Reviewerly/exHarmony huggingface dataset:
Image Enhancement Algorithm Based on GAN Neural Network

Deep underwater color images have problems such as low brightness, poor contrast, and loss of local details. In order to effectively enhance low-quality underwater images, this paper proposes an enhancement method based on GAN (Generative Adversarial Network). This paper studies low-light image enhancement algorithms, aiming to improve the quality of low-light images by studying some technical means and methods, and restore the original scene information of low-quality images, so as to obtain natural and clear images with complete details and structural information. In order to verify the effectiveness of this method, image databases such as DIARETDB0 and SID are used as the research object, combined with multi-scale Retinex color reproduction contrast-constrained adaptive histogram equalization to compare the performance of the enhanced algorithm. The results show that the processed image is better than other image enhancement methods in terms of color protection, contrast enhancement, and image detail enhancement. The proposed method significantly improves the indicators proposed in the article.
Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing

Nested named entity recognition (NER) has been receiving increasing attention.Recently, Fu et al. (2020) adapt a span-based constituency parser to tackle nested NER.They treat nested entities as partially-observed constituency trees and propose the masked inside algorithm for partial marginalization.However, their method cannot leverage entity heads, which have been shown useful in entity mention detection and entity typing.In this work, we resort to more expressive structures, lexicalized constituency trees in which constituents are annotated by headwords, to model nested entities.We leverage the Eisner-Satta algorithm to perform partial marginalization and inference efficiently.In addition, we propose to use (1) a two-stage strategy (2) a head regularization loss and (3) a head-aware labeling loss in order to enhance the performance.We make a thorough ablation study to investigate the functionality of each component.Experimentally, our method achieves the state-ofthe-art performance on ACE2004, ACE2005 and NNE, and competitive performance on GENIA, and meanwhile has a fast inference speed.Our code will be publicly available at: github.com/LouChao98/nner_as_parsing.
Integrated Blockchain-Deep Learning Approach for Analyzing the Electronic Health Records Recommender System

Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with better security. It avoids third-party intervention to ensure better security of the data. Deep learning is another booming field that is mostly used in computer applications. This work proposes an integrated environment of a blockchain-deep learning environment for analyzing the Electronic Health Records (EHR). The EHR is the medical documentation of a patient which can be shared among hospitals and other public health organizations. The proposed work enables a deep learning algorithm act as an agent to analyze the EHR data which is stored in the blockchain. This proposed integrated environment can alert the patients by means of a reminder for consultation, diet chart, etc. This work utilizes the deep learning approach to analyze the EHR, after which an alert will be sent to the patient's registered mobile number.
Single-Trace Attacks on Keccak

Since its selection as the winner of the SHA-3 competition, Keccak, with all its variants, has found a large number of applications. It is, for instance, a common building block in schemes submitted to NIST’s post-quantum cryptography project. In many of these applications, Keccak processes ephemeral secrets. In such a setting, side-channel adversaries are limited to a single observation, meaning that differential attacks are inherently prevented. If, however, such a single trace of Keccak can already be sufficient for key recovery has so far been unknown. In this paper, we change the above by presenting the first single-trace attack targeting Keccak. Our method is based on soft-analytical side-channel attacks and, thus, combines template matching with message passing in a graphical model of the attacked algorithm. As a straight-forward model of Keccak does not yield satisfactory results, we describe several optimizations for the modeling and the message-passing algorithm. Their combination allows attaining high attack performance in terms of both success rate as well as computational runtime. We evaluate our attack assuming generic software (microcontroller) targets and thus use simulations in the generic noisy Hamming-weight leakage model. Hence, we assume relatively modest profiling capabilities of the adversary. Nonetheless, the attack can reliably recover secrets in a large number of evaluated scenarios at realistic noise levels. Consequently, we demonstrate the need for countermeasures even in settings where DPA is not a threat.
Digital forensic analysis for source video identification: A survey

