Reviewerly/exHarmony huggingface dataset:
Beyond Neighbourhood-Preserving Transformations for Quantization-Based Unsupervised Hashing

An effective unsupervised hashing algorithm leads to compact binary codes preserving the neighborhood structure of data as much as possible. One of the most established schemes for unsupervised hashing is to reduce the dimensionality of data and then find a rigid (neighbourhood-preserving) transformation that reduces the quantization error. Although employing rigid transformations is effective, we may not reduce quantization loss to the ultimate limits. As well, reducing dimensionality and quantization loss in two separate steps seems to be sub-optimal. Motivated by these shortcomings, we propose to employ both rigid and non-rigid transformations to reduce quantization error and dimensionality simultaneously. We relax the orthogonality constraint on the projection in a PCA-formulation and regularize this by a quantization term. We show that both the non-rigid projection matrix and rotation matrix contribute towards minimizing quantization loss but in different ways. A scalable nested coordinate descent approach is proposed to optimize this mixed-integer optimization problem. We evaluate the proposed method on five public benchmark datasets providing almost half a million images. Comparative results indicate that the proposed method mostly outperforms state-of-art linear methods and competes with end-to-end deep solutions.
Evaluating adversarial robustness in simulated cerebellum

It is well known that artificial neural networks are vulnerable to adversarial examples, in which great efforts have been made to improve the robustness. However, such examples are usually imperceptible to humans, and thus their effect on biological neural circuits is largely unknown. This paper will investigate the adversarial robustness in a simulated cerebellum, a well-studied supervised learning system in computational neuroscience. Specifically, we propose to study three unique characteristics revealed in the cerebellum: (i) network width; (ii) long-term depression on the parallel fiber-Purkinje cell synapses; (iii) sparse connectivity in the granule layer, and hypothesize that they will be beneficial for improving robustness. To the best of our knowledge, this is the first attempt to examine the adversarial robustness in simulated cerebellum models. The results are negative in the experimental phase -- no significant improvements in robustness are discovered from the proposed three mechanisms. Consequently, the cerebellum is expected to be vulnerable to adversarial examples as the deep neural networks under batch training. Neuroscientists are encouraged to fool the biological system in experiments with adversarial attacks.
Why computational teaching? the advantage of using the maple software in teaching exact sciences

The computational thinking becomes more and more an essential part of modern teaching. In this context, the resources, including the collection of online lessons plan, videos and other resources were created to provide a better understanding of CT. But the most important are software resources, because of their flexibility and wide application tools. The aim of the present paper is to have an introduction trip focusing on the advantages of using powerful software like MAPLE in computational teaching. Some examples are focused on representing the objects and basic algebraic manipulation commands, very successfully used in teaching sciences.
Literacy and Reading Programmes for Children and Young People: Case Studies from around the Globe

This chapter examines the contributions made to children's and teen literacy development and community building, and despite international variations, there are many examples of best practice from which all library professionals can learn. Throughout the world, there are hundreds of different libraries and other organisations involved in establishing successful literacy and reading programmes for children and young adults. Not only school libraries, but public libraries and other non-profit organisations also play an important role in providing literacy services to children and young adults. Unfortunately, many developing nations are still facing great challenges in reducing illiteracy, particularly gender-based disparities in education. The eradication of illiteracy is vital to the progression of any society, as it helps to alleviate poverty and improve the quality of education and life over the long term.
Online Multi-Granularity Distillation for GAN Compression

Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images, however, they are burdensome to deploy on resource-constrained devices due to ponderous computational costs and hulking memory usage. Although recent efforts on compressing GANs have acquired remarkable results, they still exist potential model redundancies and can be further compressed. To solve this issue, we propose a novel online multi-granularity distillation (OMGD) scheme to obtain lightweight GANs, which contributes to generating high-fidelity images with low computational demands. We offer the first attempt to popularize single-stage online distillation for GAN-oriented compression, where the progressively promoted teacher generator helps to refine the discriminator-free based student generator. Complementary teacher generators and network layers provide comprehensive and multi-granularity concepts to enhance visual fidelity from diverse dimensions. Experimental results on four benchmark datasets demonstrate that OMGD successes to compress 40x MACs and 82.5X parameters on Pix2Pix and CycleGAN, without loss of image quality. It reveals that OMGD provides a feasible solution for the deployment of real-time image translation on resource-constrained devices. Our code and models are made public at: https://github.com/bytedance/OMGD.
ANALISIS WACANA & ANALISIS WACANA KRITIS BERITA KONFLIK BURUH P.T. FREEPORT INDONESIA

