Reviewerly/exHarmony huggingface dataset:
Carbon Dioxide is Not the Guilty

Calculations show that the influence of the CO2 on the temperature is much less than one percent, negligible, therefore. The new water cycle, discovered by this author, is demonstrated physically and mathematically, showing the influence of certain human activities on the natural cycles and thus on the climate, that is, not as such influence been said to us up to now.The “science” on global warming would never discover the new water cycle, because it considers the atmosphere as a warming and monolithic body only as well as eliminates the water from the atmosphere. Climate events depend on temperature differences, not on a temperature alone. Global temperature differences make no sense. Global temperatures do not serve to explain climatic events, because these happen locally or regionally. In view of this, global temperatures of 1.0, 1.5 or 2.0 °C by 2100 or lower global temperatures than these ones for the current days do not cause floods, droughts, hot air at one side of the planet and a terribly cold at the opposite one, storms, tornadoes, hurricanes etc. Each one of these events has its own causes and consequences. Temperature differences are the driving force for the atmospheric events. If the water vapor in the atmosphere existed in the direct relation with the temperature, the Sahara would be the most humid place on Earth.
A hybrid model for recommender system in e-commerce

With the popularity of the Internet, e-commerce has grown rapidly. Many online applications use recommender system to predict user preferences and make predictions on items based on user preference information to provide a better service experience and increase sales of items. Since online applications often add new items into the system, it is difficult to recommend new items to users without any feedback from them. Similarly, when a new user registers to the system, it becomes very difficult to recommend items to the new user because there is no previous purchase history of the new user. This condition is known as cold-start problem. There are two types of cold-start problem: user cold-start problem and item cold-start problem. In order to solve the cold-start problem, we propose a hybrid approach that combines traditional machine learning methods and neural network techniques. For the user cold-start problem, we extract additional information between users and items and convert the information into latent features as a basis. Experiments will be done with real-world datasets to verify the effectiveness of this method on the user cold-start problem.
Fast exemplar-based fringe extrapolation technology

When using the Fourier transform method to demodulate the phase from an interferogram, the Gibbs effect often occurs at the edge of the phase picture. Fringe extrapolation is a straightforward method to suppress the Gibbs effect. Currently, many algorithms are used in fringe extrapolation, and exemplar-based methods are one such alternative. However, the traditional exemplar-based algorithm is based on the Criminisi algorithm, which is extremely time-consuming. In this study, the structure-guided image-completion method was applied to interferogram extrapolation. Simulations and experiments show that the new method reduces computer memory requirements and saves computing time. Compared with extrapolation using the Criminisi algorithm, the speed can be increased by tens or even hundreds of times.
Using embedding learning to improve the accuracy of binary sentiment analysis on few-shot text data

Sentiment analysis of text data is an important task in natural language processing. Taking the product review system as an example, proper use of sentiment analysis can reveal the opinions of consumers, and thus adjustments can be made to products. However, most current sentiment analysis methods assume that there is a large amount of training data available for the classification model. The classification accuracy is not ideal when little data are available for analysis. Aiming at sentiment analysis of text data with few data samples, in this paper, we propose an embedded learning model, which combines convolutional neural network and bidirectional long short-term memory learning models to improve the relevance of data by dimensionality reduction and thus strengthen the classification accuracy of few-shot learning. In addition, we segment the training data and use them to train the proposed model in batches to avoid the overfitting problem that is often observed in few-shot learning models. According to the experimental results shown in this study, the accuracy of our model is about 11% on average, which is higher than that of the traditional classification methods.
A privacy protection method for camera-based elderly monitoring system using single pixel representation

The aging of population is becoming a global health concern. The elderly monitoring systems can assist senior citizens to live independently and identify emergency situations. However, adoption of camera-based monitoring systems also raises the issues of the privacy-preservation of the elderly users. This paper presents a monitoring architecture based on machine learning, edge-computing, and information encoding for remote user interface. The system comprises a top-view fisheye camera, edge-computing module and an android application. An edge computing module uses a machine learning model to identify the person in the fisheye camera images and encodes the person's activity into a colored pixel. This encoded information is transmitted to the remote user interface, which mitigates the risk of privacy leak. The experiments based on real-world images confirm the applicability of our proposed system.
Comparison K-Means and Fuzzy C-Means Methods to Grouping Human Development Index Indicators in Indonesia

