Reviewerly/exHarmony huggingface dataset:
Image harmonization based on the semantic information of foreground human

Image composition is a fundamental task of photo editing and graphic design. However, the style inconsistency of the foreground and background images will greatly degrade the quality of the composite images. The existing methods for image harmonization consider the foreground human as a whole and ignore its underlying semantic information, leading to the distortions in skin color of the foreground human. In this paper, we propose to conduct the image harmonization with the assistance of semantic understanding of foreground human. In the proposed method, a human parsing network is firstly employed to segment the foreground human into three parts: skin, hair and clothes. And thus a dataset is collected for foreground human harmonization, which contains the ground truth of foreground human parsing. Then the extracted semantic features is propagated to the image harmonization network to help the model learn the semantic information during harmonizing. With the assistance of this joint training scheme, the harmonization model enables to gain more realistic results and the distortion problem in skin color is also resolved to some extent. We conduct several comprehensive experiments on the collected dataset, the results of objective and subjective experiments prove that the proposed network outperforms the state-of-the-art methods.
Anbar: Collection and analysis of a large scale Urdu language Twitter corpus

The confluence of high performance computing algorithms and large scale high-quality data has led to the availability of cutting edge tools in computational linguistics. However, these state-of-the-art tools are available only for the major languages of the world. The preparation of large scale high-quality corpora for low-resource language such as Urdu is a challenging task as it requires huge computational and human resources. In this paper, we build and analyze a large scale Urdu language Twitter corpus Anbar. For this purpose, we collect 106.9 million Urdu tweets posted by 1.69 million users during one year (September 2018-August 2019). Our corpus consists of tweets with a rich vocabulary of 3.8 million unique tokens along with 58K hashtags and 62K URLs. Moreover, it contains 75.9 million (71.0%) retweets and 847K geotagged tweets. Furthermore, we examine Anbar using a variety of metrics like temporal frequency of tweets, vocabulary size, geo-location, user characteristics, and entities distribution. To the best of our knowledge, this is the largest repository of Urdu language tweets for the NLP research community which can be used for Natural Language Understanding (NLU), social analytics, and fake news detection.
BACE: Blockchain-based Access Control at the Edge for Industrial Control Devices of Industry 4.0

The Industrial Internet of Things is expected to attract significant investments for Industry 4.0. In this new environment, the blockchain has immediate potential in industrial applications, providing unchanging, traceable and auditable access control. However, recent work and present in blockchain literature are based on a cloud infrastructure that requires significant investments. Furthermore, due to the placement and distance of the cloud infrastructure to industrial control devices, such approaches present a communication latency that can compromise the strict deadlines for accessing and communicating with this device. In this context, this article presents a blockchain-based access control architecture, which is deployed directly to edge devices positioned close to devices that need access control. Performance assessments of the proposed approach were carried out in practice in an industrial mining environment. The results of this assessment demonstrate the feasibility of the proposal and its performance compared to cloud-based approaches.
A Robust Invariant Local Feature Matching Method for Changing Scenes

The precise evaluation of camera position and orientation is a momentous procedure of most machine vision tasks, especially visual localization. Aiming at the shortcomings of local features of dealing with changing scenes and the problem of realizing a robust end-to-end network that worked from feature detection to matching, an invariant local feature matching method for changing scene image pairs is proposed, which is a network that integrates feature detection, descriptor constitution, and feature matching. In the feature point detection and descriptor construction stage, joint training is carried out based on a neural network. In the feature point extraction and descriptor construction stage, joint training is carried out based on a neural network. To obtain local features with solid robustness to viewpoint and illumination changes, the Vector of Locally Aggregated Descriptors based on Neural Network (NetVLAD) module is introduced to compute the degree of correlation of description vectors from one image to another counterpart. Then, to enhance the relationship between relevant local features of image pairs, the attentional graph neural network (AGNN) is introduced, and the Sinkhorn algorithm is used to match them; finally, the local feature matching results between image pairs are output. The experimental results show that, compared with the existed algorithms, the proposed method enhances the robustness of local features of varying sights, performs better in terms of homography estimation, matching precision, and recall, and when meeting the requirements of the visual localization system to the environment, the end-to-end network tasks can be realized.
Ruled surfaces generated by gimbaled mirrors: II. A comparative study of gimbaled mirrors with the dual axis galvanometric scanners for XY scanning

