Reviewerly/exHarmony huggingface dataset:
Quad‐fisheye Image Stitching for Monoscopic Panorama Reconstruction

Abstract Monoscopic panorama provides the display of omnidirectional contents surrounding the viewer. An increasingly popular way to reconstruct a panorama is to stitch a collection of fisheye images. However, such non‐planar views may result in problems such as distortions and boundary irregularities. In most cases, the computational expense for stitching non‐planar images is also too high to satisfy real‐time applications. In this paper, a novel monoscopic panorama reconstruction pipeline that produces better quad‐fisheye image stitching results for omnidirectional environment viewing is proposed. The main idea is to apply mesh deformation for image alignment. To optimize inter‐lens parallaxes, unwarped images are firstly cropped and reshuffled to facilitate the circular environment scene composition by the seamless ring‐connection of the panorama borders. Several mesh constraints are then adopted to ensure a high alignment accuracy. After alignment, the boundary of the result is rectified to be rectangular to prevent gapping artefacts. We further extend our approach to video stitching. The temporal smoothness model is added to prevent unexpected artefacts in the panoramic videos. To support interactive applications, our stitching algorithm is programmed using CUDA. The camera motion and average gradient per video frame are further calculated to accelerate for synchronous real‐life panoramic scene reconstruction and visualization. Experimental results demonstrate that our method has advantages in respects of alignment accuracy, adaptability and image quality of the stitching result.
Conversational Intelligent Tutoring Systems for Online Learning: What do Students and Tutors Say?

A futuristic Technology Enhanced Learning concept, a conversational Intelligent Tutoring System (ITS) for deployment in an e-learning context, is gradually becoming a reality thanks to the continuous advancement in Artificial Intelligence and to the worldwide increasing demand for online learning, especially during the pandemic. However, we do need to consider whether such technology will support student learning or make learning more difficult. In the absence of a mature conversational Intelligent Tutoring System, this article aims to address this question indirectly through an investigation of how students and tutors in an online learning programme perceive the concept of conversational Intelligent Tutoring Systems, such as a chatbot, for online learning. This is achieved by surveying students who are currently enrolled in an online programme and interviewing the tutors on the same programme. The research concludes that ITS would very likely enhance online learning experience for both students and tutors, but there are various concerns that must be addressed.
Understanding User Experience and Satisfaction with Urban Infrastructure through Text Mining of Civil Complaint Data

With the increase in public concern about the aging of urban infrastructure and the associated risk of safety accidents, it is important to maintain the safety and serviceability of urban infrastructure in accordance with user satisfaction. Although many studies have attempted to consider user experience and satisfaction based on user surveys and civil complaint data analysis, they have had difficulty in identifying user dissatisfaction factors where users feel unsafe or uncomfortable while using the infrastructure. The main purpose of the research presented here is to understand user experience and satisfaction with urban infrastructure by text mining self-written civil complaint data. To achieve this objective, the researchers adopted the following procedures: (1) development of a civil complaint thesaurus for the text mining of civil complaint data; (2) text preprocessing of civil complaint data by using the thesaurus; and (3) keyword extraction and recognition of the relationships between the keywords to explore user-experience factors related to urban infrastructure. The research team used 2,945 bridge complaint data records and 404 tunnel complaint data records in text format from the Korean Safety e-Report database. From the collected data, the researchers developed a civil complaint thesaurus with 47 semantic relationships between words, such as Korean compound words, synonyms, and hypernym– hyponyms. As a result of keyword extraction, “breakage,” “accident,” and “road” for bridge complaints, and “entrance,” “accident,” and “breakage” for tunnel complaints were the selected words representing user experiences, and were visualized in a tag cloud. Also, critical user-experience factors such as unsafe or uncomfortable situations on bridge roads (e.g., “breakage,” “construction,” and “pothole”), and dissatisfaction factors at tunnel entrances (e.g., “streetlight,” “view,” and “sign”) were explored using semantic network analysis. The outcome of this research will contribute to identifying user-experience factors from civil complaint data and improving the safety and serviceability of urban infrastructure by considering user experience and satisfaction in infrastructure maintenance practices.
A deep learning framework to classify breast density with noisy labels regularization

