Reviewerly/exHarmony huggingface dataset:
Similarity-Aware Sampling for Machine Learning-Based Goal-Oriented Subgraph Extraction

In this paper, we explore and study the research problem of learning an effective algorithm to extract a goal-oriented subgraph, which finds applications in many graph mining scenarios, such as extracting dense/sparse subgraphs and forming effective therapy groups. Specifically, we study the research problem, Similarity-maximized Subgraph Extraction with Minimum Interaction, which aims at extracting a subgraph in which each node has the minimum numbers of neighbors and common neighbors while maximizing the similarity of the selected nodes. We first propose a reinforcement learning-based approach, named RLFG to effectively identify the resulting subgraphs. Then, we observe that directly applying RLFG on large graphs may incur the neighbor explosion problem, which forbids efficient and effective training of the learning model. To address this issue, we propose a sampling strategy with guaranteed performance, named Similarity-aware Subgraph Sampling (SA2S). Experimental results on multiple datasets show that combining our proposed RLFG and SA2S achieves significantly superior performance compared to other state-of-the-art baselines.
Mapping Brand Territories Using ChatGPT

Nowadays various tools, powered using artificial intelligence technologies, enable us to automate mundane daily tasks. A well-known recent example is ChatGPT, a large language model that generates seemingly coherent responses in conversations. This has paved the way for wider interaction between users and artificial intelligence technologies. On the other hand, in the field of advertising, defining brand territory plays an important role in market research and the marketing or communication strategy that a brand implements with its customers. Brand territory groups together a set of characteristics, attributes and values that help a brand to establish a personality and differentiate it from its competitors. Traditionally, mapping a brand territory is a largely manual, time-consuming process. In this work, we propose an approach to automate this process, creating a more efficient way of mapping brand territory. Our approach involves the webscraping of product reviews to obtain data set for subsequent analysis using ChatGPT. In this manner, we automatically determine customer perceptions with regards to certain dimensions. By analyzing customer reviews using this Large Language Model, we show that it is possible to get a broader view of how consumers perceive specific aspects of certain products or brands in an automated fashion.
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization

Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms.These are, in fact, desirable qualities for DO frameworks, such as federated edge learning techniques, in the presence of data and system heterogeneity, and in scenarios where internode communication is the main bottleneck.Although computationally demanding, Newton-type (NT) methods have been recently advocated as enablers of robust convergence rates in challenging DO problems where edge devices have sufficient computational power.Along these lines, in this work we propose Q-SHED, an original NT algorithm for DO featuring a novel bit-allocation scheme based on incremental Hessian eigenvectors quantization.The proposed technique is integrated with the recent SHED algorithm, from which it inherits appealing features like the small number of required Hessian computations, while being bandwidth-versatile at a bit-resolution level.Our empirical evaluation against competing approaches shows that Q-SHED can reduce by up to 60% the number of communication rounds required for convergence.
Adversarial Learning for Implicit Semantic-Aware Communications

Semantic communication is a novel communication paradigm that focuses on recognizing and delivering the desired meaning of messages to the destination users. Most existing works in this area focus on delivering explicit semantics, labels or signal features that can be directly identified from the source signals. In this paper, we consider the implicit semantic communication problem in which hidden relations and closely related semantic terms that cannot be recognized from the source signals need to also be delivered to the destination user. We develop a novel adversarial learning-based implicit semantic-aware communication (iSAC) architecture in which the source user, instead of maximizing the total amount of information transmitted to the channel, aims to help the recipient learn an inference rule that can automatically generate implicit semantics based on limited clue information. We prove that by applying iSAC, the destination user can always learn an inference rule that matches the true inference rule of the source messages. Experimental results show that the proposed iSAC can offer up to a 19.69 dB improvement over existing non-inferential communication solutions, in terms of symbol error rate at the destination user.
Enhanced Federated Learning on Non-Iid Data via Local Importance Sampling

As a privacy preserving framework, federated learning (FL) has been prevailing in numerous distributed scenarios. In this work, to enhance FL on non-iid data, we propose an explicit FL scheme with local importance sampling, Fed-Lis. Theoretically, we derive the convergence bound of Fed-Lis and obtain the design rule of the optimal sampling strategies. Experimentally, we formulate the algorithm and evaluate it on CIFAR-10. The results indicate that Fed-Lis reaps better accuracy, sampling efficiency, as well as robustness on non-iid data. Notably, as a non-iid FL solution from the local sampling aspect, Fed-Lis exhibits theoretical compatibility for non-convex loss functions. Furthermore, as a local sampling operation, Fed-Lis can be easily migrated into emerging FL frameworks.
Machine Learning in the Medical Field: A Comprehensive Overview

