Reviewerly/exHarmony huggingface dataset:
Transformation-Interaction-Rational Representation for Symbolic Regression: A Detailed Analysis of SRBench Results

Symbolic Regression searches for a parametric model with the optimal value of the parameters that best fits a set of samples to a measured target. The desired solution has a balance between accuracy and interpretability. Commonly, there is no constraint in the way the functions are composed in the expression or where the numerical parameters are placed, which can potentially lead to expressions that require a nonlinear optimization to find the optimal parameters. The representation called Interaction-Transformation alleviates this problem by describing expressions as a linear regression of the composition of functions applied to the interaction of the variables. One advantage is that any model that follows this representation is linear in its parameters, allowing an efficient computation. More recently, this representation was extended by applying a univariate function to the rational function of two Interaction-Transformation expressions, called Transformation-Interaction-Rational ( TIR ). The use of this representation was shown to be competitive with the current literature of Symbolic Regression. In this article, we make a detailed analysis of these results using the SRBench benchmark. For this purpose, we split the datasets into different categories to understand the algorithm behavior in different settings. We also test the use of nonlinear optimization to adjust the numerical parameters instead of Ordinary Least Squares. We find through the experiments that TIR has some difficulties handling high-dimensional and noisy datasets, especially when most of the variables are composed of random noise. These results point to new directions for improving the evolutionary search of TIR expressions.
Forecasting the Distortion in Solar Radiation during Midday Hours by Analyzing Solar Radiation during Early Morning Hours

Knowing the fluctuation of solar radiation is essential for reliable and safe operation of power systems with a high share of solar PV power plants. This paper introduces a novel method for forecasting the distortion in global solar radiation during the midday time (during the peak of solar radiation at which the generation of the PV plants is maximum), by analyzing the solar radiation during the very early hours. This paper shows that there is a strong relation between the condition of the solar radiation during the very early minutes of the day (when the generation of the PV plants is low) and the condition of the solar radiation during the midday hours (when the output of the PV plants is enormous). This relation can be utilized to help power systems operators in determining the safe share of PV plants that can be fed to power systems. We analyzed real data of one complete year for two sites in Saudi Arabia to approve this approach. The difference between each two readings was calculated for the hours 7 a.m. and 8 a.m. Then, the negative, zero, and amplitude of the difference was used to formulate a distortion index (DI) that predicts the distortion/fluctuation in the global radiation. It was concluded that the DI could classify the days into three categories: clean, distorted, and medium level according to the value of the DI. The accuracy of this approach was 85.2% and the error was 14.8%.
Indoor occupancy detection and counting system based on boosting algorithm using different sensor data

This research examined how to estimate a room's occupancy utilizing datasets using data collected from various sensors with boosting methods. Different sensors were used to extract features that indicate the occupancy level and the relative changes (PIR motion detectors, CO2 sensors, plug loads, lighting loads, electricity use and Wi-Fi access points). The proposed two-layer method increases the reliability and accuracy of occupancy detection. Five machine learning models were trained and tested using data on occupancy (Logistic Regression, Multi-Layer Perceptron Classifier, Support Vector Machine, Light Gradient Boosting Machine and Gradient Boosting). Accuracy, Precision, F1-score, Recall and ROC were the performance parameters used. Thus, using the Python tool, the proposed optimized model gives better detection and estimated accuracy of occupancy in the indoor environment. As a result, data fusion in conjunction with the Light GBM algorithm's gradient boosting-based categorical features and PSO optimization techniques may be used to predict occupancy data with high accuracy and F1-score (both 99%).
The Conditional Influence of Poverty, Inequality, and Severity of Poverty on Economic Growth in Sub-Saharan Africa

Poverty and inequality represent major policy syndromes that are relevant in the achievement of most United Nations’ Sustainable Development Goals (SDGs) in sub-Saharan Africa (SSA), while economic growth is also essential for the achievement of attendant SDGs. This study extends existing literature by assessing the conditional influence of poverty, income inequality, and severity of poverty on economic growth. The focus is on 42 countries in SSA with data from 1980 to 2019. The Gini index is used to measure income inequality. Poverty is measured in terms of the poverty headcount ratio, while the severity of poverty is computed as the squared of the poverty gap index. The empirical evidence is based on quantile regressions to assess how income inequality and poverty dynamics affect economic growth throughout the conditional distribution of economic growth. Our main finding shows that the negative response of economic growth to poverty is a decreasing function of economic growth. In other words, the incidence of poverty in reducing economic growth decreases with increasing levels of economic growth. In two specifications, the effect of inequality is negative in bottom quantiles and positive in top quantiles of the conditional distribution of economic growth. Policy implications are discussed, especially as it pertains to (1) the relevance of poverty in mitigating economic growth in SSA contingent on initial levels of economic growth and (2) comparative incidences of poverty and inequality in affecting economic growth.
Reducing false positive rate with the help of scene change indicator in deep learning based real-time face recognition systems

