Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Applications to Solve Solar Power Problems

The large-scale introduction of renewable energy sources throughout the world continues to grow and becomes more and more economically attractive. This makes the energy of the Sun a demanded energy resource not only for use in the construction of plants, but also in households. However, the use of solar energy is associated with great difficulties in predicting the generation of electricity due to its dependence on meteorological conditions, and there is an acute issue of forecasting the generation of solar power plants into the existing grid. One approach to solving this complex problem is the use of machine learning algorithms. With a correctly selected training model, such algorithms are capable of predicting the amount of electricity generation for the day ahead with high accuracy (up to 95%). In this article, the authors propose a solution to the urgent problem of predicting energy generation from solar power plants using machine learning systems.
News Popularity Prediction with Local-Global Long-Short-Term Embedding

Predicting news popularity is an essential topic in the news industry. It is challenging because numerous factors influence public response to the news. This paper presents \(F^4\), a neural model to predict news popularity by learning news embedding from global, local, long-term and short-term factors. \(F^4\) integrates a sentence encoding module to represent the local context of each news story; a heterogeneous graph-based module to capture the short-term information propagation from current buzz words to each news story; a sequential module to extract long-term popularity features in entity sequence; and an attention module to learn global news-entity correlations. Extensive experiments on real-world Chinese and English news datasets demonstrated that \(F^4\) outperforms state-of-the-art baselines in predicting and ranking news popularity.
MBTI personality classification using Apache Spark

Personality determines how person make decisions, speak or react on different situations. In this paper explained shortly the specifics of Myers-Briggs Type Indicator personality classification, then details of preparation of the experiment to run on Apache Spark platform. In experiment three different classification algorithms (Logistic Regression, Naive Bayes, Support Vector Machine) are used to train and predict MBTI personality pairs on a Kaggle dataset consisting of 8675 users tweets. In the end explained the data preprocessing and algorithm training, testing, validation details and results. The models with different vector combinations have been compared, and results have been described.
Using an Unsupervised Neural Network to Detect and Categorize Offensive Language in Social Media

The offensive language in present social media could harm the mental health of the minors. The paper aims to identify the offensive content in the posts published on the Twitter social network. More concrete, we categorize the text content of the tweets according to the characteristics they meet: offensive vs. non-offensive tweets, non-targeted tweets vs. tweets targeted on someone, and, more specifically, tweets targeted on an individual, on a group of people, or else targeted towards another category (i.e. towards an organization, a situation, an event, or an issue). This multi-level hierarchical categorization behaves like a top-down decision tree process that classifies the tweets against a tree like ontological taxonomy. This is an offensiveness taxonomy, which defines the above mentioned offensive tweet categories. We use an unsupervised neural network for this hierarchical categorization, as applied on the OLID (Offensive Language Identification Dataset) data set. The OLID dataset consists of tweets, and it was offered as benchmark at Task 6 of the SemEval 2019 competition, a task which actually inspired our paper.
Characterization Study of Outlier Detection Algorithms in Different Datasets

Outliers are findings with abnormal values compared with the rest of the data. Many algorithms have been developed for outlier detection in static data. However, very few algorithms are built in real time for identification of anomalies. This is due to the less processing time available for these types of data. Unsupervised algorithms are more commonly used in real time due to the unavailability of the ground truth. This paper discusses three such algorithms and its performance in different datasets. For the analysis, two datasets with normal distribution and random walk distribution are created artificially and outlier points are imbibed into this data at random points. In order to analyze the performance of the algorithms detection rate and false alarm rate are selected as evaluation parameters. Receiver Operator Characteristics (ROC) curve is used to compare the results. This study illustrates the significance of selecting an appropriate algorithm for outlier detection based on the type of distribution and reveals the impact of evaluation parameters while performing this selection.
Lexical Normalization of Japanese Tweets Using Related Images

Twitter is noisy and contains many nonstandard words. Furthermore, in Japanese tweets, many words have multiple variant notations. Therefore, the use of such noisy data may interfere with tasks such as identifying potential communities. In this paper, based on the assumption that words with the same meaning will have similar related images, we propose a method of normalization for nonstandard words and variant notations in Japanese tweets using related images. First, we collect images related to a word from Bing and use OpponentSIFT features to properly represent the content of those images. Next, we use clustering to narrow down the set of images to extract related images. Finally, we determine the similarity between words based on the similarity of the sets of related images.
A Model for Ship-Helicopter Cooperative Anti-Submarine Tracking based on Favorable Situation