Machine learning utilization in medicine has increased interest over the last few years. With its impressive results in treating diseases and medical conditions, it will be important to understand and analyze how the scientific community has used it. Thus, opening up space for new research and opportunities in medicine. The objective of this study is to review the literature on machine learning applications in the medical sector. Therefore, we conducted an extensive research by reviewing recent studies and surveys on machine-learning health solutions. As a result, we offer, in this paper, a fresh study affirming the foundations and necessities of a machine learning application in the medical field. We also provide a breakdown of current research trends, which highlights future research opportunities.
Model Pruning for Efficient Over-the-Air Federated Learning in Tactical Networks

This paper considers federated learning (FL) in a tactical network (TN), by adopting computationally efficient over-the-air aggregation (OTA) to train a global model at a parameter server (PS). Memory limited edge devices ideally require to store a deep neural network (DNN) model that is compact in size but has high accuracy. This work explores the effects on the accuracy of the model due to the compression technique of pruning, as the model parameters are aggregated OTA. Further, we quantify the effects of Rayleigh fading and additive white Gaussian noise (AWGN) on the accuracy of the DNN model, at different signal-to-noise ratios (SNRs), exploring the size-accuracy-SNR trade-off for both uncompressed and pruned versions of the DNN model. Simulation results, particularly at high SNR, show little difference in accuracy among the uncompressed, 30%, and 50% pruned models, but the sizes of the pruned models are reduced roughly by 0.23x and 0.40x than that of uncompressed model's size, respectively. Pruning the model to 70% results in 0.58x reduction in size but the cost is significant lowering of accuracy, even at high SNR. Depending upon the availability of resources at edge devices, size-accuracy-SNR trade-offs can be exploited as various overlapping trends are obtained in the simulation results.
Modelling Reoccurrence of Events in an Event-Based Graph Database for Asset Management

Asset maintenance is a crucial stage in the asset life cycle to ensure that assets can operate with maximum efficiency. Managing historical information on asset maintenance is critical to predict asset condition and performance. Furthermore, any asset may undergo a maintenance event multiple times, necessitating a proper method to model the reoccurrence of an event. Event-based databases centred on graph database is a suitable approach to model assets with their maintenance history, as they are built based on property graphs, allowing flexible storing of multiple attributes. This paper proposed three temporal graph data models to address the problem of storing and modelling multiple interval timestamps for multiple events to support the reoccurrence of maintenance events in an asset management scenario. All data models are described by different relationships and are built on multigraphs grounded on graph theory to support many-to-many (m:n) relationships. Evaluation is carried out based on synthetic datasets to evaluate both approaches based on two objectives, to evaluate the query performance of both approaches, and to assess the ability to query the interval timestamp. We found that the second data model is efficient in querying small amounts of data; however, the third data model is more efficient when querying large amounts of data, a characteristic of big data. The time complexity of the query on both the second and third data models based on differing amounts of data are quadratic times O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), corresponding to the nested iteration in the query algorithm. Operations based on graph traversals on our second and third data models enable the query of interval timestamps of the events. The proposed data models provide the foundation for modelling multiple related entities for any event-based data management based on graph databases and are not limited to asset management scenarios.
A Blockchain-Based Framework for Secure Digital Asset Management

In the current age of digital world with the emergence of metaverse, digital assets are increasingly recognized and become more and more valuable. Unlike real-world assets, managing digital contents is more challenging since their associated information might be leaked widely on the Internet, making them worthless. Traditional digital asset management (DAM) systems based on third-party authorities and centralized databases have various weaknesses, threatening the benefits of stakeholders. In this paper, we propose a blockchain-based DAM framework utilizing smart contract, InterPlanetary File System (IPFS), and multi-layer encryption mechanisms for access control of digital assets in the metaverse. Our proposed design eliminates the intervention of intermediaries and offers a wide range of advanced security features such as resistance against data leakage and data alteration without trust assumptions among participants. Besides, key features of blockchain are leveraged to provide the system with immutability, traceability and transparency of information. To prove the feasibility of our design, we build a Decentralized Application (DApp) operating as a marketplace for digital assets using the proposed DAM framework. Experimental results indicate that the framework is more cost-effective than existing platforms, while advanced security features are integrated and automation is maximized.
Deep Reinforcement Learning for Resource Allocation in Blockchain-Based Federated Learning

