Reviewerly/exHarmony huggingface dataset:
SEQ2SEQ++: A Multitasking-Based Seq2seq Model to Generate Meaningful and Relevant Answers

Question-answering chatbots have tremendous potential to complement humans in various fields. They are implemented using either rule-based or machine learning-based systems. Unlike the former, machine learning-based chatbots are more scalable. Sequence-to-sequence (Seq2Seq) learning is one of the most popular approaches in machine learning-based chatbots and has shown remarkable progress since its introduction in 2014. However, chatbots based on Seq2Seq learning show a weakness in that it tends to generate answers that can be generic and inconsistent with the questions, thereby becoming meaningless and, therefore, may lower the chatbot adoption rate. This weakness can be attributed to three issues: question encoder overfit, answer generation overfit, and language model influence. Several recent methods utilize multitask learning (MTL) to address this weakness. However, the existing MTL models show very little improvement over single-task learning, wherein they still generate generic and inconsistent answers. This paper presents a novel approach to MTL for the Seq2Seq learning model called SEQ2SEQ++, which comprises a multifunctional encoder, an answer decoder, an answer encoder, and a ternary classifier. Additionally, SEQ2SEQ++ utilizes a dynamic tasks loss weight mechanism for MTL loss calculation and a novel attention mechanism called the comprehensive attention mechanism. Experiments on NarrativeQA and SQuAD datasets were conducted to gauge the performance of the proposed model in comparison with two recently proposed models. The experimental results show that SEQ2SEQ++ yields noteworthy improvements over the two models on bilingual evaluation understudy, word error rate, and Distinct-2 metrics.
Projected Augmented Reality to Guide Manual Precision Tasks: An Alternative to Head Mounted Displays

Augmented reality (AR) devices are gaining popularity in industrial development and healthcare as they provide information that would not be accessible in a rapid and intuitive way. Head-mounted displays dominate in this field and are currently being comprehensively tested. Alongside its informative function, AR can be used to steer the user's actions to aid complex or high precision tasks. This is the case in surgery, which is recently seeing the development of ad-hoc head-mounted displays to meet the requirements of safety, ergonomics, and reliability. However, head-mounted displays are subject to perceptual problems that can affect their use in delicate and demanding tasks. This article aims to evaluate projected AR as an alternative to head mounted displays (HMDs) when accurate guidance on the surface is needed. We directly compare them in a user study and evaluate both user accuracy and user perception to assess whether projected AR can be a practical and useful paradigm for precision manual tasks. Ten users performed tracing trajectory tasks under the guidance of an HMD and a projected AR device. Three accuracy levels were quantitatively tested: 0.5, 1, and 2 mm. Statistical analysis showed no significant difference in the accuracy of the two AR visualization modes, whereas the user perception assessment revealed statistical differences in virtual-to-real perception and visual discomfort. The quantitative results of this article proved that both technologies can guide manual precision tasks with the same accuracy, but projected AR features some perceptual advantages.
Comparative Study Between MobilNet Face-Mask Detector and YOLOv3 Face-Mask Detector

During this Covid-19, face masks are used to avoid cross-contamination as part of an infection protection strategy. Wearing a face mask can help avoid infection by preventing individuals from coming into contact with pathogens. When someone coughs, speaks, or sneezes, there is a chance that the infection will spread into the air and affect those nearby. So to prevent the rate of spreading, face masks are highly mandatory. Tracking every individual manually is an expensive task; therefore, we save a lot of time, cost and effort by automating this process. This proposed automation can be done using Artificial Neural Networks. YOLOv3 and MobileNetv2 are popular architectures used in different object detection applications. Hence, paper compares the above architectures by their performance, accuracy from outputs of both under different scenarios.
Social Media and Social Relationship among Youth: A Changing Pattern and Impacts in Bangladesh

