Reviewerly/exHarmony huggingface dataset:
Learning to Detect Keypoints of Muti-type UAVs with OpenPose Network

Keypoint detection is mainly a single-type target detection. In this paper, we present an approach for multi-type target keypoints detection. To allow for a learning-based approach, we developed large scale-dataset of multiple-type of UAVs and customized the OpenPose network architecture. The architecture is designed to jointly learn part locations and their association via two branches of the same sequential prediction process. The proposed approach learns from three different (Skylark fixed-wing, the Phantom 4 multi-rotor, and aggregated) UAVs datasets. Furthermore, to evaluate our approach, we conduct an extensive experiment on all three datasets. The results show the effectiveness of the proposed approach with respect to accuracy and dramatically improves the speed compared to other state-of-the-art methods.
A MapReduce Approach to Automatic Key File Updates for SPT (Squeeze, Pack and Transfer) Algorithm

SPT algorithm helps in reducing network congestion by using loseless compression methodology. It helps in improving network usage by efficiently utilizing the available network resources and reducing size of dataset by using inbuilt compression algorithm. This paper epitomizes the use of MapReduce for finding out symmetrical binary keys for SPT (Squeeze Pack and Transfer) algorithm from various data files available on public cloud domains. This paper describes the use of MapReduce functionality which can help in finding out best contender keys for creating generic binary key file for the SPT algorithm which is used for binary compression. This paper discusses the manipulation of MapReduce for creating an automated system which can extract regular binary patterns from data files available on public cloud domain and inculcating them in generic key file which can be used for compressing and decompressing files with the help of the SPT algorithm. It can not only eliminate the manual generation of the keys for SPT algorithm but also it helps in reducing the time required for creation and updates of the key files.
Binary Convolutional Neural Networks for Facial Action Unit Detection

Facial Action Unit (AU) detection is essential for automatic emotion analysis. However, most AU detection researches pay attention to real-valued deep neural networks, which include heavy multiplication operations and require high computation and memory. In this paper, we propose binary convolutional neural networks for AU detection. The main contributions of our work include the following two aspects: (1) we propose a multi-layer decision fusion strategy (MDFS), which combines multiple prediction channels to produce the final result. (2) MDFS is encoded with binary parameters and activations (BMDFS) instead of real values to reduce memory cost and accelerate convolutional operations. Our proposed method is evaluated on BP4D database. MDFS and BMDFS obtain the overall F1 score of 65.5% and 54.8%, respectively. Moreover, BMDFS achieves 32× memory saving and 4.6 × speed up compared to real-valued models. Experimental results show that our approach achieves promising good performance.
Extraction method of weld centerline based on fast median filtering and morphological processing

In order to ensure that the nondestructive testing climbing robot can track the weld more accurately and in real time, a method of weld edge detection and centerline extraction is designed. The histogram-based method is used to achieve fast median filtering of the image, and the background and target weld are segmented by adaptive thresholding. To obtain the contour information of the weld, the binarized image is subjected to secondary filtering and area filling combined with the opening operation of morphology and the erosion operation. After Canny edge detection and Hough transform linear detection, the straight line equations on both sides of the weld are obtained, and the centerline position of the weld is calculated. Experimental result show that this method greatly reduces the amount of calculation, shortens the image processing time, and can accurately identify the center of the weld.
Automatic Recognition System of Motion Posture Based on Information Video Processing Technology

With the rapid development of science and technology, information video processing technology has become a research field that has attracted widespread attention. Automatic recognition of motion gestures is an important research object in the field of information technology. It has been widely used in education, medical treatment and finance, and has promoted the further development of this technology. The application of information video processing technology in motion posture and automatic recognition of motion posture contribute to better scientific education and technical diagnosis. This article is based on the research of automatic motion detection system based on video information processing technology. Based on the summary and analysis of relevant research work at home and abroad, this paper mainly studies the detection of moving posture targets, the segmentation of moving posture targets, and the automatic recognition of moving posture tracking in video sequences based on the characteristics of the motion posture. This research aims to effectively improve the effect of automatic recognition of sports high poses while appropriately reducing the computational complexity. The research focuses on finding simpler and more informative automatic recognition methods of sports poses, and a large number of targeted methods have been carried out. This research uses these algorithms to extract moving images from videos, and proposes an automatic recognition method of motion gestures based on information video processing technology. Experimental results show that the average error rate of the system designed in this paper is 89.5%, which can meet the current application requirements for automatic recognition of motion gestures.
A new method for duplicate document image detection with page layout

