Reviewerly/exHarmony huggingface dataset:
Approximate Range Counting under Differential Privacy

Range counting under differential privacy has been studied extensively. Unfortunately, lower bounds based on discrepancy theory suggest that large errors have to be introduced in order to preserve privacy: Essentially for any range space (except axis-parallel rectangles), the error has to be polynomial. In this paper, we show that by allowing a standard notion of geometric approximation where points near the boundary of the range may or may not be counted, the error can be reduced to logarithmic. Furthermore, our approximate range counting data structure can be used to solve the approximate nearest neighbor (ANN) problem and k-NN classification, leading to the first differentially private algorithms for these two problems with provable guarantees on the utility.
Scalable Text Analysis with Efficient Distributed Word Representation

Author(s): Xue, Zijun | Advisor(s): Cho, Junghoo | Abstract: The demand for Natural Language Processing has been thriving rapidly due to the various emerging Internet services such as social networks, e-commerce, intelligent assistant, etc. The large volume of natural language data and the new market requirements have brought significant challenges and opportunities for academia and industry. The challenges include (1) efficiently processing a massive volume of data and devising a highly scalable architecture, (2) extending the horizon of the current natural language processing to tackle problems that cannot be handled well. This thesis demonstrates several advanced techniques to tackle the challenges by boosting scalability and efficiency or extending the horizon of existing core NLP techniques on different granularities. This thesis introduces (i) a neural-based lexical simplification algorithm that utilizes contextualized word embedding to capture the meaning more accurately, (ii) a sample-efficient algorithm that solves extreme large-output classification for contrastive representation learning. (iii) a scalable and efficient algorithm for the probabilistic topic model LDA.First, we propose amplified negative sampling and rigorous mathematical analysis of the famous negative sampling method for the word level distributed representations. Specifically, we distinguish the impact of various loss functions for the contrastive representation learning process. Our theoretical analysis provides a better understanding of a few empirical observations that are well known among practitioners when negative sampling is employed. We also propose the amplified negative sampling based on our analysis. It's a simple yet efficient method that can effectively improve the negative sampling efficiency and boost the downstream tasks' performance. Second, we propose the neural simplicity ranking (NSR) model that utilizes the contextualized word representations in an unconventional way for lexical simplification for the sentence level tasks. This model creatively uses the contextualized word embeddings beyond its original masked language model (MLM) task to identify the relatedness among candidate words and the original words. By redesigning the feature set for the simplicity measure and the ranking scheme, the NSR model achieves new state-of-the-art performance. Finally, we propose sd-LDA for the paragraph-level tasks, a scalable disk-based LDA model with sparse-prior sampling. The sd-LDA brings a highly optimized disk-based design that reduces the memory consumption by two orders of magnitude, making it possible to do large topic analysis on mobile devices. A sparse prior sampling technique is introduced in this work to accelerate the processing time. The sd-LDA algorithm can effectively reduce the memory consumption and processing time at the same time without loss of performance.
Financial Development and Economic Growth: Evidence from Sub-Saharan Africa

This study aims to empirically investigate the short and long term, and the causality impacts of financial development on economic growth in some chosen nations in Sub-Saharan Africa (SSA). To bring out the short and long-run features, the Autoregressive Distributed Lag Model (ARDL) techniques have been estimated. The study shapes an aggregate variable of financial development through the Principal Component Analysis and applies the Cross-sectional dependency-based unit root tests. According to the empirical findings of the study, it is proposed that the execution and quickening of effective financial sector reforms and the implementation of growth-oriented policies that would boost the financial sector by policymakers.
Crop Selection Prediction Model for Agricultural and Food Consumption Trend

This study develops a crop selection prediction model using agricultural and food consumption trend. Under the expected utility hypothesis (EUH), we devise a crop selection mechanism where farmers choose crops providing the maximum expected utility from their profit. Then, feed forward neural network (FFNN) model is used for the prediction of the expected utility, and vector autoregressive (VAR) model is constructed with atypical Big Data based on web-based text query concerning consumption trend. We apply models to the exclusive consumption trend panel data provided by the Rural Development Administration (RDA) for six specific regions. Empirical results show that various crops are predicted to provide the maximum expected utility and that the prediction model can be improved by considering atypical Big Data.
Investigating Constraints and Strategies Faced by the Non-English Students in Answering Reading Comprehension TOEFL Test

