Reviewerly/exHarmony huggingface dataset:
Detection of Residual Film on the Field Surface Based on Faster R-CNN Multiscale Feature Fusion

After the residual film recycling machine recovers the film, some small pieces of the film will remain on the surface of the field. To solve the problem of collecting small pieces of film, it is necessary to develop a piece of intelligent picking equipment. The detection of small pieces of film is the first problem to be solved. This study proposes a method of an object detection algorithm fusing multi-scale features (MFFM Faster R-CNN) based on improved Faster R-CNN. Based on the Faster R-CNN model, the feature pyramid network is added to solve the problem of multiscale change of residual film. The convolution block attention module is introduced to enhance the feature extraction ability of the model. The Soft-NMS algorithm is used instead of the NMS algorithm to improve the detection accuracy of the model in the RPN network. The experimental results show that the model is able to effectively detect surface residual film in complex environments, with an AP of 83.45%, F1-score of 0.89, and average detection time of 248.36 ms. The model is compared with SSD and YOLOv5 under the same experimental environment and parameters, and it is found that the model not only ensures high-precision detection but also ensures real-time detection. This lays the theoretical foundation for the subsequent development of field surface residual film intelligent picking equipment.
Applied machine learning in hematopathology

An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.
A Density Peaking Clustering Algorithm for Differential Privacy Preservation

The privacy protection problem in data mining has received increasingly attention and is a hot topic of current research.To address the problems of large accuracy loss and instability of clustering results of clustering algorithms under differential privacy protection requirements, a density peak clustering algorithm for differential privacy protection (DP-chDPC) is proposed.Firstly, the original DPC algorithm is improved, by using the dichotomy method to automatically determine the truncation distance to avoid the subjectivity of manual selection, and by setting the threshold of local density and center offset distance to automatically obtain the clustering center, which overcomes the uncertainty of the original DPC algorithm to select the clustering center based on the decision graph.Then, noise is added to the local density by using the Laplace mechanism to realize the differential privacy protection of the algorithm during the clustering analysis.Finally, the Chebyshev distance is used to replace the Euclidean distance to calculate the distance matrix, which reduces the interference on the clustering results after the algorithm adds noise, and reduces the loss of clustering accuracy, so that the stability of the algorithm is improved.The experimental results show that the DP-chDPC algorithm can effectively reduce the loss of clustering accuracy after the algorithm adds noise, and the clustering results are more stable.
Quantum for 6G communication: A perspective

Abstract In the technologically changing world, the demand for ultra‐reliable, faster, low power, and secure communication has significantly risen in recent years. Researchers have shown immense interest in emerging quantum computing (QC) due to its potentials of solving the computing complexity in the robust and efficient manner. It is envisioned that QC can act as critical enablers and strong catalysts to considerably reduce the computing complexities and boost the future of sixth generation (6G) and beyond communication systems in terms of their security. In this study, the fundamentals of QC, the evolution of quantum communication that encompasses a wide spectrum of technologies and applications and quantum key distribution, which is one of the most promising applications of quantum security, have been presented. Furthermore, various parameters and important techniques are also investigated to optimise the performance of 6G communication in terms of their security, computing, and communication efficiency. Towards the end, potential challenges that QC and quantum communication may face in 6G have been highlighted along with future directions.
Adaptive KNN-Based Extended Collaborative Filtering Recommendation Services

In the current era of e-commerce, users are overwhelmed with countless products, making it difficult to find relevant items. Recommendation systems generate suggestions based on user preferences, to avoid information overload. Collaborative filtering is a widely used model in modern recommendation systems. Despite its popularity, collaborative filtering has limitations that researchers aim to overcome. In this paper, we enhance the K-nearest neighbor (KNN)-based collaborative filtering algorithm for a recommendation system, by considering the similarity of user cognition. This enhancement aimed to improve the accuracy in grouping users and generating more relevant recommendations for the active user. The experimental results showed that the proposed model outperformed benchmark models, in terms of MAE, RMSE, MAP, and NDCG metrics.
No Matter How You Slice It: Machine Unlearning with SISA Comes at the Expense of Minority Classes