Breast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist's noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures.A multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus.The combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71.The proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist's label allows for a better estimation of the unknown ground truth labels.
Determination of the Most Accurate Horizontal to Tilted Sky-Diffuse Solar Irradiation Transposition Model for the Capital Cities in MENA Region

Accurate solar radiation data is essential in designing, evaluating and optimizing solar energy systems. The meteorological recorded data is the mainly source of the solar irradiance data. Since solar irradiance incident on a specific tilted surface is not frequently recorded, the horizontal to tilted solar irradiation transposition models (HTTM) use to convert the measurable components of solar irradiation (global horizontal, sky-diffuse and ground reflected) to global tilted solar irradiation with high accuracy. The importance of the solar transposition model is in determining the optimum tilt angle of solar energy harvesters which is one of the important design parameters for maximizing solar radiation incident on the solar collectors. This paper introduces a statistical procedure to Figure out the transposition model that is closest to the real model in the MENA region without needs for measured data. Also it provided a summary of optimum tilt angles and transposition models that recommended by local researchers for specific locations in MENA region. This study showed that the transposition models depend on the angle of inclination of the solar collector in addition to the location. The study identified models with deviation rates about 3% for most cities, which is an engineering reasonable percentage, and this encourages the authors to recommend this approach to determine more accurate transposition models for wider regions of the world. On the other hand, it showed that all models failed to achieve an acceptable deviation rate for high tilt angles especially vertical surfaces, which have great engineering applications. The authors advise researchers to take care when adopting a transposition model that has been validated at low tilt angles to apply it to high tilt angles and building façaades. The study is also reveal that, the reduction in total annual global solar irradiation is not exceed than 1% due to the offset of tilt angle from the optimum angle for all considered transposition models and for all sites.
Estimation of Pb Content Using Reflectance Spectroscopy in Farmland Soil near Metal Mines, Central China

The contamination of farmlands with hazardous metals from mining puts the safety of agricultural commodities at risk. For remediation, it is crucial to map the spatial distribution of contaminated soil. Typical sampling-based procedures are time-consuming and labor-intensive. The use of visible, near-infrared, and short-wave infrared reflectance (VNIR-SWIR) spectroscopy to detect soil heavy metal pollution is an alternative. With the aim of investigating a methodology of detecting the most sensitive bands using VNIR-SWIR spectra to find lead (Pb) anomalies in agriculture soil near mining activities, the area in Xiaoqinling Mountain, downstream from a series of active gold mines, was selected to test the feasibility of utilizing VNIR-SWIR spectroscopy to map soil Pb. A total of 115 soil samples were collected for laboratory Pb analysis and spectral measurement. Partial least squares regression (PLSR) was adopted to estimate the soil Pb content by building the prediction model, and the model was optimized by finding the optimal number of bands involved. The spatial distribution of Pb concentration was mapped using the ordinary kriging (OK) interpolation method. This study found that five spectral bands (522 nm, 1668 nm, 2207 nm, 2296 nm, and 2345 nm) were sensitive to soil Pb content. The optimized prediction model’s coefficient of determination (R2), residual prediction deviation (RPD), and root mean square error (RMSE) were 0.711, 1.860, and 0.711 ln(mg/kg), respectively. Additionally, the result of OK interpolation was convincing and accurate (R2 = 0.775, RMSE = 0.328 ln(mg/kg)), comparing maps from estimated and ground truth data. This study proves that it is feasible to use VNIR-SWIR spectral data for in situ estimation of the soil Pb content.
Integration of Edge Computing and Blockchain for Provision of Data Fusion and Secure Big Data Analysis for Internet of Things

