Reviewerly/exHarmony huggingface dataset:
Classification of Sleep Videos Using Deep Learning

Videosomnography (VSG) is a group of video-based methods used to record and label sleep versus awake states in humans. Traditional behavioral-VSG (B-VSG) labeling requires visual inspection of the video by a trained technician to determine whether a subject is sleep or awake. B-VSG is not used to label sleep stages (e.g., slow wave or REM sleep), rather it solely labels whether a subject is asleep or awake at a particular time. In this paper we describe an automated VSG sleep detection system which uses deep learning approaches to label frames in a sleep video as "sleep" or "awake" in young children. We examine 3D Convolutional Networks (C3D) and Long Short-term Memory (LSTM) relative to motion information from selected Groups of Pictures of a sleep video and test temporal window sizes for back propagation. We compared our proposed VSG methods to traditional B-VSG sleep-awake labels. C3D had an accuracy of approximately 90% and the proposed LSTM method improved the accuracy to more than 95%. The analyses revealed that estimates generated from the proposed LSTM-based method with long-term temporal dependency are suitable for automated sleep or awake labeling.
Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network

Fingerprint classification is a significant process by which identification procedure can be accelerated. Feature extraction might be afflicted with rotation. Thus, all images get through an introduced criterion to rectify rotated images. The core point of fingerprints is utilized widely in both classification and recognition process. In some cases, however, inaccurate location of it might contribute to incorrect categorization. Therefore, the common point is initiated for the purpose of better performance. Features are extracted according to the way ridges’ angles are distributed across images. Plus, kernel smoothing technique is used to enhance the process. Generalized regression neural network (GRNN) and Probabilistic neural network (PNN) are employed to classify fingerprints in four categories. Fingerprint verification competition (FVC) database is used to evaluate and train the networks. The simulation is performed by MATLAB and 97.4% accuracy is achieved for both GRNN and PNN.
Zap Q-Learning for Optimal Stopping Time Problems

The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$. We build on the dynamic programming approach taken by Tsitsikilis and Van Roy, wherein they propose a Q-learning algorithm to estimate the optimal state-action value function, which then defines an optimal stopping rule. We provide insights as to why the convergence rate of this algorithm can be slow, and propose a fast-converging alternative, the "Zap-Q-learning" algorithm, designed to achieve optimal rate of convergence. For the first time, we prove the convergence of the Zap-Q-learning algorithm under the assumption of linear function approximation setting. We use ODE analysis for the proof, and the optimal asymptotic variance property of the algorithm is reflected via fast convergence in a finance example.
LOCC protocols with bounded width per round optimize convex functions

We start with the task of discriminating finitely many multipartite quantum states using LOCC protocols, with the goal to optimize the probability of correctly identifying the state. We provide two different methods to show that finitely many measurement outcomes in every step are sufficient for approaching the optimal probability of discrimination. In the first method, each measurement of an optimal LOCC protocol, applied to a [Formula: see text]-dimensional local system, is replaced by one with at most [Formula: see text] outcomes, without changing the probability of success. In the second method, we decompose any LOCC protocol into a convex combination of a number of “slim protocols” in which each measurement applied to a [Formula: see text]-dimensional local system has at most [Formula: see text] outcomes. To maximize any convex functions in LOCC (including the probability of state discrimination or fidelity of state transformation), an optimal protocol can be replaced by the best slim protocol in the convex decomposition without using shared randomness. For either method, the bound on the number of outcomes per measurement is independent of the global dimension, the number of parties, the depth of the protocol, how deep the measurement is located, and applies to LOCC protocols with infinite rounds, and the “measurement compression” can be done “top-down” — independent of later operations in the LOCC protocol. The second method can be generalized to implement LOCC instruments with finitely many classical outcomes: if the instrument has [Formula: see text] coarse-grained final measurement outcomes, global input dimension [Formula: see text] and global output dimension [Formula: see text] for [Formula: see text] conditioned on the [Formula: see text]th outcome, then one can obtain the instrument as a convex combination of no more than [Formula: see text] slim protocols; that is, [Formula: see text] bits of shared randomness suffice.
Improving Mammography Quality Through EQUIP.

