Reviewerly/exHarmony huggingface dataset:
A novel temperature-strain synchronous measurement method

• A measurement method for temperature field and strain field coupling measurement. • A new calibration panel is designed for infrard camera and visible camera. • A new method of temperature field and strain field coupling is presented. • The proposed method has been evaluated using simulation experiments and actual experiments. Thermal-mechanical coupling deformation is inevitable in the operation of materials and structures. Traditional deformation researches have mainly focused on temperature or deformation measurement at a specific temperature, but few studies have investigated the coupling relationship between temperature and deformation in a non-uniform thermal environment. This study presents a novel method of measuring the temperature and strain distribution of an object synchronously. A calibration object and its calibration method were designed to be utilized in the synchronous calibration of both the infrared and visible cameras, and a matching method of the infrared image and the visible image was proposed. The data in the non-uniform temperature field and the non-uniform strain field corresponded accurately using the proposed method. A simulation experiment and actual thermal coupling deformation experiments were utilized to verify the effectiveness and accuracy of the proposed method. The proposed method can realize the accurate correspondence of the temperature and strain fields in the measurement area and provide technical support for studying the actual thermal-mechanical coupling deformation of materials and structures.
Explaining presupposition projection in (coordinations of) polar questions

This article starts off with the observation that in certain cases, presuppositions triggered by an element inside a question nucleus may fail to project. In fact, in what looks like coordinated structures involving polar questions, presupposition projection patterns are exactly parallel to what is observed when the corresponding assertions are coordinated. The article further shows that these facts do not fall out straightforwardly from existing theories of polar questions, (apparent) coordinations of questions, and presupposition projection. It then proposes a trivalent extension of inquisitive semantics such that the observed pattern can be understood in terms of existing theories of presupposition projection. The proposal has the following properties: (a) apparent coordinations of questions are indeed coordinations of questions, and (b) the semantic denotation of polar questions is asymmetric with respect to the “yes” and “no” answers.
Matrix Factorization with Topic and Sentiment Analysis for Rating Prediction

In today's online services, users' feedback such as numerical rating, textual review, time of purchase, and so on for each item is often encouraged to provide. Managers of online services utilize the feedback to improve the quality of their services, or user experience. For example, many recommender systems predict the items that the users may like and purchase in the future using users' historical ratings. With the increase of user data in the systems, more detailed and interpretable information about item features and user sentiments can be extracted from textual reviews that are relative to ratings. In this paper, we propose a novel topic and sentiment matrix factorization model, which leverages both topic and sentiment drawn from the reviews simultaneously. First, we conduct topic analysis and sentiment analysis of reviews using Latent Dirichlet Allocation (LDA) and lexicon construction technique, respectively. Second, we combine the user consistency, which is calculated from his/her reviews and ratings, and helpful votes from other users of reviews to obtain a reliability measure to weight the ratings. Third, we integrate these three parts into the matrix factorization framework for the prediction of ratings. Our experimental comparison using Amazon datasets indicates that the proposed method significantly improves performance compared to traditional matrix factorization up to 14.12%.
Spatial Assembly Networks for Image Representation Learning

It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures. Image augmentations, such as random translation, cropping, and resizing, can be used to improve the robustness of deep neural networks under spatial transforms. However, changes in object part configurations, spatial layout of object, and scene structures of the images may still result in major changes in the their feature representations generated by the network, creating significant challenges for various visual learning tasks, including representation or metric learning, image classification and retrieval. In this work, we introduce a new learnable module, called spatial assembly network (SAN), to address this important issue. This SAN module examines the input image and performs a learned re-organization and assembly of feature points from different spatial locations conditioned by feature maps from previous network layers so as to maximize the discriminative power of the final feature representation. This differentiable module can be flexibly incorporated into existing network architectures, improving their capabilities in handling spatial variations and structural changes of the image scene. We demonstrate that the proposed SAN module is able to significantly improve the performance of various metric / representation learning, image retrieval and classification tasks, in both supervised and unsupervised learning scenarios.
Secure Hybrid Encryption in the Standard Model from Hard Learning Problems

We present chosen-ciphertext secure hybrid encryption systems in the standard model from the learning with errors problem and low-noise learning parity with noise problem. The systems consist of public-key key encapsulation mechanisms that are not chosen-ciphertext secure. The systems are more efficient than the existing chosen-ciphertext secure hybrid encryption systems in the standard model based on the same hard learning problems.
Ethical Responsibility of the University Teacher in Online Teaching and Evaluation

