Reviewerly/exHarmony huggingface dataset:
Synchronization of multiple mobile reservoir computing oscillators in complex networks

We investigate collective behavior of multiply moving reservoir computing oscillators. Each moving oscillator is fitted to a same dynamical system and then wanders a network navigating by random walk strategy. Interestingly, we find that the moving oscillators gradually present a coherent rhythmic behavior when their number is large enough. In this situation, temporal and spatial characteristics of each moving oscillator are in excellent agreement with that of their learned dynamical system under consideration. Remarkably, we show that these reservoir computing oscillators can exhibit significantly distinct collective behaviors that resemble bifurcation phenomenon when changing a critical reservoir parameter. Furthermore, we find that when studying a continuous chaotic system, intermittent synchronization emerges among these reservoir computing oscillators. But there is no evidence of enhanced correlation in the associated laminar length sequence. Our work provides a feasible framework for studying synchronization phenomena of mobile agents in nature when merely observed information is available.
Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets

Deep learning (DL) has achieved remarkable successes in many disciplines such as computer vision and natural language processing due to the availability of "big data". However, such success cannot be easily replicated in many nuclear engineering problems because of the limited amount of training data, especially when the data comes from high-cost experiments. To overcome such a data scarcity issue, this paper explores the applications of deep generative models (DGMs) that have been widely used for image data generation to scientific data augmentation. DGMs, such as generative adversarial networks (GANs), normalizing flows (NFs), variational autoencoders (VAEs), and conditional VAEs (CVAEs), can be trained to learn the underlying probabilistic distribution of the training dataset. Once trained, they can be used to generate synthetic data that are similar to the training data and significantly expand the dataset size. By employing DGMs to augment TRACE simulated data of the steady-state void fractions based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark, this study demonstrates that VAEs, CVAEs, and GANs have comparable generative performance with similar errors in the synthetic data, with CVAEs achieving the smallest errors. The findings shows that DGMs have a great potential to augment scientific data in nuclear engineering, which proves effective for expanding the training dataset and enabling other DL models to be trained more accurately.
The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm

To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible.
Improving metric-based few-shot learning with dynamically scaled softmax loss

The metric-based learning framework has been widely used in data-scarce few-shot visual classification. However, the current loss function limits the effectiveness of metric learning. One issue is that the nearest neighbor classification technique used greatly narrows the value range of similarity between the query and class prototypes, which limits the guiding ability of the loss function. The other issue is that the episode-based training setting randomizes the class combination in each iteration, which reduces the perception of the traditional softmax losses for effective learning from episodes with various data distributions.To solve these problems, we first review some variants of the softmax loss from a unified perspective, and then propose a novel Dynamically-Scaled Softmax Loss (DSSL). By adding a probability regulator (for scaling probabilities) and a loss regulator (for scaling losses), the loss function can adaptively adjust the prediction distribution and the training weights of the samples, which forces the model to focus on more informative samples. Finally, we found the proposed DSSL strategy for few-shot classifiers can achieve competitive results on four generic benchmarks and a fine-grained benchmark, demonstrating the effectiveness of improving the distinguishability (for base classes) and generalizability (for novel classes) of the learned feature space.
Enhancing Prediction Models' Performance for Breast Cancer using SMOTE Technique

