Reviewerly/exHarmony huggingface dataset:
ANITA's text analysis services for fighting online illegal trafficking of drugs, weapons and false charity claims: A lateral thinking approach

ANITA platform aims at improving investigation capabilities of law enforcement agencies (LEAs) by delivering a set of tools and techniques to efficiently address online illegal trafficking of counterfeit/falsified medicines, novel psychoactive substances (NPS), drugs, and weapons with Artificial Intelligence (AI). Expert.ai has developed a dedicated written content analysis engine to support investigative activity. The market already offers several solutions adopting AI and in particular Machine Learning (ML) to support investigative activities. The particularity of the work carried out in the ANITA project is to adopt a lateral thinking approach and the supervised generation of knowledge from the analysed contents. Rather than focusing on searching for specific proper names of substances, weapons, NPS, etc. that are constantly evolving in considerable speed, we propose to concentrate on searching for precursors, namely collateral concepts related with the target of the investigation. Is the case of accessories for weapons such as sights for rifles or glass vessels used to produce the drugs themselves? This approach is not compatible with the use of ML, but the use of deep semantic analysis of the text is necessary. This article reports the system's ability to automatically identify precursors and generate stimuli and knowledge which the investigator must validate; that enhances the creative and intuitive approach (serendipity) to investigation combining the best of the detective's skills with the power of AI and in particular Natural Language Understanding (NLU).
Organisational justice and job engagement predicating work performance

This paper aims to determine the predicating roles that organisational justice and job engagement in the workplace play in improving work performance. The study utilised a mixed methodology that encompassed factor analysis, and analysis of variance using simple and multiple regressions aiming to test the research hypotheses and to explore the nature of relationship among the research variables. This study's findings reveal that both job engagement and organisational justice have a significant positive effect on work performance. They also show that both organisational justice and job engagement establish employees' work performance and that job engagement significantly mediates the effect of justice on the employee's performance level. The chosen sample of 328 employees was markedly small to be considered an accurate representation of the entire airline industry in Jordan. In addition, because the items in the survey questionnaire were fixed, some important pieces of information might not have been captured for analysis.
Dimensional Reduction of Data for Anomaly Detection and Speed Performance using PCA and DBSCAN

Anomaly detection is the major problem facing by many of industries. It includes network intrusion and medical sciences. Several fields like Astronomy and research also facing difficulties in finding effective anomaly detection. They have included several techniques to solve such problems. Clustering is the technique which has been employed by many of the researchers. The most commonly used algorithm to perform clustering is DBSCAN. It is well known clustering algorithm used in data mining and Machine learning. It is referred as Density based spatial clustering of application with noise. Because of its high complexity in computation, it must be decreased in terms of dimensionality of data points. PCA is a method used then to reduce dimensionality and produced a new data set which is again undergo DBSCAN. Here by the nature of the test results was precise there by such a methodology can be adjusted. The mix of PCA and DBSCAN was acutely confirmed and resultant examination shows that a speedup of 25% was improved while the quality was 80% diminishing the dimensionality of informational index of half.
Classification of Natural Textures using Rule Based Rank Matrix

This paper derives a new frame work for the classification of natural textures based on gradient rank vectors derived on a 2 X 2 grid. This paper identified the ambiguity in deriving ranks when two or more positions of the grid possess the same value. To attend this ambiguity and without increasing the total number of rank vectors on d positions this paper derived a rule based rank vector frame work. This paper replaced the 2 X 2 grid with the column position of the Rule based Rank Word Matrix (RRWM). The range of column positions will be d! for d positions. This paper then divides RRW texture image, into a 3 X 3 grid and derives cross and diagonal rule based rank words. From this, the present paper derived Rule based Rank Word-Cross and Diagonal Texture Matrix (RRW-CDTM) and derives GLCM features for effective texture classification. The experimental results on various texture databases revels the classification accuracy of the proposed method. The proposed method is compared with the state of art local based approaches.
Fake Research Detection using Weighting Algorithm in Netspam Framework

