Reviewerly/exHarmony huggingface dataset:
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling

The recently proposed FixMatch achieved state-of-the-art results on most semi-supervised learning (SSL) benchmarks. However, like other modern SSL algorithms, FixMatch uses a pre-defined constant threshold for all classes to select unlabeled data that contribute to the training, thus failing to consider different learning status and learning difficulties of different classes. To address this issue, we propose Curriculum Pseudo Labeling (CPL), a curriculum learning approach to leverage unlabeled data according to the model's learning status. The core of CPL is to flexibly adjust thresholds for different classes at each time step to let pass informative unlabeled data and their pseudo labels. CPL does not introduce additional parameters or computations (forward or backward propagation). We apply CPL to FixMatch and call our improved algorithm FlexMatch. FlexMatch achieves state-of-the-art performance on a variety of SSL benchmarks, with especially strong performances when the labeled data are extremely limited or when the task is challenging. For example, FlexMatch outperforms FixMatch by 14.32% and 24.55% on CIFAR-100 and STL-10 datasets respectively, when there are only 4 labels per class. CPL also significantly boosts the convergence speed, e.g., FlexMatch can use only 1/5 training time of FixMatch to achieve even better performance. Furthermore, we show that CPL can be easily adapted to other SSL algorithms and remarkably improve their performances. We open source our code at https://github.com/TorchSSL/TorchSSL.
Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction

Sememes are defined as the atomic units to describe the semantic meaning of concepts.Due to the difficulty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed.However, previous methods heavily rely on word or character embeddings, and ignore the fine-grained information.In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character.The Glyph enhanced Chinese Character representation (GCC) is used to assist sememe prediction.We experiment and evaluate our model on HowNet, which is a famous sememe knowledge base.The experimental results show that our method outperforms existing non-external information models.
Progressive Guided Fusion Network with Multi-modal and Multi-scale Attention for RGB-D Salient Object Detection

The depth map contains abundant spatial structure cues, which makes it extensively introduced into saliency detection tasks for improving the detection accuracy. Nevertheless, the acquired depth map is often with uneven quality, due to the interference of depth sensors and external environments, posing a challenge when trying to minimize the disturbances from low-quality depth maps during the fusion process. In this article, to mitigate such issues and highlight the salient objects, we propose a progressive guided fusion network (PGFNet) with multi-modal and multi-scale attention for RGB-D salient object detection. Particularly, we first present a multi-modal and multi-scale attention fusion model (MMAFM) to fully mine and utilize the complementarity of features at different scales and modalities for achieving optimal fusion. Then, to strengthen the semantic expressiveness of the shallow-layer features, we design a multi-modal feature refinement mechanism (MFRM), which exploits the high-level fusion feature to guide the enhancement of the shallow-layer original RGB and depth features before they are fused. Moreover, a residual prediction module (RPM) is applied to further suppress background elements. Our entire network adopts a top-down strategy to progressively excavate and integrate valuable information. Compared with the state-of-the-art methods, experimental results demonstrate the effectiveness of our proposed method both qualitatively and quantitatively on eight challenging benchmark datasets.
Real-Time Image Deblurring and Super Resolution Using Convolutional Neural Networks

A picture provides the necessary information to the user. However, not all photos will do so since the backdrop is blurred. Blurry within the image could be a condition due to various factors like camera shifting, inappropriate aiming, aperture use and different external influences. Such photos are called deteriorated photographs which have been degraded. Owing to the rapid body activity, haze is sometimes created and can be categorized into two separate types, i.e., uniform and non-uniform. Distorted pictures of poor quality have, however, received fewer attentions. There are several forms a corrupted file can be measured and reconstituted. These issues can be overcome by adding a profound learning strategy, like a convolutional neural network, which simultaneously allows deblurring and super resolution. Photo super resolution is an efficient means of taking high resolution pictures with low resolution photographs. Using the advantages of super resolution, neural networks are superior experimentally to current deep learning algorithms. By this suggested model, we show, scientifically, that the consistency and quantity of our system is obtained. We may achieve good quality resolution in documented and unknown amounts of blur.
A Novel Missing Data Imputation Algorithm for Deep Learning-Based Anomaly Detection System in IIoT Networks

