Reviewerly/exHarmony huggingface dataset:
Research on the application of "Rain Classroom" in teaching reform based on extensive data analysis

The big data technology is gradually applied to the field of education, and the development of the corresponding supporting teaching system has become the direction of new education reform, which has spawned various intelligent teaching platforms. "Rain Classroom" is a new teaching platform that combines online teaching technology and offline teaching resources and is a typical platform for building a hybrid teaching system. By revealing the problems in the traditional teaching mode, this paper discusses the principles and specific contents of the construction of the teaching system of "Rain Classroom" in the big data environment, proposes the system construction of the teaching reform based on "Rain Classroom" and the content of the curriculum construction, and finally analyzes the advantages and effects.
Low-light image enhancement with contrast regularization

Because the processing of existing low-light images undergoes multiple sampling processing, there is serious information degradation, and only clear images are used as positive samples to guide network training, low-light image enhancement processing is still a challenging and unsettled problem. Therefore, a multi-scale contrast learning low-light image enhancement network is proposed. First, the image generates rich features through the input module, and then the features are imported into a multi-scale enhancement network with dense residual blocks, using positive and negative samples to guide the network training, and finally using the refinement module to enrich the image details. Experimental results on the dataset show that this method can reduce noise and artifacts in low-light images, and can improve contrast and brightness, demonstrating its advantages.
A Note on Reinforcement Learning

In the past decade, deep reinforcement learning (DRL) has drawn much attention in theoretical research, meanwhile, it has seen huge success across multiple application areas, such as combinatorial optimization, recommender systems, autonomous driving, intelligent healthcare system and robotics. As one of three basic machine learning paradigms, reinforcement learning concerns with how intelligent agents learn in an interactive environment through trial and error to maximize the total cumulative reward of the agents. Even though many progresses of reinforcement learning have been presented, there are still many challenging research topics due to the complexity of the problems.
Legal Regulation on Abuse of Dominant Market Position in China’s Internet Field

The legal regulation of abuse of market dominance in the Internet field needs to comprehensively consider the characteristics of subject specificity, subjective malice, illegal behavior, and harmful consequences. The abuse of market dominance in the Internet field differs from other industries in behavior, mainly including monopoly prices, predatory pricing, restricted transactions, tying or attaching unreasonable trading conditions, differential treatment, etc. This paper analyzes the main abuse of market dominance in the Internet field through cases, and studies its relevant legal regulations and preventive measures.
Taking Into Account Age Characteristics in the Formation of Intellectual and Creative Abilities of Elementary School Students

Annotation: this article focuses on taking into account the age characteristics of a younger student in the formation of his ability for intellectual creativity. The article describes the psychologist - pedagogical views of the development of creative abilities.
The role of hand size in body representation: a developmental investigation

Knowledge of one's own body size is a crucial facet of body representation, both for acting on the environment and perhaps also for constraining body ownership. However, representations of body size may be somewhat plastic, particularly to allow for physical growth in childhood. Here we report a developmental investigation into the role of hand size in body representation (the sense of body ownership, perception of hand position, and perception of own-hand size). Using the rubber hand illusion paradigm, this study used different fake hand sizes (60%, 80%, 100%, 120% or 140% of typical size) in three age groups (6- to 7-year-olds, 12- to 13-year-olds, and adults; N = 229). We found no evidence that hand size constrains ownership or position: participants embodied hands which were both larger and smaller than their own, and indeed judged their own hands to have changed size following the illusion. Children and adolescents embodied the fake hands more than adults, with a greater tendency to feel their own hand had changed size. Adolescents were particularly sensitive to multisensory information. In sum, we found substantial plasticity in the representation of own-body size, with partial support for the hypothesis that children have looser representations than adults.
Faktor-faktor penentu kepuasan pengguna learning management system di program studi akuntansi Universitas Widyatama

This study aims to identify the determinants of user satisfaction with a learning management system (LMS) in the Accounting Study Program of Widyatama University. The population in this study was students of the 2017–2021 accounting study program. The sample used was 194 people who were taken randomly. The data analysis technique used is covariance-based structural equation modeling. The results of the study stated that the implementation of e-learning lectures was more flexible; there was good support for the e-learning system; the technology used in e-learning lectures is acceptable; participants have high motivation in e-learning lectures; instructors have an important role in e-learning lectures; and participant achievement in e-learning lectures is very good. However, e-learning lectures have not been able to replace traditional (face-to-face) learning. And e-learning lectures have an effect on learning achievement.
Intelligent Distributed Computing Paradigm