With the development of artificial intelligence, more and more applications rely on a large amount of high-quality data. Due to data island and security concerns, most of data is scattered on various devices and difficult to obtain. Federated learning (FL) is a promising paradigm to allow distributed devices cooperating to train a shared model without sharing raw data. However, the traditional FL is easy to be attacked because of single-point failure and it cannot avoid devices uploading fake or low-quality model updates. To this end, blockchain is integrated into FL to establish a secure model training ecosystem by maintaining an immutably distributed ledger. However, different data quality of raw data, diverse energy resources of devices, and different trust degree of devices make it challenging for blockchain-enabled FL efficient and reliable. Therefore, in this paper, we design a fine-grained resource allocation scheme for blockchain-enabled FL with considering the credit of devices, data quality, and energy resources. We first propose a credit-based blockchain-enabled FL to jointly execute FL training and blockchain establishment. Then we formulate the resource allocation problem with considering credit, data quality, precision, latency, and energy resources. A deep-reinforcement learning based algorithm is designed to solve the problem, and BlockSim is used to build the blockchain-enabled FL platform. Simulation results demonstrate the effectiveness of our proposed scheme on precision, latency and energy consumption, compared with traditional blockchain-enabled FL.
Web-Based Combat Vehicle Learning System for Enhanced Education at the Armor Training Centre

This paper focus on the development and implementation of web-based learning for the Armored Crew Commander Course, specifically focusing on the technical data pertaining to armored tank vehicles at the Armor Training Academy. The development of this system is essentially due to the problem of the current learning method being in conventional ways by using a pile of module books. This course needs to be distinctively integrated from the four key sections of armored vehicles, potentially resulting in challenges when attempting to pinpoint the exact vehicle outputs during training management. To provide an accurate result, the Rule-Based method and Brute Force Pattern Matching Algorithm were used in this system to extract many data points from tank vehicles. The extra features in this system were created to meet the feature criterion in web-based learning. With the implementation of this system, it will be possible to provide effective learning management for armoured students by studying the course in a more sophisticated, flexible, and effective manner. It can also help them make the proper decisions on the individual training operation day. As a result, this approach can help armoured students discover and study all the technical data in armoured vehicles.
Dynamic Obstacle Avoidance in a Complex Environment using Meta-heuristic Algorithms for 4-wheel Robot

Dynamic obstacle avoidance is one of the most important tasks for autonomous navigation of robots. Accordingly, several algorithms have been proposed to determine the velocity at which the robot can drive while avoiding obstacles. Meta-heuristic algorithms are a class of optimization methods that are inspired by natural processes and social behavior. This paper uses meta-heuristic algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to determine the optimal velocity. The algorithm contains formulas and methods that help control the 4-wheel robot. Also, this work are experimented in a dynamic and complex virtual environment. The experimental environment is set up as dynamic 45 obstacles. The experiments are consists of about two methods and six parameter sets for considering various situation. When implementing to experiment about obstacle avoidance with proposed methods, collision counts and travel times are decreased 0.74 times and about 9.88 on average with PSO.
Densely U-Net Models for Human Embryo Segmentation

Selection of viable embryo based on the morphological appearance is important for achieving higher success rate of in vitro fertilization treatment. Conventional grading of human embryo by clinical embryologists is prone to variety of expert experiences and subjectivities. Images taken using time-lapse microscopy could be graded using artificial intelligence techniques. As background of the embryo contains many artifacts, segmenting the embryos from the background of images is a crucial step for evaluating the quality of the embryo. In this study, we performed comparative analysis of human embryo (blastocyst) image segmentation using U-Net based architecture. Two modified U-Net architectures for image segmentation were implemented. The first model obtained by replacing the encoder or contraction section of U-Net with pretrained DenseNet121 models, while keeping the original U-Net decoder. The second model is worked out by replacing all convolutional blocks in the initial U-Net model with dense blocks while preserving the model's symmetry. The experimental result on the test set proved that the proposed modified U-Net model performed better than the original U-Net model. The best result was achieved for the second model called as Densely U-Net model with a 99.8% accuracy, 96.6% Dice Coefficient, 93.5% Jaccard Index and 96.9% Precision.
Theoretical Analysis and Performance Evaluation for Federated Edge Learning with Integrated Sensing, Communication and Computation

Edge artificial intelligence (AI) provides potential solutions for ubiquitous intelligent access and services in future 6G networks, by integrating sensing, communication and computation (SCC) at network edges. On the other hand, federated edge learning (FEEL) is identified as one of the key candidates for edge AI due to its advantages on distributed learning and privacy protection. Nevertheless, SCC-assisted FEEL (SCC-FEEL) is still an open issue to meet the development of 6G networks, which is thus considered in this work. Particularly, each device derives training data samples by sensing the surrounding environment, and aggregates local training models to an edge server through an efficient over-the-air computation (AirComp) technique. Moreover, it is challenge currently to evaluate the SCC-FEEL performance theoretically and quantitatively, which is essential for efficient resource management. To address these issues, convergence analysis is theoretically conducted, which reveals that either the sensing data size or the AirComp aided communication error would influence the SCC-FEEL model training performance. Based on the convergence analysis, we derive an average training error (ATE) metric that can evaluate the SCC-FEEL performance. Numerical results validate the effectiveness of our proposed SCC-FEEL, and verify the feasibility of ATE.
Gender Recognizer Based on Human Face using CNN and Bottleneck Transformer Encoder

