Reviewerly/exHarmony huggingface dataset:
Low Computational Cost Machine Learning

According to recent reports, over the course of 2018, the volume of data generated, captured and replicated globally was 33 Zettabytes (ZB), and it is expected to reach 175 ZB by the year 2025. Managing this impressive increase in the volume and variety of data represents a great challenge, but also provides organizations with a precious opportunity to support their decision-making processes with insights and knowledge extracted from massive collections of data and to automate tasks leading to important savings. In this context, the ﬁeld of machine learning has attracted a notable level of attention, and recent breakthroughs in the area have enabled the creation of predictive models of unprecedented accuracy. However, with the emergence of new computational paradigms, the ﬁeld is now faced with the challenge of creating more eﬃcient models, capable of running on low computational power environments while maintaining a high level of accuracy. This thesis focuses on the design and evaluation of new algorithms for the generation of useful data representations, with special attention to the scalability and eﬃciency of the proposed solutions. In particular, the proposed methods make an intensive use of randomization in order to map data samples to the feature spaces of polynomial kernels and then condensate the useful information present in those feature spaces into a more compact representation. The resulting algorithmic designs are easy to implement and require little computational power to run. As a consequence, they are perfectly suited for applications in environments where computational resources are scarce and data needs to be analyzed with little delay. The two major contributions of this thesis are: (1) we present and evaluate eﬃcient and data-independent algorithms that perform Random Projections from the feature spaces of polynomial kernels of diﬀerent degrees and (2) we demonstrate how these techniques can be used to accelerate machine learning tasks where polynomial interaction features are used, focusing particularly on bilinear models in deep learning.
THE INCREASING THE INTRODUCTION OF BALANCED NUTRITIOUS FOOD THROUGH COOKING CLASS ACTIVITY ON CHILDREN OF 3-4 YEARS OLD AT PPT PELANGI KECAMATAN PAKAL SURABAYA

The research purpose is to increase the introduction of balanced nutritions food. The research method used was Classroom Action Research (CAR). The researcher subjects were 22 children of 3-4-year-old children at PPT Pelangi, Pakal, Surabaya. Data collection techniques included: planning, implementation, observation, and reflection. This research consisted of pre-cycle, cycle I and cycle II with 2 meetings/ cycle. The research result showed that in the pre-cycle, it obtained 46,6 %, there was An increase in the first cycle of 61,4 %, it showed an increase in the number of children who developed well, although it was not maximally yet, in the second cycle, It increased as 83 % it showed an increase in the percentage of development with very good growth.&#x0D;
Attitudes Towards Blended Learning : A Study of Leadership Training Education (Diklat PIM) IV Year 2018 Training Participants

The advancement of information technology is one of the driving factors for implementing e-Government. Nevertheless, the learning process in government official capacity development is now conceivably conducted online. This research aims to discover government officials' attitudes towards blended learning in a population of 40 respondents of Leadership Training Education or Diklat PIM IV in 2018. This research is conducted with a quantitative methodology approach. The T-test used to test the first research hypotheses. A one-way ANOVA was performed for examining the second and third hypotheses. The result shows there is a favorable attitude towards blended learning among the Diklat PIM IV 2018 training participants. There are significant differences in attitudes toward blended learning based on gender but there is no significant difference in attitudes towards blended learning based on age classification.
Reverse Tracking Graph Based on Dynamic Path Planning

This paper gives substance to Dynamic Path Planning focusing on reverse tracking method. The development of this method are proposed and expected to reduce the algorithm scanning the whole graph repeatedly. In this paper, several approachment will be presented sequentially. First, analysis and modeling of the obstacle and environment, pre-path planning, Depth First Search for availability path planning, and improvement of the Dijkstra algorithm for the shortest path. There-in the proposed model is defined by adopting the reverse feature in the Depth First Search algorithm in the finding of the availability path on the graph.
Vision-Language Navigation with Random Environmental Mixup

Vision-language Navigation (VLN) tasks require an agent to navigate step-by-step while perceiving the visual observations and comprehending a natural language instruction. Large data bias, which is caused by the disparity ratio between the small data scale and large navigation space, makes the VLN task challenging. Previous works have proposed various data augmentation methods to reduce data bias. However, these works do not explicitly reduce the data bias across different house scenes. Therefore, the agent would overfit to the seen scenes and achieve poor navigation performance in the unseen scenes. To tackle this problem, we propose the Random Environmental Mixup (REM) method, which generates cross-connected house scenes as augmented data via mixuping environment. Specifically, we first select key viewpoints according to the room connection graph for each scene. Then, we cross-connect the key views of different scenes to construct augmented scenes. Finally, we generate augmented instruction-path pairs in the cross-connected scenes. The experimental results on benchmark datasets demonstrate that our augmentation data via REM help the agent reduce its performance gap between the seen and unseen environment and improve the overall performance, making our model the best existing approach on the standard VLN benchmark. The code have released: https://github.com/LCFractal/VLNREM.
Demand Analysis of Online Chinese Behavior Expression in Wireless Sensor Network

Due to the common progress and interdependence of wireless sensor networks and language, Chinese semantic analysis under wireless sensor networks has become more and more important. Although there are many research results on wireless networks and Chinese semantics, there are few researches on the influence and relationship between them. Wireless sensor networks have strong application relevance, and the key technologies that need to be solved are also different for different application backgrounds. In order to reveal the basic laws and development trends of online Chinese semantic behavior expression in the context of wireless sensor networks, this paper adopts big data analysis methods and semantic model analysis methods and constructs semantic analysis models through PLSA method calculations, so that the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="bold-italic">λ</mi> </math> construction process conforms to this research topic. Research the accuracy and applicability of the semantic analysis model. Through word extraction of 1.05 million word data of 1,103 documents on Baidu Tieba, HowNet, and citeulike websites, the data set was integrated into a data set, and the PLSA model was verified with this data set. In addition, through the construction of the wireless sensor network, the semantic analysis results in the expression of Chinese behavior are obtained. The results show that the accuracy of the data set extracted from 1103 documents increases with the increase of the number of documents. Second, after using the PLSA model to perform semantic analysis on the data set, the accuracy of the data set is improved. Compared with traditional semantic analysis, the model and the big data analysis framework have obvious advantages. With the continuous development of Internet big data, the big data methods used to count Chinese semantics are also constantly updated, and their efficiency is constantly improving. These updated semantic analysis models and statistical methods are constantly eliminating the uncertainty of modern online Chinese. The basic laws and development trends of statistical Chinese semantics also provide new application scenarios for online Chinese behavior. It also laid a ladder for subsequent scholars.
PENGARUH MODEL PEMBELAJARAN JOYFULL LEARNING TERHADAP KEMAMPUAN MENULIS CERITA FANTASI MELALUI VIRTUAL ONLINE OLEH SISWA KELAS VII SMP NEGERI 2 SIPOHOLON TAHUN AJARAN 2020/2021

This study aims to determine the effect of the Joyfull learning model on the ability to write fantasy stories by class VII students of SMP Negeri 2 Sipoholon in the 2020/2021 Academic Year. The population in this study amounted to 110 people consisting of 4 classes namely VII-1, VII-2, VII-3, and VII-4. The sample in this study amounted to 28 people who were taken by a simple random sampling process.The research method used in this research is the experimental method. Hypothesis testing is done by using the t test. From data processing, the mean value of Pre-test = 61.07, standard deviation = 8.43, standard error = 1.62. In the post-test, the average value = 77.67, standard deviation = 8.18, standard error = 1.57. Based on the normality test, the results of the pre-test and post-test were normally distributed. The value of F is obtained from the table with a level of significance = 0.05 or F_0 (n1 – 1n2 - 1) F_count t_table , which is 7.37 > 2.05.Thus H_0 (nil hypothesis) is rejected and H_a (alternative hypothesis) is accepted. This proves that the joyfull learning model has an effect on the ability to write fantasy stories by class VII students of SMP Negeri 2 Sipoholon for the 2020/2021 Academic Year.
English Teachers’ Perception on English Teacher’s Classroom Management at SMK Negeri 1 Pariaman

This is a qualitative research design.The purposes of this research are to analyze of the English Teachers' Perception on English teachers' Classroom Management at SMK Negeri 1 Pariaman and to find out factors that influence the perception of English teacher's Classroom Management at SMK Negeri 1 Pariaman.Descriptive qualitative research is used by this research.The English teachers of SMK Negeri 1 Pariaman are the sample of this research.To analyze and identify of English teachers' perception on English Teacher's Classroom Management at SMK Negeri 1 Pariaman.This research used two instruments, they are video recording and field note.The techniques of collecting the data used by the researcher are interview, observation, documentation.Then the data will be analyzed by using descriptive qualitative strategy.The result showed that the teacher's language within the room used English and Indonesian.She used English for regarding 70%, then she translated it into Indonesian.The students' language within the room used Indonesian for regarding 50%.Students' mistakes were usually in synchronic linguistics, pronunciation, vocabulary and language use.The teacher roles within the teaching and learning method were as a planner, informer, and monitor.
Enhancement of Night Time Video using Dark Channel Prior IP Accelerator