The developments in the field of computations and services have been witnessed for decades. Traditionally, computation was done using mainframes, generally referred to as mainframe computing. Gradually, the advent of cloud computing was seen. Cloud computing provides computation and services on a pay-as-you-go basis, with accessibilities anytime, anywhere. However, with the increasing demands and requirements of faster and reliable services, distributed computing has come into the picture, which is able to provide access to services in a distributed fashion in different geographical areas. Moreover, it is being integrated with intelligent systems to design distributed intelligent systems, so that intelligent systems are able to function in a distributed manner and provide services efficiently to the end users. Thus, the objective of this chapter is to provide an overview of intelligent distributed computing. Additionally, the chapter lists the applications of this technology in real life and also addresses the challenges encountered in its practical implementation.
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document

Given the recent proliferation of false claims online, there has been a lot of manual fact-checking effort. As this is very time-consuming, human fact-checkers can benefit from tools that can support them and make them more efficient. Here, we focus on building a system that could provide such support. Given an input document, it aims to detect all sentences that contain a claim that can be verified by some previously fact-checked claims (from a given database). The output is a re-ranked list of the document sentences, so that those that can be verified are ranked as high as possible, together with corresponding evidence. Unlike previous work, which has looked into claim retrieval, here we take a document-level perspective. We create a new manually annotated dataset for this task, and we propose suitable evaluation measures. We further experiment with a learning-to-rank approach, achieving sizable performance gains over several strong baselines. Our analysis demonstrates the importance of modeling text similarity and stance, while also taking into account the veracity of the retrieved previously fact-checked claims. We believe that this research would be of interest to fact-checkers, journalists, media, and regulatory authorities.
Systematic Analysis and Comparison of Security Advice as Datasets

A long list of documents have been offered as security advice, codes of practice, and security guidelines for building and using security products, including Internet of Things (IoT) devices. To date, little or no systematic analysis has been carried out on the advice datasets themselves. Towards addressing this, with IoT as a case study, we begin with an informal analysis of two documents offering advice related to IoT security -- the ETSI Provisions and the UK DCMS Guidelines -- and then carry out what we believe is the first systematic analysis of these advice datasets. Our analysis explains in what ways the ETSI Provisions are a positive evolution of the UK DCMS Guidelines. We also suggest aspects of security advice warranting special attention by those offering security advice. Such parties may find the systematic analysis method, which categorizes advice into predefined categories, to be of general interest beyond IoT itself.
Predesign of a projector alignment system for structured light illumination systems

Structured light illumination systems can measure a target’s two-dimensional height profile. Recent advancements in computational power and algorithms have enabled the predesign of optical systems where all experimental variables are determined prior to use in the laboratory. We present a predesign process to align the projector of a custom-structured light illumination system. The predesign method uses the native optical design file to simulate the residual aberrations and can perform a trade space analysis to tailor the alignment variables to desired preferences. The presented method can predict the experimental focus position to 1.6 μm root mean square over a broad range of physical settings.
Boosting Semi-Supervised 3D Object Detection with Semi-Sampling

Current 3D object detection methods heavily rely on an enormous amount of annotations. Semi-supervised learning can be used to alleviate this issue. Previous semi-supervised 3D object detection methods directly follow the practice of fully-supervised methods to augment labeled and unlabeled data, which is sub-optimal. In this paper, we design a data augmentation method for semi-supervised learning, which we call Semi-Sampling. Specifically, we use ground truth labels and pseudo labels to crop gt samples and pseudo samples on labeled frames and unlabeled frames, respectively. Then we can generate a gt sample database and a pseudo sample database. When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them. Our semi-sampling can be regarded as an extension of gt-sampling to semi-supervised learning. Our method is simple but effective. We consistently improve state-of-the-art methods on ScanNet, SUN-RGBD, and KITTI benchmarks by large margins. For example, when training using only 10% labeled data on ScanNet, we achieve 3.1 mAP and 6.4 mAP improvement upon 3DIoUMatch in terms of mAP@0.25 and mAP@0.5. When training using only 1% labeled data on KITTI, we boost 3DIoUMatch by 3.5 mAP, 6.7 mAP and 14.1 mAP on car, pedestrian and cyclist classes. Codes will be made publicly available at https://github.com/LittlePey/Semi-Sampling.
Benin's opposition may again face election exclusion

Headline BENIN: Opposition may again be excluded from elections
Interfacing the Metaverse: Are We Ready for the Distributed Wearable Smartphone?