Industrial Internet of Things (IIoT) devices and applications are increasing in rate due to their effective usage in business and industrial operations. IIoT sector is always exposed to major cyber-attacks due to its internet-based infrastructure. To protect these Critical Infrastructures (CI), several Anomaly Detection Systems (ADSs) are proposed in existing systems. Even though the implemented ADS models identify malicious activities occurring in network, they often encounter the problem of missing values in input dataset collected. Missing values is one of the serious problems that have to be addressed. Existing missing value handling techniques do not achieve better performance especially in deep learning-based ADS models. To handle these problems, a new type of ADS system is proposed. The proposed ADS system implements a novel algorithm named Enhanced DNN for Missing Data Imputation (EDMDI). EDMDI solves missing data problem in dataset and allows the Deep Neural Network (DNN) classifier to efficiently classify normal and anomaly data. The experimental results achieved depict that the proposed model exhibits an overall improvement with respect to system accuracy, and it achieves a higher attack detection rate than existing ADS systems. This algorithm can be implemented in crucial areas where missing data problem occurs frequently.
Exploration and Practice on the Construction of Computer First-class Major

This paper analyzes the current situation of the construction of the first-class computer major, and puts forward the construction scheme of the first-class computer major in local colleges based on the concept of outcome based education (OBE). The reform scheme of the construction of the first-class computer major is elaborated from four aspects: training objectives, curriculum system, teaching mode, and evaluation mechanism.
Federated Graph Classification over Non-IID Graphs

Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems. Similar to other domains, multiple local systems, each holding a small set of graphs, may benefit from collaboratively training a powerful graph mining model, such as the popular graph neural networks (GNNs). To provide more motivation towards such endeavors, we analyze real-world graphs from different domains to confirm that they indeed share certain graph properties that are statistically significant compared with random graphs. However, we also find that different sets of graphs, even from the same domain or same dataset, are non-IID regarding both graph structures and node features. To handle this, we propose a graph clustering federated learning (GCFL) framework that dynamically finds clusters of local systems based on the gradients of GNNs, and theoretically justify that such clusters can reduce the structure and feature heterogeneity among graphs owned by the local systems. Moreover, we observe the gradients of GNNs to be rather fluctuating in GCFL which impedes high-quality clustering, and design a gradient sequence-based clustering mechanism based on dynamic time warping (GCFL+). Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed frameworks.
Blockchain insisted resilience enhancement of power electricity markets using distributed energy trading

Abstract The resilience of the power grid is taking a vital role in the energy supply and distribution process. But due to the rapid growth of integration of renewable distributed energy sources to the traditional power grid, the nature of the trading system is shifted from the centralized to decentralized or distributed manner. Blockchain is one of the most emerging security technologies that change the dimension of the financial and energy sector with openness and complete freedom. Blockchain implemented distributed energy trading promotes decentralized electricity markets. The physical system consists of the planning of the routing of energy from one place to another. A user can not send power to another customer in any severe outage or natural disaster in a traditional system. The user cannot participate in the trade globally. So in this proposed methodology, the connections of each customer to the central grid through several microgrids have been shown. In this work, an innovative approach in trading and finding a solution to a payment method is proposed. Ethereum blockchain-based smart contracts completed the entire trading system in automation mode in our proposed system. Any power outage at one place can be compensated by routing a new path of energy inflow from another active source, which enhances the system’s resiliency to a certain degree. While trading, no third parties will be engaged so that the transaction is efficient and fast.
Human Pose Estimation in UAV-Human Workspace

A 6D human pose estimation method is studied to assist autonomous UAV control in human environments. As autonomous robots/UAVs become increasingly prevalent in the future workspace, autonomous robots must detect/estimate human movement and predict their trajectory to plan a safe motion path. Our method utilize a deep Convolutional Neural Network to calculate a 3D torso bounding box to determine the location and orientation of human objects. The training uses a loss function that includes both 3D angle and translation errors. The trained model delivers <10-degree angular error and outperforms a reference method based on RSN.
A Deep Decomposable Model for Disentangling Syntax and Semantics in Sentence Representation

Recently, disentanglement based on a generative adversarial network or a variational autoencoder has significantly advanced the performance of diverse applications in CV and NLP domains.Nevertheless, those models still work on coarse levels in the disentanglement of closely related properties, such as syntax and semantics in human languages.This paper introduces a deep decomposable model based on VAE to disentangle syntax and semantics by using total correlation penalties on KL divergences.Notably, we decompose the KL divergence term of the original VAE so that the generated latent variables can be separated in a more clear-cut and interpretable way.Experiments on benchmark datasets show that our proposed model can significantly improve the disentanglement quality between syntactic and semantic representations for semantic similarity tasks and syntactic similarity tasks.
Uso de herramientas de la Web 2.0 en la metodología aula invertida: una opción para clases a distancia en tiempos de COVID-19

