Reviewerly/exHarmony huggingface dataset:
Evaluation of eCognition Developer and Orfeo ToolBox Performances for Segmenting Agrophotovoltaic Systems from Sentinel-2 Images

A deep energy transformation will be tried out across the globe in the next decades in order to detect potential green and renewable sources able to replace fossil fuels. Among the various alternatives, photovoltaic technology, recognized as sustainable, clean, and environmentally friendly essence, is considered one of the most relevant solutions. To date, the Remotely Piloted Aircraft System has been largely used to inspect solar parks albeit the treatment of very high-resolution satellite images through object-based models may be a valid option. In this work, the potentialities of two segmentation approaches (multi-resolution and mean-shift algorithms, implemented in eCognition Developer and Orfeo Toolbox software, respectively) in extracting photovoltaic panels from Sentinel-2 time series were explored and compared. Such techniques were tested in Montalto di Castro in Viterbo (Italy). Multi-resolution algorithm was applied by varying scale and shape parameters between 20 and 100 and 0.1 and 0.5, respectively. Conversely, the mean-shift approach was used by considering the default values of spatial radius and range radius. Their segmentation outcomes were compared on the base of i) minimum Euclidean Distance 2 (ED2), calculated in AssesSeg environment, ii) segmentation polygons statistics and areas value, and, lastly, iii) their performance in terms of processing time, versatility, ability of handling heavy data, and cost. ECognition Developer demonstrated a better performance in segmenting Sentinel 2-images for extracting PV systems in terms of segmentation parameters management and outcomes interpretation ability.
Comparing Univariate and Multivariate Time Series Models for Technical Debt Forecasting

Technical debt (TD) is a successful and widely used metaphor that expresses the quality compromises that can yield short-term benefits but may negatively affect the overall quality of a software product in the long run. There is a vast variety of techniques and methodologies that have been proposed over the past years to enable the identification and estimation of TD during the software development cycle. However, it is only until recently that researchers have turned towards the investigation of methods that focus on forecasting its future evolution. Getting insights on the future evolution of TD can enable on-time decision-making and allow stakeholders to plan preventive strategies regarding TD repayment. In our previous studies, we have investigated time series analysis and Machine Learning techniques in order to produce reliable TD forecasts. In our current attempt, we aim to explore the capabilities of a statistical ARIMA model both in a univariate and a multivariate fashion. More specifically, the present paper investigates whether the adoption of an ARIMA model that takes into account, in addition to the TD value itself, various TD-related indicators may lead to more accurate TD predictions than its univariate alternative. For this purpose, dedicated models are constructed, evaluated, and compared on a dataset of five long-lived, open-source software applications.
The Impact of Quantum Computing on Businesses

Despite the fact that data science continues in a constant evolution, there are still many problems that are still impossible to solve due to processing issues, either due to the exorbitant amount of data collected, or related to the different types of data to be processed. Quantum computing in the last decade has experienced a significant boom which has allowed researchers to pose problems that until now were impossible to solve in the classical paradigm. Quantum computing promises gains by solving how we solve these challenging computational problems. Currently, creating added value and achieving new experiences for the consumer, enhanced with it combination with artificial intelligence and machine learning, being a determining factor to promote business competitive advantage. This article aims to assess the impact of quantum computing on businesses competitive advantage, through a qualitative bibliographic analysis. In this context, an extensive research was carried out in this area on Web of Science and Scopus for the period 2016–2021. The survey covers 162 papers selected for this study, 92 from Scopus and 70 from Web of Science.
Application of microsensors and support vector machines in the assessment of lower limb posture correction in adolescents

Summary Automatic monitoring of physical activity provides reliable data support for the maintenance of people's physical health and provides more scientific data for studying the relationship between physical activity and disease. In this article, a research on the evaluation of adolescent lower limb posture correction based on collaborative filtering and microsensors is carried out. Aiming at the problem that the human body posture monitoring system needs multiple nodes, this article simplifies the existing human body posture monitoring model and establishes a human body model with four degrees of freedom of the lower limbs. On this basis, the micro‐sensor assembly scheme and monitoring node layout scheme for obtaining model description parameters are determined. Methods such as collaborative filtering and support vector machines are used in the process of sensor data processing and correction effect evaluation. Finally, this article uses the constructed human body posture monitoring system to carry out the recognition test of the specific posture of the human body. The experimental results show that the evaluation scheme can effectively monitor and evaluate the effect of lower limb posture correction in adolescents.
A semi-supervised algorithm for detecting extremism propaganda diffusion on social media