Test of English as a Foreign Language (TOEFL) is widely used as a measurement for standardized foreign language students.One of the sections in TOEFL assessed as the part considered by the students is reading comprehension Non-English students surely have the power to accomplish TOEFL test, especially the reading section, even though there may be some struggle or constraints they face.Descriptive qualitative research design was implemented in this study in order to investigate the constraints and strategies in accomplishing reading comprehension on the TOEFL test.The totals of participants were 18 students who have passed the ITP TOEFL test.The results indicate there are three most aspects regarding to the students' struggles of answering the question of reading comprehension, those are vocabulary limitations, hard to find the specific information on the passage, and troubled to identify some features, such as tone, objectives, or course in the passage.Aside from this, any strategies definitely composed to adjust those constraints.Further explanations would be elaborated in this paper.The results of this study would be impeccable to give any suggestions for the students or teachers to upgrade their understanding of the TOEFL test on reading comprehension.
Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

We advocate a new resource allocation framework, which we term resource rationing, for wireless federated learning (FL). Unlike existing resource allocation methods for FL, resource rationing focuses on balancing resources across learning rounds so that their collective impact on the federated learning performance is explicitly captured. This new framework can be integrated seamlessly with existing resource allocation schemes to optimize the convergence of FL. In particular, a novel "later-is-better" principle is at the front and center of resource rationing, which is validated empirically in several instances of wireless FL. We also point out technical challenges and research opportunities that are worth pursuing. Resource rationing highlights the benefits of treating the emerging FL as a new class of service that has its own characteristics, and designing communication algorithms for this particular service.
Genetically generated ASCII trees

This project, inspired by the article: "On genetic algorithms and Lindenmayer systems" by Gabriela Ochoa, implements an ASCII tree-generation program using L-systems.
Pengembangan Sistem Informasi Pembelian, Penjualan dan Persediaan Motor Bekas menggunakan Analisis Fast-Slow-Non Moving

Abstract — Motorcycle Company is engaged in selling used motorcycles, processing motorcycle inventory data to manage the optimal number of purchases and sales. Based on monthly sales data, it can be predicted the need to buy a used vehicle to the seller. Through FSN (Fast-moving, Slow-moving and Non-moving) analysis is to consider quantity, the rate of consumption of item and how often they are issued or used. The results of analysis can be used to guide the decision of the right amount of inventory to be placed in the warehouse, the frequency to buy or even delete certain items in stages. In order for the FSN analysis to be carried out successfully, it is necessary to analyze the items based on their respective categories, within a certain period of time. The purpose of this research is to design and build an information system for the sale of used motor vehicles, purchase of used motorcycles by considering the optimal inventory using the FSN method.&#x0D; &#x0D; Keywords — Inventory Management, Selling-buying Used Motorcycle, Fast-moving, Slow-moving and Non-moving Anylysis.
QuranTree.jl: A Julia Package for Quranic Arabic Corpus

QuranTree.jl is an open-source package for working with the Quranic Arabic Corpus (Dukes and Habash, 2010). It aims to provide Julia APIs as an alternative to the Java APIs of JQuranTree. QuranTree.jl currently offers functionalities for intuitive indexing of chapters, verses, words and parts of words of the Qur’an; for creating custom transliteration; for character dediacritization and normalization; and, for handling the morphological features. Lastly, it can work well with Julia’s TextAnalysis.jl and Python’s CAMeL Tools.
Measurement of e-school User Experience, Does it Support Blended Learning during the Covid-19 Pandemic ?

This study aims to measure the User Experience of e-school in supporting blended learning during the Covid-19 Pandemic. The object of this research is the parent school of LPKA Class I Palembang, namely: SDN 25, SMPN 22, and SMAN 11 Palembang. Because of this, all of the parent schools implemented an e-school web system adapted from the LPKA Class I Palembang filial e-school. The method used in this study was UEQ which was introduced by (Schrepp, 2018). The results of UEQ measurements on parent-school students show that: a). web e-school SDN 25 Palembang, on the scale of attractiveness, clarity, and accuracy are categorized as "above average" while the scale of efficiency, stimulation, and novelty are categorized as "good". b). web e-school SMPN 22 Palembang, on the scale of attractiveness, clarity, and efficiency are categorized as "above average" while the scale of accuracy and stimulation are categorized as "good". For novelty scale in the "Excellent" category. c). Web e-school of SMAN 11 Palembang, on a scale of attractiveness, efficiency, stimulation, and novelty is categorized as "good" while clarity and accuracy are categorized as "above average". For novelty scale in the "Excellent" category. Meanwhile, what parent-school teachers feel about the e-school web, on a scale of attractiveness, clarity, efficiency, accuracy, and novelty are categorized as "above average". Meanwhile, the other scale, stimulation, is categorized as "below average". So in other words, the web-adapted e-school from the LPKA Class I Palembang filial e-school as a whole has been able to support Blended-Learning learning during the Covid-19 Pandemic, although improvements still need to be made.
Supporting Creation of New iStar Extensions through a Process