&lt;p&gt;The goal of the metaverse is to provide a synchronized interoperability of real and virtual worlds across multiple domains and applications. The main challenge is to make the metaverse interface both immersive and responsive to user actions. In this paper, we propose a new concept called a distributed wearable smartphone, which provides a synergistic integration of head-mounted displays, wearable sensors and other smart devices to translate user context and actions across real and virtual metaverse domains. The proposed architecture is flexible and can be composed from any set of devices, depending on the target application. The key benefits of the proposed approach are the realistic 3D representation and the semi-virtual user experience, which can be extended to a wide range of different applications in industry, education, healthcare and entertainment.&lt;/p&gt;
SAR image despeckling using a CNN guided by high-frequency information

Preserving image details is a challenging and significant task for synthetic aperture radar (SAR) image despeckling. In this paper, we proposed a high-frequency information extraction module based on block-matching and 3D filtering for SAR image despeckling (SAR-BM3D) and wavelet transformation to guide the denoisers based on a convolutional neural network (CNN). Firstly, SAR-BM3D is employed to conduct the initial speckle removal, and then Haar wavelet transformation takes the task of extracting high-frequency information of the initial despeckled image. The noisy image and the high-frequency information are merged together to be the input of the CNN, which makes it easier for the CNN-based denoisers to restore the textures and details of the SAR image. Extensive experiments on synthetic and real SAR images have been validated, which show that our method can effectively remove the speckle noise and improve the detail-preserving capacity of the CNN-based denoiser.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. Type of peer review: Single anonymous Conference submission management system: Morressier Number of submissions received: 27 Number of submissions sent for review: 27 Number of submissions accepted: 21 Acceptance Rate (Submissions Accepted / Submissions Received × 100): 77.8 % Average number of reviews per paper: 1.00 Total number of reviewers involved: 18 Contact person for queries: Name: Prof. Pramod Gopinath and Dr. Mohamed Ameen P Affiliation: International School of Photonics - Cochin University of Science and Technology Email: photonicsdirector@cusat.ac.in
FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity

Federated Learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects the parameters of local models without accessing their local data. However, a significant challenge in FL is handling the heterogeneity of local data distribution, which often results in a drifted global model that is difficult to converge. To address this issue, current methods employ different strategies such as knowledge distillation, weighted model aggregation, and multi-task learning. These approaches are referred to as asynchronous FL, as they align user models either locally or post-hoc, where model drift has already occurred or has been underestimated. In this paper, we propose an active and synchronous correlation approach to address the challenge of user heterogeneity in FL. Specifically, our approach aims to approximate FL as standard deep learning by actively and synchronously scheduling user learning pace in each round with a dynamic multi-phase curriculum. A global curriculum is formed by an auto-regressive auto-encoder that integrates all user curricula on the server. This global curriculum is then divided into multiple phases and broadcast to users to measure and align the domain-agnostic learning pace. Empirical studies demonstrate that our approach outperforms existing asynchronous approaches in terms of generalization performance, even in the presence of severe user heterogeneity.
A Covert-Aware Anonymous Communication Network for Social Communication

To effectively protect the communication content and communication behavior of social networks, anonymous communication technologies are widely used. However, the anonymous communication networks represented by Tor and I2P lack the covertness of the control plane design, which leads to important user behavioral characteristics in the process of accessing anonymous communication networks. Therefore, network monitors can analyze users' communication behavior by tracking these characteristics. In this paper, the concept of covert measurement is proposed. On this basis, a software-defined anonymous communication network architecture is presented, which also considers the covertness of the anonymous communication network control plane and the data plane. According to the theoretical analysis and experimental results, the anonymous communication network architecture proposed in this paper has better anonymity and usability than traditional anonymous communication networks, such as Tor.
Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations

Recent work has suggested post-hoc explainers might be ineffective for detecting spurious correlations in Deep Neural Networks (DNNs). However, we show there are serious weaknesses with the existing evaluation frameworks for this setting. Previously proposed metrics are extremely difficult to interpret and are not directly comparable between explainer methods. To alleviate these constraints, we propose a new evaluation methodology, Explainer Divergence Scores (EDS), grounded in an information theory approach to evaluate explainers. EDS is easy to interpret and naturally comparable across explainers. We use our methodology to compare the detection performance of three different explainers - feature attribution methods, influential examples and concept extraction, on two different image datasets. We discover post-hoc explainers often contain substantial information about a DNN's dependence on spurious artifacts, but in ways often imperceptible to human users. This suggests the need for new techniques that can use this information to better detect a DNN's reliance on spurious correlations.
An Interpretable Neuron Embedding for Static Knowledge Distillation

