Reviewerly/exHarmony huggingface dataset:
Interpretable Mixture of Experts

The need for reliable model explanations is prominent for many machine learning applications, particularly for tabular and time-series data as their use cases often involve high-stakes decision making. Towards this goal, we introduce a novel interpretable modeling framework, Interpretable Mixture of Experts (IME), that yields high accuracy, comparable to `black-box' Deep Neural Networks (DNNs) in many cases, along with useful interpretability capabilities. IME consists of an assignment module and a mixture of experts, with each sample being assigned to a single expert for prediction. We introduce multiple options for IME based on the assignment and experts being interpretable. When the experts are chosen to be interpretable such as linear models, IME yields an inherently-interpretable architecture where the explanations produced by IME are the exact descriptions of how the prediction is computed. In addition to constituting a standalone inherently-interpretable architecture, IME has the premise of being integrated with existing DNNs to offer interpretability to a subset of samples while maintaining the accuracy of the DNNs. Through extensive experiments on 15 tabular and time-series datasets, IME is demonstrated to be more accurate than single interpretable models and perform comparably with existing state-of-the-art DNNs in accuracy. On most datasets, IME even outperforms DNNs, while providing faithful explanations. Lastly, IME's explanations are compared to commonly-used post-hoc explanations methods through a user study -- participants are able to better predict the model behavior when given IME explanations, while finding IME's explanations more faithful and trustworthy.
The Connotative Meanings In “The Internship” Movie’s Dialogue: A Semantics Study

The aims of this study are to describe and analyze connotative meanings of the internship movie’s dialogue. The researcher divides the data into two parts which are primary and secondary data. The primary data consists of the movies and the script were taken from the internet. The method used in this study is qualitative descriptive and documentation technique. The main theory that the researcher used is the theory of semantics proposed by leech (1991). The result of this study shows that the connotative meaning of the internship movie’s dialogue consists of three types of connotative meanings both positive, negative and neutral connotations. Furthermore, this study shows that the word or the phrase selected and used by the characters in the movie depended on the condition or situation and with who they are talking to.
Seam Cerving and Salient Detection for Thumbnail Photos

Image resizing is a process of processing images or images with the aim of changing the size of the image. The most commonly used methods are cropping or scaling. Scaling is changing the size of the image based on the scale. Contents in the image are not considered in scaling. Seam carving often uses energy functionality that is useful as a determinant of the pixel level contained in an image. Seam is a connecting path of image pixels both vertically and horizontally that is passed by a low energy function. Changing the image size using seam carving is considered better than cropping and scaling. However, the seam carving method still cannot protect the object that is considered the most important. In overcoming this weakness, we can use a combination of seam carving algorithm with salient detection. In this research, we will improve the two methods which function as thumbnail maker. The results of the salient detection of the most important areas of the image will be detected and as a reference in resizing the image (seam carving) The dataset uses 200 images. The accuracy value is calculated by distributing questionnaires to 100 respondents and producing an acceptance rate of 78% so that the results are Very Natural/Natural.
Deep Learning with Label Noise: A Hierarchical Approach

Deep neural networks are susceptible to label noise. Existing methods to improve robustness, such as meta-learning and regularization, usually require significant change to the network architecture or careful tuning of the optimization procedure. In this work, we propose a simple hierarchical approach that incorporates a label hierarchy when training the deep learning models. Our approach requires no change of the network architecture or the optimization procedure. We investigate our hierarchical network through a wide range of simulated and real datasets and various label noise types. Our hierarchical approach improves upon regular deep neural networks in learning with label noise. Combining our hierarchical approach with pre-trained models achieves state-of-the-art performance in real-world noisy datasets.
Convolutional Neural Network (CNN) with Randomized Pooling

Abstract Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it has been widely used in image processing for image classification, object identification, semantic segmentation etc. It has overcome the constraint of traditional machine learning approaches. There has been a lot of effort done to improve the accuracy of CNN in many application areas, but there has been a lesser amount of work done to reduce the computational complexity of this model. There is a need to improve CNN's complexity problem. Here we have introduced randomized pooling (RANDpool) to CNN. Randomized pooling has reduced the computational complexity cost of CNN. We used MNIST dataset to demonstrate CNN with randomized pooling. This paper used randomization technique to reduce the dimensions of image instead of max, min or average pooling that have very extensive computations.
Data-Driven Image Interaction-Based Software Infrastructure for Graphic Design Research and Implementation

