Reviewerly/exHarmony huggingface dataset:
Structural Control on Gold Mineralisation Revealed by Remote Sensing and Gis Techniques in Central North Darfur State, West Sudan

[vc_row][vc_column][vc_column_text]Dr. Badreldin A.H. Hassan Remote Sensing and Seismological Sciences, Authority, National Center for Research, Khartoum, Sudan Prof. Khalid A. Elsayed Zeinelabdein Department of Geology, Faculty of Petroleum and Minerals, Al Neelain University, Khartoum, Sudan Purpose: This research is focused on the structural controls on gold mineralisation in the central North Darfur State, west Sudan. The region […]
Professional Perception on Criminal Identification through Bitemarks in Portugal

This study is aimed at examining the judgment of Portuguese criminal justice professionals on the importance of bitemarks for human identification in a crime context.It describes the results of a survey published in the Google Forms platform, in which 65 individuals took part.We were able to conclude that, in general, the respondents agree that bitemarks are important for human identification and should be used frequently in legal settings.The results obtained in this study suggest that those who've worked in crimes involving bitemarks attach more importance to the forensic analysis of these injuries than those who don't have experience in these cases.
Analisis Dan Evaluasi Sistem Drainase (Studi Kasus: Ruas Jl. Coklat, Kel. Bongkaran Kec. Pabean Cantikan, Kota Surabaya)

The city of Surabaya is one of the big cities in Indonesia. Like other cities, Surabaya also has problems, especially problems regarding flooding. The problem of flooding in the city of Surabaya can be classified into 2 (two). Namely, drainage problems due to natural factors and man-made. One of the roads that are still regularly flooded in Surabaya when it rains is Jalan Coklat. In this study the authors analyzed using secondary data from related agencies from field observations. These data include rainfall data, inundation data, tide data and dimensions of existing canals and other complementary structures. From these data, it can be obtained that the average rainfall value is 97.54 mm / day, the rain intensity is 214.88 mm / hour for the 10th period, while the Flood Debit Q Planning is 0.95 m3 / sec, and the discharge in the existing channel can be 0, 849 m3 / second. From the Tide Data, it is obtained that the peak of the tide is 150 cm high on December 13, 2016 and the lowest low tide is 130 cm on January 25, 2016. From the calculation and analysis results in the area that affects the occurrence of inundation is the channel dimension with Width: 50 x Height: 80 cm From the analysis of the calculation of the redesign using the box culvert, the dimensions of each box are obtained, namely the width of the box culvert: 80 x height: 100 x length: 120 cm for the 10th period with a capacity of 2.227 m3 / second. If the rainfall is high in the area and the water level in the Kalimas river is overflowing, the results of the analysis need to install a floodgate with the dimensions of the sluice gate Width: 60 x Height: 180 x Thickness: 1 cm so that when there is back water in the jl area . Brown floodgates function and when the rainfall is also high, the floodgates are already operating, so to reduce water puddles, it is necessary to use the help of a pump. From the analysis results, the pump capacity is 2.5 m3 / second.
On decomposition of intuitionistic fuzzy prime submodules

This article is in continuation of the first author's previous paper on intuitionistic fuzzy prime submodules, [13].In this paper, we explore the decomposition of intuitionistic fuzzy submodule as the intersection of finite many intuitionistic fuzzy prime submodules.Many other forms of decomposition like irredundant and normal decomposition are also investigated.
Introduction to Markov Processes

SummaryWe introduce Markov processes using probability kernels. This allows us to define state-space models with wildly different state-spaces and dynamics in a common framework. We study two basic sets of properties of the probability distributions of such processes: the evolution of marginal distributions via recursions, and the structure of conditional distributions. In terms of the latter, we discuss the notion of conditional independence; when the Markov process consists of two components, , we study the distribution of conditional on ; we call the process whose distribution is this conditional distribution a partially observed Markov process. We show that state-space models are instances of this framework.
RANCANG BANGUN SISTEM PAKAR DIAGNOSA HAMA KAKAO MENGGUNAKAN METODE BACKWARD CHAINING DENGAN TEKNIK BEST-FIRST SEARCH PADA DESA WAEPANDAN KEC. KEPALA MADAN KAB. BURU SELATAN

&lt;p&gt;&lt;em&gt;Waepandan Village is a village located in Districk The Head of Madan, South Buru Regency and his community believes that by way of gardening, one of the results of his plantation is cocoa. Cocoa farmers in Waepandan Village are still unable to control and change the types of pests that attack the rampant. The purpose of this study is to create an expert system that can protect and diagnose the symptoms that can be diagnosed with cocoa plants and provide accurate solutions and treatments. This research makes an application of an expert system that can help farmers in Waepandan Village who are concerned about cocoa pests so that farmers get a solution to control cocoa pests.&lt;/em&gt;&lt;/p&gt;
Parallel Ants Colony Optimization Algorithm for Dimensionality Reduction of Scientific Documents