The theory developed in the first part of this study [ J. Opt. Soc. Am. A 38 , 1884 ( 2021 ) JOAOD6 0740-3232 10.1364/JOSAA.435621 ] is extended to an investigation of the ruled surface produced by a gimbaled mirror and its applications to formulate a new criterion that uses the base curve of the ruled surface as a reference scale to divide the scan field into near and far zones and to investigate ruled surfaces produced by the 1D mirror-scanning systems, such as the monogon, the polygon, and the galvanometric scanners as the special cases of that produced by a gimbaled mirror. Results obtained from the two applications described above lead to placing the ruled surface produced by scanners of different architectures on a platform and then bridging them up by the ruled surfaces they produced for a performance comparison. Although all these scanners may have very different scanning geometries and different application areas, there is one thing making them comparable: the key parameters of the ruled surfaces they produced. As an example, we compare the ray deviation power of a gimbaled mirror with that achievable by a dual galvo scanner for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>X</mml:mi> <mml:mi>Y</mml:mi> </mml:math> scanning by way of the key parameters of the ruled surfaces they produced.
Most acceptable and least capable target sizes for zoom-and-click tasks on mobile touchscreens- implications for user performance and applications to interface design

Using mobile devices with small touchscreens to click on icons or links was problematic for users, especially when the layout was dense and full of targets. Most of existing literatures studied proper target sizes in clicking tasks by testing user performance with predetermined options of targets. Larger targets were suggested for elders based on limited number of alternatives in discrete sizes. However, multi-touch technology now allows users to manipulate target sizes by zooming with a pinch gesture in a continuous manner. A speed-accuracy balanced target size (the Most Acceptable Target Size, MATS), i.e. a target not only large enough to ensure accuracy in clicking but also small enough to avoid unnecessary operation in zooming, becomes more relevant in this context. The current study recorded the MATS determined subjectively by young and elder users in a zoom-and-click task on mobile devices using 5-, 7- and 10-inch touchscreens with four levels of display sparsity. User performance was measured together with the Least Capable Target Size (LCTS, the target just large enough for the user to click with above-chance accuracy) indicative of users’ capability. Results showed that young and elder users preferred similar MATS (7.42 mm on average). The 10-inch device required the smallest LCTS but the largest MATS due to greater viewing distance and the unfamiliar 7-inch device required the largest LCTS. Reduction of display density can cause larger MATS required and possibly elicit speed-accuracy trade-offs for elders to adapt.
Remote Electronic Engineering Teaching during Lockdown: Lessons Learned

The devastating effects of COVID-19 pandemic led the Spanish government to adopt exceptional emergency regulations that forced the confinement of the entire population and, therefore, the cessation of on-site teaching. This forced the migration of teaching methodologies to virtual formats in a few weeks. The lack of specific resources and experience in online teaching, and the need of a rapid response meant that the measures adopted fostered a hybrid model between online and face-to-face teaching. This paper presents the context and the approach taken in the methodological adaptation of three subjects of Electronic Engineering with different characteristics. In addition, the results of the evaluation of these measures by students, obtained through a survey, are presented. The results show that virtualisation has produced a fictitious improvement in students’ grades, as the assessment evidences. The students considered very positive the use of synchronous learning as opposed to asynchronous format, as well as all the activities that promoted direct communication between the student and the lecturer. Another generalised opinion extracted from the results of the survey is that the new measures have implied an excess of work for students. The experience gained and the students’ opinions can be very useful for the design of blended learning formats, which seem to be a very promising alternative in the future of University, although this change seems to be non-trivial in such experimental studies as engineering.
Multi-GMTI decentralized tracking via consensus LMB density fusion

