Reviewerly/exHarmony huggingface dataset:
A new synthetic feature generation scheme based on artificial immune systems for robust offline signature verification

Identity verification based on offline handwritten signatures is required in various financial and administrative domains using paper documents. Commonly, the performance of signature verification systems is altered by the lack of sufficient authentic signatures. To overcome such shortcoming, various data augmentation schemes were proposed to generate synthetic samples. Since both genuine and synthetic signatures undergo a feature generation process inside the verification system, we propose to directly apply data augmentation on features instead of the signature image. Such an approach can give more pertinent synthetic features, than those derived from synthetic signatures. The proposed features generator is based on mutation, cloning, and resources competition mechanisms of artificial immune systems. It is evaluated on two descriptors by using SVM classifier in the verification step. As a first descriptor, we introduce the multi-oriented local difference features. The second descriptor is the histogram of templates which is used for comparison aims, since it showed satisfactory performance for handwritten signature characterization. Experiments conducted on CEDAR, GPDS-300, and MCYT-75 datasets, highlight the effectiveness of synthetic features, which allow at least an improvement of 8% when the verification system is developed by using one real signature. Also, the proposed MLDF outperforms both HOT and several state of the art features including LBP and SigNet.
Videoconferencing fatigue and online student engagement among Filipino senior high school students: A mixed methods study

Introduction The ubiquity of online classes during the COVID-19 pandemic induced the widespread use of videoconferencing applications. However, the prolonged use of these applications can lead to videoconferencing fatigue. Drawing from an online survey sample of 215 senior high school students from a selected private university in Manila, Philippines, this mixed methods study examines videoconferencing fatigue and its relationship with online student engagement (OSE) during the COVID-19 pandemic. Materials and methods This study utilized a convergent triangulation research design. The quantitative strand was cross-sectional in nature. The constructs were measured using the Zoom Exhaustion and Fatigue Scale and Online Student Engagement Scale. Bivariate and multivariate statistical tests were used to determine the significance of the relationships between variables. The qualitative strand utilized a descriptive design. Narrative data were collected through an open-ended survey question and analyzed using content analysis. Results Quantitative results indicate moderate to high levels of videoconferencing fatigue among student respondents. Moreover, higher levels of OSE were observed among those who endured higher visual fatigue and attended videoconferences more days a week. Qualitative findings demonstrate various manifestations of videoconferencing fatigue among students and relate it with decreased energy to perform academic tasks and learning absorption. However, the qualitative analysis also reveals students’ perceived need for videoconferencing to learn online. Discussion The nuanced insights from both strands highlight that despite their perceived negative impacts of videoconferencing, students had to tolerate visual videoconferencing fatigue in order to engage meaningfully in online classes.
Excluding Russia from the Bologna Process: What is Behind This?

The exclusion of Russia from the Bologna process creates serious challenges for the higher education system. The beginning reform will affect the whole institution of higher learning of our country. This, in turn, opens up new opportunities to improve the efficiency of the entire system. The introduction of three basic elements, namely: a bank of pedagogical ideas, a triple helix model and a system for introducing creative technologies into the educational process, can allow us to form a qualitatively new level of training of specialists in the country.
Cone of Vision as a Behavioural Cue for VR Collaboration

Mutual awareness of visual attention is essential for collaborative work. In the field of collaborative virtual environments (CVE), it has been proposed to use Field-of-View (FoV) frustum visualisations as a cue to support mutual awareness during collaboration. Recent studies on FoV frustum visualisations focus on asymmetric collaboration with AR/VR hardware setups and 3D reconstructed environments. In contrast, we focus on the general-purpose CVEs (i.e., VR shared offices), whose popularity is increasing due to the availability of low-cost headsets, and the restrictions imposed by the pandemic. In these CVEs collaboration roles are symmetrical, and the same 2D content available on desktop computers is displayed on 2D surfaces in a 3D space (VR screens). We prototyped one such CVE to evaluate FoV frustrum visualisation within this collaboration scenario. We also implement a FoV visualisation generated from an average fixation map (AFM), therefore directly generated by users' gaze behaviour which we call Cone of Vision (CoV). Our approach to displaying the frustum visualisations is tailored for 2D surfaces in 3D space and allows for self-awareness of this visual cue. We evaluate CoV in the context of a general exploratory data analysis (EDA) with 10 pairs of participants. Our findings indicate that CoV is beneficial during shifts between independent and collaborative work and supports collaborative progression across the visualisation. Self-perception of the CoV improves visual attention coupling, reduces the number of times users watch the collaborator's avatars and offers a consistent representation of the shared reality.
Protecting The Vulnerable

