Reviewerly/exHarmony huggingface dataset:
Very Short-Term Solar Irradiance Forecasting using Multilayered Long-Short Term Memory

The intermittent nature of the resources (inputs: sunlight) results in intermittent energy output (power) from the photovoltaic energy systems. Therefore, accurate prediction of the photovoltaic output is a key area of research across the globe. Direct normal irradiance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$DNI$</tex> ) forecasts are essential for the planning of a distributed energy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$DE$</tex> ) powered facility or concentrated solar power systems. Some areas are more prone to have random weather conditions, one such city is Melbourne, Australia. This makes the forecasting a challenging task for conventional approaches and ther is a need for more sophisticated algorithms to makes accurate predictions. Accurate forecasting aids in optimally scheduling of HVAC loads, demand side management, trading of transactive energy, asset optimization and data analytics. In this manuscript a faster, efficient and less exhaustive algorithm for quarter-hour (15 minutes) forecasts for sites prone to weather irregularities is presented. This study discusses a case study using a machine learning based multi-layered long-short term memory neural networks <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(LSTM-NN$</tex> ) with Adam optimizer are used to forecast <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$DNI$</tex> . This research addresses the best forecasting for subtropical conditions specifically and evaluates its performance by comparing with the most used methods including <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ARIMA,\ ARMA,\ SVR$</tex> , Persistence, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AR$</tex> . The proposed nested <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LSTM$</tex> model was assessed by comparing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$RMSE(27.35),\ MAE(6.879)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$MBE(0.42)$</tex> values with other aforementioned methods. The results reveal the optimal performance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$LSTM$</tex> with reduced operational time, improved precision, and accuracy.
Supervised Acute Lymphocytic Leukemia Detection and Classification Based - Empirical Mode Decomposition

Acute lymphocytic leukemia (ALL) is a potentially fatal disease that affects both children and adults. ALL spreads quickly in the human body and kills within weeks. Hematologists examine the blood and bone marrow to see if ALL is present. Manual blood testing methods, which have been used for a long time, might be slow. This study provided an advanced computerized mechanism for classifying leukemia into two types: normal cell leukemia and blast cell leukemia. To begin, advanced image preprocessing algorithms were used and applied to acquire data augmentation, enhancement, and transformation. Furthermore, the K-means clustering technique was used to accurately segment the appropriate nuclei from the background. Moreover, most salient features have been extract via an empirical mode decomposition (EMD) based Hilbert Hung Transform (HHT). The extracted features have been fed toward supervised classifiers in order to the classify the nuclei. A neural networks (NNs) classifier with the Bayesian regularization (BR) method have been implemented. Moreover, support vector machine (SVM), KNN, Random forest, Nave Bayes, Logistic regression, and Decision trees classifiers have been utilized, tested, and compared with NNs. Experimental results show that NNs outperformed a classification accuracy of 98.7%, sensitivity of 99.3%, and specificity of 98.1%.
Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks

When modelling multivariate continuous time series, a common issue is to find that the original processes that generated the data are nonlinear or that they drift away from the original distribution as the system evolves over time. In these scenarios, using a linear model such as a Gaussian dynamic Bayesian network (DBN) can result in severe forecasting inaccuracies due to the structure and parameters of the model remaining constant without considering either the elapsed time or the state of the system. To approach this problem, we propose a hybrid model that combines a model tree with DBNs. The model first divides the original data set into different scenarios based on the splits made by the tree over the system variables and then performs a piecewise regression in each branch of the tree to obtain nonlinear forecasts. The experimental results on three different datasets show that our model outperforms standard DBN models when dealing with nonlinear processes and is competitive with state-of-the-art time series forecasting methods.
Detecting code vulnerabilities by learning from large-scale open source repositories

