Reviewerly/exHarmony huggingface dataset:
∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization

Tasks across diverse application domains can be posed as large-scale optimization problems, these include graphics, vision, machine learning, imaging, health, scheduling, planning, and energy system forecasting. Independently of the application domain, proximal algorithms have emerged as a formal optimization method that successfully solves a wide array of existing problems, often exploiting problem-specific structures in the optimization. Although model-based formal optimization provides a principled approach to problem modeling with convergence guarantees, at first glance, this seems to be at odds with black-box deep learning methods. A recent line of work shows that, when combined with learning-based ingredients, model-based optimization methods are effective, interpretable, and allow for generalization to a wide spectrum of applications with little or no extra training data. However, experimenting with such hybrid approaches for different tasks by hand requires domain expertise in both proximal optimization and deep learning, which is often error-prone and time-consuming. Moreover, naively unrolling these iterative methods produces lengthy compute graphs, which when differentiated via autograd techniques results in exploding memory consumption, making batch-based training challenging. In this work, we introduce ∇-Prox, a domain-specific modeling language and compiler for large-scale optimization problems using differentiable proximal algorithms. ∇-Prox allows users to specify optimization objective functions of unknowns concisely at a high level, and intelligently compiles the problem into compute and memory-efficient differentiable solvers. One of the core features of ∇-Prox is its full differentiability, which supports hybrid model- and learning-based solvers integrating proximal optimization with neural network pipelines. Example applications of this methodology include learning-based priors and/or sample-dependent inner-loop optimization schedulers, learned with deep equilibrium learning or deep reinforcement learning. With a few lines of code, we show ∇-Prox can generate performant solvers for a range of image optimization problems, including end-to-end computational optics, image deraining, and compressive magnetic resonance imaging. We also demonstrate ∇-Prox can be used in a completely orthogonal application domain of energy system planning, an essential task in the energy crisis and the clean energy transition, where it outperforms state-of-the-art CVXPY and commercial Gurobi solvers.
Retracted: Application of Neural Network Algorithm Combined with Bee Colony Algorithm in English Course Recommendation

[This retracts the article DOI: 10.1155/2021/5307646.].
DBM-CNER: A Dual-Branch Multifeature Model for Chinese Named Entity Recognition

The Chinese Named Entity Recognition (CNER) is difficult to classify words accurately because of the lack of natural separators. Character embedding can effectively avoid the cumulative errors caused by word division, but carries relatively insufficient inter-character sequence information. In order to solve the problem of insufficient feature representation in Chinese NER model, this paper proposes a new CNER model (DBM-CNER), which has a dual-branch encoder structure. One branch is a new Transformer structure, in which the multi-head self-attention has dual-position encodings, and the other is a feedforward multi-scale dilated convolution fusion mechanism. This structure can better extract the global and local dependencies between characters in text from multiple perspectives, and enhance the ability to extract multi-level context features. Compared with other CNER models, the experimental results on the benchmark datasets in three different fields show that the proposed model is superior to state-of-the-art works.
Demonstrating JumpMod: Haptic Backpack that Modifies Users' Perceived Jump

Vertical force-feedback is extremely rare in mainstream interactive experiences. This happens because existing haptic devices capable of sufficiently strong forces that would modify a user's jump require grounding (e.g., motion platforms or pulleys) or cumbersome actuators (e.g., large propellers attached or held by the user). To enable interactive experiences to feature jump-based haptics without sacrificing wearability, we propose JumpMod, an untethered backpack that modifies one's sense of jumping. JumpMod achieves this by moving a weight up/down along the user's back, which modifies perceived jump momentum—creating accelerated & decelerated jump sensations. Our device can render five distinct effects: jump higher, land harder/softer, pulled higher/lower, which we demonstrate at SIGGRAPH 2023 Emerging Technologies in two jump-based VR experiences.
Consensus One-step Multi-view Subspace Clustering (Extended abstract)

Multi-view clustering has attracted increasing attention in data mining communities. Despite superior clustering performance, we observe that existing multi-view subspace clustering methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices; and isolate the processes of affinity learning, multiple information fusion and clustering. Both factors may cause insufficient utilization of multi-view information, leading to unsatisfying clustering performance. This paper proposes a novel consensus one-step multi-view subspace clustering (COMVSC) method to address these issues. Instead of directly fusing affinity matrices, COMVSC optimally integrates discriminative partition-level information, which is helpful in eliminating noise among data. Moreover, the affinity matrices, consensus representation and final clustering labels are learned simultaneously in a unified framework. Extensive experiment results on benchmark datasets demonstrate the superiority of our method over other state-of-the-art approaches.
ONLINE ENVIRONMENT: AUTONOMY OR LIMITATION?

