Reviewerly/exHarmony huggingface dataset:
A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion

Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.
On probabilistic term rewriting

We study the termination problem for probabilistic term rewrite systems. We prove that the interpretation method is sound and complete for a strengthening of positive almost sure termination, when abstract reduction systems and term rewrite systems are considered. Two instances of the interpretation method—polynomial and matrix interpretations—are analyzed and shown to capture interesting and nontrivial examples when automated. We capture probabilistic computation in a novel way by means of multidistribution reduction sequences, thus accounting for both the nondeterminism in the choice of the redex and the probabilism intrinsic in firing each rule.
Effective Attention Modeling for Neural Relation Extraction

Relation extraction is the task of determining the relation between two entities in a sentence.Distantly-supervised models are popular for this task.However, sentences can be long and two entities can be located far from each other in a sentence.The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via coreference.Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence.Also, the words in a sentence do not contribute equally in identifying the relation between the two entities.To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism.Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.
Governance, capital flight and industrialisation in Africa

The study examines the role of governance in modulating the effect of capital flight on industrialisation in Africa. The empirical evidence is based on Generalised Method of Moments and governance is bundled by principal component analysis, namely (i) political governance from political stability and “voice and accountability”; (ii) economic governance from government effectiveness and regulation quality; and (iii) institutional governance from corruption-control and the rule of law. First, governance increases industrialisation whereas capital flight has the opposite effect; and second, governance does not significantly mitigate the negative effect of capital flight on industrialisation. Policy implications are discussed.
Impact of E-Learning vs Traditional Learning on Student’s Performance and Attitude

Background: Recently, with the proliferation of internet technology, the E-learning has become an essential method and new epitome that is widely used and implemented by educational institutions across the globe. Objective: The main objective of the current survey was to study the impact of E-learning on the students’ academic performance. Methodology: It was a facility-based and quasi-experimental research design that carried out in Najran University College of nursing during the period from January to August 2019. By adopting a purposive sampling technique, 80 under-graduate nursing students (40 experimental group+40 as controls) that aged 21-24 years old had been recruited to participate in the current survey. Final exam results and a self-administered questionnaire were used for collecting data. Results: The findings revealed that the mean scores obtained by students in the final exam by the E-learning group (Experimental) are statistically significantly higher than those for the traditional group (controls) (t=3.45, df=37, p=0.002). Additionally, the results showed that the mean of the students’ overall satisfaction with the traditional face-to-face lectures in the control group was 6.26, while the mean of the students’ overall satisfaction with E-learning in the experimental group was 8.74. The difference between students’ attitudes was significant (p=0.015) in favor of the experimental group. Conclusion: The key findings of the present study show a significant difference in learning outcomes besides positive attitudes between online and traditional learners which can be a viable alternative learning method for higher education. It also contributes to the current literature in the area of online instruction and E-learning.
Querying and Annotating Model Histories with Time-Aware Patterns

Models are not static entities: they evolve over time due to changes. Changes may inadvertently and surprisingly violate constraints imposed. Therefore, the models need to be monitored for compliance. On the one hand, in traditional design-time applications, new and evolving requirements impose changes on a model over time. These changes may accidentally break design rules. Further, the growing complexity of the models may need to be tracked for manageability. On the other hand, newer applications use models at runtime; building runtime abstractions that are used to control a system. Adopters of these approaches will need to query the history of the system to check if the models evolved as expected, or to find out the reasons for a particular behavior. Changes over models at runtime are more frequent than changes over design models. To cover these demands, we argue that a flexible and scalable approach for querying the history of the models is needed to study the evolution and for compliance sake. This paper presents a set of extensions to a model query language inspired in the Object Constraint Language (the Epsilon Object Language) for traversing the history of a model, and for making temporal assertions that will allow the elicitation of historic information. As querying long histories may be costly, the paper presents an approach that annotates versions of interest as they are observed, in order to provide efficient recalls in possible future queries. The approach has been implemented in a model indexing tool, and is demonstrated through a case study from the autonomous and self-adaptive systems domain.
Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), and ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discover that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally, we provide in-depth experiment results comparing the effectiveness of the major works in each category.
Automated Document Analysis for Quick Personal Health Record Creation.