The flipped classroom is an active method where information in delivered before class and the time allocated for it is dedicated to achieving more complex learning objectives. Diverse resources are used in each phase of the methodology. However, Web 2.0 or "Social Web" tools allow greater personal and remote interaction.To describe the perception about flipped classroom methodology implemented using web 2.0 tools in 48 medical students from the University Mayor.The students were invited to view four video-classes available on Edpuzzl. During the activity, questions about the topic were inserted to promote understanding, research, and teamwork, then the topic was discussed using Case-Based Learning through the Quizizz program. A survey composed of 12 closed items and an open question was applied to analyze the perceptions of students. Data were analyzed using the SPSS and Atlas-Ti programs.A high satisfaction rate was observed about the way in which the content, discussion and communication of the information was presented. The methodology encouraged the development of generic competences, but it was not always positively perceived.The flipped classroom methodology developed with Edpuzzle was very well evaluated by students. This teaching method may be particularly useful in the epidemiological context of COVID-19 and other situations which require distant learning.
Generic Mechanism for Reducing Repetitions in Encoder-Decoder Models

Encoder-decoder models have been commonly used for many tasks such as machine translation and response generation.As previous research reported, these models suffer from generating redundant repetition.In this research, we propose a new mechanism for encoderdecoder models that estimates the semantic difference of a source sentence before and after being fed into the encoder-decoder model to capture the consistency between two sides.This mechanism helps reduce repeatedly generated tokens for a variety of tasks.Evaluation results on publicly available machine translation and response generation datasets demonstrate the effectiveness of our proposal.
Network Attack and Defense Modeling and System Security Analysis: A Novel Approach Using Stochastic Evolutionary Game Petri Net

At present, most network security analysis theory assumes that the players are completely rational. However, this is not consistent with the actual situation. In this paper, based on the effectiveness constraints on both sides with network attack and defense, with the help of stochastic Petri net and evolutionary game theory, the Petri net model of network attack and defense stochastic evolutionary game is reconstructed, the specific definition of the model is given, and the modeling method is given through the network connection relationship and attack and defense strategy set. Using this model, a quantitative analysis of network attack events is carried out to solve a series of indicators related to system security, namely, attack success rate, average attack time, and average system repair time. Finally, the proposed model and analysis method are applied to a classic network attack and defense process for experimental analysis, and the results verify the rationality and accuracy of the model and analysis method.
Visual Analysis System for Fine-Grained Inline Relationship of Air Quality Data

<p indent=0mm>With the rapid development of data observation and digital computing technology, the granularity of air quality data tends to be fine, which provides a data basis for visualizing data of air quality and understanding the transformation law of air pollutants. This paper proposes a visual analysis system for inline features of fine-grained air quality data. We design a spatial-temporal data dimensional model to enhance the spatial-temporal expression of the data. We propose a deep learning based scheme that embeds high-dimensional air quality data into a low-dimensional feature space, which supports interactive visual analysis. We implement a multi-view linkage visualization system which assists discover the fine-grained inline relationship in the spread of air pollution and understand the data distribution characteristics of pollution transmission routes. User cases and user evaluation results show that our system is effective.
Social Studies Education Responding to the Challenges of the 21st Century: A Critique of Learning Practices in Elementary Education

21st Century Education is inseparable from technology and information. Technology and Information are the most important components and have always been the needs of the increasingly rapid era, especially in the environment of elementary school children (SD / MI). Writing this article aims to see the relevance of social studies learning in SD/MI with 21st Century skills and find out what skills are acquired through social studies learning in SD/MI. So that they are able to master the skills needed in the 21st century. This research is a conceptual (empirical) study that is library research. This was done, because the researcher used a synthetic analysis of various Sinta accredited articles and books related to social studies education in the 21st century. The results of the study illustrate that social studies learning in SD/MI is still in line with the needs and demands of the 21st century. /MI has 4 capabilities. First, social skills. Second, communication skills. Third, networking or collaboration skills. Fourth, communication skills. The 21st century itself provides demands for skills that must develop in the social studies learning process. So that in the social studies teaching process innovation is needed in various models and learning methods
Playing Atari with Hybrid Quantum-Classical Reinforcement Learning