Humanity goes through one of the most dynamic stages, characterized by profound structural changes in all areas of life. It was computer science, the age of robots, the digital age, etc., all forming a new environment of life and a new consciousness about them. In a relatively short time, the role of the teacher has undergone a dramatic change. In the context of the current pandemic, higher education teachers have had to refocus on new practices that ensure continuity of teaching and evaluation. This chapter examines the ethical competencies of the university professor from the perspective of online teaching and evaluation. Since the teaching activity has “moved” in the online environment, it is necessary to respect ethical and legal rules in the digital space. In the first part of the chapter, the author gives a presentation on the current meanings of the concept of ethical competence and will analyze in the second part the main digital and ethical competencies that university teachers must possess, with the European Parliament’s Recommendation on Key Competences (2006) as a benchmark. EU Regulation 679/2016 on the protection of personal data has to be respected in the context of transitioning from classical to online education.
Automated Paraphrase Quality Assessment Using Recurrent Neural Networks and Language Models

The ability to automatically assess the quality of paraphrases can be very useful for facilitating literacy skills and providing timely feedback to learners. Our aim is twofold: a) to automatically evaluate the quality of paraphrases across four dimensions: lexical similarity, syntactic similarity, semantic similarity and paraphrase quality, and b) to assess how well models trained for this task generalize. The task is modeled as a classification problem and three different methods are explored: a) manual feature extraction combined with an Extra Trees model, b) GloVe embeddings and a Siamese neural network, and c) using a pretrained BERT model fine-tuned on our task. Starting from a dataset of 1998 paraphrases from the User Language Paraphrase Corpus (ULPC), we explore how the three models trained on the ULPC dataset generalize when applied on a separate, small paraphrase corpus based on children inputs. The best out-of-the-box generalization performance is obtained by the Extra Trees model with at least 75% average F1-scores for the three similarity dimensions. We also show that the Siamese neural network and BERT models can obtain an improvement of at least 5% after fine-tuning across all dimensions.
CNN Architectures to Recognize Handwritten Telugu Characters

Artificial neural nets are backbones of many AIs predictive analogies. As common models in many of advancing domains: machine learning, artificial intelligence, and deep learning, they have led their own cognitive ways in developing highly expressive variants that work on image recognitions. From text mining to natural language processing, many of these domains have found neural networks in producing the expected accuracy. This work focuses on recognizing handwritten Telugu character vowels using convolution neural nets. We experimentally demonstrated the performance of CNN on varied architectures in predicting the handwritten Telugu vowels.
Postdigital Truths: Educational Reflections on Fake News and Digital Identities

The lives of young people have successfully been infiltrated by multiple online actors—many unbeknownst to them. In this chapter, we wonder how young people manage the information they receive from different sources and how they think about and deal with manipulated information, also known as fake news. We are taking a postdigital approach that allows us to describe the normalisation of interactions with digital materials. Students’ lives are entangled and their digital identities are no longer limited to only local feelings of belonging, but are shaped by complex social contextual relationships with multiple cultures. The chapter examines what it may mean when young people find it increasingly easy to connect with like-minded peers and seek positive reinforcements of their views and actions on the topics of their daily life. In this contribution, we illustrate our thinking by presenting vignettes from two projects where young people reflected on their encounters with fake news, and how they manage information they receive online in general. The vignettes are analysed first by foregrounding material actors through actor–network theory, then overlaid with a cultural-historical activity theory framework so we can hone in on socio-material details and what this means for education in postdigital times.
Research on Spoofing Jamming of Integrated Navigation System on UAV

In order to conduct trajectory spoofing on Unmanned Aerial Vehicle (UAV) effectively, based on the analysis of the GPS spoofing jamming principle, combined with the control characteristics of the GPS/INS integrated navigation system to carry out the UAV spoofing trajectory planning. Firstly, according to the characteristics of UAV flight, the initial mobility constraint model of UAV is established. Secondly, the spoofing trajectory of UAV is planned under this constraint condition. Two trajectory planning algorithms for extension line and tangent line are proposed. Finally, simulation verification and analysis are conducted according to the proposed algorithms. The results show that both planning methods can deflect the UAV flight path, however, the planning method set by tangent is more strongly biased than the planning method set by the extension line, and the final spoofing effect is better.
Offline Writer Identification Using Convolutional Neural Network and VLAD Descriptors

