Reviewerly/exHarmony huggingface dataset:
Study of trend and fluctuations of global solar radiation over Egypt

Climatological spatiotemporal variability of global solar radiation (GSR) and its trend analysis, fluctuations and abrupt changes based on seven sites (Cairo, Arish, Barrani, Matrouh, Aswan, Asyut, Kharga) in Egypt were studied during the period 1985–2018. Fifth-generation dataset of European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis of the global climate and weather for the past 4 to 7 decades (ERA5) data after validation with available observed GSR data has been used. Monthly, seasonal and annual GSR values have marked variations. The results revealed that the highest values of coefficient of variation occurred at stations in winter, while the lowest values occurred in summer. Moreover, in winter and spring, all stations presented a positive Mann-Kendell (MK) trend and almost all stations in annual except Aswan, Asyut and Kharga, whereas in autumn, all station presented a negative MK trend, summer in all stations except Barrani and Cairo. The fluctuations in the annual GSR were investigated using the cumulative sum charts (CUSUM) method, where all stations have alternating trend between negative and positive CUSUM along period.
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label

Zhang et al. (2020) proposed to formulate fewshot intent classification as natural language inference (NLI) between query utterances and examples in the training set.The method is known as discriminative nearest neighbor classification or DNNC.Inspired by this work, we propose to simplify the NLI-style classification pipeline to be the entailment prediction on the utterance-semantic-label-pair (USLP).The semantic information in the labels can thus been infused into the classification process.Compared with DNNC, our proposed method is more efficient in both training and serving since it is based upon the entailment between query utterance and labels instead of all the training examples.The DNNC method requires more than one example per intent while the USLP approach does not have such constraint.In the 1-shot experiments on the CLINC150 (Larson et al., 2019) dataset, the USLP method outperforms traditional classification approach by >20 points (in-domain accuracy).We also find that longer and semantically meaningful labels tend to benefit model performance, however, the benefit shrinks as more training data is available.
A Visual Enhancement Quality of Digital Medical Image Based on Bat Optimization

This work proposes the Bat algorithm to denoise the medical image with different types of noise.• Use several types of noise such as Gaussian noise, salt & pepper noise, speckle noise.• Bat algorithm achieved the best results in noise removal and enhance medical images.• The proposed Bat optimization image denoising achieves better RMSE and PSNR values than other techniques (Gaussian filter, median filter, bilateral filter, and wiener filter).• Observed the PSNR performance of the proposed method (60.6, 55.6, 64.9,63.6)Gaussian noise, salt-and-pepper noise, speckle noise, and Poisson noise on order.Improve medical image visualization is a critical preliminary step before further imagery processing like analyzing texture, extracting features, and segmentation.Imagery noises in medical images are frequently occurred as a consequence of different artificial processes such as acquisition, sending and receiving, and storing & retrieving processes.As a result, the quality of image visualization is degraded.Therefore, a de-noise process is important in order to maintain good image quality for medical purposes.In this paper, medical image enhancement aims to de-noise as much as possible while maintaining detailed features and edges.This work employed an optimization algorithm called "Bat" to enhance the quality of the medical images and also compare it with other methods such as Gaussian filter, median filter, and bilateral and Wiener filter.Obtained image quality was evaluated using range of reference metrics, like, peak signal to noise ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM), and signal to noise ratio (SNR).Bat algorithm achieved the best PSNR, SNR, MSE, SSIM values compared to other filters.Findings presented in this research showed that the PSNR performance of the proposed method is (60.6, 55.6, 64.9, 63.6), MSE is (1.125, 1.43, 2.95, 1.15), Gaussian noise, salt-andpepper noise, speckle noise, Poisson noise on order.
Lithological Mapping for a Semi-arid Area Using GEOBIA and PBIA Machine Learning Approaches with Sentinel-2 Imagery: Case Study of Skhour Rehamna, Morocco

