Reviewerly/exHarmony huggingface dataset:
Image Classification of Skin Cancer Based on Cancer Malignancy Levels in Melanocytes Using Deep Convolutional Neural Network (DCNN)

Skin cancer is one of the most common diseases in the world, including Indonesia. This disease is caused by food which can be toxic to the human body and the effects of global warming. Especially melanoma, a type of skin cancer that occurs in melanocyte cells is a very serious threat. In the United States, although melanoma is only 5% of all skin cancer cases, melanoma has been the cause of death in 75% of all skin cancer deaths in that country (Hendaria, et al., 2013). Melanoma has a shape similar to a mole, birthmark or beauty mark that is often overlooked. To recognize the presence of melanoma early on, This study used a deep convolutional neural network method to classify skin cancer that occurs in melanocytes, where melanoma grows. The deep learning method used has a good level of accuracy and is good for image recognition. Dermoscopic skin cancer image is used as input for image processing. Before entering the classification stage, the image is processed with an image pre-processing process, namely Scaling, Contrast enhancement and Thresholding. After testing in this study, it was concluded that the proposed method has the ability to classify skin cancer that occurs in melanocytes quite well with an accuracy of 84.0%. The deep learning method used has a good level of accuracy and is good for image recognition. Dermoscopic skin cancer image is used as input for image processing.
Drawing on a Sculpted Space of Actions: Educating for Expertise while Avoiding a Cognitive Monster

Avoiding a Cognitive Monster Although expertise is usually considered as a positive outcome of education and practice in domains as varied as sports, science, music and politics, there are also concerns about negative effects of expertise. Since expertise is facilitated largely by implicit, automatic cognitive and brain processes, it can also lead to undesirable consequences in the form of stereotypical, discriminatory or inflexible responses. Indeed, such responses can at times even be inconsistent with the explicit and intentional choices of an expert. Explaining this phenomenon, it is argued that an expert’s performance can be considered as selecting in a given situation a preferred option for action from a ‘Sculpted Space of Actions’ (Keestra, 2014), which contains more, more complex and better differentiated action representations than a beginner’s space of actions. Integrating this account with the cognitive neuroscientific theory of Predictive Processing, it is argued how mitigating undesirable effects of expertise depends upon awareness and control of the processes involved. Education should therefore provide for insights in and techniques for controlling the cognitive and brain processes that constitute expertise.
Mining Commonsense and Domain Knowledge from Math Word Problems.

Current neural math solvers learn to incorporate commonsense or domain knowledge by utilizing pre-specified constants or formulas. However, as these constants and formulas are mainly human-specified, the generalizability of the solvers is limited. In this paper, we propose to explicitly retrieve the required knowledge from math problemdatasets. In this way, we can determinedly characterize the required knowledge andimprove the explainability of solvers. Our two algorithms take the problem text andthe solution equations as input. Then, they try to deduce the required commonsense and domain knowledge by integrating information from both parts. We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
PENGEMBANGAN MODUL PEMBELAJARAN MATEMATIKA DENGAN PENDEKATAN SAINTIFIK

The purpose of this study is to develop a learning module with a scientific approach to valid and practical number pattern material, by going through the stages or steps of a scientific approach in the form of observing, asking questions, gathering information, reasoning, and communicating. The development procedure used in this research is the 4-D model consisting of 4 stages, namely the define stage, the design stage, the develop stage, and the dissemination stage. The results of this development are seen from the results of module validation and module practicality. The module is validated by 5 validators consisting of 2 mathematical material experts, 1 linguist, one design expert and one mathematics subject teacher for class VIII MTs Al-Munawwaroh. The result of the whole module validation with the scientific approach that has been carried out shows an average score of 4.42 with a very valid category. While the practicality of the module is seen from the results of the practicality questionnaire that were tested by all 15 students of MTs Al-Munawwaroh showing that the module with the scientific approach produced is very practical with a result of 97.85%. Based on the results of the study, it was found that the module with a scientific approach on the number pattern material developed could be used and was feasible for use in MTs Al-Munawwaroh
Artificial Intelligence Algorithm & Laws of Quantum Mechanics

The electronic characteristics of molecules and molecular wave functions can be predicted using artificial intelligence. A team of researchers from the University of Warwick, the Technical University of Berlin, and the University of Luxembourg discovered a novel AI technology that might be used to speed up the design of medicinal compounds or new materials. Artificial intelligence and machine learning algorithms are frequently employed to forecast our buying habits and recognize our faces or handwriting. Artificial Intelligence is establishing itself as a critical instrument for scientific discoveries in scientific study. In chemistry, artificial intelligence has proven to be useful in predicting the results of experiments or simulations of quantum systems. To accomplish this, AI must be able to incorporate the fundamental rules of physics in a systematic manner. Quantum computing is a new type of computing that is based on quantum mechanics. It carries out computations using quantum units under control. Quantum computers can only address problems that traditional computers can solve in terms of computable problems, but in terms of computing efficiency, some known quantum algorithms are tenfold quicker than traditional general-purpose computers due to the existence of quantum superposition.
ANALISIS SURVIVAL PENDERITA GAGAL GINJAL DENGAN PENDEKATAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE

Survival analysis is a statistical method where the outcome variable that is considered is the time until the occurrence of an event or called an event. Survival analysis in the medical world can be used to test the survival of patients suffering from a disease, one of which is kidney failure. Based on the results of the 2013 Basic Health Research, 0.2% of the total population of Indonesia, namely 499,800 people, experienced kidney failure. Kidney failure is a process of decreasing kidney function that requires renal replacement therapy, in the form of hemodialysis or kidney transplantation. If the survival data involve independent variables that are thought to affect the survival time, then the Cox Proportional Hazard (PH) regression approach can be developed which can be developed for the Multivariate Adaptive Regression Spline (MARS) approach. The MARS approach is a nonparametric regression approach with high-dimensional data, namely data with an independent variable of 3≤𝑝≤20 and a data sample of 50≤N≤1000. This study aims to describe the characteristics of patients with kidney failure, identify the factors that affect the survival time of patients with kidney failure and determine the MARS model. The MARS approach is obtained from a combination of functional basis, maximum interaction, and minimum observation by trial and error. Based on the model that has been formed using the MARS approach, it is found that the most influential variables on the survival time of patients with kidney failure are age, urea levels, and creatine levels.Key words: Renal failure, hemodialysis, survival analysis, nonparametric, MARS
KEY DATA EXTRACTION AND EMOTION ANALYSIS OF DIGITAL SHOPPING BASED ON BERT

Purpose: The objective of this paper is to focus on extracting the key words about the product quality and the customer experience with the same in a more efficient and accurate way by pre-training the Bidirectional Encoders Representations from Transformers (BERT) model with the quality domain knowledge and classifying the result with deep learning technique.Methodology: Dataset is considered to be amazon reviews which is a combination of single product-based customer reviews and several products and their reviews which is of medium – large size. This dataset is subjected to initial process of cleaning, data wrangling, Exploratory Data Analysis with pre-trained BERT along with a neural network classifier. The BERT classifier is loaded along with tokenizer in the input modules. The BERT model is configured and training for fine-tuning. The prediction is done based on the final fine tuning.Findings: BERT model along with TF-IDF topic extraction model was implemented to analyse the trend and theme of the outbreak which eventually helped to analyse the public concerns and appropriate health support. Fine tuning of Chinese BERT model and softmax neural network layer was used to train the model to classify into three sentiments which resulted in 75.65% accuracy. Higher accuracy was expected to obtain but was in need to improve in the modelling and more datasets from different parts of the world will lead to much more accuracy in regards with public concerns. A function is generated to output a sample permutation and thus its replication which will be a single statistic. We consider a hypothesis that the words distribution is of with same identity and setting a value of probability as with minimum value of 5.9. When the p-value values to 0.0 which gives as the null hypothesis is invalid. The baseline will be TF-IDF model with logistic regression. Here a prediction function along with prediction matrix values is generated. The model weights and tuning are interpreted with the help of Eli5 library. The pre-train model is initialized and the configuration is used with layers of encoding and pooling with dimensionality of 768.With this initialization, logits for the input sequence is generated.Originality/value: The BERT model which is pre-trained is enables with tokenizing the input dataset which is taken as amazon Alexa product review dataset. While the input is loaded, pre-cleaning process is done such as managing the equal negative and positive comments so that the predictions can be made easy. In order to identify how much is the difference between negative and positive comments we implement the testing of permutation and from that calculating the p-value.
Parameter Measurement of Live Animals Based on the Mirror of Multiview Point Cloud

Scale and standardization are essential to the prosperity of the breeding industry. During large-scale, standardized breeding, the selective breeding of good livestock breeds hinges on the accurate measurement of body parameters for live animals. However, the complex shooting environment brings several urgent problems, such as the missing of many local data in the point cloud and the difficulty in the automatic acquisition of body data. To solve these problems, this paper proposes a method for parameter measurement of live animals based on the mirror of multiview point cloud. Firstly, the acquisition and stitching principles were given for the multiview point cloud data on body parameters of live animals. Next, the authors presented a way to make up for the data missing areas in the point cloud. Finally, this paper acquires the body mirror data of live animals and scientifically calculates the body parameters. The proposed measurement method was proved effective through experiments.
Presentation: Building an object-oriented Python interface for the Generic Mapping Tools