A successful digital society is, in part, predicated on people having secure access to digitally-delivered services when they need it. It has long been recognised that parts of society are not able to access digital services without assistance, often as a result of economic precarity. During the COVID-19 pandemic, the importance of third and voluntary sector organisations in providing assisted digital access has come to the fore. As access to essential everyday services moved to digital-only and family and friendship networks of support became disrupted by the pandemic, for many, voluntary and third sector organisations were the source of digital assistance to claim welfare, pay bills, take part in education and purchase food. Our study explores the types of assisted digital access that voluntary and third sector organisations have provided thus far during the COVID-19 pandemic. We capture the dimensions of this assistance and evaluate the steps such organisations take to ensure that this access is safe for both the assister and assisted. From these findings we set out a security strategy with supporting design principles that combines digital security with human security in a security approach we term 'positive-first'.
ColorCook: Augmenting Color Design for Dashboarding with Domain-Associated Palettes

Visualization dashboards serve as an information presentation that uses a tiled layout of key metrics visualized in charts for collaborative decision-making. Existing work has developed tools and techniques for computational color design. Much of these efforts have focused on selecting effective color palettes for independent charts while few attempts have been made to support the expressive color design of multiple coordinated charts in dashboards. In this work, we describe ColorCook, an interactive system that helps design expressive and effective dashboard colorings using domain-associated palettes. ColorCook employs an integrated color workflow for dashboarding, consisting of color selection, assignment, and adjustment. We evaluated ColorCook through a crowdsourcing experiment and a user study. The results of our evaluation indicated that ColorCook is useful for effective and expressive color design.
Data Breaches

Recent events have shown that online booking is vulnerable to hacking incidents such as data breaches. The primary purpose of this study is to investigate the effect of the risky decision-making factors on consumer post breach behavior. After a data breach, most of the companies offer monitoring services to restore customer trust and encourage them for future purchases. However, little research has been done to understand the impact of these monitoring services on consumer behavior. In this study, we examine whether monitoring services can mitigate the impact of risk perception on online booking. We utilized the Marriott data breach of November 2018 as the context. We manipulate data breach severity in our vignettes. The research model was tested using data gathered from 298 Mechanical Turk respondents. Our vignette-based survey design allowed us to incorporate situational details thought to be important in risky decision-making in a data breach context. We found strong support for our research model including the positive moderating effect of company suggested monitoring on online booking intention. The findings of this study could help firms in developing more influential post-breach monitoring services.
Virtual reality learning environments: using high-fidelity avatars to enhance distance learning experience

ABSTRACTABSTRACTVirtual Learning Environments (VLEs) became the primary space for education during the Coronavirus disease (COVID-19) lockdowns. One of the additions that specifically VR has to offer is its potential to incorporate some of the qualities that define interpersonal communication and typically lack in distance-learning VLEs. This study's aim is exploration of those qualities to appreciate Virtual Reality Learning Environments (VRLEs) as a supplementary tool for distance learning and its impact on knowledge acquisition processes, with the particular objectives of evaluating usability, usefulness, and realism of Non-Verbal Cues (NVCs) capture. Toward this end a multi-user VRLE prototype was developed and utilized for the delivery of a live lecture by a real professor of a MSc programme. The professor's body motion and facial expressions were captured in real-time and solved onto a high-fidelity avatar. In total, a group of 20 students audited the lecture via the VRLE and evaluated the modalities and experience offered compared to conventional VLEs. Additionally, knowledge acquisition was directly correlated to another group of 20 students, who audited the same lecture only via conventional VLEs. Evaluation results provide important insight regarding the learning experience between the two settings and how incorporation of NVCs can have a positive impact.KEYWORDS: Virtual realityavatarsmotion capturenon-verbaldistance learning AcknowledgementsThe research work has been supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the "First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant" (Project Number: HFRI-FM17-1168).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe research work has been supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the "First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant" (Project Number: HFRI-FM17-1168).
Coupled Global–Local object detection for large VHR aerial images

