Reviewerly/exHarmony huggingface dataset:
Exploiting Domain Knowledge to Address Class Imbalance in Meteorological Data Mining

We deal with the problem of class imbalance in data mining and machine learning classification algorithms. This is the case where some of the class labels are represented by a small number of examples in the training dataset compared to the rest of the class labels. Usually, those minority class labels are the most important ones, implying that classifiers should primarily perform well on predicting those labels. This is a well-studied problem and various strategies that use sampling methods are used to balance the representation of the labels in the training dataset and improve classifier performance. We explore whether expert knowledge in the field of Meteorology can enhance the quality of the training dataset when treated by pre-processing sampling strategies. We propose four new sampling strategies based on our expertise on the data domain and we compare their effectiveness against the established sampling strategies used in the literature. It turns out that our sampling strategies, which take advantage of expert knowledge from the data domain, achieve class balancing that improves the performance of most classifiers.
Computerized System to Audit and Sharing Feature of Medical Life History

Rapid technological evolve brings great changes. As technology advances, it replaces the old way of working or doing things. With the electronic format of paperwork, the efforts of organizations are greatly reduced. But it gives rise to new problems i.e. how to store, access and update the records. As the data is available online, it may affect data’s privacy and security. A healthcare domain greatly advances with new innovations. The documents can be saved easily and accessed everywhere. A patient and system can effectively use this system to access his medical records, medical history and currently prescribe medicines. It gives the patient as well as his family to easily access the data, sharing of medical reports and use signed prescriptions at the pharmacy anytime anywhere. The data should be available but need to take care of data security and privacy. The patient must be the owner of data. Using blockchain technology, data will be available in a decentralized manner. Using digitized signaturethe authentication of data can be verified. With the use of cryptography, data will be secure. While consensuses algorithm used for the reliability in a network. The system provides privacy using privacy agreement storage. Only with permission from the patient or authorized person the data can be used. The owner will be able to see, who is using his data and for what purpose. The system will be efficient to handle the data electronically as well as effective in confidentiality, authenticity and patient-centric.
Patient Zer0

Patient Zer0 is an interactive generative artwork, designed around the poem “In Memory of Anyone Unknown to Me” by Elizabeth Jennings and created during the first confinements imposed by the COVID-19 pandemic in 2020. While it has been showcased in several online exhibitions, this article details and analyses, for the first time, the artwork's algorithmic approach, as well as its aesthetics, the different media components, and the artist's intentions behind their inclusion and combination. In line with Springgay's, Irwin's, and Kind's a/r/tography, a recent creative research method is presented here, a/r/cography, which is complemented by a phenomenological dimension, as the author experienced the events from a physical, intellectual, and emotional perspective, in isolation, while at home in Portugal. The whole process was documented in a digital journal, which not only supported the underlying research but also documented the artwork variants and evolution. Patient Zer0 was entirely coded in Processing.js.
Output Power Prediction of Solar Photovoltaic Panel Using Machine Learning Approach

Solar power-based photovoltaic energy conversion could be considered one of the best sustainable sources of electric power generation. Thus, the prediction of the output power of the photovoltaic panel becomes necessary for its efficient utilization. The main aim of this paper is to predict the output power of solar photovoltaic panels using different machine learning algorithms based on the various input parameters such as ambient temperature, solar radiation, panel surface temperature, relative humidity and time of the day. Three different machine learning algorithms namely, multiple regression, support vector machine regression and gaussian regression were considered, for the prediction of output power, and compared on the basis of results obtained by different machine learning algorithms. The outcomes of this study showed that the multiple linear regression algorithm provides better performance with the result of mean absolute error, mean squared error, coefficient of determination and accuracy of 0.04505, 0.00431, 0.9981 and 0.99997 respectively, whereas the support vector machine regression had the worst prediction performance. Moreover, the predicted responses are in great understanding with the actual values indicating that the purposed machine learning algorithms are quite appropriate for predicting the output power of solar photovoltaic panels under different environmental conditions.
Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method