Dimensionality reduction is crucial in Machine Learning, to obtain main characteristics. The method of selecting characteristics that we will use is a multivariate filter, where we will jointly evaluate the relevance between the characteristics; using unsupervised learning. For which we will use information from Institute of Education Sciences, and application of TF-IDF to obtain the weights of each word in each document. To perform the dimensionality reduction, the PUFSACO (Parallelization Unsupervised future selection based on Ant Colony Optimization) algorithm will be applied, due to the large amount of information that will be processed. The output of PUFSACO will be the input of the classification algorithm. The present work proposes to parallelize the UFSACO algorithm (Unsupervised future selection based on Ant Colony Optimization). Being the basis of PUFSACO, comparing the computational time to validate the improvement of the proposed algorithm, the results show that applying parallelization improves 117% than the original algorithm.
Knowledge Graph Embeddings in Geometric Algebras

Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space. Existing KG embedding approaches model entities andrelations in a KG by utilizing real-valued , complex-valued, or hypercomplex-valued (Quaternionor Octonion) representations, all of which are subsumed into a geometric algebra. In this work,we introduce a novel geometric algebra-based KG embedding framework, GeomE, which uti-lizes multivector representations and the geometric product to model entities and relations. Ourframework subsumes several state-of-the-art KG embedding approaches and is advantageouswith its ability of modeling various key relation patterns, including (anti-)symmetry, inversionand composition, rich expressiveness with higher degree of freedom as well as good general-ization capacity. Experimental results on multiple benchmark knowledge graphs show that theproposed approach outperforms existing state-of-the-art models for link prediction.
Sidecar Learning: a new e-learning platform for librarians

This article describes how two librarians at the University of Arizona created a new e-learning tool, Sidecar Learning, and implemented tutorials aimed at health sciences students and researchers. Librarians at the University of Arizona have used these interactive tutorials to reach thousands of students. Sidecar Learning was created to provide librarians with a scalable means of teaching thousands of students how to use complicated library databases.Virtual Projects are published on an annual basis in the Journal of the Medical Library Association (JMLA) following an annual call for virtual projects in MLAConnect and announcements to encourage submissions from all types of libraries. An advisory committee of recognized technology experts selects project entries based on their currency, innovation, and contribution to health sciences librarianship.
Vehicle Steering System for People with Disability (PWD Type: Armless) Using Gesture Recognition & Control

Gesture recognition and control is a relatively modern concept, humans through their body gestures generatephysical control signals in order to interact and control technologies around them. It has found importantapplication in the field of robotics, electrical appliances, computer gaming, fitness accessories and other sectors.This paper showcases a new mechanism which assists people with disabilities, specifically people who don’t havecompletely developed arms or have lost their arms in an accident during their lifetime. The mechanism employsaccelerometer to recognise gesture and generate control signals accordingly. These signals then have been used tosteer a vehicle. Accelerometer is installed on a module which is strapped on to the tip of disabledperson’s amputatedarm. This mechanismbeing electronic in nature is an upgrade from previously available mechanical drivingassistance mechanisms.
On Detecting and Enforcing the Non-Relational Constraints Associated to Dyadic Relations in MatBase

MatBase is a prototype data and knowledge base management expert intelligent system based on the Relational, Entity-Relationship, and (Elementary) Mathematical Data Models. Dyadic relationships are quite common in data modeling. Besides their relational-type constraints, they often exhibit mathematical properties that are not covered by the Relational Data Model. This paper presents and discusses the MatBase algorithm that assists database designers in discovering all non-relational constraints associated to them, as well as its algorithm for enforcing them, thus providing a significantly higher degree of data quality.
Multi-channel Deep 3D Face Recognition

Face recognition has been of great importance in many applications as a biometric for its throughput, convenience, and non-invasiveness. Recent advancements in deep Convolutional Neural Network (CNN) architectures have boosted significantly the performance of face recognition based on two-dimensional (2D) facial texture images and outperformed the previous state of the art using conventional methods. However, the accuracy of 2D face recognition is still challenged by the change of pose, illumination, make-up, and expression. On the other hand, the geometric information contained in three-dimensional (3D) face data has the potential to overcome the fundamental limitations of 2D face data. We propose a multi-Channel deep 3D face network for face recognition based on 3D face data. We compute the geometric information of a 3D face based on its piecewise-linear triangular mesh structure and then conformally flatten geometric information along with the color from 3D to 2D plane to leverage the state-of-the-art deep CNN architectures. We modify the input layer of the network to take images with nine channels instead of three only such that more geometric information can be explicitly fed to it. We pre-train the network using images from the VGG-Face \cite{Parkhi2015} and then fine-tune it with the generated multi-channel face images. The face recognition accuracy of the multi-Channel deep 3D face network has achieved 98.6. The experimental results also clearly show that the network performs much better when a 9-channel image is flattened to plane based on the conformal map compared with the orthographic projection.
Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios

Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate text from source to target language. While this approach grants extensive control over the output of the system, the cost of formalising the needed linguistic knowledge is much higher than training a corpus-based system, where a machine learning approach is used to automatically learn to translate from examples. In this paper, we describe different approaches to leverage the information contained in rule-based machine translation systems to improve a corpus-based one, namely, a neural machine translation model, with a focus on a low-resource scenario. Three different kinds of information were used: morphological information, named entities and terminology. In addition to evaluating the general performance of the system, we systematically analysed the performance of the proposed approaches when dealing with the targeted phenomena. Our results suggest that the proposed models have limited ability to learn from external information, and most approaches do not significantly alter the results of the automatic evaluation, but our preliminary qualitative evaluation shows that in certain cases the hypothesis generated by our system exhibit favourable behaviour such as keeping the use of passive voice.
PENGGUNAAN GAME EDUKASI UNTUK MENINGKATKAN HAPALAN ANGKA DI TK DIPONEGORO SURABAYA

This study aimed to determine the use of educational games to improve memorization of 1-20 numbers in TK Diponegoro Surabaya. This school is located at Jalan Kedung Sroko No.17 Surabaya. The sample of this study was the children of B group Diponegoro Kindergarten. The authors used descriptive qualitative methods. The author focused on how to apply the use of educational games to improve memorization numbers 1-20 at Tk Diponegoro Surabaya. The author made observations during the 3x meeting. Each meeting, they play a different educational game but with the same content. The subject of the research was Diponegoro Kindergarten with 20 students. The results showed that students were very enthusiastic in learning to memorize numbers 1-20 as well as improving children's fine motor skills..&#x0D; Keywords: educational games, memorizing, numbers 1-20
Breast Cancer Detection via Mammographic Images : A Survey

Breast cancer is a top killer disease for women globally. The long term survival rate of women can be improved through early and effective screening of breast cancer cells. Currently, a mammogram is the recommended tool for breast cancer screening since it can identify breast cancer cells several years before physical signs appear and it is cost effective. This paper analyzes mammographic detection of breast cancer by providing an explanation on development and classification of Breast Cancer, Image representation models for breast tumor, mammography technologies, a discussion on various mammographic signs of breast cancer, breast cancer feature extraction techniques, popular breast cancer classification techniques, comparative analysis of existing mammogram breast cancer databases, and a review of mammographic breast cancer detection studies are presented. Finally, a highlight on future work is given.
Curbing Cyber Scam (Phishing, Vishing and Spoofing) through the Use of Biometrics System for Final Authentication at the Point of Withdrawal from A Bank Account

Fraudulent practices carried out daily on individuals and organizations using the internet and related technologies (cybercrime) are becoming alarming. It is an act that does not seem to end any time soon. So, in order reduce the statistics, there is need to seek for possible solutions to this menace. &#x0D; Phishing, vhishing, pharming and spoofing has become the mainstay of hackers (‘yahoo boys’) on unsuspecting bank account holders and financial institutions; who have become victims and losing their fortune/ hard earned monies to cybercrimes. &#x0D; In order to safeguard bank accounts and its holders from these nefarious acts, this paper is seeking to examine and recommend ways through which financial transactions have to be fully authenticated by the account holder through the use of biometrics. There by, improving customer experiences and reducing financial fraud to a large extent. Howard Berg (2019), “Biometrics can deliver a new era in digital authentication for financial institutions.”
English as a Foreign Language (EFL) Students’ Ability in Identifying Moral Values within Narrative Texts

The aimed of the research is to find out the students’ ability in identifying moral values of narrative text. The research design of this research was a descpritive quantitative. The researcher analyzed the data from students’ test and on the data analysis, the researcher has got result from the students’ test, there are: The students who got excellent level is 0 (0%), very good level is 29 (60%), good 13 (28%) and 6 come into fair level (12%), while 0 students got poor level (0%). The means score of the students’ test is 77, it means that the students’ ability in identifying moral values of narrative text is Good
HATE SPEECH CONCEPT IN MEDIA DISCOURSE

