Reviewerly/exHarmony huggingface dataset:
Research on Image Recognition Technology of Transmission Line Icing Thickness Based on LSD Algorithm

Icing on transmission lines has become one of the important factors that endanger the safe and stable operation of transmission lines. The timely identification of ice thickness on transmission lines can effectively prevent the damage caused by ice disasters. In order to improve the measurement accuracy of icing thickness of transmission lines, this paper proposed a linear detection algorithm based on LSD algorithm to detect icing thickness of transmission lines. Firstly, image processing methods such as image pre-processing, morphological processing, and edge detection are used to pre-process the icing image of the transmission line. Then, the edge of the icing image is detected using the LSD algorithm. Finally, the ratio of the image pixels to the actual diameter of the wire is used to find out of ice thickness. The experimental results show that the error of this method is 0.0443, which can be used to measure the thickness of icing.
A channel of communication through secured 3-party AQKDP

In quantum cryptography, quantum key distribution protocols (QKDPs) make use of quantum mechanisms to circulate treatment secrets as well as public conversations to look for viewers and confirm the accuracy of a session key. Having said that, social conversations call for additional communication rounds in between a sender as well as the receiver and expense precious qubits. By comparison, timeless cryptography supplies handy techniques that make it possible for efficient key confirmation and user verification.
A Multi-Strategy Batch Mode Active Learning Algorithm for Image Classification

Active learning is often used to alleviate the problem of insufficient labeled samples. A batch of samples is selected by batch mode active learning for labeling each iteration, but when a single querying strategy is adopted, the selected samples will exist redundant information, resulting in additional labeling costs. To solve this problem, a multi-strategy batch mode active learning is proposed. Based on uncertainty strategy, clustering and similarity measures are introduced into our algorithm. The convolutional auto-encoder (CAE) and clustering are used to measure the representativeness of unlabeled samples, and then, the distance measure is used to measure the similarity between unlabeled samples and labeled samples. Based on the MNIST, CIFAR-10 and Alzheimer’s disease neuroimaging initiative (ADNI) datasets, the experimental results show that the algorithm can effectively improve the model performance.
Book Marks of the Personal Library of the German Bibliophile Prince George of Anhalt

For many years, the Rare Books Department (Book Museum) of the Russian State Library has been conducting up-to-date work on the description of ex-libris, which contributes to the disclosure of the Department’s collections. The main goal of this research is to identify, record, study, publish, and thereby show the variety and richness of the ownership marks found on books. This article is devoted to the book marks of the German bibliophile Prince George III of Anhalt (1507—1553) from the collection of the Russian State Library. In the Russian-language research literature, Prince George’s book marks have not been considered before. The highly valued private library, later named after the owner — “Georgs-Bibliothek”, used to be part of the Land Library in Dessau (Germany). A small part of this famous book collection came after World War II to the V.I. Lenin State Library of the USSR and is now stored in the Book Museum. On the example of the small fragment of Prince George’s famous library, the article traces the gradual appearance and development of the unique ex-libris of this collection, reveals the literary and bibliophile interests of the owner, and establishes the circle of his communication. In the course of the study, about a hundred owner’s marks were recorded, thanks to which there were identified more than 120 publications from the personal collection of Prince George of Anhalt. The article presents the main types of its ex-libris (handwritten, gift, and super-ex-libris), which are reproduced and described in detail.
EDGE Enhancement Network for Weakly Supervised Semantic Segmentation

It is difficult and expensive to obtain labels for image semantic segmentation tasks. This has led to more and more researches focusing on weakly supervised semantic segmentation (WSSS) with image-level labels and the class activation maps (CAM) is often used to locate objects. The performance of WSSS methods largely depends on the accuracy of the generated CAMs, but current methods can only perform rough locali/ation, especially cannot fit CAM to the object’s edge well. Therefore, we propose the Edge Enhancement Network (EEN), which uses the shallow features of the network to enhance edge information. This allows us to obtain more accurate CAMs that fit the edges only through image-level labels. It greatly improves the accuracy of pseudo ground truth and easily reaches the level of current mainstream methods. After many experiments, it has reached 67.0% mIoU on the Pascal VOC2012 validation set, which exceeds the latest method under the same training settings.
Micro-video Production and Its Application in English Teaching

