Reviewerly/exHarmony huggingface dataset:
Territoriality in Hybrid Collaboration

Hybrid Collaboration, where remote and co-located team members work together using different devices and tools, has already been trending in recent years (e.g., through globalization and international cooperation) but experienced a further boost since the outbreak of the COVID-19 pandemic. The reason behind this surge in hybrid practices is probably that the crisis revealed aspects of remote collaboration which proved functional and which many decision makers (in industry as well as academia) plan to retain for the future. Thus, hybrid collaboration is an extremely timely topic which should be further studied in the context of CSCW. One major CSCW-anchored concept that has most intensively been researched in co-located collaboration settings where it is usually inherently related to spatial aspects and proximity, is territoriality. Already work on territoriality in fully distributed, remote settings has shown that there are significant differences due to the characteristics of the scenario. In this paper, we focus on territoriality in hybrid settings where we identified a significant research gap, and present the results of a user study with 22 teams consisting of four people each (distributed across two locations at two different universities), collaborating on a problem-solving task. Our findings reveal that more dimensions and communication channels, in addition to space, might strongly impact territoriality and territorial behavior in hybrid collaboration. Besides classical spatial territories also auditory territories frequently emerged. In addition, visibility of and accessibility to certain territories need to be rethought. We discuss these novel findings also regarding their interplay with earlier ones and derive design implications for CSCW systems supporting hybrid collaboration.
The tabular Strathbogie batholith in central Victoria: Response to comments by C. D. Branch

"The tabular Strathbogie batholith in central Victoria: Response to comments by C. D. Branch." Australian Journal of Earth Sciences, ahead-of-print(ahead-of-print), pp. 1–2
Argentine Universities: Problems, COVID-19, ICT &amp; Efforts

In this paper the general objective was determine the situation of Argentine universities: Problems, COVID-19, ICT &amp; efforts. Methodology, in this research, 36 documents have been selected, carried out in the period 2016 - 2021; including: scientific articles, review articles and information from websites of recognized organizations. The keywords used in the searches were: Argentine universities, COVID-19 and ICT. Results, Argentine universities have many difficulties, like other Latin American universities. The pandemic has reduced access to economic resources. However, Argentine universities are making many efforts to fulfill their social role. Conclusions, the oldest university in Argentina is the National University of Córdoba, founded in 1613. Higher education in Argentina is primarily public, where direct entry is offered to students. The professional careers preferred by Argentine students are engineering, law and administration. There are also very internationally recognized private universities. Many universities are struggling to quickly set up their virtual platforms, before classes start. The difficult economic situation that the country is going through for several years, the health emergency and social isolation due to the pandemic, have paralyzed public and private university activity. It is important and urgent that Argentine universities strengthen the adoption of ICT and also promote innovation.
Improving Building Extraction by Using Knowledge Distillation to Reduce the Impact of Label Noise

Building extraction using deep learning techniques has advantages but relies on a large number of clean labeled samples to train the model. Complex appearance and tilt shots often cause many offsets between building labels and true locations, and these noises have a considerable impact on building extraction. This paper proposes a new knowledge distillation-based building extraction method to reduce the impact of noise on the model and maintain the generalization of the model. The method can maximize the generalizable knowledge of large-scale noisy samples and the accurate supervision of small-scale clean samples. The proposed method comprises two similar teacher and student networks, where the teacher network is trained by large-scale noisy samples and the student network is trained by small-scale clean samples and guided by the knowledge of the teacher network. Experimental results show that the student network can not only alleviate the influence of noise labels but also obtain the capability of building extraction without incorrect labels in the teacher network and improve the performance of building extraction.
Active learning for data efficient semantic segmentation of canine bones in radiographs

X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with O(1) time complexity.
Suppression of the conjugate signal for broadband computed imaging via synthetic phase modulation

We present synthetic-phase-modulated interferometric synthetic aperture microscopy (SPM-ISAM), a method to perform 3D object reconstructions from data acquired with confocal broadband interferometric microscopy (BIM) that reconstructs images virtually free of coherent and depth-dependent defocus artifacts. This is achieved by implementing a sinusoidal SPM method in combination with an ISAM reconstruction algorithm that uses relatively low-modulation frequencies compared with acquisition frequencies. A theoretical framework and numerical results are provided here.
Research on Training Effectiveness of Professional Maintenance Personnel Based on Virtual Reality and Augmented Reality Technology

