Reviewerly/exHarmony huggingface dataset:
Efficient Comprehensive Element Identification in Large Scale Spectral Analysis with Interpretable Dimension Reduction

In the broad domain of spectral analysis, the identification of present elements is a major task for qualitative analysis and a crucial preliminary for the following quantitative evaluation. Classic approaches require manual work with prior knowledge, which is time-consuming. To improve this process, neural-network-based methods have been introduced in the last decades. However, the scope of work is limited since usually only a small number of elements are covered. Compared to previous work, in this paper, we set up a new baseline by proposing a comprehensive framework capable of identifying the most common (up to 28) elements precisely and efficiently. Various neural network architectures are evaluated on large-scale simulation datasets and real measurements from the industry. Besides, to reduce the computational and data storage cost under big data industrial settings, our approach utilizes our previous work to select important features to reduce the data dimension while maintaining the prediction performance and interpretability. Compared to other dimension reduction baseline methods, our approach outperforms by achieving the best prediction accuracy and providing an intuitive data reduction result. Overall, results on real measurement data prove the feasibility of our approach as a general framework for large-scale spectral identification, and the application of the feature selection method can reduce 80% parameters and 96.9% FLOPs of CNN networks with even better test accuracy on real-world data.
Overcoming Challenges of Synthetic Data Generation

There are several shortcomings in current methods of generating synthetic data using Generative Adversarial Networks (GANs). First, they tend to only emulate certain attributes of the original dataset. Second, they do not effectively model unbalanced discrete columns, long tails, or bimodal distributions of continuous columns. Lastly, these approaches often do not consider the potential for information leakage from the generated data. We propose UniformGAN, a GAN with a novel uniform loss function, which addresses these challenges and provides strong privacy guarantees using differential privacy. UniformGAN pre-processes datasets to transpose each column into a uniform distribution. We use a modified Deep Convolutional Generative Adversarial Network (DCGAN) architecture in which we replace ReLU activation functions with the more robust SeLU, which has significantly better performance and better convergence properties, and apply Dense-Sparse-Dense training to our network. We also use differential privacy to add noise to the discriminator during training. Along with UniformGAN, we provide a configurable command-line tool to generate and evaluate synthetic datasets on numerous metrics. It allows users to generate synthetic datasets from CTGAN, TableGAN, UniformGAN, or a custom framework and analyze the resultant datasets. This tool will help data scientists and industry users compare different synthetic dataset generation models and enable them to improve existing methods. We evaluated UniformGAN using multiple datasets, including the Adult, Covertype, and Credit Kaggle datasets, as well as two insurance-related Kaggle datasets. The results show that, when used on datatsets containing a large number of continuous columns, UniformGAN out performs other methods by producing synthetic data with similar correlations and distributions as the original dataset while ensuring privacy.
Solar Irradiance Prediction Using Transformer-based Machine Learning Models

This paper presents a study of irradiance prediction using a transformer-based machine learning model for the photovoltaic (PV) renewable energy system. We explore the forecast of irradiance using ten years of data from the Texas Mesonet Data Archive at the Reese Center in Lubbock, Texas. After training our transformer model with 90% of the data, we show that it can fit the irradiance trend successfully, while the testing phase with the remaining 10% of the dataset indicates that the transformer can predict irradiance trends that align with the observed values. Additionally, we borrow the concept of rolling LSTMs to generate a rolling transformer in order to extrapolate values of irradiance even when the observed values are not available. Our extrapolation results show that the transformer can extrapolate irradiance values accurately in the short-term, but it is less precise in the long-term. To remedy this, we aim to explore more thoroughly the hyperparameter configuration of our model in order to move towards our goal of including machine learning methods in the control of PV systems.
Cyberbully Detection Using BERT with Augmented Texts

Detecting cyberbullying in texts is an essential task as it curtails and identifies s ocial p roblems. I n t his p aper, we propose an architecture called Augmented BERT which combines both data augmentation techniques and BERT for detecting cyberbullying content in texts. Many techniques have been used in prior works to augment existing data for classification tasks and BERT had been applied in many text classification problems. However, there is a lack of annotated cyberbullying texts and obtaining annotated texts is hard and expensive. We propose to use various GAN-based and autoencoder-based data augmentation techniques to generate annotated data. The augmented texts can be used to fine-tune BERT. We choose to use HateBERT which is already pre-trained on abusive language to detect cyberbullying texts. Experimental results show an increased improvement over other cyberbullying detection models.
Cyber Security of Electric Vehicle Charging Infrastructure: Open Issues and Recommendations

