Reviewerly/exHarmony huggingface dataset:
Interval type-3 fuzzy fractal approach in sound speaker quality control evaluation

This article is presenting a proposal for the mathematical definition of interval type-3 fuzzy sets, as a basis for later constructing all the theory needed for building interval type-3 fuzzy systems for different areas of application. It is well known that the most used form of fuzzy logic is the one called type-1, which later evolved into interval and general type-2. Recently, we have seen that type-2 fuzzy has outperformed type-1 in many real problems when there is high level of uncertainty, dynamic situations or nonlinearity. For these reasons it is worthwhile to explore the new area of type-3 fuzzy logic, and in this paper, we are putting forward some mathematical definitions of type-3 fuzzy sets and systems, as a starting point for later proposing their applications. In addition, we illustrate the potential of interval type-3 in a decision-making application in quality control. The aim of this study is to show the advantages of type-3 in a quality control problem that can be viewed as a decision-making process under uncertainty that estimates product quality Speaker manufacturing requires strict quality control and a combination of type-3 with fractal theory is able to outperform previous works with type-2 and type-1 in quality control. Simulation results of the proposed approach are compared with the results provided by human experts in sound speaker quality control to validate the effectiveness of the type-3 fuzzy fractal method in handling the uncertainty of this problem.
Improving malicious email detection through novel designated deep-learning architectures utilizing entire email

In today's email dependent world, cyber criminals often target organizations using a variety of social engineering techniques and specially crafted malicious emails. When successful, such attacks can result in significant harm to physical and digital systems and assets, the leakage of sensitive information, reputation damage, and financial loss. Despite the plethora of studies on the detection of phishing attacks and malicious links in emails, there are no solutions capable of effectively, quickly, and accurately coping with more complex email-based attacks, such as malicious email attachments. This paper presents the first fully automated malicious email detection framework using deep ensemble learning to analyze all email segments (body, header, and attachments); this eliminates the need for human expert intervention for feature engineering. In this paper, we also demonstrate how an ensemble framework of deep learning classifiers each of which are trained on specific portions of an email (thereby independently utilizing the entire email) can generalize better than popular email analysis methods that analyze just a specific portion of the email for analysis. The proposed framework is evaluated comprehensively and with an AUC of 0.993, the proposed framework's results surpass state-of-the-art malicious email detection methods, including human expert feature-based machine learning models by a TPR of 5%.
Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: A case study Guilan province

The applicability of a combined fuzzy best-worst method (FBWM) and geographic information system (GIS) was investigated to find the optimal location of a solar power plant site in Guilan province, which has a temperate and humid climate. Fifteen criteria were determined based on the guidelines and performance of photovoltaic (PV) systems and divided into four main categories: environmental, economic, technical, and social. The data were achieved from satellite images and organisations. The FBWM was applied to weight the criteria based on the opinion of the decision-maker, and then spatial analysis was done by GIS to find the potential sites for the solar power plant. The result of this study indicates that among the total area of Guilan province, about 0.74% is “the most suitable” region for solar power plants and 0.46, 0.54, 0.53, and 97.73% are in the class of “suitable”, “relatively suitable”, “least suitable” and “limited”. Four alternatives of A1, A2, A3 and A4 with an area of larger than 60,638 m2 which is the required area for a 10 MW solar power plant and a distance from power lines of less than 2 km, were selected. The techno-economic aspects were conducted among the four alternatives, which show that about a 35% increase of the feed-in tariff is necessary to decrease the payback period of the most economical site (A3) from 6.7 to 5 y, which encourages the investors of a PV power plant. Also, the greenhouse gas (GHG) emission analysis indicates that the construction of a 10 MW power plant on A3 could prevent the release of 10,113 t of GHG into the atmosphere annually.
A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network

