Reviewerly/exHarmony huggingface dataset:
Expert System For Diagnosing Diseases in Toddlers Using The Certainty Factor Method

Disease is an abnormal condition in the body that causes body misalignment. There are various types of diseases that threaten humans, both parents and children. This disease can be caused by germs, bacteria, viruses, toxins, organ failure to function, and also by inherited/hereditary diseases. The difficulty of parents to find out the disease suffered by their children is one of the problems of parents today. So, we need a system to help with this predicament. The purpose of making this application is to provide information quickly and accurately in solving problems to help consult about diseases in toddlers aged 0-5 years. In addition, to find out ways to make programs that are expert systems using programming languages for artificial intelligence applications, namely PHP and Mysql, the certainty factor method is applied in web form. Using the certainty factor method is a decision-making strategy that starts from the section premise to conclusion. The result of system implementation is that the user chooses from the symptoms that already exist in the system based on the existing symptoms then processed, from the process the system provides information on diseases in children suffered by toddlers. From the results of testing this expert system has been able to diagnose diseases in children. After the diagnosis, the types of diseases and solutions will appear. Diagnosing disease in children by using this certainty factor is expected to make it easier to diagnose disease in children
Creativity in times of pandemic: Interviewing Dra. María Cándida Moraes

In this interview, Dr. Maria Candida Moraes shares her opinions and points of view on issues related to creativity and how it can operate in situations such as the pandemic from complex thinking and a transdisciplinary perspective. Maria Candida, recognized by ASOCREA with the “Ricardo Marín Creativity Award”, is a world reference in an ecosystem pedagogy. From this ecosystem point of view, it provides us with a new look at creativity, as a complex and transdisciplinary process, and therefore, relational, emergent and transcendent self-ecoorganizer. In times of crisis such as the pandemic, education and creativity are more essential than ever to face them successfully. She explains how she has turned the pandemic into “creative adversity” by dedicating himself to writing a synthesis book of his entire intellectual and academic life with the title “The Ecosystem Paradigm: for a new ecology of human learning”. His words always bring light to the mind, peace to the heart and openness to the conscience.
Analysis of The Noise Level of The Diesel Engine with 1100 RPM in The Indoor Condition

One of the weaknesses of diesel engines that most users complain about is the vibration and the noise. Therefore, it is very important to know the noise level of an engine in a room to consider the allowed duration of the exposure time, which is the aim of this study. The instrument used in this study was the Sound Level Meter, by first determining the radius of the hemisphere used in determining the location for the data collection. The measurement was carried out four times, where the largest value was considered as the main data, at four points. The result in this study indicated that the noise level of the diesel engine with a speed of 1100 rpm was 92.805 dBA with the background noise in the diesel engine room of 51.6 dB. Based on government regulations, the maximum exposure time to the noise at this level is 2 hours.
Delivery Information System Web-Based Design at Bandung SPP Posts

Central processing post office in Bandung is a division of PT. Pos Indonesia is in charge of all postal shipments, from the sender to the intended recipient. The main issue raised by this study is the slow process of POS SPP Bandung receiving and delivering goods.This study uses a descriptive research method, from the research that has been done, there are factors that hinder the process of receiving and delivering goods at POS SPP Bandung, as they still use a system that we consider less efficient and less efficient, so it will Interfere with the process of receiving and delivering goods. Therefore, in this paper, a Goods Delivery Information System was designed using Unified Modeling Language (UML) and implemented using PHP programming language, Framework Code version 4 and MySQL database. With this application system, it is expected that employees can make it easier and speed up the process of inputting goods into and out of the Bandung SPP Post office.
Analysis K-Nearest Neighbor Method in Classification of Vegetable Quality Based on Color

In this research, the process of applying the K-Nearst Neighbor (KNN) method will be carried out, which is a classification method for a collection of data based on the majority of categories and the goal is to classify new objects based on attributes and sample samples from training data. So that the desired output target is close to the accuracy in conducting learning testing. The results of the test of the K-Nearest Neighbor method. It can be seen that from the K values ??of 1 to 10, the percentage of the results of the analysis of the K-NN method is higher than the results of the analysis of the K-NN method. And from the K value that has been tested, the K 2 value and the K 9 value have the largest percentage so that the accuracy is also more precise. As for the results of testing the K-Nearest Neighbor method in data classification. As for the author's test using a variation of the K value of K-Nearest Neighbor 3,4,5,6,7,8,9. Has a very good percentage of accuracy compared to only K-NN. The test results show the K-Nearest Neighbor method in data classification has a good percentage accuracy when using random data. The percentage of variation in the value of K K-Nearest Neighbor 3,4,5,6,7,8,9 has a percentage of 100%.
Implementation of The C4.5 Algorithm in Describing The Trends of The Human Consciousness and Unconsciousness