Intelligent computing provides efficient, real-time, and secure data analysis services for the Internet of Things (IoT). As the number of IoT devices increases, IoT generates massive, diverse, and multisourcing datasets that can be used to improve IoT services further. Models trained by intelligent computing from a single system or sensor are often not global, and sending all data directly to the computing platform wastes network bandwidth and may cause network congestion and even privacy leakage. To ensure IoT applications’ quality of service and privacy, we propose a framework that integrates edge computing and blockchain to provide lightweight data fusion and secure data analysis for IoT. We propose a lightweight data fusion method that can reduce the amount of data at the node level and prevent network congestion and bandwidth waste. Furthermore, we propose a hierarchical fuzzy hashing method to check and locate anomalies of IoT machine learning models to ensure the validity of IoT intelligent computing and the security of sensitive data. Finally, we demonstrate the effectiveness of the method proposed in this paper through experiments.
Cloud Storage Data Access Control Scheme Based on Blockchain and Attribute-Based Encryption

Cloud storage is a popular model of the application in various fields, and the security of storage data and access permission have been widely considered. Attribute-based encryption (ABE) provides fine-grained user access control and ensures data confidentiality. However, current ABE access control schemes rely on trusted cloud servers and provide a low level of security. To solve these problems of traditional encryption schemes, we propose a blockchain-based and ABE cloud storage data access control scheme. In this article, blockchain and smart contract technology are the core elements to ensure data integrity and build a decentralized verification method for outsourcing results. This application can minimize the reliance on servers in the cloud environment. Based on the ciphertext-policy ABE algorithm, the proposed scheme supports a hidden access policy to avoid the risk of privacy leakage. In addition, we adopt outsourcing technology and predetected decryption algorithms to reduce the computational overhead of local and outsourced servers. Security analysis and performance evaluation show that our proposed scheme has high computational efficiency and satisfies the condition of indistinguishability under the chosen-ciphertext attacks.
Informed Patch Enhanced HyperGCN for skeleton-based action recognition

Human skeleton, as a compact representation of action, has attracted numerous research attentions in recent years. However, skeletal data is too sparse to fully characterize fine-grained human motions, especially for hand/finger motions with subtle local movements. Besides, without containing any information of interacted objects, skeleton is hard to identify human–object interaction actions accurately. Hence, many action recognition approaches that purely rely on skeletal data have met a bottleneck in identifying such kind of actions. In this paper, we propose an Informed Patch Enhanced HyperGraph Convolutional Network that jointly employs human pose skeleton and informed visual patches for multi-modal feature learning. Specifically, we extract five informed visual patches around head, left hand, right hand, left foot and right foot joints as the complementary visual graph vertices. These patches often exhibit many action-related semantic information, like facial expressions, hand gestures, and interacted objects with hands or feet, which can compensate the deficiency of skeletal data. This hybrid scheme can boost the performance while keeping the computation and memory load low since only five extra vertices are appended to the original graph. Evaluation on two widely used large-scale datasets for skeleton-based action recognition demonstrates the effectiveness of the proposed method compared to the state-of-the-art methods. Significant accuracy improvements are reported using X-Sub protocol on NTU RGB+D 120 dataset.
<scp>DevOps</scp> benefits: A systematic literature review

Abstract Among current IT work cultures, DevOps stands out as one of the most adopted worldwide. The focus of this culture is on bridging the gap between development and operations teams, enabling collaborative effort toward quickly producing software, without sacrificing its quality and support. DevOps is used to tackle a variety of issues; as such, there are differing benefits reported by authors when performing their analysis. For this research, we aim to reach consensus on the DevOps benefits reported in existing literature. To accomplish this objective, two systematic literature reviews. The first intends to find all benefits reported in the literature, while the second review will be used to map the benefits found in the first one with DevOps implementation case studies, providing empirical evidences of each benefit. To strengthen the results, the concept‐centric approach is used. During this research it was possible to observe that the most reported benefits are aligned with the DevOps premises of better collaboration between developers and operators, delivering software and products quicker. Based on DevOps implementation case studies, most reported benefits include a faster time to market as well as improvements in synergy and automation. Less reported benefits include a reduction in failed changes and security issues.
Disentangling Obstacles to Knowledge Co-Production for Early-Career Researchers in the Marine Sciences

