Reviewerly/exHarmony huggingface dataset:
Cascading Fault Early Warning and Location Method of Transmission Networks Based on Wide Area Time-Series Power System State

With the promotion of energy security strategy and the access of the high penetration of renewable energy, the related methods of situational awareness in the traditional transmission network may be inapplicable for modern power grids. For the early warning and location of the cascading faults caused by power flow shift, a novel method for security situation prediction in the transmission network is put forward based on Long Short Term Memory. Firstly, the power flow model is used to construct the early warning framework of transmission network security situation based on LSTM, and ADASYN oversampling is used for sample equilibrium. Secondly, the validity of the proposed method is verified by the open-source platform “Grid2Op” in the modified IEEE 36-bus system. Results indicate that the early warning accuracy for cascading failure can achieve around 90% with prediction time less than 1 second. Various simulation results demonstrate that the proposed method can improve the accuracy of security situation warning, shorten the time for identifying abnormality of the transmission network and provide reliable support for the operation and maintenance of the transmission network.
Patient-Friendly Medical Data Security Sharing Scheme Based on Blockchain and Proxy Re-encryption

In recent years, with the wide application of blockchain technology, blockchain-based medical data sharing has become a major hot spot in the current research on medical data sharing. Using the decentralized, tamper-proof and traceable features of blockchain, data sharing of electronic medical data in various medical institutions has been realized. With the help of privacy protection technology, the security and privacy of medical data sharing links have been improved. However, there are still problems. For example, patients cannot manage their medical data independently and cannot guarantee the confidentiality of their private keys. And the problems arising from the actual situation of patients are often ignored. In this paper, we propose a secure medical data sharing scheme based on blockchain and proxy re-encryption. The scheme enables patients to manage their own medical data directly and uses improved distributed key management technology to ensure the confidentiality of patients' private key. In addition, we use a combination of on-chain and off-chain for medical data storage to ensure high efficiency and the independence of hospital medical data. We also design a blockchain-based off-chain database security verification algorithm.
Visualizing Rule-based Classifiers for Clinical Risk Prognosis

Deteriorating conditions in hospital patients are a major factor in clinical patient mortality. Currently, timely detection is based on clinical experience, expertise, and attention. However, healthcare trends towards larger patient cohorts, more data, and the desire for better and more personalized care are pushing the existing, simple scoring systems to their limits. Data-driven approaches can extract decision rules from available medical coding data, which offer good interpretability and thus are key for successful adoption in practice. Before deployment, models need to be scrutinized by domain experts to identify errors and check them against existing medical knowledge. We propose a visual analytics system to support health-care professionals in inspecting and enhancing rule-based classifier through identification of similarities and contradictions, as well as modification of rules. This work was developed iteratively in close collaboration with medical professionals. We discuss how our tool supports the inspection and assessment of rule-based classifiers in the clinical coding domain and propose possible extensions.
Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System

Achieving at least some level of explainability requires complex analyses for many machine learning systems, such as common black-box models. We recently proposed a new rule-based learning system, SupRB, to construct compact, interpretable and transparent models by utilizing separate optimizers for the model selection tasks concerning rule discovery and rule set composition. This allows users to specifically tailor their model structure to fulfil use-case specific explainability requirements. From an optimization perspective, this allows us to define clearer goals and we find that—in contrast to many state of the art systems—this allows us to keep rule fitnesses independent. In this paper we investigate this system’s performance thoroughly on a set of regression problems and compare it against XCSF, a prominent rule-based learning system. We find the overall results of SupRB’s evaluation comparable to XCSF’s while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control can aid in subsequently providing explanations for both training and final structure of the model.
AutoML With Parallel Genetic Algorithm for Fast Hyperparameters Optimization in Efficient IoT Time Series Prediction

With the development of artificial intelligence and the improvement of hardware computing power, deep learning models have become widely used in the Internet of Things (IoT) field, especially for analyzing spatiotemporal data collected by wireless sensors. Recurrent neural networks (RNNs) such as long short-term memory (LSTM) network are generally used for these time-series data. Hyperparameter settings of model training are regarded as essential factors for the performance of deep learning models. Manually optimizing hyperparameters not only cost more resources but also be more likely to set hyperparameters that follow stereotypes, resulting in unreasonable hyperparameter settings and poor model performance. As one of the most important fields in automated machine learning research, automated hyperparameter optimization (HPO) mainly includes grid search, hyperparameter search based on genetic algorithm, etc. Whereas these methods have their own drawbacks. In this article, an automated HPO method based on parallel genetic algorithm (PGA) is proposed. According to the process of PGA, this article divided HPO into several stages, including population initialization, fitness function, tournament selection, crossover operators, mutation operators, subgroup exchange, and end of evolution. Then, the proposed HPO method is implemented in LSTM models and tested on two different time-series datasets collected by real-world IoT sensors. By comparing our proposed method with other mainstream HPO methods in different datasets, it is proved that our HPO method based on PGA shows a better performance on both time costs and prediction results.
Fine-Tuning Language Models to Mitigate Gender Bias in Sentence Encoders

