Reviewerly/exHarmony huggingface dataset:
Real-Time Voice Cloning System Using Machine Learning Algorithms

This real-time voice cloning system uses machine learning algorithms and has a lot of ease of use applications like bringing back the capacity to speak naturally users, who have missing their voices and unable to provide a lot of novel training instances. Using a neural technique, from speaker confirmation to multi-speaker text-to-speech synthesis, transfer learning is possible. In this paper, the model will strive to provide output that is completely independent of any pre-existing record while dealing with pre-existing information in the datasets. The model is built on the dataset’s base, but it is not confined to it.
Development and Implementation of a Regional Earthquake Early Warning System in Northern India

The Himalayan region has experienced many devastating earthquakes, due to the collision of the Indian Plate with the Eurasian plate. The movement of the Indian Plate is continuous but for a long time, this region has not released the accumulated energy. This accumulated energy makes this region more vulnerable to major to great earthquakes in near future. The urbanization and initialization in this region have magnified the effect of earthquakes by many folds. Earthquake Early Warning (EEW) systems are being used to mitigate the effects of earthquakes in many countries, by sending alerts of upcoming damaging waves to the users. The development of an EEW system in Uttarakhand was started in 2014 as a pilot project after validating its feasibility, which is now operational for the public. The operation EEW system includes (i) a central processing server at Roorkee; (ii) a warning server at Google cloud platform; (iii) around 160 sensors in Kumaun and Garhwal region of Uttarakhand; (iv) 80 public sirens at schools, hospitals, district emergency centers, etc., and (v) a smartphone for the public. This paper elaborates on the different components and architecture of the implemented EEW system. Further, this paper also includes information about the tasks carried out to aware the public about earthquake safety tips and the EEW system to utilize its potential at the user end.
Towards Understanding of Deep Reinforcement Learning Agents Used in Cloud Resource Management

Cloud computing resource management is a critical component of the modern cloud computing platforms, aimed to manage computing resources for a given application by minimizing the cost of the infrastructure while maintaining a Quality-of-Service (QoS) conditions. This task is usually solved using rule-based policies. Due to their limitations more complex solutions, such as Deep Reinforcement Learning (DRL) agents are being researched. Unfortunately, deploying such agents in a production environment can be seen as risky because of the lack of transparency of DRL decision-making policies. There is no way to know why a certain decision is made. To foster the trust in DRL generated policies it is important to provide means of explaining why certain decisions were made given a specific input. In this paper we present a tool applying the Integrated Gradients (IG) method to Deep Neural Networks used by DRL algorithms. This allowed to obtain feature attributions that show the magnitude and direction of each feature’s influence on the agent’s decision. We verify the viability of the proposed solution by applying it to a number of sample use cases with different DRL agents.
Learning Shape-Preserving Autoencoder for the Reconstruction of Functional Data from Noisy Observations

We propose a new autoencoder preserving input functions’ general shape (monotonicity, convexity) after their reconstruction without imposing a priori constraints. These properties are inherent to the coefficients of the Bernstein-Durrmeyer polynomials that serve here as theoretical descriptors. Their estimates, computed from noisy observations by the coder, play the role of latent variables. The approach is purely nonparametric, i.e., no prior finite-dimensional model is assumed. The answer to the question of how many latent variables should be used for an acceptable reconstruction accuracy of a family of functional data is inferred from learning based on the proposed approximation of the Akaike Information Criterion. A distinguishing feature of this autoencoder is that the coder and encoder are designed as precomputed and stored matrices with the Bernstein polynomial entries. Thus, after selecting the number of latent variables, the autoencoder usage has a low computational complexity since it is linear with respect to observations. The proposed computational algorithms are tested on real data arising in mechanical engineering when control of damping vibrations is necessary.
Solving Complex Sequential Decision-Making Problems by Deep Reinforcement Learning with Heuristic Rules

Deep reinforcement learning (RL) has demonstrated great capabilities in dealing with sequential decision-making problems, but its performance is often bounded by suboptimal solutions in many complex applications. This paper proposes the use of human expertise to increase the performance of deep RL methods. Human domain knowledge is characterized by heuristic rules and they are utilized adaptively to alter either the reward signals or environment states during the learning process of deep RL. This prevents deep RL methods from being trapped in local optimal solutions and computationally expensive training process and thus allowing them to maximize their performance when carrying out designated tasks. The proposed approach is experimented with a video game developed using the Arcade Learning Environment. With the extra information provided at the right time by human experts via heuristic rules, deep RL methods show greater performance compared with circumstances where human knowledge is not used. This implies that our approach of utilizing human expertise for deep RL has helped to increase the performance of deep RL and it has a great potential to be generalized and applied to solve complex real-world decision-making problems efficiently.
CNN-Based Quantification of Blood Vessels Lumen in 3D Images