Machine learning methods are widely used to identify common, repeatedly occurring bugs and code vulnerabilities. The performance of a machine-learned model is bounded by the quality and quantity of training data and the model’s capability in extracting and capturing the essential information of the problem domain. Unfortunately, there is a storage of high-quality samples for training code vulnerability detection models, and existing machine learning methods are inadequate in capturing code vulnerability patterns. We present Developer , 1 1 Developer = D etecting cod E V ulnerabiliti E s at the L arge scale by learning from OP en sourc E R epositories. a novel learning framework for building code vulnerability detection models. To address the data scarcity challenge, Developer automatically gathers training samples from open-source projects and applies constraints rules to the collected data to filter out noisy data to improve the quality of the collected samples. The collected data provides many real-world vulnerable code training samples to complement the samples available in standard vulnerable databases. To build an effective code vulnerability detection model, Developer employs a convolutional neural network architecture with attention mechanisms to extract code representation from the program abstract syntax tree. The extracted program representation is then fed to a downstream network – a bidirectional long–short term memory architecture – to predict if the target code contains a vulnerability or not. We apply Developer to identify vulnerabilities at the program source-code level. Our evaluation shows that Developer outperforms state-of-the-art methods by uncovering more vulnerabilities with a lower false-positive rate.
Gaussian-wave-packet model for single-photon generation based on cavity quantum electrodynamics under adiabatic and nonadiabatic conditions

For single-photon generation based on cavity quantum electrodynamics, we investigate a practical model assuming a Gaussian wavepacket. This model makes it possible to comprehensively analyze the temporal dynamics of an atom-cavity system with both adiabatic and nonadiabatic conditions using analytical expressions. These results enable us to clarify the relationship between pulse width and maximum success probability, over the full range of coupling regimes, for arbitrary detunings. We demonstrate how to achieve a high success probability while keeping a short pulse width by optimizing the cavity transmittance parameter. Our formulations provide a practical tool for efficient single-photon generation in a wide variety of experimental platforms.
Prospection of pyrochlore and microlite mineral groups through Raman spectroscopy coupled with artificial neural networks

Abstract Niobium (Nb) and tantalum (Ta) concentrated in pyrochlore and microlite mineral groups, respectively, have attracted worldwide attention due to their importance to aerospace and electronics industries. This manuscript addresses the use of Raman spectroscopy coupled with artificial neural networks (ANNs) for improving the identification and characterization of mineral species belonging to pyrochlore and microlite mineral groups. Spectral data were collected in the 100–1400 cm −1 range and two baseline corrections, namely Asymmetric Least Squares (ALS) and Piecewise Linear Fitting (PLF) were performed and compared. In most cases, ALS achieved better performance in the removal of background noise with no elimination of important features of the original spectrum. The ANNs were fed with balanced datasets and based on different topologies with logistics, hyperbolic tangent, and rectified linear unit activation functions in the hidden layers.
AWESOME: an auction and witness enhanced SLA model for decentralized cloud marketplaces

Abstract In recent decades, the world has witnessed cloud computing as an essential technology that changes the traditional application Development and Operation (DevOps) lifecycle. However, current cloud software DevOps and Service Level Agreement (SLA) management often face challenges of 1) selecting the best fitting service providers, customizing services and planning capacities for large-scale distributed applications; 2) guaranteeing high-quality and trustworthy SLAs among multiple service providers; 3) enhancing the interoperability of cloud services across different providers; and 4) designing effective incentive models among stakeholders. This paper proposes a novel framework called Auction and Witness Enhanced trustworthy SLA for Open, decentralized service MarkEtplaces (AWESOME) to build a trustworthy cloud marketplace and address the above challenges. The proposed framework contains four subsystems: a customizable graphical user interface, an auction-based service selection model, a witness committee management mechanism, and a smart contract factory orchestration. We developed a prototype AWESOME decentralized application (DApp) based on the Ethereum blockchain. Extensive experiments are designed to evaluate the latency and cost of our model. The experimental results demonstrate that our model is economical and feasible.
Personalized Travel Recommendation System Using Average Cumulative Rating Matrix Factorization Technique: Concept and Framework

Recommendation systems in travel applications have a purpose to provide custom-made results to travelers while making a travel plan. These recommendation systems should be adaptable if user preferences change dynamically. To get custom-made results, the recommendation systems should be provided with traveler’s interests such as traveler’s specifications, preferences concerning destinations, type of activities they are very much interested to do in their travel plan. However, current recommendation systems are unable to fetch required features from travelers and destination places. Moreover, current systems are lacking to recommend destination places by considering social interest and their experience (i.e. recommendations by considering many traveler’s interests, for example, when two travelers interest matches the places visit by the first traveler can be suggested to the second traveler or vice-versa and travelers experience concerning particular destination place). To address the issues and problems of the current system, we propose and implement a tourist recommendation system which is termed as Average Cumulative Rating (ACR) that supports the extraction of rating and experience which is in the form of text description. The overall score is computed based on rating and traveler experience and feed to the traditional Matrix Factorization (MF) technique for providing custom results for travelers.
An Anatomy of Crypto-Enabled Cybercrimes