Micro-video English teaching resources are used in English teaching and conform to the development direction of modern education technology. They have the advantages of visualization of learning content, diversification of learning resources, convenient use of resources, concentration of learning content, and entertainment of learning process. Based on the micro-video production process, this article explains in detail to explain the method of using Camtasia Studio to make micro-videos, including initial production, post-production and corresponding skills, and solves the key technical problems of micro-video production. It also proposes the use of micro-videos in English teaching about the application strategy: pay attention to the interactive design of micro-videos, choose resources that give full play to the specialties of micro-videos, put students in a real English language environment, and improve students' English autonomous learning ability.
Motion Driven Temporal Adaptive Enhanced Graph Convolutional Networks for Skeleton-Based Action Recognition

In this work, we propose a novel motion driven temporal adaptive enhanced graph convolutional network (MTA-EGCN) for skeleton-based action recognition. Firstly, we use skeleton motion to adaptively strengthen the features of key frames in action recognition. This is motivated by an intuition that the frames in a moment with drastic motion are rather informative. Secondly, to capture spatiotemporal information, we propose a cross-spatiotemporal graph convolution that extends the receptive field from a frame to three adjacent frames. Moreover, by coupling these proposals with other GCN methods, our method aids in further performance improvements. Extensive experiments on the two large-scale datasets, i.e., NTU-RGBD and Kinetics-Skeleton, demonstrate that our method is effective.
Reinforcement Learning Versus Generative Adversarial Networks for Video Summarization

Video summarization is the method of generating a brief and representative video clip from the original video preserving its main content. Numerous approaches have been introduced due to the exponential increase of online videos and the success of deep learning in computer vision tasks. In this paper, we review video summarization methods with an indepth focus on unsupervised methods based on reinforcement learning and generative adversarial networks for their promising results. We discuss the paradigm used for each of these two approaches, conducting our experiments, focusing mainly on the impact of the networks using the TVSum dataset. We found that these methods' deep networks produce a quite flat distribution for frames' importance scores. We notice also that for some methods, regardless of having a good final F-score result, the networks do not impact the results as intended leaving the rest of the summarization pipeline to overrule the generated summary output.
Directed Gaussian graphical models with toric vanishing ideals

Directed Gaussian graphical models are statistical models that use a directed acyclic graph (DAG) to represent the conditional independence structures between a set of jointly normal random variables. The DAG specifies the model through recursive factorization of the parametrization, via restricted conditional distributions. In this paper, we make an attempt to characterize the DAGs whose vanishing ideals are toric ideals. In particular, we give some combinatorial criteria to construct such DAGs from smaller DAGs which have toric vanishing ideals. An associated monomial map called the shortest trek map plays an important role in our description of toric Gaussian DAG models. For DAGs whose vanishing ideal is toric, we prove results about the generating sets of those toric ideals.
A Brief Introduction of Chinese Taboos in Chinese Education

short-paper A Brief Introduction of Chinese Taboos in Chinese Education Share on Author: Mengxi Liu Sichuan University, China Sichuan University, ChinaView Profile Authors Info & Claims CIPAE 2021: 2021 2nd International Conference on Computers, Information Processing and Advanced EducationMay 2021 Pages 45–48https://doi.org/10.1145/3456887.3456898Online:09 June 2021Publication History 0citation16DownloadsMetricsTotal Citations0Total Downloads16Last 12 Months16Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An approach to identify the hidden patterns in the datasets for patients with the multiple chronic diseases

The article considers the problem of revealing the hidden patterns in the datasets on patients with the multiple chronic diseases and proposes the approach which implements a search for the database of the associative rules with the given threshold of confidence and the predetermined quantitative composition of the right and left parts of the rules. The influence of the preliminary processing results of the features in the dataset, in particular, such feature as the age of patients, on the final solution of the problem of revealing the hidden patterns has been analyzed. The examples of identifying the associative rules in the datasets for patients with the multiple chronic diseases have been given, including the example of identifying the patterns for patients with the type 2 diabetes mellitus.
Built-in translation on Web Messenger

