Reviewerly/exHarmony huggingface dataset:
A proposal of an assembly workstation for car panel fitting aided by an augmented reality device

In the automotive sector, panel fitting operations represent a delicate phase of the assembly process, during which car bodywork components are manually aligned attempting to comply with spacing tolerances. Digital technologies currently promoted by the Industry 4.0 approach, such as collaborative robots and augmented reality (AR), could assist workers for reducing execution times of this “trial and error” procedure. On the one hand, human-robot collaboration (HRC) can enhance assembly process efficiency, by combining human dexterity and cognitive capabilities with robot repetitiveness and support in heavy load handling; on the other, AR can provide interactive instructions to guide workers during the process. In this regard, the present paper is focused on the development of a novel assembly workstation using HRC and AR to support workers during car panel fitting operations. The system is based on the implementation of an algorithm able to convert the values on panel spacing, which are measured by laser gauge positioned on the collaborative robot, in the instructions for the worker to correctly carry out the panel fitting operation. The paper finally presents the application of the system to a real assembly example.
Learning Fine-Grained Motion Embedding for Landscape Animation

In this paper we focus on landscape animation, which aims to generate time-lapse videos from a single landscape image. Motion is crucial for landscape animation as it determines how objects move in videos. Existing methods are able to generate appealing videos by learning motion from real time-lapse videos. However, current methods suffer from inaccurate motion generation, which leads to unrealistic video results. To tackle this problem, we propose a model named FGLA to generate high-quality and realistic videos by learning Fine-Grained motion embedding for Landscape Animation. Our model consists of two parts: (1) a motion encoder which embeds time-lapse motion in a fine-grained way. (2) a motion generator which generates realistic motion to animate input images. To train and evaluate on diverse time-lapse videos, we build the largest high-resolution Time-lapse video dataset with Diverse scenes, namely Time-lapse-D, which includes 16,874 video clips with over 10 million frames. Quantitative and qualitative experimental results demonstrate the superiority of our method. In particular, our method achieves relative improvements by 19% on LIPIS and 5.6% on FVD compared with state-of-the-art methods on our dataset. A user study carried out with 700 human subjects shows that our approach visually outperforms existing methods by a large margin.
Batteries, camera, action! Learning a semantic control space for expressive robot cinematography

Aerial vehicles are revolutionizing the way filmmakers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work, we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip. Next, we analyze correlations between descriptors and build a semantic control space based on cinematography guidelines and human perception studies. Finally, we learn a generative model that can map a set of desired semantic video descriptors into low-level camera trajectory parameters. We evaluate our system by demonstrating that our model successfully generates shots that are rated by participants as having the expected degrees of expression for each descriptor. We also show that our models generalize to different scenes in both simulation and real-world experiments. Data and video found at: https://sites.google.com/view/robotcam.
Transformers in Pedestrian Image Retrieval and Person Re-Identification in a Multi-Camera Surveillance System

Person Re-Identification is an essential task in computer vision, particularly in surveillance applications. The aim is to identify a person based on an input image from surveillance photographs in various scenarios. Most Person re-ID techniques utilize Convolutional Neural Networks (CNNs); however, Vision Transformers are replacing pure CNNs for various computer vision tasks such as object recognition, classification, etc. The vision transformers contain information about local regions of the image. The current techniques take this advantage to improve the accuracy of the tasks underhand. We propose to use the vision transformers in conjunction with vanilla CNN models to investigate the true strength of transformers in person re-identification. We employ three backbones with different combinations of vision transformers on two benchmark datasets. The overall performance of the backbones increased, showing the importance of vision transformers. We provide ablation studies and show the importance of various components of the vision transformers in re-identification tasks.
Time-frequency domain pattern analysis of Tai Chi 12 GONG FA based on skeleton key points detection

The purpose of this study is to develop an intelligent automatic scoring system of Tai Chi 12 GONG FA based on machine learning to assist the teaching, training and promotion of 12 GONG FA. On the basis of obtaining the action videos of the practitioners with different exercise levels, the two-dimensional human skeleton key point recognition technology was used to extract the different behavior patterns of fine and bad actions by analyzing the complex periodic changes of the spatial distance of different key points in the time domain; And the artificial neural network was used to reduce the dimension of features predict whether the actions conform to the norms. The system only uses monocular RGB video as input, and by the proposed algorithm of recognizing fine action and bad action, 12 kinds of action can be well identified. The intelligent scoring system based on key point detection achieves similar results with the experts' score.
InterNet+: A Light Network for Hand Pose Estimation