The aim of this work is to develop a method for automated, fast and accurate geometric modeling of blood vessels from 3D images, robust to image limited resolution, noise and artefacts. Within the centerline-radius paradigm, convolutional neural networks (CNNs) are used to approximate the mapping from the image cross-sections to vessel lumen parameters. A six-parameter image formation model is utilized to derive conditions for this mapping to exist, and to generate images for the CNN training, validation and testing. The trained networks are applied to real-life time-of-flight (TOF) magnetic resonance images (MRI) of a blood-flow phantom. Excellent agreement is observed between the predictions made by the CNN and those obtained via model fitting as the reference method. The latter is a few orders of magnitude slower than the CNN and suffers from local minima problem. The CNN is also trained and tested on publicly available contrast-enhanced (CE) computed tomography angiography (CTA) clinical datasets. It accurately predicts the coronary-tree lumen parameters in seconds, compared to hours needed by human experts. The method can be an aid to vascular diagnosis and automated annotation of images.
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets

We study the ability of pretrained large language models (LLM) to answer questions from online question answering fora such as Stack Overflow. We consider question-answer pairs where the main part of the answer consists of source code. On two benchmark datasets—CoNaLa and a newly collected dataset based on Stack Overflow—we investigate how a closed-book question answering system can be improved by fine-tuning the LLM for the downstream task, prompt engineering, and data preprocessing. We use publicly available autoregressive language models such as GPT-Neo, CodeGen, and PanGu-Coder, and after the proposed fine-tuning achieve a BLEU score of 0.4432 on the CoNaLa test set, significantly exceeding previous state of the art for this task.
r-softmax: Generalized Softmax with Controllable Sparsity Rate

Nowadays artificial neural network models achieve remarkable results in many disciplines. Functions mapping the representation provided by the model to the probability distribution are the inseparable aspect of deep learning solutions. Although softmax is a commonly accepted probability mapping function in the machine learning community, it cannot return sparse outputs and always spreads the positive probability to all positions. In this paper, we propose r-softmax, a modification of the softmax, outputting sparse probability distribution with controllable sparsity rate. In contrast to the existing sparse probability mapping functions, we provide an intuitive mechanism for controlling the output sparsity level. We show on several multi-label datasets that r-softmax outperforms other sparse alternatives to softmax and is highly competitive with the original softmax. We also apply r-softmax to the self-attention module of a pre-trained transformer language model and demonstrate that it leads to improved performance when fine-tuning the model on different natural language processing tasks.
Weighted Hamming Metric and KNN Classification of Nominal-Continuous Data

The purpose of the article is to develop a new metric learning algorithm for combination of continuous and nominal data. We start with Euclidean metric for continuous and Hamming metric for nominal part of data. The impact of specific feature is modeled with corresponding weight in the metric definition. A new algorithm for automatic weights detection is proposed. The weighted metric is then used in the standard knn classification algorithm. Series of numerical experiments show that the algorithm can successfully classify raw, non-normalized data.
Hierarchical Classification of Adverse Events Based on Consumer’s Comments

This paper focuses on autonomously classifying adverse events based on consumers’ comments regarding health and hygiene products. The data, comprising over 152,000 comments, were collected from e-commerce sources and social media. In the present research, we propose a language-independent approach using machine translation, allowing for unified analysis of data from various countries. Furthermore, this study presents a real-life application, making it potentially beneficial for subsequent scientific research and other business applications. A distinguishing feature of our approach is the efficient modeling of colloquial language instead of medical jargon, which is often the focus of adverse event research. Both hierarchical and non-hierarchical classification approaches were tested using Random Forest and XGBoost classifiers. The proposed feature extraction and selection process enabled us to include tokens important to minority classes in the dictionary. The F1 score was utilized to quantitatively assess the quality of classification. Hierarchical classification allowed for faster classification processes than the non-hierarchical approach for the XGBoost classifier. We obtained promising results for XGBoost; however, further research on a wider range of categories is required.
Sentiment Analysis Using Machine Learning Approach Based on Feature Extraction for Anxiety Detection