Machine unlearning using the SISA technique promises a signifi cant speedup in model retraining with only minor sacrifices in performance.Even greater speedups can be achieved in a distribution-aware setting, where training samples are sorted by their individual unlearning likelihood.Ye t, the side effects of these techniques on model performance are still poorly understood.In this paper, we lay out the impact of SISA unlearning in settings where classes are imbalanced, as well as in settings where class membership is correlated with unlearning likelihood.We show that the performance decrease that is associated with using SISA is primarily carried by minority classes and that conventional techniques for imbalanced
Quantum monte carlo for economics: Stress testing and macroeconomic deep learning

Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. We are the first to study whether Quantum Monte Carlo (QMC) algorithm can improve the runtime of economic applications and challenges in doing so. We provide a detailed introduction to quantum computing and especially the QMC algorithm. Then, we illustrate how to formulate and encode into quantum circuits (a) a bank stress testing model with credit shocks and fire sales, (b) a neoclassical investment model solved with deep learning, and (c) a realistic macro model solved with deep neural networks. We discuss potential computational gains of QMC versus classical computing systems and present a few innovations in benchmarking QMC.
Mutually aided uncertainty incorporated dual consistency regularization with pseudo label for semi-supervised medical image segmentation

Semi-supervised learning has contributed plenty to promoting computer vision tasks. Especially concerning medical images, semi-supervised image segmentation can significantly reduce the labor and time cost of labeling images. Among the existing semi-supervised methods, pseudo-labelling and consistency regularization prevail; however, the current related methods still need to achieve satisfactory results due to the poor quality of the pseudo-labels generated and needing more certainty awareness the models. To address this problem, we propose a novel method that combines pseudo-labelling with dual consistency regularization based on a high capability of uncertainty awareness. This method leverages a cycle-loss regularized to lead to a more accurate uncertainty estimate. Followed by the uncertainty estimation, the certain region with its pseudo-label is further trained in a supervised manner. In contrast, the uncertain region is used to promote the dual consistency between the student and teacher networks. The developed approach was tested on three public datasets and showed that: 1) The proposed method achieves excellent performance improvement by leveraging unlabeled data; 2) Compared with several state-of-the-art (SOTA) semi-supervised segmentation methods, ours achieved better or comparable performance.
Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology that can give connected nodes means of security, transparency, and distribution. IoT devices could guarantee data centralization and availability with shared ledger technology. Federated learning (FL) is a new type of decentralized machine learning (DML) where clients collaborate to train a model and share it privately with an aggregator node. The integration of Blockchain and FL enabled researchers to apply numerous techniques to hide the shared training parameters and protect their privacy. This study explores the application of this integration in different IoT environments, collectively referred to as the Internet of X (IoX). In this paper, we present a state-of-the-art review of federated learning and Blockchain and how they have been used in collaboration in the IoT ecosystem. We also review the existing security and privacy challenges that face the integration of federated learning and Blockchain in the distributed IoT environment. Furthermore, we discuss existing solutions for security and privacy by categorizing them based on the nature of the privacy-preservation mechanism. We believe that our paper will serve as a key reference for researchers interested in improving solutions based on mixing Blockchain and federated learning in the IoT environment while preserving privacy.
D-HRSP: Dataset of helpful reviews for service providers

Most common services are now provided through mobile applications; thus, the importance of mobile application reviews has increased. Service providers and developers seek helpful reviews to find useful information to improve their services. However, with currently existing indicators, e.g., star rating systems, it is difficult to identify reviews that are directly related to the quality of the service. Thus, in this study, we defined helpful mobile application reviews for service providers and developers based on the components of an existing service quality evaluation model. We also provide the D-HRSP (dataset of helpful reviews for service providers), which is a labeled dataset that can be used to examine helpful reviews. We also report the experimental results obtained with simple natural language processing techniques and machine learning and deep learning classification models. The experimental results demonstrate that the proposed definition can help address real-life problems and create opportunities for additional research into the identification of helpful mobile application reviews.
Hybrid image analysis model for hashtag recommendation through the use of deep learning methods