The paper provides the study of the topical issue of modern education which defines online learning as an environment characterized by both autonomy and limitation for all the participants of the educational process. The reasons for raising this issue have been investigated and listed as follows: necessary moving to an online due to the pandemic of recent years; technical, methodological and technological lack of preparedness of teachers to implement the educational process in a new environment; a low level of motivation of teachers and students to work online efficiently, even though having a high level of digital proficiency. The authors consider the autonomy of an online learning environment in terms of the power of digital tools to be used in solving methodological problems and designing a pedagogical scenario in the logic of achieving planned educational results. Teachers’ and students’ autonomy in an online environment is realized in: a wide choice of different ways to present educational information; a variety of technologies to increase educational motivation; the possibility of implementing the ideas of microlearning; gamification of the educational process; monitoring the psychological well-being of a students, a positive attitude; a variety of individual trajectories of learning and a significantly changed role of students; the possibility of synchronous and asynchronous work, the presenting forms of the students’ projects and its evaluation, monitoring of students’ achievements. The limitations of an online environment as the authors conclude are mostly created by the participants of the educational process. This is an automatic rejection of new requirements and conditions and resistance to changes. The teachers’ lack of preparedness to solve the educational tasks in an online environment is doubled with the poor technical conditions of the physical environment. A major role in appearing the limitations online is also played by the methodological illiteracy of the teachers and the thoughtless use of digital solutions with no reference to educational tasks. The redesign of the pedagogical scenario and role changing of all the participants of the educational process could solve the problem of limitation of an online educational environment.
These Are All Just Persistent URLs, No?

I am beginning to walk through each question of the FAIR Implementation Profile (FIP) Ontology. My goal is to construct and share a populated model of people’s articulations – aka declarations – of choices they’ve made or with challenges they face with regard to addressing each question, as well as the considerations they associate with any such choice or challenge. The first question for which I’m seeking declarations is F1-D: What globally unique, persistent,...
A Subpixel-level Parallel Alignment Technology Oriented to EMU Train Undercarriage Image

In order to realize efficient fault recognition and utilization of the high-speed train (EMU) undercarriage image obtained by detection robot, image alignment technology play an important role. Firstly, research the original image data collection process of the EMU fault detection robot in the practical working site; secondly, perform image enhancement such as gamma transformation and Hanning window adding on the original image; thirdly, compared with the image alignment algorithm based on feature extraction, a Fourier transform-based image alignment algorithm is proposed; finally, the affine processing with global transform parameters acquired from image alignment is given. This paper conducts experimental verification and comparative analysis, and selects a convolutional neural network (CNN) model based on deep residual structure to carry out this experiment. Through comparison with three typical image alignment algorithms based on feature extraction, it shows that the Fourier transform-based image alignment algorithm has significantly improved in the accurate alarm rate, mistaken alarm rate, and omitted alarm rate with strong robustness. Based on the intrinsic subpixel-level precision of domain transformation alignment algorithm, and the parallel image processing method proposed in this paper, the on-site image preprocessing, high-precision alignment, and automatic fault recognition can be realized stably and reliably within a reasonable time.
Blind Image Quality Assessment via Multiperspective Consistency

Blind image quality assessment (BIQA) has made significant progress, but it remains a challenging problem due to the wide variation in image content and the diverse nature of distortions. To address these challenges and improve the adaptability of BIQA algorithms to different image contents and distortions, we propose a novel model that incorporates multiperspective consistency. Our approach introduces a multiperspective strategy to extract features from various viewpoints, enabling us to capture more beneficial cues from the image content. To map the extracted features to a scalar score, we employ a content-aware hypernetwork architecture. Additionally, we integrate all perspectives by introducing a consistency supervision strategy, which leverages cues from each perspective and enforces a learning consistency constraint between them. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments on five representative datasets. The results demonstrate that our method outperforms state-of-the-art techniques on both authentic and synthetic distortion image databases. Furthermore, our approach exhibits excellent generalization ability. The source code is publicly available at https://github.com/gn-share/multi-perspective.
ANALISIS PENGGUNAAN MEDIA VIDEO PEMBELAJARAN DALAM MENINGKATKAN MINAT BELAJAR SISWA KELAS 5 SDN 2 TAMBAKRIGADUNG