The maintenance training method based on Virtual Reality (VR) and Augmented Reality (AR) technology has the characteristics of safety, no space limitation, and good reusability. Compared with the traditional training method, it can reduce the training cost, shorten the training period, and improve training effectiveness. Therefore, more and more maintenance training use VR and AR to replace training based on actual equipment to improve training effectiveness. However, in the context of multi-level tasks, there is still no clear research conclusion on how to choose training methods, maximize the advantages of each training method, and achieve higher training effectiveness. In response to this problem, this study constructed three training platforms based on VR, AR, and actual equipment, designed three maintenance tasks at different levels, and created a comparative analysis of the training effects of 60 male trainees under the three tasks and three training platforms. The results show that for single-level maintenance tasks, the training effect of the traditional group was significantly better than that of the AR group and the VR group. For multi-level maintenance tasks, the training effect of AR group was significantly better than that of the VR group. With the increasing difficulty of maintenance tasks, the training efficiency of the AR group was more than 10% higher than that of the VR group and traditional group and the AR group had less cognitive load. The conclusions of this study can provide a theoretical basis for the selection of training methods and evaluation design and help to formulate training strategies, thereby shortening the training period of professional maintenance personnel.
Impact of demography on linguistic aspects and readability of reviews and performances of sentiment classifiers

This study investigates how user demographics influence the linguistic characteristics and readability of the reviews and the performance of sentiment classification methods. Two restaurant review datasets representing consumers of distinct demographics, such as English language fluency levels, socio-culture, and geographic locations, are compared. It is observed that demographic dissimilarities affect various linguistic attributes, such as sentence structure and vocabulary usage of reviews. The Mann–Whitney U test indicates significant differences in these two sets of reviews on various linguistic aspects. Regarding the readability of the two corpora, no noticeable difference is perceived by Flesch Reading Ease (FRE) test. The discrepancy in the results of lexicon-based and machine learning-based sentiment classification methods in two corpora suggests that demography has an influential role in the performance of sentiment classifiers.
Robust stimulated Raman shortcuts to adiabatic passage with deep learning

Abstract One of the challenging tasks in quantum control is to manipulate quantum systems with high fidelity and as fast as possible. Simulated Raman shortcuts to adiabatic passage with invariant-based optimal control is an efficient technique accurately used to transfer population between two quantum states in three-level systems. This technique requires tuning parameters continuously which results in analog quantum control. However, a digital quantum controller design is of great importance in the era of digital quantum computing. Here, we use deep reinforcement learning to obtain digital Stokes and pump fields that can realize fast and accurate population transfer between states with the same parity in the three-level Λ configuration. We find that deep reinforcement learning follows exactly theshortcuts to adiabaticity (STA) based on dynamical invariant and leads to a robust population transfer against systematic errors and dephasing. This is a promising enhancement in digital quantum information processing.
A Semantic Similarity-Based Identification Method for Implicit Citation Functions and Sentiments Information

Automated citation analysis is becoming increasingly important in assessing the scientific quality of publications and identifying patterns of collaboration among researchers. However, little attention has been paid to analyzing the scientific content of the citation context. This study presents an unsupervised citation detection method that uses semantic similarities between citations and candidate sentences to identify implicit citations, determine their functions, and analyze their sentiments. We propose different document vector models based on TF-IDF weights and word vectors and compare them empirically to calculate their semantic similarity. To validate this model for identifying implicit citations, we used deep neural networks and LDA topic modeling on two citation datasets. The experimental results show that the F1 values for the implicit citation classification are 88.60% and 86.60% when the articles are presented in abstract and full-text form, respectively. Based on the citation function, the results show that implicit citations provide background information and a technical basis, while explicit citations emphasize research motivation and comparative results. Based on the citation sentiment, the results showed that implicit citations tended to describe the content objectively and were generally neutral, while explicit citations tended to describe the content positively. This study highlights the importance of identifying implicit citations for research evaluation and illustrates the difficulties researchers face when analyzing the citation context.
The Application of New Educational Concepts in Digital Educational Media