Content tags of a given image are the basic mechanism used in social networking services. One of the most commonly used websites like Instagram, Facebook, or Twitter is the hashtag. In this paper, we propose a hybrid solution for automatic image description extraction. A network architecture consists of a module for removing main objects from the image for additional classification of other elements on the background via U-NET (neural model designed for segmentation). Obtained images are classified by multi-branch convolutional networks. The results of such a classification are used to describe the probability of belonging to particular classes. Then, the probability of belonging to each class is used to find related linguistic values through the Skip-Gram module. The main advantage of the solution is the possibility of extending the network with additional branches to increase the number of main classes of objects. The proposal was analyzed through the object's classification, the use of learning transfer, and image description on two datasets, i.e. Animals-10 and HARRISON16. Experimental results show that the proposal is a very flexible solution for extending the model to other classes without the need to overtrain the entire architecture and reach higher classification accuracy than other models for social networking services.
GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning

Due to the dynamics of wireless channels and limited wireless resources (i.e., spectrum), deploying federated learning (FL) over wireless networks is challenged by frequent FL parameter transmission errors and incomplete FL model aggregation from devices. In order to overcome these challenges, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> l <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> bal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MO</u> del <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RE</u> use strategy (GoMORE) that reuses global FL models received at previous FL training iterations to replace erroneous local models caused by imperfect wireless transmissions. We analytically prove that the proposed GoMORE is strictly superior over the existing strategy, especially at low signal-to-noise ratios (SNRs). Based on the derived expression of weight divergence, we optimize the number of devices that participate in the model aggregation to maximize the FL performance with limited communication resources. Numerical results verify that the proposed GoMORE successfully approaches the performance upper bound by an ideal transmission. It also mitigates the negative impacts of non-independent and non-identically distributed (non-IID) data while achieving over 5 dB reduction in energy consumption.
Arrival-Time Detection With Multiscale Wavelet Analysis and Source Location of Acoustic Emission in Rock

In order to overcome the difficulties of traditional arrival-time picking techniques applied to acoustic emission (AE) events with low signal-to-noise ratio (SNR) and complex waveforms, this work proposed a new time picking method. It combines the advantage of the wavelet transform technique, the ratio of short-term average to long-term average (STA/LTA) method, and the Akaike information criterion (AIC), which is called the wavelet-STA/LTA-AIC (WSA) picker. This work assessed the performance of picking arrival times of the WSA picker by locating the AE events from pencil-lead breaking on the marble panel (artificial sources) and tensile cracking sources on the granite. For all experiment results, the proposed WSA picker shows less location error compared with the AIC picker, regardless of geometry and source type. For location results of the pencil-lead breaking events on the marble panel, the average location errors are 1.4 cm (AIC picker) and 0.6 cm (WSA picker). For location results of the tensile cracking events on granite, the WSA picker is more concentrated in the region of the fracture surface and has fewer wrong-locating points at the specimen edge when compared with the AIC picker. It demonstrates that the proposed WSA picker can improve the accuracy and capability of AE location and can be a viable method for arrival-time picking.
Short-term prediction of PV power based on fusions of power series and ramp series

Photovoltaic (PV) power generation is highly volatile and intermittent, which poses a major challenge to the prediction and intra-day scheduling of PV power. In order to effectively improve the prediction accuracy of PV power, this paper proposes a short-term prediction model through the fusion of power series and ramp series based on CNN-LSTM models. In the proposed hybrid model, both the power series prediction module and the ramp series prediction module are used to simultaneously predict the PV power, and then the results of both modules are combined by fully connected layers and corrected by a residual network to obtain the final prediction results. The prediction effects of the proposed model are illustrated by a multi-level evaluation system of prediction errors, including the evaluation of overall prediction errors, local prediction errors, and ramp point prediction errors. Case studies show that the ramp series of PV power can better characterize the fluctuation of PV power and that the proposed model has a better prediction effect than other models.
Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