Breast cancer (BC) is a critical public health concern, and the development of accurate prediction models is crucial for early detection. However, predicting BC using imbalanced datasets poses challenges for achieving accurate predictions. This study aims to enhance the performance of BC prediction models by employing the Synthetic Minority Over-sampling Technique (SMOTE) to address the imbalance in the target class of the dataset. Two approaches are employed to evaluate the models: the first approach utilizes the original Breast Cancer Coimbra Dataset (BCCD), while the second approach utilizes SMOTE to balance the target class in the BCCD. The results of the performance comparison between the two approaches demonstrate that the utilization of SMOTE significantly improves the performance of the BC prediction models. For instance, the Fine Tree, Coarse Tree, and Medium Tree models achieved accuracy rates of 60.9%, 52.2%, and 60.9%, respectively, with the SMOTE implementation. The Quadratic SVM and Cubic SVM models achieved accuracy rates of 73.9% with SMOTE. The Fine Gaussian SVM model achieved accuracy rates of 65.2 % and 80% without and with SMOTE, respectively. Similarly, the Coarse Gaussian SVM model achieved accuracy rates of 52.2% and 60% without and with SMOTE, respectively. The Medium KNN and Weighted KNN models both achieved accuracy rates of 73.9% without SMOTE and 76% with SMOTE. Furthermore, Bagged Trees achieved accuracy rates of 69.6% without SMOTE and 80% with SMOTE, while Subspace Discriminant achieved accuracy rates of 73.9% without SMOTE and 80% with SMOTE. The Optimized LogitBoost model achieved accuracy rates of 73.9% without SMOTE and 88% with SMOTE, and AdaBoost using Bayesian Optimization achieved accuracy rates of 52.2% without SMOTE and 76% with SMOTE. This study demonstrated that implementing SMOTE to balance the dataset leads to improved accuracy in BC prediction models.
Building GPU TEEs using CPU Secure Enclaves with GEVisor

Trusted execution environments (TEEs) have been proposed to protect GPU computation for machine learning applications operating on sensitive data. However, existing GPU TEE solutions either require CPU and/or GPU hardware modification to realize TEEs for GPUs, which prevents current systems from adopting them, or rely on untrusted system software such as GPU device drivers. In this paper, we propose using CPU secure enclaves, e.g., Intel SGX, to build GPU TEEs without modifications to existing hardware. To tackle the fundamental limitations of these enclaves, such as no support for I/O operations, we design and develop GEVisor, a formally verified security reference monitor software to enable a trusted I/O path between enclaves and GPU without trusting the GPU device driver. GEVisor operates in the Virtual Machine Extension (VMX) root mode, monitors the host system software to prevent unauthorized access to the GPU code and data outside the enclave, and isolates the enclave GPU context from other contexts during GPU computation. We implement and evaluate GEVisor on a commodity machine with an Intel SGX CPU and an NVIDIA Pascal GPU. Our experimental results show that our approach maintains an average overhead of 13.1% for deep learning and 18% for GPU benchmarks compared to native GPU computation while providing GPU TEEs for existing CPU and GPU hardware.
Recognizing Geochemical Anomalies Associated with Mineral Resources Using Singularity Analysis and Random Forest Models in the Torud-Chahshirin Belt, Northeast Iran

Identifying the local geochemical anomalies from stream sediment samples is challenging in regional-scale exploration programs. For this purpose, some robust and reliable techniques must be applied to distinguish the geochemical targets from the background values. In this research, a procedure of several tools, including singularity mapping (SM), random forests (RF), success-rate curves, and the t-Student method, were employed to analyze the geochemical anomalies within the intrusive-plutonic Torud-Chahshirin belt (TCB), northeast Iran. In this regard, the success-rate curves were initially applied to extract efficient geochemical signatures. Then, singularity analysis was used on the selected geochemical elements (Au, Cu, Pb, and Zn), which were transformed via centered log-ratio (clr) transformation. In the next step, due to the complexity of the ore-forming processes in the TCB, the structural factors (e.g., fault intersection and faults with different orientations) were determined. Based on the success-rate curves, NE-trending faults and fault density were distinguished as critical structural criteria. Afterward, the RF model as a robust machine learning algorithm was executed on the four efficient SM-based geochemical layers and two efficient structural factors. The anomaly map derived by the RF model (Accuracy = 98.85% and Error = 1.15%) illustrates a very high relationship with Cu ± Au mineral occurrences. Therefore, the RF algorithm assisted by the singularity method is more trustworthy for highlighting the weak geochemical prospectivity areas in the TCB.
Multimodal sentiment analysis: A survey

Multimodal sentiment analysis has emerged as a prominent research field within artificial intelligence, benefiting immensely from recent advancements in deep learning. This technology has unlocked unprecedented possibilities for application and research, rendering it a highly sought-after area of study. In this review, we aim to present a comprehensive overview of multimodal sentiment analysis by delving into its definition, historical context, and evolutionary trajectory. Furthermore, we explore recent datasets and state-of-the-art models, with a particular focus on the challenges encountered and the future prospects that lie ahead. By offering constructive suggestions for promising research directions and the development of more effective multimodal sentiment analysis models, this review intends to provide valuable guidance to researchers in this dynamic field.
EPASAD: ellipsoid decision boundary based Process-Aware Stealthy Attack Detector

