithms for traffic classification and prioritization for 4G/5G traffic is researched. The best algorithms for this case are RF and ANN with accuracy 0.99. The main contribution of the research is significant reduction in the number of features used to classify traffic, from 54 to 18, which gives a significant traffic classification speed improvement, at cost of accuracy degradation only by 0.03 for the test dataset.
Federated Learning for Pedestrian Detection in Vehicular Networks

Vehicular connectivity is foreseen to increase road safety by enabling connected vehicle applications. On the other hand, machine learning (ML) methods are provisioned to increase road safety by supporting object detection and assisted driving. Recently, distributed ML methods, which rely on data transmission between a parameter server and vehicular edge devices, are introduced to develop intelligent transportation systems. In this paper, we investigate the feasibility of the usage of a distributed ML algorithm, federated learning (FL), to detect pedestrians by using vehicular networks. We first provide a comprehensive overview of the proposed scheme, then highlight the methodology to enable FL-based pedestrian detection from the images obtained by vehicle cameras. We further present experimental validation results for communication resource utilization, and pedestrian detection accuracy by using convolutional neural networks (CNNs) and deep neural networks (DNNs) layers in our model architecture for an FL scheme. We obtain 90% pedestrian detection accuracy with our FL scheme.
Privacy-Preserving User Abnormal Behavior Detection in 5G Networks

Network Data Analytics Function (NWDAF) plays a key role in autonomous network management and security using machine learning (ML) techniques. However, not all ML methods such as convolutional neural networks, deep language models can be deployed in the core network due to their high computational requirements. Besides, telcos may prefer handing over ML analytics to experienced third parties instead of implementing themselves. In this paper, we show that delay-tolerant tasks of NWDAF can be offloaded to public third party cloud providers while preserving user and data privacy by employing homomorphic encryption (HE). We focus on the problem of abnormal behaviour detection for a group of user equipment (UE) and train various DL models with plaintext data. We demonstrate that inferencing on encrypted data is as accurate and precise as plaintext inferencing and we quantify the performance degradation in terms of running times. We believe that offloading NWDAF tasks to the cloud allows telcos to focus on expanding their core capabilities, and reduce capital and operational expenditures.
Coding for the Gaussian Channel in the Finite Blocklength Regime Using a CNN-Autoencoder

The development of delay-sensitive applications that require ultra high reliability created an additional challenge for wireless networks. This led to Ultra-Reliable Low-Latency Communications, as a use case that 5G and beyond 5G systems must support. However, supporting low latency communications requires the use of short codes, while attaining vanishing frame error probability (FEP) requires long codes. Thus, developing codes for the finite blocklength regime (FBR) achieving certain reliability requirements is necessary. This paper investigates the potential of Convolutional Neural Networks autoencoders (CNN-AE) in approaching the theoretical maximum achievable rate over a Gaussian channel for a range of signal-to-noise ratios at a fixed blocklength and target FEP, which is a different perspective compared to existing works that explore the use of CNNs from bit-error and symbol-error rate perspectives. We explain the studied CNN-AE architecture, evaluate it numerically, and compare it to the theoretical maximum achievable rate and the achievable rates of polar coded quadrature amplitude modulation (QAM), Reed-Muller coded QAM, multilevel polar coded modulation, and a TurboAE-MOD scheme from the literature. Numerical results show that the CNN-AE outperforms these benchmark schemes and approaches the theoretical maximum rate, demonstrating the capability of CNN-AEs in learning good codes for delay-constrained applications.
Adaptive Technique of Impulsive Noise Removal in Color Digital Images

This paper describes a new technique for removing impulsive noise in digital color images. The proposed method uses a measure of the pixel distortion caused by the impulsive noise, expressed as the sum of the Euclidean distances between the central pixel and its closest neighbors in the filtering window. This corruption measure is used to determine a weighted average of the pixels from the local neighborhood that is not affected by the distorted pixels. The filtering output is obtained using a soft switching scheme, where the denoising result is determined as a weighted average of the central pixel of a processing window and its robust estimate. The weighting coefficient is calculated based on the amount of distortion of the central pixel. Thus, if the central pixel is highly distorted, it is replaced by its robust estimate, otherwise it is not significantly changed. The parameters that determine the image enhancement result are adaptively adjusted by using a noise-resistant image contrast measure. The proposed filter design is able to effectively suppress impulsive noise while preserving fine image details and edges. Comparison with existing algorithms shows that the proposed method provides better denoising results than the most efficient filters designed for impulsive noise suppression in color images. Moreover, the new method is very fast and can be used in real-time denoising scenarios.