Reviewerly/exHarmony huggingface dataset:
TYPES OF WORD FORMATION USED IN THE 16th CHAPTER OF HARRY POTTER AND THE SORCERER’S STONE NOVEL WRITTEN BY J. K. ROWLING

Word formation is the process of creating new words based on the word itself, or the other words. It means the new words can be created by shortens the words itself, combining them to the other words, add affixes to give grammatical information, etc. The aim of this study is to know the types of word formation used in a literary work, in this case, a novel. The object of this study is the 16th chapter of Harry Potter and the Sorcerer’s Stone written by J. K. Rowling. Type of this study is descriptive. The data were analyzed by using O’Grady &amp; Archibald theory about word formation. The result shows that there were 340 words with the frequency 755 times have word formation process. Inflection (64.1%) becomes the most frequently used, and compounding (4.5%) becomes the less frequently used.
A Scheme for Optical Reservoir Computers with Atomic Memory

Optical systems have been identified as one of the promising alternative hardware implementations for machine learning applications due to low latency, high bandwidth and lower energy consumption from the use of passive optics [1] . Reservoir computers (RCs) are interesting as they are versatile and easily trained recurrent neural networks - preforming particularly well on time series data [2] . RCs lend themselves particularly well an optics implementation, as the the reservoirs remained fixed, and the trained weights exchanged, allowing for reuse of one reservoir for multiple problems. It has been demonstrated that optic-to-electric conversion introduces uncertainty and time delay in the implementation of optical reservoir computers [3] , thus motivating research into all-optical RCs. First implementations of an all-optical RC used feedback loop laser setups with fixed delay and dynamics, limiting state space which can be represented in the reservoir, but allowing quick data throughput [2] , [4] . We propose an all-optical setup with the internal states of the reservoir which are stored in an optical memory, allowing for more complex internal dynamics. We present an electro-optical implementation as first result, and outline how this can be implemented all-optically by using atomic vapor based memories [5] .
Structure-Preserving Deraining With Residue Channel Prior Guidance

Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures. To solve this problem, we propose a Structure-Preserving Deraining Network (SPDNet) with RCP guidance. SPDNet directly generates high-quality rain-free images with clear and accurate structures under the guidance of RCP but does not rely on any rain-generating assumptions. Specifically, we found that the RCP of images contains more accurate structural information than rainy images. Therefore, we introduced it to our deraining network to protect structure information of the rain-free image. Meanwhile, a Wavelet-based Multi-Level Module (WMLM) is proposed as the backbone for learning the background information of rainy images and an Interactive Fusion Module (IFM) is designed to make full use of RCP information. In addition, an iterative guidance strategy is proposed to gradually improve the accuracy of RCP, refining the result in a progressive path. Extensive experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves new state-of-the-art results. Code: this https URL
From linear algebra to quantum information

Anticipating the realization of quantum computers, we propose the most reader-friendly exposition of quantum information and qubits theory. Although the latter lies within framework of linear algebra, it has some fl avor of quantum mechanics and it would be easier to get used to special symbols and terminologies. Quantum mechanics is described in the language of functional analysis: the state space (the totality of all states) of a quantum system is a Hilbert space over the complex numbers and all mechanical quantities are taken as Hermite operators. Hence some basics of functional analysis is necessary. We make a smooth transition from linear algebra to functional analysis by comparing the elements in these theories: Hilbert space vs. fi nite dimensional vector space, Hermite operator vs. linear map given by a Hermite matrix. Then from Newtonian mechanics to quantum mechanics and then to the theory of qubits. We elucidate qubits theory a bit by accommodating it into linear algebra framework under these precursors.
PKM PADA TK BINA ANAPRASA UNTUK MENINGKATKAN KEMAMPUAN TENAGA PENDIDIK DALAM BIDANG TEKNOLOGI INFORMASI

