Reviewerly/exHarmony huggingface dataset:
Aplikasi Pembelajaran Konsep Ekologi Menggunakan Augmented Reality Pada Android

This study aims to design an interactive, effective and efficient ecological learning application; implementation of the Waterfall method in system development. This data was obtained through (1) Field Research, (2) Literature Study (3) Questionnaire. Using C# programming scripts. The results of this study are an application for learning the concept of ecology with Augmented Reality technology that can run on devices whose operating system is Android 6.0 (marshmallow) to Android 10. This application contains subject matter such as understanding ecology, understanding and components of ecosystems, units of living things, interactions between living things, energy flows, symptoms and impacts of ecological crises, and finally, biogeochemical cycles. The percentage of question results yielded 220%.
Referring Transformer: A One-step Approach to Multi-task Visual Grounding

As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or require the designing of complex task-specific one-stage architectures. In this paper, we propose a simple one-stage multi-task framework for visual grounding tasks. Specifically, we leverage a transformer architecture, where two modalities are fused in a visual-lingual encoder. In the decoder, the model learns to generate contextualized lingual queries which are then decoded and used to directly regress the bounding box and produce a segmentation mask for the corresponding referred regions. With this simple but highly contextualized model, we outperform state-of-the-arts methods by a large margin on both REC and RES tasks. We also show that a simple pre-training schedule (on an external dataset) further improves the performance. Extensive experiments and ablations illustrate that our model benefits greatly from contextualized information and multi-task training.
SYSTEMATIC IMPROVEMENT OF THE WORK OF THE CLASS TEACHER IN THE PEDAGOGICAL AND PSYCHOLOGICAL STUDY OF STUDENTS

This article provides detailed information on how to systematically improve the behavior and skills of each teacher in dealing with students, raising the level of activity of the class teacher in the pedagogical and psychological study of students. Relevant conclusions have been made on the basis of research in this area.
Analysis of one-hidden-layer neural networks via the resolvent method

We compute the asymptotic empirical spectral distribution of a non-linear random matrix model by using the resolvent method. Motivated by random neural networks, we consider the random matrix $M = Y Y^\ast$ with $Y = f(WX)$, where $W$ and $X$ are random rectangular matrices with i.i.d. centred entries and $f$ is a non-linear smooth function which is applied entry-wise. We prove that the Stieltjes transform of the limiting spectral distribution satisfies a quartic self-consistent equation up to some error terms, which is exactly the equation obtained by [Pennington, Worah] and [Benigni, Peche] with the moment method approach. In addition, we extend the previous results to the case of additive bias $Y=f(WX+B)$ with $B$ being an independent rank-one Gaussian random matrix, closer modelling the neural network infrastructures encountering in practice. Our approach following the \emph{resolvent method} is more robust than the moment method and is expected to provide insights also for models where the combinatorics of the latter become intractable.
Quantum switch of quantum switches

Recent results have shown that quantum theory is compatible with novel causal structures where events happen without a definite causal order. In particular, the quantum switch describes a process in which two quantum channels act in a coherent superposition of their two possible orders. Furthermore, the quantum switch can perform communication tasks that are impossible within the framework of the standard quantum Shannon theory. The present paper considers the scenario of one-shot heralded qubit communication using a higher-order quantum switch constructed from two quantum switches. Specifically, we show that two quantum switches put in a superposition of their alternative causal orders can transmit a qubit, without any error, with a probability strictly higher than that achievable with each quantum switch. We discuss three examples that demonstrate this communication advantage. Notably, a higher-order quantum switch not only can outperform useful quantum switches but also becomes useful as a resource even if the quantum switches making it up are useless.
THE IMPLEMENTATION OF LEARNING ENGLISH VOCABULARY THROUGH ASYNCHRONOUS CLASS

Online Learning certainly has challenges for students and teachers in carrying out English learning activities. This activity is carried out through asynchronous classes due to the limitations of students' devices on each cellphone. When conducting asynchronous classes for the students studying English, social media can make students motivated to learn because of the openness in learning. This study aims to motivate the students to be more enthusiastic in learning English in appropriating Youtube and WhatsApp in the implementation of which does not promote social media. Employing the descriptive qualitative method, this activity can determine the percentage of their ability and experience in studying vocabulary in English as a Foreign method during a pandemic. It is found that this activity received a positive response but there were shortcomings due to having objections in downloading learning files on social media and experiencing signal difficulties. Moreover, they get waivers where the students can ask their problems from what they did to the teacher freely. It seems that the students who learn through social media can find new knowledge which should be extra and openly guided by the teacher during online learning. Keywords :  Asynchronous, Vocabulary, English, Youtube, WhatsApp
Learning Nonparametric Volterra Kernels with Gaussian Processes