In recent years, many digital devices have been equipped with a video camera that allows videos to be recorded in good quality, free of charge and without restrictions. Concurrently, the widespread use of digital videos via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter and WhatsApp is becoming increasing important. However, security challenges have emerged and are spreading worldwide. These issues may lead to serious problems, particularly in situations where video is a key part of decision-making in crimes, including movie piracy and child pornography. Thus, to increase the trustworthiness of using digital video in daily life, copyright protection and video authentication must be used. Although source camera identification based on digital images has attracted many researchers’ attention, less research has been performed on the forensic analysis of videos due to certain challenges, such as compression, stabilization, scaling, and cropping, as well as differences between frame types that can occur when a video is stored in digital devices. Thus, there are insufficient large standard digital video databases and updated databases with new devices based on new technologies. The goal of this paper is to offer an inclusive overview of what has been done over the last decade in the field of source video identification by examining existing techniques, such as photo response nonuniformity (PRNU) and machine learning approaches, and describing some popular video databases.
Underwater Submarine Path Planning Based on Artificial Potential Field Ant Colony Algorithm and Velocity Obstacle Method

Navigating safely in complex marine environments is a challenge for submarines because proper path planning underwater is difficult. This paper decomposes the submarine path planning problem into global path planning and local dynamic obstacle avoidance. Firstly, an artificial potential field ant colony algorithm (APF-ACO) based on an improved artificial potential field algorithm and improved ant colony algorithm is proposed to solve the problem of submarine underwater global path planning. Compared with the Optimized ACO algorithm proposed based on a similar background, the APF-ACO algorithm has a faster convergence speed and better path planning results. Using an inflection point optimization algorithm greatly reduces the number and length of inflection points in the path. Using the Clothoid curve fitting algorithm to optimize the path results, a smoother and more stable path result is obtained. In addition, this paper uses a three-dimensional dynamic obstacle avoidance algorithm based on the velocity obstacle method. The experimental results show that the algorithm can help submarines to identify threatening dynamic obstacles and avoid collisions effectively. Finally, we experimented with the algorithm in the submarine underwater semi-physical simulation system, and the experimental results verified the effectiveness of the algorithm.
A blockchain-based audit approach for encrypted data in federated learning

The development of data-driven artificial intelligence technology has given birth to a variety of big data applications. Data has become an essential factor to improve these applications. Federated learning, a privacy-preserving machine learning method, is proposed to leverage data from different data owners. It is typically used in conjunction with cryptographic methods, in which data owners train the global model by sharing encrypted model updates. However, data encryption makes it difficult to identify the quality of these model updates. Malicious data owners may launch attacks such as data poisoning and free-riding. To defend against such attacks, it is necessary to find an approach to audit encrypted model updates. In this paper, we propose a blockchain-based audit approach for encrypted gradients. It uses a behavior chain to record the encrypted gradients from data owners, and an audit chain to evaluate the gradients’ quality. Specifically, we propose a privacy-preserving homomorphic noise mechanism in which the noise of each gradient sums to zero after aggregation, ensuring the availability of aggregated gradient. In addition, we design a joint audit algorithm that can locate malicious data owners without decrypting individual gradients. Through security analysis and experimental evaluation, we demonstrate that our approach can defend against malicious gradient attacks in federated learning.
Detection of Image Steganography Using Deep Learning and Ensemble Classifiers

In this article, the problem of detecting JPEG images, which have been steganographically manipulated, is discussed. The performance of employing various shallow and deep learning algorithms in image steganography detection is analyzed. The data, images from the BOSS database, were used with information hidden using three popular steganographic algorithms: JPEG universal wavelet relative distortion (J-Uniward), nsF5, and uniform embedding revisited distortion (UERD) at two density levels. Various feature spaces were verified, with the discrete cosine transform residuals (DCTR) and the Gabor filter residuals (GFR) yielding best results. Almost perfect detection was achieved for the nsF5 algorithm at 0.4 bpnzac density (99.9% accuracy), while the detection of J-Uniward at 0.1 bpnzac density turned out to be hardly possible (max. 56.3% accuracy). The ensemble classifiers turned out to be an encouraging alternative to deep learning-based detection methods.
Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging

Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images—437 benign, 210, malignant, and 133 normal—were used to train and validate the models in this study. To diagnose malignant lesions, we have performed training, hyperparameter tuning, cross-validation, and testing with a random forest model. This resulted in a binary classification accuracy of 78.5% (65.1–84.1%) for the maximal (full multivariate) cross-validated model for a combination of radiomic groups.
Effect of virtual reality technology as intervention for people with kinesiophobia: A meta‐analysis of randomised controlled trials