The complementary information from multiple ground moving target indication (GMTI) sensors at different positions can be combined for collaboratively de-blinding, thereby reducing the adverse effects caused by the view blind zone (VBZ) in a multi-view scenario and Doppler blind zone (DBZ) inherent in GMTI sensors. This paper attempts to apply the fusion of the latest labeled random finite set (LRFS) densities to achieve this goal. During the LRFS density fusion, the problem of label inconsistency is a major challenge. This problem is particularly prominent in the fully distributed fusion without fusion centers and is further exacerbated when the VBZ and DBZ problems coexist. Aiming at the brand-new problem of label inconsistency in the presence of the VBZ and DBZ, we firstly design an effective fusion scheme for the LRFS density fusion. Furthermore, we propose a consensus geometric average (GA) fusion for labeled multi-Bernoulli (LMB) densities, where the core two parts include: a two-step density association with coarse association first and then fine association, and a consensus fusion of “combination” plus “fusion”. Finally, numerical examples verify the effectiveness of the proposed fusion, showing that it is significantly superior to local trackers.
Elevator button tracking and localization for multi-storey navigation

Elevator button recognition in an indoor multi-storey environment has been a challenging task amidst the whole scenario of indoor navigation on a mobile robot. In this paper, we integrate various computer vision approaches for the task of button recognition and tracking in an indoor multi-storey environment. To overcome the problem of detecting elevator buttons, we have prepared a framework that uses various preprocessing techniques combined with object detection and tracking approaches to recognize the buttons. Initially, a single-shot object detector YOLOv3 locates the original positions of the target buttons using region over intersection based approach to produce bounding boxes over the required objects. Then we use a part-based tracking algorithm Deep-SORT that follows the detected buttons in realtime to counter the hard movements of the camera. lastly, we take the bounding box coordinate information of the detected buttons and make a semantic map, which can be used to recreate a complete layout of the button panel even with partially detected buttons or a frame consisting of partial button information.
Analysis of Dense Descriptors in 3D Face Recognition

In the past years, a revolution took place in the world of technology and developed rapidly in all areas, covering various aspects of life. One of the hottest topics that researchers work in is computer vision including artificial intelligence. As it has a great importance, it represents the basics for many applications that are currently used in various sectors. The technology of biometric recognition has progressively developed specially in security for identification purposes. Such technology is face recognition, which uses facial information of humans to recognize people. In this paper 3D (three-dimensional) face recognition approach is proposed by using dense descriptors Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Gabor with Support Vector Machine (SVM). LTP technique which is a variant and extension of LBP. LBP and LTP have been used for feature information extraction individuality and merging with Gabor from the 3D images, and then the SVM technique is employed to classify and recognize the faces according to extracted features. The database that depended in this work is Three-Dimensional Face Recognition Database (Texas 3DFRD). The accuracy obtained from the proposed model was about 94.9% after many attempts the better one is selected.
Ancient Russian Niello: Experimental Modeling and Research of the Microstructure

The paper presents the results of structural studies of model niello compositions reproducing historical niello inlays on encolpion crosses from the Suzdal Opolye. The specifics of the formation of inlay phases at different stages of its melting are analyzed. Comparison of the modeling results and historical niello shows that the microstructural features of archaeological samples correspond to niello production by melting.
Exploring the knowledge domain of earthquake prediction based on bibliometric analysis and text mining

Earthquake prediction is one of the important themes of earthquake research, and it is also a very difficult scientific problem in the world. In this study, a bibliometric analysis is conducted on the scientific publications about earthquake prediction indexed in SCIE (Science Citation Index Expanded) and SSCI (Social Sciences Citation Index) databases during the past two decades (1998–2017). The subject categories, annual and journal distributions, leading countries/regions and institutions are investigated in this field. The main research topics are identified through text mining method. The research trends are explored by keyword co-occurrence analysis and bursting keywords detection techniques. The results of this study are helpful for scholars in this field to find the knowledge structure and important participants. It is also helpful for scholars to seize the current research hotspots and future development trends in this field.
A machine learning approach to predict emotional arousal and valence from gaze extracted features

In the last years, many studies have been investigating emotional arousal and valence. Most of them have focused on the use of physiological signals such as EEG or EMG, cardiovascular measures or skin conductance. However, eye related features have proven to be very helpful and easy to use metrics, especially pupil size and blink activity. The aim of this study is to predict emotional arousal and valence levels which are induced during emotionally charged situations from eye related features. For this reason, we performed an experimental study where the participants watched emotion-eliciting videos and self-assessed their emotions, while their eye movements were being recorded. In this work, several classifiers such as KNN, SVM, Naive Bayes, Trees and Ensemble methods were trained and tested. Finally, emotional arousal and valence levels were predicted with 85 and 91% efficiency, respectively.
The semantic typology of visually grounded paraphrases