A model for ship-helicopter cooperative antisubmarine tracking was established based on the favorable situation. Some typical favorable situations were put forward based on the principle analyzed for judging a favorable situation. The position selection model was constructed for ship-helicopter cooperative anti-submarine tracking based on the favorable situation, and a decision-making process is proposed for ship’s maneuvering, which is helpful to provide reference for operational decision-making.
"A COMPARISON OF DEEP LEARNING ARCHITECTURES FOR THE GENERATION OF 3D DATA"

Faced with several Deep Learning architectures for generating artificial images, there is a need to identify which are the best for each use case.To compare several networks with the generative architectures Autoencoder, Variational Autoencoder, and Generative Adversarial Networks in the 3D MNIST dataset, 12 models with different hyperparameters were created.After training, the models were compared with loss functions to assess the difference between the original and artificial data, so that greater complexity did not translate into better performance, indicating the Autoencoder models as the best cost-benefit.
Chinese Named Entity Recognition Based on BERT and Neural Network

In the task of named entity recognition, most of the existing models map the vectorized word representation to a vector matrix, ignoring the context semantic information, and the ability to extract text feature information is weak. To solve this problem, this paper presents a model based on the combination of BERT and neural network–Chinese named entity recognition model based on BERT-Conv-RBiGRU (BCRB). Firstly, the word vector based on the context information is generated by the pretraining of the BERT model. The output character vector is used to extract the local features of the text through the convolution operation, and the deep semantic features of the text are extracted through BiGRU, and the Residual Network is introduced to avoid the gradient dissipation caused by the increase of neural network depth. Finally, CRF is selected to output the sequence. The experimental results show that the model achieves very good results on MSRA and OntoNotes datasets, with F values of 94.95% and 77.74% respectively.
Smart Security Monitoring for IIoT Platform

An IIoT platform is expected to bring both near-term and long-term benefits. It should reduce costs, increase productivity and bring new values. However, there are concerns about security and it is also not easy to find engineers who have know-hows or experience in developing a secured IIoT system. These facts are obstacles for people to adopt and use IIoT platform. Conversely, if there is a platform which has embedded security and common base functions on which a customer may concentrate on realizing their business logic, without having to deal with the complexities, the introduction of an IIoT solution can be expected to proceed swimmingly. This article focuses on the approach for security counterplan as well as security monitoring for an ideal IIoT platform.
STUDENTS’ PERCEPTION TOWARD TEACHERS’ ATTITUDE, TEACHING STYLE, AND TEACHING STRATEGIES IN LEARNING ENGLISH

Teachers’ attitudes, teaching styles, and teaching strategies are needed to motivate students’ enthusiasm and interest in learning English. This study aims to analyze students’ perceptions of teachers’ attitudes, styles, and strategies. This study uses quantitative method with a survey design which used a questionnaire as the research instrument to get data. The study is conducted by using Google Form to get the data from the participants. The population of this study is the students of English Department of Zainul Hasan Islamic University Genggong. The sample used in this study are thirty-six students that are from several high schools around the area of Kraksaan. The collected data of this study is then analyzed by using a statistical formula to get the percentage of each question. The results shows that most of the teachers high schools around the area of Kraksaan have good attitudes and strategies in teaching English.
Neural Network Generative Models for Radio Frequency Data

Neural Networks (NN) provide great flexibility in modeling non-linear relationships and have recently proved to be very valuable in Radio Frequency (RF) domain, e.g. NN based Digital Signal Processing (DSP) provide equal or better performance than traditional DSP function blocks. One key requirement of NN models is their need for large amounts of training data to mitigate model overfitting. If this data requirement is satisfied it leads to models that generalize well, but does not hold in cases of limited training data. Generating high fidelity data can help to mitigate data scarcity. In this paper we demonstrate the application of Generative Adversarial Network (GAN) based RF data generation capability for training interference removing NN models. We developed two GAN models that learn to generate RF data with tone jammers. The first is a "Sequence to Sequence GAN model", which learns the jammer data distribution by learning to map clean RF signals to impaired RF signals with a jammer present. The second is a physics driven "Tone GAN" model, which learns the frequency, phase, and magnitude of the interferer from a limited data distribution and applies this impairment to the clean signal to generate impaired waveforms similar to the GAN training data. Experimental results show that the new data generated from the trained GAN models can be used to augment the original NN model, by fine-tuning them via the GAN generated data, leading to improved performance and generalization.
ScaleNet: An Unsupervised Representation Learning Method for Limited Information