In this study, selected machine learning (ML) approaches were used to detect anxiety in Indonesian-language YouTube video comments about COVID-19 and the government's program. The dataset consisted of 9706 comments categorized as positive and negative. The study utilized ML approaches, such as KNN (K-Nearest Neighbors), SVM (Support Vector Machine), DT (Decision Tree), Naïve Bayes (NB), Random Forest (RF), and XG-Boost, to analyze and classify comments as anxious or not anxious. The data was preprocessed by tokenizing, filtering, stemming, tagging, and emoticon conversion. Feature extraction (FE) is performed by CV (count-vectorization), TF-IDF (term frequency-inverse document frequency), Word2Vec (Word Embedding), and HV (Hashing-Vectorizer) algorithms. The 24 of the ML and FE algorithms combinations were used to achieve the best performance in anxiety detection. The combination of RF and CV obtained the best accuracy of 98.4%, which is 14.3% points better than the previous research. In addition, the other ML methods accuracy was above 92% for CV, TF-IDF, and HV, while KNN obtained the lowest accuracy.
Anomaly Detection of Motion Capture Data Based on the Autoencoder Approach

Anomalies of gait sequences are detected on the basis of an autoencoder strategy in which input data are reconstructed from their embeddings. The denoising dense low-dimensional and sparse high-dimensional autoencoders are applied for segments of time series representing 3D rotations of the skeletal body parts. The outliers - misreconstructed time segments - are determined and classified as abnormal gait fragments. In the validation stage, motion capture data registered in the virtual reality of the Human Dynamics and Multimodal Interaction Laboratory of the Polish-Japanese Academy of Information Technology equipped with Motek CAREN Extended hardware and software are used. The scenarios with audio and visual stimuli are prepared to enforce anomalies during a walk. The acquired data are labeled by a human, which results in the visible and invisible anomalies extracted. The neural network representing the autoencoder is trained using anomaly-free data and validated by the complete ones. AP (Average Precision) and ROC-AUC (Receiver Operating Characteristic – Area Under Curve) measures are calculated to assess detection performance. The influences of the number of neurons of the hidden layer, the length of the analyzed time segments and the variance of injected Gaussian noise are investigated. The obtained results, with AP $$=0.46$$ and ROC-AUC $$=0.71$$ , are promising.
SAP Cloud Integration: Features and Connectivity

This chapter explores SAP Cloud Integration, focusing on its connectivity features and integration capabilities with Integration Suite. It provides an in-depth overview of the platform, including its web user interface and tools like Content Modifier, Outbound OData Channel, and Groovy Script. The chapter covers adapters, configuration examples, connectivity options, and communication security, emphasizing practical application and authentication methods. It serves as a comprehensive guide for setting up and configuring SAP Cloud Integration, offering valuable insights into its capabilities within the SAP BTP Integration Suite.
SAP Cloud Integration: Development Part II

This chapter delves into the advanced development functions available in SAP Cloud Integration. It explores design object features, such as call elements, routing configurations, and security setups. With practical examples and illustrations, readers gain insights into content enrichers, polling enrichers, router configurations, and message-level security implementations, facilitating the creation of robust integration projects using SAP Cloud Integration.
TwitterEmo: Annotating Emotions and Sentiment in Polish Twitter

This article presents TwitterEmo, a new dataset for emotion and sentiment analysis in Polish. TwitterEmo provides a non-domain-specific and colloquial language dataset, which includes Plutchik’s eight basic emotions and sentiment annotations for 36,280 tweets collected over a one-year period. Additionally, a sarcasm category is included, making this dataset unique in Polish computational linguistics. Each entry was annotated by at least four annotators. We present the results of the evaluation using several language models, including HerBERT and TrelBERT. The TwitterEmo dataset is a valuable resource for developing and training machine learning models, broadening possible applications of emotion recognition methods in Polish, and contributing to social studies and research on media bias.
Genetic Programming-based AutoML for EEG Signal Classification - A Comparative Study

End-to-end Machine Learning (ML) applications using complex data often need to investigate several alternatives for the data modeling pipeline before a good solution is found. This process, which is time-consuming and subjective, can benefit from an automated solution design by using Automated Machine Learning (AutoML). End-toend AutoML allows automated data preparation, modeling, and evaluation of ML pipelines, increasing the chances of arriving at a good solution. AutoML can implement this optimization with different strategies. Among them, Genetic Programming (GP) stands out for its ability to create pipelines of arbitrary format, allowing high interpretability and the customization of information from the data context. This paper proposes and compares two approaches of end-to-end AutoML optimized with GP for a time series classification problem, the classification of Electroencephalogram (EEG) signals. We selected this dataset because of the signals’ high complexity, spatial and temporal co-variance, and nonstationarity. For the AutoML experiments, four different domain-based data characterization measures are evaluated. The analysis of the data characterization measures shows that using only spectral or time-domain features does not lead to pipelines with good predictive performance. Our experimental results also reveal how AutoML can generate more accurate and interpretable solutions than the literature’s complex and ad hoc models. The proposed approach makes it easier to analyze dimensional reduction through fitness convergence, tree depth, and extracted features.
Meta-Learning Approach for Noise Filter Algorithm Recommendation

