Reviewerly/exHarmony huggingface dataset:
Penerapan Media Pembelajaran Audio Visual Terhadap Peningkatan Hasil Belajar Peserta Didik Pada Mata Pelajaran PAI Kelas VII 6 Di SMP Negeri 7 Makassar

The purpose of this study was to find out how the Application of Pendengaran Penglihatan Learning Alat Improve Student Learning Outcomes in PAI Subjects Class VII 6 at SMP Negeri 7 Makassar and to determine student learning outcomes using Pendengaran Penglihatan Learning Alat in PAI learning class VII 6 in SMP Negeri 7 Makassar. This research is a Classroom Action Research (CAR) which consists of two cycles, where each cycle consists of two meetings. The sample in this study was class VII 6 students at SMP Negeri 7 Makassar with a total of 28 students. The techniques used in collecting data in this study were observation, interviews, tests, and documentation. The test consists of two types, namely pre-test and post-test, the pre-test is carried out at the beginning of the meeting before the students are given treatment or the learning process by applying Audio Visual learning media while the post-test is carried out at the end of each meeting in each cycle. The data analysis technique used in this study is to use the formula for calculating the percentage and average value (Mean) for both student activity sheet data analysis and student learning outcomes. The results of this study are the application of Audio Visual learning media in PAI subjects is able to increase the sense of responsibility individually and in groups and the application of this Audio Visual learning model can improve student learning outcomes in PAI subjects. Thus the increase in student learning outcomes can be proven by the value of students who have increased and are getting better. The average value in the pre-test is 69.28 with a percentage of 42%, the average post-test value in the first cycle is 76.96 with a percentage of 71.42% and the post-test average value in the second cycle is 91, 42 with a percentage of 92.85%. The data proves that student learning outcomes can increase and have met the class success indicator, namely 80% with the application of Audio Visual learning media.
Meta-Learning for Color-to-Infrared Cross-Modal Style Transfer

Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly-available datasets that can be used for such training are limited in their size and diversity. To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors. We evaluate six contemporary stylization methods on four publicly-available IR datasets - the first comparison of its kind - and find that CMST is highly effective for DNN-based detectors. Surprisingly, we find that existing data-driven methods are outperformed by a simple grayscale stylization (an average of the color channels). Our analysis reveals that existing data-driven methods are either too simplistic or introduce significant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We find that MLST leads to more complex stylizations without introducing significant image artifacts and achieves the best overall detector performance on our benchmark datasets.
A Note on Improved Results for One Round Distributed Clique Listing

In this note, we investigate listing cliques of arbitrary sizes in bandwidth-limited, dynamic networks. The problem of detecting and listing triangles and cliques was originally studied in great detail by Bonne and Censor-Hillel (ICALP 2019). We extend this study to dynamic graphs where more than one update may occur as well as resolve an open question posed by Bonne and Censor-Hillel (2019). Our algorithms and results are based on some simple observations about listing triangles under various settings and we show that we can list larger cliques using such facts. Specifically, we show that our techniques can be used to solve an open problem posed in the original paper: we show that detecting and listing cliques (of any size) can be done using $O(1)$-bandwidth after one round of communication under node insertions and node/edge deletions. We conclude with an extension of our techniques to obtain a small bandwidth $1$-round algorithm for listing cliques when more than one node insertion/deletion and/or edge deletion update occurs at any time.
Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images

Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. However, most of the algorithms rely on the fully annotated datasets which are expensive to generate. In this work, we proposed a novel weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: (1) extracting discriminative areas in a histopathology image by employing the Multiple Instance Learning (MIL) algorithm based on Transformers, (2) representing the image by constructing a graph using the discriminative patches, and (3) classifying the image into its Gleason grades by developing a Graph Convolutional Neural Network (GCN) based on the gated attention mechanism. We evaluated our algorithm using publicly available datasets, including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross validated the algorithm on an independent dataset. Results show that the proposed model achieved state-of-the-art performance in the Gleason grading task in terms of accuracy, F1 score, and cohen-kappa. The code is available at https://github.com/NabaviLab/Prostate-Cancer.
TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization

Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.
Quantum federated learning based on gradient descent

Federated learning is a distributed learning framework in machine learning, and has been widely studied recently. Generally speaking, there are two main challenges, high computational cost and the security of the transmitted message, in the federated learning process. To address these challenges, we utilize some intriguing characteristics of quantum mechanics to propose a framework for quantum federated learning based on gradient descent. In the proposed framework, it consists of two components. One is a quantum gradient descent algorithm, which has been demonstrated that it can achieve exponential acceleration in dataset scale and quadratic speedup in data dimensionality over the classical counterpart. Namely, the client can fast-train gradients on a quantum platform. The other is a quantum secure multi-party computation protocol that aims to calculate federated gradients safely. The security analysis is shown that this quantum protocol can resist some common external and internal attacks. That is, the local gradient can be aggregated securely. Finally, to illustrated the effectiveness of the proposed framework, we apply it to train federated linear regression models and successfully implement some key computation steps on the Qiskit quantum computing framework.
PANSATZ: Pulse-based Ansatz for Variational Quantum Algorithms

We develop and implement a novel pulse-based ansatz, which we call PANSATZ, for more efficient and accurate implementations of variational quantum algorithms (VQAs) on today's noisy intermediate-scale quantum (NISQ) computers. Our approach is applied to quantum chemistry. Specifically, finding the ground-state energy associated with the electron configuration problem, using the variational quantum eigensolver (VQE) algorithm for several molecules. We manage to achieve chemical accuracy both in simulation for several molecules and on one of IBM's NISQ devices for the $H_2$ molecule in the STO-3G basis. Our results are compared to a gate-based ansatz and show significant latency reduction - up to $7\times$ shorter ansatz schedules. We also show that this ansatz has structured adaptivity to the entanglement level required by the problem.
A Quantum Information Theoretic View On A Deep Quantum Neural Network

We discuss a quantum version of an artificial deep neural network where the role of neurons is taken over by qubits and the role of weights is played by unitaries. The role of the non-linear activation function is taken over by subsequently tracing out layers (qubits) of the network. We study two examples and discuss the learning from a quantum information theoretic point of view. In detail, we show that the lower bound of the Heisenberg uncertainty relations is defining the change of the gradient descent in the learning process. We raise the question if the limit by Nature to two non-commuting observables, quantified in the Heisenberg uncertainty relations, is ruling the optimization of the quantum deep neural network. We find a negative answer.
Exact Selective Inference with Randomization

We introduce a pivot for exact selective inference with randomization. Not only does our pivot lead to exact inference in Gaussian regression models, but it is also available in closed form. We reduce the problem of exact selective inference to a bivariate truncated Gaussian distribution. By doing so, we give up some power that is achieved with approximate maximum likelihood estimation in Panigrahi and Taylor (2022). Yet our pivot always produces narrower confidence intervals than a closely related data splitting procedure. We investigate the trade-off between power and exact selective inference on simulated datasets and an HIV drug resistance dataset.
Simulation of Networked Quantum Computing on Encrypted Data

Due to the limited availability of quantum computing power in the near future, cryptographic security techniques must be developed for secure remote use of current and future quantum computing hardware. Prominent among these is Universal Blind Quantum Computation (UBQC) and its variations such as Quantum Fully Homomorphic Encryption (QFHE), which herald interactive and remote secure quantum computing power becoming available to parties that require little more than the ability to prepare and measure single qubits. Here I present a simulation of such a protocol, tested classically on the simulation platform LIQ$Ui|\rangle$ and then later adapted to and run on the recently released IBM 16-qubit quantum chip using their beta cloud service. It demonstrates the functionality of the protocol and explores the effects of noise on potential physical systems that would be used to implement it. BSc Thesis from the University of Edinburgh, December 2017
Application of Unsupervised Domain Adaptation for Structural MRI Analysis