The fractional differential algorithm has a good effect on extracting image textures, but it is usually necessary to select an appropriate fractional differential order for textures of different scales, so we propose a novel approach for haptic texture rendering of two-dimensional (2D) images by using an adaptive fractional differential method. According to the fractional differential operator defined by the Grünvald–Letnikov derivative (G–L) and combined with the characteristics of human vision, we propose an adaptive fractional differential method based on the composite sub-band gradient vector of the sub-image obtained by wavelet decomposition of the image texture. We apply these extraction results to the haptic display system to reconstruct the three-dimensional (3D) texture force filed to render the texture surface of two-dimensional (2D) images. Based on this approach, we carry out the quantitative analysis of the haptic texture rendering of 2D images by using the multi-scale structural similarity (MS-SSIM) and image information entropy. Experimental results show that this method can extract the texture features well and achieve the best texture force file for 2D images.
Containerised Application Profiling and Classification Using Benchmarks

Along with the rise of cloud and edge computing has come a plethora of solutions that regard the deployment and operation of different types of applications in such environments. Infrastructure as a service (IaaS) providers offer a number of different hardware solutions to facilitate the needs of the growing number of distributed applications. It is critical in this landscape to be able to navigate and discover the best-suited infrastructure solution for the applications, taking into account not only the cost of operation but also the quality of service (QoS) required for any given application. The proposed solution has two main research developments: (a) the creation and optimisation of multidimensional vectors that represent the hardware usage profiles of an application, and (b) the assimilation of a machine learning classification algorithm, in order to create a system that can create hardware-agnostic profiles of a vast variety of containerised applications in terms of nature and computational needs and classify them to known benchmarks. Given that benchmarks are widely used to evaluate a system’s hardware capabilities, having a system that can help select which benchmarks best correlate to a given application can help an IaaS provider make a more informed decision or recommendation on the hardware solution, not in a broad sense, but based on the needs of a specific application.
Beyond Simple Argumentation: Image Learnable Transformation For Efficient Reinforcement Learning

Deep reinforcement learning(RL) suffers from high complexity space exploration and a substantial amount of time calculation problem, when it handles tasks which need to process high-dimensional interaction image datasets. Many data augmentation algorithms alleviated these problems by applying basic image transformation methods. However, they did not quickly explore the effective policy space and requires manual design for different tasks. To alleviate this issue and improve sample-efficiency of RL algorithms, we propose a new data augmentation model called learnable image transformation RL, which contains affine transformation of autonomous learning and image embedding based on attention mechanism. It reduces half time of previous baseline and improve the accuracy and sample-efficiency in reinforcement learning tasks. We also show that the generation of countermeasure samples with noisy and image transformation improve the ability of network by extracting better semantical representations and the robustness of the model. Experimental results show the proposed method’s usefulness.
Research on the processing of verb-noun anomalous collocation with components ellipsis

Verb-Noun Anomalous Collocations are generally accompanied by the ellipsis or metaphor of components, which bring difficulties to the processing of collocations. In this paper we are focus on the processing of the components ellipsis style. Our method is to deconstruct or restore the anomalous collocations. Firstly, we divide components ellipsis into 2 kinds: predicate ellipsis and preposition ellipsis. Secondly, we build a set of special word-description system based on the Generation Lexicon theory. This knowledge representation system can restore Anomalous Collocations which caused by components ellipsis and interprets their combination mechanism and the generation processing. Then we restore the a anomalous collocation to normal collocation by adding ingredients to process it. Experiments show that this kind of thinking is effective.
Remarks on a Commutative Quaternion Neural Network–based Controller and Its Application in Controlling a Robot Manipulator

This study examines the possibility of using a commutative quaternion neural network in control system applications. A multi-layer commutative quaternion neural network and its training algorithm are derived and the network is applied to develop a feedforward-feedback controller, with the network input consisting of a reference output and some tapped-delay input-output sets of the controlled plant while the network output is employed to synthesise the control input. Training of the commutative quaternion neural network in the control system is conducted in real-time by integrating feedback error learning. To evaluate the effectiveness of a commutative quaternion neural network-based controller, computational experiments on trajectory tracking control of a three-link robot manipulator are conducted. Simulation results show the suitability of the commutative quaternion neural network for controlling the robot manipulator and the characteristics of the commutative quaternion neural network-based controller are clarified when compared with those of a quaternion neural network-based controller.
Text Classification of Modern Mongolian Documents Using BERT Models

