Reviewerly/exHarmony huggingface dataset:
Development of New Models for the Estimation of Hourly Components of Solar Radiation: Tests, Comparisons, and Application for the Generation of a Solar Database in Morocco

All studies of solar systems need the hourly values of solar fluxes related to the different components of solar radiation. However, for most sites, measurements are not available. The goal of this paper is to establish an hourly solar radiation data bank for the entirety of Morocco. This data bank will contain the energetic components (global, diffuse, direct, and inclined solar radiation) and the spectral components (ultraviolet <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mtext>UV</mtext> </mrow> </msub> </math> , infrared <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mtext>IR</mtext> </mrow> </msub> </math> , and photosynthetically active radiation <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msub> <mrow> <mi>I</mi> </mrow> <mrow> <mtext>PAR</mtext> </mrow> </msub> </math> ). To create this database, in the beginning, we build up models for predicting the various components of the hourly solar radiation, from measurements provided by the different stations owned by the Laboratory of Solar Energy and Environment (LESE). Then, these equations will be verified by using statistical tests. Moreover, we compared our results with those obtained by similar studies. Finally, based on the daily database of global radiation provided by the Laboratory of Solar Energy and Environment, we created an hourly database that unites the various components of solar radiation over 10 years for the entirety of Morocco. Regarding the validity study, the statistical indicators showed that the used models have minimal errors that do not exceed, for the global radiation, -2.93% in Rabat, -3.9% in Tangier,-3.28% in Marrakech, and -0.85% in Fez, and for diffuse radiation, -1.09% in Rabat, -0.68% in Fez,-3.08% in Tangier, and -2.49% in Marrakech. These results show the good quality of the used estimation models. Thus, the data bank that we have realized will fill the gap of the hourly solar data and will meet the needs of engineers, installers of solar systems, and researchers who often need an extensive database.
Stay professional and efficient

Bug reports in a repository are generally organized line by line in a list-view, with their titles and other meta-data displayed. In this list-view, a concise and precise title plays an important role that enables project practitioners to quickly and correctly digest the core idea of the bug, without carefully reading the corresponding details. However, the quality of bug report titles varies in open-source communities, which may be due to the limited time and unprofessionalism of authors. To help report authors efficiently draft good-quality titles, we propose a method, named iTAPE, to automatically generate titles for their bug reports. iTAPE formulates title generation into a one-sentence summarization task. By properly tackling two domain-specific challenges (i.e. lacking off-the-shelf dataset and handling the low-frequency human-named tokens), iTAPE then generates titles using a Seq2Seq-based model. A comprehensive experimental study shows that iTAPE can obtain fairly satisfactory results, in terms of the comparison with three latest one-sentence summarization works, as well as the feedback from human evaluation.
Insider threat prediction based on unsupervised anomaly detection scheme for proactive forensic investigation

The complexity, concealment and infrequency of malicious internal actions make it difficult to detect insider threats. In the process of traditional reactive forensic investigation, analysis and interpretation of the digital evidence are performed after a crime has been committed. Even if insiders can be detected, they have already caused huge damage. In this paper, we propose a novel general unsupervised anomaly detection scheme based on cascaded autoencoders (CAEs) and joint optimization network. Our core idea is to utilize CAEs to do data purification among unlabeled digital evidence, then jointly optimize the dimension reduction and density estimation network to avoid sub-optimal problems. Based on this scheme, we design an end-to-end insider threat prediction framework for proactive forensic investigation, through which we can make real time response to prevent the harmful influences of insider threats in advance. We extract the tractable and scalable feature representation automatically through the data driven Bidirectional Long Short-Term Memory (BiLSTM) feature extractor, waiving the time-consuming and customarily expert dependable feature engineering work. Additionally, a hypergraph correction module is applied to solve the commonly existed relatively high false positive rate problem in insider threat detection. We evaluate our scheme and framework on public benchmark datasets. The empirical experiments demonstrate that our models outperform state-of-the-art unsupervised methods.
Image-based rock typing using grain geometry features