Object detection in large aerial images generally requires splitting each image into local images in the preprocessing step, and even the state-of-the-art models currently use this preprocessing method. However, image splitting often leads to deficiencies in contextual information and incomplete detection of oversized objects. At present, many object detection methods are designed to deal with large images. However, they require complex additional structures or training steps, and their applicability is limited. To address these problems, we propose the Coupled Global–Local (CGL) network, which can be easily embedded in frequently used detection models, to efficiently capture more information. Specifically, we employ a multiscale feature fusion module to share information between the global and local branches. Furthermore, a new convolution method is proposed to adaptively adjust the receptive field for better feature extraction. In addition, we find that detection results from global branches in the existing global–local architecture hinder the performance improvement on details when the detection results from different-resolution branches are fused. Therefore, on the global branch, a proposal filter and a nonlocal suppression (NLS) algorithm are developed to prevent small positive proposals and remove unqualified detection boxes easily and efficiently, respectively. We conduct extensive experiments on the DOTA-1.0, DOTA-1.5, and DOTA-2.0 data sets. The results demonstrate that CGL can significantly improve the detection performance of various baseline models for large very-high-resolution (VHR) aerial images without bells and whistles.
Joint learning of frequency and spatial domains for dense image prediction

Current artificial neural networks mainly conduct the learning process in the spatial domain but neglect the frequency domain learning. However, the learning course performed in the frequency domain can be more efficient than that in the spatial domain. In this paper, we fully explore frequency domain learning and propose a joint learning paradigm of frequency and spatial domains. This paradigm can take full advantage of the combined preponderances of frequency learning and spatial learning; specifically, frequency and spatial domain learning can effectively capture intrinsic global and local information, respectively. To achieve this, an innovative but effective linear learning block is proposed to conduct the learning process directly in the frequency domain. Together with the prevailing spatial learning operation, i.e., convolution, a powerful and scalable joint learning framework is further proposed. Exhaustive experiments on the diverse Benchmark datasets — KITTI, Make3D, and Cityscapes demonstrate the effectiveness and superiority of the proposed joint learning paradigm in dense image prediction tasks, including self-supervised depth estimation, ego-motion estimation, and semantic segmentation. In particular, the proposed model can achieve performance competitive to those of state-of-the-art methods in all three tasks, even without pretraining. Moreover, the proposed model reduces the number of parameters by over 78% for self-supervised depth estimation on the KITTI dataset while retaining the time complexity on par with other state-of-the-art methods; this provides a great chance to develop real-world applications. We hope that the proposed method can encourage more research in cross-domain learning. The codes are publicly available at https://github.com/shaochengJia/FSLNet.
Efficient computation offloading for ethereum DApps

This paper covers a novel solution to reduce the gas consumed by Decentralized Applications (DApps) running on Ethereum Virtual Machine (EVM). Smart contracts extend the capabilities of the Blockchain, but the strict gas policy of the EVM limits the implementation of various DApps. On the other hand, Oracle provides a means to use an external execution environment for smart contracts, but its functional limitations and additional costs limit the growth of the DApp market to DApp users. To address the gas consumption crisis of DApps, we designed an event-based resource offloading solution that allows DApp developers to run their own compute servers. By adopting our solution, DApp users only pay the constant and minimized amount of gas fee, while the external server pays highly reduced amount of gas fee instead. We describe the solution in detail with a sample DApp model and mathematically demonstrate the effectiveness of the solution compared to an Oracle-based implementation. Furthermore, we conducted empirical experiment of actual DApp using EVM and verified the reduction of the gas consumption.
Perceptual quality assessment for fine-grained compressed images

Recent years have witnessed the rapid development of image storage and transmission systems, in which image compression plays an important role. Generally speaking, image compression algorithms are developed to ensure good visual quality at limited bit rates. However, due to the different compression optimization methods, the compressed images may have different levels of quality, which needs to be evaluated quantificationally. Nowadays, the mainstream full-reference (FR) metrics are effective to predict the quality of compressed images at coarse-grained levels (the bit rates differences of compressed images are obvious), however, they may perform poorly for fine-grained compressed images whose bit rates differences are quite subtle. Therefore, to better improve the Quality of Experience (QoE) and provide useful guidance for compression algorithms, we propose a full-reference image quality assessment (FR-IQA) method for compressed images of fine-grained levels. Specifically, the reference images and compressed images are first converted to Y C b C r color space. The gradient features are extracted from regions that are sensitive to compression artifacts. Then we employ the Log-Gabor transformation to further analyze the texture difference. Finally, the obtained features are fused into a quality score. The proposed method is validated on the fine-grained compression image quality assessment (FGIQA) database, which is especially constructed for assessing the quality of compressed images with close bit rates. The experimental results show that our metric outperforms mainstream FR-IQA metrics on the FGIQA database. We also test our method on other commonly used compression IQA databases and the results show that our method obtains competitive performance on the coarse-grained compression IQA databases as well. • A full-reference image quality assessment method for fine-grained compressed image. • Extracting features from image gradient and texture. • Achieving the best performance on the FGIQA database.
Distributed multiple model filtering for markov jump systems with communication delays