The document images often appear in the digital library, social media, e-mail etc. The duplicate copies of the same content bring burden to the management system and waste network traffic and storage resources. This paper proposes a new algorithm for detecting the duplicate document images in large-scale image data sets. The key idea of the proposed algorithm lies in taking advantage of the characteristics of the document image that is structured because of the page layout. In this paper, the text lines are exacted to be taken as elements features of the document image and the Fréchet Distance is introduced to measure the similarity of these features. The experimental results of different types of electronic documents show the advantages of the proposed algorithm in accuracy and stability.
Development of the learning game environment "Bukashka" ("Ladyb ug") Using client web technologies

The theme "Algorithmization and programming" is one of the most difficult in the school informatics course. The computer programs that implement models of microworlds in which virtual actors operate help to explain the basic concepts of this theme for schoolchildren.The best visualization can be achieved using learning environments with drawing actors. The learning environment "Kangaroo Ru" provides the most simple model. It was developed in the early 1990s as a part of an educational project for IBM PS/2 computers running the DOS operating system.The usage of "Kangaroo Ru" is fraught with difficulties on modern computers. This article describes a case of the learning environment that is "Kangaroo Ru" compatible, but is implemented using web technologies HTML, CSS and JavaScript. It is runnable in web browsers of a wide range of hardware platforms.
Various Legal Factors Extraction Based on Machine Reading Comprehension

With the rapid growth of legal cases, professionals are under pressure to go through lengthy documents and grasp informative pieces of text in the limited time. Most of the existing techniques focus on simple legal information retrieval task, such as name or address of the prosecutor or the defendant, which can be easily accomplished with the help of handcrafted patterns or sequence labeling methods. Yet complicated texts always challenge such pattern-based methods and sequence labeling approaches. These texts state the same facts or describe the same events, but they do not share common or similar patterns. In this paper, we design a unified framework to extract legal information in various formats, including directly extracted information (a piece of span) and information that needs to be deduced. The framework follows the methodology to answer questions in machine reading comprehension (MRC) tasks. We treat the extraction fact labels as the counterpart of questions in MRC task and propose several strategies to represent them. We construct several datasets regarding different cases for training and testing. Our best strategy achieves up to 4% enhancement in F1 score on each dataset compared to the MRC baseline.
Residual Net Use on FSRCNN for Image Super-Resolution

In this paper, we present a new method based on Accelerating the Super-Resolution Convolutional Neural Network (FSRCNN) and residual network for the aim of image super-resolution. In our works, we fuse the network structure of FSRCNN and skip connections as the first step of our method, this part can transfer the original features to the training results effectively, in addition, we also change the deconvolution layer to a sub-pixel convolution layer. Our experimental results show that our method is easy to converge in training works and the image reconstruction result also has better quality. The proposed method has better effects corresponding to the reconstruct images on the Set5, Setl4 andGeneral-100 data sets, its average PSNR can increase about 0.38dB.
Deep Component Based Age Invariant Face Recognition in an Unconstrained Environment

Age Invariant face recognition is one of the challenging problems in pattern recognition. Most existing face recognition algorithms perform well under controlled conditions where the data set is collected with careful cooperation with the individuals. However, in most real-world applications, the user usually has little or no control over environmental conditions. This paper proposes efficient deep component-based age-invariant face recognition algorithm in an unconstrained environment. The algorithm detects face from an image, align the face and extract the facial components (eye, mouth and nose). Each facial component is then trained using deep neural network. Thus, deep features are extracted from each component. Support vector machine is then used in classification stage. Experiments are conducted on two challenging benchmarks: AgeDb30 and Pins-Face. Results have shown significant improvement when compared with the state of the art baseline approach.
The Coding Strategy for the Mandarin Speech Conveying Sarcasm in Acoustic and Articulatory Domain