Image-based rock typing is carried out to quantitatively assess the heterogeneity of the reservoir specimen at a pore scale by classifying an image of a heterogeneous rock sample into a number of relatively homogeneous regions. Image-based rock typing can be treated as a special application of texture classification in the field of the digital core. In conventional texture classification algorithms, a single size window or a set of windows with different size are applied to scan the image to extract various local structure features, and then a classification algorithm is used to classify the image into different regions where each region possesses unique structure features. Due to the local features are extracted within a window, it is still challenging to identify the class of the voxels close to the boundary between different regions. In this paper, a rock typing method is proposed, which uses the geometry features of the grains instead of local structure features for classification. Inspired by the fact that in some cases the heterogeneity of the reservoir is mainly affected by the sedimentary process, which means each rock type always has certain specific grain features such as size and sphericity. To this kind of rock samples, the proposed grain-based rock typing algorithm can effectively address the boundary ambiguousness problem. In this study, the grains of the rock sample are partitioned firstly, and then their geometry features are calculated. Then a support vector machine algorithm is used to classify these grains into different rock types. Finally, the pore voxels are given a rock type, which is identical to its nearest grain. The proposed method shows excellent performance in the heterogeneous samples whose grains are available to be partitioned and distinguishable.
Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions

Abstract Mixture of linear experts (MoE) model is one of the widespread statistical frameworks for modeling, classification, and clustering of data. Built on the normality assumption of the error terms for mathematical and computational convenience, the classical MoE model has two challenges: (1) it is sensitive to atypical observations and outliers, and (2) it might produce misleading inferential results for censored data. The aim is then to resolve these two challenges, simultaneously, by proposing a robust MoE model for model-based clustering and discriminant censored data with the scale-mixture of normal (SMN) class of distributions for the unobserved error terms. An analytical expectation–maximization (EM) type algorithm is developed in order to obtain the maximum likelihood parameter estimates. Simulation studies are carried out to examine the performance, effectiveness, and robustness of the proposed methodology. Finally, a real dataset is used to illustrate the superiority of the new model.
Classification and recognition of online hand-written alphabets using Machine Learning Methods

Abstract The hand-written alphabet recognition and classification plays an important role in pattern recognition, computer vision as well as image processing. In last few decades, a plethora of applications based on this area are developed such as sign identification, multi lingual learning systems etc. This paper classifies samples of hand-written alphabets into different classes using various machine learning methods. The challenging factor in hand written alphabets recognition lie in variations of style, shape and size of the letters. In this paper a simplified and accurate methodology is proposed based upon engineered features which are evaluated and tested using MatLab tool in comparison to other existing methods. The proposed system achieves a substantial amount of accuracy of 98% as compared to the state of the art approaches.
Fuzzy k-nearest neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise

This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN). Real-life data-sets often do not comply with monotonic constraints due to class noise. MonFkNN incorporates a new calculation of fuzzy memberships, which increases robustness against monotonic noise without the need for relabeling. Our proposal has been designed to be adaptable to the different needs of the problem being tackled. In several experimental studies, we show significant improvements in accuracy while matching the best degree of monotonicity obtained by comparable methods. We also show that MonFkNN empirically achieves improved performance compared with Monotonic k-NN in the presence of large amounts of class noise.
Code-based vulnerability detection in Node.js applications

With one of the largest available collection of reusable packages, the JavaScript runtime environment Node.js is one of the most popular programming application. With recent work showing evidence that known vulnerabilities are prevalent in both open source and industrial software, we propose and implement a viable code-based vulnerability detection tool for Node.js applications. Our case study lists the challenges encountered while implementing our Node.js vulnerable code detector.
Reversible Data Hiding for AMBTC Compressed Images Based on Matrix and Hamming Coding

In this paper, we propose a two-layer data hiding method by using the Hamming code to enhance the hiding capacity without causing significantly increasing computation complexity for AMBTC-compressed images. To achieve our objective, for the first layer, four disjoint sets using different combinations of the mean value (AVG) and the standard deviation (VAR) are derived according to the combination of secret bits and the corresponding bitmap, following Lin et al.’s method. For the second layer, these four disjoint sets are extended to eight by adding or subtracting 1, according to a matrix embedding with (7, 4) Hamming code. To maintain reversibility, we must return the irreversible block to its previous state, which is the state after the first layer of data is embedded. Then, to losslessly recover the AMBTC-compressed images after extracting the secret bits, we use continuity feature, the parity of pixels value, and the unique number of changed pixels in the same row to restore AVG and VAR. Finally, in comparison with state-of-the-art AMBTC-based schemes, it is confirmed that our proposed method provided two times the hiding capacity comparing with other six representative AMBTC-based schemes while maintaining acceptable file size of steog-images.
TPACK’s pedagogy and the gradual release of responsibility model coalesce: integrating technology into literacy teacher preparation

(2021). TPACK's pedagogy and the gradual release of responsibility model coalesce: integrating technology into literacy teacher preparation. Journal of Research on Technology in Education. Ahead of Print.
Real-time semantic segmentation via sequential knowledge distillation