Abstract The paper aims at providing the users with a web messenger application that translates messages sent by sender to the receiver. The translation is provided by Google Translate API. This helps in having a seamless conversation experience. Any language that is supported by Google Translate can be sent and received by the users. It is planned to provide transfer of graphic files as well as other file types. Interestingly, this method of communication is the most popular form. Encryption and Decryption is provided by employing AES Algorithm function set. The encryption and decryption make it safe for communication by protecting the conversation from outside threats. In order to have a conversation between two people normally in their own preferred language, they have to rely on using Google Translate solely by copying the message text and finding the translation. This web application we have proposed combats that problem by providing simultaneous translation of text messages to the user’s preferred language. Languages that can be translated are the same as what Google Translate provides. ISO 639-1 language codes are used as language code by the sending entity to send the data to the receiving entity. Accuracy have been identified as same as what Google Translate provides, above 60%.
Multi-object Tracking with Long Short -Term Memory

Multi-object tracking is an important problem in the field of computer vision. However, it has to face a lot of challenges, such as missed targets, occlusion, similar appearance, and the complex movement of objects. In order to cope with the above issues, we propose a multi-object tracking framework with Long Short-Term Memory (LSTM). Firstly, in order to handle the occlusion and complex motion of objects in the tracking process, a LSTM motion model is established. Secondly, a two-stage tracking framework is applied to deal with the missed targets and ID switches, in which trajectories are clustered and optimized after solving the network flow model. Experimental results on the public MOT16 dataset demonstrate that our algorithm has a promising performance compared with other state-of-art methods.
Toward Agent-based Interactive Systems to Support Rehabilitation Process

Rehabilitation on a regular basis is essential to recover functional ability for patients with physical disabilities. However, performing correct movements without the therapist's guidance is considered to be a challenging problem. To this end, we propose an interactive system to support patients through rehabilitation, where a movement guidance agent is included. Our system recognizes and assists patients while engaging in rehabilitation activities. The proposed approach consists of three significant steps: (1) the estimation of patient movement through the camera; (2) the detection of patient movement; and (3) the detected movements, are then observed by an agent in order to instruct correct movement, if the movement was incorrectly performed. To this end, we propose an agent-based system that allows users to perform rehabilitation exercises anywhere with the proper guidance system. The proposed system is applied to track patient activities in real-time, with lightweight devices such as mobile phones.
Speckle pattern modulation for high-resolution displacement measurements

For digital image speckle correlation (DISC), a novel approach is introduced where an ensemble average over multiple different speckle patterns is calculated. As a result, the measurement uncertainty of the displacement is reduced by an order of magnitude without deteriorating the spatial resolution. This enables precise surface displacement field measurements in the micrometer range with a measurement uncertainty lower than 100 nm at a spatial resolution below 20 µm. By using a digital micromirror device (DMD) for illumination modulation, measuring rates in the range of 25 Hz are possible while each measurement is based on 80 images.
Reversible data hiding method in encrypted images using secret sharing and Huffman coding

Early works on reversible data hiding in encrypted images (RDHEI) usually generate only one encrypted image from the original image, if the encrypted image is lost or maliciously damaged by an attacker, all information about the original image will be lost. Thus, storing images in distributed servers is an effective solution. Therefore, this paper proposes an RDHEI method using (k, n)-threshold secret sharing. It is not a traditional secret sharing technique, but takes its principles and ideas. In this algorithm, we combine the image encryption and sharing process to generate n shares and send them to the cloud for storage. It maintains the advantage of (k, n)-threshold secret sharing technique that only at least k shares can successfully recover the original image while less than k shares cannot. In addition, the size of each share is smaller than the original image, so that it can save storage spaces. By Huffman coding the pixel difference in the encrypted image block, secret data are embedded into these shares. It is a full reversible method that data extraction and image recovery are performed separately and losslessly. Simulation results, comparisons and security analysis are demonstrated to show superior performances of the proposed algorithm.
Construction and Application of Knowledge Graph for Commanders of Synthetic Brigade

Synthetic brigade is the decisive force to carry out ground assault missions, Under the conditions of informationized warfare, battlefield information presents the characteristics of multi-source heterogeneity. How to transform information advantage into decision advantage is a big challenge for commanders. In this paper, the knowledge graph technology is used to design and implement the intelligent question and answer system for the commander of the synthetic brigade, which can effectively improve the combat effectiveness of the troops.
Analytic comparison of various classifier algorithms

