Reviewerly/exHarmony huggingface dataset:
Teaching Action of Virtual Reality Video Creation Based on Viewers’ Cognitive Behavior

How to make good use of videos to promote the school, so that potential students and parents can have a more comprehensive understanding of the characteristics of the school, is becoming more and more important.This study aimed to explore the action research of film and television design action learning in the international volunteer service of University of Science and Technology (HKUST) by cooperating with Pei Hwa Independent High School (PHHS) in Malaysia to shoot a virtual reality (VR) enrollment promotional video.It explored how an action learning plan could achieve the United Nations Sustainable Development Goals (SDGs) and recorded the process of creation and teaching design models.A total of ten students from the International Volunteer Film and Television Service Team of Chung Chou University of Science and Technology (CCUT) were selected as the research subjects for a two-week course with 88 sessions.Qualitative data were collected through observation, interviews, and document analysis.From planning to implementation, this action research on teaching action of VR video creation of viewers' cognitive behavior was divided into the planning period, the action research and shooting period, and the action learning post-production period.Conclusions verified that HKUST students' practical ability and learning attitude were improved after the action research, and the specialties they have studied in Taiwan can obtain imaging results and the output of imaging education abroad.
Classification of Arabic fricative consonants according to their places of articulation

&lt;span&gt;Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate.&lt;/span&gt;
A Learning Vector Quantization Architecture for Transfer Learning Based Classification in Case of Multiple Sources by Means of Null-Space Evaluation

We present a method, which allows to train a Generalized Matrix Learning Vector Quantization (GMLVQ) model for classification using data from several, maybe non-calibrated, sources without explicit transfer learning. This is achieved by using a siamese-like GMLVQ-architecture, which comprises different sets of prototypes for the target classification and for the separation learning of the sources. In this architecture, a linear map is trained by means of GMLVQ for source distinction in the mapping space in parallel to the classification task learning. The respective null-space projection provides a common data representation of the different source data for an all-together classification learning.
Filter-based Discriminative Autoencoders for Children Speech Recognition

Children speech recognition is indispensable but challenging due to the diversity of children's speech. In this paper, we propose a filter-based discriminative autoencoder for acoustic modeling. To filter out the influence of various speaker types and pitches, auxiliary information of the speaker and pitch features is input into the encoder together with the acoustic features to generate phonetic embeddings. In the training phase, the decoder uses the auxiliary information and the phonetic embedding extracted by the encoder to reconstruct the input acoustic features. The autoencoder is trained by simultaneously minimizing the ASR loss and feature reconstruction error. The framework can make the phonetic embedding purer, resulting in more accurate senone (triphone-state) scores. Evaluated on the test set of the CMU Kids corpus, our system achieves a 7.8% relative WER reduction compared to the baseline system. In the domain adaptation experiment, our system also outperforms the baseline system on the British-accent PF-STAR task.
Research on Cloud Computing Security Technology of Power System

The integrated function of cloud computing technology can effectively guarantee the security of power system data, and lay a solid foundation for the healthy and stable development of power system. At present, the application of cloud computing security technology in the power system is still in the primary stage, and there is a large space for improvement in both the application depth and the strategy level. On account of this, this paper first analyzes the overall security requirements of power system and the concept and connotation of cloud computing security technology in power system. Then it proposes the security structure target of power system. Finally, this paper analyses and studies the power system architecture of cloud computing security and applied technologies of cloud computing security.
Implementation of artificial intelligence development strategy in the Russian Federation in the educational programs of aerospace engineering training of Bauman Moscow State Technical University

As a part of the National Strategy on Artificial Intelligence development in the Russian Federation, the specific features of training in USSN 24.00.00 Aviation and Rocket-Space Engineering at “Mechanical Engineering” (SM) Department of Bauman Moscow State Technical University were considered. The analysis of self-established educational standards and the classification of artificial intelligence course modules, implemented at the SM Department, is carried out. Individual courses that already use methods of artificial intelligence or are promising for the introduction of such methods are presented.
List Decoding of 2-Interleaved Binary Alternant Codes

This paper is concerned with list decoding of 2-interleaved binary alternant codes. The principle of the proposed algorithm is based on a combination of a list decoding algorithm for (interleaved) Reed-Solomon codes and an algorithm for (non-interleaved) alternant codes. A new upper bound on the decoding radius is derived and the list size is shown to scale polynomially in the code parameters. While it remains an open problem whether this upper bound is achievable, the provided simulation results show that a decoding radius exceeding the binary Johnson radius can be achieved with a high probability of decoding success by the proposed algorithm.
Speeding Up Fermat’s Factoring Method using Precomputation