On January 1, 2017, the U.S. Food and Drug Administration launched an initiative to improve mammography image quality. This initiative, called the Enhancing Quality Using the Inspection Program, or EQUIP, entails a series of questions that have become part of annual mammography facility inspections required under the Mammography Quality Standards Act. This article examines the EQUIP initiative, discusses the initiative's early results, and explores the criteria for quality mammography images, with a focus on optimal positioning and compression.
Properties of interval-valued intuitionistic fuzzy vector spaces

In this paper we introduced and studied the properties of interval-valued intuitionistic fuzzy vector spaces (in brief IVIF space) and its IVIF standard basis.We use the concept of max-union and min-intersection algebra to define interval-valued ituitionistic fuzzy vector space.
Spatial-Temporal Recommendation for On-demand Cinemas

The on-demand cinema is an emerging offline entertainment venue, guiding a new mode of watching movies in recent years. As a breakthrough in the development of the post-movie industry, the on-demand cinemas rely on private booths, high-quality hardware and rich movie resources to provide audiences with new and fresh watching experiences. The recommendation system for on-demand cinemas is to recommend to cinemas movies that may be of interest to their potential audiences, and provide an individualized recommendation service for preparing movie storage of on-demand cinemas to meet the audiences’ preferences and instant watching needs. The characteristics implied in the audience behaviors of on-demand cinemas make the recommendation method for them different from those for online videos, items in offline stores or a group of users. In this paper, we describe the challenges and build a system for this application scenario, which fuses the historical on-demand records of cinemas, the POI (Point of Interest) information around cinemas and the content descriptions of movies, and explores the temporal dynamics and spatial influences rooted in audience behaviors. A WeChat applet customized for on-demand cinema staffs/hosts, as the client of our system, has been put into in practice.
Self Efficacy of Pre-Service English Teachers in Using English as a Language Instruction

Field Experience Program or Praktek Pengalaman Lapangan (PPL) are carried out by students to fulfill the requirements for the formation of the education profession. But in the process of PPL, the pre-service English teachers of English Language Education Study Program at the University of PGRI Ronggolawe Tuban often experience problems related to their efficacy when teaching in English as language of instruction.This study aims to determine the efficacy of English Language Education students who run the Field Experience Program (PPL) in using English as the language of instruction, as well as what factors influence their efficacy in using English as the language of instruction. It is a phenomenological qualitative research which the choice of subject or data source was done by purposive sampling which focused on selected informants who were appropriate for in-depth studies (Nana Syaodih, 2007: 101). The subjects are pre-service English teachers who had finished undergoing PPL in the period between November 2017 to January 2018. Data collection techniques used are observation, interviews, and documentation, and using descriptive inductive analysis.The results of this study reveal that self-efficacy of all pre-service English teachers (six students) as subjects in this study in terms of the use of English as the language of instruction during PPL has varied self-efficacy on dimensions of magnitude, strength and generality. Only 1 out of 6 students had negative self-efficacy in all dimensions, and 1 (one) person showed positive self-efficacy in all dimensions; magnitude, strength and generality. While the others vary for all three dimensions. Those six students used both English and Indonesian in teaching English. The reason was because they were not confident (2 people), and for the emphasis on certain material delivered so that it is more easily understood (4 people).This study found that the factors of success experience and psychological factors were the strongest in forming self-efficacy of the 6 PPL pre-service teachers in using English as the language of instruction during field experience program. Furthermore, it is only slightly influenced by other people's success factors and social persuasion. The more experience and the better the psychological condition of students, the more positive their self-efficacy. Two students, namely Mg and Su, have negative self-efficacy due to the lack of teaching experience and poor psychological conditions because they felt uncomfortable and underappreciated in the school where they carried out the field experience program.
INCREASING STUDENTS’ VOCABULARY ACQUISITION BY MASTER AND MASTERY CARDS GAME

The objective of this research is to explain how the Master and Mastery cards game improves students’ vocabulary acquisition and participation learning process of applying Master and Mastery cards game in teaching vocabulary for tenth-grade students of a vocational school. This research was carried out with a classroom action research design. Field notes were the instruments to collect the qualitative data. While the vocabulary test and gained the quantitative data that strengthen the quantitative data. The increasing of the test 1 and test 2 was 22.83 point. Responding to that result the teacher-researcher decided to stop the cycle at cycle two. According to the researcher and co-observer’s observation students participated actively while playing Master and Mastery vocabulary cards game. This research significantly increased students’ vocabulary acquisition as well as participation.Keywords: Master and Mastery Cards Game, Vocabulary Acquisition, Participation
PERANCANGAN MULTIMEDIA INTERAKTIF UNTUK MATERI PERLINDUNGAN ANAK TERHADAP PELECEHAN SEKSUAL BERBASIS MOBILE