Although large-scale labeled data are essential for deep convolutional neural networks (ConvNets) to learn high-level semantic visual representations, it is time-consuming and impractical to collect and annotate large-scale datasets. A simple and efficient unsupervised representation learning method named ScaleNet based on multi-scale images is proposed in this study to enhance the performance of ConvNets when limited information is available. The input images are first resized to a smaller size and fed to the ConvNet to recognize the rotation degree. Next, the ConvNet learns the rotation-prediction task for the original size images based on the parameters transferred from the previous model. The CIFAR-10 and ImageNet datasets are examined on different architectures such as AlexNet and ResNet50 in this study. The current study demonstrates that specific image features, such as Harris corner information, play a critical role in the efficiency of the rotation-prediction task. The ScaleNet supersedes the RotNet by ~7% in the limited CIFAR-10 dataset. The transferred parameters from a ScaleNet model with limited data improve the ImageNet Classification task by about 6% compared to the RotNet model. This study shows the capability of the ScaleNet method to improve other cutting-edge models such as SimCLR by learning effective features for classification tasks.
Face Recognition by Wavelet Transformation, Deep FFNN and ANFIS Approach

The paper presents the results of selection of multilayer Feed-Forward Neural Networks (FFNN) and Adaptive Neuro-Fuzzy Interface Systems (ANFIS) for face recognition using Daubechies wavelet image pre-processing. Neural structures have been investigated with two hidden layers in different ratios between hidden neurons in Levenberg-Marquardt learning processes. FFNNs have been synthesized with observed accuracies 100.00 % and minimal MSE indicators, respectively 8.4654e-04, 0.0016 and 5.6520e-04. A neuro-fuzzy classifier was found with achieved minimum RMSE = 2.8091e-05 and 100.00 % accuracy in Hybrid learning algorithm and Pi-shaped membership function.
Improving Goal-Oriented Visual Dialogue by Asking Fewer Questions

An agent who adaptively asks the user questions to seek information is a crucial element in designing a real-world artificial intelligence agent. In particular, goal-oriented visual dialogue, which locates an object of interest from a group of visually presented objects by asking verbal questions, must be able to efficiently narrow down and identify objects through question generation. Several models based on GuessWhat?! and CLEVR Ask have been published, most of which leverage reinforcement learning to maximize the success rate of the task. However, existing models take a policy of asking questions up to a predefined limit, resulting in the generation of redundant questions. Moreover, the generated questions often refer only to a limited number of objects, which prevents efficient narrowing down and the identification of a wide range of attributes. This paper proposes Two-Stream Splitter (TSS) for redundant question reduction and efficient question generation. TSS utilizes a self-attention structure in the processing of image features and location features of objects to enable efficient narrowing down of candidate objects by combining the information content of both. Experimental results on the CLEVR Ask dataset show that the proposed method reduces redundant questions and enables efficient interaction compared to previous models.
Scale Invariants from Gaussian-Geometric Moments

The Gaussian geometric moment is obtained by adding a Gaussian kernel to the geometric moment, which has better characteristic expression ability and robustness than the geometric moment. Based on the construction theory of geometric moment invariants, the translation and rotation invariants of Gaussian geometric moments can be easily constructed. However, due to the existence of the Gaussian kernel, the method of constructing scale invariants in geometric moments is no longer applicable. In this paper, by introducing the variable factor σ, the Gaussian geometric moment invariants for scale transformation is constructed, and the construction method of this scale invariants will not affect the translation invariance and rotation invariance. Numerical experiments verify the validity of scale invariants, and at the same time have better numerical stability than geometric moments.
Full Stack Image Annotation by 4d to 2d Label Transfer

Accurate image annotations are a critical component in the development of any deep learning model in computer vision. There are different approaches in their acquisition depending on the task: semantic segmentation maps and bounding boxes require a vast amount of human effort, accurate depth maps are hard to obtain without laser scanners, and the procedures for other tasks (e.g. optical flow) are even more challenging. In this work, we present a pipeline that extracts accurate annotations for computer vision tasks by leveraging 3D modelling techniques and structure from motion. Since all the structure information comes from a video, it removes the need for classical 2D annotation in the case when video is available. The pipeline is evaluated on several KITTI benchmark scenes, showing its potential.
Quantum Information Science in the Optics Curriculum

Optics is an essential backbone of quantum information science (QIS). Infusing QIS in the optics curriculum is much needed, but challenging. We report an approach transitioning from classical matrix optics to qubits and quantum gates.
Pareto-Optimal Active Learning with Cost