Deep model-based semantic segmentation has received ever increasing research focus in recent years. However, due to the complex model architectures, existing works are still unable to achieve high accuracy in real-time applications. In this paper, we propose a novel Sequential Prediction Network (termed SPNet) to seek a better trade-off between accuracy and efficiency. SPNet is also an end-to-end encoder-decoder architecture, which introduces a sequential prediction method to spread the contextual information from the low-level layers to the high-level layers. Besides, the proposed method is equipped with a stream Spatial Semantic and Edge Loss (termed SEL) and an adversarial network at multiple resolutions, which greatly improves the segmentation accuracy with a negligible increase in computation cost. To further utilize the extra unlabeled data, we present a knowledge distillation scheme to distill the structured knowledge from cumbersome to compact networks. Without using any pre-trained model, our method achieves state-of-the-art performance among exiting real-time segmentation models on several challenging datasets. Impressively, on the Cityscapes test dataset, it obtains 75.8% mIoU at a speed of 61.2 FPS.
Post COVID-19 Higher Education Empowered by Virtual Worlds and Applications

The COVID-19 pandemic forced higher education institutions to switch to remote learning environments, for which many universities lacked prior contingency plans and/or infrastructures to run all of their courses online. Now, it has become compulsory that higher education institutions invest in educational technologies in order to survive and thrive in such a challenging platform, as this has become the `New Norm' in higher education. This paper investigates literature related to post COVID-19 universities' experiences in applying virtual worlds and applications in higher education worldwide, to achieve a developed remote learning for their students in a risk-free educational environment. The paper highlights the advantages and suitability of virtual world technologies for higher education's requirements in this period, and provides examples from universities globally, which applied virtual worlds and applications in their students' education post pandemic. The paper also delivers recommendations to higher education institutions when considering virtual world technologies for their online learning activities.
Learning to handle exceptions

Exception handling is an important built-in feature of many modern programming languages such as Java. It allows developers to deal with abnormal or unexpected conditions that may occur at runtime in advance by using try-catch blocks. Missing or improper implementation of exception handling can cause catastrophic consequences such as system crash. However, previous studies reveal that developers are unwilling or feel it hard to adopt exception handling mechanism, and tend to ignore it until a system failure forces them to do so. To help developers with exception handling, existing work produces recommendations such as code examples and exception types, which still requires developers to localize the try blocks and modify the catch block code to fit the context. In this paper, we propose a novel neural approach to automated exception handling, which can predict locations of try blocks and automatically generate the complete catch blocks. We collect a large number of Java methods from GitHub and conduct experiments to evaluate our approach. The evaluation results, including quantitative measurement and human evaluation, show that our approach is highly effective and outperforms all baselines. Our work makes one step further towards automated exception handling.
Experiencing with electronic image stabilization and PRNU through scene content image registration

This paper explores content-based image registration as a means of dealing with and understanding better Electronic Image Stabilization (EIS) in the context of Photo Response Non-Uniformity (PRNU) alignment. A novel and robust solution to extrapolate the transformation relating the different image output formats for a given device model is proposed. This general approach can be adapted to specifically extract the scale factor (and, when appropriate, the translation) so as to align native resolution images to video frames, with or without EIS on, and proceed to compare PRNU patterns. Comparative evaluations show that the proposed approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. Furthermore, a tracking system able to revert back EIS in controlled environments is designed. This allows one to investigate the differences between the existing EIS implementations. The additional knowledge thus acquired can be exploited and integrated in order to design and implement better future PRNU pattern alignment methods, aware of EIS and suitable for video source identification in multimedia forensics applications.
Evaluating and Improving Adversarial Attacks on DNN-Based Modulation Recognition

The discovery of adversarial examples poses a serious risk to the deep neural networks (DNN). By adding a subtle perturbation that is imperceptible to the human eye, a well-behaved DNN model can be easily fooled and completely change the prediction categories of the input samples. However, research on adversarial attacks in the field of modulation recognition mainly focuses on increasing the prediction error of the classifier, while ignores the importance of decreasing the perceptual invisibility of attack. Aiming at the task of DNNbased modulation recognition, this study designs the Fitting Difference as a metric to measure the perturbed waveforms and proposes a new method: the Nesterov Adam Iterative Method to generate adversarial examples. We show that the proposed algorithm not only exerts excellent white-box attacks but also can initiate attacks on a black-box model. Moreover, our method decreases the waveform perceptual invisibility of attacks to a certain degree, thereby reducing the risk of an attack being detected.
WITHDRAWN: IOT-HARPSECA: A SECURE DESIGN AND DEVELOPMENT SYSTEM OF ROADMAP FOR DEVICES AND TECHNOLOGIES IN IOT SPACE