The rapid development of technology has digitized customer payment behavior towards a cashless society.To a certain extent, this has created a feast for miscreants to commit fraud.According to Nilson (2020), global fraud loss is projected to reach over $35 billion by 2025.Consequently, the need for a novel method to prevent this menace is undisputed.This research was conducted on the IEEE-CIS Fraud Detection Dataset provided by Vesta Corporation.Based on the logic of labeling for converting the entire account to ''Fraud=1'' once the credit card has fraud, we navigate the research process towards predicting fraudulent credit cards rather than fraudulent transactions.The key idea behind the proposed model is user separation, in which we divide users into old and new people before applying CatBoost and Deep Neural Network to each category, respectively.In addition, a variety of techniques to improve detection accuracy, namely handling heavily imbalanced datasets, feature transformation, and feature engineering, are also presented in detail in this paper.The experimental results showed that our model performed well, as we obtained AUC scores of 0.97 (CatBoost) and 0.84 (Deep Neural Network).
Body Part-Level Domain Alignment for Domain-Adaptive Person Re-Identification With Transformer Framework

Although existing domain-adaptive person re-identification (re-ID) methods have achieved competitive performance, most of them highly rely on the reliability of pseudo-label prediction, which seriously limits their applicability as noisy labels cannot be avoided. This paper designs a Transformer framework based on body part-level domain alignment to solve the above-mentioned issues in domain-adaptive person re-ID. Different parts of the human body (such as head, torso, and legs) have different structures and shapes. Therefore, they usually exhibit different characteristics. The proposed method makes full use of the dissimilarity between different human body parts. Specifically, the local features from the same body part are aggregated by the Transformer to obtain the corresponding class token, which is used as the global representation of this body part. Additionally, a Transformer layer-embedded adversarial learning strategy is designed. This strategy can simultaneously achieve domain alignment and classification of the class token for each human body part in both target and source domains by an integrated discriminator, thereby realizing domain alignment at human body part level. Compared with existing domain-level and identity-level alignment methods, the proposed method has a stronger fine-grained domain alignment capability. Therefore, the information loss or distortion that may occur in the feature alignment process can be effectively alleviated. The proposed method does not need to predict pseudo labels of any target sample, so the negative impact caused by unreliable pseudo labels on re-ID performance can be effectively avoided. Compared with state-of-the-art methods, the proposed method achieves better performance on the datasets that are in line with real-world scene settings. The source codes of this paper will be available at https://github.com/lhf12278/BPDA.
Randomizing multi-product formulas for Hamiltonian simulation

Quantum simulation, the simulation of quantum processes on quantum computers, suggests a path forward for the efficient simulation of problems in condensed-matter physics, quantum chemistry, and materials science. While the majority of quantum simulation algorithms are deterministic, a recent surge of ideas has shown that randomization can greatly benefit algorithmic performance. In this work, we introduce a scheme for quantum simulation that unites the advantages of randomized compiling on the one hand and higher-order multi-product formulas, as they are used for example in linear-combination-of-unitaries (LCU) algorithms or quantum error mitigation, on the other hand. In doing so, we propose a framework of randomized sampling that is expected to be useful for programmable quantum simulators and present two new multi-product formula algorithms tailored to it. Our framework reduces the circuit depth by circumventing the need for oblivious amplitude amplification required by the implementation of multi-product formulas using standard LCU methods, rendering it especially useful for early quantum computers used to estimate the dynamics of quantum systems instead of performing full-fledged quantum phase estimation. Our algorithms achieve a simulation error that shrinks exponentially with the circuit depth. To corroborate their functioning, we prove rigorous performance bounds as well as the concentration of the randomized sampling procedure. We demonstrate the functioning of the approach for several physically meaningful examples of Hamiltonians, including fermionic systems and the Sachdev-Ye-Kitaev model, for which the method provides a favorable scaling in the effort.
A Super-Resolution Flexible Video Coding Solution for Improving Live Streaming Quality

