Reviewerly/exHarmony huggingface dataset:
The class imbalance problem in deep learning

Deep learning has recently unleashed the ability for Machine learning (ML) to make unparalleled strides. It did so by confronting and successfully addressing, at least to a certain extent, the knowledge bottleneck that paralyzed ML and artificial intelligence for decades. The community is currently basking in deep learning's success, but a question that comes to mind is: have all of the issues previously affecting machine learning systems been solved by deep learning or do some issues remain for which deep learning is not a bulletproof solution? This question in the context of the class imbalance becomes a motivation for this paper. Imbalance problem was first recognized almost three decades ago and has remained a critical challenge at least for traditional learning approaches. Our goal is to investigate whether the tight dependency between class imbalances, concept complexities, dataset size and classifier performance, known to exist in traditional learning systems, is alleviated in any way in deep learning approaches and to what extent, if any, network depth and regularization can help. To answer these questions we conduct a survey of the recent literature focused on deep learning and the class imbalance problem as well as a series of controlled experiments on both artificial and real-world domains. This allows us to formulate lessons learned about the impact of class imbalance on deep learning models, as well as pose open challenges that should be tackled by researchers in this field.
What Did My AI Learn? How Data Scientists Make Sense of Model Behavior

Data scientists require rich mental models of how AI systems behave to effectively train, debug, and work with them. Despite the prevalence of AI analysis tools, there is no general theory describing how people make sense of what their models have learned. We frame this process as a form of sensemaking and derive a framework describing how data scientists develop mental models of AI behavior. To evaluate the framework, we show how existing AI analysis tools fit into this sensemaking process and use it to design AIFinnity , a system for analyzing image-and-text models. Lastly, we explored how data scientists use a tool developed with the framework through a think-aloud study with 10 data scientists tasked with using AIFinnity to pick an image captioning model. We found that AIFinnity ’s sensemaking workflow reflected participants’ mental processes and enabled them to discover and validate diverse AI behaviors.
The metaverse, online communities, and (real) urban space

The metaverse, online communities, and (real) urban spaceWith the COVID-19 pandemic, technological advancements and investments accelerated to create an alternative to the real world that makes it possible for various activities and experiences to be handled online.Among these developments, the metaverse comes to the fore because it makes possible real and virtual experiences simultaneously, regardless of the time and space the user exists in, and it acts as a mediator and medium to bring these two environments together.This article discusses the possible impacts of advancements in the metaverse on (real) urban space considering the socio-spatial dialectic through theory synthesis and adaptation.An overall evaluation of the socio-spatial impacts of this is also included and opened up for discussion.Based on a literature review, it is expected that technological developments like the metaverse will introduce a new organization to physical and virtual socio-spatial relations, and new socio-technological groups will be created in them and by using them.
Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia

In order to satisfy increasing energy demand and mitigate global warming worldwide, the implementation of photovoltaic (PV) clean energy installations needs to become common practice. However, solar energy is known to be dependent on several random factors, including climatic and geographic conditions. Prior to promoting PV systems, an assessment study of the potential of the considered location in terms of power yield should be conducted carefully. Manual assessment tools are unable to handle high amounts of data. In order to overcome this difficulty, this study aims to investigate various artificial intelligence (AI) models—with respect to various intuitive prediction benchmark models from the literature—for predicting solar energy yield in the Ha’il region of Saudi Arabia. Based on the daily data, seven seasonal models, namely, naïve (N), simple average (SA), simple moving average (SMA), nonlinear auto-regressive (NAR), support vector machine (SVM), Gaussian process regression (GPR) and neural network (NN), were investigated and compared based on the root mean square error (RMSE) and mean absolute percentage error (MAPE) performance metrics. The obtained results showed that all the models provided good forecasts over three years (2019, 2020, and 2021), with the naïve and simple moving average models showing small superiority. The results of this study can be used by decision-makers and solar energy specialists to analyze the power yield of solar systems and estimate the payback and efficiency of PV projects.
Wearable Sensors-Based Hand Gesture Recognition for Human–Robot Collaboration in Construction