As the Internet of Services (IoT) is emerging in all ways it is essential to make the road map for securing in all ways. The framework was designed in the novel IoT Hardware Platform Security Advisor (IoT-HarPsecA) where the framework was evaluated. Based on this framework it is designed in all the ways and implemented in hardware as well as software. But this proposed work is an extension to that by implementing more security by including the concept of a digital signature. By implementing the digital signature concept it is aware of the intermediate intruders. If they modified the data or messages which are sent then the signature will be invalidated suggesting that the message is subjected to modification. Thus the security is maintained. There are many available schemes. One among them is the RSA (Rivest–Shamir–Adleman) algorithm. By implementing the concepts like key generation, key distribution the security was adopted.
The mobile phone technology, gender inclusive education and public accountability in Sub-Saharan Africa

This study assesses the relevance of mobile phone technology in complementing gender inclusive education (i.e. primary, secondary and tertiary) to promote public accountability (i.e. involving horizontal, vertical and diagonal accountability dynamics). The study utilizes the generalized method of moments (GMM) technique to establish the empirical evidence based on 48 Sub-Saharan African countries for the period 2005–2018. The following findings are documented from the linkages between mobile phone technology, inclusive education and public accountability. First, the interactions between mobile phone technology and inclusive education promote public accountability. Second, with regard to net effects, while unexpected negative signs are established, the corresponding positive interactive effects indicate that enhancing the penetration of mobile phone technology beyond some critical thresholds ensures positive net effects. Hence, policy makers should ensure that mobile phone technology penetration exceeds the established thresholds in order for gender inclusive education to positively affect public accountability.
Hybrid Multi-Objective Grey Wolf Search Optimizer and Machine Learning Approach for Software Bug Prediction

Software bugs (or malfunctions) pose a serious threat to software developers with many known and unknown bugs that may be vulnerable to computer systems, demanding new methods, analysis, and techniques for efficient bug detection and repair of new unseen programs at a later stage. This chapter uses evolutionary grey wolf (GW) search optimization as a feature selection technique to improve classifier efficiency. It is also envisaged that software error detection would consider the nature of the error when repairing it for remedial action instead of simply finding it either faulty or non-defective. To address this problem, the authors use bug severity multi-class classification to build an efficient and robust prediction model using multilayer perceptron (MLP), logistic regression (LR), and random forest (RF) for bug severity classification. Both tests are performed on two software error datasets, namely Ant 1.7 and Tomcat.
Modeling social interaction and intention for pedestrian trajectory prediction

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the fact that in addition to pedestrians there are other kinds of objects (cars, dogs, bicycles, motorcycles, etc.) with a great influence on the subject pedestrian's future trajectory. Most existing methods neglect the intentions of the pedestrian, which can be obtained by the key points of the subject pedestrian's face. Therefore, rich category information about the subject pedestrian's surroundings and face key points plays a great role in promoting the modeling of pedestrian movement. Motivated by this idea, this paper tries to predict a pedestrian's future trajectory by jointly using various categories and the relative positions of the subject pedestrian's surroundings and the key points in his face. We propose a data modeling method to effectively unify rich visual features about categories, interaction and face key points into a multi-channel tensor and build an end-to-end fully convolutional encoder–decoder attention model based on convolutional long–short-term memory utilizing this tensor. We evaluate and compare our method with several existing methods on 5 crowded video sequences from the public dataset multi-object tracking (MOT) -16. Experimental results show that our method outperforms state-of-the-art approaches, with less prediction error.
A Hybrid Deep Learning Model For Online Fraud Detection

Nowadays, credit cards are becoming more and more widely used for both online and offline transactions. But along with this trend comes more credit card fraud. According to the Nilson report the global loss to credit card fraud is expected to reach $35 billion this year, so there is a desperate need for accurate and efficient fraud detection systems. In this paper, we propose a deep-learning-based method to tackle this problem. We employed multiple techniques, including feature engineering, memory compression, mixed precision, and ensemble loss to boost the performance of our model. The model is trained and evaluated on the IEEE-CIS fraud dataset provided by Vesta Corporation consisting of over 1 million records. Experiments show that our model outperforms traditional machine-learning-based methods like Bayes and SVM.