The paper analyses cyber security challenges of smart cities with a particular focus on the intelligent integrated and interconnected electric vehicle (EV) charging infrastructure. The analysis indicates that not all innovative elements and smart city solutions have adequate cybersecurity protection. Digital technologies vary considerably in terms of the level of potential risks, with certain novel technologies — such as V2G, smart charging, and smart energy management — posing higher risks than others. It is intended to lay a foundation for securing EV charging infrastructure by analysing problem context and data to be protected, including attack surfaces and cybersecurity threats and vulnerabilities in the EV ecosystem, analysing standardisation for the EV connection to the charging infrastructure, and providing a set of recommendations and best practices to securing EV charging infrastructure.
An AutoEncoder-based Numerical Training Data Augmentation Technique

This paper aims to automatically augment numerical tabular data by using the variational autoencoder model. For this, we try to solve the problem of class imbalance in numerical data and to improve the performance of the classification model by augmenting the training data. In this paper, we propose a new augmentation technique called ‘D-VAE’ which performs data augmentation through variational autoencoder with discretization for numerical columuns; D-VAE artificially increases the number of records and the number of columns for a given tabular data. The main features of the proposed technique are to kperform discretization and feature selection in the preprocessing process. For the discretization process, we use k-means algorithm, through which records within a given table are grouped, and then converted into one-hot vectors according to the clustering results. In addition, for memory efficiency, we reduced the number of parameters of the VAE model by using a relatively small number of features through feature selection called REFCV. To evaluate the performance of the proposed technique, we conducted various experiments by numerical data augmentation ratio using four open datasets.
Leveraging Subgraph Structure for Exploration and Analysis of Bitcoin Address

The growing acceptance and popularity of cryptocurrencies have boosted the digital financial markets, which have also increased crime risk due to their anonymity and decentralization. Appropriately monitoring decentralized cryptocurrency, particularly Bitcoin, can prevent participants from financial loss and benefit the community. Therefore, in this paper, we build the first Bitcoin address subgraph dataset called BASD-8, which contains 3,830 labeled Bitcoin address subgraphs, and we study the structural characteristics of these subgraphs, aiming at identifying eight common types of Bitcoin addresses to distinguish between normal and abnormal addresses. Three methods are utilized to exploit subgraph patterns: complex network, machine learning, and empirical analysis. Specifically, we calculate ten vital metrics of subgraphs as features to train address classifiers using basic machine learning models. Also, a graph neural network model is trained as a graph-level classifier, and the experimental results with the best f1-score of 91.35% illustrate the effectiveness of our dataset and study methods. Furthermore, we conduct a detailed empirical pattern analysis combining the subgraph structures and the definitions of each category of Bitcoin addresses.
Distributed Node Classification with Graph Attention Networks

Developing scalable machine learning solutions that meet the demand of real-world applications is crucially important in domains such as cybersecurity, smart grids, and social networks. Despite the rising interest in the analysis of graph data using big data frameworks, existing works are usually limited to the adoption of conventional machine learning models, due to their simplicity and availability as off-the-shelf algorithms in popular libraries. As a result, resorting to more sophisticated models such as deep neural networks for graph analysis in a fully distributed learning setting is still an open challenge. In this paper, we propose a distributed workflow for node classification in graphs. We focus on graph attention networks, and devise a distributed model training approach leveraging the Apache Spark, GraphX, and Horovod frameworks. Our workflow consists of feature engineering, graph partitioning, model deployment, and model training stages, which take place in a fully distributed manner. Experimental results show that leveraging graph partitioning is a feasible strategy for distributed model training on multiple workers equipped with GPUs. Specifi-cally, the randomized and informed graph partitioning strategies analyzed in our experiments present satisfactory results in terms of both accuracy and scalability with two benchmark graph datasets for node classification.
Robust Personalized Federated Learning under Demographic Fairness Heterogeneity