The development of robotic machines has shown the potential to promote automation in construction. One of the critical enablers of human–robot collaboration is a user-friendly interface to support their interactions. Compared with other interfaces, hand gesture is an effective communication channel on construction sites. This article proposes a system for recognition of construction workers’ hand gestures using wearable sensors on fingers. The system starts with synchronizing, normalizing, and smoothing finger motions. Then, the motion data are extracted through a sliding window and fed into an enhanced fully convolutional neural network (FCN) for the hand gesture recognition. The system was tested through a system validation test and achieved the precision and recall of 85.7% and 93.8%, respectively. A pilot study demonstrated the use of the proposed system to interact with a robotic dump truck. The system was further compared with vision-based recognition methods to quantitatively and qualitatively assess their relative benefits and limitations.
Rotation-Invariant Feature Learning via Convolutional Neural Network With Cyclic Polar Coordinates Convolutional Layer

Convolutional neural networks (CNNs) have been demonstrated to be powerful tools to automatically learn effective features from large datasets. Though features learned in CNNs are approximately scale-, translation-, and position-invariant, and their capacity in dealing with image rotations remains limited. In this article, a novel cyclic polar coordinate convolutional layer (CPCCL) is proposed for CNNs to handle the problem of rotation invariance for feature learning. First, the proposed CPCCL converts rotation variation into translation variation using polar coordinates transformation, which can easily be handled by CNNs. Moreover, cyclic convolution is designed to completely handle the translation variation converted from rotation variation by conducting convolution in a cyclic shift mode. Note that the proposed CPCCL is capable of generalization and can be used as a preprocessing layer for classification CNNs to learn the rotation-invariant feature. Extensive experiments over three benchmark datasets demonstrate that the proposed CPCCL can clearly handle the rotation-sensitive problem in traditional CNNs and outperforms several state-of-the-art rotation-invariant feature learning algorithms.
Rank minimization via adaptive hybrid norm for image restoration

Rank minimization methods have achieved promising performance in various image processing tasks. However, there are still two challenging problems in the existing works. One is that most of the current methods only regularize singular values by using a single l1-norm, such as the well-known nuclear norm minimization (NNM) and the weighted nuclear norm minimization (WNNM). Consequently, many small singular values are shrunk to zero, which is unbeneficial for restoring image details. The other is that how to adaptively evaluate the importance of each singular value is still a suspending problem. In this paper, we propose a novel rank minimization method, namely adaptive hybrid norm minimization (AHNM) model, to solve the above problems. Specifically, for each singular value, we employ l2-norm to compensate for l1-norm, and introduce a significance factor to assess its importance adaptively. More importantly, we show that closed-form solutions for all subproblems can be derived simply by using alternating optimization. With the aid of the proposed AHNM model, we further develop a general yet effective image restoration algorithm based on the nonlocal self-similarity (NSS) of images. Numerous experimental results demonstrate that the proposed AHNM model consistently outperforms many state-of-the-art restoration methods, including model-based methods and deep learning-based methods.
How well do investor sentiment and ensemble learning predict Bitcoin prices?

Investor sentiment is widely recognized as the major determinant of cryptocurrency prices. Although earlier research has revealed the influence of investor sentiment on cryptocurrency prices, it has not yet generated cohesive empirical findings on an important question: How effective is investor sentiment in predicting cryptocurrency prices? To address this gap, we propose a novel prediction model based on the Bitcoin Misery Index, using trading data for cryptocurrency rather than judgments from individuals who are not Bitcoin investors, as well as bagged support vector regression (BSVR), to forecast Bitcoin prices. The empirical analysis is performed for the period between March 2018 and May 2022. The results of this study suggest that the addition of the sentiment index enhances the predictive performance of BSVR significantly. Moreover, the proposed prediction system, enhanced with an automatic feature selection component, outperforms state-of-the-art methods for predicting cryptocurrency for the next 30 days.
Local Quantum Uncertainty and Quantum Interferometric Power in an Anisotropic Two-Qubit System