Abstract Extremist online networks reportedly tend to use Twitter and other Social Networking Sites (SNS) in order to issue propaganda and recruitment statements. Traditional machine learning models may encounter problems when used in such a context, due to the peculiarities of microblogging sites and the manner in which these networks interact (both between themselves and with other networks). Moreover, state-of-the-art approaches have focused on non-transparent techniques that cannot be audited; so, despite the fact that they are top performing techniques, it is impossible to check if the models are actually fair. In this paper, we present a semi-supervised methodology that uses our Discriminatory Expressions algorithm for feature selection to detect expressions that are biased towards extremist content ( Francisco and Castro 2020 ). With the help of human experts, the relevant expressions are filtered and used to retrieve further extremist content in order to iteratively provide a set of relevant and accurate expressions. These discriminatory expressions have been proved to produce less complex models that are easier to comprehend, and thus improve model transparency. In the following, we present close to 70 expressions that were discovered by using this method alongside the validation test of the algorithm in several different contexts.
“They fabricated lies against us and described us in the harshest of ways”

Abstract Over the past decade, Islamic State (ISIS) has made numerous attempts to propagate their beliefs on a global scale via a range of social media platforms (e.g. Twitter ), enabling them to reach an extensive audience within a very short time span; when successful, people enlist as supporters of their ideas and, essentially, become radicalised. ISIS also achieve this through publishing propaganda materials, such as the two online magazines Dabiq and Rumiyah ( Heidarysafa et al. 2019 ). In this paper, our focus lies with the former. Through a transitivity analysis of three issues from Dabiq , this paper explores how the in-group (the believers) and the Other (the non-believers) are represented in the magazine. The transitivity framework is useful here because it exposes the linguistic choices that people make and, in turn, reveals how they perceive their world. To retrieve both quantitative and qualitative findings, the UAM Corpus Tool ( O’Donnell 2016 ) is employed.
A Multilayer Approach to the Security of Blockchain Networks of the Future

Decentralized computing and blockchain technology play a significant role in the implementation of modern digital economy business models. The most noticeable trends in this economy are the diversification and convergence of platforms and services, which is often achieved through undesirable fragmentation of the overall IT landscape. Business solutions represented by different blockchain networks turn out to be poorly connected, data exchange between them is difficult. The search for ways to overcome barriers between different decentralized networks leads to an increase in the importance of cross-platform integration solutions, providing the necessary level of interoperability. Such solutions must be secure both in terms of confidentiality and fault tolerance. Below is a vision of the architecture of integration gateways using the ODAP-2PC protocol, which provides crash fault-tolerance for the integration of various networks. This architecture provides transparency of interaction, reliability and continuity of audit in digital asset exchange systems or payment systems with increased requirements for interoperability.
Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments

Visual crowdsensing applications using built-in cameras in smartphones have recently attracted researchers' interest. Making the most out of the limited resources to acquire the most helpful images from the public is a challenge in disaster recovery applications. Proposed solutions should adequately address several constraints, including limited bandwidth, limited energy resources, and interrupted communication links with the command center or server. Furthermore, data redundancy is considered one of the main challenges in visual crowdsensing. In distributed visual crowdsensing systems, photo sharing duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region would be available to the server. Methods for recognizing and removing redundant data provide a range of benefits, including decreased transmission costs and energy consumption overall. To handle the interrupted communication with the server and the restricted resources of the sensor nodes, this paper proposes a distributed visual crowdsensing system for full-view area coverage. The target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest. Then, based on the criteria for full-view area coverage, a specific data structure theme is developed to represent each photo with a set of features. The geometric context parameters of each photo are utilized to extract the features of each photo based on the full-view area coverage criteria. Finally, data redundancy removal algorithms are implemented based on the proposed clustering scheme to eliminate duplicate photos. As a result, each sensor node may filter redundant photographs in dispersed contexts without requiring high computational complexity, resources, or global awareness of all photos from all sensor nodes inside the target area. Compared to the most recent state-of-the-art, the improvement ratio of the added values of the photos provided by the proposed method is more than 38%. In terms of traffic transfer, the proposed method requires fewer data to be transferred between sensor nodes and between sensor nodes and the command center. The overall reduction in traffic exceeds 20% and the overall savings in energy consumption is more than 25%. It was evident that in the proposed system, sending photos between sensor nodes, as well as between sensor nodes and the command center, consumes less energy than existing approaches due to the considerable amount of photo exchange required. Thus, the proposed technique effectively transfers only the most valuable photos needed.
Improving handwritten digit recognition using hybrid feature selection algorithm