The security of many public-key cryptosystems and protocols relies on the difficulty of factoring a large positive integer n into prime factors. The Fermat factoring method is a core of some modern and important factorization methods, such as the quadratic sieve and number field sieve methods. It factors a composite integer n=pq in polynomial time if the difference between the prime factors is equal to ∆=p-q≤n^(0.25) , where p&gt;q. The execution time of the Fermat factoring method increases rapidly as ∆ increases. One of the improvements to the Fermat factoring method is based on studying the possible values of (n mod 20). In this paper, we introduce an efficient algorithm to factorize a large integer based on the possible values of (n mod 20) and a precomputation strategy. The experimental results, on different sizes of n and ∆, demonstrate that our proposed algorithm is faster than the previous improvements of the Fermat factoring method by at least 48%.
Using Chained Views and Follow-Up Queries to Assist the Visual Exploration of the Web of Big Linked Data

The Web of Linked Open Data (LOD) provides access to a great number of dynamic datasets containing valuable information to support decision-making processes in diverse application domains while being publicly accessible and up-to-date. While information visualization techniques are useful to explore, analyze, and explain relationships within LOD data, the existing tools are limited to visualizing a single dataset at a time and, often, use static and preprocessed data. In this article, we leverage the linked aspect of LOD to support the dynamic integration of data into a visualization system by connecting views and distributed LOD datasets using the so-called follow-up queries. We demonstrate how our approach uses dynamic SPARQL queries to integrate external data into the exploration flow through visualization techniques and enrich the ongoing analysis. We ran a semi-structured interview to assess the usefulness of our approach, which results were encouraging while showing its relevance to explore big linked data.
A Novel Client Sampling Scheme for Unbalanced Data Distribution Under Federated Learning

Federated learning is one computation paradigm used to address privacy preservation and efficient collaboration computing nowadays. Especially, in the environment where edge devices are facing different data scenarios, it is a challenge to enhance the prediction model accuracy. Since the data distributions on different edge devices might not be independent identical distributions, and also due to the communication obstacles existing in the modern complicated wireless world, it is an essential problem to sample which client devices to contribute to the server learning model. In this paper, instead of making the assumption on uniform distributed data sources, we assume the agnostic data distribution presumption. One indicator called client reward is defined applicable on the proposed client sampling algorithm. Combing with the redefined loss functions on the agnostic data distribution, a novel client sampling scheme is proposed and tested on real world datasets. The experiment results show that the client sampling scheme improves prediction accuracy on unbalanced data sources from different edge devices and achieves reasonable computing efficiency.
Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting

Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention(GAA) module and an Iterative Cross Guidance(ICG) strategy. The GAA module relies on the epipolar geometry cues and learns the geometry-aware guidance from one view to another, which is beneficial to make the corresponding regions in two views consistent. However, learning guidance from co-existing missing regions is challenging. To address this issue, the ICG strategy is proposed, which can alternately narrow down the missing regions of the two views in an iterative manner. Experimental results demonstrate that our proposed network outperforms the latest stereo image inpainting model and state-of-the-art single image inpainting models.
Image-Based Spammer Analysis Using Suitable Features Selection by Genetic Algorithm in OSNs

Online social networks brought about to change the fundamental of the people about the way they generate and share information. Over the last decades, gratuitous messages in the form of spam have become one of the most unplayful situations for social network users. In this paper, we report a novel framework to support precise and vigorous spam images filtering. The framework is developed based on multiple properties and features extracted by our crawler from different level of granularity, pointing to express more discriminative contents for effective spam image detection. Besides, a stochastic method used for function optimization (Genetic algorithm) based on natural genetics for suitable features selection. It can facilitate more accurate for identification of spam images by using sample training sets. By using some sample test collections, the proposed framework is compared with other state-of-the-art techniques. Our result presents a remarkably higher detection rate with a different outlook.
Survey on tracking and registration technology for mobile augmented reality

Mobile augmented reality, exploring the synergy of augmented reality and mobile computing, can superimpose images or information content over a displayed view of the physical world. It revolutionises the way how people perceive information and interact with surrounding environments, holding great potential in a wide range of applications. As one of the key technologies in mobile augmented reality, tracking and registration technology plays an important role in the system performance. In this article, we review the state-of-art tracking and registration technology for mobile augmented reality. Firstly, we introduce the basic concept and a typical pipeline of mobile augmented reality. Then, we mainly classify tracking and registration technology for mobile augmented reality into three categories: sensor-based, vision-based, and hybrid approaches, and analyse the principles and progress of each type respectively. The characteristics of each approach are discussed and compared in detail. After that, we present some mobile augmented reality applications. Finally, we discuss the challenges and opportunities in the area of mobile augmented reality.
Fundamental thresholds of realistic quantum error correction circuits from classical spin models

Mapping the decoding of quantum error correcting (QEC) codes to classical disordered statistical mechanics models allows one to determine critical error thresholds of QEC codes under phenomenological noise models. Here, we extend this mapping to admit realistic, multi-parameter noise models of faulty QEC circuits, derive the associated strongly correlated classical spin models, and illustrate this approach for a quantum repetition code with faulty stabilizer readout circuits. We use Monte-Carlo simulations to study the resulting phase diagram and benchmark our results against a minimum-weight perfect matching decoder. The presented method provides an avenue to assess fundamental thresholds of QEC circuits, independent of specific decoding strategies, and can thereby help guiding the development of near-term QEC hardware.
An Intelligent Chatbot for Admission System of an Educational Institute and Prediction of User Interest in Taking Admission