Hand pose estimation from RGB images has always been a difficult task, owing to the incompleteness of the depth information. Moon et al. improved the accuracy of hand pose estimation by using a new network, InterNet, through their unique design. Still, the network still has potential for improvement. Based on the architecture of MobileNet v3 and MoGA, we redesigned a feature extractor that introduced the latest achievements in the field of computer vision, such as the ACON activation function and the new attention mechanism module, etc. Using these modules effectively with our network, architecture can better extract global features from an RGB image of the hand, leading to a greater performance improvement compared to InterNet and other similar networks.
Intelligent Technologies in E-Learning: Personalization and Interoperability

Many educational systems are focused predominantly on such an organization of the learning process to be uniform for all learners. This approach is not the best for many learners, especially for those with various difficulties or interests. Strategies for personalization and adaptation can increase the tutoring quality and ensure efficient learning for groups of learners with different needs, goals, or interests. Intelligent technologies can enable the personalization of the learning process. However, no one system can contain all the knowledge needed for all learners, so interoperability between e-learning systems is of greater importance. Standards are needed for ensuring interoperability, but they are not sufficient. This paper first analyzes and classifies sources for learning on the Internet. The findings help to develop a knowledge model needed for a personalized recommendation of web-based sources, complementary to the learning content that a well-organized intelligent educational system can propose to learners. Having in mind specifics of the ontologies needed for modeling the learning domain, a model of the ontological repository for storing ontologies that can be useful for building intelligent educational systems is suggested. We also discussed mapping problems between ontologies and modeling e-learning standards.
A comprehensive E-commerce customer behavior analysis using convolutional methods

E-commerce is one of the most widely preferred business models all around the world. People can order items and have them in days or maybe in hours without visiting the shops. They count on the reviews of the online items to purchase them, so reviews have become one of the most significant metrics. Modern day algorithms and models can be made to decide the reviews seen by the user to judge an item fairly. We have attempted to make models that can judge the sentiment of the user writing the reviews from the text of the review, classifying it into positive, negative or neutral. Edge Computation is an emerging field in this scenario that can be very beneficial alongside capsule networks for processing the data as data capsules. In this work, we have performed a comparison of the effect of convolutional layers , attention layers and capsule network layers on base models for GRU , LSTM , Bi-GRU, and Bi-LSTM. These are done with the purpose to support the functioning of Edge Computation. The final results show that Bi-LSTM-Capsule Model (BiCapsModel) gives highest accuracy of 94.8%. The final outcome is very promising and can be used in various domains in journalism, product review and stock market. For classification , an Amazon review dataset has been considered. The dataset has various types of classified reviews based on which the emotional text analysis can be performed efficiently covering each aspect.
Dynamic Window Approach with Human Imitating Collision Avoidance

The autonomous navigation in the crowded environment is a challenging task due to the sensor occlusion and the complex nature of the abstract social interactions. And yet, humans are capable of navigating in such complex environment. In this paper, we propose an effective navigation method that combines the learning-based and model-based methods in a way that a cost function that includes human imitation factor learned via deep learning is integrated into the dynamic window approach (DWA) [1]. The experiments conducted on simulations show that by training the robot to imitate the human trajectory, our navigation method is safer and more efficient than the state-of-the-art methods. Additionally, we successfully deployed a physical robot in an actual environment, and we validate that our navigation quality shares similar tendency with human in the path length, travel time, and the collision avoidance.
Efficient fine-texture image retrieval using deep multi-view hashing