With the development and progress of society, great changes have taken place in educational concepts and teaching models compared with the past. Faced with the new educational concept of advocating diversified teaching modes and all-round talent training, the teaching space under the traditional single-fixed teaching mode is insufficient. The field of digital media education is a form of education with the development of information technology. The purpose of this paper is to find out the construction form of teaching space under the new educational concept that adapts to the development of the social era and to respond to the constantly updated and emerging educational concept and teaching mode. The process is as follows: based on the collected education data, mining the specific factors that will affect the application ability of teachers’ digital education resources and building a multiple machine learning regression model using these objective and significant features to predict the score of teachers’ digital education resources application ability. Through the comparison and optimization of model performance, a more suitable prediction method was found. MSE, MAE, RMSE, and MAPE are used as performance evaluation indicators to compare the performance of each model. It is found that there are multilayer linear regression &lt; mild gradient advance &lt; extreme gradient advance &lt; random forest in each indicator. In addition, in the two integration models, bagging idea represented by the random forest is more suitable for this group than two gradient boosting.
The Open Language Archives Community: a 20-year update

Purpose This paper reports on the first 20 years of the Open Language Archives Community (OLAC), comprehensive infrastructure for indexing and discovering language resources. Design/methodology/approach We begin with the original vision, assess progress relative to the original requirements, and identify ongoing challenges. Findings Based on the overview of OLAC history and recent developments and on the analysis of the situation in the language archives area as a whole, the authors propose an agenda for a more sustainable future for open language archiving. Originality/value This paper examines the progress of OLAC and discusses improvements in such areas as participation, access, and sustainability.
Understanding the Implications of Manifest V3

The world of browser extensions is undergoing a major transformation. Google Chrome is spearheading a transition that changes how extensions work and what they are capable of doing, and the rest of the major browsers are likely to follow suit. Some of these changes are controversial, as they have significant implications for many very popular chrome extensions. The changes are embodied within a refashioning in how the manifest.json is written (by defining a new manifest_version 3); thus, these changes are commonly referred to as “manifest v3.”
Analysis of Feature Selection Methods for Network Traffic Classification

Network traffic classification has Considerable importance with the rapid growth of current Internet networks and their online applications. In the research conducted in this area, machine learning algorithms have been widely used due to the importance of the accuracy issue at the traffic classification. One of the most essential and useful steps in machine learning algorithms is feature selection. Because it reduces redundant features, and it affects accuracy. This paper examines the impact of feature selection methods on traffic classification methods regarding the importance of this issue. In this paper, three strategies are presented for feature selection: the gain ratio, information gain, and weight by SVM models to be applied to SVM and Naïve Bayes machine learning algorithms. Also, considering the importance of encrypted data and their growing trend, this work explores separately the impact of the above-mentioned methods on encrypted and non-encrypted data from the NIMS data set separately. The results of the experiments show that the gain ratio in encrypted data obtained a better accuracy of 97.30% compared to other methods, as well as for non-encrypted data of 99.90%.
A Fuzzy Neural Network-Based Evaluation Method for Physical Education Teaching Management in Colleges

A novel Fuzzy Neural Network (FNN) teaching quality assessment model of physical education (PE) is presented at colleges and universities to enhance the validity of PE teaching quality evaluation. It is being done to enhance the accuracy of quality evaluations of PE instruction. In the first phase, out of 4 aspects of teaching material, teaching method, teaching attitude, and teaching effect, a multi-index assessment process of university physical education teacher performance based on the analytic hierarchy process (AHP) is created. The effectiveness of college PE instructors is assessed using this approach. The FNN model is used to develop a teaching quality assessment model for college PE courses. The FNN's parameter is the score data, and the FNN's output vector is equipped with better college PE (excellent, good, average, and low). In terms of assessing the instructional excellence of PE courses in colleges and universities, FNN has been proven to have superior classification accuracy, specificity, sensitivity, and F1 score when compared to other methods. When compared to other countries, this is the case. The proposed approaches resulted in a score of 96% for accuracy, 95% for specificity, 90% for sensitivity, and an F1 score of 94% for performance. The effectiveness of the proposed approach is shown by comparing the outcomes to those of standard physical education teaching strategies.
CHALLENGES OF GOING BACK TO CLASS AFTER THE PANDEMIC

