Reviewerly/exHarmony huggingface dataset:
The Effect of Dicto Comp Technique on Students’ Ability in Writing Descriptive Paragraph at Mas Al- Falah Dusun Jawa

Dicto-comp is a method that uses dictating techniques in writing, where students can write about what they hear from the teacher. With that students are trained not only in terms of writing but also in terms of listening to the words or sentence sentences from the teacher. Increasing students' writing skills with the dicto comp method can be compared with descriptive paragraph learning. where descriptive text is a way to describe a place, object or person. In this study the researchers conducted a description of the people with the dicto comp method. The population in this study were all class XI MAS AL-FALAH DUDUN JAWA in Academic Year 2019/2020consisting of 80 people. The research sample was taken using a saturated sample technique with the experimental method. The average student learning outcomes using Dicto-comp techniuqe = 79,53 with a standard deviation of 7.70 while those that do not use = 67,38 with a standard deviation of 10.35. from the difference in mean values from real level a = 0.05 indicates that students who get treatment Dicto-comp techniuqe is better than those who don't get it. So that it can be seen that the use of Dicto-comp techniuqe can affect students' in writing at MAS AL-FALAH DUDUN JAWA in Academic Year 2019/2020
Interactive Artistic Multi-style Transfer

Abstract Artistic style transfer is to render an image in the style of another image, which is a challenge problem in both image processing and arts. Deep neural networks are adopted to artistic style transfer and achieve remarkable success, such as AdaIN (adaptive instance normalization), WCT (whitening and coloring transforms), MST (multimodal style transfer), and SEMST (structure-emphasized multimodal style transfer). These algorithms modify the content image as a whole using only one style and one algorithm, which is easy to cause the foreground and background to be blurred together. In this paper, an iterative artistic multi-style transfer system is built to edit the image with multiple styles by flexible user interaction. First, a subjective evaluation experiment with art professionals is conducted to build an open evaluation framework for style transfer, including the universal evaluation questions and personalized answers for ten typical artistic styles. Then, we propose the interactive artistic multi-style transfer system, in which an interactive image crop tool is designed to cut a content image into several parts. For each part, users select a style image and an algorithm from AdaIN, WCT, MST, and SEMST by referring to the characteristics of styles and algorithms summarized by the evaluation experiments. To obtain richer results, the system provides a semantic-based parameter adjustment mode and the function of preserving colors of content image. Finally, case studies show the effectiveness and flexibility of the system.
Arbitrary Conditional Distributions with Energy

Modeling distributions of covariates, or density estimation, is a core challenge in unsupervised learning. However, the majority of work only considers the joint distribution, which has limited utility in practical situations. A more general and useful problem is arbitrary conditional density estimation, which aims to model any possible conditional distribution over a set of covariates, reflecting the more realistic setting of inference based on prior knowledge. We propose a novel method, Arbitrary Conditioning with Energy (ACE), that can simultaneously estimate the distribution $p(\mathbf{x}_u \mid \mathbf{x}_o)$ for all possible subsets of unobserved features $\mathbf{x}_u$ and observed features $\mathbf{x}_o$. ACE is designed to avoid unnecessary bias and complexity -- we specify densities with a highly expressive energy function and reduce the problem to only learning one-dimensional conditionals (from which more complex distributions can be recovered during inference). This results in an approach that is both simpler and higher-performing than prior methods. We show that ACE achieves state-of-the-art for arbitrary conditional likelihood estimation and data imputation on standard benchmarks.
Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems

The optimization step in many machine learning problems rarely relies on vanilla gradient descent but it is common practice to use momentum-based accelerated methods. Despite these algorithms being widely applied to arbitrary loss functions, their behaviour in generically non-convex, high dimensional landscapes is poorly understood. In this work, we use dynamical mean field theory techniques to describe analytically the average dynamics of these methods in a prototypical non-convex model: the (spiked) matrix-tensor model. We derive a closed set of equations that describe the behaviour of heavy-ball momentum and Nesterov acceleration in the infinite dimensional limit. By numerical integration of these equations, we observe that these methods speed up the dynamics but do not improve the algorithmic threshold with respect to gradient descent in the spiked model.
Impact of Ontology in Aviation Incident and Accident Knowledge Repository