Now a day’s our life has become more dependent on social media. Social has opened many opportunity for business so, whenever customer wants to buy new product they will look for other people’s opinion. Social media has also have major drawback for business strategies which is spammers. Spammers create spam surveys about various products which mislead a consumer. This online opinion plays important role in business strategies, while positive opinion gives good publicity and market on the other side negative opinion gives bad publicity and market which affects the service providers. To avoid this spammers there have been many research but very have work on user and review related feature. In this investigation we propose a classification system using heterogeneous information network NetSpam framework. This system will classify spam and non-spam reviews using NetSpam algorithm and naïve bayes classifier for sentiment analysis which will provide positive and negative value of the product review. And furthermore if wants to search top product, user can use search query, in addition to that it will display recommendation product on the basis of user’s point of interest.
System for Recognition and Evaluation of Handwritten Arithmetic Expressions

Recognition of mathematical expressions today is a very interesting area of application of deep learning.The problem is quite complex and requires a complex system to solve it.The problem becomes even more complex if arithmetic expressions are handwritten.The use of convolutional neural networks yields satisfactory results but leaves some room for improvement.In this paper, a simplified variant of the above problem is solved by using an approach that consists of extraction and classification of individual symbols using convolutional neural networks followed by syntactic parsing of the expression taking into account the symbol positions.
Complementizer functional sequence: the contribution of Italo-Romance

In this paper, we discuss the realization of complementizers in different Romance languages. We show that some Italo-Romance varieties exhibit two forms of complementizers, which select for indicative versus subjunctive embedded clauses. We show that the constraints on the licensing of these two complementizers are determined by properties of the matrix selecting predicate. While all embedding verbs have a sentient feature, only verbs embedding subjunctive clauses also exhibit an emotive feature. We propose that in French and standard Italian, the same verbs select for the same complementizers. However, in these languages, ‘indicative’ and ‘subjunctive’ complementizers are syncretic.
Bad Science

In this episode of Open Science Talk, we are joined by the founder of the campaign #bulliedintobadscience, Corina Logan. Logan is a Senior Researcher at the Department of Human Behavior, Ecology and Culture at the Max Planck Institute for Evolutionary Anthropology. She explains what she means by "Bad Science", including important terms like P-hacking/data fishing and HARKing. She also talks about how Open Science could help in the fight against bad science.&#x0D; The #BulliedIntoBadScience (BIBS) campaign consists of early career researchers leading individuals and institutions in adopting open practices to improve research rigor (from all fields, not only the sciences).&#x0D; The host of this episode is Erik Lieungh.&#x0D; This episode was first published 3 December 2018.
Inheritance and its type in Object Oriented Programming using C++

Reusability is one in every of most vital advantage of C++ programming language. C++ categories are often reused in many ways in which. Once the parent (Base) category has been written it are often changed by another technologist to suit their needs. the most plan of inheritance is making new categories, reusing the properties of the present base category. The mechanism of etymologizing a replacement category (Child/Derived Class) from associate Existing category (Base/Parent Class) is termed inheritance. The previous category is stated because the base (Parent) category and therefore the new category is termed the derived category (Child) or taxonomic group. A derived category includes all options of the generic base category so adds qualities specific to the derived category. This paper reflects the learning of the Inheritance conception and its varieties victimization C++ (oops)
Organization of Pedagogical Control of Trainees’ Physical Preparedness Based on on-Line Service “As Phcsc Rld” Application

The article presents the results of on-line service “AS PhCSC GTO” application in pedagogical control implementation in an educational institution in automation of information processing for management of trainees’ physical training in a comprehensive school. On-line service “AS PhCSC GTO” application in pedagogical control implementation in an educational institution allows providing the main task (achieving the model level of physical preparedness (Gold, Silver, Bronze) of the main group) with effective solution, and also trainees support for physical preparedness correction.
30TH EUROPEAN SUMMER SCHOOL IN LOGIC, LANGUAGE, AND INFORMATION (ESSLLI 2018) CO-SPONSORED BY THE ASSOCIATION FOR SYMBOLIC LOGIC Sofia, Bulgaria August 6–17, 2018

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
The NSRDB PV Resource Product: Spectral Solar Radiation Data on Inclined Surfaces&amp;#160;