This paper proposes a method for writer identification and retrieval. The focus of these two tasks is to extract discriminative features from handwriting documents. Traditional artificial features require researchers' experience and professional knowledge in language and handwriting, while the automatic features are extracted from large data handwriting samples. The proposed method uses local features and has better performance. We extract features via the convolutional neural network, subsequently aggregating to obtain the global feature vector. During the process, the system first enhances and preprocesses the handwriting samples and then feeds the patches into the ResNet50 that pretrained on ImageNet. Finally, the local features, i.e., the output of ResNet50, are aggregated into a global feature vector by the VLAD method. The evaluation is employed on the ICDAR2013 and CVL data sets. Experiments show this approach is close to the state-of-the-art methods, and it has good stability and robustness.
Synergic learning for noise-insensitive webly-supervised temporal action localization

Webly-supervised temporal action localization (WebTAL) leverages web videos to train localization models without requiring manual temporal annotations. WebTAL is extremely challenging since video-level labels on the web are always noisy, seriously damaging the overall performance. Most state-of-the-art methods filter out noise before training, which will inevitably reduce the training samples. In contrast, we propose a preprocessing-free WebTAL framework along with a new synergic learning paradigm to alleviate the noise interference. Specifically, we introduce a synergic task called Spatio-Temporal Order Prediction (STOP) for spatio-temporal representation learning. This task requires a network to arrange permuted spatial crops and temporal clips, thereby learning the inherent spatial semantics and temporal interactions in videos. Instead of pre-extracting features with the well-trained STOP, we design a novel synergic learning paradigm called Warm-up Synergic Training (WST) to iteratively generate better spatio-temporal representations and improve action localization results. In this synergic fashion, experimental results show that the interference caused by label noise will be largely mitigated. We demonstrate that our method outperforms all other WebTAL methods on two public benchmarks, THUMOS'14 and ActivityNet v1.2.
Improved evolution of generative adversarial networks

Generative adversarial networks (GANs) achieved relevant results regarding the production of realistic samples. However, the training of GANs has issues that affect the stability and convergence of the algorithm. One line of research to tackle these issues uses Evolutionary Algorithms to drive the training and evolution of GANs, such as COEGAN. In this work, we propose COEGAN-v2, an extension of COEGAN that allows the use of spectral normalization and upsampling layers through the variation operators. Additionally, we use the loss functions of RaSGAN in training and also as fitness. Results show that COEGAN-v2 is more efficient and achieves better outcome quality when compared to the original COEGAN version and also regular GANs.
Collaborative Approaches for Security of Cloud and Knowledge Management Systems: Benefits and Risks

Cloud Computing pattern is becoming more and more trendy, due to the enormous decrease in the time, cost, and effort to conversation software program development needs. A knowledge management approach is highly utilized in enterprises in appliances such as intelligence methods, employment authority, supercomputer learning, in the public domain, protection, and conventional administration. Knowledge management remains believed towards remaining a few of these categories issues parts. To identify, develop, describe, repository, or disseminate knowledge, the existing devices, the equipment, or techniques that must not be stayed knowledgeable to achieve something the agreement needs the intended used for growth. This study article explores the innumerable characteristics similar to clarifications used aimed at the benefits and consequences of applying cloud computing in knowledge management systems, presenting the greatest degree of security applied to the knowledge management enterprise is a challenging that realizes exactly how to be resolved by applying cloud computing. Furthermore, cloud computing is competent to maintain knowledge management ahead and all around simultaneously with technology.
Performance Evaluation of the JK-nearest Neighbor Online Signature Verification Parameters

There are several classification algorithms used for signature verification purposes. The k-nearest neighbor (KNN) algorithm was previously used in online signature verification, but in this paper, we present an evaluation of the online signature verification using the JKNN classifier, which is a generalized case of the KNN classifier. An optimal classifier is used to evaluate the algorithm's main parameters that provide the most accurate verification results. The evaluation of each parameter of the algorithm is presented and tested using the SVC2004 database. Our results show that JKNN can provide accurate results by optimizing the main parameters of the algorithm. Both false acceptance rate and false rejection rate behave differently with different values of each parameter; the least average error rate can be achieved by choosing the parameters' optimal values.
Forecast of complex financial big data using model tree optimized by bilevel evolution strategy

