Reviewerly/exHarmony huggingface dataset:
$$\mathsf {FABBOO}$$ - Online Fairness-Aware Learning Under Class Imbalance

Data-driven algorithms are employed in many applications, in which data become available in a sequential order, forcing the update of the model with new instances. In such dynamic environments, in which the underlying data distributions might evolve with time, fairness-aware learning cannot be considered as a one-off requirement, but rather it should comprise a continual requirement over the stream. Recent fairness-aware stream classifiers ignore the problem of class distribution skewness. As a result, such methods mitigate discrimination by "rejecting" minority instances at large due to their inability to effectively learn all classes. In this work, we propose \(\mathsf {FABBOO}\), an online fairness-aware approach that maintains a valid and fair classifier over a stream. \(\mathsf {FABBOO}\) is an online boosting approach that changes the training distribution in an online fashion based on both stream imbalance and discriminatory behavior of the model evaluated over the historical stream. Our experiments show that such long-term consideration of class-imbalance and fairness are beneficial for maintaining models that exhibit good predictive- and fairness-related performance.
The ability of personas: An empirical evaluation of altering incorrect preconceptions about users

False preconceptions about users can result in poor design, product development, and marketing decisions, so rectifying these preconceptions is essential for organizations. This research quantitatively evaluates the ability of data-driven personas to alter decision makers’ preconceptions about their online social media users. We conduct a within-participant experiment of 31 professionals carrying out a workplace task scenario. The participants’ conceptions of user attributes are recorded both before and after interacting with personas created from real user analytics data. Using statistical analysis to compare the responses, we find that personas had a significant effect on changing preconceptions about the audience segments. After interacting with personas, 81% of the participants changed their preconceptions of the audience, and 94% of the participants maintained or increased the accuracy of their perceptions of the audience after engaging with the personas. Moreover, the confidence of the participants in their responses increased. However, two participants did not change their preconceptions even when faced with contradictory factual information, which highlights the need for accompanying initiatives to align user preconceptions with user data fully.
Software-defined data protection

Most modern data processing pipelines run on top of a distributed storage layer, and securing the whole system, and the storage layer in particular, against accidental or malicious misuse is crucial to ensuring compliance to rules and regulations. Enforcing data protection and privacy rules, however, stands at odds with the requirement to achieve higher and higher access bandwidths and processing rates in large data processing pipelines. In this work we describe our proposal for the path forward that reconciles the two goals. We call our approach "Software-Defined Data Protection" (SDP). Its premise is simple, yet powerful: decoupling often changing policies from request-level enforcement allows distributed smart storage nodes to implement the latter at line-rate. Existing and future data protection frameworks can be translated to the same hardware interface which allows storage nodes to offload enforcement efficiently both for company-specific rules and regulations, such as GDPR or CCPA. While SDP is a promising approach, there are several remaining challenges to making this vision reality. As we explain in the paper, overcoming these will require collaboration across several domains, including security, databases and specialized hardware design.
Varying-Coefficient Stochastic Differential Equations with Applications in Ecology

Abstract Stochastic differential equations (SDEs) are popular tools to analyse time series data in many areas, such as mathematical finance, physics, and biology. They provide a mechanistic description of the phenomenon of interest, and their parameters often have a clear interpretation. These advantages come at the cost of requiring a relatively simple model specification. We propose a flexible model for SDEs with time-varying dynamics where the parameters of the process are nonparametric functions of covariates, similar to generalized additive models. Combining the SDE and nonparametric approaches allows for the SDE to capture more detailed, non-stationary, features of the data-generating process. We present a computationally efficient method of approximate inference, where the SDE parameters can vary according to fixed covariate effects, random effects, or basis-penalty smoothing splines. We demonstrate the versatility and utility of this approach with three applications in ecology, where there is often a modelling trade-off between interpretability and flexibility. Supplementary materials accompanying this paper appear online.
Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events

Operational Risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technological failures and failures in the execution & administration of processes. However, due to the large amount of qualitative information, the uncertainty and the low frequency at which these risk events are generated in an organization, their modeling is still a technological challenge. This paper takes up this challenge and presents a fuzzy convolutional deep-learning model to estimate, based on the Basel III recommendations, the ORLoss Component (OR-LC) in an organization. The proposed model integrates qualitative information as linguistic random variables, as well as risk events data from different sources using multi-dimensional fuzzy credibility concepts. The results show the stability of the proposed model with respect to the OR-LC estimation from both structural and dimensional point of views, making it an ideal tool for modeling OR from the perspective of: (a) the regulators (Basel Committee on Banking Supervision) by allowing the integration of experts' criteria into the OR-LC; (b) the insurers by allowing the integration of risk events from different sources; and (c) organizations and financial entities by allowing the a priori evaluation of the OR-LC of new financial products based on technological platforms and electronic channels.
Behavioral Intention of Students in Higher Education Institutions Towards Online Learning During COVID-19