Supervised learning algorithms require a set of labeled training data. In many engineering applications, acquiring and accurately labeling the training data can be time consuming, burdensome, and costly. Active learning is an area of machine learning that selects observations in an unlabeled set to be passed to an oracle to retrieve the ground truth label, thereby improving the efficiency of the labeling and training process. However, most active learning algorithms only consider model improvement and, therefore, ignore cost considerations. The active learning algorithms that do consider cost require the practitioner to specify the trade-off between model improvement and cost. We propose an active learning with cost method that does not require this trade-off to be specified by randomly sampling observations from the Pareto optimal frontier. Further, we propose an extension to this method that accounts for uncertainty in the cost estimate of labeling an observation. These methods are evaluated on publicly available data sets, and the numerical experiments demonstrate that the proposed methods can produce models that achieve similar performance to standard active learning algorithms while reducing the labeling cost.
A Leading Indicator Approach with Data Mining Techniques in Analysing Bitcoin Market Value

In the last decade as a result of the changes in business landscape new payment systems have evolved.Some of the Consumers, business stakeholders, investors and individuals turned to different types of payment systems and virtual currencies for various reasons.Peer to peer architectured Bitcoin which uses a blockchain mechanism is one of these approaches that found place in our lives.In this study, a leading indicator focused data mining methodology has been followed in analyzing Bitcoin market value and bitcoin valuation.Several classification and clustering algorithms applied to the data following a literature review, pre-processing of the data and conceptual framework formation.Finaly performances of these supervised and unsupervised machine learning techniques with rules discovered have been compared, assessed and presented for this type of problem and research domains.
Sentiment Classification of Sinhala Content in Social Media: An Ensemble Approach

We focus on the binary classification of Sinhala social media content in the sports domain using machine learning algorithms. In particular, we improve upon the accuracy achieved in a previous study of ours that utilized word and character N-grams. We use the base learners from that study to implement a probability-based stacking ensemble approach. This is done by creating a base learner library of 1066 base learners, using 13 different algorithms and different N-gram feature extraction methods. Different base learner combinations from the library are then stacked together to find the best stacking ensemble model. The best stacking ensemble model achieves an accuracy of 83.8% which is an improvement of over 1.5% of our previous study.
G-BEAM: Graph-Based Exploration And Mapping for Autonomous Vehicles

A novel solution to the problem of exploration and mapping of an unknown environment by an autonomous vehicle is presented. A hierarchical control system is adopted: a low-level reactive controller manages obstacle avoidance, and two high-level strategies are in charge of mapping and navigation tasks. The decision strategy implemented at the high-level is named G-BEAM, standing for “Graph-Based Exploration And Mapping It builds a reachability graph used both as a trajectory planning tool and as a map. The reachability graph representation requires less storage resources with respect to a more traditional occupancy-map, and it can be directly exploited to compute the system’s path towards a given target or unexplored locations. The latter are ranked according to the expected information gain that is realized when they are visited. Such information gain is then used in the cost function of the navigation strategy, which is based on a receding horizon concept. The controller has been successfully tested in various simulated environments. Comparison with other approaches in state of the art shows promising performance.
Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation

Previous research has shown that simple methods of augmenting machine translation training data and input sentences with translations of similar sentences (or fuzzy matches), retrieved from a translation memory or bilingual corpus, lead to considerable improvements in translation quality, as assessed by a limited set of automatic evaluation metrics. In this study, we extend this evaluation by calculating a wider range of automated quality metrics that tap into different aspects of translation quality and by performing manual MT error analysis. Moreover, we investigate in more detail how fuzzy matches influence translations and where potential quality improvements could still be made by carrying out a series of quantitative analyses that focus on different characteristics of the retrieved fuzzy matches. The automated evaluation shows that the quality of NFR translations is higher than the NMT baseline in terms of all metrics. However, the manual error analysis did not reveal a difference between the two systems in terms of total number of translation errors; yet, different profiles emerged when considering the types of errors made. Finally, in our analysis of how fuzzy matches influence NFR translations, we identified a number of features that could be used to improve the selection of fuzzy matches for NFR data augmentation.
cl-psoatransrun: An Efficiently Executable Specification of PSOA RuleML in Common Lisp

The open-source cl-psoatransrun system, a realization for reasoning in PSOA RuleML is discussed. cl-psoatransrun, written in Common Lisp follows the paradigm of functional programming for the central transformation steps, necessary for translating the object- and graph-oriented PSOA syntax to relational Prolog clauses. We describe the parsing of the PSOA RuleML presentation syntax, and we provide a detailed analysis of the transformation steps. cl-psoatransrun can serve as an alternative of the reference implementation PSOATransRun, providing both fast translation times and feature parity. Additionally, the flexibility of the transformation functions provides an ideal environment for developing the PSOA RuleML specification.