Despite the successes of recent works in quantum reinforcement learning, there are still severe limitations on its applications due to the challenge of encoding large observation spaces into quantum systems. To address this challenge, we propose using a neural network as a data encoder, with the Atari games as our testbed. Specifically, the neural network converts the pixel input from the games to quantum data for a Quantum Variational Circuit (QVC); this hybrid model is then used as a function approximator in the Double Deep Q Networks algorithm. We explore a number of variations of this algorithm and find that our proposed hybrid models do not achieve meaningful results on two Atari games - Breakout and Pong. We suspect this is due to the significantly reduced sizes of the hybrid quantum-classical systems.
Emotion Stimulus Detection in German News Headlines

Emotion stimulus extraction is a fine-grained subtask of emotion analysisthat focuses on identifying the description of the cause behind an emotionexpression from a text passage (e.g., in the sentence am happy that I passedmy exam the phrase passed my exam corresponds to the stimulus.). Previouswork mainly focused on Mandarin and English, with no resources or models forGerman. We fill this research gap by developing a corpus of 2006 German newsheadlines annotated with emotions and 811 instances with annotations ofstimulus phrases. Given that such corpus creation efforts are time-consumingand expensive, we additionally work on an approach for projecting the existingEnglish GoodNewsEveryone (GNE) corpus to a machine-translated German version.We compare the performance of a conditional random field (CRF) model (trainedmonolingually on German and cross-lingually via projection) with a multilingualXLM-RoBERTa (XLM-R) model. Our results show that training with the Germancorpus achieves higher F1 scores than projection. Experiments with XLM-Routperform their respective CRF counterparts.
How Can South Korea Teach, Lead, and Better Engage with the Asia-Pacific in Shaping Data Governance for the 5G Era?

executive summary: This essay explores how South Korea might contribute to the crafting of a new strategy designed to strengthen and better align regional approaches to data governance. main argument South Korea has not always fit cleanly into various U.S.-, Japanese-, or Chinese-led efforts to shape data governance in the Asia-Pacific. Yet there are few countries as important to the success of any collaborative effort to improve and better align regional data governance practices. South Korea not only is at the forefront of designing and producing a range of next-generation technologies but also is in the midst of a multiyear effort to reform its own data governance policies to better deploy these technologies. Moreover, South Korea has been a champion of prior efforts within APEC and other forums to address barriers to information flows. Identifying opportunities to learn from, build on, and further sharpen Seoul's existing efforts would benefit both South Korea and numerous regional stakeholders. policy implications • States and international bodies interested in regional data governance can learn from South Korea's ongoing process of wrestling with the debates inherent in data governance and reforming its frameworks. These insights include how public policy can benefit from inviting input from industry, civil society, and other stakeholders outside government into decision-making processes. • Both formal legislation and statements by the Moon administration are suggestive of South Korean goals for data governance that overlap with U.S. priorities, such as aims to safeguard privacy rights, bolster digital development, and enable secure e-commerce. Still, divergences remain between how both countries have dealt with potential trade-offs in these goals, which may complicate efforts to advance joint regional leadership. • In light of the novel and complex questions that have arisen during the Covid-19 pandemic, South Korea should increase its engagement with an array of other countries reviewing their uses of big data tools in managing the crisis. Dialogue on this issue could take place on a bilateral basis—including with Taiwan, Singapore, Australia, and India—or within multilateral groupings, such as APEC or the G-20.
Quality Metrics for Atmospherically Distorted Images

This paper describes a number of quality metrics for quantifying the quality of atmospherically distorted images and corrected images. Metrics that require a truth image (SSIM, MSE) and no-reference metrics (NIQE, BRISQUE) will be reviewed.
Adapting Entities across Languages and Cultures

How would you explain Bill Gates to a German? He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts. This type of translation is called adaptation in the translation community. Until now, this task has not been done computationally. Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education. We propose two automatic methods and compare them to human results for this novel NLP task. First, a structured knowledge base adapts named entities using their shared properties. Second, vector-arithmetic and orthogonal embedding mappings methods identify better candidates, but at the expense of interpretable features. We evaluate our methods through a new dataset of human adaptations.
Application of blockchain technology in “smart-digital hospital” cyber physical systems

Health informatization as an urgent problem has not yet been resolved. Quality medical care, intellectual level, communication infrastructure, management process, control of equipment, sensors are presented as a “smart-digital hospital” cyberphysical system with modern information technologies. The most important condition for “smart-digital” hospitals is that health workers inform patients about patients using the Internet of Things and mobile applications, and ensure their comfort.