Descriptive discourse analysis (DDA) considers discourse as a bigger language unit than sentence and as merely neutral linguistic phenomenon, meanwhile critical discourse analysis (CDA) (Foucault) sees discourse as constructed linguistic practice which builds social practice to change or keep power dominance. If the focus of DDA is on cohesion-coherence of text in making the unity of textual meaning, the focus of CDA is on the totality of relation of discourse which reveals and hidden to uncover the relation of power dominance among the groups inside. In DDA conclusion of the text of Freeport mine labor conflict news is: “Intimidation act of the strike labors towards the non-strike labors has caused PT Freeport to stop the production activity”. In CDA the text of the same news will be interpreted as: “Salary range based on race discrimination in Freeport labors increases horizontal conflict which is reconciled through dialogues under the control of company, so it guarantees labor’s obedience and productivity and covers the reality at once that Freeport gets so big benefit, Freeport exploitation has destroyed environment, and Timika people are still in poverty“.
Unifying Nonlocal Blocks for Neural Networks

The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks. Although having shown excellent performance, they still lack the mechanism to encode the rich, structured information among elements in an image or video. In this paper, to theoretically analyze the property of these nonlocal-based blocks, we provide a new perspective to interpret them, where we view them as a set of graph filters generated on a fully-connected graph. Specifically, when choosing the Chebyshev graph filter, a unified formulation can be derived for explaining and analyzing the existing nonlocal-based blocks (e.g., nonlocal block, nonlocal stage, double attention block). Furthermore, by concerning the property of spectral, we propose an efficient and robust spectral nonlocal block, which can be more robust and flexible to catch long-range dependencies when inserted into deep neural networks than the existing nonlocal blocks. Experimental results demonstrate the clear-cut improvements and practical applicabilities of our method on image classification, action recognition, semantic segmentation, and person re-identification tasks.
PERSONAL SELF-DEVELOPMENT, TRAINING AND EDUCATION: THE PSYCHOLOGICAL DIMENSION

The article is devoted to the psychological inquiry of personal self-development and its connection with education and training. The focus has been made on the terminological aspect of the problem. It has been concluded that personal self-development is the process of person’s activity in the social and cultural environment which includes three main phases: the phase of adaptation; the phase of individualization; the phase of integration. There are four main positions regarding the relationship between training and education in the context of personality self-development: training and education are indivisible processes; education is carried out in an extracurricular form; education is completely outside the training, but forms an axiological unity with it; personal education is mainly carried out by communities and groups that are not related to the field of training. There are three main approaches to the relationship between development and training: complete denial of any connection between development and training; identification of these two processes; the interpretation of development as a dual process, including training and maturation (in this case, development is recognized as a broader concept than training). Personal self-development is impossible without education and training.Key words: Personality, Personal Self-development, Mind, Training, Education, Culture.
Violence detection in video game metadata using ConvLSTM

The automatic detection of violent situations is relevant to monitor exposure to violence, both in the context of the analysis of real video and video generated in virtual environments, namely in simulated scenarios or virtual/mixed reality applications, such as serious games. In this paper, we propose a deep neural network to identify violent videos, with an approach capable of working in real video and synthetic video. An efficient detector of the 2D pose together with a multiple person tracker is used to extract motion features from the video sequence that will be fed directly into the proposed network. The proposed convolutional neural network is a recurrent neural network for a Spatio-temporal prediction that has convolutional structures in both the input-to-state and state-to-state transitions, which enables the analysis of local motion in the video. By stacking multiple ConvLSTM layers and forming an encoding-forecasting structure, we obtain a network model for the violence detection problem and more general spatiotemporal sequence forecasting problems. The inputs for the model correspond to sequences of keypoints extracted from the skeletons present in each frame originating an output corresponding to the classification of the video. The model was trained and evaluated with an innovative dataset that contains violent videos from a popular fighting game and non-violent videos related to people's daily lives. Comparison of the results obtained with the state-of-the-art techniques revealed the promising capability of the proposed method in recognizing violent videos with 100% precision, although it is not as robust as other datasets. Conv-LSTM units are shown to be an effective means for modelling and predicting video sequences.
An Empirical Study Using the Cognitive Learning RM2A Model for a STEM Project Based on Gesture-based Technology