Visually grounded paraphrases (VGPs) are different phrasal expressions describing the same visual concept in an image. Previous studies treat VGP identification as a binary classification task, which ignores various phenomena behind VGPs (i.e., different linguistic interpretation of the same visual concept) such as linguistic paraphrases and VGPs from different aspects. In this paper, we propose semantic typology for VGPs, aiming to elucidate the VGP phenomena and deepen the understanding about how human beings interpret vision with language. We construct a large VGP dataset that annotates the class to which each VGP pair belongs according to our typology. In addition, we present a classification model that fuses language and visual features for VGP classification on our dataset. Experiments indicate that joint language and vision representation learning is important for VGP classification. We further demonstrate that our VGP typology can boost the performance of visually grounded textual entailment.
Optimization of the parallel semi-Lagrangian scheme in the YHGSM based on the adaptive maximum wind speed

The Yin-He global spectral model (YHGSM), embodies a parallel semi-Lagrangian solver and has two schemes implemented: maximum wind speed scheme and on-demand communication scheme. Maximum wind speed communication adopts a single and fixed data structure, which has a large communication overhead. Although the overhead of on-demand communication is reduced, it is still pretty huge. In this paper, a novel adaptable approach is proposed in which a monthly maximum wind speed is used in the YHGSM. This approach reduces the difference between the actual wind speed and the maximum wind speed used in the model; in turn, the communication overhead in the trajectory computation is further reduced. Experiments show that in the maximum wind speed scheme and on-demand schemes, the communication overheads with the adaptive maximum wind speed are significantly reduced. In addition, in a ten-day forecast with the on-demand communication scheme, the total overhead for the semi-Lagrangian computing and the total parallel execution time are also both reduced, and the reduction ratio increases as the number of nodes increases.
Building Cultural Heritage Reference Collections from Social Media through Pooling Strategies: The Case of 2020’s Tensions Over Race and Heritage

Social networks constitute a valuable source for documenting heritage constitution processes or obtaining a real-time snapshot of a cultural heritage research topic. Many heritage researchers use social networks as a social thermometer to study these processes, creating, for this purpose, collections that constitute born-digital archives potentially reusable, searchable, and of interest to other researchers or citizens. However, retrieval and archiving techniques used in social networks within heritage studies are still semi-manual, being a time-consuming task and hindering the reproducibility, evaluation, and open-up of the collections created. By combining Information Retrieval strategies with emerging archival techniques, some of these weaknesses can be left behind. Specifically, pooling is a well-known Information Retrieval method to extract a sample of documents from an entire document set (posts in case of social network’s information), obtaining the most complete and unbiased set of relevant documents on a given topic. Using this approach, researchers could create a reference collection while avoiding annotating the entire corpus of documents or posts retrieved. This is especially useful in social media due to the large number of topics treated by the same user or in the same thread or post. We present a platform for applying pooling strategies combined with expert judgment to create cultural heritage reference collections from social networks in a customisable, reproducible, documented, and shareable way. The platform is validated by building a reference collection from a social network about the recent attacks on patrimonial entities motivated by anti-racist protests. This reference collection and the results obtained from its preliminary study are available for use. This real application has allowed us to validate the platform and the pooling strategies for creating reference collections in heritage studies from social networks.
Resource Types linked in Academic Reading Lists

Reading List Systems are widely used in tertiary education as a pedagogical tool and for tracking use of copyrighted material. This paper explores the types of resources that are linked in reading lists, in particular the inclusion of electronic materials. A mixed-methods approach was employed in which we first performed a transaction log analysis on reading lists across a university, covering five years (2016 to 2020). We then used a questionnaire to gain feedback from academics about their experience with linking resources. Our results show a growing number of digital resources being used in reading lists, and indicate faculty-based differences in the types of resources linked. We also identify that many academics struggle with successfully linking resources, and do not perceive the process to be user friendly. The paper recommends a number of interventions to improve the reading list experience for academics.