Reviewerly/exHarmony huggingface dataset:
A Visual Data Storytelling Framework

While the consumption of visual information becomes a daily commodity integrated into our lives, data visualisation is dominated by dashboards and charts. The main contribution of this article is an advanced way to visualise data as a data story. We converged paradigms from digital storytelling, serious games, and data visualisation to turn data into useful insights. The creation, management, and analysis of data have been increasingly given more attention in industry and professional practices. However, the potential of packaging data and analytic results into easily digestible and visually explorable content intended for non-professional audiences has not yet been investigated to its full extent. We contributed towards overcoming the gap between data analytics and data presentation. By integrating a story-like environment and entertainment into data visualisation, we explore the possibilities of efficiently communicating data and insights to general audiences in a casual context. We present this modular approach to customising messages for visual data storytelling from an information and communication perspective, including a test prototype developed to illustrate our data storytelling framework.
Research on the Application of Visual Recognition in the Engine Room of Intelligent Ships

In the engine room of intelligent ships, visual recognition is an essential technical precondition for automatic inspection. At present, the problems of visual recognition in marine engine rooms include missing detection, low accuracy, slow speed, and imperfect datasets. For these problems, this paper proposes a marine engine room equipment recognition model based on the improved You Only Look Once v5 (YOLOv5) algorithm. The channel pruning method based on batch normalization (BN) layer weight value is used to improve the recognition speed. The complete intersection over union (CIoU) loss function and hard-swish activation function are used to enhance detection accuracy. Meanwhile, soft-NMS is used as the non-maximum suppression (NMS) method to reduce the false rate and missed detection rate. Then, the main equipment in the marine engine room (MEMER) dataset is built. Finally, comparative experiments and ablation experiments are carried out on the MEMER dataset to verify the strategy's efficacy on the model performance boost. Specifically, this model can accurately detect 100.00% of diesel engines, 95.91% of pumps, 94.29% of coolers, 98.54% of oil separators, 64.21% of meters, 60.23% of reservoirs, and 75.32% of valves in the actual marine engine room.
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation

The rapid progress in clinical data management systems and artificial intelligence approaches enable the era of personalized medicine. Intensive care units (ICUs) are ideal clinical research environments for such development because they collect many clinical data and are highly computerized. Goal: We designed a retrospective clinical study on a prospective ICU database using clinical natural language to help in the early diagnosis of heart failure in critically ill children. Methods: The methodology consisted of empirical experiments of a learning algorithm to learn the hidden interpretation and presentation of the French clinical note data. This study included 1386 patients' clinical notes with 5444 single lines of notes. There were 1941 positive cases (36% of total) and 3503 negative cases classified by two independent physicians using a standardized approach. Results: The multilayer perceptron neural network outperforms other discriminative and generative classifiers. Consequently, the proposed framework yields an overall classification performance with 89% accuracy, 88% recall, and 89% precision. Conclusions: This study successfully applied learning representation and machine learning algorithms to detect heart failure in a single French institution from clinical natural language. Further work is needed to use the same methodology in other languages and institutions.
Efficient Virtual Machine Migration Algorithms for Data Centers in Cloud Computing

As the technology is growing at a rapid pace, there is an increased demand for various cloud resources and resultant of which is the establishment of large number of cloud data centers (CDCs). A single cloud data center consumes large amount of energy, and eventually, it will lead to the higher operational cost and emission of carbon. To reduce the consumption of energy with better utilization of resources, different virtual machine (VM) and its consolidated approaches have been considered for the dynamic utilization of resources. In this paper, proposed enhanced artificial bee colony (PEA) has been proposed for better migration and placement of various VMs and physical machine (PM) dynamically. There are two distinct phases in this algorithm. Firstly, selection for the location of PM with access delay to the location where it needs to be migrated and secondly, reduction in number of VM migrations. Further, proposed approaches are compared to in terms of SLA-V, energy consumption, number of hosts shutdown, and resource utilization. Results show the gradual reduction in SLA-V by 20 and 31%, number of migrations by 16 and 25% and increase the resource utilization by 8%. There is a better improvement of 13% in energy consumption has been observed in the proposed method compared to others.
Some Reasons for the Russian Authors Transition to Foreign Journals