The Human Development Index (HDI) is an important indicator to measure the success of efforts to improve people's quality of life. The increase in the human development index in Indonesia is not accompanied by an even distribution of the human development index in every district/city in Indonesia. To facilitate the government in making policies and plans in overcoming the uneven HDI in Indonesia, it is necessary to group districts/cities in Indonesia based on HDI indicators. This study discusses the use of the K-means and Fuzzy C-Means algorithms with a total of 4 clusters. The grouping results obtained summarize that most districts/cities in Papua Island have low HDI indicators. The achievement of the HDI indicator in the medium category on the K-Means and Fuzzy C-Means methods is the same, spread across all major islands in Indonesia. However, the Nusa Tenggara Islands generally have a medium HDI indicator achievement. The achievements of the HDI indicators with high categories in the K-Means and Fuzzy C-Means methods are mostly found on the islands of Sumatra, Java, Kalimantan, and Sulawesi. The achievement of the HDI indicator in the very high category in the K-Means and Fuzzy C-Means methods is found in provincial capitals in several provinces in Indonesia as well as in big cities in Indonesia. The results of this study indicate that the S_DBW index and C_index values of the Fuzzy c-means method are smaller than the K-Means method, namely 2.312 and 0.105.
Descriptive Analysis of Human Emotions Based on Eye pupils

Facial emotional expressions are viewed as the most descriptive way to understand the human’s state of temperament during confronting communication. In this work numerous statistical approaches have been applied on human eye pupil with static images of Chicago face dataset (CFD) to analyze and classify the considered categories for emotions which are Happy, Fear, Anger and Neutral. The aim of this study is to develop the specific architecture for image processing domain after applying different enhancement techniques on human eye pupil for analysis &amp; recognition of the facial expressions. This work is divided into three phases initially in the first phase data preprocessing is performed to prepare according to the requirement of work and also the color images are converted in to negative by applying the pixel intensity controlled mechanism. Second phase define the boundary to compute the feature by using Circular Hough Transform algorithm. Lastly statistical approaches are applied on extracted features to corporate the central point of pupil. This corporation the central point presents the effects of emotions. While comparing peoples of different Age groups it is concluded that pupil constricted on Anger at different levels on different age groups. If further it is discussed about cross cultural and gender wise comparison then Happy Emotion effects most and resulted towards dilated pupil same like that Anger emotion effects most on constricting the pupil size.
Eye-tracking calibration to control a COBOT

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Bayesian belief networks

This comprehensive Dictionary brings together an extensive range of definitive terms in ecological economics. Assembling contributions from distinguished scholars, it provides an intellectual map to this evolving subject ranging from the practical to the philosophical.
Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering

Over the last few years, various multi-view graph clustering methods have shown promising performances. However, we argue that these methods can have limitations. In particular, they are often unnecessarily complex, leading to scalability problems that make them prohibitive for most real-world graph applications. Furthermore, many of them can handle only specific types of multi-view graphs. Another limitation is that the process of learning graph representations is separated from the clustering process, and in some cases these methods do not even learn a graph representation, which severely restricts their flexibility and usefulness. In this paper we propose a simple yet effective linear model that addresses the dual tasks of multi-view attributed graph representation learning and clustering in a unified framework. The model starts by performing a first-order neighborhood smoothing step for the different individual views, then gives each one a weight corresponding to its importance. Finally, an iterative process of simultaneous clustering and representation learning is performed w.r.t. the importance of each view, yielding a consensus embedding and partition of the graph. Our model is generic and can deal with any type of multi-view graph. Finally, we show through extensive experimentation that this simple model consistently achieves competitive performances w.r.t. state-of-the-art multi-view attributed graph clustering models, while at the same time having training times that are shorter, in some cases by orders of magnitude.
Learning to Discriminate Adversarial Examples by Sensitivity Inconsistency in IoHT Systems

Deep neural networks (DNNs) have been widely adopted in many fields, and they greatly promote the Internet of Health Things (IoHT) systems by mining health-related information. However, recent studies have shown the serious threat to DNN-based systems posed by adversarial attacks, which has raised widespread concerns. Attackers maliciously craft adversarial examples (AEs) and blend them into the normal examples (NEs) to fool the DNN models, which seriously affects the analysis results of the IoHT systems. Text data is a common form in such systems, such as the patients’ medical records and prescriptions, and we study the security concerns of the DNNs for textural analysis. As identifying and correcting AEs in discrete textual representations is extremely challenging, the available detection techniques are still limited in performance and generalizability, especially in IoHT systems. In this paper, we propose an efficient and structure-free adversarial detection method, which detects AEs even in attack-unknown and model-agnostic circumstances. We reveal that sensitivity inconsistency prevails between AEs and NEs, leading them to react differently when important words in the text are perturbed. This discovery motivates us to design an adversarial detector based on adversarial features, which are extracted based on sensitivity inconsistency. Since the proposed detector is structure-free, it can be directly deployed in off-the-shelf applications without modifying the target models. Compared to the state-of-the-art detection methods, our proposed method improves adversarial detection performance, with an adversarial recall of up to 99.7% and an F1-score of up to 97.8%. In addition, extensive experiments have shown that our method achieves superior generalizability as it can be generalized across different attackers, models, and tasks.
The CMIP TT Data Citation for a sustainable continuation of a requested and established service

