Search

In this work, Schottky diodes based on amorphous indium-gallium-zinc-oxide (IGZO) were fabricated on cellulose microfiber paper substrate. Silver lines used as the Schottky barrier were printed, in parallel to thermally evaporated Cr/Au ohmic contact, using a dispense printer. The morphological and electrical characteristics of the devices are presented. The fabricated diodes exhibited rectification ratios ranging from 3.4 to 34.9 at ±1V with ON voltages that range from 1.1V to 1.4 V, for device lengths (Ag to Au distance) from 145 µm to 894 µm. The diodes were characterized in a temperature range between 25°C and 80°C. They showed a decrease of the ON current when increasing temperature, which is mainly attributed to the change of the cellulose microstructure. Indeed, an opposite of the ON current behavior was registered when the diode was realized on a polyimide substrate. The realized flexible paper-based diodes offer a potential promising choice for printed environmental-friendly electronics.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

Agile development is increasingly taught at universities worldwide. Project courses are redesigned in order to better fit these methods, both with respect to content taught and how courses are organised. This position paper builds on experience with reviews as a feedback practice in a bachelor level project course. Reviews are a key element in agile development, but there has been little discussion in the software engineering education literature on the role of such reviews in improving feedback and assessment. Through examples of course improvement work in a course with about 140 students in 24 teams, we show how review practices are tailored to better comply with principles of good feedback practice which intend to empower students and help self-regulate learning. We argue that reviews can provide formative assessment and improve the learning outcome. Finally, we conclude with five lessons learned from three years of continuous improvement.

The European Union (EU) regulations require the pilots of Unmanned Aircraft System (UAS) to register and pass a basic theory exam from 2021. Therefore, it is essential to systematically gather information for pilots for utilizing UAS in a safe and meaningful manner especially in Nordic weather conditions. This book chapter describes the Nordic challenges for UAS operations. These can be categorized into two main categories: technological and operational. Based on the extensive literature review and practical experience of the authors, both categories of challenges especially with relevance to the severe Arctic weather conditions are presented. The relevant weather conditions include changing the density of the air, dust and solid particles clouds, extreme light conditions, freezing rain, heavy and gusty wind, heavy clouds, ice fog, rain, mist and fog combinations, rapid temperature changes, snow, storm and hailstorm, temperature crossing 0 C several times within 24 hours, wind shear and whirlwind. Many technical and operational challenges (e.g., weather-related phenomena) are overlapping on both categories and can be mitigated partly by technological advances and partly by operational preparedness. Finally, the future challenges and needs in UAS research are also discussed.

Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading. Agency for Science, Technology and Research (A*STAR) Accepted version This work was supported by the Agency for Science, Technology and Research (A*STAR) through its Singapore–Germany Academic-Industry (2+2) International Collaboration under Grant A1990b0060. Paper no. TSG-01805-2019.

Image classification needs to consider image degradations in practice because an image classification network trained with clean images works poorly for degraded images while digital images usually include some degradations such as JPEG compression. To tackle this problem, a common approach is training classification networks on degraded images with various levels of degradation, e.g. various quality factors for JPEG compression. However, the classification networks do not usually have enough accuracy for clean images because the classification networks have been averagely trained for various levels of degradation. This paper aims to construct a classification network of degraded images without sacrificing the performance of clean images. This paper proposes a network to learn the classification of degraded images and degradation levels of degraded images as multi-task learning. Learning the classification of degraded images is the main task of multi-task learning. On the other hand, learning degradation levels is a sub-task of multi-task learning and reinforces the classification ability. In the proposed network, a feature extractor of the classification is trained with image features acquired from a classification network of clean images by using consistency regularization with the cosine similarity loss. The experimental results using different types of degradations, including JPEG compression, Gaussian blur, Gaussian noise, and salt-and-pepper noise, show that our proposed network has enough ability to classify degraded images without sacrificing the performance for clean images.

The fifth-generation (5G) mobile system is now being deployed across the world and the scale of 5G subscribers is growing quickly in many countries. The attention of academia and industry is increasingly shifting towards the sixth generation (6G) and many pioneering works are being kicked off, indicating an important milestone in the history of 6G. At this juncture, an overview of the current state of the art of 6G research and a vision of future communications are of great interest. This paper thus investigates up-to-date 6G research programs, ambitions, and main viewpoints of representative countries, institutions and companies worldwide. Then, the key technologies are outlined and a vision on ``What 6G may look like?" is provided. This paper aims to serve as an enlightening guideline for interested researchers to quickly get an overview when kicking off their 6G research.

This paper analyzes the behavior of transient switching silicon carbide devices influenced by the parasitics of printed circuit board and the component itself. The parasitics in power and gate driver loops are described and investigated. Double pulse test simulations for silicon carbide devices are conducted in this report and verified through the circuit simulation. The parasitics in the printed circuit board are added based on the real experience printed circuit board design and the parasitics in silicon carbide devices are added based on the manufacturer modelling. The parasitic inductances in the simulation are varied +/- 30% to understand the effect of the parasitics. The drain and source parasitic inductances and the gate parasitic resistance are simulated separately to understand the individual effect on the ringing of silicon carbide devices. The ringing impact on the reliability of devices is also analyzed in this paper.