Knowledge co-production involving researchers and non-academic actors is becoming increasingly important for tackling sustainability issues. Coastal and marine social-ecological systems are one example where knowledge co-production is important, yet also particularly challenging due to their unique characteristics. Early-Career Researchers (ECRs) often face specific obstacles when engaging in the process of knowledge co-production. In this perspective paper, we shed light on the particular characteristics of knowledge co-production in marine social-ecological systems and the obstacles ECRs in the marine sciences face. Based on these obstacles, we discuss actions that can be taken at various organizational levels (institutional, community, supervisor, and individual) in order to leverage change towards a more inclusive environment for ECRs engaging in knowledge co-production. We conclude that both bottom-up (individual to institutions) and top-down (institutions to individual) actions are required. However, we emphasize the responsibilities of institutions to create conditions in which the needs of ECRs are met. This will be necessary to adequately support ECRs engaging in knowledge co-production and thus contribute to tackling sustainability challenges in coastal and marine social-ecological systems.
Novel Mixed Reality Use Cases for Pilot Training

This study explored novel mixed reality (MR) use cases for pilot training using a mix of methods rooted in the general innovation theory of dynamic capabilities. The aim was to identify areas of improvement for various aspects of the flight training based on MR, in a socially and economically sustainable manner. Multiple surveys and workshops have been conducted with flight instructors, administrative staff, pilots and student pilots. The main result of this study is a systematic identification of the three most promising MR use cases: interactive theory training, cockpit procedure, and outside check training. These results are important because they inform the development of technical didactic tools for pilot training. The applicability of MR technologies to accommodate diverse user needs and preferences is addressed, while also considering aspects of economical sustainability.
Generating Quality Threat Intelligence Leveraging OSINT and a Cyber Threat Unified Taxonomy

Today’s threats use multiple means of propagation, such as social engineering, email, and application vulnerabilities, and often operate in different phases, such as single device compromise, lateral network movement, and data exfiltration. These complex threats rely on advanced persistent threats supported by well-advanced tactics for appearing unknown to traditional security defenses. As organizations realize that attacks are increasing in size and complexity, cyber threat intelligence (TI) is growing in popularity and use. This trend followed the evolution of advanced persistent threats, as they require a different level of response that is more specific to the organization. TI can be obtained via many formats, with open-source intelligence one of the most common, and using threat intelligence platforms (TIPs) that aid organizations to consume, produce, and share TI. TIPs have multiple advantages that enable organizations to quickly bootstrap the core processes of collecting, analyzing, and sharing threat-related information. However, current TIPs have some limitations that prevent their mass adoption. This article proposes AECCP, a platform that addresses some of the TIPs limitations. AECCP improves quality TI by classifying it accordingly a single unified taxonomy , removing the information with low value, enriching it with valuable information from open-source intelligence sources, and aggregating it for complementing information associated with the same threat. AECCP was validated and evaluated with three datasets of events and compared with two other platforms, showing that it can generate quality TI automatically and help security analysts analyze security incidents in less time.
Robust Design of Federated Learning for Edge-Intelligent Networks

Mass data traffics, low-latency wireless services and advanced artificial intelligence (AI) technologies have driven the emergence of a new paradigm for wireless networks, namely edge-intelligent networks, which are more efficient and flexible than traditional cloud-intelligent networks. Considering users’ privacy, model sharing-based federated learning (FL) that migrates model parameters but not private data from edge devices to a central cloud is particularly attractive for edge-intelligent networks. Due to multiple rounds of iterative updating of high-dimensional model parameters between base station (BS) and edge devices, the communication reliability is a critical issue of FL for edge-intelligent networks. We reveal the impacts of the errors generated during model broadcast and model aggregation via wireless channels caused by channel fading, interference and noise on the accuracy of FL, especially when there exists channel uncertainty. To alleviate the impacts, we propose a robust FL algorithm for edge-intelligent networks with channel uncertainty, which is formulated as a worst-case optimization problem with joint device selection and transceiver design. Finally, simulation results validate the robustness and effectiveness of the proposed algorithm.
EXECUTE: Exploring Eye Tracking to Support E-learning

The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students&#x2019; attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91&#x0025;. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs.