Reviewerly/exHarmony huggingface dataset:
Kernel Regression Over Graphs Using Random Fourier Features

This paper proposes efficient batch-based and online strategies for kernel regression over graphs (KRG). The proposed algorithms do not require the input signal to be a graph signal, whereas the target signal is defined over the graph. We first use random Fourier features (RFF) to tackle the complexity issues associated with kernel methods employed in the conventional KRG. For batch-based approaches, we also propose an implementation that reduces complexity by avoiding the inversion of large matrices. Then, we derive two distinct online strategies using RFF, namely, the mini-batch gradient KRG (MGKRG) and the recursive least squares KRG (RLSKRG). The stochastic-gradient KRG (SGKRG) is introduced as a particular case of the MGKRG. The MGKRG and the SGKRG are low-complexity algorithms that employ stochastic gradient approximations in the regression-parameter update. The RLSKRG is a recursive implementation of the RFF-based batch KRG. A detailed stability analysis is provided for the proposed online algorithms, including convergence conditions in both mean and mean-squared senses. A discussion on complexity is also provided. Numerical simulations include a synthesized-data experiment and real-data experiments on temperature prediction, brain activity estimation, and image reconstruction. Results show that the RFF-based batch implementation offers competitive performance with a reduced computational burden when compared to the conventional KRG. The MGKRG offers a convenient trade-off between performance and complexity by varying the number of mini-batch samples. The RLSKRG has a faster convergence than the MGKRG and matches the performance of the batch implementation.
Deep Bayesian Unsupervised Lifelong Learning

Lifelong Learning (LL) refers to the ability to continually learn and solve new problems with incremental available information over time while retaining previous knowledge. Much attention has been given lately to Supervised Lifelong Learning (SLL) with a stream of labelled data. In contrast, we focus on resolving challenges in Unsupervised Lifelong Learning (ULL) with streaming unlabelled data when the data distribution and the unknown class labels evolve over time. Bayesian framework is natural to incorporate past knowledge and sequentially update the belief with new data. We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations. To efficiently maintain past knowledge, we develop a novel knowledge preservation mechanism via sufficient statistics of the latent representation for raw data. To detect the potential new clusters on the fly, we develop an automatic cluster discovery and redundancy removal strategy in our inference inspired by Nonparametric Bayesian statistics techniques. We demonstrate the effectiveness of our approach using image and text corpora benchmark datasets in both LL and batch settings.
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes

Abstract. Geophysical sensors combined with machine learning algorithms were used to understand the pedosphere system and landscape processes and to model soil attributes. In this research, we used parent material, terrain attributes, and data from geophysical sensors in different combinations to test and compare different and novel machine learning algorithms to model soil attributes. We also analyzed the importance of pedoenvironmental variables in predictive models. For that, we collected soil physicochemical and geophysical data (gamma-ray emission from uranium, thorium, and potassium; magnetic susceptibility and apparent electric conductivity) by three sensors (gamma-ray spectrometer, RS 230; susceptibilimeter KT10, Terraplus; and conductivimeter, EM38 Geonics) at 75 points and analyzed the data. The models with the best performance (R2 0.48, 0.36, 0.44, 0.36, 0.25, and 0.31) varied for clay, sand, Fe2O3, TiO2, SiO2, and cation exchange capacity prediction, respectively. Modeling with the selection of covariates at three phases (variance close to zero, removal by correction, and removal by importance) was adequate to increase the parsimony. The results were validated using the method “nested leave-one-out cross-validation”. The prediction of soil attributes by machine learning algorithms yielded adequate values for field-collected data, without any sample preparation, for most of the tested predictors (R2 values ranging from 0.20 to 0.50). Also, the use of four regression algorithms proved to be important since at least one of the predictors used one of the tested algorithms. The performance values of the best algorithms for each predictor were higher than those obtained with the use of a mean value for the entire area comparing the values of root mean square error (RMSE) and mean absolute error (MAE). The best combination of sensors that reached the highest model performance was that of the gamma-ray spectrometer and the susceptibilimeter. The most important variables for most predictions were parent material, digital elevation, standardized height, and magnetic susceptibility. We concluded that soil attributes can be efficiently modeled by geophysical data using machine learning techniques and geophysical sensor combinations. This approach can facilitate future soil mapping in a more time-efficient and environmentally friendly manner.
Plant competition optimization: A novel metaheuristic algorithm