Talk at the Scipy Conference 2018.Live demo: http://try.gmtpython.xyzShort summaryWe are building a Python wrapper for the Generic Mapping Tools (GMT), a set ofcommand-line programs used across the Earth, Atmospheric, and Ocean Sciences toprocess and visualize geographic data. At Scipy 2017, we presented the projectgoals and an initial prototype. The feedback received led to improvements inthe design of the library, mainly the creation of an object-oriented API. Wewill present the newest developments including support for numpy arrays andpandas Dataframes, interactive visualization in the Jupyter notebook using NASAWorldWind, and more. Once again, we seek feedback from the community to guideus moving forward.AbstractThe GMT/Python library has been in development forapproximately 1 year. Much of the current design of the library was inspired bythe feedback that we received following our presentation at Scipy2017. Since then, wehave been implementing this design, establishing a solid low-level API on whichto build the rest of the library, and exploring new ways to interface with theJupyter notebook. In this talk, we will present the current state of theproject, including: the design of the low-level wrapper for the GMT C API (thegmt.clib.LibGMT class); the new object-oriented plotting API (thegmt.Figure class); the support for numpy arrays and pandas Dataframes; usingGMT's built-in topography grids and sample datasets; interactive visualizationin the Jupyter notebook using the NASA WorldWind Web Javascriptlibrary; and more. An online demo of thesefeatures is available through the Binder service at http://try.gmtpython.xyz.We will also share the lessons learned from using ctypes to build the wrapperand the changes that were required in the C API to make the wrapping process assmooth as possible when porting to other languages. Finally, we will layout ourdevelopment plans and solicit feedback and contributions to help guide thefuture of the project.GMT has an extensive feature set that goes well beyond data visualization. Ithas sophisticated algorithms for processing and interpolating data in Cartesianand spherical coordinates that is still unmatched in the Scipy ecosystem. GMTis also the basis for specialized software likeMB-System forprocessing and visualizing bathymetry and backscatter imagery data derived frommultibeam, interferometry, and sidescan sonars andGMTSAR for processing InterferometricSynthetic-Aperture Radar (InSAR) data. A well designed wrapper for the GMT CAPI is the first step to bring these powerful tools to the Scipy community. Thedata visualization landscape in Python has grown immensely in the past fewyears with the advent of Boheh, Altair, Cartopy, Holoviews, etc. GMT/Python canhelp diversify this ecosystem and bring important lessons learned during the28+ years of continuous development of GMT.
Importance and Performance Analysis on Service Quality of Lifelong Education Institutions - focused on Adult Learners

AbstractIn the rapidly changing modern society, it has become difficult to adapt to one's personal life and socialinteractions without continuous learning. Therefore, this study investigated the needs and satisfaction ofadult learners based on the SERVQUAL model for the purpose of improving the educational service qualityof lifelong educational institutions, which are organizations of organized educational activities for adults.As a result of the IPA analysis, first, problem-solving skills, excellent amenities, required curriculum, andconvenience of operation were extracted as areas for areas that should be intensively improved for alladult learners. Second, in the case of disabled learners, convenient access was extracted as a variableto be intensively improved. Thirdly, for non-disabled adult learners, commitment to learners, safe personalinformation management, required curriculum, and convenience of operation were extracted. In thisstudy, by analyzing service quality factors from the adult learner's perspective, lifelong educationinstitutions' educational service quality management plan was presented
Azul Positivo’s Community Response Strategy during the COVID-19 Pandemic in Zulia

This is a case study produced by Andrea Pacheco, Protection Officer at Oxfam in Venezuela, and Yordy Bermudez, Monitoring, Evaluation, Accountability and Learning (MEAL) Officer at Azul Positivo, as part of the Resource Pack on Community-Based Protection. The document discusses access to services, community identification, COVID-19, negative coping strategies, referrals and service mapping.
Low Power-Reduced Simple Edge Preserved Denoising Ladner Fischer Adder Method for Image Restoration

In digital image processing, noise suppression is an emerging research area, because it supports majority of the applications like pattern recognition and biometric authentication. Usually, digital images are deteriorated by salt and pepper noise, which is caused due to poor lighting conditions, physical deterioration, signal instabilities and defective sensors. The elimination of salt and pepper noise from the original digital image is a hard task. So,low power-reduced simple edge preserved denoising- ladner Fischer adder method is proposed in this research paper for image restoration. In this article, simulation is done usingMATLAB r2018aand the program code is synthesized using Xilinx Spartan-II field-programmable gate array. The simulation results showed that the proposed method outperforms several spatial filters such as median 3×3, median 5×5, and low complexity denoising method in light of peak signal to noise ratio. In this article, the proposed method showed 1.18 dB to 11.70 dB enhancement in image denoising by means of peak signal to noise ratio. The Verilog implementation of the proposed method delivers several benefits such as minimum delay, maximum operating frequency of  188.26 MHz, and lesser resources.
How to Call Functions and Use the Stack