The study addresses the problem of using hate speech in today's media space. It is studied not only by linguists, but also by journalists, lawyers and sociologists. It is difficult to distinguish between hate speech and other forms of hidden aggression. The purpose is to explore and analyze this phenomenon in various contexts. Several messages of influental people have been analysed which can be considered as hateful towards a particular group of people. Categories, which they can be divided to are distinguished. The aim is to research and speculate this term in various fields, namely: to analyze the language of hate in the media space, to differentiate terms that are used, e.g. hate speech, aggressive remarks, microaggressions and freedom of speech. Hate speech is defined as aggressive messages against people based on some characteristic such as race, color, ethnicity, gender, sexual orientation, nationality, religion, and other. It can attack, threaten or insult individual person or groups of people. Microaggression can be defined as statements which send negative messages reacted to someone’s membership in marginalized group. Whereas, aggressive remarks are not defined clearly. To achieve the goal of definining and categorizing, the author selected the most outrageous messages of politicians and public speeches as well as readers’ reaction to controversial statements. The most common hate speech topics are holocaust denial, Muslim prohibition, sexual minority rights, blasphemy, feminists’ oppression to men. There are set of rules in most countries made to stop hate speech in different areas, namely: politics, religion, social life. The article offers some prospects for further discussion. It would be particularly interesting to research influence of hate speech on state level decisions.
LibGuides: Percussion: Sound Recordings

This is a guide for students and faculty of percussion. The library has Vinyl records, Compact Discs, and Databases with recordings
Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs

Rust is an emerging programing language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and many projects start using the language. However, can Rust achieve the memory-safety promise? This paper studies the question by surveying 186 real-world bug reports collected from several origins which contain all existing Rust CVEs (common vulnerability and exposures) of memory-safety issues by 2020-12-31. We manually analyze each bug and extract their culprit patterns. Our analysis result shows that Rust can keep its promise that all memory-safety bugs require unsafe code, and many memory-safety bugs in our dataset are mild soundness issues that only leave a possibility to write memory-safety bugs without unsafe code. Furthermore, we summarize three typical categories of memory-safety bugs, including automatic memory reclaim, unsound function, and unsound generic or trait. While automatic memory claim bugs are related to the side effect of Rust newly-adopted ownership-based resource management scheme, unsound function reveals the essential challenge of Rust development for avoiding unsound code, and unsound generic or trait intensifies the risk of introducing unsoundness. Based on these findings, we propose two promising directions towards improving the security of Rust development, including several best practices of using specific APIs and methods to detect particular bugs involving unsafe code. Our work intends to raise more discussions regarding the memory-safety issues of Rust and facilitate the maturity of the language.
Aspect-Level Sentiment Difference Feature Interaction Matching Model Based on Multi-round Decision Mechanism

Sentence matching is a key problem in natural language understanding, so the research on sentence matching can be applied to a large number of known natural language processing tasks, such as information retrieval, automatic question and answer, machine translation, dialogue system, paraphrase identification etc. In a series of natural language processing tasks, we need to rely on the participation and collaboration of the sentence matching model. The performance of the sentence matching model can greatly affect the final performance of these natural language processing tasks. We propose the Al-SFIM model, which improves the matching model from the perspective of word interaction. First, we propose sentiment attention mechanism based on the distribution of aspect-level sentiment difference to improve the interaction between cross-sentence words, and use the sentiment space position perception vector to improve the interaction between intra-sentence words, so that the model has the ability to perceive the subjective sentiment difference in the process of intra-sentence word interaction and cross-sentence word interaction. Then, we introduce a multi-round decision mechanism based on the accumulation of memory state, which iteratively updates the working memory state to make matching decisions in multiple rounds, so that the model can better understand the semantic of complex sentence. Experiment results show that the AL-SFIM model has made progress in sentence matching and has better matching performance for complex, long and incomprehensible sentences.
Fuzzy-based Description of Computational Complexity of Central Nervous Systems

Computational intelligence algorithms are currently capable of dealing with simple cognitive processes, but still remain ineﬃcient compared with the human brain’s ability to learn from few exemplars or to analyze problems that have not been deﬁned in an explicit manner. Generalization and decision-making processes typically require an uncertainty model that is applied to the decision options while relying on the probability approach. Thus, models of such cognitive functions usually interact with reinforcement-based learning to simplify complex problems. Decision-makers are needed to choose from the decision options that are available, in order to ensure that the decision-makers’ choices are rational. They maximize the subjective overall utility expected, given by the outcomes in diﬀerent states and weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using the Bayes’ law. Fuzzy-based models described in this paper propose a diﬀerent – they may serve as a point of departure for a family of novel methods enabling more effective and neurobiologically reliable brain simulation that is based on fuzzy logic techniques and that turns out to be useful in both basic and applied sciences. The approach presented provides a valuable insight into understanding the aforementioned processes, doing that in a descriptive, fuzzy-based manner, without presenting a complex analysis