Chatbots are nothing but computer programs that are designed to communicate with humans like a human with the help of natural languages. Chatbots basically emulates how a human would talk in conversation. Sentiment analysis is a process of opinion extraction, thought process of an individual by analyzing a person’s language. The present work aims to develop an intelligent chatbot for an educational Institution which would converse with the candidates regarding admission requirements and would also answer their queries regarding the same, that can replace a human admission counsellor. It also aimed to develop a sentiment analyzer to analyze the chats of the said chatbot to predict the likelihood of the candidate of taking admission to the institution. The system analyzed the performance of different classification algorithm like Na’i've Byes, Maximum Entropy and SVM for the prediction. The result shows that Maxent algorithm is most efficient for the purpose.
Percepção/satisfação dos pacientes submetidos a reabordagens cirúrgicas quanto às intervenções de enfermagem no perioperatório: um estudo de caso

The aim was to analyze the influence of nursing interventions performed in the immediate pre and postoperative periods on the anxiety of patients undergoing repeat vascular surgeries. Exploratory, descriptive, mixed approach, case study type. Held at a university hospital located in the state of Rio de Janeiro. An instrument used to assess anxiety, the State-Trait Anxiety Inventory (STAI) and a semi-structured interview instrument were applied. Data analysis was performed using Content Analysis. The low level of anxiety was what stood out the most for STAI in both operative periods. When comparing the operative periods, the STAI-Trace presented an increase of 33% more in the high level of anxiety and 22% for the STAI-State. The study showed that recurrent submission to a surgical procedure, especially amputation surgeries, can cause more emotional damage to the patient than a more invasive surgery. The nurse has a vital participation in the process of rehabilitation, autonomy and self-care of the individual in his new condition, thus empowering the user to break paradigms about amputation.
Sparsifying Sparse Representations for Passage Retrieval by Top-$k$ Masking

Sparse lexical representation learning has demonstrated much progress in improving passage retrieval effectiveness in recent models such as DeepImpact, uniCOIL, and SPLADE. This paper describes a straightforward yet effective approach for sparsifying lexical representations for passage retrieval, building on SPLADE by introducing a top-$k$ masking scheme to control sparsity and a self-learning method to coax masked representations to mimic unmasked representations. A basic implementation of our model is competitive with more sophisticated approaches and achieves a good balance between effectiveness and efficiency. The simplicity of our methods opens the door for future explorations in lexical representation learning for passage retrieval.
A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network

Context.— The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges. Objective.— To establish a computer-aided diagnosis system for rapid recognition of fNRBCs by convolutional neural network. Design.— We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. The cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin staining. Then, we proposed a convolutional neural network–based, computer-aided diagnosis system to automatically discriminate features and recognize fNRBCs. Extracting methods of interested region were used to automatically segment individual cells in cell slices. The discriminant information from cellular-level regions of interest was encoded into a feature vector. Pathologic diagnoses were also provided by the network. Results.— In total, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures), the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5%, respectively, in the test set (1040 pictures). Conclusions.— We established a computer-aided diagnosis system for effective and accurate fNRBC recognition based on a convolutional neural network.
Machine Learning Identification of Self-reported COVID-19 Symptoms from Tweets in Canada

Surveillance of open-source media, such as social media, has become an essential complement to traditional surveillance data for quickly detecting changes in the occurrence of diseases in time and space. We present our method for classifying Tweets into narratives about COVID-19 symptoms to produce a dataset for downstream surveillance applications. A dataset of 10,405 tweets has been manually classified as relevant or not to self-reported symptoms of COVID-19. Five machine learning classification algorithms, with different tokenization methods, were trained on the dataset and tested. The Support vector machine (SVM) algorithm, with a term frequency-inverse document frequency (TF-IDF) 3-4 n-grams on character as the tokenization method, was the classification algorithm with the highest F1-score of 0.70. However, the training dataset showed an imbalanced classification problem. To reduce the bias of the imbalance classes, the crowdsourcing website Mechanical Turk was used to add 133 relevant tweets. This addition improved the F1-score from 0.70 to 0.77.
LSB Data Hiding in Digital Media: A Survey

Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel.Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data.Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks.In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, offers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications.This article provides a comprehensive survey on LSB data hiding in digital media.The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model.The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the different LSB data hiding methods are discussed.Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided.In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media.Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods.The article concludes with summarizing main findings and suggesting directions for future research.This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-offs between data hiding attributes.
Concentration inequalities for non-causal random fields

Concentration inequalities are widely used for analyzing machine learning algorithms. However, the current concentration inequalities cannot be applied to some non-causal processes which appear for instance in Natural Language Processing (NLP). This is mainly due to the non-causal nature of such involved data, in the sense that each data point depends on other neighboring data points. In this paper, we establish a framework for modeling non-causal random fields and prove a Hoeffding-type concentration inequality. The proof of this result is based on a local approximation of the non-causal random field by a function of a finite number of i.i.d. random variables.