Children have an important role as the next generation of nation's development. Indicating to the roles of children for the future of the nation, so it is deemed necessary to get a chance for them to be best growing. The phenomenon of sexual harassment of children is higest increasingly threathen the growth and development of children. This is due to the lack of knowledge of parents and most of children have less knowledge to prevent sexual harassment. For that purpose, an interactive multimedia android-based application for child protection against sexual harassment has made that can provide danger information and how to prevent child sexual abuse. Applications intended to children of aged 6-12 years. Interactive Multimedia are developed through the stages of Decide, Design, Develop dan Evaluate (DDD-E). In the test results of Black-box testing on mobile devices obtained general results functionality of the application is running as are expected.
Live Demonstration: A Temperature Sensor with Analog Encryption

Compact, low power sensors are being used for many applications such as IoT and bio-medical sensors. However, most sensors do not incorporate a layer of security. Chaotic Shift Keying is a type of encryption that masks data using a chaotic system that is highly sensitive to initial conditions and parameters. Combining a low power CMOS digital output temperature sensor with a chaotic stream cipher, a layer of security is added to the temperature's data. This live demonstration showcases a low power, real-time sensing system in which the temperature sensor's digital data of the current temperature is encrypted and decrypted.
Developing Computer–Based Instructional Media for English Speaking Skill at Senior High School

This research aimed at developing computer-based instructional media using Adobe Flash for the tenth graders’English speaking skill material. The studyemployed research and development method. The results showed (1) the product is a computer-based instructional media in a combined format of tutorial and drilling facilitating semiinteractive learning consisting of thanking, complimenting, congratulating, surprise and disbelief materials; (2) the expert validation showed that the product quality in terms of media is categorized very good (4.51) and in terms of the material and instructionalare categorized good (3.91). The results of the field trials showed that the quality of the product in terms of material, media, and instructional is categorized very good (4.32). In conclusion, the developed media is eligible to be implemented in speaking learning activities. Keywords: computer-based instructional media; Adobe Flash; speaking skill
Use of the Naive Bayes Function and the Models of Artificial Neural Networks to Classify Some Cancer Tumors

This study is concerned with the methods of classification and separation of observations and the use of two methods: of classification Naive Bayes, using the function of Kernell, and models of artificial neural networks using a function to show the step or sample in order to classify three types of cancer tumors (bone, lung, and breast). For a simple random sample of 150 patients, Naive Bayes was the correct classification of data for 67%, while in the method of neural network models, it was 86%. The model of artificial neural networks was found to be the best in classifying views from the Naive Bayes classification. The correct classification of bone cancer is the highest, followed by breast cancer and lung cancer. The relative importance of the variable age was the highest, then sex, and then the period of survival of the patient, and then the profession of the patient, and then the state of exit of the patient.
SUGGESTION ACTS PERFORMED BY ENGLISH INSTRUCTORS OF CONVERSATION CLASS IN LANGUAGE CENTRE OF ANDALAS UNIVERSITY

This study aimed to analyze analyzes suggestion acts that were spoken by English instructors in conversation classes at the Andalas University Language Center. The purposes purposes of this study are are to identify and explain explain the types of speech acts, taxonomies, modifications, and reasons of the instructor expressing the speech. This study is descriptive study which data are taken from two conversation class instructors in language centre of Andalas University This study is descriptive study which data are taken from two conversation class instructors in language centre of Andalas University. The data are in the form of transcriptions, research notes, and interviews. The data are in the form of transcription, research notes, and interviews. The data are in the form of The data are in the form of transcription, research notes,, and interviews. The instruments used are observation sheets, audio recordings,, and interview guidelines. The findings indicate indicate that the two instructors expressed different suggestions at each meeting. The highest intensity of appearance of suggestion acts is is seen in the first meeting of the first instructor and the third meeting of the second instructor. Furthermore, the two instructors also express express the same type of taxonomy/suggestion acts as conventionalized forms. Then, a similar fact is is found from the modified aspect, namely subjectivizer. The reasons reason why the instructor expresses suggestions are are influenced by social distance, power,, and imposition. Based on these findings, it is is implied that the suggestion actions action taken by the instructors instructors are are influenced by the culture of the community and the class context.
Performance evaluation of sampling-based large-scale clustering algorithms

Using benchmark datasets, we study the performances of three efficient clustering algorithms which find cluster centers using a fixed number of random samples. The algorithms are also compared with two other (well-known) algorithms, namely k-means and PAM. One of the efficient algorithms, CLARA, is well-known while the other two, k-means-lite and PAM-lite, were introduced recently. CLARA and PAM-lite are based on the k-medoids approach, while k-means-lite adopts the k-means approach. The study shows that k-means-lite is the most efficient, followed by PAM-lite which is faster than CLARA. PAM-lite exhibits the best balance of efficiency and accuracy; it produces the most competitive results relative to PAM which is the most accurate but most inefficient.
Advances in HDR Display Technology for Cinema Applications, Including Light‐Steering Projection