While the advent of cryptocurrencies and digital assets holds promise for improving and disrupting financial systems through offering cheap, quick, and secure transfer of value, it also opens up new payment channels for cybercrimes. A prerequisite to solving a problem is understanding the nature of the problem. Assembling a diverse set of public, proprietary, and hand-collected data including dark web conversations in Russian, we conduct the first detailed anatomy of crypto-enabled cybercrimes and highlight relevant economic issues. Our analyses reveal that a few organized ransomware gangs dominate the space and have evolved into sophisticated firm-like operations with physical offices, franchising, and affiliation programs. Their techniques also have become more aggressive over time, entailing multiple layers of extortion and reputation management. Blanket restrictions on cryptocurrency usage may prove ineffective in tackling crypto-enabled cybercrime and hinder innovations. But blockchain transparency and digital footprints enable effective forensics for tracking, monitoring, and shutting down dominant cybercriminal organizations.
Video segmentation via target objectness constraint and multi-head soft aggregation

Objectness is a notion of how a generic object looks like. Recently, matching-based methods have yielded impressive video object segmentation results. However, these methods suffer from mis-segmentation of distractors that are distant from the targets due to the lack of objectness constraint. The segmentation mask has a very low objectness when both the correct object and the distant distractor are considered as a target. Position information is also ignored in matching-based methods. In this paper, a video object segmentation method based on target objectness constraint and multi-head soft aggregation is proposed. The target objectness constraint is introduced by forcing the network to learn to predict object center and pixel-to-object boundary distances. The problem of mis-segmentation of objects or regions with similar appearance at a distance is alleviated. Besides, the target position consistency constraint is strengthened by introducing the target position information in the feature matching of adjacent frames. Furthermore, an instance attention mechanism is implemented using multi-head soft aggregation to re-calibrate channel importance. Multi-head soft aggregation enables better utilization of target feature information than global average pooling. Comprehensive experiments demonstrate that the proposed method obtains a competitive performance, surpassing many other existing methods.
Preventing oversmoothing in VAE via generalized variance parameterization

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative. This is often related to the hyperparameter resembling the data variance. It can be shown that an inappropriate choice of this hyperparameter causes the oversmoothness in the linearly approximated case and can be empirically verified for the general cases. Moreover, determining such appropriate choice becomes infeasible if the data variance is non-uniform or conditional. Therefore, we propose VAE extensions with generalized parameterizations of the data variance and incorporate maximum likelihood estimation into the objective function to adaptively regularize the decoder smoothness. The images generated from proposed VAE extensions show improved Fréchet inception distance (FID) on MNIST and CelebA datasets.
Study on the evolution of Chinese characters based on few-shot learning: From oracle bone inscriptions to regular script

Oracle bone inscriptions (OBIs) are ancient Chinese scripts originated in the Shang Dynasty of China, and now less than half of the existing OBIs are well deciphered. To date, interpreting OBIs mainly relies on professional historians using the rules of OBIs evolution, and the remaining part of the oracle's deciphering work is stuck in a bottleneck period. Here, we systematically analyze the evolution process of oracle characters by using the Siamese network in Few-shot learning (FSL). We first establish a dataset containing Chinese characters which have finished a relatively complete evolution, including images in five periods: oracle bone inscriptions, bronze inscriptions, seal inscriptions, official script, and regular script. Then, we compare the performance of three typical algorithms, VGG16, ResNet, and AlexNet respectively, as the backbone feature extraction network of the Siamese network. The results show that the highest F1 value of 83.3% and the highest recognition accuracy of 82.67% are obtained by the combination of VGG16 and Siamese network. Based on the analysis, the typical structural performance of each period is evaluated and we identified that the optimized Siamese network is feasible to study the evolution of the OBIs. Our findings provide a new approach for oracle's deciphering further.
A fablab at the periphery: Decentering innovation from São Paulo