Across the world, Higher Education Institutions (HEIs) were mandatory to transform their teaching model from face-to-face interaction to online learning. Neither the faculty nor the students were ready for this sudden transformation, most of the HEIs were practicing online learning for the sake of changing or technology adoption. COVID-19 changes the HEIs standpoint towards online delivery, as of mid of March 2020, the only online learning come to be the reasonable solution for teaching in schools and HEIs. Therefore, this study aimed to develop a research model to explore the effect of perceived enjoinment, perceived ease of use, perceived usefulness, social influence on the students' intention and behavioral towards online learning during COVID-19 in Oman. The developed model was validated by employing Structural Equation Modeling (SEM) and the data collected via online questionnaire from 191 students whom have just completed the "Educational Technology" course on Spring 2020 at A'Sharqiyah University-Oman (ASU). The main finding of this study indicating that the identified factors; PEOU and SI significantly influenced students' Behavioral Intention (BI) towards online learning, where others; PE and PU were not supported in this study.
Deep Generative Models to Counter Class Imbalance: A Model-Metric Mapping With Proportion Calibration Methodology

The most pervasive segment of techniques in managing class imbalance in machine learning are re-sampling-based methods. The emergence of deep generative models for augmenting the size of the under-represented class, prompts one to review the question of the suitability of the model chosen for data augmentation with the metric selected for the-goodness-of classification. This work defines this suitability by using newly-sampled data points from each generative model first to the degree of parity, and studying classification performance on a large set of metrics. We extend the investigation to different proportions of augmented data points for identifying the sensitivity of the metric to the degree of imbalance, leading to the discovery of an optimum proportion against the metric. The models used are GAN, VAE and RBM and the metrics include Precision, Recall, F1-Score, AUC, G-Mean and Balanced Accuracy. We offer a comparison of these models with the established class of data synthesizing counterparts on the aforementioned metrics. Deep generative models outperform the state-of-the-art on 5 metrics on multiple datasets and also comprehensively surpass the baselines. This work thereby recommends the following model-metric mappings: VAE for high Precision and F1-Score, RBM for high Recall and GAN for high AUC, G-Mean and Balanced Accuracy under various recommended proportions of the minority class.
Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.
Remote sensing of inland Sabkha and a study of the salinity and temporal stability for sustainable development: A case study from the West coast of Qatar

The inland sabkha of the Arabian Gulf is important to study for the occurrence of minerals, rocks, soil salinity, and stability of the sabkha due to the high demand for infrastructure and agriculture development region. This study describes the spectral absorptions of evaporite minerals, discriminates rocks, maps salt crusts, gypsiferous soil flats, and soil salinity, and studies the temporal stability of an inland sabkha of the Dukhan area, west coast of the State of Qatar. This was performed using satellite data of the Hyperion of EO1, ASTER of Terra, and multispectral instrument (MSI) of Sentinel-2. The occurrence of minerals in the area is detected using Hyperion data by the linear spectral unmixing (LSU) method and studied for their spatial distribution. The different geological formations of the sabkha were discriminated by using the VNIR (visible and near-infrared) and SWIR (shortwave infrared) spectral bands from ASTER by principal component analysis (PCA). The image developed by using the principal components (R:PC2, G:PC3, B:PC5) showed the formations in different tones. Salinity of the area was mapped using monthly data of MSI from 2018 to 2020 by normalized difference salinity index (NDSI) (band11-band12)/(band11 + band12). The results of the index displayed the distribution of salinity in the area. Besides, moisture of the area was studied by using the normalized difference moisture index (NDMI) (b8-b11)/(b8 + b11) and described the temporal stability of the sabkha. All the results of image analyses were validated through field and laboratory studies. The study of laboratory spectra of evaporite minerals namely gypsum, anhydrite, and halite present in the salt crusts and gypsiferous soil flats showed their unique spectral absorptions in between 1.4–1.5 μm and 1.9–2.0 μm whereas, the calcite and dolomite minerals of the carbonate formations exhibited deep absorptions near 2.345 and 2.495 μm respectively.
Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples’ mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible–Near Infrared–Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky-Golay First-Derivative (SG-FD) and Savitzky-Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using Partial Least Squares Regression (PLSR) coefficients and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A, and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg/kg and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using the ordinary kriging technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. This study’s findings indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas.
Image-matching enhancement using a polarized intensity-hue-saturation fusion method

Atmospheric absorption and scattering (e.g., haze) cause degradation in the image quality of outdoor scenes, which affects the image-matching process. The scale-invariant feature transform (SIFT) algorithm is not effective in haze. Edge information is required to enhance the matching process. Utilizing the polarization information expressed by the Stokes vector component S1 with its edge information can improve the keypoint localization in the matching process. In this paper, a novel, to the best of our knowledge, fusion method called polarized intensity-hue-saturation is proposed that uses polarization and depth information by fusion of a polarized haze-removed image with the estimated depth and by applying S1. The instant dehazing method uses polarized images to obtain a haze-removed image and its estimated depth map. The fused image has high spatial details required for enhancing the matching process. The experimental results show that the proposed method outperforms the existing image-matching schemes and improves the conventional SIFT matching method.
A hybrid multi-class imbalanced learning method for predicting the quality level of diesel engines