Aiming to increase Russian journals’ authority and enhance their readership, and consequently, journals’ rankings, detection of reasons for Russian authors’ transition to foreign journals is important. Recent studies demonstrated that up to one-third of Russian authors orients toward top-tiered foreign serials and do not intend to change their publication preferences. One of the obvious formal reasons is in quantitative indexes used in calculations in research organizations while evaluating the research capacity of their staff. However, external circumstances, as well as the risk of part or full restriction for Russian authors in some toptieredinternational journals lead to obstacles to the fulfillment of previous obligations. Under such conditions increase in the reputation of Russian serials, their compliance with international publishing standards, increase in their bibliometric indexes are of high importance. The paper aims to reveal the main issues caused to force Russian authors to select foreign journals and stop collaborating with domestic serials. Analyses of those reasons are believed to help detect the main vector for the successful development of Russian academic journals.
Soccer Game Summarization using Audio Commentary, Metadata, and Captions

Soccer is one of the most popular sports globally, and the amount of soccer-related content worldwide, including video footage, audio commentary, team/player statistics, scores, and rankings, is enormous and rapidly growing. Consequently, the generation of multimodal summaries is of tremendous interest for broadcasters and fans alike, as a large percentage of audiences prefer to follow only the main highlights of a game. However, annotating important events and producing summaries often requires expensive equipment and a lot of tedious, cumbersome, manual labour. In this context, recent developments in Artificial Intelligence (AI) have shown great potential. The goal of this work is to create an automated soccer game summarization pipeline using AI. In particular, our focus is on the generation of complete game summaries in continuous text format with length constraints, based on raw game multimedia, as well as readily available game metadata and captions where applicable, using Natural Language Processing (NLP) tools along with heuristics. We curate and extend a number of soccer datasets, implement an end-to-end pipeline for the automatic generation of text summaries, present our preliminary results from the comparative analysis of various summarization methods within this pipeline using different input modalities, and provide a discussion of open challenges in the field of automated game summarization.
Transient-Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead

Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. For instance, the radiation-induced misprediction probability can be so high to impede a safe deployment of DNNs models at scale, urging the need for efficient and effective hardening solutions. In this work, we propose to tackle the reliability issue both at training and model design time. First, we show that vanilla models are highly affected by transient faults, that can induce a performances drop up to 37%. Hence, we provide three zero-overhead solutions, based on DNN re-design and re-train, that can improve DNNs reliability to transient faults up to one order of magnitude. We complement our work with extensive ablation studies to quantify the gain in performances of each hardening component.
Effect of online research data management instruction on social science graduate students’ RDM skills

Prior research has suggested the value of and the need to provide consistent research data management (RDM) instruction specifically for graduate students. However, there is a lack of RDM instruction that is tailored for the social science disciplines. The effect of a four-hour, online RDM instruction intervention, designed based on the research data life cycle, on the RDM knowledge of graduate students in social science disciplines was investigated. A total of 84 students completed both pre/post knowledge assessments with 40 students randomly assigned into the intervention group receiving online instruction and 44 in the control group. A one-way ANCOVA was used for the data analysis. Results indicated that social science graduate students who received online RDM instruction had a significantly higher score in RDM knowledge than students in the control group. Moreover, the effect of the instruction on participants' RDM skills varied by their disciplines.
e-LION: Data integration semantic model to enhance predictive analytics in e-Learning

In the last years, Learning Management systems (LMSs) are acquiring great importance in online education, since they offer flexible integration platforms for organising a vast amount of learning resources, as well as for establishing effective communication channels between teachers and learners, at any direction. These online platforms are then attracting an increasing number of users that continuously access, download/upload resources and interact each other during their teaching/learning processes, which is even accelerating by the breakout of COVID-19. In this context, academic institutions are generating large volumes of learning-related data that can be analysed for supporting teachers in lesson, course or faculty degree planning, as well as administrations in university strategic level. However, managing such amount of data, usually coming from multiple heterogeneous sources and with attributes sometimes reflecting semantic inconsistencies, constitutes an emerging challenge, so they require common definition and integration schemes to easily fuse them, with the aim of efficiently feeding machine learning models. In this regard, semantic web technologies arise as a useful framework for the semantic integration of multi-source e-learning data, allowing the consolidation, linkage and advanced querying in a systematic way. With this motivation, the e-LION (e-Learning Integration ONtology) semantic model is proposed for the first time in this work to operate as data consolidation approach of different e-learning knowledge-bases, hence leading to enrich on-top analysis. For demonstration purposes, the proposed ontological model is populated with real-world private and public data sources from different LMSs referring university courses of the Software Engineering degree of the University of Malaga (Spain) and the Open University Learning. In this regard, a set of four case studies are worked for validation, which comprise advance semantic querying of data for feeding predictive modelling and time-series forecasting of students' interactions according to their final grades, as well as the generation of SWRL reasoning rules for student's behaviour classification. The results are promising and lead to the possible use of e-LION as ontological mediator scheme for the integration of new future semantic models in the domain of e-learning.
Development of a Universal-Purpose Settlement Gripper Using a Flexible Sub-arm and Triggering Mechanism