Language models are used for a variety of downstream applications, such as improving web search results or parsing CVs to identify the best candidate for a job position. At the same time, concern is growing around word and sentence embeddings, popular language models that have been shown to exhibit large amount of social bias. In this work, by leveraging the possibility to further train state-of-the-art pre-trained embedding models, we propose to mitigate gender bias by fine-tuning sentence encoders on a semantic similarity task built around gender stereotype sentences and corresponding gender-swapped anti-stereotypes, in order to enforce similarity between the two categories. We test our intuition on two popular language models, BERT-Base and DistilBERT, and measure the amount of gender bias mitigation using the Sentence Encoder Association Test (SEAT). Our solution shows promising results despite using a small amount of training data, proving that post-processing bias mitigation techniques based on fine-tuning can effectively reduce gender bias in sentence encoders.
Characterizing Coding Style of Phishing Websites Using Machine Learning Techniques

Social engineering attacks pose a major threat to an internet user’s sensitive information, such as credit card information and passwords. One of the most common of these attacks are phishing websites. These websites appear to be legitimate in hopes that a user will unknowingly input their sensitive information to the malicious website. This paper attempts to identify and characterize the coding style of phishing websites using machine learning models. We used web scraping to extract the HTML content of around 29,000 phishing websites. The phishing websites were collected from PhishTank, which publicly tracks such websites. To compare the HTML coding styles and syntax in phishing websites and legitimate websites, we used a dataset of around 36,000 legitimate websites. We eliminated websites with missing basic content. From the cleaned datasets of phishing and legitimate websites, we processed 10,800 websites’ source codes (5,400 websites per category), extracting 11 features from every website’s content. Our Random Forest model achieved the best accuracy of 94.16% in detecting phishing websites.
Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task

End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research. However, data sparsity limits the performance of end-to-end text image translation. Multi-task learning is a nontrivial way to alleviate this problem via exploring knowledge from complementary related tasks. In this paper, we propose a novel text translation enhanced text image translation, which trains the end-to-end model with text translation as an auxiliary task. By sharing model parameters and multi-task training, our model is able to take full advantage of easily-available large-scale text parallel corpus. Extensive experimental results show our proposed method outperforms existing end-to-end methods, and the joint multi-task learning with both text translation and recognition tasks achieves better results, proving translation and recognition auxiliary tasks are complementary. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
SIFSDNet: Sharp Image Feature Based SAR Denoising Network

Acquired speckle noise degrades the quality of synthetic aperture radar (SAR) images which affect their further processing and analysis. In this paper we propose a sharp image feature based SAR denoising network called SIFSDNet. This network uses a image sharpening block, which amplifies the feature content of the input noisy image. Features of this sharpened image is then extracted and concatenated along with the features of the input noisy image to get a combined feature map. The despeckled SAR image is then reconstructed from the combined feature map. The proposed method supersedes state-of-the-art classical as well as deep learning based SAR denoising methods, both in terms of visual results as well as quantitative analysis. We have shared the code of SIFSDNet at https://github.com/RTSIR/SIFSDNet.
Vehicle Detection and Classification Using Deep Neural Networks

Urbanized large cities encounter significant challenges of manual traffic control due to overpopulation, traffic congestion and land shortage. These cities have initiated implementing intelligent transportation systems by employing automatic and efficient traffic monitoring and management. Due to bad conditions in traffic, many minor incidents occur on streets, e.g., avoiding traffic rules, over-taking tendency, and high-speed and intoxicated driving, overdrive limit, unsafe lane changes on the road, etc. Sometimes these incidences provoke collisions, accidents, crimes, loss of time and human fatalities. For this reason, automatic vehicle detection and classification are essential to predict traffic congestion levels and lane control and enhance road safety and security. This automated process can solve economic, social, and environmental concerns and impact everyday living issues. In this work, automatic vehicle detection and classification have been introduced using various deep neural network frameworks, i.e., VGG16, VGG19 and YOLOv5. We used transfer learning algorithms built on a conventional neural network for native vehicle identification and classification. This research used a Bangladeshi vehicle images dataset containing 9,058 images. Next, we employed the “ImageDataGenerator” Keras data preprocessing and augmentation framework and various deep learning networks, VGG16, VGG19, and YOLOv5. Finally, the proposed VGG16 and VGG19 models' accuracies for ten classes are 75.15% and 79.57%, respectively. The YOLOv5 architecture attained the best performance with 83.02% accuracy for fifteen vehicle classes.
Stereo RGB and Deeper LIDAR-Based Network for 3D Object Detection in Autonomous Driving