In the era of world progress, the world of education has also experienced significant developments. One of them is now in several learning activities many teachers are already using video as a learning medium. Learning media is considered very important because it is one of the assistants in teaching activities. This video-shaped media helps teachers explain material to students. In addition to increasing interest in learning, students are also more enthusiastic because of the visuals in a video. Learning media can also be one of the factors that trigger students' interest in learning. Interest in learning is an inner urge to do something that can make him interested and happy. The research method used is descriptive qualitative. The purpose of this research is to analyze learning activities at SDN 2 Tambakrigadung. Precisely in grade 5 with a total of 30 students. The results obtained in this study showed that the activeness of students in voicing their opinions increased by 20% after the researchers explained the material presented using learning media in the form of videos.
TRAINING OF PERSONNEL IN INSTITUTIONS OF SECONDARY VOCATIONAL &#x0D; EDUCATION FOR THE ECONOMY OF THE KHABAROVSK TERRITORY

In the course of the study, an assessment of the dynamics of the release of middle-level specialists by educational institutions of the Khabarovsk Territory, as well as the employment of graduates of secondary vocational education is given. The results of a comparative analysis of the graduation rates of specialists in enlarged groups of professions and specialties and the needs of the economy for specialists are presented. The results of the analysis allowed us to draw conclusions about how the structure of the graduation of specialists is changing, identify key trends and offer recommendations for improving the training of middle-level specialists in the Khabarovsk Territory.
Efficient Non-Sampling Graph Neural Networks

A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operation called negative sampling. Usually, the purpose of the learning objective is to maximize the similarity between neighboring nodes while minimizing the similarity between nodes that are not close to each other. Negative sampling can reduce the time complexity by sampling a small fraction of the negative nodes instead of using all of the negative nodes when optimizing the objective. However, sampling of the negative nodes may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such disadvantages, we provide an efficient Non-Sampling Graph Neural Network (NS-GNN) framework. The main idea is to use all the negative samples when optimizing the learning objective to avoid the sampling process. Of course, directly using all of the negative samples may cause a large increase in the model training time. To mitigate this problem, we rearrange the origin loss function into a linear form and take advantage of meticulous mathematical derivation to reduce the complexity of the loss function. Experiments on benchmark datasets show that our framework can provide better efficiency at the same level of prediction accuracy compared with existing negative sampling-based models.
Use of Information Technology in Simhastha 2016 in Ujjain

Celebrated in a cycle of 12 years in four cities of India, attracted footfall of around 100 million in 2012 (at Allahabad) and another 80 million in 2016 (at Ujjain), the Kumbh Mela, the Great Religious Bathing Festival of India, is possibly the largest known conglomeration of humans on earth. Ujjain Simhastha 2016 is known as the most hi tech Kumbha in the history of Kumbha Melas. The study reveals the use of information technology in Simhastha Mela which was held in Ujjain 2016 and would help the planners and decision makers in planning and managing the Simhastha and as an opportunity to trigger the local and overall development
Emotions and Technological Affordances

Abstract Research on the ties between technological affordances or functions and users’ emotion is nascent. This chapter reviews the current work on this topic with a specific focus on the roles that digital affordances play in processes of both emotion regulation and emotion sharing and expression. Technological affordance refers to any capacity or feature of technology that shapes how users use it. Special attention is given to the implications of the affordances of interactivity, personalization, accessibility, visibility, and social cues for these affective processes. The chapter concludes with suggestions for future research on how the affordances that media and technology offer to users affect their emotional lives.
Applying Natural Language Processing to Assessment

Abstract Natural language processing (NLP), the enablement of machines to automatically process and learn human natural language data, is at the core of many real-world applications. Recent years have seen initial attempts to apply NLP in assessment. The goal of this chapter is to discuss how NLP can be applied to improve assessment in the selection context. The chapter opens with an introduction of NLP—what it is and how NLP techniques have evolved over time. It then dives into how various NLP methods can be applied in the assessment cycle, from assessment construction to analyzing assessment responses to predicting job-related outcomes. The chapter ends with a discussion of the key challenges and opportunities facing assessment researchers and practitioners when considering using NLP for assessment.
DAMPAK KEBIJAKAN DALAM JARINGAN TERHADAP PELAKSANAAN PEMBELAJARAN PENDIDIKAN DASAR PADA MASA PANDEMI COVID-19 DI KABUPATEN KONAWE UTARA