In the context of the latest growing popularity of live video streaming, ensuring high video quality has become one of the most significant challenges faced by all live streaming platforms. Insufficient uplink bandwidth is an important factor that influences these live video transmissions, affecting their bitrate and latency and consequently the associated video streaming quality. This paper proposes a novel flexible super-resolution-based video coding and uploading framework (FlexSRVC) that improves the quality of live video streaming in limited uplink network bandwidth conditions. FlexSRVC includes a flexible video coding scheme, which compresses high-resolution key and non-key video frames to a lower bitrate in order to reduce the upload delay. A new flexible bitrate adaptation algorithm is also proposed to select dynamically the number of frames to be compressed and the compression ratio by jointly considering uplink network conditions and available cloud computing resources. Trace-driven emulations demonstrate that FlexSRVC provides the same quality while reducing up to 25% of the required bandwidth compared to the original encoding method (H.264). FlexSRVC improves users' QoE by at least 50% compared to a super resolution-based method which employs reconstruction of all video frames in uplink bandwidth constrained conditions.
Towards optimal foreign object debris detection in an airport environment

Foreign object debris (FOD) can critically damage aircraft engines as well as injure personnel in an airport environment. Airfield inspectors routinely inspect runways for the presence of FOD items, which differ in material, shape, and color, using conventional and automated methods. The major shortcoming of the current methods is their inability to detect all types of foreign objects in an accurate and timely manner for removal from the airport runways. In this study, we address this shortcoming, i.e., the lack of accuracy and timeliness in detection, by developing an object detection framework to detect FOD for quick removal from the airfields. Our proposed FOD detection framework consists of (i) unmanned aerial system (UAS) for inspecting and collecting data from the airfields, (ii) data preprocessing and augmentation techniques to counter the issue of learning on limited types of foreign objects, weather conditions, and airport surface materials that are present in the data sets, and (iii) a computer vision-based object detection model to attain high accuracy and faster inference time for deployment in a real-world airport environment. We generated the training data with the UAS at an air force range and developed various models, including the You Only Look Once (YOLO) object detector family of models (one-stage object detectors) in our framework. Our models are evaluated on previously unseen data collected by the UAS and a publicly available data set of FOD images. The experiment results demonstrate that our proposed approach with a version of the YOLO model (YOLOv4) with transfer learning provides a faster inference time for FOD detection and outperforms the other models in the precision and recall metric values.
Artificial intelligence in clinical endoscopy: Insights in the field of videomics

Artificial intelligence is being increasingly seen as a useful tool in medicine. Specifically, these technologies have the objective to extract insights from complex datasets that cannot easily be analyzed by conventional statistical methods. While promising results have been obtained for various -omics datasets, radiological images, and histopathologic slides, analysis of videoendoscopic frames still represents a major challenge. In this context, videomics represents a burgeoning field wherein several methods of computer vision are systematically used to organize unstructured data from frames obtained during diagnostic videoendoscopy. Recent studies have focused on five broad tasks with increasing complexity: quality assessment of endoscopic images, classification of pathologic and nonpathologic frames, detection of lesions inside frames, segmentation of pathologic lesions, and in-depth characterization of neoplastic lesions. Herein, we present a broad overview of the field, with a focus on conceptual key points and future perspectives.
Pathological image classification via embedded fusion mutual learning

Deep learning models have been widely used in pathological image classification. However, most researches employ complex but inefficient neural networks to implement this task. And the implicit but complementary pathological knowledge between heterogeneous networks has not been fully explored. To alleviate these problems, we propose a novel method, named embedded fusion mutual learning (EFML). First, online mutual learning is carried out between two parallel heterogeneous networks, which forms two effective feature extractors. Second, an adaptive feature fusion classifier and an ensemble classifier are embedded into EFML to learn the diverse knowledge from feature maps and logits output, simultaneously. Finally, we combine the logits output and the fused feature maps to jointly supervise model training. We conducted experiments on three public datasets: BreaKHis, BACH and LC25000. Extensive experiments demonstrate the effectiveness, robustness, and generalization ability of EFML. Moreover, EFML is efficient and magnification scale independent. It can get evident performance enhancements on the high-resolution pathological images. All these firmly demonstrate its clinical practicality.
The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data

With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Finally, we describe the potential deficiencies and further in-depth insights of UDA in the field of remote sensing.
Fairness-aware Federated Matrix Factorization