The primary goal of this work is to study the effectiveness of an unsupervised domain adaptation approach for various applications such as binary classification and anomaly detection in the context of Alzheimer's disease (AD) detection for the OASIS datasets. We also explore image reconstruction and image synthesis for analyzing and generating 3D structural MRI data to establish performance benchmarks for anomaly detection. We successfully demonstrate that domain adaptation improves the performance of AD detection when implemented in both supervised and unsupervised settings. Additionally, the proposed methodology achieves state-of-the-art performance for binary classification on the OASIS-1 dataset.
Bias Mitigation Framework for Intersectional Subgroups in Neural Networks

We propose a fairness-aware learning framework that mitigates intersectional subgroup bias associated with protected attributes. Prior research has primarily focused on mitigating one kind of bias by incorporating complex fairness-driven constraints into optimization objectives or designing additional layers that focus on specific protected attributes. We introduce a simple and generic bias mitigation approach that prevents models from learning relationships between protected attributes and output variable by reducing mutual information between them. We demonstrate that our approach is effective in reducing bias with little or no drop in accuracy. We also show that the models trained with our learning framework become causally fair and insensitive to the values of protected attributes. Finally, we validate our approach by studying feature interactions between protected and non-protected attributes. We demonstrate that these interactions are significantly reduced when applying our bias mitigation.
Skit-S2I: An Indian Accented Speech to Intent dataset

Conventional conversation assistants extract text transcripts from the speech signal using automatic speech recognition (ASR) and then predict intent from the transcriptions. Using end-to-end spoken language understanding (SLU), the intents of the speaker are predicted directly from the speech signal without requiring intermediate text transcripts. As a result, the model can optimize directly for intent classification and avoid cascading errors from ASR. The end-to-end SLU system also helps in reducing the latency of the intent prediction model. Although many datasets are available publicly for text-to-intent tasks, the availability of labeled speech-to-intent datasets is limited, and there are no datasets available in the Indian accent. In this paper, we release the Skit-S2I dataset, the first publicly available Indian-accented SLU dataset in the banking domain in a conversational tonality. We experiment with multiple baselines, compare different pretrained speech encoder's representations, and find that SSL pretrained representations perform slightly better than ASR pretrained representations lacking prosodic features for speech-to-intent classification. The dataset and baseline code is available at \url{https://github.com/skit-ai/speech-to-intent-dataset}
Fewer is More: Efficient Object Detection in Large Aerial Images

Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not. This paradigm, although effective, is inefficient because the detectors have to go through all patches, severely hindering the inference speed. This paper presents an Objectness Activation Network (OAN) to help detectors focus on fewer patches but achieve more efficient inference and more accurate results, enabling a simple and effective solution to object detection in large images. In brief, OAN is a light fully-convolutional network for judging whether each patch contains objects or not, which can be easily integrated into many object detectors and jointly trained with them end-to-end. We extensively evaluate our OAN with five advanced detectors. Using OAN, all five detectors acquire more than 30.0% speed-up on three large-scale aerial image datasets, meanwhile with consistent accuracy improvements. On extremely large Gaofen-2 images (29200$\times$27620 pixels), our OAN improves the detection speed by 70.5%. Moreover, we extend our OAN to driving-scene object detection and 4K video object detection, boosting the detection speed by 112.1% and 75.0%, respectively, without sacrificing the accuracy. Code is available at https://github.com/Ranchosky/OAN.
FORMS AND METHODS OF FORMING LEADERSHIP SKILLS OF LEARNERS OF GENERAL SECONDARY EDUCATION