In face recognition systems, light direction, reflection, and emotional and physical changes on the face are some of the main factors that make recognition difficult. Researchers continue to work on deep learning-based algorithms to overcome these difficulties. It is essential to develop models that will work with high accuracy and reduce the computational cost, especially in real-time face recognition systems. Deep metric learning algorithms called representative learning are frequently preferred in this field. However, in addition to the extraction of outstanding representative features, the appropriate classification of these feature vectors is also an essential factor affecting the performance. The Scene Change Indicator (SCI) in this study is proposed to reduce or eliminate false recognition rates in sliding windows with a deep metric learning model. This model detects the blocks where the scene does not change and tries to identify the comparison threshold value used in the classifier stage with a new value more precisely. Increasing the sensitivity ratio across the unchanging scene blocks allows for fewer comparisons among the samples in the database. The model proposed in the experimental study reached 99.25% accuracy and 99.28% F-1 score values ​​compared to the original deep metric learning model. Experimental results show that even if there are differences in facial images of the same person in unchanging scenes, misrecognition can be minimized because the sample area being compared is narrowed.
Development and in-depth investigation of pre-service teachers' digital competencies based on DigCompEdu: a case study

The purpose of this study is twofold: (a) To develop digital competencies of pre-service teachers in an educational process; (b) To describing their digital competences by examining artefacts designed by pre-service teachers based on DigCompEdu framework. Holistic single case study was employed in this study and the course was examined as a single unit. The study group consisted of 40 pre-service teachers. A 14-week course has been designed to develop the digital competencies of pre-service teachers based on the DigCompEdu framework. The e-portfolios and reflection reports of 40 pre-service teachers who participated in the study were examined and evaluated according to the indicators presented for each competence within the framework of DigCompEdu. Pre-service teachers' digital competences were assessed as folows: mostly C2 level in digital resources; mostly C1 level in teaching and learning, and mostly B2 level in assessment and empowering learning. An education process that blends theoretical and practical assignments for the pre-service teachers' digital competencies to be improved was conducted in this study. It is expected that the steps that were followed in the study in the process of training pre-service teachers be directive towards researchers who wish to study this subject. It is important that contextual and cultural qualities are taken into consideration in the interpretation of the findings in the study. This study contributes to the literature in terms of evaluating the digital skills of pre-service teachers based on reflection reports and e-portfolios, instead of self-report surveys.
‘We have the best gays, folks’

In the wake of the 2016 election of Donald Trump, users on the pro-Trump online forum thedonald.win engaged in violently homophobic and Islamophobic discourses. This study uses a critical discourse analytic approach to investigate how users on this forum contradictorily invoke homosexuality to construct Muslims as sexually deviant while also situating them as homophobic and therefore incompatible with the users’ brand of American nationalism. This is an example of homonationalism, using the United States’ supposed tolerance of homosexuality to uphold American exceptionalism and paint Muslims as anti-gay and thus anti-American. While this form of homonationalism was originally formulated in the immediate aftermath of 9/11, the presidency of Donald Trump has altered the way the homonationalist project is discursively constructed on an interactional level.
Interactive method research of dual mode information coordination integration for astronaut gesture and eye movement signals based on hybrid model

The lightweight human-robot interaction model with high real-time, high accuracy, and strong anti-interference capability can be better applied to future lunar surface exploration and construction work. Based on the feature information inputted from the monocular camera, the signal acquisition and processing fusion of the astronaut gesture and eye-movement modal interaction can be performed. Compared with the single mode, the human-robot interaction model of bimodal collaboration can achieve the issuance of complex interactive commands more efficiently. The optimization of the target detection model is executed by inserting attention into YOLOv4 and filtering image motion blur. The central coordinates of pupils are identified by the neural network to realize the human-robot interaction in the eye movement mode. The fusion between the astronaut gesture signal and eye movement signal is performed at the end of the collaborative model to achieve complex command interactions based on a lightweight model. The dataset used in the network training is enhanced and extended to simulate the realistic lunar space interaction environment. The human-robot interaction effects of complex commands in the single mode are compared with those of complex commands in the bimodal collaboration. The experimental results show that the concatenated interaction model of the astronaut gesture and eye movement signals can excavate the bimodal interaction signal better, discriminate the complex interaction commands more quickly, and has stronger signal anti-interference capability based on its stronger feature information mining ability. Compared with the command interaction realized by using the single gesture modal signal and the single eye movement modal signal, the interaction model of bimodal collaboration is shorter about 79% to 91% of the time under the single mode interaction. Regardless of the influence of any image interference item, the overall judgment accuracy of the proposed model can be maintained at about 83% to 97%. The effectiveness of the proposed method is verified.
MDM-CPS: A few-shot sample approach for source camera identification