This article constructs and studies a graphic design software model based on data-driven image interactions. The semantic information of image interaction is integrated into the framework of the augmented reality label placement algorithm. A new feature map, Guidance Map, is proposed to combine image saliency information with semantic information in the task scenario, accurately describing the importance of different regions in the user’s field of view to derive a more reasonable label placement strategy. This database is different from other label placement algorithms that only use data-driven saliency detection. This paper proposes a data-driven augmented reality labeling method, which first presents the design requirements for augmented reality scene labeling placement, then designs the energy function for augmented reality scene characteristics, transforms the labeling placement problem into an optimization problem, and solves it. This paper proposes a feature representation form oriented to pixels, elements, interelement relationships, planes, and applications and quantifies the geometric features, perceptual features, and stylistic features in them. The model can fit the probability density distribution of the parts to predict the optimal element placement and element color under the target conditions.
THE UNIVERSITY STUDENTS� EXPERIENCES IN LEARNING ENGLISH USING YOUTUBE : A CASE STUDY OF INDEPENDENT LEARNING

YouTube is a website that facilitates users to share the videos they have, or just enjoy various videos uploaded by content creators. There are various kinds of videos that can be uploaded through this site, such as video blogs, music video clips, education videos, and others. Learning English independently through YouTube is a research topic which aims to find out the experiences of students while studying English and the challenges and opportunities they get. English Education in one private university in Cirebon became the object that the researcher chose and there were four female students who participants in this study. This research uses qualitative methods and data analysis uses thematic analysis. Data from this research is found by doing questionnaire and interview seasons. All participants showed positive responds. The results of this research indicate that YouTube is an application that can be used independently. Learning English by watching videos can help them improve their English skills, especially in listening and speaking skills. The benefits they get while using YouTube as a medium for learning English are interesting English learning content, good pronunciation, unfamiliar word, familiar with accent, a lot of information and knowledge through the videos that they see for learning.
“Youtube Go”: Developing EFL Learners’ Listening Skills and Social Learning Process

The sophisticated advance in digital technology, one of which is YouTube Go, has not specifically fully significantly affected the EFL learners’ English Listening enhancement and other required critical skills accompanying it. The fact is, to this day, YouTube has not yet become the most critical learning medium in the Listening Comprehension instructional program. Therefore, the study, whose paradigm employed qualitative research, aimed at diagnosing the benefits of taking YouTube Go as a major listening comprehension instructional resource. Personal journal, observation as a complete participant, and informal interviews were techniques for collecting the qualitative data. Organizing, interpreting, and conclusion drawing were ways of analyzing the data. Credibility and audibility were ensured to examine the data. The findings indicated that YouTube Go improves students’ linguistic inputs, vocabulary knowledge, productive skills, and debating skills. Besides, it develops the students’ appreciative, discriminative, comprehension, attentive, reflection, critical, evaluative, dialogic, relationship, empathetic, therapeutic, sympathetic listening skills. It also establishes the teacher-learners and students learners' social and interaction skills, learning skills, literacy skills, and life skills. It encourages teachers to assess the students’ knowledge and skills authentically. In conclusion, YouTube Go extensively enhances the students’ language skills, listening skills, social skills, and social learning process.
DETERMINAN HASIL BELAJAR ANAK: LINGKUNGAN KELUARGA, LINGKUNGAN SEKOLAH, DAN LINGKUNGAN MASYARAKAT

The success of education is determined by the ability of various parties to understand and achieve educational goals. This study aims to analyze the determinants of learning outcomes. The research method used is the survey method. Survey research method is used to provide an overview of what factors determine children's learning outcomes. The sampling technique used in this research is proportional random sampling. The sample of this study was all students of class X, totaling 71 people. Sample is part of the number and characteristics possessed by the population. The sample that will be taken from the affordable population is based on the table of determining the number of samples from Issac and Michael with an error rate of 5% as many as 57 students. The data analysis technique used in this study uses quantitative methods, to get the results of which determination factor is the highest. The results of the study found that the correlation coefficient of the family environment with learning outcomes was 0.444 (p≤ 0.01) while the determinant coefficient is 19.71 percent. Which means that children's learning outcomes are determined by the family environment by 19.71 percent. The correlation coefficient between the community environment and learning outcomes is 0.324 (p≤ 0.05) while the determinant coefficient is 10.49 percent. Which means that children's learning outcomes are determined by the community environment by 10.49 percent. The largest correlation coefficient is the school environment, which is 0.520 (p≤ 0.01) with a determinant coefficient of 27.04 percent. Which means that children's learning outcomes are determined by the community environment by 27.04 percent. The results of this study found that the highest determinant of learning outcomes was the school environment. To achieve good children's learning outcomes, support from various parties is needed, especially the environment closest to the child, namely the school environment and family environment. The school and parents must work together to achieve good learning outcomes for children.
The Impact of Threats Appraisal on Security Strategies of Computer Users: A Survey

