Reviewerly/exHarmony huggingface dataset:
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning

We show that in a cooperative $N$-agent network, one can design locally executable policies for the agents such that the resulting discounted sum of average rewards (value) well approximates the optimal value computed over all (including non-local) policies. Specifically, we prove that, if $|\mathcal{X}|, |\mathcal{U}|$ denote the size of state, and action spaces of individual agents, then for sufficiently small discount factor, the approximation error is given by $\mathcal{O}(e)$ where $e\triangleq \frac{1}{\sqrt{N}}\left[\sqrt{|\mathcal{X}|}+\sqrt{|\mathcal{U}|}\right]$. Moreover, in a special case where the reward and state transition functions are independent of the action distribution of the population, the error improves to $\mathcal{O}(e)$ where $e\triangleq \frac{1}{\sqrt{N}}\sqrt{|\mathcal{X}|}$. Finally, we also devise an algorithm to explicitly construct a local policy. With the help of our approximation results, we further establish that the constructed local policy is within $\mathcal{O}(\max\{e,\epsilon\})$ distance of the optimal policy, and the sample complexity to achieve such a local policy is $\mathcal{O}(\epsilon^{-3})$, for any $\epsilon>0$.
Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT)

In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely applied in multimedia and digital applications. The periodic nature of DFT makes it impossible to meet the periodic condition of an image's opposing edges without producing severe artifacts, which lowers the image's perceptual visual quality. On the other hand, deep learning has recently achieved outstanding results for applications like speech recognition, image reduction, and natural language processing. Convolutional Neural Networks (CNN) have received more attention than most other types of deep neural networks. The use of convolution in feature extraction results in a less redundant feature map and a smaller dataset, both of which are crucial for image compression. In this work, an effective image compression method is purposed using autoencoders. The study's findings revealed a number of important trends that suggested better reconstruction along with good compression can be achieved using autoencoders.
Misogynist Internet Objectification of Women Celebrities on Social Media Platforms (Facebook) in Bangladesh

Social Media platforms are giving open access to everyone.Everyone is getting the power to write something in someone's post hiding behind the monitor.However, the problem is that people are trying to carry their offline identity to the online platform and they are trying to impose misogynist hegemonic masculine power on women by bullying them.As celebrities' lives are very public, they are often being the victim of bullying and online harassment.Especially, women celebrities are the victim of online bullying and harassment more in comparison with male celebrities.Moreover, perpetrators think that they can say anything about them as it's not possible to identify them or there are no strict laws against cyberbullying.Because of these situations women celebrities are the victim of misogynist internet objectification, cyberbullying, and online harassment.After following the content analysis method, it's been noticed that they are being bullied for their personal life, dresses, or age.Some people are also trying to impose their thought on them regarding their matter.Women celebrities are just praised for their beauty.The appreciation regarding their work is not that noticeable among followers' comments.This study will provide a deep insight into how embodied experiences or offline identities of misogynistic people are carried away to online platforms like Facebook and how they are trying to defame women celebrities and establish power over them.The lack of proper knowledge regarding digital security is increasing the objectification of celebrities and women on social media platforms.
Peningkatan Hasil Belajar Keterampilan Membaca Melalui Strategi Pembelajaran Card Sort Pada Siswa MTs. Darut Taqwa 02 Sengonagung Purwosari

Learning outcomes in learning reading skill there are 31 students who are still under the KKM. This is because the teacher still uses the lecture method and tadribat only, so that in focused learning with books that can cause students to feel bored and students are still not able to understand the meaning or information contained in the Arabic text. The objectives of this study include; to find out how to use the card sort learning strategy, and to find out how the effect of the card sort learning strategy on improving the learning outcomes of reading skill. This research is a quantitative research type of pre-experiment. The method used is one group pretest-posttest design. The results of this study show that card sort learning strategies have an effect on improving student reading skill learning outcomes. This can be seen from the fact that the calculation is greater than the ttabel, namely 11,614 &gt; 2,030 and Sig. (2 tailed) = 0.000 &lt; 0.05, then Ho was rejected and Ha was accepted. So it can be concluded that there is an influence on the use of card sort learning strategies on improving the learning outcomes of reading skill students of Junior High School Darut Taqwa 02 Sengonagung Purwosari.
MANAJEMEN SARANA DAN PRASARANA PENDIDIKAN DI MTSN 2 KARAWANG

