Reviewerly/exHarmony huggingface dataset:
Instant Quantization of Neural Networks using Monte Carlo Methods

Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose Monte Carlo methods to quantize the weights and activations of pre-trained neural networks without any re-training. By performing importance sampling, we obtain quantized low bit-width integer values from full-precision weights and activations. The precision, sparsity, and complexity are easily configurable by the amount of sampling performed. Our approach, called Monte Carlo Quantization (MCQ), is linear in both time and space, with the resulting quantized, sparse networks showing minimal accuracy loss when compared to the original full-precision networks. Our method either outperforms or achieves competitive results on CIFAR-10 and ImageNet when compared to previous methods that do require additional training.
Studying Emerging New Contexts for Museum Digitisations on Pinterest

In a SweClarin cooperation project we apply topic modelling to the texts found with pins in Pinterest boards.The data in focus are digitisations of Viking Age finds from the Swedish History Museum and the underlying research question is how they are given new contextual meanings in boards.We illustrate how topic modelling can support interpretation of polysemy and culturally situated meanings.It expands on the employment of topic modelling by accentuating the necessity of interpretation in every step of the process from capturing and cleaning the data, to modelling and visualisation.The paper concludes that the national context of digitisations of Viking Age jewellery in the Swedish History Museum's collection management system is replaced by several transnational contexts in which Viking Age jewellery is appreciated for its symbolical meanings and decorative functions in contemporary genres for re-imagining, reliving and performing European pasts and mythologies.The emerging contexts on Pinterest also highlight the business opportunities involved in genres such as reenactment, neo-paganism, lajv and fantasy.The boards are clues to how digitisations serve as prototypes for replicas.
Extracting Relations in Texts with Concepts of Neighbours

During the last decade, the need for reliable and massive Knowledge Graphs (KG) increased. KGs can be created in several ways: manually with forms or automatically with Information Extraction (IE), a natural language processing task for extracting knowledge from text. Relation Extraction is the part of IE that focuses on identifying relations between named entities in texts, which amounts to find new edges in a KG. Most recent approaches rely on deep learning, achieving state-of-the-art performances. However, those performances are still too low to fully automatize the construction of reliable KGs, and human interaction remains necessary. This is made difficult by the statistical nature of deep learning methods that makes their predictions hardly interpretable. In this paper, we present a new symbolic and interpretable approach for Relation Extraction in texts. It is based on a modeling of the lexical and syntactic structure of text as a knowledge graph, and it exploits Concepts of Neighbours, a method based on Graph-FCA for computing similarities in knowledge graphs. An evaluation has been performed on a subset of TACRED (a relation extraction benchmark), showing promising results.
Distance Learning as a Challenge of the Times: An E-Learning Experience During the COVID-19 Pandemic

The article presents a systematic analysis of responses to the challenges posed by the need to move to distance learning during quarantine through COVID-19. It is presented the state of the art at the beginning of quarantine and step-by-step strategy of transition according to stages of preparation, rules coordination, work with HR, technological maintenance, planning educational process, content creation and quality assurance. There are presented results of the survey, which revealed the problems and possible steps to overcome them. An innovative model for organizing technological learning environment for high-quality e-transformation of learning is proposed.
Steering information in quantum network

In this contribution, we investigate the possibility that one member of a quantum network can steer the information that encoded in the state of other member. It is assumed that, these members have a direct or indirect connections. We show that, the steerability increases at small values of the channel' strength. Although, the degree of entanglement between the direct interacted nodes is smaller than that displayed for the non-interacted nodes, the possibilities of steering a member of the direct interacted nodes and the non-direct nodes are almost similar.
Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.
Synergy and Communication of IoT Industry Development Strategies in the Perspective of Low Carbon Economy