Although deep neural networks have shown well-performance in various tasks, the poor interpretability of the models is always criticized. In the paper, we propose a new interpretable neural network method, by embedding neurons into the semantic space to extract their intrinsic global semantics. In contrast to previous methods that probe latent knowledge inside the model, the proposed semantic vector externalizes the latent knowledge to static knowledge, which is easy to exploit. Specifically, we assume that neurons with similar activation are of similar semantic information. Afterwards, semantic vectors are optimized by continuously aligning activation similarity and semantic vector similarity during the training of the neural network. The visualization of semantic vectors allows for a qualitative explanation of the neural network. Moreover, we assess the static knowledge quantitatively by knowledge distillation tasks. Empirical experiments of visualization show that semantic vectors describe neuron activation semantics well. Without the sample-by-sample guidance from the teacher model, static knowledge distillation exhibit comparable or even superior performance with existing relation-based knowledge distillation methods.
Paradox of the Image in Digital Communications

When analyzing the visual aspect of modern culture, it is necessary to consider the duality of the “digital image” concept, which paradoxically combines the opposition of mutually exclusive principles of information representation—digital (discrete, “text”) and analog (based on similarity). The popular notion in science that the visual (analogue) aspect predominates in modern culture is not entirely accurate and does not take into account that the image today functions predominantly in digital form, which involves sampling contradicting the analog principle. Digital technologies represent visual information in discrete code which is alien to it and even remove the distinction between types of information. The balance between the two principles of representation is restored through the specialization of discretization technologies. The lack of awareness of this fact can be considered as one of the symptoms of a split between the perception by an ordinary user and the point of view of a holder of special knowledge, without which a digital society cannot exist. This splitting, which becomes noticeable only in system analysis, is a characteristic feature of modern digital culture.
Revisiting Attention Weights as Explanations from an Information Theoretic Perspective

Attention mechanisms have recently demonstrated impressive performance on a range of NLP tasks, and attention scores are often used as a proxy for model explainability. However, there is a debate on whether attention weights can, in fact, be used to identify the most important inputs to a model. We approach this question from an information theoretic perspective by measuring the mutual information between the model output and the hidden states. From extensive experiments, we draw the following conclusions: (i) Additive and Deep attention mechanisms are likely to be better at preserving the information between the hidden states and the model output (compared to Scaled Dot-product); (ii) ablation studies indicate that Additive attention can actively learn to explain the importance of its input hidden representations; (iii) when attention values are nearly the same, the rank order of attention values is not consistent with the rank order of the mutual information(iv) Using Gumbel-Softmax with a temperature lower than one, tends to produce a more skewed attention score distribution compared to softmax and hence is a better choice for explainable design; (v) some building blocks are better at preserving the correlation between the ordered list of mutual information and attention weights order (for e.g., the combination of BiLSTM encoder and Additive attention). Our findings indicate that attention mechanisms do have the potential to function as a shortcut to model explanations when they are carefully combined with other model elements.
Fast DistilBERT on CPUs

Transformer-based language models have become the standard approach to solving natural language processing tasks. However, industry adoption usually requires the maximum throughput to comply with certain latency constraints that prevents Transformer models from being used in production. To address this gap, model compression techniques such as quantization and pruning may be used to improve inference efficiency. However, these compression techniques require specialized software to apply and deploy at scale. In this work, we propose a new pipeline for creating and running Fast Transformer models on CPUs, utilizing hardware-aware pruning, knowledge distillation, quantization, and our own Transformer inference runtime engine with optimized kernels for sparse and quantized operators. We demonstrate the efficiency of our pipeline by creating a Fast DistilBERT model showing minimal accuracy loss on the question-answering SQuADv1.1 benchmark, and throughput results under typical production constraints and environments. Our results outperform existing state-of-the-art Neural Magic's DeepSparse runtime performance by up to 50% and up to 4.1x performance speedup over ONNX Runtime. Source code is publicly available at https://github.com/intel/intel-extension-for-transformers.