Reviewerly/exHarmony huggingface dataset:
Online support vector quantile regression for the dynamic time series with heavy-tailed noise

In this paper, we propose an online support vector quantile regression approach with an ɛ-insensitive pinball loss function, called Online-SVQR, for dynamic time series with heavy-tailed noise. Online-SVQR is robust to heavy-tailed noise, as it can control the negative influence of heavy-tailed noise by using a quantile parameter. By using an incremental learning algorithm to update the new samples, the coefficients of Online-SVQR reflect the dynamic information in the examined time series. During each incremental training process, the nonsupport vector is ignored while the support vector continues training with new updated samples. Online-SVQR can select useful training samples and discard irrelevant samples. As a result, the training speed of Online-SVQR is accelerated. Experimental results on one artificial dataset and three real-world datasets indicate that Online-SVQR outperforms ɛ-support vector quantile regression in terms of both sample selection ability and training speed.
Post-processing correction method for surface solar irradiance forecast data from the numerical weather model using geostationary satellite observation data

The numerical weather prediction (NWP) model still produces forecast errors. In this study, a correction method for the data from the NWP model is developed to obtain more skillful forecast data. The proposed method fuses information about surface global horizontal solar irradiance (GHI) from the NWP model and the satellite observations and gives corrected forecast data over the domain of the NWP model. There are three steps in the correction method algorithm. First, grid cells of the NWP model are grouped into clusters based on atmospheric conditions and regions. Second, cumulative distribution functions of the GHI from both datasets are inferred for each cluster separately. Last, correction functions are built using the statistical transformation. The correction effect is evaluated using satellite observation data as the reference. These analyses are performed using data for Japan. The correction function generally improves the forecast quality measured with the mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE). When the forecast quality is measured with MBE, the improvement lasts for two forecast days, but when measured with MAE and RMSE, the improvement lasts for only one forecast day. The correction effects have seasonal and regional characteristics. The correction is effective from spring to autumn and the correction effect weakens in winter. Forecast quality at all ground observation stations in Japan, except for those in northern Japan, is improved owing to the correction. In contrast, the forecast quality at stations in northern Japan tends to be degraded.
Distributed ledger technology based robust access control and real-time synchronization for consumer electronics

Consumer electronics or daily use home appliances are the basic necessity of every household. With the adoption of IoT in consumer electronics, this industry is set to rise exponentially. In recent times, the demand for consumer electronics rises amidst the pandemic due to a paradigm shift from in-office culture to work from home. Despite intelligent IoT devices, smart home configuration, and appliances at our disposal, the rudimentary client-server architecture fails to provide facilities like full access control of data and devices, transparency, secured communication, and synchronization between multiple devices, etc. to the users.To overcome these limitations, Blockchain technology has been adopted in recent years, however, it has its own set of limitations in its widespread implementation. Hence, we propose a methodology using the IOTA platform, a distributed ledger technology (DLT) for secured communication between consumer electronics devices and appliances.The implementation provides access control, interoperability, data storage, and management with an exploratory insight towards a decentralized micro-payment use-case between Electric cars and charging stations.
Assessment and categorization of empirical models for estimating monthly, daily, and hourly diffuse solar radiation: A case study of Iran

Knowledge of diffuse radiation is essential to design and monitor passive and active solar systems. Measuring diffuse radiation is not common in most meteorological stations. Therefore, developing empirical models for many regions is inevitable. In the current study, a comprehensive review was conducted on all available monthly, daily, and hourly empirical model formats (MFs) to estimate the horizontal diffuse fraction of solar radiation (DFSR). A total of 153 monthly mean daily MFs, 159 daily MFs, and 30 hourly MFs were cited. Monthly and daily MFs were classified into six sub-categories, while hourly MFs were classified into two sub-categories. Eleven new empirical regression MFs were introduced to estimate DFSR over five cities of Iran. Results showed that errors in all 11 newly developed models are not significantly different even at the 10% probability level. The yearly average of DFSR for Bandarabbas, Kermanshah, Mashhad, Tabriz, and Zahedan were 12.41, 9.04, 7.65, 8.99, and 9.93 MJ m−2, respectively. The generality of the newly developed models was proved by fitting them to data of four meteorological stations in the U.S.A. Comparing the newly developed models and the seven existing models showed that the newly developed models were more accurate in fitting the data.
AOM-MPA: Arabic Opinion Mining using Marine Predators Algorithm based Feature Selection