Virtual reality links the real world with a virtual environment via human movements interacting with machines. Gesture-based technology (GBT), an interactive virtual reality method, can enable teachers and students to use computers or virtual environments to interact with educational resources. Based on GBT and STEM education literature reviews, this research designed an RM2A cognitive learning model for a GBT-based STEM project. The RM2A model included four cognitive learning pathways— rote learning to assimilation, rote learning to accommodation, meaningful learning to assimilation, and meaningful learning to accommodation. To evaluate the effectiveness of the RM2A model, this research employed an empirical study based on a STEM project with "O" state university student participants in the US. The results from the data analysis indicate that the GBT-based RM2A model led students’ development of self-learning, collaborative learning, problem-solving, and creativity development. This research will be helpful for those who study either virtual reality or the educational implementation of GBT by better explaining the link between virtual reality resources and students’ learning.
A Survey on Leukemia Detection using Image Processing Techniques

Leukemia detection and diagnosis by inspecting the blood cell images is an intriguing and dynamic exploration region in both the Artificial Intelligence and Medical research fields. There are numerous procedures created to look at blood tests to identify leukemia illness, these strategies are the customary methods and the deep learning (DL) strategy. This survey paper presents a review on the distinctive conventional strategies and Deep Learning and Machine Learning methods towards that have been utilized in leukemia illness diagnosis dependent on platelets images and to analyze between the two methodologies in nature of appraisal, exactness, cost and speed. This article covers 11 research papers, 9 of these examinations were in customary strategies which utilized image handling and AI (ML) calculations, such as, K-closest neighbor (KNN), K-means, SVM, Naïve Bayes, and 2 investigations in cutting edge procedures which utilized Deep Learning, especially Convolutional Neural Networks (CNNs) which is the most generally utilized in the field leukemia detection since it is profoundly precise, quick, and has the smallest expense. What's more, it dissects various late works that have been presented in the field including the dataset size, the pre-owned procedures, the acquired outcomes, and so forth. At last, in view of the led study, it very well may be reasoned that the proposed framework CNN was accomplishing immense triumphs in the field whether in regards to highlights extraction or classification time, precision and also a best low cost in the identification of leukemia.
Matroid Intersection: A pseudo-deterministic parallel reduction from search to weighted-decision.

We study the matroid intersection problem from the parallel complexity perspective. Given two matroids over the same ground set, the problem asks to decide whether they have a common base and its search version asks to find a common base, if one exists. Another widely studied variant is the weighted decision version where with the two matroids, we are given small weights on the ground set elements and a target weight W, and the question is to decide whether there is a common base of weight at least W. From the perspective of parallel complexity, the relation between the search and the decision versions is not well understood. We make a significant progress on this question by giving a pseudo-deterministic parallel (NC) algorithm for the search version that uses an oracle access to the weighted decision.The notion of pseudo-deterministic NC was recently introduced by Goldwasser and Grossman [Shafi Goldwasser and Ofer Grossman, 2017], which is a relaxation of NC. A pseudo-deterministic NC algorithm for a search problem is a randomized NC algorithm that, for a given input, outputs a fixed solution with high probability. In case the given matroids are linearly representable, our result implies a pseudo-deterministic NC algorithm (without the weighted decision oracle). This resolves an open question posed by Anari and Vazirani [Nima Anari and Vijay V. Vazirani, 2020].
Research Work In Programs For Masters Of Economics

Currently, organization of educational process in higher educational establishments requires special attention to be paid to training offered within Master programs. Acquisition of skills in research work has major implications as Masters are to be verse in searching for and processing a wide range of legislative sources and research publications, be capable of operating different types of data, possess well-developed abilities of generalization and deduction, of logical reasoning and argumentation, of presenting and publishing research findings. Research work is deemed a basic mode of individual research activities and cognition procedures with postgraduates. Relevance of the topic discussed is based on postgraduates developing skills in identifying research problems, searching for solution to the problems identified, accompanied by relevant skills in effective organization and time management. The study offers options for organization of internship for postgraduates specializing in economics based on scientific research with assignments formulated to correspond to the topics of Master theses. The first semester is the introductory stage including research-centered review of relevant theories and theoretical approaches; the second semester is devoted to extensive studies in existing methods and methodologies; in the third semester practical experiments and tests are used to verify and assess the knowledge gained. Reports on internship taken are regarded a preliminary stage in preparation and writing of the Master qualification paper.
From Beginner to Master: A Survey for Deep Learning-based Single-Image Super-Resolution

Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their targets, such as reconstruction efficiency, reconstruction accuracy, and perceptual accuracy. Specifically, we first introduce the problem definition, research background, and the significance of SISR. Secondly, we introduce some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Thirdly, we provide a detailed investigation of SISR and give some domain-specific applications of it. Fourthly, we present the reconstruction results of some classic SISR methods to intuitively know their performance. Finally, we discuss some issues that still exist in SISR and summarize some new trends and future directions. This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field. An investigation project for SISR is provided in https://github.com/CV-JunchengLi/SISR-Survey.
비대면 교육 유형별 만족도, 선호도 조사와 교육 방향

The Journal of Studies in Language 37.2, 149-160. In this paper I research the learners’ satisfaction and preference about the non-face-to-face education according to the types, compare which type is suitable to the learners and consider the education’s directions. Because the learners have week points to learn actively by self-directed learning method, these difficulties become more and more in the environment of online education. In this untact environment, among several types, I’d like to show which is the more effective type and which the learners like better. Also when I showed the untact class type simultaneously, we could get some different results. About these problems I’d like to research the learning time’s convenience, the repetitive learning’s convenience, the teaching contents’ quality, the school attendance. (Uiduk University)
Waveguide-integrated plasmonic photodetectors and activation function units with phase change materials

With a rapidly growing amount of data generated and processed, a search for more efficient components and architectures such as neuromorphic computing that are able to perform a more and more complex operations in more efficient way continue. Here we show that thin films of chalcogenide phase change materials (semiconductors) can serve as building blocks for novel type of plasmonic components that operate seamlessly in both electrical and optical domains without the need for repeated electrical-to-optical conversions. In consequence, novel waveguide-integrated devices were proposed that are able to operate simultaneously as photodetectors and activation function units and that are based on low-loss plasmonic waveguide platform and phase change materials. Theoretically predicted coupling efficiency exceeding 95 % and extremally low insertion losses of 0.01 dB/um in connection with an enhanced light-matter interaction provided by plasmonics enables a realization of very efficient and compact photodetectors and activation function units. A detection and threshold mechanism involve a Joule heating of phase change materials through internal or external metal contacts. With this paper, different arrangements and operation conditions were analyzed to ensure most efficient on-chip signal processing.
Application of Artificial Intelligence in Integrated Circuits

Abstract Integrated circuit and artificial intelligence technology are inseparable. Artificial intelligence technology depends on the learning ability and computing ability of computers and other machines. In this process, integrated circuits provide hardware support for the operation of artificial intelligence algorithms. For a long time, the application research of artificial intelligence technology in the field of integrated circuit mainly focuses on circuit fault analysis and artificial intelligence chips. Especially in recent years, due to the rapid development of technology, the combination of the two has become more closely, and more scientists and researchers have invested in the interdisciplinary research of the two disciplines. In this paper, the application of artificial intelligence technology in the field of integrated circuits is explored, that is, its applications in the three aspects of chip, integrated circuit fault diagnosis and circuit design optimization is discussed, and the relationship between artificial intelligence technology and integrated circuit is reflected. Finally, it is concluded that the cross research and application of integrated circuit and artificial intelligence technology will have an important impact on the level of science and technology and the level of human social life.
Influence Patterns for Explaining Information Flow in BERT

While attention is all you need may be proving true, we do not know why: attention-based transformer models such as BERT are superior but how information flows from input tokens to output predictions are unclear. We introduce influence patterns, abstractions of sets of paths through a transformer model. Patterns quantify and localize the flow of information to paths passing through a sequence of model nodes. Experimentally, we find that significant portion of information flow in BERT goes through skip connections instead of attention heads. We further show that consistency of patterns across instances is an indicator of BERT's performance. Finally, We demonstrate that patterns account for far more model performance than previous attention-based and layer-based methods.