Abstract In recent decades, many tech spaces have emerged worldwide to promote innovation. Based on ethnographic research, this article examines one of such initiatives in Brazil—a public laboratory of digital fabrication located in a low‐income neighborhood in the periphery of São Paulo. While scholars have exposed the neoliberal aspects of fablabs, this article aims to de‐center hegemonic understandings of innovation by attending to its situated practices. Analyzing the techno‐optimist aspirations and institutional legacies behind this laboratory, I explain how the US‐based fablab model was reconfigured in light of community concerns and previous Latin American experiments of digital inclusion. Against a monolithic image of tech collectives, I show how lab workers cultivated a diverse range of audiences and creative practices, specifically those of working‐class women. The article concludes with a call for more anthropological attention to overlooked tech practices as a means to imagine fairer and more solidary forms of innovation.
Multiprojector Multicamera Structured Light Surface Scanner

Structured light surface scanners are usually limited to a single projector and cannot provide a full surface profile in a single scan.Multiple projectors must be employed to extend the imaging area over the whole surface of the imaged object.Using multiple projectors is a challenging problem due to inter-projector interference which make pattern decoding hard.We describe a practical approach to multi-projector structured light surface scanning based on sinusoidal fringe patterns and temporal phase shifting which allows simultaneous projector employment.In our approach a different set of temporal phase shifts is assigned to each projector to enable efficient and robust separation of observed combined patterns into the contributions of each projector.The proposed approach does not place any restrictions on projector placement nor on the upper limit of the number of projectors used.We demonstrate the applicability of the proposed approach on a structured light surface scanner comprised of three projectors and six cameras.
Query Path Generation via Bidirectional Reasoning for Multihop Question Answering From Knowledge Bases

Multihop question answering from knowledge bases (KBQA) is a hot research topic in natural language processing. Recently, the graph neural network-based (GNN-based) methods have achieved promising results as the KB can be organized as a knowledge graph (KG). However, they often suffered from the sparsity of the KG which was detrimental to the structure encoding and reasoning capabilities of GNN. Specifically, a KG is a sparse graph linked by directed relations and previous studies have paid scant attention to the directional characteristic of relations in the KG, limiting the patterns of relation path that GNN-based approaches could resolve. This study proposes a bidirectional recurrent GNN (BRGNN) to tackle these difficulties. To model the bidirectional information of relations, all adjacent relations of an entity are grouped by their directions, and they are separately aggregated into the entity representation in outward and inward directions. For the reasoning process, BRGNN simultaneously considers the neighbor relations in both directions to cover more patterns of relation paths and improve the recall of answers. Extensive experiments on three benchmarks: WebQuestionsSP, ComplexWebQuestions, and MetaQA, verify that BRGNN can answer more questions by taking into account the directional information, and it is competitive to all state-of-the-art approaches.
Path planning for aerial mobility in urban scenarios: the SMARTGO project

This paper presents the SMARTGO project aimed at developing an integrated planning framework to enable safe and autonomous flight operations in urban environment. To this aim, several constraints must be taken into account such as aircraft performance, navigation and control aspects, deconfliction with other UAVs, and minimization of the risk connected to the flight. A detailed overview of the project goals and their relevance with respect to the open literature is provided, along with a description of the main technical development, i.e., the envisaged planning strategy and the creation of context map to serve as the base for the planning. Some preliminary planning results are also presented.
A Comprehensive Blockchain Framework for (COVID-19)Vaccine Program Registration, Supply Chain and Side Effects

The history of mankind witnessed a lot of pandemics throughout its years. Many of which caused fatalities and created severe consequences socially and economically. The latest example is the COVID-19 pandemic that got the world into a heated race to contain by developing immunization vaccines. Many of the vaccines came into production in record time which raises questions about their side effects. Also, since vaccines are sensitive medical products, they require continuous cold supply chain monitoring and tracking. It is clear how vital it is to link any occurring side effects to their causes to determine whether it is a manufacturing problem or a supply chain management issue. Additionally, and to properly establish this link, an effective beneficiaries’ registration process is required. Therefore, it is necessary to establish a comprehensive system that combines vaccines cold supply chain management solution with side-effects follow-up data and individuals registration records. To achieve these goals, the technology of blockchain was selected to develop a framework for the vaccines program. Blockchain is known as the architecture behind cryptocurrencies, however, it proved its reliability and effectiveness in different use cases. Blockchain special characteristics were the reason behind adopting it to establish a blockchain-based platform for vaccines cold supply chain associated with traceable side-effects information records combined with beneficiaries registration data. The connection between the three parts of our system framework is important to achieving the utmost benefits out of the vaccine program in terms of compliance with standard measures and its link to side-effects and beneficiaries metadata.