In this paper, from the perspective of a low-carbon economy, the development mode and influencing factors of the IoT industry are studied, and development strategies are proposed in a targeted manner. Through the analysis of relevant literature, the connotation of the concept of cooperation and dissemination of IoT industry development strategy under low-carbon economy is enriched, emphasizing its systemic, multisubjectivity, and low-carbon effect, and its mechanistic effect is analyzed and different types of models are analyzed and established. The analysis of the model of IoT industry development strategy cooperation and dissemination in low-carbon economy finds that there is industrial variability in the overall trend of continuous increase of IoT industry development strategy cooperation and dissemination in the low-carbon economy, and the reasonable industrial categories and their characteristics are pointed out through cluster analysis. Based on the viewpoint of the IoT industry and regional differentiation, the analysis establishes the path analysis model of enterprise, industry, and region. From the perspective of enterprises using game analysis method research shows that the development process of IoT industry under low-carbon economy first adopts the important benchmarking significance of enterprise technology innovation behavior and its effect on potential enterprises and then pays attention to the government financial and tax policies and market cultivation on the coincidence between different types of IoT enterprises. The research shows that there are fault paths, distorted paths, and pathway types in the development of the IoT industry under a low-carbon economy. Finally, based on the policy analysis, we summarize our shortcomings and the above-mentioned development path views and make suggestions for better policy formulation of IoT industry development in a low-carbon economy from enterprise-level, industry level, and regional level. The above perspectives, conclusions, and policy recommendations on the synergy and dissemination of development strategies will have important positive theoretical and practical guidance for promoting the development of the low-carbon economy and IoT industry.
An Autocontouring Method for the Kidneys Using an Adaptive Weighted Multikernel Support Vector Machines

In radiotherapy, the location of the target area is very important. If the target area is small, the treatment is not complete, so the location of the target area is generally larger than the actual cancerous site. However, the damage of radiotherapy to normal cells is the same. In order to reduce the damage to the body as much as possible, we need to complete the most suitable target area. This paper uses an adaptive weighted multikernel support vector machine, which solves the parameter problem in the traditional multikernel support vector machine. The new AW-SVM can adjust the kernel weights adaptively. We completed our experiment on the abdominal MR dataset, using DSI as an evaluation indicator, and the experimental results showed its excellent classification performance. The minimum value of DSI in all results is 0.9654 (more than 0.7 is acceptable).
Generative modeling for label-free glomerular modeling and classification.

Generative modeling using GANs has gained traction in machine learning literature, as training does not require labeled datasets. This is perfect for applications in biological datasets, where large labeled datasets are often difficult and expensive to acquire. However, generative models offer no easy way to encode real images into feature-sets, something that is desirable for network explainability and may yield potentially informative image features. For this reason, we test a VAE-GAN architecture for label-free modeling of glomerular structural features. We show that this network can generate realistic looking synthetic images, and be used to interpolate between images. To prove the biological relevance of the network encodings, we classify small-labeled sets of encoded glomeruli by biopsy Tervaert class and for the presence of sclerosis, obtaining a Cohen's kappa values of 0.87 and 0.78 respectfully.
Research on Web Data Mining Based on Topic Crawler

This paper analyzes the method of Web information data mining based on topic crawler. This paper puts forward the architecture of Web information search and data mining, and introduces the key technology and operation principle of the architecture. After analyzing the functions and shortcomings of ordinary crawler, this paper focuses on the working principle, implementation method and performance analysis of this crawler, as well as the functions of this crawler different from other crawlers and its application in Web information search and data mining system. The experimental results show that the crawler can get all kinds of information resources on the world wide web, which is helpful to the monitoring and management of network cultural content.
DropEdge: Towards the Very Deep Graph Convolutional Networks for Node Classification