The purpose of this study was to find out and obtain information about the management of educational facilities and infrastructure at MTsN 2 Karawang. The analytical method used is descriptive qualitative. The research took place at MTsN 2 Karawang in November-December 2021. The principal, vice principal in charge of facilities and infrastructure, maintenance support staff, and students at MTsN 2 Karawang became research informants. Interviews, observations, and reporting studies were all used to collect data. After that, the data is analyzed by reducing it, presenting it, and drawing conclusions. Triangulation of sources, techniques, and hypotheses is a technique of validity. The results show that: (1) Preparation for the management of facilities and infrastructure at MTsN 2 Karawang begins with a work meeting and the preparation of an activity agenda led by the principal, then maintenance is divided into 2 categories, namely periodic maintenance and routine maintenance. Then prepare a one-year budget plan made by the school principal and the Budget Drafting Team. Furthermore, representatives from the field of facilities and infrastructure specifically choose the structure of the maintenance of facilities and infrastructure, as well as their respective job desks in each division, which are known by the principal. The final step is to socialize students at school. (2) School maintenance processes and techniques begin with frequent and routine maintenance. Routine maintenance includes the maintenance of classrooms, teacher offices, and special rooms such as libraries, computer laboratories, and science laboratories. The school also performs routine maintenance on air conditioners, printers and copiers, as well as upgrades computer programs. (3) Obstacles faced by schools in preparing for repairs to facilities and infrastructure, including problems that arise unexpectedly and require immediate repair of these facilities. The lack of knowledge of users of school facilities and infrastructure, partnering with external parties who are often less sensitive, and cultural differences between individuals in the use of facilities and infrastructure are among the process and technological obstacles faced. including problems that arise unexpectedly and require immediate repair of these facilities. The lack of knowledge of users of school facilities and infrastructure, partnering with external parties who are often less sensitive, and cultural differences between individuals in the use of facilities and infrastructure are among the process and technological obstacles faced. including problems that arise unexpectedly and require immediate repair of these facilities. The lack of knowledge of users of school facilities and infrastructure, partnering with external parties who are often less sensitive, and cultural differences between individuals in the use of facilities and infrastructure are among the process and technological obstacles faced.
A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-trees
Bispectral Neural Networks

We present a neural network architecture, Bispectral Neural Networks (BNNs) for learning representations that are invariant to the actions of compact commutative groups on the space over which a signal is defined. The model incorporates the ansatz of the bispectrum, an analytically defined group invariant that is complete -- that is, it preserves all signal structure while removing only the variation due to group actions. Here, we demonstrate that BNNs are able to simultaneously learn groups, their irreducible representations, and corresponding equivariant and complete-invariant maps purely from the symmetries implicit in data. Further, we demonstrate that the completeness property endows these networks with strong invariance-based adversarial robustness. This work establishes Bispectral Neural Networks as a powerful computational primitive for robust invariant representation learning
Responsibility: An Example-based Explainable AI approach via Training Process Inspection

Explainable Artificial Intelligence (XAI) methods are intended to help human users better understand the decision making of an AI agent. However, many modern XAI approaches are unintuitive to end users, particularly those without prior AI or ML knowledge. In this paper, we present a novel XAI approach we call Responsibility that identifies the most responsible training example for a particular decision. This example can then be shown as an explanation: "this is what I (the AI) learned that led me to do that". We present experimental results across a number of domains along with the results of an Amazon Mechanical Turk user study, comparing responsibility and existing XAI methods on an image classification task. Our results demonstrate that responsibility can help improve accuracy for both human end users and secondary ML models.
Variational optimization of tensor-network states with the honeycomb-lattice corner transfer matrix

We develop a method of variational optimization of the infinite projected entangled pair states on the honeycomb lattice. The method is based on the automatic differentiation of the honeycomb-lattice corner transfer matrix renormalization group. We apply the approach to the antiferromagnetic Heisenberg spin-1/2 and ferromagnetic Kitaev models on the honeycomb lattice. The developed formalism gives quantitatively accurate results for the main physical observables and has a necessary potential for further extensions.
Design and Realization of Computer Network Virtual Experiment Economic Teaching Platform Based on Mathematical Image and Signal Processing

The emergence of computer network virtual experiment teaching provides the possibility to solve the limitations of hardware equipment and time for cultivating computer talents in colleges and universities. Although the network virtual experiment teaching platform can provide learners with an autonomous learning environment, the design of these modules still has problems such as weak integration with the teaching management system, immature functions, and high cost. In order to make the computer network virtual experiment economic teaching platform better integrate with the teaching management system, reduce the cost of investment and provide a better system environment for the development of functions; this research will design the virtual teaching platform through the method of mathematical image and signal processing. By analyzing the functional requirements of the users of the teaching platform, the overall design of the system is determined, and the designed system is specifically implemented and designed for performance testing and functional testing. The functional test results are combined with expectations. The average response time of the system in the performance test results is 0.30 s, which is in line with the expected results. The experiments show that the system designed and developed in this study works well.
Learning Open-World Object Proposals without Learning to Classify

Object proposals have become an integral preprocessing steps of many vision pipelines including object detection, weakly supervised detection, object discovery, tracking, etc. Compared to the learning-free methods, learning-based proposals have become popular recently due to the growing interest in object detection. The common paradigm is to learn object proposals from data labeled with a set of object regions and their corresponding categories. However, this approach often struggles with novel objects in the open world that are absent in the training set. In this paper, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories. Therefore, we propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlap with any ground-truth object (e.g., centerness and IoU). This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization on COCO, as well as cross-dataset evaluation on RoboNet, Object365, and EpicKitchens. Finally, we demonstrate the merit of OLN for long-tail object detection on large vocabulary dataset, LVIS, where we notice clear improvement in rare and common categories.
Combining federated and active learning for communication-efficient distributed failure prediction in aeronautics