We propose our own optimization methods to speed up the work and compare different approaches for high-quality implementation of the idea. The subproblem of the first stage in image processing is a ...
<b>hetGP</b>: Heteroskedastic Gaussian Process Modeling and Sequential Design in <i>R</i>

An increasing number of time-consuming simulators exhibit a complex noise structure that depends on the inputs.For conducting studies with limited budgets of evaluations, new surrogate methods are required in order to simultaneously model the mean and variance fields.To this end, we present the hetGP package, implementing many recent advances in Gaussian process modeling with input-dependent noise.First, we describe a simple, yet efficient, joint modeling framework that relies on replication for both speed and accuracy.Then we tackle the issue of data acquisition leveraging replication and exploration in a sequential manner for various goals, such as for obtaining a globally accurate model, for optimization, or for contour finding.Reproducible illustrations are provided throughout.
DMGAN: Discriminative Metric-based Generative Adversarial Networks

With the proposed of Generative Adversarial Networks (GANs), the generative adversarial models have been extensively studied in recent years. Although probability-based methods have achieved remarkable results in image synthesis tasks, there are still some unsolved challenges that are difficult to overcome. In this paper, we propose a novel model, called Discriminative Metric-based Generative Adversarial Networks (DMGANs), for generating real-like samples from the perspective of deep metric learning. To be specific, the generator is trained to generate realistic samples by reducing the distance between real and generated samples. Instead of outputting probability, the discriminator in our model is conducted as a feature extractor, which is well constrained by introducing a combination of identity preserving loss and discriminative loss. Meanwhile, to reduce the identity preserving loss, we calculate the distance between samples and their corresponding center and update these centers during training to improve the stability of our model. In addition, a data-dependent strategy of weight adaption is proposed to further improve the quality of generated samples. Experiments on several datasets illustrate the potential of our model.
Image Feature Correspondence Selection: A Comparative Study and a New Contribution

Image feature correspondence selection is pivotal to many computer vision tasks from object recognition to 3D reconstruction. Although many correspondence selection algorithms have been developed in the past decade, there still lacks an in-depth evaluation and comparison in the open literature, which makes it difficult to choose the appropriate algorithm for a specific application. This paper attempts to fill this gap by evaluating eight competing correspondence selection algorithms including both classical methods and current state-of-the-art ones. In addition to preselected correspondences, we have compared different combinations of detector and descriptor on four standard datasets. The diversity of those datasets cover a wide range of uncertainty factors including zoom, rotation, blur, viewpoint change, JPEG compression, light change, different rendering styles and multiple structures. We have measured the quality of competing correspondence selection algorithms in terms of four performance metrics - i.e., precision, recall, F-measure and efficiency. Moreover, we propose to combine the strengths of eight competing methods by combining their correspondence selection results. Extensive experimental results are reported to demonstrate the superiority of several fusion strategies to individual methods, which suggests the possibility of adaptively combining those methods for even better performance.
Are Noisy Sentences Useless for Distant Supervised Relation Extraction?

The noisy labeling problem has been one of the major obstacles for distant supervised relation extraction. Existing approaches usually consider that the noisy sentences are useless and will harm the model's performance. Therefore, they mainly alleviate this problem by reducing the influence of noisy sentences, such as applying bag-level selective attention or removing noisy sentences from sentence-bags. However, the underlying cause of the noisy labeling problem is not the lack of useful information, but the missing relation labels. Intuitively, if we can allocate credible labels for noisy sentences, they will be transformed into useful training data and benefit the model's performance. Thus, in this paper, we propose a novel method for distant supervised relation extraction, which employs unsupervised deep clustering to generate reliable labels for noisy sentences. Specifically, our model contains three modules: a sentence encoder, a noise detector and a label generator. The sentence encoder is used to obtain feature representations. The noise detector detects noisy sentences from sentence-bags, and the label generator produces high-confidence relation labels for noisy sentences. Extensive experimental results demonstrate that our model outperforms the state-of-the-art baselines on a popular benchmark dataset, and can indeed alleviate the noisy labeling problem.
Distributed Machine Learning through Heterogeneous Edge Systems

Many emerging AI applications request distributed machine learning (ML) among edge systems (e.g., IoT devices and PCs at the edge of the Internet), where data cannot be uploaded to a central venue for model training, due to their large volumes and/or security/privacy concerns. Edge devices are intrinsically heterogeneous in computing capacity, posing significant challenges to parameter synchronization for parallel training with the parameter server (PS) architecture. This paper proposes ADSP, a parameter synchronization model for distributed machine learning (ML) with heterogeneous edge systems. Eliminating the significant waiting time occurring with existing parameter synchronization models, the core idea of ADSP is to let faster edge devices continue training, while committing their model updates at strategically decided intervals. We design algorithms that decide time points for each worker to commit its model update, and ensure not only global model convergence but also faster convergence. Our testbed implementation and experiments show that ADSP outperforms existing parameter synchronization models significantly in terms of ML model convergence time, scalability and adaptability to large heterogeneity.
Development of Moodle E-Learning Media in Industrial Revolution 4.0 Era