&amp;lt;p&amp;gt;Global horizontal irradiance (GHI) and direct normal irradiance (DNI), representing broadband solar radiation measured on a horizontal surface and those received in a narrow beam along the direction of the incoming sunlight, respectively, are the most frequently used quantities to assess the solar resource in specific locations or large-scale areas. However, GHI and DNI indirectly, and often deficiently, represent the amount of radiation that is converted into electric power by photovoltaic (PV) panels that are usually installed at tilt angles or on solar tracking systems to maximize the power output. Spectral distribution of surface radiation, affects the PV performance due to the spectral response of semiconductor materials and the solar cell designs to split spectral radiation, but this information is not directly informed from GHI and DNI data. To address this issue, we developed a Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) to simultaneously compute spectral radiation over horizontal and inclined surfaces using the physical properties that can be inferred by surface- or satellite-based radiometers. It utilizes the optical properties of aerosols and a pre-computed lookup table of cloud transmittance to efficiently solve spectral radiances that can be spectrally and angularly weighted to directly match the PV response. This new model has been implemented in the National Solar Radiation Data Base (NSRDB) to quantify the solar resource that is available for conversion by a PV plant. The data are referred to as the NSRDB PV Resource product to be distinguished from the routine solar resource product from satellite observations. This study will review the input data and mechanics to develop the PV Resource product as well as the procedures to access the product. To provide a critical reference in the data applications, we will evaluate the PV Resource data using surface observations from stations operated by the National Renewable Energy Laboratory (NREL), University of Oregon, and the First Solar, Inc.&amp;lt;/p&amp;gt;
Publishing Correlated Social network Data with Differential Privacy

The application of social network collects a large amount of user data and sensitive data, which may reveal potential privacy information through analysis.At present, the differential privacy protection model gives a rigorous and quantitative representation and proof of privacy disclosure risk, which greatly guarantees the availability of data.Recently, differential privacy is very popular.However, differential privacy assumes that data sets are independent.In real life, few data sets are completely independent.In social networks, nodes have edges that are related.This paper proposes a solution to use differential privacy on correlation social network data and designs a mechanism for correlation social network data publishing.Reduce the large amount of noise added when the association graph data is published with differential privacy.Considering the degree of correlation between nodes in the graph data, this paper first proposed the degree of association between nodes and calculated the degree of association between each node to calculate the sensitivity of association.The correlation sensitivity is used to determine the noise level in the implementation of differential privacy.Then the hierarchical random graph model is used to add noise satisfying differential privacy to edge connection probability to generate the pending layout.Finally, the feasibility and effectiveness of the method are verified by the statistical analysis of degree distribution, aggregation coefficient and onedimensional structural entropy.
Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification

© 2017 IEEE. Genetic programming (GP) is a well-known evolutionary computation technique, which has been successfully used to solve various problems, such as optimization, image analysis, and classification. Transfer learning is a type of machine learning approach that can be used to solve complex tasks. Transfer learning has been introduced to GP to solve complex Boolean and symbolic regression problems with some promise. However, the use of transfer learning with GP has not been investigated to address complex image classification tasks with noise and rotations, where GP cannot achieve satisfactory performance, but GP with transfer learning may improve the performance. In this paper, we propose a novel approach based on transfer learning and GP to solve complex image classification problems by extracting and reusing blocks of knowledge/information, which are automatically discovered from similar as well as different image classification tasks during the evolutionary process. The proposed approach is evaluated on three texture data sets and three office data sets of image classification benchmarks, and achieves better classification performance than the state-of-the-art image classification algorithm. Further analysis on the evolved solutions/trees shows that the proposed approach with transfer learning can successfully discover and reuse knowledge/information extracted from similar or different problems to improve its performance on complex image classification problems.
Hierarchies

Hierarchies are networks that begin with a top node and always add nodes below other nodes. There are no lateral connections so there is only one path connecting any two nodes. Although typically depicted as tree diagrams, hierarchies can also be represented as embedded rectangles. The semantic network model was initially constructed as a hierarchical network model that proposed reaction times to verify statements required retrieving categories and their associated features from hierarchically stored knowledge. Hierarchies also store intuitive ontologies that may differ from scientific ontologies. The different characteristics of hierarchies, networks, and matrices provide a basis for selecting an appropriate diagram for representing knowledge. One characteristic that should influence the selection is whether there are no paths (matrices), one path (hierarchies), or multiple paths (network) between concepts.
The Use of YouTube as a Learning Tool in Teaching Listening Skill