Investigating the favorable configurations for non-classical correlations preservation has remained a hotly debated topic for the last decade. In this regard, we present a two-qubit Heisenberg spin chain system exposed to a time-dependent external magnetic field. The impact of various crucial parameters, such as initial strength and angular frequency of the external magnetic field along with the state’s purity and anisotropy of the spin-spin on the dynamical behavior of quantum correlations are considered. We utilize local quantum uncertainty (LQU) and quantum interferometric power (QIP) to investigate the dynamics of quantum correlations. We show that under the critical angular frequency of the external magnetic field and the spin-spin anisotropy, quantum correlations in the system can be successfully preserved. LQU and QIP suffer a drop as the interaction between the system and field is initiated, however, are quickly regained by the system. This tendency is confirmed by computing a measure of non-classical correlations according to the Clauser–Horne–Shimony–Holt inequality. Notably, the initial and final preserved levels of quantum correlations are only varied when variation is caused in the state’s purity.
IoT-Enabled Healthcare Data Analysis in Virtual Hospital Systems Using Industry 4.0 Smart Manufacturing

Background: The world is transitioning to Industry 4.0, representing the transition to digital, fully machine-driven environments and cyberphysical systems. Industry 4.0 comprises various technologies and innovations that enable development in multiple perspectives, which are implemented in many different sectors. Problem: The major challenges are the high cost, high rate of failure, security and privacy issues, and there is a need for highly skilled labor for applying healthcare data analysis. Aim: To resolve these issues, we employ the proposed system of Industry 4.0 smart manufacturing for IoT-enabled healthcare data analysis in virtual hospital systems with machine learning (ML) techniques. Methods: The proposed system contains five alternative solutions under smart manufacturing. First, the healthcare data analysis is applied for Weber’s syndrome. That is, this will be used to analyze Weber’s syndrome during its consistent treatment. Second, the IoT-enabled healthcare data handling system works based on edge-assisted edge computing that is used to apply IoT to the healthcare data handling system. The healthcare data analysis in virtual hospital systems uses machine learning for driving data synthesis. Finally, the Industry 4.0 smart manufacturing is applied to the IoT-enabled healthcare data analysis to realize efficient data digitization, especially in smart hospitals with smart sensors for virtual IoT-enabled devices surveillance of Weber’s syndrome. Result: The data digitization based on Industry 4.0 smart manufacturing analysis is considered for data processing, storage and transmission. The proposed system is 62% more efficient than the other analyzed methods. The identification of Weber’s syndrome is 69.8% more efficient than the existing midbrain stroke syndrome identification. The processing and storage of data results are 45.78% more efficient than the current encryption method. Finally, the priority-aware healthcare data analysis based on ML provides 63.4% efficient, faster and more accurate diagnoses in the personalized treatment.
Neural network for ordinal classification of imbalanced data by minimizing a Bayesian cost

Ordinal classification of imbalanced data is a challenging problem that appears in many real world applications. The challenge is to simultaneously consider the order of the classes and the class imbalance, which can notably improve the performance metrics. The Bayesian formulation allows to deal with these two characteristics jointly: It takes into account the prior probability of each class and the decision costs, which can be used to include the imbalance and the ordinal information, respectively. We propose to use the Bayesian formulation to train neural networks, which have shown excellent results in many classification tasks. A loss function is proposed to train networks with a single neuron in the output layer and a threshold based decision rule. The loss is an estimate of the Bayesian classification cost, based on the Parzen windows estimator, which is fitted for a thresholded decision. Experiments with several real datasets show that the proposed method provides competitive results in different scenarios, due to its high flexibility to specify the relative importance of the errors in the classification of patterns of different classes, considering the order and independently of the probability of each class.
Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images

Medical centers and healthcare providers have concerns and hence restrictions around sharing data with external collaborators. Federated learning, as a privacy-preserving method, involves learning a site-independent model without having direct access to patient-sensitive data in a distributed collaborative fashion. The federated approach relies on decentralized data distribution from various hospitals and clinics. The collaboratively learned global model is supposed to have acceptable performance for the individual sites. However, existing methods focus on minimizing the average of the aggregated loss functions, leading to a biased model that performs perfectly for some hospitals while exhibiting undesirable performance for other sites. In this paper, we improve model "fairness" among participating hospitals by proposing a novel federated learning scheme called Proportionally Fair Federated Learning, short Prop-FFL. Prop-FFL is based on a novel optimization objective function to decrease the performance variations among participating hospitals. This function encourages a fair model, providing us with more uniform performance across participating hospitals. We validate the proposed Prop-FFL on two histopathology datasets as well as two general datasets to shed light on its inherent capabilities. The experimental results suggest promising performance in terms of learning speed, accuracy, and fairness.
Semantic-Aware Sensing Information Transmission for Metaverse: A Contest Theoretic Approach