Sentiment analysis is an active research domain of the current era, thanks to its vast applications. The main objective is to classify the polarity of the given text as negative, positive, or neutral. Thus, researchers’ focus shifted towards the aspect or feature-based sentiment analysis because overall polarity does not determine the people’s views towards certain features. Therefore, Aspect-Based Sentiment Analysis (ABSA) helps us to identify the sentiments about various aspects of different services and products. However, their accurate identification and extraction are still challenging for the research community due to the complex nature of natural languages. This paper presents a method named Attention-based Multi-Channel Gated Recurrent Neural Network (Att-MC-GRU), which extracts aspects and analyzes textual reviews to predict or classify their sentiments. It introduces the hybrid approach by combining word embedding, part of speech (POS) tags, and contextual position information. The main novelty lies in proposal of a Multi-Channel Gated Recurrent Neural Network (MC-GRU), in contrast to the existing studies that consider Recurrent Neural Networks (RNN) comprising only a single input channel. In addition, word embedding, POS tags, and contextual position information collectively improve the identification and prediction accuracy of aspects and their associated sentiments. Due to the application of the filtering by the attention mechanism that figured out first the significant words, which helps to determine entities’ aspects related to the sentiment expressed. The empirical analysis proves the proposed approach’s effectiveness compared to the existing techniques in the relevant literature using standard datasets. According to the empirical analysis, the proposed model performs better in the F1-measure, with an overall achievement of 94% in the task of aspect extraction and 93% in the classification of sentiment.
Evaluating the Performance of Machine Learning and Deep Learning Techniques to HyMap Imagery for Lithological Mapping in a Semi-Arid Region: Case Study from Western Anti-Atlas, Morocco

Accurate lithological mapping is a crucial juncture for geological studies and mineral exploration. Hyperspectral data provide the opportunity to extract detailed information about the geology and mineralogy of the Earth’s surface. Machine learning (ML) and deep learning (DL) techniques provide an accurate and effective mapping of various types of lithologies in arid and semi-arid regions. This article discusses the use of machine learning algorithms, specifically Support Vector Machines (SVM), one-dimensional Convolutional Neural Network (1D-CNN), random forest (RF), and k-nearest neighbor (KNN), for lithological mapping in a complex area with strong hydrothermal alteration. The study evaluates the performance of the four algorithms in three different zones in the Ameln valley shear zone (AVSZ) area at eastern Kerdous inlier, Moroccan western Anti-Atlas. The results demonstrated that 1D-CNN achieved the best classification results for most lithological units. Additionally, the LK-SVM demonstrated good mapping results compared to the other SVM models, as well as RF and KNN. Our study concludes that the combination of the CNN and HyMap data can provide the most accurate lithologic mapping for the three selected region, with an overall accuracy of ~95%. However, this study highlights the challenges in identifying different lithological units using remotely sensed data due to spectrum similarities induced by similar chemical and mineralogical compositions. This study emphasizes the importance of carefully considering and evaluating ML and DL methods for lithological mapping studies, then recommends the high-resolution hyperspectral data and DL models for accurate results. The implications of this study would be fascinating to exploration geologists for Mineral Prospectivity Mapping (MPM), especially in selecting the most appropriate techniques for highly accurate mineral mapping in metallogenic provinces.
Causal structure in the presence of sectorial constraints, with application to the quantum switch

Existing work on quantum causal structure assumes that one can perform arbitrary operations on the systems of interest. But this condition is often not met. Here, we extend the framework for quantum causal modelling to situations where a system can suffer sectorial constraints, that is, restrictions on the orthogonal subspaces of its Hilbert space that may be mapped to one another. Our framework (a) proves that a number of different intuitions about causal relations turn out to be equivalent; (b) shows that quantum causal structures in the presence of sectorial constraints can be represented with a directed graph; and (c) defines a fine-graining of the causal structure in which the individual sectors of a system bear causal relations. As an example, we apply our framework to purported photonic implementations of the quantum switch to show that while their coarse-grained causal structure is cyclic, their fine-grained causal structure is acyclic. We therefore conclude that these experiments realize indefinite causal order only in a weak sense. Notably, this is the first argument to this effect that is not rooted in the assumption that the causal relata must be localized in spacetime.