Appears in: ICERI2022 Proceedings Publication year: 2022Pages: 6273-6279ISBN: 978-84-09-45476-1ISSN: 2340-1095doi: 10.21125/iceri.2022.1549Conference name: 15th annual International Conference of Education, Research and InnovationDates: 7-9 November, 2022Location: Seville, Spain
Investigate Evolutionary Strategies for Black-box Attacks to Deepfake Forensic Systems

Recently, the rising of deepfake generation techniques, cyber security against misinformation has become a popular topic among the research community. To improve the robustness of deepfake detection, attacks such as adversarial examples are studied with the aim to exploring weaknesses and security leaks. While most adversarial example generators are based on the assumption of white-box attack, in this paper, we focus on a more realistic black-box attack scenario using evolutionary approaches. A wide range of evolutionary strategies such as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution along with their quantum-inspired versions, are evaluated. The black-box attacks are shown to be highly effective against state-of-the-art forensics, exposing a vulnerability in current defense techniques. Analysis of the performance of different evolutionary strategies used for attacking also reveals insights on possible solutions to counter against the attacks.
Deep Learning Based Architecture for Entity Extraction from Covid Related Documents

Entity extraction from the text data in the biomedical domain has an essential role in biomedical research. In natural language processing entity extraction task aims to identify the terms into predefined categories. With the emergence of the covid-19, covid related digital resources increased drastically and the new type of entities is introduced. State-of-the-art named entity extraction models is heavily relying on domain-specific resources which are hard to perform adequately on covid related data. In this paper, we proposed a deep-learning-based architecture for named entity recognition. The experiment was performed on the CORD-NER dataset which was released by the University of Illinois. We compare the performance of different deep learning-based architectures on this data for a named entity recognition task.
Team performance assessment and improvement in an agile methodology framework

Improving the performance of work teams is a key issue for any organization because it has a direct impact on the productivity and quality involved in the generation of its products and/or in the provision of the services it offers. The performance of a team can be increased through improvements in the performance of each of its members or the team as a whole. There are several mechanisms to try to achieve performance improvements, however, for a correct orientation of possible actions, it is necessary to evaluate the current performance of the team and each of its members to highlight possible weaknesses or opportunities for improvement. An improvement process is by nature continuous, changes are incorporated gradually and at the same time, the achievements must be reinforced. Agile methods are closely aligned with improvement performance of teams. For instance, Scrum establishes Retrospective Meetings which are exclusively dedicated to continuous improvement of teamwork. This paper proposes a model for assesment and improvement of team performance within an agile methodological framework. In addition, a support tool that implements the model is presented. The model and its tool have been developed and refined through its application for more than a decade in two software engineering subjects. In these subjects, we apply the Project-Based Learning (PBL) teaching method, according to this the students work as a team throughout the subject in a software development project. Although our proposal has been generated in the academic field, we think that it can also be applied and be useful in an industrial context.
On the safe-haven and hedging properties of Bitcoin: new evidence from COVID-19 pandemic

Purpose This study aims to investigate the safe-haven and hedging properties of Bitcoin against a wide variety of conventional assets before and during the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach This paper uses a smooth transition regression (STR) to jointly test the hedging properties of Bitcoin in normal conditions and Bitcoin's safe-haven properties in extreme stock market conditions. Findings Highlighting the results, the authors show that Bitcoin is able to provide safe-haven feature during the COVID-19 pandemic period while Bitcoin serves as a hedge tool in the pre-COVID-19 pandemic period. The findings also show that the prowess of the safe-haven/hedge nature is sensitive to the type of the asset market and the time horizon when switching from daily to weekly frequency data. Originality/value This is one of the first studies that conduct a combined analysis of the safe-haven and hedging capabilities of Bitcoin against several asset classes using an STR method. This study uses the longest sample period to yet, allowing researchers to examine Bitcoin's safe-haven and hedging features both before and after the COVID-19 pandemic.