YouTube is not only a platform that individuals share, upload, comment on videos; but also, teachers and educators can make practical and effective use of it to the best as great that students can have more an advantage or profit gained. This study aims to carry out a systematic or formal inquiry to discover and examine the facts of how active and distinguished. YouTube can be in the educational process and how it is beneficial for language teachers to enhance the skills of students. The study exhibits different theoretical frameworks that equip the employment of technology to increase the learning/teaching process. In using multimedia, video clips used to develop teachers’ abilities for using technology in classrooms. The objectives of this research are to give knowledgeably and clear explanations about teaching students’ listening skills through English video. The researcher emerges a questionnaire and applies it to third-semester of college students of Ma’soem University to give confirmation and show the advantages of technology in the academic field. The paper investigates classes where computers can be utilized, and also appear the defiance that faces teachers and educators regarding this application. The researchers terminated that YouTube is a necessary tool in classrooms as it appeals to the attention of students and involves their mentality and creativity. It also assists cover the materials exhaustive, especially language. YouTube leads the fun element into classes, through meet the interests of students. Equivalent findings have a remarkable impact on the learning process as the students will realize the educational environment more uplift and inspire. Besides, they notice the material presented worth studying, and this method, they would admire the attempt expend in explaining the information. The research aspires to be of worth to teachers for the operation of technology and students to have a superior apprehension of the materials extend.
Azure Storage

Over the years, the two foundational components that define Azure as a hyperscale cloud continue to be unmentioned pillars: compute and storage. Compute and storage are the foundational pillars for every private and public cloud. Infrastructure as a service is pretty much about compute and storage. We focused on compute in Chapter 2 , and in this chapter, we dedicate time to cloud-based storage.
First revision of the manuscript nhess-2019-135

This paper deals with the topic of defining a new combined drought indicator (CDI) capable to anticipate crop drought events.To do so, authors combined a meteorological indicator (SPI), a soil moisture indicator (SMAI) and a vegetation indicator (NDVIA).Authors established four levels of alerts with the corresponding actions and assessed this new indicator comparing monthly alerts with crop damage provided by the agricultural insurance.The research carried out in this paper is of interest, and I think it is adequate to NHESS journal.The manuscript is in general well-structured and the results that follows seems very reasonable to me.Correlation between the proposed CDI and crop damage is correctly presented.
2019 Benjamin Franklin Medal in Life Science presented to James P. Allison, Ph.D.

In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advance zeroing neural network (CT-AZNN) model is shown for solving continuous-time augmented Sylvester matrix equation (CT-ASME). Secondly, a recently published discretization formula is further investigated with the optimal sampling gap of the discretization formula proposed. Then, for solving F-ASME, a novel DT-AZNN model is proposed based on the discretization formula. Theoretical analyses on the convergence property and the perturbation suppression performance of the DT-AZNN model are provided. Moreover, comparative numerical experimental results are conducted to prove the effectiveness and robustness of the proposed DT-AZNN model for solving F-ASME.
PRIVACY PROTECTION AND INTRUSION AVOIDANCE FOR CLOUDLET-BASED MEDICAL DATA SHARING

Remote health monitoring and older health care has become a popular application with the advance of wearable medical devices. Privacy protection and intrusion avoidance for cloudlet- based medical data sharing data collected from patients through wearable devices ( such as heartbeat, blood pressure, etc.) must be passed to cloud-run applications to implement various services such as expert advice, emergency assistance, etc. The cloud storage system provides distributed clients with convenient file storage and sharing services. To solve integrity, we present identity based data outsourcing , outsourcing and original auditing concerns about outsourced documents, the program is equipped with an ideal feature that factilitates existing recommendations to protect outsourcing data.
Perancangan dan Pembuatan Sistem Informasi Peminjaman dan Pengembalian Dokumen Rekam Medis Rawat Jalan di RSF Mangusada Badung

Registration of loans and refunds of medical records at RSD Mangusda Badung still there is an obstacle that is the loan and refunds data of medical records in the period of 1x24 hours hours is not complete and, there is no description of loan and return timeÂ of medical record documents in the expedition. This research aims to design and create information system for loans and refunds theÂ medical records by using waterfall method at RSD Mangusada Badung. This research was conducted in RSD Mangusada Badung. The method of this research used is a qualitative method with the waterfall system development method. The data collection methods used are interviews, observations and documentation. The result of this research is the design and creation of a loan information system for the return of medical record documents using waterfall method is expected to facilitate the officer filing at Puskesmas Banjarsengon to do their duties.
Practical stability analysis of perturbed fuzzy control system related to unperturbed fuzzy control system

The behavior of solutions of a perturbed fuzzy control system with respect to an original unperturbed fuzzy control system is investigated. Notions of practical stability, asymptotic stability and instability are introduced. Sufficient conditions of stability properties are given with the help of Lyapunov-like functions.