Personalized federated learning (PFL) gives each client in a federation the power to obtain a model tailored to their specific data distribution or task without the client forfeiting the benefits of training in a federated manner. However, the concept of demographic group fairness has not been widely studied in PFL. Further, fairness heterogeneity – when not all clients enforce the same local fairness metric – has not been studied at all. To fill this gap, we propose Fair Hypernetworks (FHN), a personalized federated learning architecture based on hypernetworks that is robust to statistical (e.g., non-IID and unbalanced data) and fairness heterogeneity. We theoretically show that granting clients the ability to independently choose multiple (possibly conflicting) fairness constraints, such as demographic parity or equalized odds, does not break previously proven generalization bounds on hypernetworks used in the federated setting. Additionally, we empirically test FHN against several baselines in multiple fair federated learning settings, and we find t hat F HN outperforms all other federated baselines when handling clients with heterogeneous fairness metrics. We further demonstrate the scalability of FHN to show that minimal degradation to the accuracy and the fairness of the clients occurs when the federation grows in size. Additionally, we empirically validate our theoretical analysis to show FHN generalizes well to new clients. To our knowledge, our FHN architecture is the first to consider tolerance to fairness heterogeneity which gives clients the freedom to personalize the fairness metric enforced during local training.
Dual Graphs of Polyhedral Decompositions for the Detection of Adversarial Attacks

Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. This paper illustrates how one can utilize the dual graph to detect and analyze adversarial attacks in the context of digital images. When an image passes through a network containing ReLU nodes, the firing or non-firing at a node can be encoded as a bit (1 for ReLU activation, 0 for ReLU non-activation). The sequence of all bit activations identifies the image with a bit vector, which identifies it with a polyhedron in the decomposition and, in turn, identifies it with a vertex in the dual graph. We identify ReLU bits that are discriminators between non-adversarial and adversarial images and examine how well collections of these discriminators can ensemble vote to build an adversarial image detector. Specifically, we examine the similarities and differences of ReLU bit vectors for adversarial images, and their non-adversarial counterparts, using a pre-trained ResNet-50 architecture. While this paper focuses on adversarial digital images, ResNet-50 architecture, and the ReLU activation function, our methods extend to other network architectures, activation functions, and types of datasets.
Power Data Blockchain Sharing Scheme based on Homomorphic Encryption

In the process of cross-agency flow of power data, only sharing the results of the operational analysis of power data rather than the source data is an effective sharing method that can not only meet the needs of data value mining, but also prevent source data leakage and protect data rights and interests. Hence, a power data blockchain sharing scheme based on homomorphic encryption was proposed, which making the data sharing rules and computing trustworthiness with smart contracts, and prevented data leakage with homomorphic encryption algorithms. Further, a power data sharing platform based on the sharing scheme was built and designed with Hyperledger Fabric as the underlying blockchain framework, so that the power data would be computed credibly in the chain code Docker container environment. Finally, our work simulated the sharing scheme with two-node deployment on one device. The response performance of the sharing operation tested by Hyperledger Caliper shows the superiority of our design.
Efficient Bayesian Network Construction for Increased Privacy on Synthetic Data

The use of synthetic data is a widely acknowledged privacy-preserving measure that reduces identity and attribute disclosure risks in micro-data. The idea is to learn the statistical properties of an original dataset, store this information in a model, and then use this model to generate artificial samples and build a synthetic dataset that resembles the original. One of the many different approaches of synthetization tools relies on describing the original dataset by using a Bayesian network. This method is implemented in the open-source tool DataSynthesizer and has proven particularly suitable for datasets with a small to moderate number of attributes. In this paper, we will substitute the greedy algorithm used for learning the Bayesian network by a substantially faster genetic algorithm. In addition, our goal is to protect particularly sensitive attributes by decreasing specific correlations in the synthetic data that may reveal personal information. We will thus show how to customize the network structures for specific machine learning tasks. Our experiments demonstrate that this technique allows to further decrease the disclosure risks and, hence, add to the applicability of synthetic data as technique for privacy preservation.
Integrated Gradients is a Nonlinear Generalization of the Industry Standard Approach to Variable Attribution for Credit Risk Models