In this chapter, we examine how to organize code into small independent units called functions. This allows us to build reusable components that we can call easily from anywhere we wish. Typically, in software development, we start with low-level components and then build on these to create higher-level applications. So far, we learned how to loop, perform conditional logic, and perform some arithmetic. Now, we examine how to compartmentalize our code into building blocks.
Laporan Sistem Informasi Posyandu Berbasis Website Dari Pt. Telkom Iot Jakarta Selatan

Posyandu is currently still widely held in every region in Indonesia, especially in RW06 Pasir Angin Village where the posyandu conducts health checks for infants both with immunizations and nutrition checks by looking at the weight of the toddler. However, unfortunately the system used in the RW06 Posyandu in the village of Pasir Angin still uses manual techniques, namely using books in the recording process and for making data reports for every toddler brought and examined at the posyandu. This underlies the authors to make a Posyandu Information System Based on Website and Internet of Things where the way to use it is that every toddler will be given an identity card and then during the registration and nutrition check process, toddler parents only need to attach the card to the RFID sensor so that the personal data starting from the name, age, and parent's name will automatically appear on the web page. By utilizing the technology of the Internet of Things and the web, posyandu officers no longer need to record every toddler who has registered and checked the nutrition manually and can save time.Keywords : Toddler, Internet of Things, Posyandu, RFID, Website
Inflation and Money Growth in Ethiopia

This study analyses money growth - inflation nexus in Ethiopia using annual datasets covering the period 1970-2009. This period was considered due to data limitations. A significant aspect of the study is that it tries to identify the optimal level of money growth using Two Regime Threshold Model. The result from the two-regime threshold model reveals that there is indeed a threshold effect in the relationship between money growth and inflation and the optimal level of money growth is estimated to be 17% which has an important policy implication. Here, money supply creates inflationary pressures only when it exceeds 17%. A percentage increase in money supply above this threshold value is expected to cause 1.47 percent increase in annual inflation indicating that monetary factors are valid sources of inflation in Ethiopia. The results imply keep the money growth below 17%. Hence, a specific monetary policy measures that could be envisaged is controlling broad money supply (M2).Inflation; money growth; two regime threshold model; Ethiopia
An evaluation Model of College English Teaching Comprehension Ability from the Perspective of Cross Culture

Due to the poor systematicness and serious homogenization of the existing evaluation models, the personalization and differentiation of the cross-cultural level are not obvious, which further affects the accuracy of the output results of the evaluation model. To solve this problem, this paper proposes a cross-cultural perspective of college English teaching comprehension ability evaluation model. From the six dimensions of knowledge and skills, teaching design, resource development, teaching implementation, teaching evaluation, reflection and development, this paper selects evaluation indicators, designs clustering algorithm, and outputs the clustering results of evaluation indicators. The analytic hierarchy process (AHP) is used to assign weights to the clustering results, and an evaluation model of English teaching comprehension ability is constructed. The experimental results show that the model has certain advantages in accuracy and recall rate, F1 value is 6.15 and 9.33 higher than the existing model, which shows that the model has good evaluation accuracy and improves the evaluation results.
RANCANGAN SISTEM INFORMASI AKADEMIK SEKOLAH BERBASIS LARAVEL

The school is a tiered and continuous educational unit in order to carry out learning and teaching activities, providing the best service facilities for students. Problems in scheduling often occur, one of which is in the world of education, where this is a fairly complicated problem between teachers and lessons. A good step in using information technology is to create a Laravel-based academic scheduling system. Laravel-based academic scheduling information system design is expected to increase time savings. Where scheduling is actually very necessary in various activities, considering that if the scheduling system is good, it will indirectly have an impact on increasing the efficiency of existing resources
The practice of speech and language processing in China

Several companies are trying push automatic speech recognition and other technologies past their current limitations.
An Overview on the Emerging Issues in Cyberlaw and Cyber Security

Today, everyone is going towards the era of digitalization and networking, which has numerous advantages in industries such as e-commerce, communication, and so on. It also gives rise to a new criminal methodology known as all of a sudden. To prevent crimes in such a virtual world, a focus on associated rules and orders is essential. Many laws and procedures have been enacted and implemented to combat these problems, such as the IT Act of 2000 and the National Cyber Security Policy. Although the term cybercrime has no legal definition, activities such as vandalism, violence, and rape are not classified as cybercrime and have no legal significance. This study focuses primarily on cyberspace difficulties, emphasizing the urgent need for reform in India's cyber legal framework as well as different issues where cyber law enforcement falls short.