Clustering is the classification of knowledge into the aspects of data that are identical. The catgory is referred to as a unit, which has made different objects that seem to be identical to one another but not to entities from other classes. The purpose of this work is to examine various data clustering algorithms. To separate the set of data into multiple parts, clustering algorithms typically use spacing metric or a distance metric. Two areas have made the extensive use of well-known clustering algorithms. The programmed and data collection are used in that field to define the scheduling technique that has been used. Because information is often a true number, where quantitative datasets are simple to incorporate and it could be used to demonstrate ratio performance. The expectation maximization (EM) k - cluster algorithm, K-means algorithm, Hierarchical Proposed method, Self-Organizing Maps (SOMs) algorithm as well as the K-means technique is the techniques in investigation. All these different classification algorithms depend on the value of the data, the number of variables and the kind of dataset. Any of the conclusions drawn are related to efficiency and quality and information specific emphasized on real time current info based.
PCOR: Private Contextual Outlier Release via Differentially Private Search

Outlier detection plays a significant role in various real world applications such as intrusion, malfunction, and fraud detection. Traditionally, outlier detection techniques are applied to find outliers in the context of the whole dataset. However, this practice neglects the data points, namely contextual outliers, that are not outliers in the whole dataset but in some specific neighborhoods. Contextual outliers are particularly important in data exploration and targeted anomaly explanation and diagnosis. In these scenarios, the data owner computes the following information: i) The attributes that contribute to the abnormality of an outlier (metric), ii) Contextual description of the outlier's neighborhoods (context), and iii) The utility score of the context, e.g. its strength in showing the outlier's significance, or in relation to a particular explanation for the outlier. However, revealing the outlier's context leaks information about the other individuals in the population as well, violating their privacy. We address the issue of population privacy violations in this paper. There are two main challenges in defining and applying privacy in contextual outlier release. In this setting, the data owner is required to release a valid context for the queried record, i.e. a context in which the record is an outlier. Hence, the first major challenge is that the privacy technique must preserve the validity of the context for each record. We propose techniques to protect the privacy of individuals through a relaxed notion of differential privacy to satisfy this requirement. The second major challenge is applying the proposed techniques efficiently, as they impose intensive computation to the base algorithm. To overcome this challenge, we propose a graph structure to map the contexts to, and introduce differentially private graph search algorithms as efficient solutions for the computation problem caused by differential privacy techniques.
Improving Deep Object Detection Backbone with Feature Layers

Deep neural networks are the frontier in object detection, a key modern computing task. The dominant methods involve two-stage deep networks that heavily rely on features extracted by the backbone in the first stage. In this study, we propose an improved model, ResNeXt101S, to improve feature quality for layers that might be too deep. It introduces splits in middle layers for feature extraction and a deep feature pyramid network (DFPN) for feature aggregation. This backbone is neither much larger than the leading model ResNeXt nor increasing computational complexity distinctly. It is applicable to a range of different image resolutions. The evaluation of customized benchmark datasets using various image resolutions shows that the improvement is effective and consistent. In addition, the study shows input resolution does impact detection performance. In short, our proposed backbone can achieve better accuracy under different resolutions comparing to state-of-the-art models.
cross site scripting attack - case study on malicious scripts

Cross-site scripting is one of the major problems occuring in Web Applications. With more connected devices which use different kind of web applications for pretty much every job, the danger of XSS attacks is increasing rapidly. In these web applications, attacker steals victim’s session details and other important info by exploiting XSS vulnerabilities.
Image Inpainting Using Edge Structure Aware Hierarchical Guidance

Image inpainting is a challenging task to recover the missing visual information of an image in a visually plausible way. To tackle this challenge, this paper proposes a new image in-painting approach that employs the edge structure knowledge to hierarchically guide image completion. The hierarchical guidance consists of the following two key components. First, a new edge structure aware fusion is proposed to encourage information fusion between image feature maps and edge structure feature maps hierarchically via a channel-wise attention mechanism. Second, a novel edge structure aware loss is proposed, with the help of the conventional image loss together, to reconstruct the refined image via hierarchically supervising side outputs from the image completion network. Extensive experimental results are provided to demonstrate that the proposed approach reconstructs images with better quantitative quality and better qualitative performance using two benchmark image datasets.
Research on Modulation Recognition Algorithm Based on Combination of Multiple Higher-Order Cumulant

The algorithm of modulation recognition using a single higher-order cumulant as the characteristic parameter is usually limited, and the recognition performance needs to be improved, for this reason, a recognition method is proposed, which uses the combination of multiple higher-order cumulants to construct the characteristic parameters, so that it contains more signal characteristics, and realizes the recognition of multiple signals of MASK, MPSK, and MFSK. The MATLAB simulation shows that it has a better recognition rate.