Reviewerly/exHarmony huggingface dataset:
Deep Learning and Fuzzy Rule-Based Hybrid Fusion Model for Data Classification

Data mining is the promising field that attracted the industries to manage huge volumes of data. The most effective and challenging techniques of data mining is data classification. The main intension of this research is to design and develop a data classification strategy based on hybrid fusion model using the deep learning approach, Adaptive Lion Fuzzy System (ALFS), and Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS). The hybrid model consists of three phases: In the first phase, the data is classified using ALFS and the rule base of the fuzzy system is updated by optimally generating the rules using adaptive lion optimization (ALA) from the training data. The second step is the fuzzification process, which converts the scalar values in the training data into fuzzy values with the help of membership function, which is based on Adaptive Genetic Fuzzy System (AGFS). Finally, the classified score of data instances is determined using defuzzification process, which converts the linguistic variable into fuzzy score. In the second phase, the data is classified using Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS), which is used for selecting the optimal fuzzy rules. In the third phase, the data is classified using deep learning networks. The outputs from three phases are fused together using the hybrid fusion model for which the weighed fusion is employed. The performance of the system is validated using three different datasets that are available in UCI machine learning repository. The proposed hybrid model outperforms the existing methods with sensitivity of 0.99, specificity of 0.9350, and accuracy of 0.9411, respectively.
Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.
KNN-Contrastive Learning for Out-of-Domain Intent Classification

The Out-of-Domain (OOD) intent classification is a basic and challenging task for dialogue systems.Previous methods commonly restrict the region (in feature space) of In-domain (IND) intent features to be compact or simplyconnected implicitly, which assumes no OOD intents reside, to learn discriminative semantic features.Then the distribution of the IND intent features is often assumed to obey a hypothetical distribution (Gaussian mostly) and samples outside this distribution are regarded as OOD samples.In this paper, we start from the nature of OOD intent classification and explore its optimization objective.We further propose a simple yet effective method, named KNN-contrastive learning.Our approach utilizes K-Nearest Neighbors (KNN) of IND intents to learn discriminative semantic features that are more conducive to OOD detection.Notably, the density-based novelty detection algorithm is so well-grounded in the essence of our method that it is reasonable to use it as the OOD detection algorithm without making any requirements for the feature distribution.Extensive experiments on four public datasets show that our approach can not only enhance the OOD detection performance substantially but also improve the IND intent classification while requiring no restrictions on feature distribution.Code is available.
Scaling quantum approximate optimization on near-term hardware

Abstract The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems. However, the viability of the QAOA depends on how its performance and resource requirements scale with problem size and complexity for realistic hardware implementations. Here, we quantify scaling of the expected resource requirements by synthesizing optimized circuits for hardware architectures with varying levels of connectivity. Assuming noisy gate operations, we estimate the number of measurements needed to sample the output of the idealized QAOA circuit with high probability. We show the number of measurements, and hence total time to solution, grows exponentially in problem size and problem graph degree as well as depth of the QAOA ansatz, gate infidelities, and inverse hardware graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
Wide and Deep Graph Neural Network With Distributed Online Learning

Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates for decentralized tasks. In these scenarios, the underlying graph often changes with time due to link failures or topology variations, creating a mismatch between the graphs on which GNNs were trained and the ones on which they are tested. Online learning can be leveraged to retrain GNNs at testing time to overcome this issue. However, most online algorithms are centralized and usually offer guarantees only on convex problems, which GNNs rarely lead to. This paper develops the Wide and Deep GNN (WD-GNN), a novel architecture that can be updated with distributed online learning mechanisms. The WD-GNN consists of two components: the wide part is a linear graph filter and the deep part is a nonlinear GNN. At training time, the joint wide and deep architecture learns nonlinear representations from data. At testing time, the wide, linear part is retrained, while the deep, nonlinear one remains fixed. This often leads to a convex formulation. We further propose a distributed online learning algorithm that can be implemented in a decentralized setting. We also show the stability of the WD-GNN to changes of the underlying graph and analyze the convergence of the proposed online learning procedure. Experiments on movie recommendation, source localization and robot swarm control corroborate theoretical findings and show the potential of the WD-GNN for distributed online learning.
Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images

Recently, few-shot object detection based on fine-tuning has attracted much attention in the field of computer vision. However, due to the scarcity of samples in novel categories, obtaining positive anchors for novel categories is difficult, which implicitly introduces the foreground–background imbalance problem. It is difficult to identify foreground objects from complex backgrounds due to various object sizes and cluttered backgrounds. In this article, we propose a novel context information refinement few-shot detector (CIR-FSD) for remote sensing images. In particular, we design a context information refinement (CIR) module to extract discriminant context features. This module uses dilated convolutions and dense connections to capture rich context information from different receptive fields and then uses a binary map as the supervision label to refine the context information. In addition, we improve the region proposal network (RPN). Concretely, the RPN is fine-tuned on novel categories, and the constraint of non-maximum suppression (NMS) is relaxed, which can obtain more positive anchors for novel categories. Experiments on two remote sensing public datasets show the effectiveness of our detector.
Error mitigation for variational quantum algorithms through mid-circuit measurements

Noisy intermediate-scale quantum algorithms require novel paradigms of error mitigation. To obtain noise-robust quantum computers, each logical qubit is equipped with hundreds or thousands of physical qubits. However, it is not possible to use memory-consuming techniques for current quantum devices having at most hundreds or thousands of physical qubits on their own. For specific problems, valid quantum states have a unique structure as in the case of Fock states and $W$ states where the Hamming weight is fixed, and the evolution takes place in a smaller subspace of the full Hilbert space. With this preknowledge, some errors can be detected during the evolution of the circuit, by filtering the states not obeying the pattern through postselection. In this paper, we present mid-circuit postselection schemes for frequently used encodings such as one-hot, binary, gray, and domain-wall encoding. For the particular subspace of one-hot states, we propose a method that works by compressing the full Hilbert space to a smaller subspace, allowing projecting to the desired subspace without using any ancilla qubits. We demonstrate the effectiveness of the approach for the quantum alternating operator ansatz algorithm. Our method is particularly suitable for the currently available hardware, where measuring and resetting are possible, but classical conditional operators are not.
Relevance of the College of American Pathologists guideline for validating whole slide imaging for diagnostic purposes to cytopathology