Purpose: This study investigated the coding strategy for the speech conveying two opposing attitudes, i.e., sarcasm and praising, based on the utterances elicited by role-play dialogues. Method: Using an electromagnetic articulography (EMA), we collected audio signals and kinematic data of the articulators (including tongue and lips) from 33 native speakers of Mandarin. Results: For sarcastic speech, acoustic analysis showed a lower fundamental frequency, a higher intensity, and a slower speech rate, while articulatory analysis exhibited a higher tongue position, a smaller lip opening, and narrower ranges of lingual and lip movement in both horizontal and vertical dimensions than in praising speech. Based on random forest model, the rates of attitudinal speech recognition were around 75%, using both acoustic and articulatory parameters. Conclusion: Results indicate that both acoustic parameters and segmental articulation play roles in encoding sarcasm and praising attitudes. Also, the findings offer valuable insights on conducting attitudinal speech synthesis and recognition from an articulatory perspective.
entity2item: Leveraging Knowledge Graph Embedding for Item Recommendation

Traditional recommender systems only rely on the historical interaction information of users and items, so they often suffer from sparsity problem. Therefore, researchers utilize auxiliary data to alleviate this problem and improve the quality of recommendation. Recently knowledge graph as a kind of auxiliary data has attracted increasing attention, which can extract the semantic information of users and items. However, how to make full use of the information from knowledge graph to build a better recommendation method is still facing numerous challenges. To this end, we propose entity2item, a novel method of using the information from knowledge graphs to assist item recommendation. Specifically, entity2item takes translation-based model as the knowledge graph embedding approach to learn the feature vectors of entities. Then we consider that the items in recommender system are highly correlated with the entities in knowledge graph, so these vectors are introduced to the recommendation module to enrich the expression of items. In the end, the vectors of users and items are input into a deep recommender system for training. Extensive experiments have been conducted on three real-world datasets, the results demonstrate that our method outperforms the state-of-the-art baselines. Even in sparse scenarios, it can still maintain satisfactory performance.
Special Symbol Attacks On NLP Systems

Adversarial attacks/perturbations are becoming important for NLP research, as it has been shown recently that text-attacking adversaries can degrade an NLP model's performance without the victim's knowledge. This has far-reaching implications, especially when an NLP system is deployed in critical applications such as health or finance. In fact, the robustness of state-of-the-art models such as NLP transformers have increasingly been scrutinised due to their vulnerability against adversarial perturbations such as TextFooler and BERT-Attack. These methods, however, focus on changing words, which at times, ruins the readability and semantics of the sample. This paper introduces Special Symbol Text Attacks ‘SSTA’, a technique to improve the performance of language adversarial perturbations using special symbols that have downstream task information associated with them even though that should not have been the case. Our tests show that introducing such symbols which are meaningless to a human within a sentence, can perturb the sample in a particular direction. When this technique is used with TextFooler, which is a recent benchmark for the creation of NLP adversaries, through the TextAttack framework, it can improve all main evaluation metrics on three sentiment classification tasks and fake news detection. A simple, novel and symbol-specific adversarial learning technique is then introduced to reduce the influence of such special symbols.
Construction of a New Network Information Resource Storage System

On the basis of introducing the function of information storage system, this paper analyzes the advantages and disadvantages of three kinds of traditional information resource storage systems: Direct Attached Storage (DAS), network attached storage (NAS) and storage area network (SAN) ct-Based Storage). Compared with the traditional storage mode, the storage system is intelligent because of the introduction of the concept of object. With the further development of object storage, this storage mode can realize all the functions of block or file, and also has the intelligent mechanism of self-management and self adaptation that block and file storage system do not have.
DIGITAL EDUCATIONAL ENVIRONMENT OF A MODERN UNIVERSITY

The article deals with the formation of the modern digital educational environment of the university and the implementation of educational programs using distance learning technologies, the main elements of the digital educational environment of the university, as well as the ways of developing the digital educational environment of the University in 2021.
Verification in the Grand Challenge

chapter Share on Verification in the Grand Challenge Authors: Jim Woodcock Search about this author , Janet Barnes Search about this author , Rod Chapman Search about this author , Simon Foster Search about this author , Thomas Santen Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 125–156https://doi.org/10.1145/3477355.3477363Online:02 October 2021Publication History 0citation2DownloadsMetricsTotal Citations0Total Downloads2Last 12 Months2Last 6 weeks1 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
Collision-Free Trajectory Generation for Multiple UAVs with Sensing Constraints