The purpose of source camera identification (SCI) is to identify the source device of target images, so as to ensure the source reliability of digital images. However, most state-of-the-art results require sufficient training samples which are hard to obtain in practice. In this work, we propose an approach based on multi-distance measures and coordinate pseudo-label selection (MDM-CPS) approach to solve the problem of few-shot sample databases. Based on semi-supervised learning, this approach iteratively expands and updates the labeled database. Our approach drastically reduces the interference of noisy pseudo-labels in training and ensures highly-confident prediction of the pseudo-label samples. Through comprehensive experiments, our approach has achieved the best performance in few-shot sample scenarios of the common benchmark databases (i.e., Dresden database and VISION database) in the field of source camera identification.
Mel-S3R: Combining Mel-spectrogram and self-supervised speech representation with VQ-VAE for any-to-any voice conversion

The self-supervised speech representation (S3R) has succeeded in many downstream tasks, such as speaker recognition and voice conversion thanks to its high-level information. Voice conversion (VC) is a task to convert the source speech into a target speaker's voice. Though S3R features effectively encode content and speaker information, spectral features contain low-level acoustic information that is complementary to the S3R. As a result, solely relying on the S3R features for VC may not be optimal. In order to seek speech representation carrying both high-level learned information and low-level spectral details for VC, we proposed a three-level attention to combine Mel-spectrogram (Mel) and S3R, denoted as Mel-S3R. In particular, S3R features are high-level learned representations extracted by a pre-trained network with self-supervised learning. Whereas Mel is the spectral feature representing the acoustic information. Then the proposed Mel-S3R is used as the input of any-to-any VQ-VAE-based VC and the experiments are performed as a downstream task. Objective metrics and subjective listening tests have demonstrated that the proposed Mel-S3R speech representation facilitates the VC framework to achieve robust performance in terms of both speech quality and speaker similarity.
Efficient spatiotemporal context modeling for action recognition

Contextual information is essential in action recognition. However, local operations have difficulty in modeling two distant elements, and directly computing the dense relations between any two points brings huge computation and memory burden. Inspired by the recurrent 2D criss-cross attention (RCCA-2D) in image segmentation, we propose a recurrent 3D criss-cross attention (RCCA-3D) that factorizes the global relation map into sparse relation maps to model long-range spatiotemporal context with minor costs for video-based action recognition. Specifically, we first propose a 3D criss-cross attention (CCA-3D) module. Compared with the CCA-2D which only works in space, it can capture the spatiotemporal relationship between the points in the same line along the direction of width, height and time. However, only replacing the two CCA-2Ds in the RCCA-2D with our CCA-3Ds cannot model the spatiotemporal context in videos. Therefore, we further duplicate the CCA-3D with a recurrent mechanism to transmit the relation between the points in a line to a plane and finally to the whole spatiotemporal space. To make the RCCA-3D adaptive for action recognition, we propose a novel recurrent structure rather than directly extending the original 2D structure to 3D. In the experiments, we make a thorough analysis of different structures of RCCA-3D, verifying the proposed structure is more suitable for action recognition. We also compare our RCCA-3D with the non-local attention, showing that the RCCA-3D requires 25% fewer parameters and 30% fewer FLOPs with even higher accuracy. Finally, equipped with our RCCA-3D, 3 networks achieve better and leading performance on 5 RGB-based and skeleton-based datasets.
Non-binary IoU and progressive coupling and refining network for salient object detection

Recently, many salient object detection (SOD) methods decouple image features into body features and edge features, which imply a new development direction in the field of SOD. Most of them mainly focus on how to decouple features, but the fusion method for the decoupled features can be further improved. In this paper, we propose a network, namely Progressive Coupling and Refining Network (PCRNet), which allows the progressive coupling and refining of the decoupled features to get accurate salient features. Furthermore, a novel loss, namely Non-Binary Intersection over Union (NBIoU), is proposed based on the characteristics of non-binary label images and the principle of Intersection over Union (IoU) loss. Experimental results show that our NBIoU performance surpasses binary cross-entropy (BCE), IoU and Dice on non-binary label images. The results on five popular SOD benchmark datasets show that our PCRNet significantly exceeds the previous state-of-the-art (SOTA) methods on multiple metrics. In addition, although our method is designed for SOD, it is comparable with previous SOTA methods on multiple benchmark datasets for camouflaged object detection without any modification on the network structure, verified the robustness of the proposed method.
A vehicle classification model based on deep active learning