Machine Learning has proven useful in the recent years as a way to achieve failure prediction for industrial systems. However, the high computational resources necessary to run learning algorithms are an obstacle to its widespread application. The sub-field of Distributed Learning offers a solution to this problem by enabling the use of remote resources but at the expense of introducing communication costs in the application that are not always acceptable. In this paper, we propose a distributed learning approach able to optimize the use of computational and communication resources to achieve excellent learning model performances through a centralized architecture. To achieve this, we present a new centralized distributed learning algorithm that relies on the learning paradigms of Active Learning and Federated Learning to offer a communication-efficient method that offers guarantees of model precision on both the clients and the central server. We evaluate this method on a public benchmark and show that its performances in terms of precision are very close to state-of-the-art performance level of non-distributed learning despite additional constraints.
Digital Development Path of Music Appreciation Based on the Kalman Filter

For a long time, due to the influence of curriculum orientation and examination-oriented education, the learning of music appreciation courses has not been paid much attention. This makes the teaching method in music appreciation teaching single, the classroom effect cannot reach the expected learning goal, and the classroom teaching efficiency becomes low, which urgently needs a new learning method to deal with these problems. Focusing on the digital education background of the new curriculum reform, this paper investigates the teaching nature, teaching methods, teaching design and evaluation, classroom construction, and student ability development of music appreciation. In this paper, the Kalman filter algorithm is used, and the optimal transformation order determined in advance is used to suppress the noise in music and improve the sound quality of music appreciation. If the order of the full-band signal model is selected as 10, and the number of sub-bands is selected as 4, then the order of the sub-band signal model should be at least greater than or equal to 2. In the music appreciation teaching test based on the Kalman filter, it was found that 35.6% of the students believed that the music appreciation course based on the Kalman filter could completely improve the learning efficiency, and 49% of students believe that the teaching of music appreciation class based on the Kalman filter can improve a large part of the learning efficiency. Overall, 85.5% of the students believed that the music appreciation class based on the Kalman filter is of great help in improving the learning efficiency, and the Kalman filter denoising method proposed in this paper has an obvious effect, which is a new attempt to promote the digital development of music appreciation.
Teaching with Commentaries

Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In this paper, we take steps towards extending the scope of teaching. We propose a flexible teaching framework using commentaries, learned meta-information helpful for training on a particular task. We present gradient-based methods to learn commentaries, leveraging recent work on implicit differentiation for scalability. We explore diverse applications of commentaries, from weighting training examples, to parameterising label-dependent data augmentation policies, to representing attention masks that highlight salient image regions. We find that commentaries can improve training speed and/or performance, and provide insights about the dataset and training process. We also observe that commentaries generalise: they can be reused when training new models to obtain performance benefits, suggesting a use-case where commentaries are stored with a dataset and leveraged in future for improved model training.
HAMBATAN IMPLEMENTASI KOMUNIKASI ILMIAH AKSES TERBUKA: TINJAUAN SISTEMATIS

Barriers to the implementation of open access globally need to be studied further using a systematic review. The purpose of this study is to look at the barriers from various countries in implementing open access. This study uses a systematic review method with a qualitative approach. The data collection method is in the form of planning, implementation, and synthesis of search results. This study uses Google Scholar to capture all research on implementing scientific communication with open access in the global realm. The search results on Google Scholar using Boolean logic found 273 articles. After the elimination process based on research objectives became 16 articles. This study shows that the biggest obstacle in implementing open access is the perception of the researchers (33.3%) which indicates that the quality of open access journals is less guaranteed, that is, many of them fall into the category of predatory journals. In addition, another obstacle identified was policy (25.6%). The government or stakeholders must make policies that regulate the standards for the quality of open access journals, the editorial board, and the time intervals for publishing journals. Other obstacles include financial factors where many researchers have minimal research funds but open access publications require large funds (23.1%), followed by copyright (10.3%) and language (7.7%) because not all researchers are fluent in English. The results of this study can be used as a reference for further research in assessing the obstacles that have also been happened in Indonesia.
MENINGKATKAN KETERAMPILAN LITERASI INFORMASI MASYARAKAT MELALUI PROGRAM WISATA LITERASI

Information literacy has a very important role in everyday life. This study aims to describe literacy tourism destinations and efforts to improve people's information literacy skills through literacy tourism programs. The research method uses a qualitative approach, data collection techniques use literature studies by utilizing various sources obtained from books, scientific articles in journals, and various other sources obtained on the internet. The results show that 1) libraries, reading houses, community reading parks, reading corners, and the like can be used as appropriate literacy tourism destinations to improve information literacy skills and obtain relevant information. 2) Improving public information literacy skills. The literacy tourism program can be carried out by holding various program activities such as library tours, user education, giving rewards, storytelling, educational games, as well as digitizing facilities and services. The research concludes that the literacy tourism program is very important to improve the information literacy skills of the community. Many libraries, reading houses, community reading parks, reading corners, and the like can be used as appropriate literacy tourism destinations in order to improve people's information literacy skills. Researchers suggest that the government, libraries, literacy activists, and the community work together in an effort to improve literacy skills and culture in Indonesia.