Social media is a platform for people to share their varied points of view on life, as well as their experiences, facts, videos, and other information. Digitalization brings the world into more connectivity, increases social diversity and variety, and leads to a significant change in the structure, systems, traditions, and norms. In the twenty-first century, communication is the most important part of social and professional communication. In the last 15 years, social media has changed the way, how we communicate. Faster communication and cheaper ways have become the most valuable things in the modern world. In the past 5 years, Bangladesh has also been influenced by western culture and has seen a rapid growth in social media use. Social media platforms have become deeply integrated into many day-to-day interactions in an increasingly digital society. The vast majorities of Bangladeshis own a Smartphone and use social media on a regular basis. Because of the digital attachment, how the users' emotional well-being as well as offline interpersonal relationships have been impacted by their use of relationships. Social media is the platform for people to share their varied points of view on life, as well as their experiences, facts, videos, and other information. This article explored social relationships in the face of growing dependency on social media or virtual communication. It has also been figured out that the risk factor is mainly related to social media use and spread mainly among youth in Bangladesh. The findings suggest the presence of cyber bullying, hate speech, and blasphemy have been polarizing the relationship in parochial senses - communal, ethnic, lingual, and others. Based on secondary sources, the article indicates the necessity of dealing with several issues like socialization and mental health issues on a national level and how they affect our lives.
A Lower Bound for the <i>n</i>-queens Problem

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)A Lower Bound for the n-queens ProblemMichael Simkin and Zur LuriaMichael Simkin and Zur Luriapp.2185 - 2197Chapter DOI:https://doi.org/10.1137/1.9781611977073.86PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The n-queens puzzle is to place n mutually non-attacking queens on an n × n chessboard. We present a simple two stage randomized algorithm to construct such configurations. In the first stage, a random greedy algorithm constructs an approximate toroidal n-queens configuration. In this well-known variant the diagonals wrap around the board from left to right and from top to bottom. We show that with high probability this algorithm succeeds in placing (1–o(1))n queens on the board. In the second stage, the method of absorbers is used to obtain a complete solution to the non-toroidal problem. By counting the number of choices available at each step of the random greedy algorithm we conclude that there are more than ((1–o(1)) ne–3)n solutions to the n-queens problem. This proves a conjecture of Rivin, Vardi, and Zimmerman in a strong form. Recently, using different methods, Bowtell and Keevash proved the same lower bound for the toroidal problem, giving an independent proof of the result. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Stuffed Toy as an Appealing Tangible Interface for Children

In recent years, there is a growing phenomenon with children playing mobile devices. Overplaying mobile devices, however, may not lead to a balanced childhood. Lack of physical touch-and-feel stimulus is detrimental to children. Can touch-and-feel stuffed toys be made more appealing to children for a wider variety on their playtime diet? We explore a stuffed toy controller which integrates elements from both digital and make-believe games in an attempt to enrich children interaction experience. A user study was conducted to evaluate the effectiveness of the proposed controller while another children-oriented user study shows that make-believe augmented reality games played by the toy controller can enhance children’s interest in stuffed toys.
Darknet Public Hazard Entity Recognition Based on Deep Learning

Due to the strong protection of anonymity, Darknet has been exploited by criminals to distribute harmful content and banned items, such as drugs, weapons, and malware, which are regarded as public hazard entities. The task of public hazard entity recognition can help to detect and analyze malicious activities in the Darknet. This paper focuses on the research of Chinese public hazard entity recognition in the field of illegal drugs. To evade detection and surveillance, Chinese public hazard entities in the Darknet usually utilize disguised forms, like homophones and multi-entities in a sentence, which makes it harder to identify them using traditional entity recognition methods. In this paper, we present an effective deep learning-based multi-information fusion model to identify Chinese public hazard entities in the Darknet. Specifically, we introduce the grammatical information by adding Pinyin and lexical features, and strengthen the semantic features by adding the word vectors from one advanced pre-trained language representation model. Then we combine these three parts with a classical sequence annotation architecture used in general named entity recognition to form our ultimate model. At last we construct a real dataset from drugs-related groups in the Darknet and conduct several experiments to evaluate our model. The experimental result verifies that our proposed model gains a good performance on the recognition of Darknet public hazard entities.
User Experience of Real and Virtual Products: a Comparison of Perceived Product Qualities

Virtual reality (VR) enables immersive experiences where users can explore products in the virtual world. In the last few years VR technologies have developed so much that the idea of substituting experiences with real products through those with virtual representation is growing, even with all inaccuracies of the computer-generated experience. If these virtual experiences are adapted as real, how huge will be the opportunities to increase the effectiveness of product development processes? For example, what if we can virtualize main parts of prototyping because we can get valuable feedback on a virtual representative? Thus, understanding the possibilities of VR to create products and evaluate their user experience (UX) is gaining in importance. While previous research analyzed the UX of interactive products, only few studies have investigated the UX of VR-product representations compared to the real world. In order to get a first step towards understanding the UX of virtual products, we conducted a product test that compares the UX of real products with their representations in virtual reality. In an empirical study, our subjects judged three different products with respect to their hedonic and pragmatic qualities by evaluating them through the AttrakDiff 2 questionnaire. The results from our user study revealed that there are in fact no significant differences in the user experience for two out of the three products. However, one object was rated significantly better in virtual reality compared to the real counterpart. Furthermore, qualitative user statements have been analyzed, showing that the users either valued or disliked different aspects of the products depending on whether these products were perceived in real life or VR: In summary, our study shows that there is already a high level of comparability between real and virtual product experience. At the same time, it reveals some question approaches that still have to be answered when experiencing virtual product representatives as if they were real.
Towards Enhancement of the Lexicon Approach for Hindi Sentiment Analysis