Recently, the automatic process of Arabic opinion mining (AOM) attracts the intention of scientific researchers, especially with the real challenges of the arabic language as dialectal language, Arabizi format, which combine multilingual forms. The prepossessing aims to realize a filtering operation on text, then TF-IDF is applied in order to transform the text to numerical vector. The main drawback of TF-IDF arises on the dimensionality. Thus, the feature selection plays a vital role for selecting the relevant words using swarm intelligence algorithms. The primary objective of this paper is to use the Marine Predators Algorithm (MPA) to determine the nature of opinions in the Arabic language in order to select the most pertinent terms. The experimental study is conducted on OCA data-set, and the results indicate that the proposed algorithm MPA outperforms other algorithms inspired by swarm intelligence and others from the literature in terms of accuracy and selection ratio.
A Novel Multi-View Clustering Method for Unknown Mapping Relationships Between Cross-View Samples

The existing multi-view clustering algorithms require that a sample in a view is completely or partially mapped onto one or more samples in a different corresponding view. However, this requirement could not be satisfied in many practical applications. Fortunately, there is a common cognition that the graph structure formed from each view should be as consistent as possible. Thus, this paper proposes a novel multi-view clustering method for unknown mapping relationships between cross-view samples based on the framework of non-negative matrix factorization, as an attempt to solve this problem. The objective function is designed by effectively building reconstruction error terms, local structural constraint terms, and cross-view mapping loss terms by exploring cross-view relationships. The experimental results show that the proposed method not only performs well to reveal the real mapping relationships between cross-view samples but also outperforms the comparison algorithms on the obtained clustering results.
Random Sampling in Corpus Design: Cross-Context Generalizability in Automated Multicountry Protest Event Collection

What is the most optimal way of creating a gold standard corpus for training a machine learning system that is designed for automatically collecting protest information in a cross-country context? We show that creating a gold standard corpus for training and testing machine learning models on the basis of randomly chosen news articles from news archives yields better performance than selecting news articles on the basis of keyword filtering, which is the most prevalent method currently used in automated event coding. We advance this new bottom-up approach to ensure generalizability and reliability in cross-country comparative protest event collection from international and local news in different countries, languages, sources and time periods, which entails a large variety of event types, actors, and targets. We present the results of comparing our random-sample approach with keyword filtering. We show that the machine learning algorithms, and particularly state-of-the-art deep learning tools, perform much better when they are trained with the gold standard corpus from a randomly selected set of news articles from China, India, and South Africa. Finally, we also present our approach to overcome the major ethical issues that are intrinsic to protest event coding.
Image Character Recognition using Convolutional Neural Networks

This paper aims the study and implementation of Convolutional Neural Network (CNN) for Image character recognition. Handwritten Character Recognition involves recognition of texts present in digital images and documents and processing them for various applications such as machine translation, pattern recognition and so on. This paper studies the use of CNN in detecting and recognizing handwritten text images with a higher accuracy. The CNN model is tested on English handwritten characters and validated on its performance. The model performs feature extraction from images through multiple layers. These are later used for training the model and thereby recognizing characters.
User defined weight based budget and deadline constrained workflow scheduling in cloud

Abstract Cloud computing is a technology that is being used by scientists for the execution of huge scale workflows as it has various features such as availability, faster, cheaper, elasticity, and high accessibility. In addition, the users are charged on a pay per use basis which makes it more efficient to be used by various clients. Scheduling workflows in the cloud is a difficult task as it takes into account the dependencies of workflows along with quality of service (QoS) demands. The problem becomes more challenging when there are multiple QoS objectives which are often disagreeing to each other. A lot of research has been done to minimize makespan and execution cost under deadline and budget constraints. This article presents a workflow scheduling algorithm based on Jaya to minimize them both and returns solutions according to the weights a user gives. The algorithm has been compared with Min‐Min, Max‐Min, Round Robin, heterogeneous earliest finish time first, first come first serve, minimum completion time, dynamic heterogeneous earliest finish time first in WorkflowSim. The results are compared based on both cost and time. In the end, a trade‐off between the objectives has been shown. Results show that Jaya performs much better than other algorithms when averaged over several iterations.
Understanding and optimizing packed neural network training for hyper-parameter tuning