Drones are used for various purposes including surveillance and surveying. A drone usually hovers in place during its mission, consuming a large amount of energy to maintain its position. This can be fatal, particularly for small-to-medium-sized drones with relatively short flight time capability. If drones could settle on other structures while performing their missions, this would save a significant amount of energy. To this end, drones can be equipped with the ability to hang onto or from various structures using a gripper device. However, urban areas have many structures with vertical planes such as building walls and streetlights, making it difficult for the drone to settle. Therefore, a method of settling on the walls of structures, as opposed to hanging, would be preferable in this case. Further, for a gripper to settle not only on a plane but also on curved surfaces, the angle of the gripper must be adjustable to fit the shape of the structure. In this work, a gripper was designed to have a center arm and a sub-arm. To increase stability further during settlement, the sub-arm was composed of a rigid body and a flexible pad. Sets of experiments were conducted to optimize the attachment pad type and the shape of the flexible pad. Further, a supporting beam was incorporated into the gripper to prevent the drone from sagging under its own weight. By utilizing the impact force generated upon colliding with the structure, the gripper designed in this research could stably settle over a wide range of flight velocities. The sub-arm with the flexible pad could adjust the gripping angle, and the gripper could cope with various surfaces with curvatures larger than 70 mm.
Coordinated Vulnerability Disclosure programme effectiveness: Issues and recommendations

Coordinated Vulnerability Disclosure (CVD) programmes leverage a global network of independent security researchers (hackers) to support pre- and post-deployment security. Organisations are increasingly adopting Bug Bounty Programmes (BBPs) and Vulnerability Disclosure Programmes (VDPs) to outsource work from internal security teams, and are able to utilise the results from a programme to help shape their Software Development Life Cycle (SDLC) processes. Motivated by the question How effectively are organisations utilising CVD programmes?, we aim to address two issues concerning the operation of CVD programmes. First, it is necessary to identify the pre- and post-launch issues faced by programme operators that inhibit effective operation. Second, organisations stand to benefit if they are able to use the results of a CVD programme outside of the typical reporting-triaging information flow between a hacker and the operator. As such, it is useful to explore how the results of a CVD programme influence change across the SDLCs of real-world organisations and measure the extent to which this occurs. We report upon the results of a qualitative study based on the outcomes of 39 survey responses and eight semi-structured interviews with individuals involved in the operation of CVD programmes. It is found that the fears and issues faced by organisations are similar to those identified in earlier studies, suggesting that there has been little development in preventing prevalent problems faced by CVD programme operators. High volumes of low-quality, low-value reports still burden operators and consume resources. It is also found that organisations use the information contained within vulnerability reports to influence change in a number of security activities, namely testing, communication processes, and the specification of security requirements. Finally, based on the responses from the surveys and interviews, we provide recommendations to those looking to establish a CVD programme.
Blind Restoration of Atmospheric Turbulence-Degraded Images Based on Curriculum Learning