Several applications, such as Human-Robot interactions and offline advertising platforms, perform gender recognition based on a human face to profile their audience. These applications demand gender recognition that can operate in real-time on a low-cost or CPU device. This work proposes a gender recognizer based on a human face using a Convolutional Neural Network (CNN) and Bottleneck Transformer Encoder (BTE) that renders low parameters and operation. BTE is offered to support the primary CNN feature extractor in learning the global representation of the feature maps efficiently. This work uses three face gender datasets benchmark, namely UTKFace, Labeled Faces in the Wild (LFW), and Adience, to train and validate the proposed network. The CNN network consisted of the BTE achieves competitive accuracy compared to the state-of-the-art network. The recognizer can operate in real-time on a CPU with 150 frames per second.
Towards the Security of AI-Enabled UAV Anomaly Detection

Unmanned aerial vehicles (UAVs) are increasingly adopted to perform various military, civilian, and commercial tasks in recent years. To assure the reliability of UAVs during these tasks, anomaly detection plays an important role in today's UAV system. With the rapid development of AI hardware and algorithms, leveraging AI techniques has become a prevalent trend for UAV anomaly detection. While existing AI-enabled UAV anomaly detection schemes have been demonstrated to be promising, they also raise additional security concerns about the schemes themselves. In this paper, we perform a study to explore and analyze the potential vulnerabilities in state-of-the-art AI-enabled UAV anomaly detection designs. We first validate the existence of security vulnerability and then propose an iterative attack that can effectively exploit the vulnerability and bypass the anomaly detection. We demonstrate the effectiveness of our attack by evaluating it on a state-of-the-art UAV anomaly detection scheme, in which our attack is successfully launched without being detected. Based on the understanding obtained from our study, this paper also discusses potential defense directions to enhance the security of AI-enabled UAV anomaly detection.
Multi-Contrast MRI Image Translation via Pathology-Aware Generative Adversarial Networks

Multi-contrast magnetic resonance imaging (MRI) sequences reflect different tissue characteristics, and thus such sequences are crucial for clinical and non-clinical applications. However, the acquisition of these MRI sequences is both financially and computationally expensive. In order to alleviate these expenses, cross-modality MRI image translation schemes have been proposed to transform an MRI image of one contrast (or modality) to another. Image translation has been primarily based on deep learning architectures, especially generative adversarial networks (GAN). Most of the GAN-based methods focus on matching voxel values and anatomical structures, irrespective of whether the region is normal or abnormal. In this paper, we present a pathology-aware GAN (Pa-GAN) architecture which exploits MRI contrast images and segmentation masks for pathological areas in order to explicitly differentiate between normal and pathological tissues in the multi-contrast MRI image translation process. This framework leads to the synthesis of MRI images with better anatomical details in the normal & abnormal regions. We employed edge-based, gradient-based, and mean-absolute-error (MAE) loss functions in order to ensure the structural integrity of the synthesized MRI images. Extensive experiments were conducted on the BraTS 2015 dataset for T1-weighted (T1W)-to-T2-weighted (T2W) MRI image translation. The proposed architecture achieved promising qualitative and quantitative results with average structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics of 0.972 and 34.83 dB, respectively.
Fuzzy Logic Assisted Client Selection and Energy-Efficient Joint Optimization for Hierarchical Federated Learning

In this paper, we investigate multi-criteria client selection and energy consumption minimization for hierarchical federated learning (HFL) to deal with clients' heterogeneity and limited energy. To the best of our knowledge, this is the first work to investigate multi-criteria client selection for HFL. A fuzzy logic assisted client selection (FLACS) scheme is proposed, where multiple criteria are taken into account, including the distance, clients' battery capacity and computational resource. The FLACS scheme enables a significant performance gain in terms of the clients' average normalized suitability over the previous schemes. A joint communication and learning factors optimization (JCLFO) algorithm is proposed to minimize the system energy consumption. Thanks to the derived closed-form expressions for the optimal aggregation intervals, computation frequency and transmission power, the JCLFO algorithm can achieve the optimal performance in terms of the system energy consumption and converge within only 3 iterations, with a significant complexity reduction over exhaustive search.