Abstract Plant competition is a fundamental process in plant communities. In one neighbourhood, different plants compete with each other to access shared resources. This paper presents a novel evolutionary algorithm, plant competition optimization (PCO) algorithm, inspired by plant competition processes. In this algorithm, each feasible solution to an optimization problem is assumed to be a plant, with the underlying assumption that each plant grows in competition with its neighbours. In contrast to other techniques inspired by natural phenomena, we attempted to fit the formulation with a known plant growth model, Richards' growth model, and simulate what happens in nature. As the number of plants in a specific area increases, the available resources are decreased, and competition occurs in smaller areas. So, to use this aspect of competition, we develop some mathematical formulation to simulate the decrease of neighbouring area for each plant related to its size to compete on the share resources with the other neighbouring plants. This competition will conduct a smart local search around the most fitted solutions in the optimization context. Furthermore, the reproduction is simulated by producing some seeds in their neighbouring area, which a few of them can migrate to far distances as well. Summing up together, the most powerful plants can grow more. Under competition pressure, their neighbouring area will decrease more than the others, producing more seeds in the next generation. Just as happened in nature, the losers of this competition will die. Our algorithm's efficiency is shown by performing numerical tests on well‐known optimization problems and comparing the results with the other evolutionary algorithms. The results confirm that PCO is effective and efficient in finding sub‐optimal solutions and gives better results than genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), grasshopper optimisation algorithm (GOA), dragonfly algorithm (DA), salp swarm Algorithm (SSA), and comparable results with whale optimization algorithm (WOA) and marine predators algorithm (MPA) on multimodal optimization functions because it efficiently explores the entire search space efficiently and intelligently.
A hybrid IoT services recommender system using social IoT

Nowadays, the increasing number of smart objects and devices which are connected to the Internet leads to the introduction of numerous IoT services. However, due to the overloading of information, many users suffer from several difficulties in obtaining useful and relevant services that meet their needs and interests. Recommender Systems (RSs) provide an efficient solution to this complex problem. Yet, the existing IoT recommender systems are based on objects owned by the users themselves because it is a serious problem, in IoT recommender systems, to find alternative sources of the missing data to fulfill the service requirements. Whereas, the IoT data generated by various objects is inefficiently used. An efficient IoT service recommendation can be reached based on Social IoT (SIoT) using the generated data by various IoT devices owned by friends and friends of friends. In this paper, we propose a hybrid technique that combines implicit collaborative filtering and ontology to recommend personalized IoT services to users. Ontology is used for modeling the SIoT, where we incorporate the social relationships among objects into the recommendation process alongside the ratings while collaborative filtering predicts ratings and generates recommendations. The evaluation results show that our proposed recommendation technique outperforms the collaborative filtering using SIoT on its own without SIoT in terms of personalization and recommendation accuracy.
A novel lifelong machine learning-based method to eliminate calibration drift in clinical prediction models

Clinical prediction models (CPMs) constructed based on artificial intelligence have been proven to have positive impacts on clinical activities. However, the deterioration of CPM performance over time has rarely been studied. This paper proposes a model updating method to solve the calibration drift issue caused by data drift.This paper proposes a novel model updating method based on lifelong machine learning (LML). The effectiveness of the proposed method is verified in four tumor datasets, and a comprehensive comparison with other model updating methods is performed.Changes in data distributions cause model performances to drift. The four compared model updating methods have different effects in terms of improving the discrimination and calibration abilities of the tested models. The LML method proposed in this study improves model performance better than or equivalent to the other methods. The proposed method achieved a mean AUC of 0.8249, 0.8780, 0.8261, and 0.8489, a mean AUPRC of 0.7782, 0.9730, 0.4655, and 0.5728, a mean F1 of 0.6866, 0.9552, 0.2985, and 0.3585, and a mean estimated calibration index (ECI) of 0.0320, 0.0338, 0.0101, and 0.0115 using colorectal, lung, breast and prostate cancer datasets.The LML framework simultaneously monitors model performance and the distribution of disease risk characteristics, enabling it to effectively address the performance degradation caused by gradual and sudden data drifts and provide reasonable explanations for the causes of performance degradation.Monitoring model performance and the underlying data distribution can promote model life cycle iteration with "development-deployment-maintenance-monitoring" as the core, which, in turn, ensures that the model can provide accurate predictions, guides the model update process and explains the causes of model performance changes.
Optimal Tuning of Quantum Generative Adversarial Networks for Multivariate Distribution Loading