This paper introduces a method for the nonparametric Bayesian learning of nonlinear operators, through the use of the Volterra series with kernels represented using Gaussian processes (GPs), which we term the nonparametric Volterra kernels model (NVKM). When the input function to the operator is unobserved and has a GP prior, the NVKM constitutes a powerful method for both single and multiple output regression, and can be viewed as a nonlinear and nonparametric latent force model. When the input function is observed, the NVKM can be used to perform Bayesian system identification. We use recent advances in efficient sampling of explicit functions from GPs to map process realisations through the Volterra series without resorting to numerical integration, allowing scalability through doubly stochastic variational inference, and avoiding the need for Gaussian approximations of the output processes. We demonstrate the performance of the model for both multiple output regression and system identification using standard benchmarks.
Adversarial Intrinsic Motivation for Reinforcement Learning

Learning with an objective to minimize the mismatch with a reference distribution has been shown to be useful for generative modeling and imitation learning. In this paper, we investigate whether one such objective, the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution, can be utilized effectively for reinforcement learning (RL) tasks. Specifically, this paper focuses on goal-conditioned reinforcement learning where the idealized (unachievable) target distribution has full measure at the goal. We introduce a quasimetric specific to Markov Decision Processes (MDPs), and show that the policy that minimizes the Wasserstein-1 distance of its state visitation distribution to this target distribution under this quasimetric is the policy that reaches the goal in as few steps as possible. Our approach, termed Adversarial Intrinsic Motivation (AIM), estimates this Wasserstein-1 distance through its dual objective and uses it to compute a supplemental reward function. Our experiments show that this reward function changes smoothly with respect to transitions in the MDP and assists the agent in learning. Additionally, we combine AIM with Hindsight Experience Replay (HER) and show that the resulting algorithm accelerates learning significantly on several simulated robotics tasks when compared to HER with a sparse positive reward at the goal state.
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification

Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving performance on fine-grained tasks. In this work, we analyse the contrastive fine-tuning of pre-trained language models on two fine-grained text classification tasks, emotion classification and sentiment analysis. We adaptively embed class relationships into a contrastive objective function to help differently weigh the positives and negatives, and in particular, weighting closely confusable negatives more than less similar negative examples. We find that Label-aware Contrastive Loss outperforms previous contrastive methods, in the presence of larger number and/or more confusable classes, and helps models to produce output distributions that are more differentiated.
Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation

This paper introduces a concept of layer aggregation to describe how information from previous layers can be reused to better extract features at the current layer. While DenseNet is a typical example of the layer aggregation mechanism, its redundancy has been commonly criticized in the literature. This motivates us to propose a very light-weighted module, called recurrent layer aggregation (RLA), by making use of the sequential structure of layers in a deep CNN. Our RLA module is compatible with many mainstream deep CNNs, including ResNets, Xception and MobileNetV2, and its effectiveness is verified by our extensive experiments on image classification, object detection and instance segmentation tasks. Specifically, improvements can be uniformly observed on CIFAR, ImageNet and MS COCO datasets, and the corresponding RLA-Nets can surprisingly boost the performances by 2-3% on the object detection task. This evidences the power of our RLA module in helping main CNNs better learn structural information in images.
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. cross entropy (CE) to improve their learnability. Here, we propose to use Jensen-Shannon divergence as a noise-robust loss function and show that it interestingly interpolate between CE and MAE with a controllable mixing parameter. Furthermore, we make a crucial observation that CE exhibit lower consistency around noisy data points. Based on this observation, we adopt a generalized version of the Jensen-Shannon divergence for multiple distributions to encourage consistency around data points. Using this loss function, we show state-of-the-art results on both synthetic (CIFAR), and real-world (WebVision) noise with varying noise rates.
Personality Prediction Using Through Cv Analysis