With the advancement of network and computer technologies, virtual cyberspace keeps evolving, and Metaverse is the main representative. As an irreplaceable technology that supports Metaverse, the sensing information transmission from the physical world to Metaverse is vital. Inspired by emerging semantic communication, in this paper, we propose a semantic transmission framework for transmitting sensing information from the physical world to Metaverse. Leveraging the in-depth understanding of sensing information, we define the semantic bases, through which the semantic encoding of sensing data is achieved for the first time. Consequently, the amount of sensing data that needs to be transmitted is dramatically reduced. Unlike conventional methods that undergo data degradation and require data recovery, our approach achieves the sensing goal without data recovery while maintaining performance. To further improve Metaverse service quality, we introduce contest theory to create an incentive mechanism that motivates users to upload data more frequently. Experimental results show that the average data amount after semantic encoding is reduced to about 27.87% of that before encoding, while ensuring the sensing performance. Additionally, the proposed contest theoretic based incentive mechanism increases the sum of data uploading frequency by 27.47% compared to the uniform award scheme.
Machine Learning Developments and Applications in Solid‐Earth Geosciences: Fad or Future?

Abstract After decades of low but continuing activity, applications of machine learning (ML) in solid Earth geoscience have exploded in popularity. This special collection provides a snapshot of those applications, which range from data processing to inversion and interpretation, for which ML appears particularly well suited. Inevitably, there are variations in the degree to which these methods have been developed. We hope that the progress seen in some areas will inspire efforts in others. Challenges remain, including the formidable task of how geoscience can keep pace with developments in ML while ensuring the scientific rigor that our field depends on, but with improvements in sensor technology and accelerating rates of data accumulation, the methods of ML seem poised to play an important role for the foreseeable future.
Evaluating semantic similarity and relatedness between concepts by combining taxonomic and non-taxonomic semantic features of WordNet and Wikipedia

Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disambiguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical coverage. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic similarity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/meronyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe concepts. We present a novel technique for extracting ‘is-a’ and ‘part-whole’ relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia’s semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks.
PRCBERT: Prompt Learning for Requirement Classification using BERT-based Pretrained Language Models

Software requirement classification is a longstanding and important problem in requirement engineering. Previous studies have applied various machine learning techniques for this problem, including Support Vector Machine (SVM) and decision trees. With the recent popularity of NLP technique, the state-of-the-art approach NoRBERT utilizes the pre-trained language model BERT and achieves a satisfactory performance. However, the dataset PROMISE used by the existing approaches for this problem consists of only hundreds of requirements that are outdated according to today's technology and market trends. Besides, the NLP technique applied in these approaches might be obsolete. In this paper, we propose an approach of prompt learning for requirement classification using BERT-based pretrained language models (PRCBERT), which applies flexible prompt templates to achieve accurate requirements classification. Experiments conducted on two existing small-size requirement datasets (PROMISE and NFR-Review) and our collected large-scale requirement dataset NFR-SO prove that PRCBERT exhibits moderately better classification performance than NoRBERT and MLM-BERT (BERT with the standard prompt template). On the de-labeled NFR-Review and NFR-SO datasets, Trans_PRCBERT (the version of PRCBERT which is fine-tuned on PROMISE) is able to have a satisfactory zero-shot performance with 53.27% and 72.96% F1-score when enabling a self-learning strategy.
A Detailed Survey on Federated Learning Attacks and Defenses

A traditional centralized method of training AI models has been put to the test by the emergence of data stores and public privacy concerns. To overcome these issues, the federated learning (FL) approach was introduced. FL employs a privacy-by-design architecture to train deep neural networks utilizing decentralized data, in which numerous devices collectively build any machine learning system that does not reveal users’ personal information under the supervision of a centralized server. While federated learning (FL), as a machine learning (ML) strategy, may be effective for safeguarding the confidentiality of local data, it is also vulnerable to attacks. Increased interest in the FL domain inspired us to write this paper, which informs readers of the numerous threats to and flaws in the federated learning strategy, and introduces a multiple-defense mechanism that can be employed to fend off threats.