The enhancement of night-time video using Dark Channel Prior IP accelerator is proposed in this paper. Night time video processing is difficult due to low brightness, low contrast and high noise in the video. The above problems affect the accuracy and may results in failures of object detection in night time video. Dark Channel Prior (DCP) filter is used to improve the visibility, brightness and contrast of the video at night time. Processing speed is challenging task on real time application of DCP algorithm for night time video enhancement. Hence, DCP algorithm is implemented on FPGA (ALVEO Board) to increase the speed of video processing.
SVA Library Research Guides: Online Resources and Services: Further Support from Public Libraries

The following guide outlines the collections, services, and support available remotely to students, faculty, and staff.
MODEL OF TIME MANAGEMENT TECHNOLOGY IN THE PROCESS OF PROFESSIONAL TRAINING OF THE FUTURE TEACHER OF MUSIC ART

The result of study in a modern institution of higher art education is focused on the personal and professional growth of the student through the development of his independence. However, personal development is inseparable from professional. In both the first and the second case, the principle of self-development is the basis. Here the ability of the person to prepare own vital activity for practice-oriented transformation which is a basis of self-realization of the person, its progressive self-development is important. In the course of self-organization there are changes in life, and the ability to develop it affects the formation of readiness to meet the requirements of the time. Here an important part of professional training in higher art education is the formation of motivation in future teachers of music art, because the activity of students is determined by internal reserves, motives, needs, desires, abilities. The technology of time management in the development of professional competencies of future music teachers is aimed at the rational use of strength and health of students, to increase the level of personal self-organization. In terms of technology, the teacher's activities include the following actions: the first stage - the motivation of students and the creation of optimal conditions for a clear distribution of the budget of educational time of self-organization; the second stage - the motivation of future teachers of music to self-diagnose the development of their individual tempo-rhythmic characteristics of self-organization (speed, accuracy, tempo, rhythm of work performed); the third stage - the motivation of future teachers of music to plan educational activities, the ability to determine the sequence of implementation of the planned program of action in the process of self-preparation, as well as the ability to analyze, evaluate and monitor the results of independent work. Time management technology contributes to the design of an intensive path of self-educational activities based on a system of methods and regulators of activity, the algorithm of rational use of time; theoretical prerequisites for this technology were: the law of saving time, the principles of scientific organization of labor, the theory of temporal dynamics of human life, individualized models of educational time (optimal, developmental, functional and pedagogical), activation of cognitive activity (algorithmization of educational activities, programmed learning, time methods) -management), the idea of saving and rational use of time, its subjective organization, issues of work, rest, etc., this technology includes consideration of the scientific component, based on the achievements of pedagogical theory and best practices.
Context-Aware Unsupervised Clustering for Person Search

The existing person search methods use the annotated labels of person identities to train deep networks in a supervised manner that requires a huge amount of time and effort for human labeling. In this paper, we first introduce a novel framework of person search that is able to train the network in the absence of the person identity labels, and propose efficient unsupervised clustering methods to substitute the supervision process using annotated person identity labels. Specifically, we propose a hard negative mining scheme based on the uniqueness property that only a single person has the same identity to a given query person in each image. We also propose a hard positive mining scheme by using the contextual information of co-appearance that neighboring persons in one image tend to appear simultaneously in other images. The experimental results show that the proposed method achieves comparable performance to that of the state-of-the-art supervised person search methods, and furthermore outperforms the extended unsupervised person re-identification methods on the benchmark person search datasets.
All-photonic architectural roadmap for scalable quantum computing using Greenberger-Horne-Zeilinger states

Linear optical quantum computing is beset by the lack of deterministic entangling operations besides photon loss. Motivated by advancements at the experimental front in deterministic generation of various kinds of multiphoton entangled states, we propose an architecture for linear-optical quantum computing that harnesses the availability of three-photon Greenberger-Horne-Zeilinger (GHZ) states. Our architecture and its subvariants use polarized photons in GHZ states, polarization beam splitters, delay lines, optical switches and on-off detectors. We concatenate topological quantum error correction code with three-qubit repetition codes and estimate that our architecture can tolerate remarkably high photon-loss rate of $11.5 \%$; this makes a drastic change that is at least an order higher than those of known proposals. Further, considering three-photon GHZ states as resources, we estimate the resource overheads to perform gate operations with an accuracy of $10^{-6}$~($10^{-15}$) to be $2.0\times10^6~(5.6\times10^7)$. Compared to other all-photonic schemes, our architecture is also resource-efficient. In addition, the resource overhead can be even further improved if larger GHZ states are available. Given its striking enhancement in the photon loss threshold and the recent progress in generating multiphoton entanglement, our scheme will make scalable photonic quantum computing a step closer to reality.
Solution of Linear Ordinary Differential Equations in Fuzzy Environment by Method of Variation of Parameters