Existing Graph Convolutional Networks (GCNs) are shallow---the number of thelayers is usually not larger than 2. The deeper variants by simply stackingmore layers, unfortunately perform worse, even involving well-known tricks likeweight penalizing, dropout, and residual connections. This paper reveals thatdeveloping deep GCNs mainly encounters two obstacles: \emph{over-fitting} and\emph{over-smoothing}. The over-fitting issue weakens the generalizationability on small graphs, while over-smoothing impedes model training byisolating output representations from the input features with the increase innetwork depth. Hence, we propose DropEdge, a novel technique to alleviate bothissues. At its core, DropEdge randomly removes a certain number of edges fromthe input graphs, acting like a data augmenter and also a message passingreducer. More importantly, DropEdge enables us to recast a wider range ofConvolutional Neural Networks (CNNs) from the image field to the graph domain;in particular, we study DenseNet and InceptionNet in this paper. Extensiveexperiments on several benchmarks demonstrate that our method allows deep GCNsto achieve promising performance, even when the number of layers exceeds30---the deepest GCN that has ever been proposed.
STUDENTS’ PERCEPTION TOWARDS THE USE OF WEBTOON TO IMPROVE READING COMPREHENSION SKILL

This research discussed about student’s perception on the use of Webtoon to improve reading comprehension skill. Webtoon is a new platform that can be used to improve reading skill by its words used and interesting pictures. The purpose of this study is to explain whether or not Webtoon can increase students’ interest in reading comprehension skill. The respondents of the study are the students of English Education students at Universitas Teknokrat Indonesia.The result of the study indicates that the students are more interested and get the motivation to improve their reading comprehension skill by using Webtoon.
Image Inpainting Using Channel Attention and Hierarchical Residual Networks

<p indent="0mm">Existing deep-learning-based inpainting methods may have some shortcomings in perceiving and presenting image information at multi-scales. For this problem, we proposed an image inpainting model based on multi-scale channel attention and a hierarchical residual backbone network. Firstly, we adopted a U-Net architecture as the generator backbone of our inpainting model to encode and decode the damaged image. Secondly, we built multi-scale hierarchical residual structures in the encoder and decoder respectively, which can improve the ability of the model to extract and express occluded image features. Finally, we designed a dilated multi-scale channel-attention block and inserted it into the skip-connection of the generator. This block can improve the utilization efficiency of low-level features in the encoder. Experimental results show that our model outperforms other classical inpainting approaches in the face, street-view inpainting tasks, both qualitatively and quantitatively.
Simulation of single-phase ground short circuit protection device

The most common type of short circuit in a three-phase system is a single-phase-to-ground short circuit, accounting for 70 to 90% of electrical faults. In this paper simulation of the protective device from single-phase-to-ground short circuit with automatic change of current setting in electrical networks of 6-10 kV voltage on the basis of Coloured Petri Net is considered. The complexity of elaborated technical systems makes the problem of their modelling actual at the stage of development with the purpose of obtaining estimations of prospective and achievable characteristics. Now the modelling theory of the dynamic discrete systems, based on the formalism of Coloured Petri Nets has a wide application. Petri Net, describing the device operation, was designed and analysed using CPN Tools Programm. The resulting Petri net is correct, as it is live, reversible and safe. Thereby designed device can provide the selective protection from single-phase-to-ground short circuit.
Object Tracking for Automatic Driving

With the development of automatic driving technology, the object tracking based on computer vision is being widely used nowadays. In this paper an overview of object tracking methods in automatic driving are presented. Kalman filtering, LSTM CNN, correlation filtering and Deep Affinity Network will be introduced. Kalman filtering and Kalman filtering extension algorithms and Correlation filtering have been combined with deep learning algorithms about object detection. Learning goals in end-to-end way the appearance of the object characteristics and correlation in several frame, including its appearance modeling study on the hierarchical characteristics of the object and its surrounding. Finally, we conclude the object tracking in automatic driving.
Covariance’s Loss is Privacy’s Gain: Computationally Efficient, Private and Accurate Synthetic Data

Abstract The protection of private information is of vital importance in data-driven research, business and government. The conflict between privacy and utility has triggered intensive research in the computer science and statistics communities, who have developed a variety of methods for privacy-preserving data release. Among the main concepts that have emerged are anonymity and differential privacy. Today, another solution is gaining traction, synthetic data. However, the road to privacy is paved with NP-hard problems. In this paper, we focus on the NP-hard challenge to develop a synthetic data generation method that is computationally efficient, comes with provable privacy guarantees and rigorously quantifies data utility. We solve a relaxed version of this problem by studying a fundamental, but a first glance completely unrelated, problem in probability concerning the concept of covariance loss. Namely, we find a nearly optimal and constructive answer to the question how much information is lost when we take conditional expectation. Surprisingly, this excursion into theoretical probability produces mathematical techniques that allow us to derive constructive, approximately optimal solutions to difficult applied problems concerning microaggregation, privacy and synthetic data.
A Development of Least Significant Bit Steganography Technique