This paper investigates the application of state-of-the-art deep-learning-based natural language processing techniques in modern Mongolian documents. In particular, we explore several methods that apply Bidirectional Encoder Representations from Transformers (BERT) models for classifying modern Mongolian legal documents. Based on our findings, we propose BERT-based models called LEGAL-BERT-Mongolian. We demonstrated two variants of LEGAL-BERT-Mongolian, i.e., uncased-LEGAL-BERT-Mongolian and cased-LEGAL-BERT-Mongolian, for classifying modern Mongolian legal documents. The uncased-LEGAL-BERT-Mongolian model achieved the best results, with a precision of 0.91, recall of 0.87, and F1 score of 0.89, whereas the cased-LEGAL-BERT-Mongolian model achieved a precision of 0.87, recall of 0.83, and F1 score of 0.85. Moreover, because the LEGAL-BERT-Mongolian models demonstrated a certain degree of confusion among the “legal,” “economy,” and “politics” categories, some distinct deep features need to be explored to properly distinguish these classification categories. Furthermore, the proposed LEGAL-BERT-Mongolian models need to be evaluated on larger datasets.
Attention-based Feature Construction and Authorship Identification

This paper uses the attention mechanism to integrate lexicon and syntactic features and identify authors. Firstly, a representation method of node embedding of syntax tree is proposed. The node of syntax tree is represented as the sum of embeddings of all its dependency arcs. The syntactic information and the association information among words are introduced into the deep learning model. Then the syntactic attention network is constructed, and the syntax-aware vector is obtained through the network. The vector incorporates information of dependencies, part-of-speech and words. Then, the representation of the sentence is obtained through the sentence attention network. Finally, the classifier is used for classification. The proposed model achieves high performance in authorship identification experiments on 3 datasets.
Continual Learning of Medical Image Classification Based on Feature Replay

The wide variety of diseases in clinical diagnosis makes it impractical to develop specific detection algorithms for each disease. Models with continual learning capabilities learn to detect new disease as needed and can eventually detect all diseases learned before. However, there are few researches on continual learning of medical image classification. In this paper, we design two kinds of continual learning tasks of medical image classification and evaluate continual learning methods in the literature. We propose a novel continual learning method based on feature replay. Our method also utilizes multiple conditional generators to improve quality of replayed samples. Comparison with other methods shows that our method achieves higher average accuracy and lower average forgetting. Inception Score and Fréchet Inception Distance show that our method generates better samples which help to overcome catastrophic forgetting significantly.
Using Synthetic Data to Train a Conversational Response Generation Model in Low Resource Settings

Response generation is a task in natural language processing where a model is trained to respond to human statements. Conversational response generators take this one step further with the ability to respond within the context of previous responses. While techniques exist for training such models, they all require large amounts of conversational data, which are not always available for low-resource languages. In this research, we make three contributions. First, we released the first Filipino conversational dataset collected from a popular Philippine online forum called the “PEx Conversations Dataset.” Second, we introduce a data augmentation methodology for Filipino data by employing a Tagalog RoBERTa model to increase the size of the existing corpora. Lastly, we published the first Filipino conversational response generator capable of generating responses related to the previous three responses. With the supplementary synthetic data, we were able to improve the performance of the response generator by up to 10.7% in perplexity, and 11.7% in content word usage as compared to training with zero synthetic data.
Investigation of educational mathematics mobile applications (EMMAs) with multi-criteria decision-making methods: A TOPSIS algorithm implementation

The number and variety of educational mathematics mobile applications (EMMAs) make it difficult to select mobile applications for mathematics learning and teaching. Therefore, in this study, multi-criteria decision-making (MCDM) techniques, which are effectively used in a wide variety of disciplines, were applied to choose among alternative applications according to specified criteria. In this context, it was aimed to determine which of the 13 considered EMMAs that work on Android-based tools and were proposed by experts according to certain features were most effective with the help of the TOPSIS algorithm, one of the popular MCDM methods. The results obtained from an evaluation using 10 criteria (4 evaluator-independent, 6 evaluator-dependent) were analysed with MATLAB. As a result, the Desmos: Graphing Calculator application was found to rank first among the 13 EMMAs in order of importance. Considering the results obtained, it can be said that the use of MCDM techniques in such decision problems can facilitate the work of decision-makers.
Named Entity Recognition of Protein Complex Incorporating Syntactic Information

In order to automatically identify protein complex named entities from unstructured biomedical literature, we propose a protein complex named entity recognition method which incorporates syntactic information. Through incorporating three types of syntactic information, including part-of-speech labels, syntactic constituents and dependency relations with a key-value memory network, the model is able to learn syntactic information features, thus solving the problem of utilizing the syntactic information insufficiently. On the data set of protein complex constructed by distant supervision method, the experimental results show that after incorporating the three syntactic information with the key-value memory network, the models outperform the original model in different degrees, indicating that our proposed method can effectively improve the performance of protein complex named entity recognition model.
Superposed Linear Representation in Reproducing Kernel Hilbert Space for Image Classification