iStar is a goal-oriented requirements modelling language which has been used by industrial and academic projects of different domains. Modelling languages are commonly extended to add new constructs giving more expressiveness. iStar is often extended to incorporate new constructs. A study performed on iStar extensions identified 96 extensions and the occurrence of problems related to their quality. It was pointed out by experts in iStar extensions the need to propose a way to support the proposal of iStar extensions systematically to prevent the problem occurrence, increase the quality of extensions, and make extension creation a less challenging task. This work investigates how iStar extensions have been created and proposes a systematic way to guide the creation of quality extensions. A process to support the creation of new iStar extensions was proposed. The process was used to propose a new iStar extension and was analysed by experts. The results point to the usefulness of the process to propose new iStar extensions.
Accounts–Cookies–Carts

An application will be developed that requires a user to log into the site. Once the user has logged in, the user’s previous data will be retrieved from the database. Finding a row in the database using the primary key is a simple matter. However, the primary key that we have been using is unknown to the user. In order to find a row in the database, the user must be able to uniquely specify the desired row. Web applications are stateless: the developer must add code so that the application will remember what the user has done recently. Because of this, it is difficult to identify users until they log into a database. For this reason, cookies were developed. Cookies allow information to be stored on a user’s computer. When the user visits a site, the stored information is sent to the site. Most e-commerce sites allow the user to enter data into a shopping cart. This allows the user to browse the site, adding items to the cart for later purchase. A shopping cart is easy to implement using Java generics. A complete application will be developed that uses a shopping cart.
IMPROVING STUDENTS’ READING ACHIEVEMENT BY USING THINK-PAIRSHARE

This research aimed to find out how Think-Pair-Share technique can improve students’achievement in reading comprehension. It used Classroom Action Research (CAR) and conductedtwo cycles, where each cycle consisted of four meetings. The subjects are 17 students of XI gradewhich consists of 7 males and 10 females. The instrument for collecting data were reading comprehensiontest, observation sheets, and interview sheet. Based on reading comprehension score, students’ scorekept improving in every test. In preliminary study the mean score was 34.1, in cycle I the mean scorewas 73.82, in cycle II the mean score was 89.41. Based on observation sheets and interview sheet, itwas found that teaching and learning process ran well. Students were active and interested in readingcomprehension. The result of the research showed that Think-Pair-Share can improve students’achievement in reading comprehension.
IMPLEMENTASI HOTSPOT LOGIN MENGGUNAKAN CAPSMAN MIKROTIK PADA WILAYAH YANG BERBEDA

The development of wireless technology is currently so fast, every communication device such as smartphones, tablets, and portable computers supports the wireless technology. It's no stranger when somewhere provides wireless networks especially if the wireless network is connected to the internet network. If the wireless network has been formed then the next problem is how the network can cover all areas or places that exist, then what about the security system, whether the wireless network is already safe than the stylists. Then, whether the administrator can look at controlling the activities of users, and what if there are two or more places with different areas but want to make the administrator centrally. By using CAPsMAN which is synergized with Userman and Radius Server using the Extended Service Set topology and connecting between regions via VPN, this wireless network will be controlled, monitored and configured centrally, bringing Authentication, Authorized and Accounting features in userman to make the network usable using an account By itself, using a VPN for interconnection ensures that account usage can be done using the same account, and applying an extended service set topology can produce a stable network without interrupted interruptions.
Internal Factors of Development of Psychological Readiness for Professional Activities in Cynologist Officers

The purpose of our research is to carry out empirical study and analysis of internal factors of the development of psychological readiness for professional activities in cynologist officers. The article gives empirical evidence of internal factors of psychological readiness for professional activities in cynologist officers. The motivational and purposive, active and operational, emotional and volitional, reflexive and controlling components of psychological readiness are outlined. Three internal factors of psychological readiness of cynologist officers for professional activities are identified and analysed with the help of factor analysis of empirical indicators: awareness of psychological readiness for professional activities, a responsible subjective position, the ability to act independently, and take decisions.
Simulation of Knee-Joint Angle Measurement for Accelerometer-Based Human Body Motion Monitoring System Using Quartus II