The aviation industry is one of the most critical industries where access to quick and detailed information is very crucial for saving human lives and preserving the life span of the expensive fleet of aircraft. One of the most important problems with accident and incident reports in the aviation industry is that they are usually manually based or accessed and, in most cases, the outcome is usually based on hardcopy reports or standalone softcopies and are fragmented on the World Wide Web, and this makes logical inference cumbersome. However, documenting accidents and incidents in a database system is a good way to make these reports easily and logically accessible as knowledge for making informed decisions on any potential aspects to take note of, in order to enhance aircraft safety. This paper presents an Ontology-based knowledge representation language to facilitate explicit interpretation of domain resources and data. Coupled with its interoperability function, ontology presents the best choice for modelling such database domain. Ontology was developed in Protege using the template of the accident and incident manuals and was tested by querying it for accident and incident information which returned results based on the search criteria and the information in its database. Access to logical information in a database system for accidents and incidents in the aviation industry would facilitate the understanding of the structure of the data that can be used by humans as a source of knowledge of the things in the domain of a function for software for further computational task or analysis would go a long way in mitigating some avoidable incidences.
IMPLEMENTATION OF AN ELECTRONIC HOSPITAL INFORMATION SYSTEM IN A DEVELOPING COUNTRY; A CASE STUDY OF FED.MED. CENTER OWERRI, NIGERIA.

Concerned by the overwhelming  increase in the population of patients attending our hospitals for medical attention and the problems associated with the sluggish, unreliable and insecure manual (paper work) method of keeping record of patient's information; the need for an electronic  information system which makes it easier ,more reliable and  more economical to keep and retrieve a patient,s information as at and when needed with high degree of speed, and precision has become inevitable in our medical system .Manual method of record keeping which has been in use in the hospital where this research was carried out was observed to be inadequate, often inaccurate and lacks flexibility. Personal interview carried out on both patients and staffs of the hospital showed that more than 70% of the respondents  prefered an electronic system of information storage and retrieval  that will reduce the time and energy wasted in sorting out files from heaps of files from racks when a patient is to be attended to.  Due to this obvious problems, the researcher was motivated to develop a software that will handle this problem
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme.

Recently, a series of algorithms have been explored for GAN compression,which aims to reduce tremendous computational overhead and memory usages whendeploying GANs on resource-constrained edge devices. However, most of theexisting GAN compression work only focuses on how to compress the generator,while fails to take the discriminator into account. In this work, we revisitthe role of discriminator in GAN compression and design a novelgenerator-discriminator cooperative compression scheme for GAN compression,termed GCC. Within GCC, a selective activation discriminator automaticallyselects and activates convolutional channels according to a local capacityconstraint and a global coordination constraint, which help maintain the Nashequilibrium with the lightweight generator during the adversarial training andavoid mode collapse. The original generator and discriminator are alsooptimized from scratch, to play as a teacher model to progressively refine thepruned generator and the selective activation discriminator. A novel onlinecollaborative distillation scheme is designed to take full advantage of theintermediate feature of the teacher generator and discriminator to furtherboost the performance of the lightweight generator. Extensive experiments onvarious GAN-based generation tasks demonstrate the effectiveness andgeneralization of GCC. Among them, GCC contributes to reducing 80%computational costs while maintains comparable performance in image translationtasks. Our code and models are available at \url{https://github.com/SJLeo/GCC}.
Student Behavior Towards Online Learning Systems during the COVID-19 Pandemic

This research was initiated from the confusion of lecturers and students, especially those who do face-to-face learning, although there are universities that specialize in online distance learning such as the Open University. This change in the online learning system changes student behavior from face-to-face to virtual. The impact of online learning media begins with the stagnation of the metamorphosed learning system for the better, especially the behavior of students who have to change a lot in online learning. This research was studied qualitatively based on journal literature and observations were made to Untirta students who experienced learning difficulties. The results of the study state that many universities make Internet-based applications that are familiar with the term e-learning (both in the form of websites and applications). Not only that, but it is also necessary to develop an internet-based information system application that is easy to use from day to day, students feel enthusiastic about online learning for internet access/good signal, and vice versa experience a dilemma if the signal is not good and student economic factors also influence.
Development of Visual Data Acquisition Systems of Household Objects