The non-parametric subspace classification to obtain excellent classification performance mainly depend on directly using the training samples as the dictionary of each class. The most typical classification algorithm is the collaborative representation-based classification. Recently, a superposed linear representation classifier that expresses recognition problems by presenting test images as the superposition of centroid and shared intra-class differences reveals that the superior performance of the collaborative representation depends to a large extent on the significant enough class separability of the controllable face datasets. However, the superposed linear representation classifier does not take account of the nonlinear features hidden in the visual features. In the paper, we propose to utilize the kernel technique to extend the superposed linear representation classifier method. our proposed method tested on several databases, such as handwritten digital recognition, image classification, and fine-grained recognition, and obtains satisfactory classification performance.
A Geometric Approach to Real-time Quality of Experience Prediction in Volatile Edge Networks

In recent years, the continuing growth of the network edge, along with increasing user demands, has led to the need for increasingly complex and responsive management strategies for edge services. Many of these strategies are cloud-based, offering near-perfect solutions at the cost of requiring massive computational power, or edge-based, offering reactive strategies to changing edge conditions. This paper presents a decentralized, pro-active Quality of Experience (QoE) based architecture designed to run on edge nodes, which allows nodes to predict optimal service providers (fog nodes) in advance and request their services. The concepts behind the components of the architecture are explained, as well as geometry-inspired design decisions to limit model size. Evaluations on an NVIDIA Jetson Nano show that the architecture can predict optimal service providers for an edge node in real-time for 5 to 20 QoS (Quality of Service) and QoE parameters, with at least 50 potential fog nodes, and that overall QoE resulting from its use is improved by 1% to 18% over previous work such as SoSwirly, depending on the scenario.
Appropriation of Digital Technology in Popular Education

The purpose of this research is to give an account of the appropriation of digital technology by a group of economically vulnerable families, within the framework of a popular education experience, in a district of metropolitan Lima. The methodology used follows the qualitative approach, with case study design, and with exploratory scope. It is concluded that, with adequate planning and strategies, digital appropriation is possible and its results are encouraging; however, the technological limitations of the social segment require constantly updating the process and even resorting to a combination of physical and digital components.
One-class Anomaly Detection with Redundancy Reduction and Momentum Mechanism

The objective of anomaly detection is to identify the sample which differs in some known data. In practice, anomaly class is usually hard to obtain and consumptive to label, while unsupervised learning and one-class classification are most widely used to solve this problem. Only a set of data from the specific class are given in the training phase, and the remaining categories will be considered as abnormal. In this paper, inspired by the success of deep learning and Support Vector Data Description (SVDD) of decision boundary-based, a novel idea that combining SVDD with redundant information reduction and momentum update mechanism named RRM-SVDD is proposed to address the anomaly detection problem. With the existence of trivial solutions for SVDD, an objective function is designed to avoid such situation by computing the dimension correlation matrix of the output vector from the feature extraction network, while optimizing it as the identity matrix to make any two dimensions as linearly independent as possible in the pretraining phase, that causes the effective for SVDD to describe the distribution of normal data in the feature space and reduce the probability of model collapse. Meanwhile, the momentum update mechanism is applied to learn the global hyperparameter center C by considering the previous epoch information in the next training period. To evaluate the performance of RRM-SVDD, related experiments on MNIST and CIFAR-10 image benchmark dataset have been conducted, achieved state-of-the-art anomaly detection accuracy and robustness in most categories than comparison methods.
The adoption of Artificial Intelligence technologies in the era of "grey tsunami": prospects and challenges

The aim of this paper is to investigate the potentiality of the use of Artificial Intelligence (AI) robots in social groups and specifically the antecedents that impact on either senior citizens’ willingness or objection to use Artificial Intelligence robots. In order to explore what are the antecedents of senior citizens’ acceptance or rejection towards the use of AI robots in the context of senior living facilities, this paper proposes a revisited Technology Acceptance Model. The model is analyzed using SEM to test the hypotheses and to check the loading of each factor. The paper shows important theoretical implications as well as for the decision makers of elderly care and for the whole society.