Reviewerly/exHarmony huggingface dataset:
STRATEGI PEMASARAN SEPEDA MOTOR BUKAN BARU PADA UD. DINA MOTOR DI KOTA BANGKINANG

This study aims to determine the strategy that should be carried out by UD. Dina Motor in Bangkinang City in marketing non-new motorbikes. The type of data used is primary data obtained through UD. Dina Motor Bangkinang City and secondary data in the form of documents, files and notes that have been published. Data collection techniques are interviews, FGD (Focus Group Discussion) and research files. Data analysis was carried out using SWOT analysis, which is mapping with strength maps, strengths, weaknesses, opportunities and threats to determine the factors of effective strategy advantage. The results showed that the map of the strength of the motorcycle marketing strategy is not new to UD. Dina Motor in Bangkinang City is in quadrant   I as a market leader, namely a strategy to support aggressive growth (growth oriented strategy).
CIPP model evaluation on learning innovation in the implementation of SFH education policy during the pandemic in Bangka Belitung Indonesia

The policy issued by the Ministry of Education and Culture regarding Study from Home (SFH) and Work from Home (WFH) to preventing the spread of Covid-19 is an interesting study to find out the implementation in three districts of Bangka Belitung. The selection of theree regions is based on the high number of confirmed cases of Covid-19 and these region is a central and border area. The importance of this study is expected to contribute technical, psychological, science and technology insight for Muhammadiyah Elementary School (MES) in Bangka Belitung. This study method was CIPP (Context, Input, Process, and Product) evaluation models by Stufflebeam. The data were collected through interviews, observation, and documentation. The data analysis were fingding the same answer from various information obtained through data condensation, data display, and conclusion drawing/ verification and deepened with introspective reflective analysis techniques. The result showed that SFH policies can still be continued but must be supported by technical quality were related to facilitating communication tools during online learning for those experiencing economic difficulties, expanding internet networks and learning quotas, facilitating synchronous learning so that it can be accessed at a premium. The adaptability of image technology and communication in the learning process is an important requirement in implementing SFH policies. Furthermore, improve human resources (teachers) was concerned with implementing training programs for teachers to explore ICT in order to optimize online learning. The results of this research can be used as a basis and give contribution for the Government and  Dikdasmen PP Muhammadiyah to consider improving the quality of education.
A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls

Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.
THE EFFECT OF COMMON KNOWLEDGE CONSTRUCTION MODEL ON PROSPECTIVE SCIENCE TEACHERS? CONCEPTUAL UNDERSTANDINGS ON WATER POLLUTION

Surface waters are in a cycle with the energy provided by the sun. Humans play an active role in this cycle of water. In thisprocess, the substances that are mixed in water change the physical, chemical and biological properties of the water and cause pollutionon the water. All living organisms are adversely affected by this pollution in their vital processes. The aim of this study used ofCommon Knowledge Construction Model is to determine the effect of science teacher candidates on conceptual understanding of waterpollution. This study was carried out with 18 science teachers’ candidate in the 4th grade of Trabzon University Fatih Education Facultyin the fall semester of 2018-2019 academic years. In the study, pre-test and post-test semi-experimental method without control groupwas used. Word Association Test (WAT) was used as data collection tool. In order to evaluate the results of the pre-test and post-test,each of the answers given to the key concepts was examined. A frequency table and concept networks were formed. As a result, it isseen that there is an increase in the number of words given to the key concepts in favor of the post-test. It was found out that theCommon Knowledge Construction Model (CKCM) had an impact on the conceptual understanding of teacher candidates about waterpollution. More studies related to CKCM should be included and teachers should be encouraged to use this model effectively in theircourses.
Differential Privacy - A Balancing Act

Data privacy is an ever important aspect of data analyses. Historically, a plethora of privacy techniques have been introduced to protect data, but few have stood the test of time. From investigating the overlap between big data research, and security and privacy research, I have found that differential privacy presents itself as a promising defender of data privacy. Differential privacy is a rigorous, mathematical notion of privacy. Nevertheless, privacy comes at a cost. In order to achieve differential privacy, we need to introduce some form of inaccuracy (i.e. error) to our analyses. Hence, practitioners need to engage in a balancing act between accuracy and privacy when adopting differential privacy. As a consequence, understanding this accuracy/privacy trade-off is vital to being able to use differential privacy in real data analyses. In this thesis, I aim to bridge the gap between differential privacy in theory, and differential privacy in practice. Most notably, I aim to convey a better understanding of the accuracy/privacy trade-off, by 1) implementing tools to tweak accuracy/privacy in a real use case, 2) presenting a methodology for empirically predicting error, and 3) systematizing and analyzing known accuracy improvement techniques for differentially private algorithms. Additionally, I also put differential privacy into context by investigating how it can be applied in the automotive domain. Using the automotive domain as an example, I introduce the main challenges that constitutes the balancing act, and provide advice for moving forward.
La dimensione sistemica del successo (o insuccesso?) nell'uso dei dati nella pandemia da COVID-19. Risultati dall'analisi comparata relativa alle app di tracciamento in nove paesi OECD