This paper mainly focuses on the real-time collision avoidance trajectory generation for multiple UAVs with limited sensing capabilities. It is assumed that each UAV has visual constraints (i.e., limited field of view (FOV)). Based on their sensed relative positions, two observation situations are considered: reciprocal observation and unidirectional observation, which can be identified through our proposed observation function. We propose a two-stage strategy for each UAV to plan its real-time trajectory, resulting a very small deviation from the initial trajectory. At the first stage, the quadrotor employs a novel visual vector field function to judge whether a collision possibly occurs, then a modified optimal reciprocal collision avoidance (ORCA) algorithm is proposed such that collision-free zones are figured out. At the second stage, the quadrotor will regenerate a smooth trajectory by solving a QP problem to return to the initial trajectory. Simulation and experiment results show the validness and effectiveness of our proposed strategy.
Fuzzy Bit-Plane-Dependence Region Competition

This paper presents a novel variational model based on fuzzy region competition and statistical image variation modeling for image segmentation. In the energy functional of the proposed model, each region is characterized by the pixel-level color feature and region-level spatial/frequency information extracted from various image domains, which are modeled by the windowed bit-plane-dependence probability models. To efficiently minimize the energy functional, we apply an alternating minimization procedure with the use of Chambolle’s fast duality projection algorithm, where the closed-form solutions of the energy functional are obtained. Our method gives soft segmentation result via the fuzzy membership function, and moreover, the use of multi-domain statistical region characterization provides additional information that can enhance the segmentation accuracy. Experimental results indicate that the proposed method has a superior performance and outperforms the current state-of-the-art superpixel-based and deep-learning-based approaches.
Geometry-independent realistic noise models for synthetic data generation

Summary Synthetic datasets are vital for the development and benchmarking of new processing and imaging algorithms as well as in the training of machine learning models. It is therefore important that such datasets are generated with realistic noise conditions making them resemble as much as possible their corresponding field datasets. Building on previously developed covariance-based noise modelling, we propose an extension of such an approach that aims to translate a noise model onto a user-defined geometry by means of Gaussian Process Regression. Starting from a synthetic data, we show that noise models can be generated and transformed into a desired geometry whilst keeping the same underlying statistical properties (i.e., covariance and variogram). The modelling procedure is subsequently applied to the ToC2ME passive noise dataset transforming the actual 69-sensor acquisition geometry into a gridded, 56-sensor array. The ability to generate realistic, geometry-independent noise models opens up a host of new opportunities in the area of survey design. We argue that by coupling the noise generation and monitoring algorithms, the placement of sensors could be further optimised based on the expected microseismic signatures as well as the surrounding noise behaviour.
Lyzeli: a tool for identifying the clues in survey research data

Survey research is one of the most used empirical research methods, and it is a means to contribute to theory development. A survey can be exploratory, descriptive, or explanatory. Whatever the objective, it will generate data that may support the construction of a theory or validate it. However, researchers must carefully evaluate data so they can transform them into useful information for science. Little is said about how complex and susceptible to human failures this process can be. We note that all the data analysis work is still highly manual and exhausting for the researcher, who is not always qualified to perform data analysis and, sometimes, can make mistakes in this process, inserting wrong numbers or calculating something wrong. Some existing tools can support this process but do not specifically focus on analyzing survey responses, combining valuable qualitative and quantitative analysis of responses. In this study, we introduce Lyzeli, a tool designed to assist researchers in analyzing and correlating survey responses. The proposed tool aims to deliver automatic detection of question types, sentiment analysis of responses, data filtering, word cloud, word count, graphics, codification for open-based questions answers.
An Exploration of College Students’ Second Language Mental Lexicon Storage Model Based on WordNet Computer Data Analysis

Vocabulary plays an extremely important role in language learning. Because of its importance, more and more researchers use lexical relevance test to study psychological vocabulary. In the past half century, there have been dozens of researches on the representation structure of bilingual mental vocabulary. Through the analysis of WordNet computer data, this study analyzes the storage mode of college students’ second language psychological vocabulary. This research is carried out from the aspects of research objects, research methods, research results and research significance. It can be seen from the statistical data that semantic responses account for the largest proportion of these two types of responses, followed by formal responses and meaningless responses, which indicates that the second language research objects are mainly stored in the semantic association of psychological vocabulary. This provides support for the semantic theory of psychological vocabulary organization model.