Kindergarten (TK) is the very important phase in child education to develop the next generation of the nation. Therefore, kindergarten teachers play an important role in the student learning. Kindergarten teachers should not only excel in conventional learning, but also in innovative learning, starting with blending Information Technology in student learning process. The purpose of the Community Partnership Program (PKM) is to improve the human resource quality in the implementation of Information Technology in administration and student learning process. The skill improvement focuses on the training of Google Applications such as Google Docs, Google Form, Google Slide, Google Sheet, and social media as well as Blogger to publish school's updated information to public.The consideration of partnering with TK Bina Anaprasa is because of the lower accreditation in C rank for over 23 years of establishment, and the lower understanding in Google and Internet application. In addition, the school do not have social media like blog or web to share information with the public. Therefore, in the short term the PKM program can improve the teachers' skills in area of Information Technology and in the long term the PKM program can establish an Internet-based kindergarten.
PENGARUH VIDEO PEMBELAJARAN INTERAKTIF TERHADAP HASIL BELAJAR PAI SISWA SMP SEPULUH NOVEMBER SIDOARJO

Teacher skills in obtaining student learning outcomes are something that needs more attention, because the form of student learning outcomes is a form of teacher success in teaching. Teachers can have their own assessments for themselves and know their teaching skills. Therefore, the learning video media here is a form of interactive media to determine student learning outcomes. The purpose of this study was to determine whether interactive learning video media had an effect on student learning outcomes. The results of this study indicate that the significance value of the data before and after using interactive video video media on student learning outcomes is 0.000 <0.05 which indicates that interactive learning video media have a significant effect on student learning outcomes.
A Spatial-Temporal based Next Frame Prediction and Unsupervised Classification of Video Anomalies in Real Time Estimation

Anomaly detection is an area of video analysis has a great importance in automated surveillance. Although it has been extensively studied, there has been little work started using CNN networks. Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. In our technique, we capture variations in scale of the patterns of motion in an image object by using optical flow dense estimation technique and train our auto encoder model using convolution long short term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time accuracy of nearly 98% for the youtube videos which are not used for either testing or training. Error between the network’s output and the target output is used to classify a video volume as normal or abnormal. In addition to the use of reconstruction error, we also use prediction error for anomaly detection. The prediction models show comparable performance with state of the art methods. In comparison with the proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.
Analysis with the Pieces Framework in the Xyz Village Service Information System

The village is the level of government below sub-district in an area, information system population data service at the village level majority still using the manual system. System like this which often results in data not valid, and sometimes there is data that does not match with the fact, the impact often goes wrong in the process of searching for population data information, both old population data and population data transfer. The same is true for letter data correspondence, such as death certificates, letters of birth certificate, transfer certificate domicile, and so on. Mail archive sometimes not obviously due to unorganized archiving properly or not stored in a databases. Problem analysis is done by using the PIECES framework approach. PIECES itself is an abbreviation of Performance, Information, Economics, Control, Efficiency, Service. These PIECES have six parts, of each of the six parts a check list is made, each check list in the as the name implies will cause problems.
Designing for teacher education: Exploring the concept of a responsive curriculum

Curriculum designers in teacher education are challenged to design curricula that react to, and anticipate, complexity and change and make a curriculum more adaptive, flexible or responsive. The aim of the current study is to enhance understanding of the responsiveness of teacher education curricula. A curriculum is a plan for learning and it organizes, sequences and stimulates learning experiences in school and in practice (Billett, 2011). We consider a curriculum as responsive when it takes individual differences and needs of learners into account, is adaptive to a variety of contexts in occupational practice and anticipates change in society (Author, 1995; Turkenburg & Vogels, 2017). A curriculum includes different levels (micro, meso), design dimensions (epistemic, social, instrumental, temporal and spatial) and actors (students and their supervisors) (Author, 2019; Van den Akker, 2013). In this participative design research a variety of design initiatives in five bachelor science teacher education programs (ISCED 6) are included. Data sources were documents, participatory observations of organized design sessions, project meetings, and just-in-time interviews. A logbook was used to keep track of the initiatives and to reflect on design activities including creative leaps. The data was analyzed qualitatively using templates. The results are expected before the Jure conference in August 2021.
Sentiment Analysis Using FFBP Neural Network for Profit of Commercial Products in Industry