Agriculture is said to be the backbone of the economy. Farmers toil hard with different kinds of crops to make good and healthy food for the country. There are more existing systems but uses outdated machine-learning techniques based on RNN( Recurrent neural network) which makes the process slower and more time-consuming. Here We are proposing a new CNN(Convolutional neural network ) based system which is fast and gives accurate results within seconds. CNN is power-efficient and is more suitable for real-time implementation. In this project, we use CNN algorithms which is very much better than the RNN algorithms used in the existing system.More parameters will be taken for the consideration of prediction in the proposed system. And we use Random Forest Regression, Multiple Linear Regression
Word Sense Induction in Persian and English: A Comparative Study

Words in the natural language have forms and meanings, and there might not always be a one-to-one match between them.This property of the language causes words to have more than one meaning; as a result, a text processing system faces challenges to determine the precise meaning of the target word in a sentence.Using lexical resources or lexical databases, such as WordNet, might be a help, but due to their manual development, they become outdated by passage of time and language change.Moreover, the lexical resources might be domain dependent which are unusable for open domain natural language processing tasks.These drawbacks are a strong motivation to use unsupervised machine learning approaches to induce word senses from the natural data.To reach the goal, the clustering approach can be utilized such that each cluster resembles a sense.In this paper, we study the performance of a word sense induction model by using three variables: a) the target language: in our experiments, we run the induction process on Persian and English; b) the type of the clustering algorithm: both parametric clustering algorithms, including hierarchical and partitioning, and non-parametric clustering algorithms, including probabilistic and density-based, are utilized to induce senses; c) the context of the target words to capture the information in vectors created for clustering: for the input of the clustering algorithms, the vectors are created either based on the whole sentence in which the target word is located; or based on the limited surrounding words of the target word.We evaluate the clustering performance externally.Moreover, we introduce a normalized, joint evaluation metric to compare the models.The experimental results for both Persian and English test data showed that the windowbased partitioningK-means algorithm obtained the best performance.
PEDAGOGICAL CONDITIONS OF SENIOR TEENAGERS’ POLYART EDUCATION BY MULTIMEDIA (DIGITAL) MEANS

The theoretical analysis of the current state of arts education in the context of challenges associated with the processes of digitization is made. It is concluded that many environments (multimedia) become the basis of the modern educational process due to the technological specifics of education digital transformation and importance, in this context, of senior teenagers’ polyart education.The pedagogical conditions that will contribute to senior teenagers’ polyart education by multimedia (digital) means are grounded, i.e.: personality-oriented involvement of students in various arts in their interaction based on the specific capabilities of multimedia (digital) means; system using of multimedia (digital) means in the process of polyart education in class and out-of-school activities; stimulating polyart activity of senior teenagers with the use of multimedia (digital) means based on their own artistic experience; organizing independent creative activity of senior teenagers by multimedia (digital) means.
Understanding political sentiment

Sentiment analysis is a widespread technique for computing the emotional loading of text to determine if it is “positive”, “negative” or “neutral”. Sentiment analysis has received broad adaptation in Twitter-based public opinion research, as it provides a framework for approximating support towards a concept of interest, such as a political party or candidate. However, we argue that much of the use of sentiment analysis, specifically in research aimed at forecasting election results using Twitter data, has been prone to a number of questionable design decisions which may contribute to the mixed track record of sentiment-assisted Twitter-based election forecasting. Crucially, previous publications (1) make no distinction between linguistic and political sentiment, thus potentially distorting their measurement validity; (2) typically analyze data at the tweet rather than the user level, thus biasing results towards prolific tweeter; and (3) treat relevant “negative” tweets as uninformative. In this chapter, we provide an overview of different forms of sentiment analysis, their uses and previous applications specifically in Twitter-based election forecasting. Then, we conduct a comprehensive empirical analysis in a novel, three-fold case study approach applying the same methodologies towards predicting three separate elections in the U.S. 2016 Democratic presidential primary in New Hampshire, South Carolina and Massachusetts. Besides replicating previously applied methodologies, we expand the sentiment analysis for Twitter-based public opinion research toolkit with a method for ordinal, intensity-focused political sentiment classification and further develop a modeling approach which incorporates negative-classified information. We present 12 vote share prediction models for all three primaries. We find that weighting Twitter data for computationally inferred user-level characteristics, such as home location and political affinity improved sentiment-based vote share prediction accuracy but find that the inclusion of negative tweets does not consistently improve analyses. Furthermore, we find that shifting analyses from the tweet to the user level benefits the resulting predictions.
Text with Image Editing Using Steganography