Accurate and reliable lithological mapping through satellite-borne remote sensing data and image classification approaches has a critical role since it can automatically and promptly identify lithological units over large areas. Most available Pixel-Object Based comparative classification studies have been applied to land use land cover (LULC) studies; however, this research aims to evaluate and compare the performance of these digital classification methods in the field of geological mapping in semi-arid areas, by integrating spectral bands and neo-bands, particularly the Minimum noise fraction (MNF) and the principal component analysis (PCA), of Sentinel-2A satellite imagery, to map the southern of Skhour Rehamna which is located at the western Moroccan Meseta. The analysis results from two different methods, namely, pixel-based image analysis (PBIA) with k-nearest neighbour (K-NN) and Random Forest (RF) machine learning algorithms (MLAs), and Geographic Object-Based Image Analysis (GEOBIA) were assessed and compared. PBIA method involved selection of training areas whether it was k-NN or RF MLAs, and produced lithological maps that exhibit “salt and pepper” effects as well as problems associated to delineating accurate lithological boundaries, while GEOBIA approach involved multi-resolution segmentation step where scale, shape and compactness parameters should be adjusted as accurate as possible, in order to segment the image into homogeneous and meaningful regions so that the resulted samples were classified using Standard Nearest Neighbour algorithm. Therefore, the resulting lithological maps were assessed by comparing both techniques using confusion matrix, overall accuracy (OA) and Kappa coefficient (K). The results show that the GEOBIA approach had higher overall agreement (83.46% OA and 0.76 K) than RF (81.92% OA and 0.72 K) and k-NN (80.79% OA and 0.70 K) PBIA approaches. Overall, the results clearly indicate the potential of GEOBIA technique for lithological mapping applications to produce more realistic maps.
Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large

Classification problems having thousands or more classes naturally occur in NLP, for example language models or document classification.A softmax or one-vs-all classifier naturally handles many classes, but it is very slow at inference time, because every class score must be calculated to find the top class.We propose the "softmax tree", consisting of a binary tree having sparse hyperplanes at the decision nodes (which make hard, not soft, decisions) and small softmax classifiers at the leaves.This is much faster at inference because the input instance follows a single path to a leaf (whose length is logarithmic on the number of leaves) and the softmax classifier at each leaf operates on a small subset of the classes.Although learning accurate tree-based models has proven difficult in the past, we are able to overcome this by using a variation of a recent algorithm, tree alternating optimization (TAO).Compared to a softmax and other classifiers, the resulting softmax trees are both more accurate in prediction and faster in inference, as shown in NLP problems having from one thousand to one hundred thousand classes.
OffendES: A New Corpus in Spanish for Offensive Language Research

Offensive language detection and analysis has become a major area of research in Natural Language Processing.The freedom of participation in social media has exposed online users to posts designed to denigrate, insult or hurt them according to gender, race, religion, ideology, or other personal characteristics.Focusing on young influencers from the wellknown social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of 47,128 Spanish comments manually labeled on offensive pre-defined categories.A subset of the corpus attaches a degree of confidence to each label, so both multi-class classification and multi-output regression studies are possible.In this paper, we introduce the corpus, discuss its building process, novelties, and some preliminary experiments with it to serve as a baseline for the research community.
A Class of New Permutation Polynomials over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </mrow> </msub> </math>

In this paper, according to the known results of some normalized permutation polynomials with degree 5 over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </mrow> </msub> </math> , we determine sufficient and necessary conditions on the coefficients <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>b</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>b</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </mfenced> <mo>∈</mo> <msubsup> <mi mathvariant="double-struck">F</mi> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </mrow> <mn>2</mn> </msubsup> </math> such that <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>f</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>x</mi> </mrow> </mfenced> <mo>=</mo> <msup> <mrow> <mi>x</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msup> <msup> <mrow> <mover accent="true"> <mi>x</mi> <mo stretchy="true">¯</mo> </mover> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>+</mo> <msub> <mrow> <mi>b</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <msup> <mrow> <mi>x</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mover accent="true"> <mi>x</mi> <mo stretchy="true">¯</mo> </mover> <mo>+</mo> <msub> <mrow> <mi>b</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mi>x</mi> </math> permutes <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </mrow> </msub> </math> . Meanwhile, we obtain a class of complete permutation binomials over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <msup> <mrow> <mn>2</mn> </mrow> <mrow> <mi>n</mi> </mrow> </msup> </mrow> </msub> </math> .
A Cryptographic View of Deep-Attestation, or How to Do Provably-Secure Layer-Linking