Vehicle classification is widely used in intelligent transportation and smart cities. However, the good performance of vehicle classification often depends on a large number of labeling data, which leads to the high cost of manual labeling. How to use a small number of labeled samples with rich features to achieve good classification performance is a challenge in vehicle classification. To solve these problems, we propose a deep active learning framework called Feature Fusion Spatial Pyramid Pooling and Re-parameterization Visual Geometry Group (FFSPP-RepVGG). The framework is mainly divided into two parts: query strategy and feature extraction module. Specifically, the query strategy uses Feature Fusion (FF) to calculate the loss, thus defining uncertainty and being able to select more valuable samples for labeling training. The feature extraction module uses Spatial Pyramid Pooling and Re-parameterization Visual Geometry Group (SPP-RepVGG) model, which can extract more reliable image features. It can reduce the cost of data labeling and have better performance at the same time. The experimental results on the BIT-Vehicle data set and the car-10 data set show that the FFSPP-RepVGG framework has superior performance than that of the comparison models.
Self-immolation among Kurdish Women: Causes and solutions

We study a photonic circuit consisting of a quantum dot, QD, coupled to a photon cavity over a wide range of temperature up to room temperature. A key component of such a system is presented here in the form of a Purcell-enhanced single-photon source based on Cavity Quantum Electrodynamics, cQED. We use a real set of pure dephasing data extracted from experimental measurements of InGaAs QD to calculate the effective QD-cavity coupling strength, the Purcell factor, and the single photon efficiency emerged from the QD-cavity system in the cases without and with detuning. In the non-detuned system, the effective coupling strength between the QD and the resonator decreases with increasing temperature, results in a decrease in efficiency. However, when the temperature of the QD-cavity system increases under Purcell effect conditions, the detuned QD-cavity system induces spontaneous emission rate enhancement. As a result, we found that the increase in efficiency can be obtained under a certain condition, when the maximum effective coupling strength and the Purcell factor are related to the spontaneous emission and the pure dephasing rates. Additionally, the influences of the pumping mechanism on the efficiency of the QD-system were examined and showed that the pumping process can be used to further increase in efficiency. Our results can be advantageous for advanced quantum optics applications once temperature is taken into account.
PRIAH: Private Alerts in Healthcare

Privacy is today a mandatory property to implement in modern systems and particularly in Healthcare systems where patients' personal data is exchanged between different persons and services. Many solutions are currently implemented for privacy preservation, mainly for EHR (Electronic Health Record) data exchange and storage. Nevertheless, when dealing with electronic alerts about the patient's emergent health state, current systems still suffer from scalability issues. Indeed, privacy implementation generally requires cryptography and interception techniques which slow down alert arrival to its destinations. In this work, we propose a Big data-based system called PRIAH which stands for PRIvate Alerts in Healthcare. In this system, all alerts contain no identification indications about the patient's identity, so that only authorized users can access patients' private data. The originality of PRIAH consists in the optimal integration of three subsystems for ensuring private alerts: A machine learning-based component for alert detection, a pseudonymization system for privacy implementation, and a Big data streaming system for scalability. The system evaluation shows that the integration of the three sub-systems has no significant overhead compared to Big data non-secure systems.
Benchmark Performance of the Multivariate Polynomial Public Key Encapsulation Mechanism

This paper presents the results of benchmarking the quantum-safe Multivariate Public Key Cryptosystem (MPPK) key encapsulation mechanism for quadratic solvable univariate polynomials. We used a benchmarking tool containing implementations of the four NIST Post-Quantum Cryptography (PQC) finalists: Kyber, McEliece, NTRU, and Saber. The benchmark demonstrates that the performance of MPPK is comparable with that of the four PQC algorithms, offering relatively fast key generation and small key sizes. Key encapsulation and decapsulation performance are comparable with the PQC schemes, with room for improvement.
Blockchain Survey for Security and Privacy in the e-Health Ecosystem

This survey paper focuses on the use of blockchain technology to ensure security and privacy properties in e-health applications. For that, background information on blockchain technology was conducted, followed by a classification of e-health applications based on data life-cycle components related to security and privacy concerns. Then, we discuss the security and privacy requirements of an e-health system and how blockchain can address these requirements, accompanied by a detailed examination of the data security and privacy solutions offered by five blockchain platforms. Lastly, we provide some guidelines and recommendations for using blockchain to secure e-health applications and preserve patient privacy. This work provides technical and intuitive insights into concepts, requirements, development, and deployment technologies for healthcare professionals and system designers interested in adopting blockchain to maintain the security and privacy of healthcare systems.