This work considers LFIVP defined under gH-differentiability with the aim of establishing a method and subsequently, a fuzzy-valued function, which is a solution to the LFIVP. The conditions for a fuzzy function to be H- differentiable and gH-differentiability are defined. It is however established that gH-differentiability concept to fuzzy-valued functions is more generalized than H-differentiable. Characterization and Stacking theorems are also used to translate LFIVP to a system of specialized ODEs. Method of variation of parameters is also extended to LFIVP. Examples are constructed to test the applicability of the methods and graphical presentation of the behavior of solutions of the constructed examples is done with the aid of Matlab 8.5 version. We recommended that further studies should be made on second and higher order LFIVP.
Research Guides: Conducting Business Research: Evaluating Sources

A how-to guide for conducting business research using resources from William & Mary Libraries and beyond.
Demonstration of Lossy Linear Transformations and Two-Photon Interference via Singular Value Decomposition

Photon-state transformations are the cornerstone of quantum optics. Recently a universal method to treat non-unitary transformations has been proposed [1] – [3] , based on singular value decomposition. In this work, we show an experimental demonstration of this approach with non-classical light, by studying the dynamics of photon pairs in a system of coupled waveguides.
DEVELOPING STUDENTS 'PRACTICAL SKILLS OF CREATIVE THINKING IN THE EDUCATIONAL PROCESS

Creativity (Latin, create - to create, - creator, creator) - the creative ability of the individual, which characterizes the readiness to produce new ideas and is part of the talent as an independent factor. The formation of creative thinking and practical skills in students is to ensure the unity of theory and practice, and therefore to increase the quality and effectiveness of education.
Improved Deep Learning Optimization Algorithm Based on Variable Speed Integral Control

The performance of neural network is closely related to the optimization algorithm. An improved SWATS algorithm is designed to solve the non-convergence problem and the local optimum oscillation problem. Adamod algorithm and a piecewise function are introduced into the SWATS algorithm to improve the neural network performance. Experiments show that the proposed algorithm in this paper is applied to different model structures and different data sets. Compared with the SWATS algorithm, our algorithm has higher accuracy and converges more quickly and stably.
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts

Preserving maximal information is one of principles of designing self-supervised learning methodologies. To reach this goal, contrastive learning adopts an implicit way which is contrasting image pairs. However, we believe it is not fully optimal to simply use the contrastive estimation for preservation. Moreover, it is necessary and complemental to introduce an explicit solution to preserve more information. From this perspective, we introduce Preservational Learning to reconstruct diverse image contexts in order to preserve more information in learned representations. Together with the contrastive loss, we present Preservational Contrastive Representation Learning (PCRL) for learning self-supervised medical representations. PCRL provides very competitive results under the pretraining-finetuning protocol, outperforming both self-supervised and supervised counterparts in 5 classification/segmentation tasks substantially.
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis

Classic and deep generalized canonical correlation analysis (GCCA) algorithms seek low-dimensional common representations of data entities from multiple ``views'' (e.g., audio and image) using linear transformations and neural networks, respectively. When the views are acquired and stored at different computing agents (e.g., organizations and edge devices) and data sharing is undesired due to privacy or communication cost considerations, federated learning-based GCCA is well-motivated. In federated learning, the views are kept locally at the agents and only derived, limited information exchange with a central server is allowed. However, applying existing GCCA algorithms onto such federated learning settings may incur prohibitively high communication overhead. This work puts forth a communication-efficient federated learning framework for both linear and deep GCCA under the maximum variance (MAX-VAR) formulation. The overhead issue is addressed by aggressively compressing (via quantization) the exchanging information between the computing agents and a central controller. Compared to the unquantized version, our empirical study shows that the proposed algorithm enjoys a substantial reduction of communication overheads with virtually no loss in accuracy and convergence speed. Rigorous convergence analyses are also presented, which is a nontrivial effort. Generic federated optimization results do not cover the special problem structure of GCCA. Our result shows that the proposed algorithms for both linear and deep GCCA converge to critical points at a sublinear rate, even under heavy quantization and stochastic approximations. In addition, in the linear MAX-VAR case, the quantized algorithm approaches a global optimum in a geometric rate under reasonable conditions. Synthetic and real-data experiments are used to showcase the effectiveness of the proposed approach.