Data citation has become a core service for data infrastructures and data repositories, integrated in author guidelines and project proposals. It provides an essential piece in Open Science supporting reuse and enabling credit by integration into the established scientific credit system of scientific references (Scholix) and the envisioned FAIR Digital Object framework. Humans and machines need to be supported for provision and access of the information.Modern data infrastructures are federated and without central funding, especially that of international collaborations. Providing and maintaining state-of-the-art data services does not fit within current funding structures, even though these infrastructures provide core services to a broad community, like the data infrastructure of the Coupled Model Intercomparison Project (CMIP).&amp;#160;For the citation service of CMIP6, currently a single person effort, the newly established CMIP Task Team (TT) Data Citation is exploring a set of options for a sustainable data citation service for CMIP7 and further WCRP activities in a cost-benefit approach. Due to the different experiences of its members, the TT brings infrastructure aspects together with the needs of researchers. A governance structure for sustainability, standard agreements and the coordination of further developments will be framed. The background to the TT and first experiences are shared.
Designing a GUI for Offline Text-Independent Writer Identification System

Due to significant changes in the software and hardware technologies, the twenty-first century became an era of information. As the user/customer now wants all the desired information at their fingertips, automation of all manual processes in every field is of utmost importance. Therefore, it is needed to design and develop algorithms capable of processing the required work without interference from human. Particularly, scriptwriter identification is a changing field for research. Handwriting is considered an identifying sign of a writer with each writer having their own characteristic style and some details that get repeated unconsciously. Throughout history, the identification of the writer of a handwritten script has been of importance, as writer identification based on handwriting has a large number of applications such as authentication, security, forensics and criminal justice. Documents that do not contain any signature for identification also need to be identified using only the handwritten data, i.e., text-independent identification. Writer identification is impactful both in fields of academics and industry with multiple applications such as paleography, forensic, financial activity, security, access control and document verification. This chapter intends to design a graphical user interface (GUI) system for text-independent writer identification methods.
Wrapper-Based Feature Selection for Big Data Using a Multi-Core Environment

In the big data feature selection research field, there have been various attempts are made to develop an efficient models for the selection of features in big data applications, due to the complicated nature of processing such data remains a major challenge. In this chapter, an exhaustive wrapper-based feature selection methodology is proposed. The proposed wrapper method of feature selection is parallelized on a multi-core CPU environment. The Euclidean separation matrices are used with the k-nearest neighbor (KNN) classifier for optimal solutions. The proposed work is focused on developing a more cost effective and reliable big data feature selection process. The proposed work aims to develop the method of feature selection such that it reaches the same level of classification accuracy and computation time as the traditional approach. An effective contribution aim is to assess the accuracy of six dataset results and to increase the accuracy value with fewer characteristics.
“Una sottospecie della lirica”: le lettere di Clemente Rebora (1913–1914)

What we intend to do in this short essay is to consider part of the epistolary of Rebora as a literary object, an object that follows the condition of poetry hand in hand (at least in certain moments) and that often goes beyond it precisely at the level of daring formal and semantic.In evaluating the epistolary under the literary aspect, in its creativity so similar to writing in verse, our pourpose is to demonstrate how at the basis of Rebora's expressiveness (or expressionism) there is a powerful endogenous drive, and that his disorder is psychological rather than cultural, or "stylistic".
Model validation in natural hazard forecasting requires an unambiguous hierarchy of uncertainties