Establishing an effective quality level prediction of diesel engines is of great significance for controlling the production quality through subsequent process improvements and reducing manufacturing costs. Data-driven methods have become the most promising methods due to their self-learning ability and independence of complex manufacturing processes. However, the dataset collected from a diesel engine assembly line is characterized by high dimensionality and multiclass imbalance. To realize the high-accuracy quality level prediction of diesel engines, this study develops a novel hybrid multi-class imbalanced learning method that combines particle swarm optimization (PSO) based wrapper method and dynamic oversampling approach with adaptive boosting (DysBoost), where PSO based wrapper method is applied to adaptively select the optimum feature subset and DySBoost is utilized to address the multi-class imbalanced classification problem. Computational experiments are conducted on three standard imbalanced datasets and one diesel engine assembly dataset, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and efficiency.
Hilbert–Schmidt speed as an efficient figure of merit for quantum estimation of phase encoded into the initial state of open n-qubit systems

Hilbert-Schmidt speed (HSS) is a special type of quantum statistical speed which is easily computable, since it does not require diagonalization of the system state. We find that, when both HSS and quantum Fisher information (QFI) are calculated with respect to the phase parameter encoded into the initial state of an n-qubit register, the zeros of the HSS dynamics are actually equal to those of the QFI dynamics. Moreover, the signs of the time-derivatives of both HSS and QFI exactly coincide. These findings, obtained via a thorough investigation of several paradigmatic open quantum systems, show that HSS and QFI exhibit the same qualitative time evolution. Therefore, HSS reveals itself as a powerful figure of merit for enhancing quantum phase estimation in an open quantum system made of n qubits. Our results also provide strong evidence for both contractivity of the HSS under memoryless dynamics and its sensitivity to system-environment information backflows to detect the non-Markovianity in high-dimensional systems, as suggested in previous studies.
Parallel Proxy Re-Encryption Workload Distribution for Efficient Big Data Sharing in Cloud Computing

Cloud computing enables users and organizations to conveniently store and share data in large volumes and to enjoy on-demand services. Security and the protection of big data sharing from various attacks is the most challenging issue. Proxy re-encryption (PRE) is an effective method to improve the security of data sharing in the cloud environment. However, in PRE schemes, offloading big data for re-encryption will impose a heavy computational burden on the cloud proxy server, resulting in an increased computation delay and response time for the users. In this paper, we propose a novel parallel PRE workload distribution scheme to dynamically route the big data re-encryption process into the fog of the network. Moreover, this paper proposes a dynamic load balancing technique to avoid an excessive workload for the fog nodes. It also uses lightweight asymmetric cryptography to provide end-to-end security for the big data sharing between users. Within the proposed scheme, the offloading overhead on the centralized cloud server is effectively mitigated. Meanwhile, the processing delay incurred by the big data re-encryption process is efficiently improved.
Visual Cascade Analytics of Large-scale Spatiotemporal Data

Many spatiotemporal events can be viewed as contagions. These events implicitly propagate across space and time by following cascading patterns, expanding their influence, and generating event cascades that involve multiple locations. Analyzing such cascading processes presents valuable implications in various urban applications, such as traffic planning and pollution diagnostics. Motivated by the limited capability of the existing approaches in mining and interpreting cascading patterns, we propose a visual analytics system called VisCas. VisCas combines an inference model with interactive visualizations and empowers analysts to infer and interpret the latent cascading patterns in the spatiotemporal context. To develop VisCas, we address three major challenges 1) generalized pattern inference; 2) implicit influence visualization; and 3) multifaceted cascade analysis. For the first challenge, we adapt the state-of-the-art cascading network inference technique to general urban scenarios, where cascading patterns can be reliably inferred from large-scale spatiotemporal data. For the second and third challenges, we assemble a set of effective visualizations to support location navigation, influence inspection, and cascading exploration, and facilitate the in-depth cascade analysis. We design a novel influence view based on a three-fold optimization strategy for analyzing the implicit influences of the inferred patterns. We demonstrate the capability and effectiveness of VisCas with two case studies conducted on real-world traffic congestion and air pollution datasets with domain experts.
A Mura Detection Model Based on Unsupervised Adversarial Learning

Mura is a phenomenon in which the displays have various uneven display defects and has the characteristics of irregular shape and different sizes.For Mura detection, traditional detection methods have the following two problems: One is the problem of data imbalance.The second is that new shapes and sizes of Mura may appear at any time during the inspection process.In response to the above problems, this paper proposes the Res-unetGAN network, which is an unsupervised anomaly detection method based on generative adversarial network.The generative network is an autoencoder structure composed of ResNet50 and UNet to learn the normal distribution of normal samples.The discriminator is a convolutional neural network based on deep separable convolution and forms a game process with the generator.The network only needs normal samples during the training process, and the network is optimized by the error loss between the original samples and the reconstructed samples.In the test, a reconstruction error score will be designed according to the reconstruction quality, and the defect in the sample will be judged by the reconstruction error score, so as to achieve the goal of anomaly detection.After repeated experiments on the Mura data set, the detection accuracy of Mura defect is better than that of several models compared.The proposed model has a unique application prospect in other industrial anomaly detection since it only requires normal samples for training.