In modern society, epistemic uncertainty limits trust in financial relationships, necessitating transparency and accountability mechanisms for both consumers and lenders. One upshot is that credit risk assessments must be explainable to the consumer. In the United States regulatory milieu, this entails both the identification of key factors in a decision and the provision of consistent actions that would improve standing. The traditionally accepted approach to explainable credit risk modeling involves generating scores with Generalized Linear Models (GLMs) - usually logistic regression, calculating the contribution of each predictor to the total points lost from the theoretical maximum, and generating reason codes based on the 4 or 5 most impactful predictors. The industry standard approach is not directly applicable to a more expressive and flexible class of nonlinear models known as neural networks. This paper demonstrates that an eXplainable AI (XAI) variable attribution technique known as Integrated Gradients (IG) is a natural generalization of the industry standard to neural networks. We also discuss the unique semantics surrounding implementation details in this nonlinear context. While the primary purpose of this paper is to introduce IG to the credit industry and argue for its establishment as an industry standard, a secondary goal is to familiarize academia with the legislative constraints – including their historical and philosophical roots – and sketch the standard approach in the credit industry since there is a dearth of literature on the topic.
Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics

Choosing a good resource configuration for big data analytics applications can be challenging, especially in cloud environments. Automated approaches are desirable as poor decisions can reduce performance and raise costs. The majority of existing automated approaches either build performance models from previous workload executions or conduct iterative resource configuration profiling until a near-optimal solution has been found. In doing so, they only obtain an implicit understanding of the underlying infrastructure, which is difficult to transfer to alternative infrastructures and, thus, profiling and modeling insights are not sustained beyond very specific situations. We present Perona, a novel approach to robust infrastructure fingerprinting for usage in the context of big data analytics. Perona employs common sets and configurations of benchmarking tools for target resources, so that resulting benchmark metrics are directly comparable and ranking is enabled. Insignificant benchmark metrics are discarded by learning a low-dimensional representation of the input metric vector, and previous benchmark executions are taken into consideration for context-awareness as well, allowing to detect resource degradation. We evaluate our approach both on data gathered from our own experiments as well as within related works for resource configuration optimization, demonstrating that Perona captures the characteristics from benchmark runs in a compact manner and produces representations that can be used directly.
Boosting Utility of Differentially Private Streaming Data Release under Temporal Correlations

Although differentially private streaming data release has been studied extensively, how to strike a good balance between privacy and utility on correlated data is still an open problem. Many existing works focus on enhancing privacy when applying differential privacy to correlated data. They show that differential privacy may suffer extra privacy leakage under correlations, and it is inevitable to resort to a small privacy budget to prevent such privacy leakage. However, there is no attempt to solve the consequential utility problem. In this work, for the first time, we propose a post-processing framework to boost the utility of differential privacy data release under temporal correlations. Specifically, we model the problem as a maximum posterior estimation given the released differentially private data and correlation model. We finally transform this problem into a nonlinear constrained programming. Our experiments demonstrate the effectiveness of the proposed approach where the utility and accuracy of differentially private data are significantly improved by nearly ten times in terms of mean square error when a strict privacy budget is given.
A Path Planning Algorithm Based on Deep Reinforcement Learning for Mobile Robots in Unknown Environment

The path planning is one of the hottest issues of the mobile robotics, which is to find an optimized path for the robot to reach the target location from a start position. In this research a Proximal Policy Optimization (PPO) algorithm, which is by far the most widely used and efficient Deep Reinforcement Learning (DRL) algorithm, is applied to plan a safe and collision-free path from the initial position to the target one for the mobile robot in an unknown environment where a global map of the environment is unavailable for the robot during the planning. The mobile robot can only obtain the local information of the environment through the Lidar installed on it. The path planning of the mobile robots is modeled as a Partially Observed Markov Decision Problem (POMDP), of which the state space is represented by the position of the target relative to the mobile robot and the distance information obtained by the 2D-lidar, the action space is represented by the position for the mobile robot to reach in the next step. The execution of motion controller is only conducted for the robot to implement the learned strategy rather than conducted in the whole learning episodes during the training phase, which resulted in a great improvement in the training efficiency. Computer simulations demonstrated the effectiveness of the proposed approach.
RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion

Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.