Deep attestation is a particular case of remote attestation, i.e., verifying the integrity of a platform with a remote verification server. We focus on the remote attestation of hypervisors and their hosted virtual machines (VM), for which two solutions are currently supported by ETSI. The first is single-channel attestation, requiring for each VM an attestation of that VM and the underlying hypervisor through the physical TPM. The second, multi-channel attestation, allows to attest VMs via virtual TPMs and separately from the hypervisor – this is faster and requires less overall attestations, but the server cannot verify the link between VM and hypervisor attestations, which comes for free for single-channel attestation. We design a new approach to provide linked remote attestation which achieves the best of both worlds: we benefit from the efficiency of multi-channel attestation while simultaneously allowing attestations to be linked. Moreover, we formalize a security model for deep attestation and prove the security of our approach. Our contribution is agnostic of the precise underlying secure component (which could be instantiated as a TPM or something equivalent) and can be of independent interest. Finally, we implement our proposal using TPM 2.0 and vTPM (KVM/QEMU), and show that it is practical and efficient.
Adaptation of Short-Term Cloud Forecasting to Global Scale and Evaluation of its Contribution to Earth Observing Satellite Planning

A short-term cloud forecasting method based on optical flows is used in order to increase the number of cloud free images acquired at the end of an Earth observation satellites planning process. Following a first validation of a satellite-based cloud forecasting method for cloud fraction forecasting, it is adapted from local to global scale using parameters optimized for several climate types. This is done using a new dataset built on the basis of two Nowcasting Satellite Application Facilities (NWC SAF) products derived from Meteosat Second Generation (MSG) images, which allowed us to create a large number of test samples 0associated to six different climate types and regularly distributed throughout a whole year. Then, two test cases are performed in order to estimate the gain in terms of cloud-free acquired images proportion between the satellite-based cloud forecasting method and a more classical numerical weather prediction (NWP) model, i.e. the high resolution configuration of the Integrated Forecasting System (IFS) developed by ECMWF and Meteo-France. The first does not consider the satellites trajectory and assumes that all samples can be acquired in a single pass. The second one is realistic and imply a mission planning simulator. In both cases, the satellite-based cloud forecasting method leads to much better cloud-free images rates.
CDCAT: A multi-language cross-document entity and event coreference annotation tool

A tool for the manual annotation of cross-document entity and event coreferences that helps annotators to label mention coreference relations in text is essential for the annotation of coreference corpora. To the best of our knowledge, CROss-document Main Events and entities Recognition (CROMER) is the only open-source manual annotation tool available for cross-document entity and event coreferences. However, CROMER lacks multi-language support and extensibility. Moreover, to label cross-document mention coreference relations, CROMER requires the support of another intra-document coreference annotation tool known as Content Annotation Tool, which is now unavailable. To address these problems, we introduce Cross-Document Coreference Annotation Tool (CDCAT), a new multi-language open-source manual annotation tool for cross-document entity and event coreference, which can handle different input/output formats, preprocessing functions, languages, and annotation systems. Using this new tool, annotators can label a reference relation with only two mouse clicks. Best practice analyses reveal that annotators can reach an annotation speed of 0.025 coreference relations per second on a corpus with a coreference density of 0.076 coreference relations per word. As the first multi-language open-source cross-document entity and event coreference annotation tool, CDCAT can theoretically achieve higher annotation efficiency than CROMER.
GenSumm: A Joint Framework for Multi-task Tweet Classification and Summarization using Sentiment Analysis and Generative Modelling

Social media platforms like Twitter act as a medium for communication among people, government agencies, NGOs, and other relief providing agencies in widespread humanitarian havoc during a disaster outbreak when other communication means might not be available. Various agencies leverage twitter's open and public features to get timely and reliable updates, thus support agencies in communicating with the people on rescue and provide immediate relief. As situational updates are mixed in millions of other tweets, an efficient system is required to extract and summarize these tweets. We have developed a novel framework that uses a deep learning-based classification model to separate the informational tweets from others and summarizes them in the current paper. Non-situational tweets mostly comprise sentiments like grief, anger, sorrow, etc. Motivated by this observation, we have solved sentiment classification and informative tweet selection tasks simultaneously using a multi-task learning (MTL) in a deep-learning framework. Our summarization approach generates clustering solutions using various existing approaches and then ensembles cluster solutions using generative modelling. A summary is formulated by extracting tweets from different clusters. The proposed approach's superior performance on four disaster-related events indicates the developed framework's efficiency over state-of-the-art techniques.
Bug Triaging: Right Developer Recommendation for Bug Resolution Using Data Mining Technique