Recently, the world has been interested in transferring data between different devices. The transmission of data must be encrypted so that the intended receiver can only read and process a secret message. Hence, the security of information has become more important than earlier. This paper proposes the least significant bit Steganography method to hide a secret message inside an image cover via using dynamic stego-key. To check the effectiveness of the proposed method, many factors are used for evaluation and compared with another method. The results illustrate more robustness at steganography since stego-key depends on the cover image to hide a secret message.
Construction and Application of E-Government Simulation Network Experimental Teaching Practice Environment under the Background of Machine Learning

With the rapid development of economy and Internet technology, e-government has been reflected in the reform of government information management and is closely related to the security of e-government networks. Existing technologies for evaluating network security can provide good evaluation results when dealing with deterministic data, but they are not sufficient to evaluate the uncertainty of information. In order to solve the problem of the construction and realization of the network training environment, a network security assessment method based on gray relational analysis and D-S theory proof is proposed. First, the scaling method is used to determine the weights of different levels of indicators, because different levels of indicators have different effects on the network state. Secondly, taking into account the differences in the degree of participation of different indicators at different levels, the participation function of each level indicator at the level is determined and a weighted amplitude coefficient to reduce the overall uncertainty is established. The network security situation is sudden and volatile. Therefore, the network security prediction method should have a good ability to deal with nonlinear problems. In this case, a network security state prediction method based on the GM performance model is proposed. The GM performance model has excellent nonlinear debugging functions. It has low requirements on the number of samples and good practicability. The experimental results show that the dynamic decision server push framework based on the XGBoost algorithm proposed in this paper has better performance than the single push method for push server, can use network resources appropriately, and improves the network environment security rate by at least 50%.
Improved Deep Hashing with Scalable Interblock for Tourist Image Retrieval

Tourist image retrieval has attracted increasing attention from researchers. Mainly, supervised deep hash methods have significantly boosted the retrieval performance, which takes hand-crafted features as inputs and maps the high-dimensional binary feature vector to reduce feature-searching complexity. However, their performance depends on the supervised labels, but few labeled temporal and discriminative information is available in tourist images. This paper proposes an improved deep hash to learn enhanced hash codes for tourist image retrieval. It jointly determines image representations and hash functions with deep neural networks and simultaneously enhances the discriminative capability of tourist image hash codes with refined semantics of the accompanying relationship. Furthermore, we have tuned the CNN to implement end-to-end training hash mapping, calculating the semantic distance between two samples of the obtained binary codes. Experiments on various datasets demonstrate the superiority of the proposed approach compared to state-of-the-art shallow and deep hashing techniques.
Deep Morphological Anomaly Detection Based on Angular Margin Loss

Deep anomaly detection aims to identify “abnormal” data by utilizing a deep neural network trained on a normal training dataset. In general, industrial visual anomaly detection systems distinguish between normal and “abnormal” data through small morphological differences such as cracks and stains. Nevertheless, most existing algorithms emphasize capturing the semantic features of normal data rather than the morphological features. Therefore, they yield poor performance on real-world visual inspection, although they show their superiority in simulations with representative image classification datasets. To address this limitation, we propose a novel deep anomaly detection algorithm based on the salient morphological features of normal data. The main idea behind the proposed algorithm is to train a multiclass model to classify hundreds of morphological transformation cases applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, making unsupervised learning straightforward. Additionally, to enhance the performance of the proposed algorithm, we replaced the cross-entropy-based loss function with the angular margin loss function. It is experimentally demonstrated that the proposed algorithm outperforms several recent anomaly detection methodologies in various datasets.