3D object detection has become an emerging task in autonomous driving scenarios. Most of previous works process 3D point clouds using either projection-based or voxel-based models. However, both approaches contain some drawbacks. The voxel-based methods lack semantic information, while the projection-based methods suffer from numerous spatial information loss when projected to different views. In this paper, we propose the Stereo RGB and Deeper LIDAR (SRDL) framework which can utilize semantic and spatial information simultaneously such that the performance of network for 3D object detection can be improved naturally. Specifically, the network generates candidate boxes from stereo pairs and combines different region-wise features using a deep fusion scheme. The stereo strategy offers more information for prediction compared with prior works. Then, several local and global feature extractors are stacked in the segmentation module to capture richer deep semantic geometric features from point clouds. After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method. The decent experimental results on the challenging KITTI detection benchmark demonstrate the effectiveness of utilizing both stereo images and point clouds for 3D object detection.
Look Both Ways: Self-supervising Driver Gaze Estimation and Road Scene Saliency

We present a new on-road driving dataset, called "Look Both Ways", which contains synchronized video of both driver faces and the forward road scene, along with ground truth gaze data registered from eye tracking glasses worn by the drivers. Our dataset supports the study of methods for non-intrusively estimating a driver's focus of attention while driving - an important application area in road safety. A key challenge is that this task requires accurate gaze estimation, but supervised appearance-based gaze estimation methods often do not transfer well to real driving datasets, and in-domain ground truth to supervise them is difficult to gather. We therefore propose a method for self-supervision of driver gaze, by taking advantage of the geometric consistency between the driver's gaze direction and the saliency of the scene as observed by the driver. We formulate a 3D geometric learning framework to enforce this consistency, allowing the gaze model to supervise the scene saliency model, and vice versa. We implement a prototype of our method and test it with our dataset, to show that compared to a supervised approach it can yield better gaze estimation and scene saliency estimation with no additional labels.
The i-waveNet project and the integrated sea wave measurements in the Mediterranean sea

i-waveNET project is deploying measurement networks of the sea state in the Mediterranean. Several marine monitoring technologies will be integrated. In particular, HF radar, wave-buoys, seismographs, and tide gauges will be integrated with numerical models establishing a new integrated monitoring system. All the information provided will be analyzed with the aim of setting a Decision System Support (DSS). The DSS will support stakeholder activities to mitigate the coastal risks linked to extreme events, by providing useful information such as marine bulletins and GIS thematic maps.
Blockchain-Based Traceability for Shipping Containers in Unimodal and Multimodal Logistics

An unprecedented amount of goods and commodities are shipped and transported globally each day through different modes of transport.Due to its complexity, the maritime industry suffers from a lack of trust and secure ownership evidence, protracted documentation procedures, and excessive data aggregation.These shortcomings are reflected in cargo processing delays and elevated costs in the shipping process.Most of today's systems and technologies leveraged for managing shipping containers in unimodal and multimodal logistics fall short of providing transparency, traceability, reliability, audit, security, and trust features.In this paper, we propose a blockchain-based solution that allows users to trace and track their container shipments in a manner that is decentralized, transparent, auditable, secure, and trustworthy.We employ the InterPlanetary File System (IPFS) to overcome the limited data storage problem.We develop smart contracts and present algorithms along with their full implementation, testing, and validation details in both unimodal and multimodal logistics.We present security and cost analyses to show that the proposed solution is secure and cost-efficient.Furthermore, we compare our proposed solution with the existing solutions to show its novelty.All developed smart contract codes are made publicly available on GitHub.
Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification

Domain generalizable (DG) person re-identification (ReID) aims to test across unseen domains without access to the target domain data at training time, which is a realistic but challenging problem. In contrast to methods assuming an identical model for different domains, Mixture of Experts (MoE) exploits multiple domain-specific networks for leveraging complementary information between domains, obtaining impressive results. However, prior MoE-based DG ReID methods suffer from a large model size with the increase of the number of source domains, and most of them overlook the exploitation of domain-invariant characteristics. To handle the two issues above, this paper presents a new approach called Mimic Embedding via adapTive Aggregation ( $$\textsf{META}$$ ) for DG person ReID. To avoid the large model size, experts in $$\textsf{META}$$ do not adopt a branch network for each source domain but share all the parameters except for the batch normalization layers. Besides multiple experts, $$\textsf{META}$$ leverages Instance Normalization (IN) and introduces it into a global branch to pursue invariant features across domains. Meanwhile, $$\textsf{META}$$ considers the relevance of an unseen target sample and source domains via normalization statistics and develops an aggregation module to adaptively integrate multiple experts for mimicking unseen target domain. Benefiting from a proposed consistency loss and an episodic training algorithm, $$\textsf{META}$$ is expected to mimic embedding for a truly unseen target domain. Extensive experiments verify that $$\textsf{META}$$ surpasses state-of-the-art DG person ReID methods by a large margin. Our code is available at https://github.com/xbq1994/META .
GIQE: Generic Image Quality Enhancement via N<sup>th</sup> Order Iterative Degradation

Visual degradations caused by motion blur, raindrop, rain, snow, illumination, and fog deteriorate image quality and, subsequently, the performance of perception algorithms deployed in outdoor conditions. While degradation-specific image restoration techniques have been extensively studied, such algorithms are domain sensitive and fail in real scenarios where multiple degradations exist simultaneously. This makes a case for blind image restoration and reconstruction algorithms as practically relevant. However, the absence of a dataset diverse enough to encapsulate all variations hinders development for such an algorithm. In this paper, we utilize a synthetic degradation model that recursively applies sets of random degradations to generate naturalistic degradation images of varying complexity, which are used as input. Furthermore, as the degradation intensity can vary across an image, the spatially invariant convolutional filter cannot be applied for all degradations. Hence to enable spatial variance during image restoration and reconstruction, we design a transformer-based architecture to benefit from the long-range dependencies. In addition, to reduce the computational cost of transformers, we propose a multi-branch structure coupled with modifications such as a complimentary feature selection mechanism and the replacement of a feed-forward network with lightweight multiscale convolutions. Finally, to improve restoration and reconstruction, we integrate an auxiliary decoder branch to predict the degradation mask to ensure the underlying network can localize the degradation information. From empirical analysis on 10 datasets covering rain drop removal, deraining, dehazing, image enhancement, and deblurring, we demonstrate the efficacy of the proposed approach while obtaining SoTA performance.
A hybrid convolutional long short-term memory (CNN-LSTM) based natural language processing (NLP) model for sentiment analysis of customer product reviews in Bangla

AbstractAbstractSentiment Analysis (SA) examines how people feel about products, services, people, organizations, events etc. Most of the Natural language processing on SA research has focused on English. In the case of Bangla, it lacks sufficient study as well as a proper dataset. All previous analyses employed KNN, NB, and other methods. We develop a natural language processing (NLP) model for separating opinion and sentiment from Bangla customer surveys. This technology isolates extreme client opinions to help with business and marketing decisions. Bangladesh is embracing e-commerce and f-commerce. Client comments and evaluations are becoming more significant in judging product or service quality, and this industry is evolving toward internet distribution. Organizations utilize client audits to check product quality. Our objective is to systematically collect client feedback and understand their product reaction. We used a hybrid CNN-LSTM based NLP model to classify Bangla texts in 3 viewpoint categories (positive, negative and neutral). We tested our model using a Bangla dataset that we generated. For our dataset, we collected polls and comments from websites and social media. Finally, among the three evaluation matrices, the f-1 score is providing the highest average, and the three-opinion technique is 87.22 percent accurate in determining the performance of our task.Subject Classification: Primary 65C99Secondary 62K99Keywords: Customer review analysisOpinion miningHybrid CNN-LSTM architectureBangla sentiment analysisNLP
ABSNFT: Securitization and Repurchase Scheme for Non-Fungible Tokens Based on Game Theoretical Analysis

The Non-Fungible Token (NFT) is viewed as one of the important applications of blockchain technology. Currently NFT has a large market scale and multiple practical standards, however several limitations of the existing mechanism in NFT markets still exist. This work proposes a novel securitization and repurchase scheme for NFT to overcome these limitations. We first provide an Asset-Backed Securities (ABS) solution to settle the limitations of non-fungibility of NFT. Our securitization design aims to enhance the liquidity of NFTs and enable Oracles and Automatic Market Makers (AMMs) for NFTs. Then we propose a novel repurchase protocol for a participant owing a portion of NFT to repurchase other shares to obtain the complete ownership. As the participants may strategically bid during the acquisition process, we formulate the repurchase process as a Stackelberg game to explore the equilibrium prices. We also provide solutions to handle difficulties at market such as budget constraints and lazy bidders.