&amp;lt;p&amp;gt;Science is rooted in the concept that a model can be tested against independent observations and rejected when necessary (model validation). However, the problem of model validation becomes formidable when we consider probabilistic models that forecast the evolution of natural &amp;quot;open&amp;quot; systems, which are characterized by the ubiquitous presence of uncertainties of different kind and the impossibility to replicate the process in laboratory.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The purpose of this talk is to clarify the conceptual issues associated with different types of uncertainty and probability in natural hazard analysis, as well as the conditions that make a hazard model testable and thus &amp;#039;scientific&amp;#039;. We discuss the limits of the classical frequentist and subjective probabilistic frameworks to validate hazard forecasting models and discuss how these difficulties paved the way for some of the strongest criticisms of hazard analysis: arguments that most of natural hazard analyses cannot be validated and are intrinsically &amp;#039;unscientific&amp;#039;, and that &amp;lt;em&amp;gt;the outcome of natural processes in general cannot be accurately predicted by mathematical models (cit)&amp;lt;/em&amp;gt;.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;We show that the proper validation of a forecasting model requires a suitable taxonomy of uncertainty embedded in a unified probabilistic framework. This taxonomy comprises three types of uncertainty: (i) the natural variability of the&amp;amp;#160; system, usually represented as stochastic processes with parameterized distributions (aleatory variability); (ii) the uncertainty in our knowledge of how the system operates and evolves, often represented as subjective probabilities based on expert opinion (epistemic uncertainty); and (iii) the possibility that our forecasts are wrong owing to system processes about which we are completely ignorant and, hence, cannot quantify in terms of probabilities (ontological error). We compare this taxonomy with other conventions for describing uncertainties (i.e., the &amp;amp;#8220;likelihood&amp;amp;#8221; and &amp;amp;#8220;confidence&amp;amp;#8221; used by the International Panel of Climate Change), the link with the probability, and their estimation using data, models, and subjective expert opinion. We show that these different uncertainties, and the testability of hazard models, can be unequivocally defined only for a well-defined &amp;lt;em&amp;gt;experimental concept&amp;lt;/em&amp;gt;, external to the model under test, that identifies collections of data, observed and not yet observed, judged to be stochastically exchangeable when conditioned on a set of explanatory variables.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;These theoretical issues are applicable to a wide range of natural hazards; here, for the sake of example, they will be discussed using real examples in volcanology, tsunami and seismic hazards.&amp;lt;/p&amp;gt;
Research on the identification method of poor students based on SVM and decision tree algorithm

In recent years, the expansion of colleges and universities has led to a sharp rise in the number of students, and the number of poor students has also increased, which has greatly increased the difficulty and workload of financial aid for poor students. In order to improve the accuracy and efficiency of poor student identification, there is an urgent need to adopt digital and intelligent measures to assist poor student identification. In this paper, we propose a method to identify needy students using SVM and decision tree algorithm. Firstly, students' campus card consumption information is preprocessed to obtain the consumption poverty index of each student by SVM classification model. Then the decision tree algorithm is used to derive the student's family poverty index based on the student's family information. Finally, the comprehensive poverty index is calculated by weighted summation. The experimental results show that the proposed method realizes the statistics and analysis of students' consumption and family information, and it can identify poor students more accurately, which effectively improves the efficiency and accuracy of poor students' identification.
UAV 3D route planning algorithm based on improved RRT

An improved algorithm based on fast extended random tree (RRT) was proposed to solve the 3d route planning problem of uav in complex environment. Firstly, the planning space modeling is carried out according to the threat factors of flight route. Secondly, in view of the large randomness of THE RRT algorithm, the heuristic distance function is introduced as the basis for the selection of nodes to be expanded, so as to increase the probability of nodes near the target being selected as nodes to be expanded, and improve the way of generating new nodes in the random tree to accelerate the convergence speed of the algorithm. Then, UAV dynamics constraints were incorporated into the new node to meet the flight path requirements. Finally, b-spline curve was used to optimize the smoothness of the initial route curvature discontinuity problem. Simulation results show that the improved algorithm has certain advantages in planning speed and route length, and can effectively solve the problem of UAV 3D route planning.
Improved foggy pedestrian detection algorithm based on YOLOv5s

To address the problem of low detection accuracy of YOLOv5s target detection algorithm in foggy traffic environment, an improved YOLOv5s-based pedestrian detection algorithm for foggy skies is proposed. The algorithm uses image defogging techniques to preprocess the data, expands the sample size by manually generating the Foggy Cityscapes-Person dataset through a new fog simulation pipeline algorithm, and enhances the network's ability to sense small targets under foggy skies by adjusting the loss function and the training method to improve the detection accuracy of pedestrians under foggy skies, resulting in an increase of the mAP value from 64.97% to The mAP value increases from 64.97% to 81.29%. The experimental results show that the YOLOv5s-ACE network model proposed in this paper effectively reduces the missing detection rate and false detection rate, and the model can quickly and accurately detect pedestrian targets in foggy sky scenes.