Modern changes of the country and society have created a need for a new generation of leaders who are able to act effectively in crisis situations and take responsibility not only for their own future, but also for the future of the state. The leading role in the formation of leadership skills belongs to institutions of general secondary education. This fact requires a change in approaches, also the implementation of new teaching forms and methods into the educational process.Leadership skills are an integrative quality that unites personal, cognitive, social and civic groups of skills. They are consist of knowledge, which is acquired in the learning process and skills formed in individual and group activities and aimed at the realization of a leadership role by learners.The formation of leadership skills, means qualitative and quantitative changes in the structure of the individual, which are manifested in the ability to perform the functions of a leader, to carry out socially significant public activities.According to the survey of 130 teachers of the Educational Complex № 157 Kyiv, the most effective organizational forms were identified (student self-government, protection of social projects, circles, volunteer projects, lessons, charity events, flash mobs, competitions, round tables, debates, trainings, summit of high school leaders, excursions, optional classes, briefings) and methods (creative tasks, problem tasks, stimulation, educational discussion, individual influence, games, peer-to-peer, open microphone, brainstorming, case studies) of forming learnersʼ leadership skills.The article describes the essence of the specified forms and methods and gives examples of their implementation in the educational process.&#x0D; Keywords: leadership skills; personal skills; social skills; cognitive skills; civic skills; organizational forms of formation of leadership skills; methods of formation of leadership skills.
FORMATION ENVIRONMENTAL COMPETENCE OF FUTURE TEACHERS OF NATURAL SPECIALTIES BY MEANS OF STEM-TECHNOLOGIES

The article examines the modernization of the didactics of teaching educational components by means of educational STEM technology in the system of professional and pedagogical training of future teachers of natural sciences. For successful pedagogical activity in this new information and high-tech society, the teacher needs to develop his capabilities much higher than those that were in the past by forming professional competences in general, hence environmental competences in particular. Educational STEM technology is a modern didactic tool, the conceptual basis of which is aimed at: forming the professional competences of the acquirers at a qualitatively new level-STEM and environmental competences, which transform the educational achievements of the acquirer into the professional activity of a specialist; are the key to the successful socialization of applicants and are designed to make them competitive on the labor market. The content, functions and structure of STEM technology were studied. The methodical features of the application of STEM technology during professional-pedagogical training with the aim of forming the environmental competence of students of higher pedagogical education have been clarified. A methodical way of introducing educational STEM technology into the system of pedagogical training of future teachers of natural sciences with the aim of forming environmental competences is proposed. An algorithm for the organization of educational and cognitive activities of applicants during the STEM technology “Tourist Secrets of Ukraine” has been developed. The abstract should contain the purpose of the research, the methods used, and the results obtained.&#x0D; Keywords: future teachers of natural sciences; educational STEM technology; environmental competence; educational and cognitive activity; educational components; professional and pedagogical training; didactics; students of higher pedagogical education.
Personalized Prediction of Offensive News Comments by Considering the Characteristics of Commenters

When reading news articles on social networking services and news sites, readers can view comments marked by other people on these articles. By reading these comments, a reader can understand the public opinion about the news, and it is often helpful to grasp the overall picture of the news. However, these comments often contain offensive language that readers do not prefer to read. This study aims to predict such offensive comments to improve the quality of the experience of the reader while reading comments. By considering the diversity of the readers' values, the proposed method predicts offensive news comments for each reader based on the feedback from a small number of news comments that the reader rated as "offensive" in the past. In addition, we used a machine learning model that considers the characteristics of the commenters to make predictions, independent of the words and topics in news comments. The experimental results of the proposed method show that prediction can be personalized even when the amount of readers' feedback data used in the prediction is limited. In particular, the proposed method, which considers the commenters' characteristics, has a low probability of false detection of offensive comments.
Highlighting Named Entities in Input for Auto-Formulation of Optimization Problems

Operations research deals with modeling and solving real-world problems as mathematical optimization problems. While solving mathematical systems is accomplished by analytical software, formulating a problem as a set of mathematical operations has been typically done manually by domain experts. Recent machine learning methods have shown promise in converting textual problem descriptions to corresponding mathematical formulations. This paper presents an approach that converts linear programming word problems into mathematical formulations. We leverage the named entities in the input and augment the input to highlight these entities. Our approach achieves the highest accuracy among all submissions to the NL4Opt Competition, securing first place in the generation track.