Purpose The amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the employment of a feature selection algorithm becomes crucial for successful classification modeling, because the inclusion of irrelevant or redundant features can mislead the modeling algorithms, resulting in overfitting and decrease in efficiency. Design/methodology/approach The minimum redundancy and maximum relevance (mRMR) and the recursive feature elimination (RFE) are two frequently used feature selection algorithms. While mRMR is capable of identifying a subset of features that are highly relevant to the targeted classification variable, mRMR still carries the weakness of capturing redundant features along with the algorithm. On the other hand, RFE is flawed by the fact that those features selected by RFE are not ranked by importance, albeit RFE can effectively eliminate the less important features and exclude redundant features. Findings The hybrid method was exemplified in a binary classification between digits “4” and “9” and between digits “6” and “8” from a multiple features dataset. The result showed that the hybrid mRMR + support vector machine recursive feature elimination (SVMRFE) is better than both the sole support vector machine (SVM) and mRMR. Originality/value In view of the respective strength and deficiency mRMR and RFE, this study combined both these methods and used an SVM as the underlying classifier anticipating the mRMR to make an excellent complement to the SVMRFE.
Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application

The existing numerous adaptive variants of differential evolution (DE) have been improved the search ability of classic DE to certain extent. Nevertheless, those variants of DE do not obtain the promising performance in solving black box problems with unknown features, which is mainly because the adaptive rules of those variants are designed according to their designers’ cognition on the problem features. To enhance the optimization ability of DE in optimizing black box problems with unknown features, a differential evolution with autonomous selection of mutation strategies and control parameters (ASDE) is proposed in this paper, inspired by autonomous decision-making mechanism of reinforcement learning. In ASDE, a historical experience archive with population features is utilized to preserve accumulated historical experience of the combination of mutation strategies and control parameters. Furthermore, the accumulated historical experience can be autonomously mapped into rules repository, and the individuals can choose the combination of mutation strategies and control parameters according to those rules. Additionally, an updating and utilization mechanism of the historical experience is designed to assure that the historical experience can be effectively accumulated and utilized efficiently. Compared with some state-of-the-art intelligence algorithms on 15 functions of CEC2015, 28 functions of CEC2017, and parameter extraction problems of the photovoltaic model, ASDE has the advantages of solution accuracy, convergence speed, and robustness in solving black box problems with unknown features.
Real-Time Object Tracking Algorithm Based on Siamese Network

Object tracking is aimed at tracking a given target that is only specified in the first frame. Due to the rapid movement and the interference of cluttered backgrounds, object tracking is a significant challenging issue in computer vision. This research put forward an innovative feature pyramid and optical flow estimation based on the Siamese network for object tracking, which is called SiamFP. The SiamFP jointly trains the optical flow and the tracking task under the Siamese network framework. We employ the optical flow network based on the pyramid correlation mapping to evaluate the movement information of the target in two contiguous frames, to increase the accuracy of the feature representation. Simultaneously, we adopt spatial attention as well as channel attention to effectively restrain the ambient noise, stress the target area, and better extract the features of the given object, so that the tracking algorithm has a higher success rate. The proposed SiamFP obtains state-of-the-art performance on OTB50, OTB2015, and VOT2016 benchmarks while exhibiting better real-time and robustness.
Privacy-Preserving Outsourced Logistic Regression on Encrypted Data from Homomorphic Encryption