Fine-texture Image Retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. However, there are very few researches focus on this special case. Due to the difference between fine-texture images and common images, general retrieval methods for common images are difficult to apply to fine-texture image retrieval. It is also a challenging issue with several obstacles: variety and complexity of appearance, as well as high requirements for retrieval accuracy. To address this issue, this study proposes a novel approach, called deep multi-view hashing (DMVH), to learn enhanced hash codes for efficient fine-texture image retrieval. We propose to use the first few layers of a deep convolutional neural network for fine-texture image presentation. To avoid overfitting, we employ L0Linear layer instead of the commonly used Linear layer in all the fully connected layers. Then we introduce a pairwise quantified similarity computed on the semantic labels. And the learning process of the model is guided by multiple labels of images. This study takes fabric as an example, and builds a dataset called MFT-fabric-v1, to validate the effectiveness of the DMVH method. On this data set, the proposed model achieved a performance with a mAP value of o.8735. The area under the pr curve is larger than other comparison methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art on the MFT-fabric-v1 dataset.
The Construction of English Smart Classroom and the Innovation of Teaching Mode under the Background of Internet of Things Multimedia Communication

There are some problems in English smart classroom, such as slow response speed of related platforms and room for improvement of teaching effect. Therefore, it is necessary to construct English smart classroom and innovate teaching mode under the background of Internet of Things (IoT) multimedia communication. The teaching objectives are divided into classroom teaching objectives, curriculum objectives, and education and training objectives, so as to design the technical support for English smart classroom, that is, to build a smart classroom integrating cloud application platform (including students’ learning cloud and teachers’ teaching cloud), teaching platform, resource platform, learning space, and interactive classroom by using Internet of Things multimedia communication technology, and build an innovative teaching mode of English wisdom classroom. The fuzzy hierarchical evaluation model is used to design the fuzzy hierarchical evaluation algorithm. Through the fuzzy hierarchical evaluation algorithm, the teaching effect of smart classroom is evaluated, and the final evaluation result is obtained. The results show that the average platform login time of the proposed method is always below 0.3 s, the average score of the summative evaluation is high, the evaluation time is always below 0.4 s, the average communication delay is 0.54 s, and the user satisfaction is always above 9.2. The practical application effect is better.
An efficient fine-grained data access control system with a bounded service number

In a data access control system oriented toward the cloud storage environment, a data owner defines attribute-based access control policies for data files to realize fine-grained data sharing. However, the existing schemes have defects in user execution efficiency and user privacy protection, and they do not consider the problems of user revocation and attribute updates. To this end, we propose a ciphertext policy attribute-based encryption method with verifiable outsourced decryption; this requires a user to complete decryption with the help of a server, but the results of the outsourced decryption can be verified independently. With this new encryption scheme and the technique of k-times anonymous authentication, a new fine-grained data access control system was constructed; this system allows a server to provide users with outsourced decryption services, and users' computation cost is independent of the size of the underlying access control policy. Moreover, the number of outsourced decryption requests is limited. In addition, the new system supports user revocation and attribute updates and it is provably secure under formal proofs. An efficiency analysis shows that it can be compared with other similar systems in terms of performance, despite the addition of several practical properties.
A Brief Guide

Facial expressions are an important way for humans to perceive emotions. The advent of facial action coding systems has enabled the quantification of facial expressions. Moreover, a large amount of annotated data facilitates the performance of deep learning for the spotting and recognition of expressions or micro-expressions. However, the study of video-based expressions or micro-expressions requires coders to have expertise while also familiar with action unit (AU) coding. This paper systematically sorts out the relationship between facial muscles and AU to make more people understand AU coding from the principle. For this purpose, we have made a brief guide to get started as quickly as possible for the beginner to code.
Robust RF Mixture Signal Recognition Using Discriminative Dictionary Learning

RF signal recognition is an important element toward RF situational awareness and dynamic spectrum management. In this work, machine learning-based signal recognition algorithms are proposed. Our key contribution is to engineer feature learning such that the classifiers can perform robustly even when a mixture of heterogeneous signal classes is observed, although the training is still done using non-mixture single-label samples. To achieve this, discriminative dictionary learning algorithms are developed with various feature-shaping constraints. The signal detection can then be done in a way reminiscent of the multi-user detection in wireless communication, employing linear equalizers. The algorithms are tested using real wideband RF measurement data. It is verified that the proposed algorithms can robustly classify the component signals even when their powers are widely different and their number is not known a priori.
Development of Vertical Text Interpreter for Natural Scene Images