Achieving fairness over different user groups in recommender systems is an important problem. The majority of existing works achieve fairness through constrained optimization that combines the recommendation loss and the fairness constraint. To achieve fairness, the algorithm usually needs to know each user’s group affiliation feature such as gender or race. However, such involved user group feature is usually sensitive and requires protection. In this work, we seek a federated learning solution for the fair recommendation problem and identify the main challenge as an algorithmic conflict between the global fairness objective and the localized federated optimization process. On one hand, the fairness objective usually requires access to all users’ group information. On the other hand, the federated learning systems restrain the personal data in each user’s local space. As a resolution, we propose to communicate group statistics during federated optimization and use differential privacy techniques to avoid exposure of users’ group information when users require privacy protection. We illustrate the theoretical bounds of the noisy signal used in our method that aims to enforce privacy without overwhelming the aggregated statistics. Empirical results show that federated learning may naturally improve user group fairness and the proposed framework can effectively control this fairness with low communication overheads.
Learning knowledge graph embedding with a dual-attention embedding network

Knowledge Graph Embedding (KGE) aims to retain the intrinsic structural information of knowledge graphs (KGs) via representation learning, which is critical for various downstream tasks including personalized recommendations, intelligent search, and relation extraction. The graph convolutional network (GCN), due to its remarkable performance in modeling graph data, has recently been studied extensively in the KGE field. However, when learning entity representations, most attention-based GCN approaches treat neighborhoods as a whole to measure their importance without considering the direction information of relations. Additionally, these approaches make relation representations perform self-update via a learnable matrix, resulting in ignoring the impact of neighborhood information on representation learning of relations. To this end, this study presents an innovative framework, namely learning knowledge graph embedding with a dual-attention embedding network (D-AEN), to jointly propagate and update the representations of both relations and entities via fusing neighborhood information. Here the dual attentions consist of a bidirectional attention mechanism and a relation-specific attention mechanism for jointly measuring the importance of neighborhoods in respectively learning entity and relation representations. Thus D-AEN enables elements like relations and entities to interact well semantically, which makes their learned representations retain more effective information of KGs. Extensive experimental results on three standard link prediction datasets demonstrate the superiority of D-AEN over several state-of-the-art approaches.
k-best feature selection and ranking via stochastic approximation

This study presents SPFSR, a novel stochastic approximation approach for performing simultaneous k-best feature ranking (FR) and feature selection (FS) based on Simultaneous Perturbation Stochastic Approximation (SPSA) with Barzilai and Borwein (BB) non-monotone gains. SPFSR is a wrapper-based method which may be used in conjunction with any given classifier or regressor with respect to any suitable corresponding performance metric. Numerical experiments are performed on 47 public datasets which contain both classification and regression problems, with the mean accuracy and R2 reported from four different classifiers and four different regressors respectively. In over 80% of classification experiments and over 85% of regression experiments SPFSR provided a statistically significant improvement or equivalent performance compared to existing, well-known FR techniques. Furthermore, SPFSR obtained a better classification accuracy and R-squared on average compared to utilising the entire feature set.
Quality of Experience Optimization in IoT Energy Services

We propose a novel Quality of Experience (QoE) metric as a key criterion to optimize the composition of energy services in a crowdsourced IoT environment. A novel importance-based composition algorithm is proposed to ensure the highest QoE for consumers. A set of experiments is conducted to evaluate the proposed approaches’ effectiveness and efficiency.
Query-guided networks for few-shot fine-grained classification and person search

Few-shot fine-grained classification and person search appear as distinct tasks and literature has treated them separately. But a closer look unveils important similarities: both tasks target categories that can only be discriminated by specific object details; and the relevant models should generalize to new categories, not seen during training. We propose a novel unified Query-Guided Network (QGN) applicable to both tasks. QGN consists of a Query-guided Siamese-Squeeze-and-Excitation subnetwork which re-weights both the query and gallery features across all network layers, a Query-guided Region Proposal subnetwork for query-specific localisation, and a Query-guided Similarity subnetwork for metric learning. QGN improves on a few recent few-shot fine-grained datasets, outperforming other techniques on CUB by a large margin. QGN also performs competitively on the person search CUHK-SYSU and PRW datasets, where we perform in-depth analysis.