Nowadays, many people work from home, owing in part to the COVID-19 pandemic. Home users are susceptible to a variety of threats emanating from the internet. Numerous studies have been conducted linking threat appraisal and its relationship to multidimensional security behaviour. The purpose of this study was to ascertain users' perceptions of threat appraisal behaviour as measured by perceived susceptibility, perceived severity, and perceived threats, as well as their relationship to security behaviour as measured by device security, password generation, proactive awareness, and updating. The results of a survey of 270 home computer users indicate that there is a mixed relationship between trait appraisal and security behaviour. Nevertheless, each dimension of trait appraisal has a significant relationship with at least one dimension of security behaviour. This study contributed significantly to both theory and practise... Keywords— Threats appraisal, security behavior, home user, structural equation modelling
How to structure citations data and bibliographic metadata in the OpenCitations accepted format

The OpenCitations organization is working on ingesting citation data and bibliographic metadata directly provided by the community (e.g., scholars and publishers). The aim is to improve the general coverage of open citations, which is still far from being complete, and use the provided metadata to enrich the characterization of the citing and cited entities. This paper illustrates how the citation data and bibliographic metadata should be structured to comply with the OpenCitations accepted format.
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution

Videos typically record the streaming and continuous visual data as discrete consecutive frames. Since the storage cost is expensive for videos of high fidelity, most of them are stored in a relatively low resolution and frame rate. Recent works of Space-Time Video Super-Resolution (STVSR) are developed to incorporate temporal interpolation and spatial super-resolution in a unified framework. However, most of them only support a fixed up-sampling scale, which limits their flexibility and applications. In this work, instead of following the discrete representations, we propose Video Implicit Neural Representation (VideoINR), and we show its applications for STVSR. The learned implicit neural representation can be decoded to videos of arbitrary spatial resolution and frame rate. We show that VideoINR achieves competitive performances with state-of-the-art STVSR methods on common up-sampling scales and significantly outperforms prior works on continuous and out-of-training-distribution scales. Our project page is at http://zeyuan-chen.com/VideoINR/ .
Computer Vision Based Virtual Sketch Using Detection

Abstract: In today's environment, and in the midst of a pandemic, online learning is the only way to learn. Students become more curious about knowledge as a result of online learning, and they choose their own learning route. Academics, on the other hand, must devote time to studying and be disciplined in their dedication in order to pass the course or exam. There are also other hurdles to Online learning. Students are losing their comprehending power since they have been accustomed to relying on their teachers and offline classes. In recent years, virtual writing and controlling systems have become demanding study areas in the fields of image processing and pattern recognition. It makes a significant contribution to progress Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV, Py
Pentingnya Penataan Arsip Dalam Menunjang Aktivitas Kerja Karyawan pada PT Anugerah Putra Langkat

Good records management is very helpful for the company. Archives/documents are one source of data because each agency needs to have a control area that is very close to archive management, so that each agency actually needs data and information. Therefore, archive arrangements must be used correctly to support the work of employees in achieving their goals. However, there are still companies that are sloppy in file management, hindering archive searches and confusing employees' work. This survey was conducted at PT. Anugerah Putra Langkat using a qualitative descriptive method and observing the company live. The results of this research reveal the fact that they have not managed their archives properly. Therefore, if documents/archives are needed immediately, this can affect and confuse the work activities of employees. Companies need to pay more attention to the placement of archives and provide equipment and placement to support the placement of archives so that employee work activities are successful.&#x0D; &#x0D; Keywords: Archive, Arrangement, Employee Activities
Flight Planning Tool an Aid for Efficient Flight Evaluation