The human mind has two properties that have different and conflicting functions. The two characteristics of the mind are the conscious mind and the subconscious mind. This study uses the C4.5 data mining algorithm to describe or see the tendency of the conscious and subconscious or the level of suggestion. Suggestibility is the most important thing in hypnotherapy. Hypnotherapy is a therapy performed under hypnosis. Hypnosis is communication with the human subconscious. The C4.5 algorithm for Data Mining is used to form a decision tree. This research will produce a decision tree that can explain the suggestive level of a series of tests that have been carried out. Testing is done with RapidMiner software to get a decision tree. The test consists of a series of tests consisting of four types of tests, where test 1 is to measuring right brain dominance , test 2 is to measure the speed of receiving instructions, test 3 is to measure a person's creativity, and test 4 is to know person's level of understanding, reasoning and imagination. The results of manual calculations were carried out in this study later with the results obtained from the results of testing with the RapidMiner software
Pengaruh Metode Pembelajaran Concept Mapping terhadap Motivasi dan Hasil Belajar Kognitif Siswa

This research was conducted at SMA Wiyata Dharma in the 2018/2019 academic year. This study aims to increase students' motivation and cognitive learning outcomes in biology subjects by using the concept mapping learning method. The type of research used in this research is quasi-experimental or quasi-experimental. The population of this study were students of class XI SMA Wiyata Dharma 2018/2019 academic year. The samples used were 39 students of class XI IPA 5 as the control class and 39 people of class XI IPA 6 as the experimental class taken with cluster random sampling technique. Data collection techniques using observation sheets and test sheets of cognitive learning outcomes. The observation sheet was given after the learning took place in the experimental class. The observation sheet aims to determine the increase in students' learning motivation. The cognitive learning outcomes test sheets are given before and after the learning process takes place, aiming to determine the increase in students' cognitive learning outcomes. Data on students' cognitive learning outcomes in this study were taken by giving pre-test and post-test to the sample class students. The test sheet used has been tested by the validator so that it can be used in this study. The results showed that the average percentage of student learning motivation in the experimental class after using the concept mapping method was 79.67 with a high motivation category. While the value of the students' cognitive learning outcomes showed that the experimental class obtained an average score of 97.43 while the control class was 98.58. The final test data of the sample class students were analyzed using t-test. based on the results of data analysis, it was obtained that tcount was 2.57 and ttable 1.46 so that tcount was greater than ttable at an error rate of 5% and dk was 76. Therefore, from the t-test analysis it can be concluded that there is a significant effect on the results. Cognitive learning of class XI SMA Wiyata Dharma students.
Pengaruh Metode Mind Map dengan Media Komik terhadap Minat Baca dan Hasil Belajar Kognitif Siswa

The main problem in learning in formal education (schools) today is the low quality of student learning. One of the factors is the teacher's ability to manage, develop methods and select learning media. In this case the researcher tried to use the mind map method with comic media. This study aims to determine the effect of the mind map method with comic media on reading interest and cognitive learning outcomes of class VIII students in biology science subjects at SMPN 10 Mataram. This type of research is a quasi-experimental. The population in this study were all eighth grade students of SMPN 10 Mataram. This sample was taken using a simple random sampling technique, namely class VIII F as the experimental class and class VIII G as the control class. Data collection in this study was taken by giving pre-test and post-test to the sample class students. Data collection techniques using a reading interest questionnaire and objective tests that have been tested for validity and reliability. Based on the results of the study showed that the students' reading interest obtained an average of 60.54 for the experimental class with a percentage of 75.67%, while the control class was 51.79 with a percentage of 64.74% which indicated that students were included in the interested category, indicating that the reading interest of students who using the mind map method with comic media is higher than the reading interest of students who use conventional methods. Based on the results of the study, it was shown that the pre-test data obtained an average value of 51.03 for the experimental class and 51.55 for the control class. The post-test results showed an average of 77.93 for the experimental class and 68.96 for the control group. The post-test data of the sample class students were analyzed using t-test. Based on the results of data analysis using t-test obtained t count &gt; t table, which is 4.33 &gt; 2.00 at an error level of 5%. This indicates that the mind map method with comic media has an effect on cognitive learning outcomes for class VIII students in biology science subjects. at SMPN 10 Mataram.
Efektivitas Model Pembelajaran Scramble pada Keterampilan Menulis Kalimat Siswa Kelas III SDN Pabuaran Tumpeng 2 Kota Tangerang