Most of the open-source projects consist of open-source bug repositories. Users report the bugs frequently to these open-source bug repositories. The bugs are typically non-technical and cannot be assigned to the correct class. Triaging these bugs is extremely time-consuming. Developers are expected to be expertise in specific areas. For example, some developers are experts of GUI and while others in Java applications. Assigning a particular bug to respective developer could save time and would help the developers according to their requirements. However, assigning correct bug to respective developer is quite difficult for triager without knowing the actual class, to which the bug belongs. Bugs are inevitable and bug triaging is a challenging, time-consuming, tedious and expensive task, particularly the software bugs. Larger software projects include more of incoming bug reports than in smaller projects. Triaging these bug reports is a tough and time consuming. Part of the bug triaging process is assigning a newly arrived bug report to a developer who can successfully resolve the bug. Automatic assignment of the bug to the right developer based on the developer’s experience and skills for the bug fixation is achieved.
Anomaly Detection and Performance Analysis by Using Big Data Filtering Techniques For Healthcare on IoT Edges

The IoT is a sensors world that detects countless physical events in our environment and transforms them into data, and transfers this data to different environments or digital systems. The usage areas of Internet of things-based technologies are constantly increasing and technologies are being developed to support the IoT infrastructure. But, in order to effectively manage the large number of big-data generate in the detection layer, it should be pre-processed and done in accordance with big-data standards. For the effective management of big data, it is imperative to improving the standards of the data set, and filtering methods are being developed for a higher quality data set. For instance, using data cleaning methods is a preprocessing method that facilitates data mining operations. In this way, more manageable data is obtained by preventing the formation of interference and big data can be managed more effectively. In this study, we investigate the efficient operation of IoT and big data originating from the internet of things. Additionally, real-time anomalous data filtering is performed on IoT edges with a data set consisting of six different data produced in real- time. Furthermore, the speed and accuracy performances of classifiers are compared, and machine learning algorithms such as the random cut forest-RCF, logistic regression-LR, naive bayes-NB, and neural network-NN classifiers are used for comparison. According to the accuracy performance values, the RCF and LR classifiers are very close, but considering the speed values, it is seen that the LR classifier is more successful in IoT systems.
Catalytic transformations in coherence theory

In 2016, Winter and Yang [A. Winter and D. Yang, Phys. Rev. Lett. 116, 120404 (2016)] provided an operational meaning to relative entropy of coherence and coherence of formation by introducing coherence distillation and dilation protocols in an asymptotic setup. Here we have provided relative entropy of coherence [via incoherent operations (IO)] and coherence of formation (via IO) and quantum incoherent relative entropy [via local quantum incoherent operations with classical communications (LQICC)] as a measure using the concept of a catalyst. We have proved an existential correspondence between asymptotic and catalytic state transformation using IO, local incoherent operations with classical communications, and LQICC. We have also discussed two very important protocols, assisted distillation and quantum incoherent state merging using a catalyst. Monotone properties of relative entropy of coherence, coherence of formation, and quantum incoherent relative entropy under the catalytic transformation are also discussed here.
An Up-Sampled Particle Filter Fusion Technique and Its Application in Synthetic Aperture Radar Imaging

Global positioning system (GPS) and inertial measurement units (IMUs) are often combined to produce navigation systems for airborne imaging platforms. The current state-of-the-art radar technology allows for radars to pulse at very high rates. GPS and IMU update rates are not fast enough to accurately report the platform position for each radar pulse. Independent GPS and IMUs cannot provide positional accuracy for long term stability. Traditional techniques, such as the Kalman and particle filter, are used to fuse GPS and IMU measurements. The Kalman filter excels for linear and Gaussian systems whereas the particle filter excels at non-linear and non-Gaussian systems. Sensor fusion techniques are used to help correct for IMU errors and provide the positional accuracy required for synthetic aperture radar (SAR) imaging applications. However, SAR requires the fusion algorithms to provide faster update rates. This paper explores the use of an up-sampled particle filter (UPF) for SAR to provide highly accurate position estimates at sampling frequencies comparable to radar pulse rates and overcome the limitations of standard interpolation techniques. This up-sampled particle filter is proven through simulations and instrumentation with a NovAtel GPS and IMU. The UPF technique allows for the GPS/IMU sampling rate to be different from the radar pulse repetition frequency (PRF) while providing accurate position solutions for each radar pulse capable of compensating for the phase history required for focusing a SAR image. The algorithms are instrumented in a SAR system and the position estimates are further validated and demonstrated through captured SAR images.
Analyzing Newspaper Articles for Text-Related Data for Finding Vulnerable Posts Over the Internet That Are Linked to Terrorist Activities

One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.