Logistic regression is a data statistical technique, which is used to predict the probability that an event occurs. For some scenarios where the storage capabilities and computing resources of the data owner are limited, the data owner wants to train the logistic regression model on the cloud service provider, while the high sensitivity of training data requires effective privacy protection methods that enable efficient model training without exposing information about the training data to untrusted cloud service providers. Recently, several works have used cryptographic techniques to implement privacy-preserving logistic regression in such application scenarios. However, on large-scale training datasets, the existing works still have the problems of long model training time and poor model performance. To solve these problems, based on the homomorphic encryption (HE), we propose an efficient privacy-preserving outsourced logistic regression (P2OLR) on encrypted training data, which enables data owners to utilize the powerful storage and computing resources of cloud service providers for logistic regression analysis without exposing data privacy. Furthermore, the proposed scheme can pack multiple messages into one ciphertext and perform the same arithmetic evaluations on multiple plaintext slots by using the batching technique and single instruction multiple data (SIMD) mechanism in HE. On three public training datasets, the experimental results show that, compared with the existing schemes, the proposed scheme has better performance in terms of the encryption and decryption time of the data owner, the storage of encrypted training data, and the training time and accuracy of the model.
Unsupervised Martian Dust Storm Removal via Disentangled Representation Learning

Abstract Mars exploration has become a hot spot in recent years and is still advancing rapidly. However, Mars has massive dust storms that may cover many areas of the planet and last for weeks or even months. The local/global dust storms are so influential that they can significantly reduce visibility, and thereby the images captured by the cameras on the Mars rover are degraded severely. This work presents an unsupervised Martian dust storm removal network via disentangled representation learning (DRL). The core idea of the DRL framework is to use the content encoder and dust storm encoder to disentangle the degraded images into content features (on domain-invariant space) and dust storm features (on domain-specific space). The dust storm features carry the full dust storm-relevant prior knowledge from the dust storm images. The “cleaned” content features can be effectively decoded to generate more natural, faithful, clear images. The primary advantages of this framework are twofold. First, it is among the first to perform unsupervised training in Martian dust storm removal with a single image, avoiding the synthetic data requirements. Second, the model can implicitly learn the dust storm-relevant prior knowledge from the real-world dust storm data sets, avoiding the design of the complicated handcrafted priors. Extensive experiments demonstrate the DRL framework’s effectiveness and show the promising performance of our network for Martian dust storm removal.
Data Augmentation-aided Convolutional Neural Network for Detection of Abnormalities in Digital Mammography

Background: The use of data augmentation techniques to addressing the challenge of network overfitting and classification error is important in deep learning. Insufficient sample data for training have the tendency to bias the trained model so that it fails to generalize well. Several studies have proposed different augmentation techniques to solve this problem. But there are some peculiarities identified with the nature of datasets when applying augmentation methods. The subtle nature of some abnormalities in digital mammography often makes it difficult to transform such datasets into different form, while preserving the structure of the abnormality. Aim: To address this, this study aims to apply a combination of carefully selected data augmentation operations on digital mammography.
Learning to Utilize Curiosity: A New Approach of Automatic Curriculum Learning for Deep RL

In recent years, reinforcement learning algorithms based on automatic curriculum learning have been increasingly applied to multi-agent system problems. However, in the sparse reward environment, the reinforcement learning agents get almost no feedback from the environment during the whole training process, which leads to a decrease in the convergence speed and learning efficiency of the curriculum reinforcement learning algorithm. Based on the automatic curriculum learning algorithm, this paper proposes a curriculum reinforcement learning method based on the curiosity model (CMCL). The method divides the curriculum sorting criteria into temporal-difference error and curiosity reward, uses the K-fold cross validation method to evaluate the difficulty priority of task samples, uses the Intrinsic Curiosity Module (ICM) to evaluate the curiosity priority of the task samples, and uses the curriculum factor to adjust the learning probability of the task samples. This study compares the CMCL algorithm with other baseline algorithms in cooperative-competitive environments, and the experimental simulation results show that the CMCL method can improve the training performance and robustness of multi-agent deep reinforcement learning algorithms.
A classification scheme to improve conclusion instability using Bellwether moving windows

Abstract Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.