Abstract Whole slide imaging (WSI) allows pathologists to view virtual versions of slides on computer monitors. With increasing adoption of digital pathology, laboratories have begun to validate their WSI systems for diagnostic purposes according to reference guidelines. Among these the College of American Pathologists (CAP) guideline includes three strong recommendations (SRs) and nine good practice statements (GPSs). To date, the application of WSI to cytopathology has been beyond the scope of the CAP guideline due to limited evidence. Herein we systematically reviewed the published literature on WSI validation studies in cytology. A systematic search was carried out in PubMed‐MEDLINE and Embase databases up to November 2021 to identify all publications regarding validation of WSI in cytology. Each article was reviewed to determine if SRs and/or GPSs recommended by the CAP guideline were adequately satisfied. Of 3963 retrieved articles, 25 were included. Only 4/25 studies (16%) satisfied all three SRs, with only one publication (1/25, 4%) fulfilling all three SRs and nine GPSs. Lack of a suitable validation dataset was the main missing SR (16/25, 64%) and less than a third of the studies reported intra‐observer variability data (7/25, 28%). Whilst the CAP guideline for WSI validation in clinical practice helped the widespread adoption of digital pathology, more evidence is required to routinely employ WSI for diagnostic purposes in cytopathology practice. More dedicated validation studies satisfying all SRs and/or GPSs recommended by the CAP are needed to help expedite the use of WSI for primary diagnosis in cytopathology.
Ambient Assisted Living: Scoping Review of Artificial Intelligence Models, Domains, Technology, and Concerns

Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work.This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems.This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI.We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern.This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines.PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.
Design and Simulation of a Solar Tracking System for PV

After installing a solar panel system, the orientation problem arises because of the sun’s position variation relative to a collection point throughout the day. It is, therefore, necessary to change the position of the photovoltaic panels to follow the sun and capture the maximum incident beam. This work describes our methodology for the simulation and the design of a solar tracker system using the advantages that the orientation and efficiency of the PV panel offer due to the latitude and the number of hours of sunshine in the testing area. This proposed methodology is experimentally validated through the implementation of a single-axis solar tracker at a specific location (36.261° latitude), which allowed the incorporation of a high-availability tracking mechanism, low precision, and low cost. Based on the results, the feasibility of this type of solar tracker for latitudes close to 36° was demonstrated, as this tracking system costs less than traditional commercial systems. Furthermore, this system increased the collection efficiency compared to a fixed device. Our results provide an excellent platform for engineering technology researchers and students to study the design theory of a sun-tracking solar system.
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing

Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing. Code and models are publicly available at https://github.com/BillyXYB/TransEditor.
2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP) Federated Learning (FL)-based IDS (2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three building blocks, namely: a key exchange protocol (for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of cyber attacks in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based IDS solutions.
An SEM-ANN analysis of the impacts of Blockchain on competitive advantage

Purpose Though Blockchain has been studied in numerous contexts, the understanding of the impacts of Blockchain in achieving competitive advantages remains unexplored. Many industries, organizations and firms are still in a “wait and see” mode. This study aims at examining the effects of the technological, organizational and environmental factors drawn from the TOE framework in generating competitive advantage. Design/methodology/approach A dual-staged deep learning structural equation modeling artificial neural network analysis was conducted on 211 samples of small and medium enterprises. Four neural network models were engaged to rank the normalized importance of each of the predictor variables. Findings The research model can expound 57.99 and 47.33% of the variance in Blockchain adoption and competitive advantage correspondingly. The study successfully identified nonlinear relationships. The theoretical and managerial contributions are useful to scholars and practitioners such as industrial players, investors, chief executive officers (CEOs), managers, decision-makers and other stakeholders that intend to use Blockchain technology. Originality/value Unlike the existing technological–organizational–environmental (TOE) framework that uses a linear model and theoretically assumes that all relationships are linear, this has been the first study, which has successfully validated that there exist nonlinear relationships in the TOE framework. Further, very little has been theorized on the impacts of Blockchain adoption on competitive advantage, especially in the context of SMEs. Therefore, this study is the first one to provide the necessary theoretical foundation that may further extend the current knowledge of Blockchain technology adoption and its impacts.
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR.
Null-text Inversion for Editing Real Images using Guided Diffusion Models

Recent large-scale text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing tools. To edit a real image using these state-of-the-art tools, one must first invert the image with a meaningful text prompt into the pretrained model's domain. In this paper, we introduce an accurate inversion technique and thus facilitate an intuitive text-based modification of the image. Our proposed inversion consists of two key novel components: (i) Pivotal inversion for diffusion models. While current methods aim at mapping random noise samples to a single input image, we use a single pivotal noise vector for each timestamp and optimize around it. We demonstrate that a direct DDIM inversion is inadequate on its own, but does provide a rather good anchor for our optimization. (ii) Null-text optimization, where we only modify the unconditional textual embedding that is used for classifier-free guidance, rather than the input text embedding. This allows for keeping both the model weights and the conditional embedding intact and hence enables applying prompt-based editing while avoiding the cumbersome tuning of the model's weights. Our null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and various prompt editing, showing high-fidelity editing of real images.