During the COVID-19 pandemic, public attention turned to contact tracing apps as a possible solution to the spread of the virus. Many countries have moved in this direction, adopting contact tracing apps, while respecting personal data protection and, for EU countries, adhering to a number of fundamental principles: voluntariness, interoperability, regulatory coverage, purpose specification, minimisation, transparency, protection, security, and timeliness. In spite of timely public policy efforts, tracking apps have not been a success in many countries, and today, when their use could be of great importance, it seems appropriate to open a reflection on the success and unsuccessfulness of a public policy that has resolutely supported the use of digital technologies for public utility purposes. This working paper proposes a comparative analysis of nine OECD countries: Australia, Korea, France, Germany, Ireland, Italy, New Zealand, Russia, Spain. It outlines the specific factors in each country's public policy that made the use of tracking apps possible, in terms of policy design in terms of objectives, instruments, public procurement selection criteria, resources and the context in which the policy was implemented. The working paper concludes with three lessons learned from the comparative analysis: the privacy paradox, the choice of a public interest technology, and the systemic interweaving that the implementation of a public policy must take into account to enhance the effectiveness of a public interest action.
The Nearest Surfing Spot Search Application in Bali Uses the Web-based

Indonesia is an archipelago that has various kinds of beaches and uniqueness. Surfing has become a very popular activity on some beaches with supportive waves. Bali is one of the tourist destinations that in addition to highlighting customs and culture, also has abundant natural wealth as well as coastal tourism destinations that can be used as surfing spots. This study aims to provide information about the location of the surfing spots and the nearest search support. The application uses the web-based Haversine formula to find the nearest surfing spot in Bali which can be used as a solution to display information and the location of surfing spots. Researchers developed this application using the waterfall method. Based on the results of questionnaire testing with 20 respondents, 35% agree and 65% highly agree, that this application can help and make it easier for tourists to find the nearest surfing spot.
Differentially Private Nonparametric Regression Under a Growth Condition

Given a real-valued hypothesis class H, we investigate under what conditions there is a differentially private algorithm which learns an optimal hypothesis from H given i.i.d. data. Inspired by recent results for the related setting of binary classification (Alon et al., 2019; Bun et al., 2020), where it was shown that online learnability of a binary class is necessary and sufficient for its private learnability, Jung et al. (2020) showed that in the setting of regression, online learnability of H is necessary for private learnability. Here online learnability of H is characterized by the finiteness of its eta-sequential fat shattering dimension, sfat_eta(H), for all eta > 0. In terms of sufficient conditions for private learnability, Jung et al. (2020) showed that H is privately learnable if lim_{\eta -> 0} sfat_eta(H) is finite, which is a fairly restrictive condition. We show that under the relaxed condition liminf_{eta -> 0} eta * sfat_eta(H) = 0, H is privately learnable, thus establishing the first nonparametric private learnability guarantee for classes H with sfat_eta(H) diverging as eta -> 0. Our techniques involve a novel filtering procedure to output stable hypotheses for nonparametric function classes.
SIMULASI PENGARUH LAJU ALIR LARUTAN NIRA TERHADAP DISTRIBUSI TEMPERATUR PADA FALLING FILM EVAPORATOR

Cane juice is the main raw material in sugar factories. The processing of cane juice to produce the highest quality sugar must be through the proper evaporation process. If the evaporation temperature is too high, the cane juice can be damaged and this will affect the quality of sugar produced. The type of evaporator that is often used to reduce water content in food is falling film evaporator. This study obtained to determine the effect of the flow rate of cane juiceon the temperature distribution at the interface and wall. This research was conducted using Matlab Software and the Runge-Kutta Method. The research variables used were the flow rate of 154, 243, and 301 l/hr and the air flow rate of 4, 8 and 10 m3/hr. The results showed that the smaller flow rate of the solution, the smaller temperature of the solution at the interface with the same position. The temperature distribution on the wall has increased at the beginning of evaporator operation, but then decreased due to evaporation.
Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription

Orthographic-To-Phonetic (O2P) Transcription is the process of learning the relationship between the written word and its phonetic transcription. It is a necessary part of Text-To-Speech (TTS) systems and it plays an important role in handling Out-Of-Vocabulary (OOV) words in Automatic Speech Recognition systems. The O2P is a complex task, because for many languages, the correspondence between the orthography and its phonetic transcription is not completely consistent. Over time, the techniques used to tackle this problem have evolved, from earlier rules based systems to the current more sophisticated machine learning approaches. In this paper, we propose an approach for Arabic O2P Conversion based on a probabilistic method: Conditional Random Fields (CRF). We discuss the results and experiments of this method apply on a pronunciation dictionary of the Most Commonly used Arabic Words, a database that we called (MCAW-Dic). MCAW-Dic contains over 35 000 words in Modern Standard Arabic (MSA) and their pronunciation, a database that we have developed by ourselves assisted by phoneticians and linguists from the University of Tlemcen. The results achieved are very satisfactory and point the way towards future innovations. Indeed, in all our tests, the score was between 11 and 15% error rate on the transcription of phonemes (Phoneme Error Rate). We could improve this result by including a large context, but in this case, we encountered memory limitations and calculation difficulties.
Influence of Information Skills and Media Literacy Instruction to Misinformation Evaluation Skills Among College Students

Accurate information is vital to the lives of every student. Thus, being able to distinguish between correct and erroneous information is a necessity. Hence, this descriptive-correlation research aimed to determine whether information skills and media literacy instruction can significantly influence the misinformation evaluation skills. The researchers administered adapted and patterned survey questionnaires to one hundred forty-one (141) BLIS college students from Regions XI and XII. Results revealed that students-respondents have high proficiency in information skills, namely computer skills, information literacy skills, and browsing and navigation skills. They have also received outstanding media literacy instruction, and they are highly skillful in evaluating and identifying misinformation. Moreover, using Multiple Linear Regression Analysis, findings revealed that both of the two independent variables, namely Information Skills and Media Literacy Instruction, can significantly influence the respondents' misinformation evaluation skills. These findings serve as the basis to further strengthen the students' information skills, and media literacy instruction as these variables positively affect college students' skills in evaluating misinformation.
A Tentative Study of Framework for Deriving Diverse Solutions Focused on Emergent Property

This paper describes a framework for methods deriving diverse solution focusing on emergence and issues based on this framework. Concretely, we show a framework based on the four groups: Surface-Large Group, Surface-Small Group, Internal-Small Group, and Internal- Large Group. These groups are based on the application area (parts) of the generated form and its scale. Moreover, As the effects of this framework, we describe the promotion of the development of diverse generation methods by accumulating knowledge based on the framework and the sophistication by cooperation between these methods. Additionally, we show two future issues: clarification of the relationship between morphological characteristics and generation methods in each group, and the need for modeling and rule setting guidelines according to the morphological characteristics of each group
Qualitative and Quantitative Analysis of Financial Public Opinion Risk Based on Big Data Analysis

Online public opinion has a strong social influence and spreads at a very fast speed. Public opinion analysis on financial public opinion reports is conducive to the relevant institutions to conveniently understand the public opinion of the event and make correct guidance and control, which is also conducive to the sustainable development of financial market. In this paper, subject model, emotion dictionary construction, public opinion analysis and other technologies are used to conduct a detailed study of financial related network public opinion, and a financial public opinion analysis model is designed. The model can effectively identify the emotional words in the text from the improved topic model, and then match the emotional words with the more comprehensive and perfect emotional dictionary in the financial field, calculate the emotional tendency value of the emotional words, so as to classify the financial text public opinion more accurately.
Two Step Verification and Data Security through The Network

Our project is basically a web based application that engages in security and the transmission of data through the network. As we know that security is now become a necessity in each and everyone’s life as there might be people who want to access your secret data for wrong purposes.So our system deals with the security issue by implementing steganography into our system. Steganography basically helps to hide secret messages and information embedded in a photograph or a video. This technique helps to prevent any third party user from accessing the secret message. iIn our project we are also using an encryption technique that is the “Tiny Encryption Algorithm”. Encryption also plays an important role in keeping the data from unauthorized people in real time environment such that the data is safe. After encryption, the file is implemented with steganography and is then transmitted through the network to the receiver. The receiver should have the same application to de-embed the file and then decrypt the same file to extract the original file. The goal of our project is to design a tool for providing security to the system during transmission of data through the network for the likes of military forces that have an essential need for communication for vehicle control, surveillance signal processing and is even more imperative in times of a war.
Attention-Guiding Effect Measurement and Gaze Frequency Analysis of Motion Illusion in Visual Search Tasks