In this paper, we study a distributed state estimation problem for Markov jump systems (MJS) over sensor networks, in which each sensor node connects with each other through wireless networks with communication delays. We assume that each sensor node maintains a buffer to store delayed data transmitted from neighbor nodes. A distributed multiple model filter is designed by using the interacting multiple model methods (IMM) and a recursive delays compensation method. In order to ensure the stability, two stability conditions are derived for boundedness of estimation errors and boundedness of error covariance. Finally, the effectiveness of the proposed methods is illustrated by simulations and experiments of maneuvering target tracking.
GIRE: Gender-Inclusive Requirements Engineering

Gender inclusion is fundamental to a prosperous society, but inequality and exclusion persist in various sectors of it. One of them is the ICT field, which is still struggling to represent the diversity of those it serves. The lack of diversity and power imbalance in software development affects the produced systems, that, instead of advancing gender inclusion, create new barriers in achieving it. Although considered neutral, software does not equally serve everyone who depends on it, favoring characteristics that are statistically more observed in those that are represented during development. As software development teams are predominantly male, it is not surprising that existing systems favor characteristics that are statistically more observed in men over characteristics observed in other genders. As technologies rapidly evolve and revolutionize the way we live, addressing this problem becomes urgent to ensure that these systems benefit everyone, regardless of their gender. As a first step towards this goal, we performed a systematic mapping study on gender issues in software engineering whose results indicated that gender impacts development and systems, but there are limited approaches for addressing it in Requirements Engineering. This study served as the foundation for proposing a conceptual model for gender-inclusive requirements. Its main objective is to facilitate discussion and analysis of gender and related concepts in the elicitation process to include them in the specification of requirements. In this paper, we extend this work by illustrating the concepts with an example, by presenting a process for using the knowledge of the model and a prototype tool that implements it, and by discussing an evaluation with 31 participants of the conceptual model's usefulness, difficulty of understanding, strengths and weaknesses, use and recommendation, and finally, its components. The results were positive as both novices and experts in conceptual modeling considered the model useful, provided comprehensive feedback on its strengths but also suggestions for improvement, and most answered positively to the questions about whether they would use and recommend it.
Privacy protection framework for face recognition in edge-based Internet of Things

Abstract Edge computing (EC) gets the Internet of Things (IoT)-based face recognition systems out of trouble caused by limited storage and computing resources of local or mobile terminals. However, data privacy leak remains a concerning problem. Previous studies only focused on some stages of face data processing, while this study focuses on the privacy protection of face data throughout its entire life cycle. Therefore, we propose a general privacy protection framework for edge-based face recognition (EFR) systems. To protect the privacy of face images and training models transmitted between edges and the remote cloud, we design a local differential privacy (LDP) algorithm based on the proportion difference of feature information. In addition, we also introduced identity authentication and hash technology to ensure the legitimacy of the terminal device and the integrity of the face image in the data acquisition phase. Theoretical analysis proves the rationality and feasibility of the scheme. Compared with the non-privacy protection situation and the equal privacy budget allocation method, our method achieves the best balance between availability and privacy protection in the numerical experiment.
Augmented Reality in Medical Education: An Industry 4.0 Enabled Innovative Approach

Industry 4.0 enabled Augmented reality (AR) is a better representation of the real world made possible by using cutting-edge visual, aural, or other tangibly improved elements communicated through innovation. In order to increase insight, AR entails superimposing visual, audible, or other tactile data over the real world. Expanded reality can be used by businesses and retailers to promote goods or services, launch innovative marketing campaigns, and compile novel customer data. This manuscript centralizes the importance of AR in education in generating interest, learning climate, content clarity, cooperation, memory, tactile turn of events, and cost-viability. It provides the history of AR in education and AR applications like human anatomy atlas 2021, holo-human, VR frog dissection, touch surgery, 4D interactive anatomy, visible body and plantale. This manuscript also highlights the challenges of Industry 4.0 enabled AR, as an absence of fundamental preparation, contingent upon the equipment, content movability issues, and expense of planning these intuitive stages and advanced education is still in its early stages. This study provides future solutions for Industry 4.0 enabled AR. It gives the analysis of traditional and Industry 4.0 enabled AR.