The purpose of this study was to find out and analyze the impact of online policies on the implementation of basic education learning during the Covid-19 pandemic in the North Konawe Regency Region. This study uses a qualitative approach. The data processing procedure used follows the interactive data analysis model of Miles &amp; Huberman (1994) which includes four interrelated sub-processes, namely data collection, data reduction, data display, and conclusion drawing/verification.The results of the study show the impact of online policies on the implementation of basic education learning during the Covid-19 pandemic in North Konawe Regency, including (1) Impacts on teachers, including; (a) teachers have difficulty managing PJJ and tend to stage the curriculum; (b) study time is reduced so that teachers do not meet teaching hours; (c) teachers have difficulty communicating with parents and other partners at home. (2) The impact on students includes: (a) Students quickly get bored and bored; (b) students' mood in learning goes up and down; (c) the assessment given by the teacher is not fair; (d) students cannot operate Android (HP); (e) the child's learning achievement decreases; (f) parents do most of the tasks; (g) the child's psychosocial condition decreases and (h) there is covert violence against students. (3) The impact on students' parents is as follows: (a) the role of parents increases; (b) economic difficulties in buying pulses and cellphones; (c) time to supervise and accompany children at home.
Local Gaussian Process Extrapolation for BART Models with Applications to Causal Inference

Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This article proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+. Supplementary materials for this article are available online.
Visual image encryption algorithm based on compressed sensing and 2D cosine -type logistic map

Abstract A new two-dimensional cosine-type Logistic map (2D-CTLM) is proposed in this paper. Performance analysis shows that 2D-CTLM has good ergodicity, complex behavior, and a wide range of chaotic regions. To study its application, an adaptive embedded high visual security image encryption scheme is proposed by combining the system with two-dimensional compression sensing to address the shortcomings of fixed embedding positions and low transmission efficiency in existing schemes. The embedding position is fixed when the ciphertext image is embedded into the carrier image. When embedded in an inappropriate location, it will affect the decryption quality and the imperceptibility of the steganographic image. Firstly, the initial value of 2D-CTLM system is generated by using the information entropy of plaintext image and counter to generate random measurement matrix, which is used to compress and encrypt plaintext image from two directions at the same time. Secondly, the compressed cipher image is scrambled by the initial matrix generated by the GOL (game of life) rules in two-dimensional cellular automata to obtain the ciphertext image. Finally, the most suitable embedding position in the carrier image is found by combining information entropy and edge entropy. Then the unquantized ciphertext image is embedded into it by ST (slant transform) embedding method to obtain a high visual security image. To improve the transmission efficiency, the color carrier image can be selected to transmit three gray plaintext images at the same time, maintain high-quality reconstruction effect. SHA-512 and the information entropy of plaintext image are used to generate the initial value of encryption and measurement matrix, enhance the correlation between the algorithm and plaintext image, and effectively resist known plaintext and selective plaintext attacks. Simulation results and performance analysis show that compared with the existing encryption schemes, the encryption scheme has excellent visual effect, good decryption quality and good robustness.
<scp>StainGAN</scp>: Learning a structural preserving translation for white blood cell images

Abstract Analysis of white blood cells in blood smear images plays a vital role in computer‐aided diagnosis for the analysis and treatment of many diseases. However, different techniques for blood smear preparation result in images with large appearance variations, which limits the performance of large‐scale machine learning algorithms. In this paper, we propose StainGAN, an image translation framework to transform the conventional Wright‐stained white blood cell images into their rapidly‐stained counterpart. Moreover, we designed a cluster‐based learning strategy that does not require manual annotations and a multi‐scale discriminator that incorporates a richer hierarchy of the spatial context to generate sharper images with better semantic consistency. Experimental results on multiple real‐world datasets prove the effectiveness of our proposed framework. Moreover, we show that the transformed images from StainGAN can be used to boost the downstream segmentation performance under the label‐limiting scenario.