There are two mechanisms of visual attention, which have different developmental processes. One is passive bottom-up attention and the other is active top-down attention. LED guide lights attract attention by the movement of light. Although such a bottom-up attention-grabbing display is intuitive, it has a disadvantage for constant use. In this study, we focused on the attention-inducing effect of the motion illusion to induce the illusion of motion in still images. The purpose of this study was to propose a display with high attention-getting effect by incorporating optical illusions. In this study, we used an illusory image of moving gratings to give the illusion of motion by placing black and white gratings in front of a gray background. To investigate the attention-inducing effects of illusory images, we conducted a visual search task with stimuli of illusory images that were not related to the task. In the visual search task, we tested whether the target in the upright disturbance was tilted to the left or right. As a result, we found that the optical illusion reduced the reaction time when guiding attention to the target. In addition, we measured the number of eye movements during the task and analyzed the number of gazes by using a gaze-measuring device. As a result, we found that the number of gazes decreased when the illusion induced the target's attention. The results suggest that the motion-induced optical illusion induces visual attention.
Efficient and Privacy-preserving Distributed Learning in Cloud-Edge Computing Systems

Machine learning and cloud computing have been integrated in diverse applications to provide intelligent services. With powerful computational ability, the cloud server can execute machine learning algorithm efficiently. However, since accurate machine learning highly depends on training the model with sufficient data. Transmitting massive raw data from distributed devices to the cloud leads to heavy communication overhead and privacy leakage. Distributed learning is a promising technique to reduce data transmission by allowing the distributed devices to participant in model training locally. Thus a global learning task can be performed in a distributed way. Although it avoids to disclose the participants' raw data to the cloud directly, the cloud can infer partial private information by analyzing their local models. To tackle this challenge, the state-of-the-art solutions mainly rely on encryption and differential privacy. In this paper, we propose to implement the distributed learning in a three-layer cloud-edge computing system. By applying the mini-batch gradient decent, we can decompose a learning task to distributed edge nodes and participants hierarchically. To improve the communication efficiency while preserving privacy, we employ secure aggregation protocol in small groups by utilizing the social network of participants. Simulation results are presented to show the effectiveness of our proposed scheme in terms of learning accuracy and efficiency.
Source Identification for Mixtures of Product Distributions

We give an algorithm for source identification of a mixture of k product distributions on n bits. This is a fundamental problem in machine learning with many applications. Our algorithm identifies the source parameters of an identifiable mixture, given, as input, approximate values of multilinear moments (derived, for instance, from a sufficiently large sample), using $2^{O(k^2)}n^{O(k)}$ arithmetic operations. Our result is the first explicit bound on the computational complexity of source identification of such mixtures. The running time improves previous results by Feldman, O’Donnell, and Servedio (FOCS 2005) and Chen and Moitra (STOC 2019) that guaranteed only learning the mixture (without parametric identification of the source). Our analysis gives a quantitative version of a qualitative characterization of identifiable sources that is due to Tahmasebi, Motahari, and Maddah-Ali(ISIT 2018).
Methodology of Inducing Divergent Thinking Utilizing Human Unique Ideation with Concept Dictionary

It is generally agreed today that there are various ideation methods, but there isn't an established ideation method for computer to utilize human ideation. In this thesis, I investigate the way of inducing divergent thinking utilizing human unique ideation with concept dictionary. In some preceding study, it is said that making words with conceptual control by computer in process of thought tends to make better ideation. Following the reasoning, I hypothesize that an idea generator makes words scattered conceptually widely in divergent thinking leads to generate novel or interesting ideas. The method of inducing divergent thinking is an generator's making three words related with the last word and computer's choosing the one conceptually far from words that have been generated ever. They do these action in turns two sets of five times and after that the examinee generates ideas. First, each of 16 examinees uses the software as divergent thinking and after that makes new ideas as many as they can. I do this experiment two times on different themes. On one theme, each of the similarity by control exceeds each of them by no control. On the other theme, the similarity are little different. Second, I evaluate all ideas in the point of view of novelty and interestingness. The result is that average estimates of control exceed them of no control, but they aren't significant difference between them.