As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue. To achieve better utilization of the shared resources, we explore the idea of jointly training multiple neural network models on a single GPU in this paper. We realize this idea by proposing a primitive, called pack. We further present a comprehensive empirical study of pack and end-to-end experiments that suggest significant improvements for hyperparameter tuning. The results suggest: (1) packing two models can bring up to 40% performance improvement over unpacked setups for a single training step and the improvement increases when packing more models; (2) the benefit of the pack primitive largely depends on a number of factors including memory capacity, chip architecture, neural network structure, and batch size; (3) there exists a trade-off between packing and unpacking when training multiple neural network models on limited resources; (4) a pack-aware Hyperband is up to 2.7X faster than the original Hyperband, with this improvement growing as memory size increases and subsequently the density of models packed.
Higher Education’s Certificates Model based on Blockchain Technology

Abstract Blockchain technology has revolutionized economic transactions, and it is expected to spread and influence other fields. It can create innovative and destructive effects on the systems that need to be executed, store, verified, and continually update digital data among participating parties. Moreover, it provides security, privacy, trust, and transparency. In the education field, there is a shy presence of some blockchain applications, and the majority of them have a pilot character. Hence, a model based on blockchain is introduced in this study, which aims to provides easy to apply for certificates, as well as share and verify these certificates with a third party. Moreover, the mechanism of the proposed model proposes permanent distributed hashes records of students’ certificates in Higher Education to reduce forgery. In conclusion, the study discussed the opportunities, benefits, features, and challenges are represented by deploying this model in the Higher Education field.
Multiscale quantum harmonic oscillator algorithm with guiding information for single objective optimization

The multiscale quantum harmonic oscillator algorithm (MQHOA) is a competitive heuristic optimization algorithm that has been successfully implemented in many applications. This paper proposes a novel way to optimize MQHOA to further improve its performance. The idea is to use the historical information in the evolutionary iterative process of the algorithm to derive a direction in the quantum harmonic oscillator (QHO) process and as a multi scale in the M process, then take the guidance information as the parameter to generate a new solution. The combination of these processes, i.e., MQHOA combined with the guidance information, is called GI-MQHOA. The experimental results show that the guidance information is of great significance for the exploration and exploitation of MQHOA. The proposed algorithm was evaluated on the CEC2014 test suite, and shown to be comparable to other state-of-the-art swarm intelligence and heuristic algorithms. The principle of using guiding information is simple and effective and can be easily transplanted to other heuristic algorithms.
The mediating effect of literacy of LMS between self-evaluation online teaching effectiveness and self-directed learning readiness

The aim of this study was to analyze whether the self-evaluation online teaching effectiveness (SEOTE) and literacy of learning manage system (LLMS) did significantly have effect on the level of self-directed learning readiness (SDLR). Furthermore, it was another purpose to examine whether the LLMS was a significant mediating effect between SEOTE and SDLR. Pearson correlation, multiple linear regression, and mediated regression analysis were conducted for this study. This study included 210 online college students in Korea who responded to three web-survey questionnaires (SEOTE, LLMS, and SDLR). The bivariate (Pearson) correlation analysis showed that SEOTE significantly influenced on SDLR (r=524, p<.01) and LLMS was positively associated with SDLR (r=487, p<.01). The multiple linear regression revealed that SEOTE and LLMS significantly predicted SDLR and explained 31.4% of variance in SDLR. The results of mediated regression analysis revealed that LLMS did have a significant indirect or mediating effect with SEOTE in predicting SDLR.
Transformers for Multi-label Classification of Medical Text: An Empirical Comparison

Recent advancements in machine learning-based multi-label medical text classification techniques have been used to help enhance healthcare and aid better patient care. This research is motivated by transformers’ success in natural language processing tasks, and the opportunity to further improve performance for medical-domain specific tasks by exploiting models pre-trained on health data. We consider transfer learning involving fine-tuning of pre-trained models for predicting medical codes, formulated as a multi-label problem. We find that domain-specific transformers outperform state-of-the-art results for multi-label problems with the number of labels ranging from 18 to 158, for a fixed sequence length. Additionally, we find that, for longer documents and/or number of labels greater than 300, traditional neural networks still have an edge over transformers. These findings are obtained by performing extensive experiments on the semi-structured eICU data and the free-form MIMIC III data, and applying various transformers including BERT, RoBERTa, and Longformer variations. The electronic health record data used in this research exhibits a high level of label imbalance. Considering individual label accuracy, we find that for eICU data medical-domain specific RoBERTa models achieve improvements for more frequent labels. For infrequent labels, in both datasets, traditional neural networks still perform better.
Reinforcement Learning for Edge Device Selection Using Social Attribute Perception in Industry 4.0