Abstract Aims and Objective To explore the effectiveness of Virtual Reality Technology in reducing kinesiophobia in people. Background Kinesiophobia is an important psychosocial factor affecting the pain experience and has a significant negative impact on rehabilitation. Virtual reality technology has been widely used in the treatment of phobias, mental disorders and anxiety disorders. However, the effect of virtual reality technology on people with kinesiophobia has been reported with inconsistent results. Design A meta‐analysis of randomised controlled trials. Methods This study systematically searched PubMed, Web of Science, PsycINFO, CINAHL, Embase, Cochrane Library, Medline, Scopus and four Chinese databases. The standardised mean difference (SMD) was calculated using random‐effects models, and the Cochrane Collaboration's tool was used to assess the risk of bias in each study. The PRISMA 2020 checklist provided by the EQUATOR network was used. Results Eleven randomised controlled trials involving a total of 488 subjects were included. Meta‐analysis showed the effect sizes of virtual reality intervention on kinesiophobia (SMD = −0.53, 95% CI [−0.90, −0.17], p = .004). Virtual reality intervention was more effective in reducing kinesiophobia in people with chronic low back pain (SMD = −1.00, 95% CI [−1.71, −0.29], p = .006). Compared with fully immersive virtual reality (SMD = −0.29, 95% CI [−0.62, 0.05], p = 0.09), non‐immersive virtual reality was more effective in reducing kinesiophobia (SMD = −0.66, 95% CI [−1.24, −0.09], p = 0.02). Compared with virtual reality intervention alone (SMD = −0.35, 95% CI [−1.40, 0.71], p = 0.52), virtual reality combined with exercise was more effective in reducing kinesiophobia (SMD = −0.59, 95% CI [−0.95, −0.22], p = 0.002). Conclusions Virtual reality technology has the potential to reduce the degree of kinesiophobia in people. In addition, virtual reality technology was more effective in people with chronic low back pain; non‐immersive virtual reality was more effective in reducing kinesiophobia; and virtual reality technology combined with exercise was more effective in reducing kinesiophobia than virtual reality intervention alone. Clinical nursing staff should be encouraged to use virtual reality to speed up patient recovery. However, to achieve immersion and apply this technology to different diseases, more studies are required to provide clearer suggestions. Relevance to clinical practice This study suggests that healthcare staff should pay attention to kinesiophobia, and early identification and intervention of kinesiophobia can help patients recover their health and improve the quality of nursing.
Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation

Session-based recommendation (SBR) aims to predict the user's next action based on the ongoing sessions. Recently, there has been an increasing interest in modeling the user preference evolution to capture the fine-grained user interests. While latent user preferences behind the sessions drift continuously over time, most existing approaches still model the temporal session data in discrete state spaces, which are incapable of capturing the fine-grained preference evolution and result in sub-optimal solutions. To this end, we propose Graph Nested GRU ordinary differential equation (ODE), namely GNG-ODE, a novel continuum model that extends the idea of neural ODEs to continuous-time temporal session graphs. The proposed model preserves the continuous nature of dynamic user preferences, encoding both temporal and structural patterns of item transitions into continuous-time dynamic embeddings. As the existing ODE solvers do not consider graph structure change and thus cannot be directly applied to the dynamic graph, we propose a time alignment technique, called t-Alignment, to align the updating time steps of the temporal session graphs within a batch. Empirical results on three benchmark datasets show that GNG-ODE significantly outperforms other baselines.
Blockchain: A business model innovation analysis

The adoption of blockchain-based technologies by organisations can bring benefits in terms of firms' profitability, productivity and efficiency, making companies rethink their existing business models. However, as the technology is still developing and the research on the implications of the different types of blockchain networks (i.e. public, private, consortium) is scarce, their role in business model innovation requires closer attention. To address this gap, the paper provides a conceptual insight into the role of blockchain technology in companies with different value configurations by examining the technological conditions that can impact business models and probing the role of technology benefits in driving company value. The analysis contributes to the literature by discussing the business implications of innovative technologies and uncovering their positive and negative consequences for the value creation, delivery and capture activities. Such analysis sheds light on the functions of blockchains that have a differentiating impact on business processes. Also, the paper puts forward managerial implications by discussing the paths of business model innovation using blockchain technologies.
Can We Use Word Embeddings for Enhancing Guarani-Spanish Machine Translation?