The amount of data from users in the form of reviews, comments and opinions in Hindi language is tremendously increasing on social media, blogs and online forums due to which sentiment analysis of Indian languages has turned out to be a predominant research area. Lexicon-based approaches are being used for analysing sentiments but they may not be as accurate as machine learning approaches. To enhance and improve the lexicon approach, a frequency score generation weighting scheme algorithm is proposed. Novel rules for handling some linguistic features such as negation, intensifiers are also implemented. Experiments performed to evaluate the performance of the proposed algorithm shows that the performance of lexicon-based sentiment analysis is increased significantly.
Real-time panoramic-deformation measurement of cabin structure of launch vehicle based on camera network

<p indent="0mm">To satisfy the requirement of real-time panoramic-deformation measurement of a cabin structure in a large-diameter launch vehicle, a camera-network deformation-measurement system composed of 24 cameras and real-time three-dimensional (3D) deformation-measurement software based on CPU parallel computing was developed. The 360° full-circle deformation measurement of the inner surface of a cylindrical coordinate system and real-time measurement of the local deformation of a local Cartesian coordinate system were successfully realized. The camera-network measurement system consisted of 12 sets of binocular systems. The field of view of a single set of binocular systems was 3.6 m×3.6 m, and a certain overlap area was set between two adjacent binocular systems to ensure continuity of measurements. Photogrammetry and binocular stereo vision were used to reconstruct the 3D coordinates of the coded points, and coordinate transformation between the local and global coordinate systems was realized using the coded points. Finally, coordinate unification of the 12 sets of binocular systems was performed. A single set of binocular systems could be connected to a computer. By implementing the seed-point-based parallel algorithm and initial guess-estimation-algorithm-based deformation-transfer scheme, real-time 3D displacement and surface-strain measurements at 70000 points per second were successfully realized. On the basis of the 360° full-circle deformation-measurement results in the cylindrical coordinate system, high-precision calculation of the diffusion angle was successfully realized. The standard deviation of the static noise in the three directions was less than 0.01 mm, and the standard deviation of the static noise in the strain was less than 50 microstrains.
Outlier removal method for the refinement of optically measured displacement field based on critical factor least squares and subdomain division

Abstract As a representative type of outlier, abnormal data in displacement measurement has often inevitably occurred in full-field optical metrology and significantly affected further evaluation, especially when calculating the strain field by differencing the displacement. In this study, an outlier removal method is proposed which can recognize and remove the abnormal data in an optically measured displacement field. An iterative critical factor least squares (CFLS) algorithm is developed which distinguishes the distance between the data points and the least squares plane to identify the outliers. A successive boundary point algorithm is proposed to divide the measurement domain to improve the applicability and effectiveness of the CFLS algorithm. The feasibility and precision of the proposed method are discussed in detail through simulations and experiments. The results show that the outliers are reliably recognized and the precision of the strain estimation is greatly improved by using these methods.
Research on Intelligent Function Design of Vocational Education System under Mobile Learning Mode

Because the traditional vocational education system has intelligent functions, long running time, and low recommendation accuracy of vocational education resources, the vocational education system under mobile learning mode is designed. The system is designed through the registration and login function module, course learning function module, examination and evaluation function module, communication and interaction function module, mobile learning function module, and personalized recommendation function module. In the personalized recommendation function module, the user’s preference for vocational education resources is obtained by calculating the user’s interest weight, as well as personalized recommendation of vocational education resources. The simulation results show that the intelligent function of vocational education system can operate normally under the designed mobile learning mode, the operation efficiency is high, and the effect of Vocational Education Resource Recommendation is good.
Comparison between the pollution of Mariute lagoon bottom sediments by heavy metals before and after 2015, north Nile Delta, Egypt