Preprocessing techniques can increase the quality or even enable Machine Learning algorithms. However, it is not simple to identify the preprocessing algorithms we should apply. This work proposes a methodology to recommend a noise filtering algorithm based on Meta-Learning, predicting which algorithm should be chosen based on a set of features calculated from a dataset. From synthetics datasets, we created the meta-data from an extracted set of meta-features and the f1-score performance metric calculated from the DT, KNN, and RF classifiers. To perform the suggestion, we used a meta-ranker that returns the rank of the best algorithms. We selected three noise filtering algorithms, HARF, GE, and ORBoost. To predict the f1-score, we used the PCT, RF, and KNN algorithms as meta-rankers. Our results indicate that the proposed solution acquired over 60% and 80% accuracy when considering a top-1 and top-2 approach. It also shows that the meta-rankers, when compared with a random choice and single algorithms as a baseline, provided an overall performance gain for the Machine Learning algorithm.
Agile project management and investment projects: Dilemmas and challenges

New technologies and changeable environment more and more lead to evolution from traditional to agile project management. This approach was applied a quarter century ago to software development projects, but now it has been spread to all other projects. In this paper, basic characteristics of Agile approach as well as possibilities of its application in specific type of projects, investment ones are considered..
Round The Table, Education without the 2d frame constraints: a WebVR experience from a glocal perspective

Round-the-Table, as a researcher-led initiative, was an experimental virtual roundtable in a 3D format that invited twenty-one organisations worldwide from education, research, and technology to open a broad dialogue about a more sustainable, inclusive, interactive, and accessible educational environment, which may help pedagogical communication beyond the 2D frame.This was made possible by the implementation of a Web-VR platform supported by Mozilla, by which each participant had the opportunity to co-create with the organisers, a collaborative immersive sensory experience, together with the simultaneous dialogue between Local and Global.Participants were asked two critical questions: 'decentralised education' and 'phygital exchanges' : how can we work beyond the 2d frame and how to distribute tasks between physical and digital.The responses were by far diverse, but it was indeed possible to map a cohesive picture from this cloudy but colourful panorama.
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

Abstract. This work introduces a model for all-sky-image-based cloud and direct irradiance nowcasting (MACIN), which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASIs). With a synthetic setup based on simulated cloud scenes, the model and its components are validated in depth. We train a convolutional neural network on real ASI images to identify clouds. Cloud masks are generated for the synthetic ASI images with this network. Cloud height and motion are derived using sparse matching. In contrast to other studies, all derived cloud information, from both ASIs and multiple time steps, is combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times of up to 20 min. For the cloud masks derived from the ASI images, we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud-base heights is 4 % and cloud motion error is in the range of ±0.1ms-1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than 1 min. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500 m×500 m. Errors for area nowcasts as required, e.g., for photovoltaic plants, are smaller compared with errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.
The Grey Correlation Algorithm is Used to Evaluate the Quality of Independent Innovation Talents Training in Universities

The quality evaluation of college students' innovation and entrepreneurship education is very important.This paper constructs a quality evaluation index system of innovation and entrepreneurship education for college students with 5 dimensions and 21 sub-indexes, establishes an evaluation model based on grey relational degree algorithm, and makes an empirical study of 8 universities in N City, and puts forward corresponding suggestions.The quality evaluation of college students' innovation and entrepreneurship education is very important.This paper constructs a quality evaluation index system of innovation and entrepreneurship education for college students with 5 dimensions and 21 sub-indexes, establishes an evaluation model based on grey relational degree algorithm, and makes an empirical study of 8 universities in N City, and puts forward corresponding suggestions.Grey relational degree algorithm is one of the grey system analysis methods.It is a method to measure the degree of correlation among factors according to the similarity or dissimilarity of the development trend among factors, namely "grey relational degree" [3].In this paper, the grey relational algorithm is used to determine the evaluation index system, establish the quality evaluation model of college students' innovation and entrepreneurship education, and make an empirical evaluation.