What is most needed in the development of intelligent robots is visual information. Especially, Domestic Service Robots (DSR) in carrying out their duties in a complex and dynamic environment such as a household. To accomplish the task given in such environment, various kinds of recognition including household object recognition are necessary. The first step in object recognition is object learning, which one of the processes needs visual information. This information is provided by a visual sensor such as a second version of Microsoft Kinect (Kinect V2). Kinect V2 provides data such as, color information, depth information, and near infrared information. To make the object learning process simple and trackable, the development of object data acquisition systems is needed. In general, the captured data will be incorporated with labels such as the name of household object and the corresponding pose. To facilitate the object labeling, we develop smartphone applications to enable simple user interaction. To capture visual information in several poses, we make a turntable that can rotate synchronously during the data acquisition process. To extract the visual information of the object autonomously, an object extraction is inevitable. To this end, we propose an object extraction based on combination of information captured from Kinect V2. Our proposed method using a probabilistic method which integrates several Gaussian Mixture Models (GMM). Evaluation of proposed systems has been done through several experiments. Based on conducted experiments, our systems can capture the household object with size specifications greater than 3 cm × 3 cm × 2 cm, smaller than 28 cm × 28 cm × 30 cm, and with a weight not exceeding 800 g. In addition, the proposed systems can extract 40 objects (each of which covered 40 poses) with the F1-score of 76.43%.
Application of Mooc in French Language Teaching

With the rapid development of globalization, international communication methods are becoming more and more diverse, and international communication is getting closer. Simple English teaching can no longer meet the needs of today's social communication. More and more language teaching has joined the teaching tasks at this stage, and is being gradually promoted and strengthened. French, as a language with a large number of learners, has entered the syllabus of colleges and universities early. Although the teaching of French language has developed for many years, there are still many problems. The application of MOOC in French language teaching can change some current situations of French teaching to a certain extent, such as short class hours, large capacity and so on. The application of MOOC can solve these problems. This paper analyzes the current situation of French teaching in colleges and universities and the application of MOOC in French language teaching at present.
Light-driven mechanism of a photothermal conversion infrared image generation chip

An infrared image generation chip (IR-IGC) based on the photothermal conversion effect was developed. It can transform visible light images into infrared (IR) images. However, full 256 IR grayscales of an 8-bit IR grayscale image cannot always be completely captured due to the short integral time of the IR imaging devices. In this paper, we propose a novel light-driven mechanism of the IR-IGC to break the limitation on the integral time of IR imaging devices. A digital micro-mirror device (DMD) working in its binary mode was adopted for generating a light-driven image sequence including 8 binary images. The IR-IGC was driven by these 8 binary images in turns circularly during a heating period. The heating period was divided into a rising period and a holding period. The temperature of the IR-IGC gradually rised during the rising period and then stabilized during the holding period. After the heating period, the IR-IGC entered a cooling period to dissipate heat and got ready for generating the next frame of IR image. The IR imaging device can capture full 256 grayscales by integrating the IR radiation during the holding period of the IR-IGC. The thermal response curves of the IR-IGC under different light-driven waveforms were simulated. The heating period was set to 20 ms. For the grayscale of 255, the rising period was about 5.89 ms, the holding period was about 13.84 ms, and the cooling period was about 8.32 ms. The maximum temperature rise corresponding to the grayscale of 255 was about 123.71 K. The temperature change caused by unit grayscale was 0.36–0.61 K. We also experimentally tested the performance of the IR-IGC under the light-driven mechanism. Clear IR images with 256 grayscales were captured. And the measured thermal response curve of the IR-IGC conformed to the simulation curve. The light-driven mechanism proposed in this paper can also be extended to generate IR images with a grayscale greater than 8-bit, such as 12-bit and 16-bit.
Understanding Mobile Phone Use in Electricity Scarce Context: Extended TAM for Sub-Saharan African Countries

In many developing countries, rural, remote and nomadic regions seem to be off the national grid power infrastructure leading to power scarcity, which could affect the presence and user experience of electronic devices such as radio and mobile phones. Considering the above, this study examined the presence of mobile phones in a rural community and the effect of electricity scarcity in the village on mobile phone user experience. A qualitative methodology was adopted. The context was purposefully selected to include communities with scarce electricity supply. 10 participants were purposively and conveniently sampled from the village and face to face interviews conducted to collect data. Based on the data analysis, the study found that mobile phones are present in electric scarce communities and that they are used for communication, socialization and businesses. People in electricity scarce communities’ power their mobile phones through various means including power banks, solar panel systems, car batteries and generators. The effect of scarce electricity on mobile phone user experience include users missing calls and missed opportunities included job related ones. Considering the usefulness of mobile phones and power to keep it running, as found in this study, the intention of rural dwellers to own, power and use mobile phones is high and this conforms to the perceived usefulness, perceived benefit and perceived performance constructs of the extended Technological Acceptance Model in the Sub-Saharan African context. The sample size for this study was small and hence is recommended that future studies consider a larger population.
The Involvement of School Teachers in the Prosess of Practice-Oriented Training of Students at Pedagogical University