This manuscript study the comparison between the pollution of Mariout bottom sediment by heavy metals in 2014 and 2017.All means of heavy metals of bottom sediments collected in 2017 more than that collected in 2014 except Ni.The mean concentrations of heavy metals in Mariute bottom sediments of 2014 and 2017 were arranged in descending order as follows: Fe> Mn> Zn> Cu >.Pb> Ni > Cr > Cd.Degree of contamination (Dc) of the bottom sediments of 2014 and 2017 are ranged from low to moderate degree of contamination (the same except in sample 1).The potential ecological risk index (RI) was low ecological risk in sediments of 2014 and 2017.
CNN-Based Hand Grasping Prediction and Control via Postural Synergy Basis Extraction

The prediction of hand grasping and control of a robotic manipulator for hand activity training is of great significance to assist stroke patients to recover their biomechanical functions. However, the human hand and the figure joints have multiple degrees of freedom; therefore, it is complex to process and analyze all the collected data in hand modeling. To simplify the description of grasping activities, it is necessary to extract and decompose the principal components of hand actions. In this paper, the relationships among hand grasping actions are explored by extracting the postural synergy basis of hand motions, aiming to simplify hand grasping actions and reduce the data dimensions for robot control. A convolutional neural network (CNN)-based hand activity prediction method is proposed, which utilizes motion data to estimate hand grasping actions. The prediction results were then used to control a stimulated robotic model according to the extracted postural synergy basis. The prediction accuracy of the proposed method for the selected hand motions could reach up to 94% and the robotic model could be operated naturally based on patient’s movement intention, so as to complete grasping tasks and achieve active rehabilitation.
Special affine multiresolution analysis and the construction of orthonormal wavelets in <i>L</i><sup>2</sup>(ℝ)

The aim of this study is to construct both the continuous and discrete special affine wavelets in L2(R). Firstly, we introduce the continuous special affine wavelet transform via the convolution operations in the special affine Fourier domain. Subsequently, we investigate all the fundamental properties of the proposed transform using special affine Fourier transforms. Secondly, the discrete orthonormal special affine wavelets are constructed by introducing the notion of the special affine multiresolution analysis (MRA) in L2(R). Besides, a fast wavelet transform associated with special affine MRA is also presented. Towards the culmination, the formulation of special affine MRA starting from a given scaling function is briefly studied.
Intelligent Question Answering System by Deep Convolutional Neural Network in Finance and Economics Teaching

The question answering link in the traditional teaching method is analyzed to optimize the shortcomings and deficiencies of the existing question-and-answer (Q&amp;A) machines and solve the problems of financial students’ difficulty in answering questions. Firstly, the difficulties and needs of students in answering questions are understood. Secondly, the traditional algorithm principle by the Q&amp;A system is introduced and analyzed, and the problems and defects existing in the traditional Q&amp;A system are summarized. On this basis, deep learning algorithms are introduced, the long short-term memory (LSTM) neural network and convolutional neural network (CNN) are combined, and a Q&amp;A system by long short-term memory-convolutional neural network (LSTM-CNN) is proposed, the gated recurrent unit (GRU) attention mechanism is introduced, and the algorithm is optimized. Finally, the design experiments to determine the nearest parameters of the neural network algorithm and verify the effectiveness of the algorithm are carried out. The results show that for the LSTM-CNN, the effect is the best when dropout = 0.5. After introducing the attention mechanism optimization, the effect is the best when dropout = 0.6. The test results of the comparison between the recommended algorithm and the traditional Q&amp;A model algorithm show that the LSTM-CNN algorithm maintains the ability of the LSTM algorithm to arrange information in chronological order. After being combined with the CNN algorithm, the language features of the sentence can be extracted more deeply, the semantic feature information can be captured more accurately from the sentence, and better performance can be maintained when processing more complex sentences. The introduction of a BANet can simultaneously obtain the past and future information so that the algorithm can more appropriately combine it with the context to retrieve the semantic features, and the effectiveness of the model has been greatly improved. The research results have played an optimizing role in improving the Q&amp;A effect of finance and economics teaching and provided a reference for research in related fields.
Nature-inspired metaheuristic algorithms for optimization

With the advancement of technologies in engineering science, there has always been an advent of optimization for further refinement. Albeit not abruptly but still with considerable consistency, multi-objective metaheuristic optimization methods extend their roots in almost all complex engineering problems. This chapter gives an overview of the most interesting class, that is, nature-inspired optimization algorithms evolved in due course of time and inspiration from nature. A mathematical analysis is kept prominent in all sections for a better understanding of the method to the young researchers.