Machine translation for low-resource languages, such as Guarani, is a challenging task due to the lack of data.One way of tackling it is using pretrained word embeddings for model initialization.In this work we try to check if currently available data is enough to train rich embeddings for enhancing MT for Guarani and Spanish, by building a set of word embedding collections and training MT systems using them.We found that the trained vectors are strong enough to slightly improve the performance of some of the translation models and also to speed up the training convergence.
Analysing the Sentiment of Air-Traveller: A Comparative Analysis

Airport service quality is considered to be an indicator of passenger satisfaction.However, assessing this by conventional methods requires continuous observation and monitoring.Therefore, during the past few years, the use of machine learning techniques for this purpose has attracted considerable attention for analysing the sentiment of the air traveller.A sentiment analysis system for textual data analytics leverages the natural language processing and machine learning techniques in order to determine whether a piece of writing is positive, negative or neutral.Numerous methods exist for estimating sentiments which include lexical-based methodologies and directed artificial intelligence strategies.Despite the wide use and ubiquity of certain strategies, it remains unclear which is the best strategy for recognising the intensity of the sentiments of a message.It is necessary to compare these techniques in order to understand their advantages, disadvantages and limitations.In this paper, we compared the Valence Aware Dictionary and sentiment Reasoner, a sentiment analysis technique specifically attuned and well known for performing good on social media data, with the conventional machine learning techniques of handling the textual data by converting it into numerical form.We used the review data obtained from the SKYTRAX website for each airport.The machine learning algorithms evaluated in this paper are VADER sentiment and logistic regression.The term frequency-inverse document frequency is used in order to convert the textual review data into the resulting numerical columns.This was formulated as a classification problem, whereby the prediction of the algorithm was compared with the actual recommendation of the passenger in the dataset.The results were analysed according to the accuracy, precision, recall and F1-score.From the analysis of the results, we observed that logistic regression outperformed the VADER sentiment analysis.
EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation

Imbalanced data cause deep neural networks to output biased results, and it becomes more serious when facing extremely imbalanced data regarding the outliers with tiny size (the ratio of the outlier size to the image size is around 0.05%). Many data argumentation models are proposed to supplement imbalanced data to alleviate biased results. However, the existing augmentation models cannot synthesize tiny outliers, which make the generated data unavailable. In this article, we propose a new augmentation model named extremely imbalanced data augmentation generative adversarial nets (EID-GANs) to address the extremely imbalanced data augmentation problem. First, we design a new penalty function by subtracting the outliers from the cropped region of generated instance to guide the generator to learn the features of outliers. After this, we combine the output value of the penalty function with the generator loss to jointly update the generator’s parameters with backpropagation. Second, we propose a new evaluation approach that adopts two outlier detectors with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -fold cross-validation to assess the availability of generated instances. We conduct extensive experiments to demonstrate the significant performance improvement of EID-GAN on two extremely imbalanced datasets, which are the industrial Piston and the Fabric datasets, and one general imbalanced dataset, i.e., the public DAGM dataset. The experimental results show that our EID-GAN outperforms the state-of-the-art (SOTA) augmentation models on different imbalanced datasets.
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning

Conventional federated learning (FL) trains one global model for a federation of clients with decentralized data, reducing the privacy risk of centralized training. However, the distribution shift across non-IID datasets, often poses a challenge to this one-model-fits-all solution. Personalized FL aims to mitigate this issue systematically. In this work, we propose APPLE, a personalized cross-silo FL framework that adaptively learns how much each client can benefit from other clients' models. We also introduce a method to flexibly control the focus of training APPLE between global and local objectives. We empirically evaluate our method's convergence and generalization behaviors, and perform extensive experiments on two benchmark datasets and two medical imaging datasets under two non-IID settings. The results show that the proposed personalized FL framework, APPLE, achieves state-of-the-art performance compared to several other personalized FL approaches in the literature. The code is publicly available at https://github.com/ljaiverson/pFL-APPLE.