Abstract Due to the importance of Critical Infrastructure (CI) in a nation’s economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems such as power grids, water, and sewage treatment facilities, oil and gas pipelines, etc. In recent times, these systems have suffered from cyber attacks numerous times. Researchers have been developing cyber security solutions for CIs to avoid lasting damages. According to standard frameworks, cyber security based on identification, protection, detection, response, and recovery are at the core of these research. Detection of an ongoing attack that escapes standard protection such as firewall, anti-virus, and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the system. Therefore, anomaly detection in physical dynamics proves an effective means to implement defense-in-depth. PASAD is one example of anomaly detection in the sensor/actuator data, representing such systems’ physical dynamics. We present EPASAD, which improves the detection technique used in PASAD to detect these micro-stealthy attacks, as our experiments show that PASAD’s spherical boundary-based detection fails to detect. Our method EPASAD overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries in various dimensions, whereas a spherical boundary treats all dimensions equally. We validate EPASAD using the dataset produced by the TE-process simulator and the C-town datasets. The results show that EPASAD improves PASAD’s average recall by 5.8% and 9.5% for the two datasets, respectively.
A Framework for Standardization of Distributed Ledger Technologies for Interoperable Data Integration and Alignment in Sustainable Smart Cities

Abstract Distributed ledger technologies (DLTs) are considered one of the foremost emerging technologies which can contribute to transform cities to smarter cities. DLT play important role in municipalities to accelerate the digitalization process toward changing the roles and services of enterprises in sustainable smart cities. Standardization of DLTs aids to reduce data and digital assets silos while decreasing vendor lock-in across distributed applications enabling a digital urban ecosystem that supports migration capabilities making it possible for cities to seamlessly achieve interoperability among DLTs and centralized digital platforms, although a few standards such as IEEE 2418, IEEE P2418.5, and ISO/TC 307 have been developed. The alignment and integration mechanisms required to support standardization of DLT for interoperable services in smart cities is lacking. Therefore, this study presents an understanding on current and open issues on standardization of DLTs in sustainable smart cities with a specific focus on data integration and alignment efforts related to interoperable DLTs. A framework is developed to promote standardization of DLTs to support integration and alignment for interoperability in smart cities. Design science research methodology was adopted based on three use case scenarios which illustrates how IOTA tangle is employs as a DLT for secured standardized communication between physical sensors, devices, and digital platforms in smart city environment. Findings from this article provide exploratory evidence demonstrating the potential uses of IOTA tangle through the developed framework applied for decentralized and centralized digital services. Based on this evidence, this study provides interface integration and alignment strategies to better exploit distributed applications full potential by improving DLT standardization in urban environment.
A Safe Genetic Algorithm Approach for Energy Efficient Federated Learning in Wireless Communication Networks

Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy.Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified.Towards mitigating the carbon footprint of FL, the current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization, by orchestrating the computational and communication resources of the involved devices, while guaranteeing a certain FL model performance target.A penalty function is introduced in the offline phase of the GA that penalizes the strategies that violate the constraints of the environment, ensuring a safe GA process.Evaluation results show the effectiveness of the proposed scheme compared to two stateof-the-art baseline solutions, achieving a decrease of up to 83% in the total energy consumption.
An Empirical Evaluation of Cyber Threat Intelligence Sharing in the ECHO Early Warning System

This paper reports on the information sharing practices of cyber competency centres representing different sectors and constituencies. The cyber competency centres participated in the form of CSIRTs employed the ECHO Early Warning System. Through a structured tabletop exercise, over 10 CSIRTS were engaged and a number of features were captured and monitored. A key research question was to determine the factors that can potentially hinder or amplify Cyber Threat Intelligence information sharing. The exercise imitated real attack scenarios using state-of-the-art tactics techniques and procedures as observed by real-world APT groups and daily incidents. The findings revealed differences in terms of timeliness, response time and handling tickets with different Traffic Light Protocol classifications, duration of handling a ticket and intention to disclose.
Ship detection based on YOLO algorithm for visible images