Knee angle measurement is important for investigating complex gait disorder, monitoring the progress of rehabilitation and provide the range of motion (ROM) during flexion or extension. The research presents an implementation of knee angle measurement in simulation by using Quartus II software. Verilog HDL is used to develop the modules of the knee angle measurement. There are ten groups of value have been used in this study. The result of the simulation is verified through the EDA tool. The knee angle measurement is compared with the value calculated by calculator. In the finding, the knee joint angle measurement in simulation has a ±17° difference as compared to the value calculated by the calculator. Thus, there is some improvement is needed to make the algorithm well suit with the healthcare applications. The benefit of this project is it can help the physiotherapists to measure the range of motion during stance and swing for treatment to regain the full function of range of motion.
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are the ideal clinical research environment for such development because they collect many clinical data and are highly computerized environments. We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36 % of total) and 3503 negative cases classified by two independent physicians using a standardized approach. The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89 % accuracy, 88 % recall, and 89 % precision. This study successfully applied learning representation and machine learning algorithms to detect heart failure from clinical natural language in a single French institution. Further work is needed to use the same methodology in other institutions and other languages.
Hercule: Representing and Reasoning about Norms as a Foundation for Declarative Contracts over Blockchain

Current blockchain approaches for business contracts are based on smart contracts, namely, software programs placed on a blockchain that are automatically executed to realize a contract. However, smart contracts lack flexibility and interfere with the autonomy of the parties concerned. We propose Hercule, an approach for declaratively specifying blockchain applications in a manner that reflects business contracts. Hercule represents a contract via regulatory norms that capture the involved parties' expectations of one another. It computes the states of norms (hence, of contracts) from events in the blockchain. Hercule's novelty and significance lie in that it operationalizes declarative contracts over semistructured databases, the underlying representation for practical blockchain such as Hyperledger Fabric and Ethereum. Specifically, it exploits the map-reduce capabilities of such stores to compute norm states. We demonstrate that our implementation over Hyperledger Fabric can process thousands of events per second, sufficient for many applications.
Understanding Prediction Discrepancies in Machine Learning Classifiers

A multitude of classifiers can be trained on the same data to achieve similar performances during test time, while having learned significantly different classification patterns. This phenomenon, which we call prediction discrepancies, is often associated with the blind selection of one model instead of another with similar performances. When making a choice, the machine learning practitioner has no understanding on the differences between models, their limits, where they agree and where they don't. But his/her choice will result in concrete consequences for instances to be classified in the discrepancy zone, since the final decision will be based on the selected classification pattern. Besides the arbitrary nature of the result, a bad choice could have further negative consequences such as loss of opportunity or lack of fairness. This paper proposes to address this question by analyzing the prediction discrepancies in a pool of best-performing models trained on the same data. A model-agnostic algorithm, DIG, is proposed to capture and explain discrepancies locally, to enable the practitioner to make the best educated decision when selecting a model by anticipating its potential undesired consequences. All the code to reproduce the experiments is available.
Theoretical basis of integrative approaches studying of STEM-education programs implementation in secondary educational institutions of Ukraine

The article examines integrative approaches of educational STEM programs implementation in secondary educational institutions. The concept of “STEM education” is not stable. The views of experts on its content are revealed through such generalized components as pedagogical technology (pedagogical process), focus on practical tasks and problem-based learning. At the same time, integrative approaches find application in the disciplines, the sequence of courses, and in a separate course. Generalization of the definitions of the concept “STEM education”, proposed by various experts, made it possible to understand it as a joint activity, interaction and cooperation of teachers, students and stakeholders, aimed at overcoming the fragmentation of knowledge by eliminating boundaries between them, integrating content components. It provides the formation of key competencies necessary for further employment and life activity in the conditions of the formation and development of new branches of knowledge. The article describes the general features of the learning organization in accordance with disciplinary, multidisciplinary, interdisciplinary and transdisciplinary approaches. In the context of transdisciplinarity, the prospects for the development of educational programs in secondary educational institutions of Ukraine is considered. It is possible to single out a number of problems accompanying the process of STEM courses developing and implementing. There are the problem of systematizing the structural and logical links between disciplines to be merged, the problem of professional readiness of teachers specializing in teaching one or two disciplines to take responsibility for mentoring and supporting student’s transdisciplinary projects and modernization of educational laboratories and making of modern STEM centres at secondary educational institution.