Learning Indonesian Language and Literature in schools is to develop language skills both orally and in writing. Most students find it difficult to develop aspects of writing. Especially in stringing words to be arranged into a sentence. This difficulty may be caused by teachers who in their delivery are less precise and almost never use appropriate media. The problems mentioned above require alternative solutions in the form of learning models that can improve students' writing skills. A model that can provide a lot of information to students in order to inspire students and improve writing skills, through the Scramble Learning Model, students can find information about various vocabularies. In this study, researchers used quantitative methods and the variety of methods used in this study was experimental research with a pre-experimental design type of experiment. The research design used was a one-group pre-test-post test. Then the data collection technique used in this research is to use test questions (pre test and post test), observation, interviews and documentation. The research analysis uses the arithmetic mean. Based on the results of the post-test hypothesis, the t-test with the results of the T-value &gt; with the T-table value, so that Ha which means 'Effectiveness of the Scramble Learning Model on Sentence Writing Skills for Class III Students at SDN Pabuaran Tumpeng 2 Tangerang City is accepted, and Ho which means "No Effectiveness". The Scramble learning model on Sentence Writing Skills for Class III Students at SDN Pabuaran Tumpeng 2 Tangerang City was rejected.
Peningkatan Hasil Belajar Bahasa Indonesia melalui Penerapan Metode Inkuiri Siswa SMPN 1 Sukamulia Semester Ganjil

Learning approach with "Application of Inquiry Method in Improving Learning Outcomes." is a learning concept that helps teachers relate the material being taught to students' real-world situations and encourages students to make connections between their knowledge and its application in their lives as family and community members. With this concept, learning outcomes are expected to be more meaningful for students. The learning process takes place naturally in the form of student activities working and experiencing, not transferring knowledge from teacher to student. A learning strategy is far more important than a result. The purpose of this classroom action research (CAR) is to find out the extent to which the learning outcomes of news text material in class VII-A students of Indonesian language lessons improve by applying the Inquiry Method. In this classroom action research (CAR) was conducted in 2 cycles, from the results of the actions taken proved to be able to improve student learning outcomes by achieving the ideal standard. From 70% in the first cycle, it can increase in the second cycle to 100%. The results of this action research indicate that the application of learning with the Inquiry Method can improve student learning mastery in Indonesian lessons with completeness reaching 100%.
Cognitive Activities during Software Engineering Process

Software engineering activities are to process a large amount of knowledge and therefore, the cognitive process is mainly involved.Studying the cognitive process involved in software engineering can greatly help us to understand the whole process and to improve software engineering research.In order to study the cognitive process, an empirical method and case studies are needed.In this talk, Dr. Xu will first introduce the empirical research method which includes dialog-based protocol and self-directed learning theory.The dialog-based protocol is based on the analysis of the dialog that occurs between programmers.The self-directed learning theory is based on the constructivist learning theory and the Bloom taxonomy.Then case studies on incremental software development and program debugging will be presented.The results of case studies will be provided, and the implications will be analyzed.
Find Thy Neighbourhood: Privacy-Preserving Local Clustering

Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MPC) to achieve the same. Our local clustering algorithm is based on the heat kernel PageRank (HKPR) metric, which produces the best-known cluster quality. En route to our final solution, we have two important steps: (i) designing data-oblivious equivalent of the state-of-the-art algorithms for computing local clustering and HKPR values, and (ii) compiling the data-oblivious algorithms into its secure realisation via an MPC framework that supports operations over fixed-point arithmetic representation such as multiplication and division. Keeping efficiency in mind for large graphs, we choose the best-known honest-majority 3-party framework of SWIFT (Koti et al., USENIX'21) and enhance it with some of the necessary yet missing primitives, before using it for our purpose. We benchmark the performance of our secure protocols, and the reported run time showcases the practicality of the same. Further, we perform extensive experiments to evaluate the accuracy loss of our protocols. Compared to their cleartext counterparts, we observe that the results are comparable and thus showcase the practicality of the designed protocols.
Private Graph Extraction via Feature Explanations

Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks. We show that additional knowledge of post-hoc feature explanations substantially increases the success rate of these attacks. Further, we investigate in detail the differences between attack performance with respect to three different classes of explanation methods for graph neural networks: gradient-based, perturbation-based, and surrogate model-based methods. While gradient-based explanations reveal the most in terms of the graph structure, we find that these explanations do not always score high in utility. For the other two classes of explanations, privacy leakage increases with an increase in explanation utility. Finally, we propose a defense based on a randomized response mechanism for releasing the explanations, which substantially reduces the attack success rate. Our code is available at https://github.com/iyempissy/graph-stealing-attacks-with-explanation.
An Efficient Data-Independent Priority Queue and its Application to Dark Pools

We introduce a secure data-independent priority queue which supports polylogarithmic-time insertion operations and constant-time deletions and read-front (aka peek) operations as opposed to the originally introduced queue by Toft (PODC '11). Moreover, we minimize the number of comparisons required to perform different operations on Toft's priority queue. Data-independent data structures—first identified explicitly by Toft, and further elaborated by Mitchell and Zimmerman (STACS '14)—serve the purpose of computing on encrypted data without executing branching code which can be used to avoid prohibitively expensive operations in secure computation applications. Focusing on the costly sorting operations, we show significant asymptotic improvements over prior privacy preserving dark pool applications. Dark pools are securities-trading venues which attain ad-hoc order privacy, by matching orders outside of publicly visible exchanges via the so-called dark pool operators. In this paper, we describe an efficient and secure dark pool (implementing a full continuous double auction) based on our new priority queue. Our construction's security guarantees are cryptographic based on secure multiparty computation (MPC), and do not require that the dark pool operators are trusted. Our construction improves upon the asymptotic efficiency attained by previous efforts. Existing cryptographic dark pools process new orders in time which grows linearly in the size of the standing order book; ours does so in polylogarithmic time. We describe a concrete implementation of our MPC protocol with malicious security in the honest majority setting. We also report benchmarks of our implementation and compare them to prior works. Our protocol reduces the total running time by several orders of magnitude over prior secure dark pool solutions.
RPM: Robust Anonymity at Scale

This work presents RPM, a scalable anonymous communication protocol suite using secure multiparty computation (MPC) with the offline-online model. We generate random, unknown permutation matrices in a secret-shared fashion and achieve improved (online) performance and the lightest communication and computation overhead for the clients compared to the state of art robust anonymous communication protocols. Using square-lattice shuffling, we make our protocol scale well as the number of clients increases. We provide three protocol variants, each targeting different input volumes and MPC frameworks/libraries. Besides, due to the modular design, our protocols can be easily generalized to support more MPC functionalities and security properties as they get developed. We also illustrate how to generalize our protocols to support two-way anonymous communication and secure sorting. We have implemented our protocols using the MP-SPDZ library suit and the benchmark illustrates that our protocols achieve unprecedented online phase performance with practical offline phases.
ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline

Machine Learning as a service (MLaaS) permits resource-limited clients to access powerful data analytics services ubiquitously. Despite its merits, MLaaS poses significant concerns regarding the integrity of delegated computation and the privacy of the server’s model parameters. To address this issue, Zhang et al. (CCS'20) initiated the study of zero-knowledge Machine Learning (zkML). Few zkML schemes have been proposed afterward; however, they focus on sole ML classification algorithms that may not offer satisfactory accuracy or require large-scale training data and model parameters, which may not be desirable for some applications. We propose ezDPS, a new efficient and zero-knowledge ML inference scheme. Unlike prior works, ezDPS is a zkML pipeline in which the data is processed in multiple stages for high accuracy. Each stage of ezDPS is harnessed with an established ML algorithm that is shown to be effective in various applications, including Discrete Wavelet Transformation, Principal Components Analysis, and Support Vector Machine. We design new gadgets to prove ML operations effectively. We fully implemented ezDPS and assessed its performance on real datasets. Experimental results showed that ezDPS achieves one-to-three orders of magnitude more efficient than the generic circuit-based approach in all metrics while maintaining more desirable accuracy than single ML classification approaches.