Abstract Ship detection is a crucial task for waterway surveillance and channel optimization, especially in close proximity to the shore. However, detecting ship in visible image‐based detection remains a challenge due to the limited nature of visible image datasets. To address this issue, the Inland Ships Data Set (ISDS) is constructed to facilitate research on ship identification. On the other hand, most detection methods struggle to accurately identify ships that are small in size. Therefore, a visible image‐based ship detection model is proposed that employs a multi‐scale weighted feature fusion structure with the YOLOv4 detection model to improve the efficacy of small ship detection. Specifically, the YOLOv4 model is improved through fusing multi‐scale feature, redesigning priori frame, and enhancing loss function. The model, named YOLOv4‐MSW (i.e. YOLOv4 based on Multi‐Scale Weighted feature fusion), exhibits improved performance on ship detection in experiments conducted on the ISDS dataset, outperforming the original YOLOv4 model by improving the average precision (AP) by 4.87% and the recall rate by 10.03%. Meanwhile, the model achieve better detection accuracy and improve the average precision rate by at least 0.86% compared to existing learned object detection methods. The code related to this work are released at https://github.com/Sunhuashan/YOLOv4‐MSW . The whole dataset is available at https://drive.google.com/drive/folders/1fzJ2fcqiko6lFwqIEGghMceoQgv‐8jBy .
Digital Sovereignty, Digital Expansionism, and the Prospects for Global AI Governance

In recent years, policymakers, academics, and practitioners have increasingly called for the development of global governance mechanisms for artificial intelligence (AI). This paper considers the prospects for these calls in light of two other geopolitical trends: digital sovereignty and digital expansionism. While calls for global AI governance promote the surrender of some state sovereignty over AI, digital sovereignty and expansionism seek to secure greater state control over digital technologies. To demystify the tensions between these trends and their potential consequences, we undertake a case analysis of digital sovereignty and digital expansionism in China, the European Union, and the United States. We argue that the extraterritoriality embedded in these three actors' policies and escalatory competitive narratives, particularly those from the US, will likely undermine substantive global AI governance cooperation. However, nascent areas of alignment or compromise, notably in data governance and technical standards, could prove fruitful starting points for building trust in multilateral fora, such as the G20 or United Nations.
Fraudulent Financial Transactions Detection Using Machine Learning

there is a critical need to identify transactional risks in financial institutions for the purpose of improving customer experience and minimizing financial loss. This current study compares various machine learning (ML) algorithms with the aim of predicting legitimate financial transactions in an effective and efficient manner. Those algorithms include variety of machine learning models that covers all types of ML. The dataset was gathered from Kaggle website for that datasets. It consists of 6,362,620 records and 10 features. The top ML model was Random Forest Classifier using the unbalanced dataset attained an accuracy (99.97 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), precession (99.96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), recall (99.97 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and F1-score (99.96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). Meanwhile, the Bagging Classifier is the best classifier with balanced dataset with an accuracy (99.96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), F1-score (99.96 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), precession (99.95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and recall (99.98 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).
Stealthy dynamic backdoor attack against neural networks for image classification

Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, which are external entity model providers that can inject backdoors into the network for illegal purposes. Current attack methods rely on manipulated images with embedded triggers to infiltrate carrier networks, but they suffer from detectable distortions and limited integration into neural networks. To delve further into the potential vulnerabilities of these models, this study introduces an innovative backdoor attack strategy, leveraging deep learning steganography through a Generative Adversarial Network (GAN). Our approach utilizes steganography to create manipulated images, capitalizing on the unique sensitivity of neural networks to minute perturbations. The network is then trained de novo with these manipulated images, creating a backdoor-infused model. Fundamentally, our method harnesses the pronounced sensitivity of DNNs to nuanced alterations. Experimental outcomes substantiate that our backdoor can be effectively integrated into the models, yielding high attack success rates. Notably, it also adeptly circumvents both contemporary state-of-the-art defense mechanisms and human inspection. Our source code is publicly accessible at https://github.com/DLAIResearch/NNSDB.