Abstract Cinematic display solutions are catching up with consumer display technologies' capabilities in both brightness and contrast. However, a mainstream rollout of HDR in cinema requires a high‐performing and cost‐effective solution. In this article, the first of a two‐part series, the authors review solutions being developed and performance specifications suggested for HDR on the big screen.
Perbandingan Fitur Smartphone, Pemanfaatan Dan Tingkat Usability Pada Android Dan iOS Platforms

Android dan iOS nowadays have become the most dominating operating system in smartphone markets. These two operating system continue to grow rapidly in terms of technology as well as the number of devices that exist in the world. However, consumers are still lacking of understanding on how to compare these two operating systems from various of aspects. So, this research is being conducted with the purpose to learn about the comparison of these two operating system in terms of features, utilizations and usability. Method that is being used in this research is descriptive analysis which in comparison of features and utilizations are using documentation and observation as data collection technique. For usability comparison is using questionnaire technique. Result of the research has shown that Android and iOS each have their unique features such as Google Hangouts with i-Message and Smart Caller ID with Facetime. Afterwards, comparison of utilizations has shown that Android is mostly being used for communication as for iOS more for information searching. Meanwhile, for descriptive result of usability has shown that Android and iOS have the same level of ease of use which is good.
Context-Dependent Semantic Parsing over Temporally Structured Data

We describe a new semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface. Multiple time series belonging to an entity of interest are stored in a database and the user interacts with the system to obtain a better understanding of the entity's state and behavior, entailing sequences of actions and questions whose answers may depend on previous factual or navigational interactions. We design an LSTM-based encoder-decoder architecture that models context dependency through copying mechanisms and multiple levels of attention over inputs and previous outputs. When trained to predict tokens using supervised learning, the proposed architecture substantially outperforms standard sequence generation baselines. Training the architecture using policy gradient leads to further improvements in performance, reaching a sequence-level accuracy of 88.7% on artificial data and 74.8% on real data.
AI Deep Learning with Multiple Labels for Sentiment Classification of Tweets

We introduce an incremental transfer learning pipeline for AI systems for ordinal classification based on multiple labels of Tweets. In this pipeline, 5 sub-models are trained and the incremental knowledge is transferred to achieve higher model complexity and better performance. Each training example has multiple labels, with a target label for each sub-model. The first sub-model is a polarity classification model for negative, neutral, and positive sentiments. The second and third sub-models are ordinal classification models for positive and negative sentiments. The fourth sub-model is a binary classification model for neutral sentiment. The last sub-model is a seven-class model for polarity and intensity classes. The proposed method is applied on the Semantic Evaluation 2018 Task 1 Affects in Tweets Subtask V-oc (ordinal classification task). We experiment with 5 word embeddings, and apply ensemble methods to combine their outputs to boost overall performance. We use weighted average and stacking technique on the proposed systems and the DeepMoji model which is retrained for transfer learning. We achieve a Pearson correlation coefficient of 0.806 on the test data of SemEval-2018, which would have ranked the 4th in the SemEval-2018 Task 1 Subtask V-oc.
Disciplinary, Asynthetic, Domain-Dependent: NARCIS a National Research Classification in Isolation

NARCIS, the National Academic Research and Collaborations Information System, is the national research portal of the Netherlands. NARCIS is governed by a knowledge organization system—a classification—by the same name. For a variety of reasons—a disciplinary base, a lack of synthesis, and domain-dependency—the NARCIS classification is highly compartmentalized and therefore inhospitable for interoperability. In addition, the classification has been revised repeatedly leading to the problems of scheme-versioning and subject ontogeny.
Continual and Multi-task Reinforcement Learning With Shared Episodic Memory

Episodic memory plays an important role in the behavior of animals and humans. It allows the accumulation of information about current state of the environment in a task-agnostic way. This episodic representation can be later accessed by down-stream tasks in order to make their execution more efficient. In this work, we introduce the neural architecture with shared episodic memory (SEM) for learning and the sequential execution of multiple tasks. We explicitly split the encoding of episodic memory and task-specific memory into separate recurrent sub-networks. An agent augmented with SEM was able to effectively reuse episodic knowledge collected during other tasks to improve its policy on a current task in the Taxi problem. Repeated use of episodic representation in continual learning experiments facilitated acquisition of novel skills in the same environment.