Automatic text recognition in natural scene images is essential for accessing information and understanding our surroundings. Scene text orientations include horizontal scene texts, arbitrarily oriented scene texts, curved scene texts, and vertically oriented scene texts. While attention has been given to horizontal, arbitrarily oriented, and curved text, limited research has been carried out on vertically oriented scene text recognition. To this end, we propose Vertical Text Interpreter, an autonomous vertically oriented scene text recognizer model. Vertical Text Interpreter detects and recognizes vertically oriented scene texts in natural scenes, including vertically-stacked texts, bottom-to-top vertical texts, and top-to-bottom vertical texts. It consists of a shared convolutional neural network, a Vertical Text Spotter, and a Vertical Text Reader. Addressing the need for a dataset for this category of scene texts, we developed a dataset, namely Vertically Oriented Scene Text 1250 Dataset, created as part of this research. The performance of the Vertical Text Interpreter is evaluated using benchmark datasets and the VOST-1250 dataset. Results show that Vertical Text Interpreter can detect and recognize different types of vertically oriented scene texts simultaneously. For future work, we can explore Vertical Text Interpreter for the contexts such as reading assistance and visual navigation systems.
Design and Development of Multiple Mobile Manipulator Robots using Gazebo-ROS

In this paper, multiple mobile manipulators are designed for handshaking system on Robot Operating System (ROS). The mobile manipulator is a robotic manipulator arm mounted on a mobile platform. The Festo Robotino mobile platform is equipped with a robotic arm manipulator in order to extend the task of delivering goods and offers enhanced flexibility and agility, increasing the arm manipulator workspace by using the mobility of Festo Robotino. ROS is powerful tools that can be used in the field of robotics and Graphical User Interface (GUI) development. The Gazebo-ROS can be used to verify the coordination protocol of multiple mobile manipulator robots. Furthermore, several simulation and experimental results have shown the effectiveness of the designed multiple mobile manipulator robots
Diffusion of theories and theoretical models in the<scp>Ibero‐American</scp>research on information behavior

Abstract Ibero‐American research on information behavior (IB) lacks the visibility typical of other parts of the world, and little is known about it in countries outside the area. The objective of this paper has therefore been to analyze the way in which Ibero‐American research incorporates various theoretical references to empirical research on IB. The results point to the existence of different focuses of research in the past 10 years, in the sense of a reduced empirical approach and a moderate to minimal use of theories in the design of such research. Furthermore, the most cited theories and models of IB at an international level are those most widely applied in this geographical area, and the use of a wide variety of theoretical frameworks has been demonstrated, which gives the research under review a cognitive, but also sociocultural, perspective. Future research should further elaborate on this issue, including other types of documents, such as conference papers, books, and theses, while taking into account the publication landscape of the geographical area in question.
Quantifying Interference via Coherence

Abstract Interference, which has its origin in the superposition principle, is one of the hallmarks of wave mechanics and constitutes a versatile resource for many information processing tasks. As such, it is actually a manifestation of coherence. Despite its long history in the development of physics, quantitative studies of interference are still an important topic. Traditionally, interference is often characterized via the fringe visibility from an operational perspective. This is experimentally convenient. However, from a theoretic perspective, it is desirable to reveal information‐theoretic aspects of interference. In this work, coherence of a state (with respect to a unitary operator parameterized by the interfering paths and phase shifts in multi‐path interference) is first quantified in terms of a generalized version of the Wigner‐Yanase skew information, two natural quantifiers of interference are then introduced via this coherence measure. Basic properties of these quantifiers of interference are further revealed, and the concepts are illustrated through several examples.
<scp>LEADING</scp> the Way: A New Model for Data Science Education

Abstract Addressing the data skills gap, namely the superabundance of data and the lack of human capital to exploit it, this paper argues that iSchools and Library and Information Science programs are ideal venues for data science education. It unpacks two case studies: the LIS Education and Data Science for the National Digital Platform (LEADS‐4‐NDP) project (2017–2019), and the LIS Education and Data Science‐Integrated Network Group (LEADING) project (2020–2023). These IMLS‐funded initiatives respond to four national digital platform challenges: LIS faculty prepared to teach data science and mentor the next generation of educators and practitioners, an underdeveloped pedagogical infrastructure, scattered and inconsistent data science education opportunities for students and current information professionals, and an immature data science network. LEADS and LEADING have made appreciable collaborative, interdisciplinary contributions to the data science education community; these projects comprise an essential part of the long‐awaited and much‐needed national digital platform.