“Editing Text With Image Steganography” is the software developed for hiding information which uses the technology called as steganography, which is the art of hiding information in ways that prevent its detection. A message in cipher text may arouse suspicion while an invisible message is not. The conventional way of protecting information was to use a standard symmetric or asymmetric key system in encryption. Steganography can also be used to place a hidden “trademark” in images, music, and software, a technique referred to as watermarking. Steganography, if however used along with cryptography then the message will become quite secure as far as cryptanalytic attacks are concerned. Now, if this cipher text is embedded in an image it is even more secure. If an encrypted message is intercepted, the interceptor knows the text is an encrypted message. With steganography, the interceptor may know the object contains a message. Data hiding is a technique that conceals data into a carrier for conveying secret messages confidentially. Digital images are widely transmitted over the Internet; therefore, they often serve as a carrier for covert communication. Images used for carrying data are termed as cover images and images with data embedded are termed as steganography images. The least significant bit substitution method, referred to as LSB.
On the interplay between data structure and loss function in classification problems

One of the central puzzles in modern machine learning is the ability of heavily overparametrized models to generalize well. Although the low-dimensional structure of typical datasets is key to this behavior, most theoretical studies of overparametrization focus on isotropic inputs. In this work, we instead consider an analytically tractable model of structured data, where the input covariance is built from independent blocks allowing us to tune the saliency of low-dimensional structures and their alignment with respect to the target function. Using methods from statistical physics, we derive a precise asymptotic expression for the train and test error achieved by random feature models trained to classify such data, which is valid for any convex loss function. We study in detail how the data structure affects the double descent curve, and show that in the over-parametrized regime, its impact is greater for logistic loss than for mean-squared loss: the easier the task, the wider the gap in performance at the advantage of the logistic loss. Our insights are confirmed by numerical experiments on MNIST and CIFAR10.
Supervising the Transfer of Reasoning Patterns in VQA

Methods for Visual Question Anwering (VQA) are notorious for leveraging dataset biases rather than performing reasoning, hindering generalization. It has been recently shown that better reasoning patterns emerge in attention layers of a state-of-the-art VQA model when they are trained on perfect (oracle) visual inputs. This provides evidence that deep neural networks can learn to reason when training conditions are favorable enough. However, transferring this learned knowledge to deployable models is a challenge, as much of it is lost during the transfer. We propose a method for knowledge transfer based on a regularization term in our loss function, supervising the sequence of required reasoning operations. We provide a theoretical analysis based on PAC-learning, showing that such program prediction can lead to decreased sample complexity under mild hypotheses. We also demonstrate the effectiveness of this approach experimentally on the GQA dataset and show its complementarity to BERT-like self-supervised pre-training.
Visualizing the Emergence of Intermediate Visual Patterns in DNNs

This paper proposes a method to visualize the discrimination power of intermediate-layer visual patterns encoded by a DNN. Specifically, we visualize (1) how the DNN gradually learns regional visual patterns in each intermediate layer during the training process, and (2) the effects of the DNN using non-discriminative patterns in low layers to construct disciminative patterns in middle/high layers through the forward propagation. Based on our visualization method, we can quantify knowledge points (i.e., the number of discriminative visual patterns) learned by the DNN to evaluate the representation capacity of the DNN. Furthermore, this method also provides new insights into signal-processing behaviors of existing deep-learning techniques, such as adversarial attacks and knowledge distillation.
Physical, Geographical, Technical, and Economic Potential for the Optimal Configuration of Photovoltaic Systems Using a Digital Surface Model and Optimization Method

The main objective of this paper is to determine the optimal configuration of photovoltaic systems for maximum photovoltaic potential in the observed area using a digital surface model and a new optimization approach. The paper describes the physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model. The essential emphasis of the paper is to present a new and different approach for determining the economic potential, which can significantly influence the decision to install photovoltaic systems. For the further successful integration of photovoltaic systems into networks and for the successful establishment of appropriate policies and directives, it is essential to properly assess the photovoltaic potential. Based on the described methodology, the potential determination is made for a completed area of 75,537 m 2 using the observed area’s digital surface model. The annual values of physical, geographical, technical and economic potentials were 19.57 GWh, 7.54 GWh, 875.50 MWh, and 19.64 kWh. The methodologies presented in the paper are based on detailed data on the intensity of solar radiation for 20 years, digital surface model of the observed area, a new optimization approach, and multi-year data on the movement of electricity prices on markets.