Loading data efficiently from classical memories to quantum computers is a key challenge of noisy intermediate-scale quantum computers. Such a problem can be addressed through quantum generative adversarial networks (qGANs), which are noise tolerant and agnostic with respect to data. Tuning a qGAN to balance accuracy and training time is a hard task that becomes paramount when target distributions are multivariate. Thanks to our tuning of the hyper-parameters and of the optimizer, the training of qGAN reduces, on average, the Kolmogorov–Smirnov statistic of 43–64% with respect to the state of the art. The ability to reach optima is non-trivially affected by the starting point of the search algorithm. A gap arises between the optimal and sub-optimal training accuracy. We also point out that the simultaneous perturbation stochastic approximation (SPSA) optimizer does not achieve the same accuracy as the Adam optimizer in our conditions, thus calling for new advancements to support the scaling capability of qGANs.
FTAP: Feature transferring autonomous machine learning pipeline

An effective method in machine learning often involves considerable experience with algorithms and domain expertise. Many existing machine learning methods highly rely on feature selection which are always domain-specific. However, the intervention by data scientists is time-consuming and labor-intensive. To meet this challenge, we propose a Feature Transferring Autonomous machine learning Pipeline (FTAP) to improve efficiency and performance. The proposed FTAP has been extensively evaluated on different modalities of data covering audios, images, and texts. Experimental results demonstrate that the proposed FTAP not only outperforms state-of-the-art methods on ESC-50 dataset with multi-class audio classification but also has good performance in distant domain transfer learning. Furthermore, FTAP outperforms TPOT, a state-of-the-art autonomous machine learning tool, on learning tasks. The quantitative and qualitative analysis proves the feasibility and robustness of the proposed FTAP.
Virtual reality and well-being in older adults: Results from a pilot implementation of virtual reality in long-term care

This paper describes the findings of a pilot implementation project that explored the potential of virtual reality (VR) technology in recreational programming to support the well-being of older adults in long-term care (LTC) homes.32 Adults in four LTC homes participated in a pilot implementation project where they viewed VR experiences of popular locations in Canada created especially for this project. Data in this paper are based on multiple viewing experiences (n = 102) over a two-week period.VR appeared to be an effective distraction from pain for the participants. Participants of this study found the VR experiences to be enjoyable and were relaxed and happy while viewing them. Most participants were attentive or focused while viewing the VR experiences, and the experiences were found to be a source of reminiscence for some of the participants. Participants related well to others around them during a majority of the experiences and the VR experiences were a point of conversation between the staff and the participants.The findings from this pilot implementation reveal that VR shows potential to enhance the physical, emotional, cognitive, and social well-being of older adults living in LTC, including those living with cognitive impairment.
Data Management in Machine Learning Systems

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala
Improving robust adaptive steganography via minimizing channel errors

Online Social Networks (OSNs) are becoming increasingly entrenched in peoples lives and a huge number of images are shared on them every day, which are well-suited platforms for image steganography. Generally, the image can be regarded as the channel for steganography. However, OSNs usually perform lossy processing on uploaded images, which invalidates most of the current non-robust steganography algorithms. To solve the problem of poor detection resistance of existing robust steganography, we divide the causes of channel errors into two parts: steganography-related and steganography-independent. First, we propose a novel method to eliminate the effect of steganography-independent part. The message is embedded in the channel-processed cover which will modify some elements, and then the corresponding elements in the original image are replaced with the modified elements to generate the stego for transmission. Then for the steganography-related part, the wet paper model is employed to minimize the channel error rate. The proposed algorithm can not only resist JPEG recompression but also enhancement filtering which was not considered in previous algorithms. Experimental results show that the proposed algorithm surpasses previous methods in terms of both robustness and security by a clear margin. Besides, take the example of one of the most complex platforms, Facebook, this method can achieve error-free steganography without error correction codes.
Using support vector machine to deal with the missing of solar radiation data in daily reference evapotranspiration estimation in China

Accurate estimation of reference evapotranspiration (ET0) is of great importance for regional water resources planning and irrigation scheduling. The FAO56 recommended Penman-Monteith (P-M) model is widely adopted as the standard method for ET0 estimation, but its application is usually restricted by limited meteorological data worldwide, especially global solar radiation (Rs). This study provided two possible solutions to deal with the missing Rs data in ET0 estimation in China mainland. In the first solution, Rs data were estimated with the Ångström-Prescott (A-P) formula and daily sunshine hours. The values of two A-P formula fundamental coefficients a and b were obtained through three ways: (1) estimated based on limited Rs measurements at 80 solar radiation measurement stations (or site-calibrated); (2) recommended by the FAO-56 manual (or FAO-recommended); and (3) estimated based on the altitude and latitude of each weather station through the support vector machine algorithm (or SVM-estimated). The second solution used the SVM algorithm and available weather variables without Rs. The results showed that the FAO-recommended coefficients a and b were separately overestimated and underestimated in China mainland, which generated the largest simulation errors of Rs. However, the transfer errors from Rs estimations to ET0 estimations were reduced by using the P-M model for all of the three kinds of coefficients. Compared with the Rs-based models, the estimation accuracy of the SVM-ET0 model yielded the highest accuracy both at the training stage (R2 = 0.979; RMSE = 0.273 mm d−1) and the testing stage (R2 = 0.973; RMSE = 0.302 mm d−1). Generally, both the P-M and the machine-learning-based methods could be used for the ET0 estimation, when only Rs data were missing. However, considering the complexity in the programming, the P-M model combining with the A-P formula with the SVM-estimated A-P coefficients is recommended for daily ET0 estimation in China.
Thermal information and teleportation in two-qutrit Heisenberg XX chain model

We employ the concepts of quantum Fisher information, quantum coherence, and fidelity to study a thermally environmental effect on the teleportation process. It is assumed that a single qutrit is teleported through a thermally environmental containing a two-qutrit Heisenberg XX chain under the external magnetic field (EMF) and Dzyaloshinski Moriya (DM) interaction. The energy spectrum of the physical Hamiltonian and its eigenstates are employed to obtain the thermal density matrix, which is used as a thermal teleportation environment. The Resort to the definition of quantum Fisher information for a single qubit system, we estimate the optimal behaviour concerning the EMF and temperature. The result shows that maximizing the estimation degree depends on increasing the EMF and minimizing it requires lowering temperature. The effect of temperature and DM coupling on the quantum coherence and fidelity of the teleported state is studied. It is shown that temperature destroyed the maximum bounds of the two physical quantity, while the DM coupling delays the effect of EMF at the same periods.
Hybrid handcrafted and learned feature framework for human action recognition

Recognising human actions in video is a challenging task in real-world. Dense trajectory (DT) offers accurate recording of motions over time that is rich in dynamic information. However, DT models lack the mechanism to distinguish dominant motions from secondary ones over separable frequency bands and directions. By contrast, deep learning-based methods are promising over the challenge though still suffering from limited capacity in handling complex temporal information, not mentioning huge datasets needed to guide the training. To take the advantage of semantical meaningful and “handcrafted” video features through feature engineering, this study integrates the discrete wavelet transform (DWT) technique into the DT model for gaining more descriptive human action features. Through exploring the pre-trained dual-stream CNN-RNN models, learned features can be integrated with the handcrafted ones to satisfy stringent analytical requirements within the spatial-temporal domain. This hybrid feature framework generates efficient Fisher Vectors through a novel Bag of Temporal Features scheme and is capable of encoding video events whilst speeding up action recognition for real-world applications. Evaluation of the design has shown superior recognition performance over existing benchmark systems. It has also demonstrated promising applicability and extensibility for solving challenging real-world human action recognition problems.
A New Method of Secure Image Encryption by Using Enhanced RSA Algorithm

The images are playing a key role in diverse areas worldwide as these images contains vast amount of essential data. The secrecy as well as privacy of the images has become an immeasurable challenge that demands more attention in order to offer well-organized and faster way of transmission form security perspectives as the images comprising gigantic amount of essential data in diverse images formats. In last decade, multifarious researchers have investigated pragmatic methods to secure the essential data contained in the multifarious images formats but due to constant technological advancements in modern communication systems such existing methods have numerous limitations. In order to, effectively resolve these threats related to the secrecy as well as privacy of the diverse images format, an enhanced RSA (Rivest–Shamir–Adleman) algorithm is investigated to provide required security to the image data during communication over diverse channels. This proposed algorithm is selected due to diverse reasons such as easy implementation as well as less computational complexity in juxtaposition with several existing algorithms explored in last decade by researchers. The selected picture encryption in the testing procedure was done in a pragmatic manner with high degree of precision in order to avoid the chances of the errors and images were secured via the XOR diffusion for confusing the hackers in transmission of the images. Although, extensive research is conducted in this field in past many years but still there is a mammoth scope of more investigation to explore full potential of this domain.