In the 5G era, the problem of data islands in various industries restricts the development of artificial intelligence technology, so data sharing is proposed. High-quality data sharing directly affects the effectiveness of machine learning models, but data leakage and abuse will inevitably occur in the process. As a consequence, in order to solve this problem, federated learning is proposed. This method uses the personalized data of multiple edge devices to train the model. The central server collects the training results of the edge devices and updates the global model, and then iteratively tests and updates the model through the edge devices. However, edge devices may have problems, such as unbalanced load and exit from the training process, which makes the training time of the model long and the effect is poor. Therefore, in the process of federated learning, the selection of reliable and high-quality edge devices becomes crucial. On this basis, in this article, we introduce reinforcement learning (RL) to preselect edge devices and obtain a set of candidate devices and then determine reliable edge devices through social attribute perception. The simulation experiment data analysis demonstrates that this scheme can improve the reliability of federated learning and complete the training process in a shorter time, the efficiency of federated learning increased by approximately 10.3%.
Unsupervised Behavior Discovery With Quality-Diversity Optimization

Quality-diversity (QD) algorithms refer to a class of evolutionary algorithms designed to find a collection of diverse and high-performing solutions to a given problem. In robotics, such algorithms can be used for generating a collection of controllers covering most of the possible behaviors of a robot. To do so, these algorithms associate a behavioral descriptor (BD) to each of these behaviors. Each BD is used for estimating the novelty of one behavior compared to the others. In most existing algorithms, the BD needs to be hand-coded, thus requiring prior knowledge about the task to solve. In this article, we introduce: autonomous robots realizing their abilities, an algorithm that uses a dimensionality reduction technique to automatically learn BDs based on raw sensory data. The performance of this algorithm is assessed on three robotic tasks in simulation. The experimental results show that it performs similar to traditional hand-coded approaches without the requirement to provide any hand-coded BD. In the collection of diverse and high-performing solutions, it also manages to find behaviors that are novel with respect to more features than its hand-coded baselines. Finally, we introduce a variant of the algorithm which is robust to the dimensionality of the BD space.
Contrastive Feature Decomposition for Image Reflection Removal

The crux of image reflection removal stems from the difficulty of recognizing the diverse reflection patterns. Typical methods optimize the modeling of background restoration by performing low-level supervision on the restored image to minimize its per-pixel difference from the groundtruth, which re-lies on substantial training samples to learn diverse reflection patterns robustly and avoid overfitting spurious reflection patterns. In this work, we perform supervision on the contrastive distribution between the predicted background and the reflection image. Specifically, our proposed method restores the background and the reflection images in parallel, and seeks to maximize the distribution consistency between the predicted background-reflection contrast and the groundtruth contrast in the latent space. Such supervision pushes the model to focus on contrastive modeling between the background and reflection image. Extensive experiments on four real-world bench-marks demonstrate that our method consistently outperforms state-of-the-art methods.
Implementing Technology for Teaching: The Use of a Mobile/Tablet Approach for Enhancing Students’ Learning (Design Interaction) Technology-Enhanced Learning (TEL)

In industrial design education, the curriculum should be structured to facilitate and advance student learning. The purpose of this work is to enhance the education by introducing mobile lectures and imbedded innovative approach to Bournemouth University (BU) Education, which will be employed by the lecturer in the years to come.
Mind the product owner: An action research project into agile release planning

This paper studies agile release planning in a software development organization with 13 development teams. It is important for software development organizations to be able to plan work in an efficient way that supports development work. The research aims to understand issues within agile release planning in the studied organization, and to make some improvement to the agile release planning practices there. The study followed canonical action research methodology completing one cycle of diagnosis, action planning, intervention, evaluation, and learning. Qualitative methods were used during these phases to identify preliminary issues, to support the choice of action, and the evaluation of those actions. The research identified issues of strain on the role of Product Owners. Sources of strain in the organization include changing priorities, the effort required to build up domain competence for new projects, and external pressure to push out new features. Additionally, there was difficulty for people participating in agile release planning to suggest improvements to the used practices due in part to the complexity and scale of planning practices in a multi-team development organization. The actions taken as part of the research provided ways for Product Owners to share knowledge between themselves, to better affect the working practices in the organization, and promoted a sense of team spirit between the Product Owners. Organizations should be mindful of their Product Owners when looking at their release planning practices. Problems for Product Owners are problems in planning for the whole organization. Having an active, collective, and structured channel for continuous improvement for Product Owners can help drive improvements to agile release planning.