The requirements of the modern system of general education determine the need to give the training of a studying pedagogical university a practical-oriented nature. Thus, the issue of how to involve teachers of general education organizations in the training process is updated. The purpose of the article is to substantiate the identified prerequisites for involving teachers of general education organizations in practical-oriented training of students in pedagogical areas, as well as to reveal the potential possibility of implementing the plan. Research methodology and methods. The foundations are a practical-oriented approach to the implementation of educational programs in the direction of «Pedagogical Education», a problematic approach that involves targeted search activity of new solutions, research in action. Applied methods: analysis of special literature, study of experience, survey, pedagogical modeling. Conclusion. A method of structuring the content of training in a task form is justified, which allows you to control the process of conscious mastery by students of pedagogical activities, to constructively approach the mastery of professional actions. It is shown in various ways that the implementation of the proposed method requires the involvement of teachers. Based on the survey data, the potential of teachers to be involved in the training of students in pedagogical fields is analyzed and conclusions are drawn about the existing reserves of motivation. A proposal is made to create vocational training platforms as new forms of vocational and educational cooperation between teachers, teachers and students, their model-semantic description is given.
PiRank: Scalable Learning To Rank via Differentiable Sorting

A key challenge with machine learning approaches for ranking is the gap between the performance metrics of interest and the surrogate loss functions that can be optimized with gradient-based methods. This gap arises because ranking metrics typically involve a sorting operation which is not differentiable w.r.t. the model parameters. Prior works have proposed surrogates that are loosely related to ranking metrics or simple smoothed versions thereof, and often fail to scale to real-world applications. We propose PiRank, a new class of differentiable surrogates for ranking, which employ a continuous, temperature-controlled relaxation to the sorting operator based on NeuralSort [1]. We show that PiRank exactly recovers the desired metrics in the limit of zero temperature and further propose a divide and-conquer extension that scales favorably to large list sizes, both in theory and practice. Empirically, we demonstrate the role of larger list sizes during training and show that PiRank significantly improves over comparable approaches on publicly available internet-scale learning-to-rank benchmarks.
PENGARUH MODEL PEMBELAJARAN INSIDE OUTSIDE CIRCLE DALAM MENYIMAK BERITA OLEH SISWA KELAS X IPA 3 SMA N. 2 SIBORONG-BORONG TAHUN PEMBELAJARAN 2020/2021

This study aims to determine the effect of the Inside Outside Circle learning model in class XSMA N. 2 Siborong-borong in the 2020/2021 academic year. In this study, the subjects consisted of onegroup, namely the experimental class. Measurements were made twice, namely before the experiment(pre-test) and after (post-test). From the results of the study, it was concluded that the ability to listen tonews from students who used the Inside Outside Circle learning model in class X SMA N.2 Siborongborongfor the 2020/2021 academic year was sufficient with an average value of 75.40. The ability tolisten to news before using the Inside Outside Circle learning model in class X SMA N. 2 Siborongborongfor the 2020/2021 Learning Year is bad with an average score of 55.81. The Inside Outside Circlelearning model has an effect on improving students' ability to listen to news. This is evident from theresults of the t test, the value of t count > t table (0.05) is obtained, namely 8.23> 2.03.
Adapting to function difficulty and growth conditions in private optimization

We develop algorithms for private stochastic convex optimization that adapt to the hardness of the specific function we wish to optimize. While previous work provide worst-case bounds for arbitrary convex functions, it is often the case that the function at hand belongs to a smaller class that enjoys faster rates. Concretely, we show that for functions exhibiting $\kappa$-growth around the optimum, i.e., $f(x) \ge f(x^*) + \lambda \kappa^{-1} \|x-x^*\|_2^\kappa$ for $\kappa > 1$, our algorithms improve upon the standard ${\sqrt{d}}/{n\varepsilon}$ privacy rate to the faster $({\sqrt{d}}/{n\varepsilon})^{\tfrac{\kappa}{\kappa - 1}}$. Crucially, they achieve these rates without knowledge of the growth constant $\kappa$ of the function. Our algorithms build upon the inverse sensitivity mechanism, which adapts to instance difficulty (Asi & Duchi, 2020), and recent localization techniques in private optimization (Feldman et al., 2020). We complement our algorithms with matching lower bounds for these function classes and demonstrate that our adaptive algorithm is \emph{simultaneously} (minimax) optimal over all $\kappa \ge 1+c$ whenever $c = \Theta(1)$.