Airborne surveillance systems have multiple sensors and communication links on board a suitable platform. They work in a cohesive manner to provide effective surveillance over the region of interest. The performance proving of such a system is challenging and requires flight trails extending over years. The test results often have to be interpreted using statistical analysis of the flight test data. An efficient way is to carefully design the flight test profiles such that enough samples can be collected during the test and multiple requirements can be tested in a single sortie. Such meticulous test strategies where both own ship platform and test targets are moving with high dynamics call for software based tool for planning of test sorties and the test points. Flight Planning Tool (FPT) plays an important role in pre-flight stage during developmental trials for analysis of the MOEs and MOPs of overall system and of various on-board sensors of an airborne multi-sensor system. The FPT provides statistical &amp; graphical analysis for sensor behaviour for various scenarios (flight trials) before actual flight test is conducted. It provides prior information on number of valid samples for sensor testing during flight trials. In addition, the tool aids in assessing number of profiles to be flown for proving each MOE. The profiles can also be optimised such that valid samples are collected for evaluation.
Efficient multi-attribute image classification through context-driven networks

Performing many simultaneous tasks on a resource-limited device is challenging due to the limited amount of available computational resources. Efficient and universal model architectures are the key to solving this problem. Existing sub-fields of machine learning, such as Multi-Task Learning (MTL), have proven that learning multiple tasks with a single neural network architecture is possible and even has the potential to improve sample efficiency, memory efficiency, and can be less prone to overfitting. In Visual Question Answering (VQA), a model ingests multi-modal input to produce text-based responses in the context of an image. Our proposed architecture merges the MTL and VQA concepts to form TaskNet. TaskNet solves the visual MTL problem using an input task to provide context to the network and guide its attention mechanism towards providing a relevant response. Our approach saves memory without sacrificing performance relative to naively training independent models. TaskNet efficiently provides multiple fine-grained classifications on a single input image and seamlessly incorporates context-specific metadata to further boost performance in a world of high variance.
Analisis Kelayakan Penerima Bantuan Covid-19 Menggunakan Metode K–Means

The government provides several types of assistance during the covid-19 pandemic that is distributed through the agencies of each village throughout Indonesia, one of which is Punggulan Village air joman subdistrict. The types of assistance that have been distributed to citizens are Cash Social Assistance (BST), Social Safety Net Assistance (JPS), Non-Cash Sembako Assistance, and Cash Direct Assistance (BLT). So far the assistance provided by Punggulan Village is still done manually, so it is possible to occur errors in the collection and distribution of assistance. To solve the problem, the author applies one of the data mining algorithms, the K-Means algorithm, to determine the recipient of covid-19 assistance that is done by collecting population data by the specified attributes. Then the data is weighted to facilitate the calculation of K-Means, after that build a system to implement the K-Means algorithm and perform testing. Population data used is 50 data recipients of aid 2021 as a sample using 3 attributes, namely, income, dependents, and other beneficiaries. The result of this system is prospective recipients of Covid-19 assistance with 2 feasible clusters (C1) and unfit (C2)
Performance Analysis of Machine Learning Algorithms using Fake News Detection

Abstract: Machine learning has vast algorithms in which each and everything is specialized to predict and compute certain functionalities and tasks. A neural network is a collection of neurons in which every neuron holds a numerical value as the output of other neurons. Furthermore, neural networks are classified more as regular neural networks, convolutional neural networks, feed-forward neural networks, and long-term memory networks. Each is specialized in unique scenarios, such as regular neural networks work better in position-based image detection and convolutional neural networks work better in edgebased detection. Therefore, this paper bases the comparison of different algorithms on a base prediction problem, which is fake news detection. This project displays various performance metrics such as accuracy score and various visualization plots like scatter, pie, and bar. so that users will know the different algorithms and their scalability on non-relatable text patterns. Keywords: Fake news detection, Machine learning, Naïve bayes Gaussian NB, Classification, Regression, Support Vector Classifier, Decision Tree Classifier.
Federated Hetero-Task Learning

To investigate the heterogeneity of federated learning in real-world scenarios, we generalize the classical federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks. We also present B-FHTL, a federated hetero-task learning benchmark consisted of simulation dataset, FL protocols and a unified evaluation mechanism. B-FHTL dataset contains three well-designed federated learning tasks with increasing heterogeneity. Each task simulates the clients with different data distributions and learning tasks. To ensure fair comparison among different FL algorithms, B-FHTL builds in a full suite of FL protocols by providing high-level APIs to avoid privacy leakage, and presets most common evaluation metrics spanning across different learning tasks, such as regression, classification, text generation and etc. Furthermore, we compare the FL algorithms in fields of federated multi-task learning, federated personalization and federated meta learning within B-FHTL, and highlight the influence of heterogeneity and difficulties of federated hetero-task learning. Our benchmark, including the federated dataset, protocols, the evaluation mechanism and the preliminary experiment, is open-sourced at https://github.com/alibaba/FederatedScope/tree/contest/v1.0.