Atmospheric turbulence-degraded images in typical practical application scenarios are always disturbed by severe additive noise. Severe additive noise corrupts the prior assumptions of most baseline deconvolution methods. Existing methods either ignore the additive noise term during optimization or perform denoising and deblurring completely independently. However, their performances are not high because they do not conform to the prior that multiple degradation factors are tightly coupled. This paper proposes a Noise Suppression-based Restoration Network (NSRN) for turbulence-degraded images, in which the noise suppression module is designed to learn low-rank subspaces from turbulence-degraded images, the attention-based asymmetric U-NET module is designed for blurred-image deconvolution, and the Fine Deep Back-Projection (FDBP) module is used for multi-level feature fusion to reconstruct a sharp image. Furthermore, an improved curriculum learning strategy is proposed, which trains the network gradually to achieve superior performance through a local-to-global, easy-to-difficult learning method. Based on NSRN, we achieve state-of-the-art performance with PSNR of 30.1 dB and SSIM of 0.9 on the simulated dataset and better visual results on the real images.
Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users' reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.
Education Initiatives to Support Earthquake Early Warning: A Retrospective and a Roadmap

Abstract As of May 2021, public alerting is now operational for the ShakeAlert earthquake early warning system for the West Coast of the United States in California, Oregon, and Washington. Successful early warning systems require the scientific and technical implementation to be coupled with social and humanitarian considerations, including education and outreach campaigns. Community engagement with the over 50 million people who live in ShakeAlert states is important to increase public safety, security, and awareness of local earthquake hazards, how to prepare, and how ShakeAlert earthquake early warning can help. Here, we describe the efforts of the ShakeAlert Educational Resources Working Group around the rollout of public alerting in the Pacific Northwest for Oregon in March 2021 and Washington in May 2021, respectively. Our initial approach was to engage in formative dialogue with community members in each ShakeAlert state, develop educational activities and animations based on the collective feedback, and then to disseminate our resources in both formal (K-16) and informal (free choice) learning environments through workshops and outreach events. The rollout of public alerting in the Pacific Northwest provided an opportunity to directly engage with various publics around a specific event, and to collaborate with local news and social media, communications and social science professionals, educators, emergency managers, and scientific and technical experts on the ShakeAlert system. Following the rollouts, we developed a strategic plan for the next five years of ShakeAlert to promote earthquake early warning, as the importance of earthquake preparedness competes with the stressors of everyday life. Because earthquake early warning systems are rapidly expanding worldwide, our education and outreach efforts provide a roadmap for building successful education and outreach campaigns, leading up to public alerting and maintaining earthquake preparedness in the public consciousness following rollout.
An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515

RGB-D cameras have become common in many research fields since these inexpensive devices provide dense 3D information from the observed scene. Over the past few years, the RealSense™ range from Intel® has introduced new, cost-effective RGB-D sensors with different technologies, more sophisticated in both hardware and software. Models D415, SR305, and L515 are examples of successful cameras launched by Intel® RealSense™ between 2018 and 2020. These three cameras are different since they have distinct operating principles. Then, their behavior concerning depth estimation while in the presence of many error sources will also be specific. For instance, semi-transparent and scattering media are expected error sources for an RGB-D sensor. The main new contribution of this paper is a full evaluation and comparison between the three Intel RealSense cameras in scenarios with transparency and translucency. We propose an experimental setup involving an aquarium and liquids. The evaluation, based on repeatability/precision and statistical distribution of the acquired depth, allows us to compare the three cameras and conclude that Intel RealSense D415 has overall the best behavior namely in what concerns the statistical variability (also known as precision or repeatability) and also in what concerns valid measurements.
A Hybrid Deep Learning Model for Real Time Hand Gestures Recognition

The performance of Hand Gesture Recognition (HGR) depends on the hand shape.Segmentation helps in the recognition of hand gestures for more accuracy and improves the overall performance compared to other existing deep neural networks.The crucial segmentation task is extremely complicated because of the background complexity, variation in illumination etc.The proposed modified UNET and ensemble model of Convolutional Neural Networks (CNN) undergoes a two stage process and results in proper hand gesture recognition.The first stage is segmenting the regions of the hand and the second stage is gesture identification.The modified UNET segmentation model is trained using resized images to generate a cost effective semantic segmentation model.The Central Processing Unit (CPU) utilization and training time taken by these models with respect to three public benchmark datasets are also analyzed.Recognition is carried out with the ensemble learning model consisting of EfficientNet B0, Effi-cientNet B4 and ResNet V2 152.Experimentation on NUS hand posture dataset-II, OUHANDS and HGRI benchmark datasets show that our architecture achieves a maximum recognition rate of 99.07%through semantic segmentation and the Ensemble learning model.