This study aims to produce moodle e-learning media that is feasible to use as a medium for learning the physics of geometric optical matter.The main problem is in the current era of industrial revolution 4.0, teachers as the front guard in the world of education are able to improve competence in facing the era of industrial revolution 4.0.One of the demands is that the teacher must be able to develop learning media according to the industrial revolution 4.0 as developed in this research, namely moodlee-learning media.This type of research is Research and Development and the development model used is the model of the development of 4-D.The instruments used in this study include learning device instruments and data collection instruments.The results of the study are that it has moodle e-learning media that is suitable for use as a medium for learning the physics of geometric optical matter.
Reinventing Ourselves: New and Emerging Roles of Academic Librarians in Canadian Research-Intensive Universities

The academic library profession is being redefined by the shifting research and scholarly landscape, the transformation in higher education, and advances in technology. A survey of librarians working in Canada’s research-intensive universities was conducted to explore new and emerging roles. This study focuses on librarians’ activities in: Research Support, Teaching and Learning, Digital Scholarship, User Experience, and Scholarly Communication. It addresses the scope and nature of the new roles, the skills required to provide new services, and the confidence librarians have in their abilities to perform the new roles. It also reports on librarians’ job satisfaction and their perceived impact on the academic enterprise.
Biomedical data analysis using neuro-fuzzy model with post-feature reduction

Now-a-days, a large volume of biomedical data are continuously generated from various biomedical devices and experiments due to the rapid technological advancement in medical science. The effective analysis of these biomedical data such as extracting the significant features biologically and diagnostically is really a challenging task. This paper proposes a Neuro-Fuzzy model with post-feature reduction to analyze these complex biomedical data. The proposed Neuro-Fuzzy approach uses class belongingness fuzzification of input patterns to handle uncertainty issues. However, the complexity of the model increases due to this fuzzy expansion of input patterns. On the other hand, all the expanded fuzzified patterns may not always be significant for model identification. To address this issue, post-feature reduction has been employed on fuzzified patterns to filter out the irrelevant, redundant and noisy features. Unlike pre-feature reduction, this allows all the features to be participated in the fuzzification process and then identify irrelevant features from the fuzzified patterns. Further, this approach allows exploring potential fuzzified features from the strong as well as weak feature set. The effectiveness of this proposed model has been tested and validated with a variety of benchmark biomedical data collected from various domains.
Reusable Mesh Signature Scheme for Protecting Identity Privacy of IoT Devices

The development of the Internet of Things (IoT) plays a very important role for processing data at the edge of a network. Therefore, it is very important to protect the privacy of IoT devices when these devices process and transfer data. A mesh signature (MS) is a useful cryptographic tool, which makes a signer sign any message anonymously. As a result, the signer can hide his specific identity information to the mesh signature, namely his identifying information (such as personal public key) may be hidden to a list of tuples that consist of public key and message. Therefore, we propose an improved mesh signature scheme for IoT devices in this paper. The IoT devices seen as the signers may sign their publishing data through our proposed mesh signature scheme, and their specific identities can be hidden to a list of possible signers. Additionally, mesh signature consists of some atomic signatures, where the atomic signatures can be reusable. Therefore, for a large amount of data published by the IoT devices, the atomic signatures on the same data can be reusable so as to decrease the number of signatures generated by the IoT devices in our proposed scheme. Compared with the original mesh signature scheme, the proposed scheme has less computational costs on generating final mesh signature and signature verification. Since atomic signatures are reusable, the proposed scheme has more advantages on generating final mesh signature by reconstructing atomic signatures. Furthermore, according to our experiment, when the proposed scheme generates a mesh signature on 10 MB message, the memory consumption is only about 200 KB. Therefore, it is feasible that the proposed scheme is used to protect the identity privacy of IoT devices.
Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.
Image decomposition based matrix regression with applications to robust face recognition

The previous matrix regression based methods mainly focus on designing a robust error term to characterize the occlusion and illumination changes. In actually, it is very challenging to give a strong model for solving the original images directly since the images contains rich and complex structure information. To address this problem, we aim to simplify the complex images and propose a simple and robust matrix regression based classification model. In our method, we firstly employ the local gradient distribution to decompose the image into a series of gradient images (LID for short). Each gradient image reveals the local structure information in different gradient orientations. Subsequently, we consider each gradient image as the diagonal block element and construct the diagonal block matrix for image representation. Nuclear norm based matrix regression model (NMR) is then applied to complete the classification tasks. The proposed model can be called ID-NMR for short. We further design a fast ADMM optimization algorithm to solve the proposed ID-NMR due to the fact that the big diagonal block matrix will increase the computational load. Experimental results show that the proposed method performs favorably compared with state-of-the-art regression based classification methods.