From its inception in the early 2000s to the present, sentiment analysis and opinion mining have taken several twists and turns. People are increasingly using social media sites to voice their opinions and communicate with others who share common ideas, thanks to advancements in technology, smartphone and internet networks, and ease of access to these services. As a result, a vast volume of data has been generated on the internet, necessitating the need to analyse it Sentiment research aids various organisations in determining how customers perceive their goods and services, as well as what improvements are needed to enhance them. The paper uses an inbuilt python library called Text Blob to perform sentiment analysis, which is the classification of tweets into positive, negative, and neutral on views on a certain product, for three platforms: twitter, Facebook, and news websites. It also discusses how Artificial Neural Networks (ANN) provide a platform to perform sentiment analysis in a much easier and therefore less time consuming process.In this article, feed-forward back propagation neural networks (FFBPNN) were used to split the task into training data, and a min-max method has been used to measure the information and analysing the sentiment accuracy rate with ANN. To have a quantitative approach to the findings and quantify the success of ANN, precision, memory, and accuracy were measured. We discovered that this kind of neural network is very effective at accurately predicting the outcome.
Data Portability in a Data-Driven World

The chapter explores the concept of data portability in a data-driven world. In the first part, it maps out the journey data takes in a data economy and investigates the valuation and cost of data. It posits that, because of data analytics, machine learning, and artificial intelligence models, “generated” data, as data that has been derived or inferred from “raw” data, is of higher value in the data market, and carries a higher cost of production. In the second part, building on this taxonomy, the requirements for the free flow of data in competitive data-centric markets are discussed: regulations on data tradability and portability. The analysis leads to doubt that the newly introduced and widely debated rules regarding portability of data under European Union law will adequately provide these prerequisites. The chapter concludes by suggesting an alternative model for data portability: rather than distinguishing between personal and non-personal data, the legal concept of data portability should be based on the allocation of value; that is, whether the value of data provides cost compensation to the service provider. Raw data should always be portable, while generated data may require a more refined regime depending on whether it is compensating costs.
Past Matters: Supporting LTL+Past in the BLACK Satisfiability Checker

LTL+Past is the extension of Linear Temporal Logic (LTL) supporting past temporal operators. The addition of the past does not add expressive power, but does increase the usability of the language both in formal verification and in artificial intelligence, e.g., in the context of multi-agent systems. In this paper, we add the support of past operators to BLACK, a satisfiability checker for LTL based on a SAT encoding of a tree-shaped tableau system. We implement two ways of supporting the past in the tool. The first one is an equisatisfiable translation that removes the past operators, obtaining a future-only formula that can be solved with the original LTL engine. The second one extends the SAT encoding of the underlying tableau to directly support the tableau rules that deal with past operators. We describe both approaches and experimentally compare the two between themselves and with the νXmv model checker, obtaining promising results.
The role of finance in inclusive human development in Africa revisited

This study investigates direct and indirect linkages between financial development and inclusive human development in data panels for African countries. It employs a battery of estimation techniques, notably: Two-Stage Least Squares, Fixed Effects, Generalized Method of Moments and Tobit regressions. The dependent variable is the inequality adjusted human development index. All dimensions of the Financial Development and Structure Database (FDSD) of the World Bank are considered. The main finding is that financial dynamics of depth, activity and size improve inclusive human development, whereas the inability of banks to transform mobilized deposits into credit for financial access negatively affects inclusive human development. Policies should be tailored to improve mechanisms by which credit facilities can be provided to both households and business operators. Surplus liquidity issues resulting from the inability of banks to transform mobilized deposits into credit can be resolved by enhancing the introduction of information sharing offices (like public credit registries and private credit bureaus) that would reduce information asymmetry between lenders and borrowers. This study complements the extant literature by assessing the nexus between financial development and inclusive human development in Africa.
Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agents

Enrollment in online courses has sharply increased in higher education. Although online education can be scaled to large audiences, the lack of interaction between educators and learners is difficult to replace and remains a primary challenge in the field. Conversational agents may alleviate this problem by engaging in natural interaction and by scaffolding learners’ understanding similarly to educators. However, whether this approach can also be used to enrich online video lectures has largely remained unknown. We developed Sara, a conversational agent that appears during an online video lecture. She provides scaffolds by voice and text when needed and includes a voice-based input mode. An evaluation with 182 learners in a 2 x 2 lab experiment demonstrated that Sara, compared to more traditional conversational agents, significantly improved learning in a programming task. This study highlights the importance of including scaffolding and voice-based conversational agents in online videos to improve meaningful learning.
An Empirical Study on College Selection of High School Students

Since 2019, the number of high school graduates in Korea has been smaller than the college admission capacity. Thus, it has been suffering from a shortage of college students. Additionally, as many young people have difficulty finding jobs after graduating from college, there is a wide variation in the admission competition rate according to the employment rate. This phenomenon will intensify in the near future, and measures are needed to resolve it. This paper aims to empirically analyze data on the college and major selections of high school students. In the existing research, the high school students college and major choices were analyzed through questionnaire surveys. However, in this paper, an empirical study on college and major selections is conducted using actual statistical data. The results of this paper are expected to be helpful for two-year colleges in attracting new students.
Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization.

Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named \texttt{SPBOpt}. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.
Data and Systems Heterogeneity: Analysis on Data, Processing, Workload, and Infrastructure

This paper is our survey toward a general understanding of the requirements for handling large volumes of heterogeneous data, and moreover, presents an overview of the employed computing techniques and technologies necessary for analyzing and processing those datasets. As of our attempt to picture how the data heterogeneity meets the systems heterogeneity, we summarize the identified key issues for multiple dimensions, including data, processing, workload, and infrastructure.
A STUDY ON THE DIFFICULTIES IN PRONUNCIATION OF ESL LEARNERS

In many ESL classes across the globe, English pronunciation is still not given enough attention. One of the primary reasons why it is overlooked is because there arent many pronunciation techniques accessible to instructors in the context of the school setting. The purpose of this research is to identify the problems in English pronunciation that learners experience as a result of the presence of Mother Tongue (MT) interference in their speech. In this article, the survey technique was used to gather information from a variety of sources, including interviews, personal conversations over the phone, and meetings. The report also included suggestions and recommendations for reducing the pronunciation difficulties that ESL learners encounter. There were a total of 102 samples gathered through Google forms in India. The findings of the study are discussed in detail in the interpretation.
Clustering Data Disabilitas menggunakan Algoritma K-Means di Kabupaten Cirebon

The Cirebon Regency Government at the end of every year always carries out social assistance counseling activities. One of the targets of counseling is to people with disabilities. Because the data for people with disabilities in Cirebon Regency is quite large, it is necessary to group them by area that has residents with people with disabilities. The K-Means method is the right method for classifying data on persons with disabilities. The grouping results are measured by the Davies Bouldin index value in order to know the optimization results of the K-Means algorithm. The results show that the largest group is based on the number of people with disabilities, so cluster_0 : low group with 50 cases, cluster_2 : medium group with 53 cases, and cluster_1 : large group with 122 cases. Cluster_0 consists of sub-districts Waled, Ciledug, Losari, Karangsembung, Lemahabang, Susukan Lebak, Pangenan, Beber, Palimanan, Plumbon, Kedawung, Klangenan, Panguragan, Gegesik, Kaliwedi, Depok, Pabuaran, Karangwareng, Tengah Tani, Plered, Greged, and Suranenggala Cluster_1 consists of the districts of Pabedilan, Babakan, Sedong, Astanajapuran, Mundu, Talun, Sumber, Dukuntang, Weru, Gunungjati, Kapetakan, Arjawinangun, Ciwaringin, Articleeman, Gempol and Jamblang. And cluster_2 consists of Susukan and Gebang sub-districts.
Data protection by design in AI? The case of federated learning

This article investigates some of the data protection implications of an emerging privacy preserving machine learning technique, i.e. federated machine learning. First, it shortly describes how this technique works and focuses on some of the main security threats it faces. Second, it presents some of the ways in which this technique can facilitate compliance with certain principles of the General Data Protection Regulation as well as some of the challenges it may pose under the latter.
Analisa Angkutan Sedimen Pada Hulu Bendung Aepodu Kabupaten Konawe Selatan

Research on Aepodu Weir Sediment Transport Analysis in South Konawe District, based on observations in the field, Aepodu Weir hasa sediment buildup that has now exceeded the height of the weirlight house. The purpose of the study was to analyze the magnitudeof Aepodu river flow and to analyze the amount of sedimenttransport that occurred in the Aepodu dam. The method used todetermine the amount of bed load transport uses stchoklitscht, whilefor transporting suspended load using forcheimer.The results of the analysis of the average flow of the Aepodu riverwere 3,604 m3/ second. Sediment transport that occurs in Aepoduweir is Bedload transport (Qb) of 291625.771 tons / year, andsuspended load transport (Qs) of 16972,423 tons / year, so that thetotal sediment transport (QT) is 308598,194 tons / year.