Abstract If a decision tree is constructed through a series of locally optimal solutions, such as the Greedy method, overfitting to the data is likely to occur. In order to avoid overfitting, many previous research have attempted to collectively optimize the structure of a decision tree by using evolutionary computation. However, if attributes of each split and their thresholds are searched simultaneously, the evaluation function becomes intermittent; thus, optimization methods assuming continuous distribution cannot be used. In this study, in order to enable efficient search assuming continuous distribution even for complicated data that contains a lot of noise and extraordinary values, such as financial time series data, the inner level search that optimizes each threshold value collectively given a specific attribute for each split in a model tree and the outer level search that optimizes the attributes of each split were performed by separate evolutionary computing. As a result, we obtained high prediction accuracy that far exceeded the performance of the conventional method.
Business Intelligence based novel Marketing Strategy Approach using Automatic Speech Recognition and Text Summarization

A novel marketing strategy approach is proposed that offers distinctive features than other competitive models. Text converted from speech between customer and sales executive is fed into text summarizer to automate customer strategy into various priorities. The approach shows that there is no need of data entry for emulating marketing tools. The need of account for customer's preferences is high, which can be stored by capturing the customer's context. Data mining and advanced analytical aspects give accurate results to help marketing managers and improve firms with solid customer strategies. The experimental results for the implementation show high outcomes in comparison to native advances in the technologies. A total of 62 test cases have been performed where an average of 12% Word Error Rate has been observed for Speech Recognition, which is optimal than other standard Speech Datasets. Furthermore, summarization compressed rate has been reported as 60%. The automated categorization module has achieved 70 % accuracy and overall F-score is 0.81349.
In search of lost time: Axiomatising parallel composition in process algebras

This survey reviews some of the most recent achievements in the saga of the axiomatisation of parallel composition, along with some classic results. We focus on the recursion, relabelling and restriction free fragment of CCS and we discuss the solutions to three problems that were open for many years. The first problem concerns the status of Bergstra and Klop's auxiliary operators left merge and communication merge in the finite axiomatisation of parallel composition modulo bisimiliarity: We argue that, under some natural assumptions, the addition of a single auxiliary binary operator to CCS does not yield a finite axiomatisation of bisimilarity. Then we delineate the boundary between finite and non-finite axiomatisability of the congruences in van Glabbeek's linear time-branching time spectrum over CCS. Finally, we present a novel result to the effect that rooted weak bisimilarity has no finite complete axiomatisation over CCS.
Personalized Recommendation of English Learning Based on Knowledge Graph and Graph Convolutional Network

With the rapid development of education, a large number of the online learning platforms have emerged. Although they provide the convenience to the students, they cannot provide students with in-depth personalized services. To solve this problem, in this paper, we propose a method to personalize the recommendation of English learning based on knowledge graph and graph convolutional network. Firstly, we construct a knowledge graph containing a large number of Junior High School English exercises, which are classified by the knowledge points. Secondly, a graph convolutional neural network is used to generate a personalized knowledge graph for each student. Finally, it provides students with in-depth personalized services by generating personalized learning path. The experimental results prove the effectiveness of our method.
How data governance technologies can democratize data sharing for community well-being

Abstract Data sharing efforts to allow underserved groups and organizations to overcome the concentration of power in our data landscape. A few special organizations, due to their data monopolies and resources, are able to decide which problems to solve and how to solve them. But even though data sharing creates a counterbalancing democratizing force, it must nevertheless be approached cautiously. Underserved organizations and groups must navigate difficult barriers related to technological complexity and legal risk. To examine what those common barriers are, one type of data sharing effort—data trusts—are examined, specifically the reports commenting on that effort. To address these practical issues, data governance technologies have a large role to play in democratizing data trusts safely and in a trustworthy manner. Yet technology is far from a silver bullet. It is dangerous to rely upon it. But technology that is no-code, flexible, and secure can help more responsibly operate data trusts. This type of technology helps innovators put relationships at the center of their efforts.
A two steps phase-shifting demodulation method using the VU factorization

In this paper, we present a two steps phase-shifting fringe pattern demodulation method using the VU factorization.Compared with other methods, this method is robust to non-constant background illuminations and modulating terms (non-normalized fringes).We use the two given fringe patterns, and from these, we synthetically generate another two images that approach the background illumination in order to obtain a total set of four images.Such a set of images is used in a matrix model that can be factored using the VU factorization method to recover the modulating phase.Our proposal is able to recover the phase without: 1) known phase shift, 2) normalized fringe patterns, and 3) any special non-linear pre-processing.Tests and results will be shown to demonstrate the feasibility of this method.