In the era of information society and 5G networks, cars are extremely important mobile information carriers. In order to meet the needs of multi-scenario business requirements such as vehicle assisted driving and in-vehicle entertainment, cars need to interact with the outside world. This interconnection and data transmission process is usually called vehicular communication (V2X, Vehicle-to-Everything). Device-to-device (D2D) communication not only has partial nature of communication, but also alleviate the current problem of spectrum scarcity of resources. The application of D2D communication in V2X can meet the requirements of high reliability and low latency, but resource reuse also brings interference. Software-defined networking (SDN) provides an optimal solution for interoperability and flexibility between the V2X and D2D communication. This paper reviews the integration of D2D and V2X communication from the perspective of SDN. The state-of-the-art and architectures of D2D-V2X were discussed. The similarity, characteristics, routing control, location management, patch scheduling and recovery is described. The integrated architecture reviewed in this paper can solve the problems of routing management, interference management and mobile management. It also overcome the disconnection problem between the D2D-V2X in terms of SDN and provides some effective solutions. - Qatar National Research Fund (QNRF) - [UREP27-020-1-003]. - Ministry of Higher Education, Malaysia (MOHE) - [FRGS/1/2018/ICT02/UKM/02/6]. - National Research Foundation of Korea (NRF) - [2019R1C1C1007277]. - Taif University (TU)- [TURSP-2020/260].

End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in the real-time pricing, scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead, and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.

Since its inception in 2015, Style Transfer has focused on texturing a content image using an art exemplar. Recently, the geometric changes that artists make have been acknowledged as an important component of style[42], [55], [62], [63]. Our contribution is to propose a neural network that, uniquely, learns a mapping from a 4D array of inter-feature distances to a non-parametric 2D warp field. The system is generic in not being limited by semantic class, a single learned model will suffice; all examples in this paper are output from one model.Our approach combines the benefits of the high speed of Liu et al. [42] with the non-parametric warping of Kim et al. [55]. Furthermore, our system extends the normal NST paradigm: although it can be used with a single exemplar, we also allow two style exemplars: one for texture and another geometry. This supports far greater flexibility in use cases than single exemplars can provide.

Crowdsourcing plays an important role in modern IT landscape, enabling the use of human information processing abilities to solve problems that are still hard for machines. One of the specific (and most demanded) applications of crowdsourcing is collecting item annotations, i.e., describing the contents of complex items with a help of labels (tags). Input received from crowdsourcing participants is typically unreliable, therefore, to increase the quality of annotations, each item is typically processed by several participants and the obtained annotations have to be aggregated. The paper considers a special case of annotating, where a set of possible labels, as well as the set of relationships between the labeled items and the labels are defined by an OWL 2 ontology (OWL QL). Such semantic item annotations turn out to be very useful in organizing large collections of items and enabling semantic search in them. In order to increase annotations quality, the paper proposes two aggregation methods - Onto Voting and OntoSB, differing in that the first one is agnostic with respect to participants' reliability and the second one accounts for variations in reliability. Simulation experiments with ontology-based annotations of varying quality show that the proposed aggregation methods increase the quality of collected ontology-based item annotations.

Electricity network modelling and grid simulations form a key enabling element for the integration of newer and cleaner technologies such as renewable energy generation and electric vehicles into the existing grid and energy system infrastructure. This paper reviews the models of the African electricity systems and highlights the gaps in the open model landscape. Using PyPSA (an open Power System Analysis package), the paper outlines the pathway to a fully open model and data to increase the transparency in the African electricity system planning. Optimisation and modelling can reveal viable pathways to a sustainable energy system, aiding strategic planning for upgrades and policy-making for accelerated integration of renewable energy generation and smart grid technologies such as battery storage in Africa.

Great Britain has reached high penetrations of intermittent Distributed Generation (DG), which has influenced many aspects of power system planning and operation. There has been a lack of technical requirements for DG to provide grid support and for network operators to monitor, control and gather detailed information of DG installations. The work reported in this paper has explored available data sources to provide better visibility of the amount, size and type of DG installed at various locations in the distribution networks and evaluated it alongside whole electricity system trends. Several of the issues associated with a high volume of generation being unobservable and uncontrollable are surveyed and discussed, such as active network management and coordination of transmission and distribution (T & D) networks. Finally, the paper discusses system resilience, emphasising that DG loss-of-mains protection continues to present a threat to system security and DG adds uncertainty to the effectiveness of automatic defence mechanisms. The latter issues were brought into focus following a system disturbance in GB on August 9th, 2019 and are reflected on in this study.

© 2021 IEEE.Coronavirus usually transmits from the animal to the human, but now, the virus transmission is between persons. Therefore, scientists and researchers are trying to develop several types of machine learning methods to defend against COVID-19. Medical images play a significant role in this time due to they can be used to recognize COVID-19 accurately. However, in this paper, we used X-Ray images, the images undergone to sharpening techniques to increase the results further. The texture techniques named local binary pattern (LBP) have been used in order to extract features. The features obtained were applied to the support vector machine (SVM). The results we achieved were 100% for all performance measurements. Our results were conspicuously superior compared to the state-of-the-art papers published.

This paper presents a hybrid technique of combining the BERT embedding method and the graph convolutional neural network. This combination is then employed to solve the text classification problem. Initially, we apply the BERT embedding method to the whole corpus in order to transform all the texts into numerical vectors. Then, the graph convolutional neural network will be applied to these numerical vectors to classify these texts into their appropriate classes. Especially, in our approach, we need only a few labeled texts for the model training. For the illustration, in this paper, we use the BBC news and the IMDB movie reviews datasets to perform our experiments, showing that the performance of the graph convolutional neural network model is better than the performances of the combination of the BERT embedding method with other classical machine learning models.

The power distribution grid is centrally managed concerning the requirements of the end-users, however, with the appearance of smart grids, new technologies arc arising. Therefore, distributed energy resources, mainly, renewables, energy storage systems, electric mobility, and power quality are viewed as encouraging contributions for improving power management. In these circumstances, this paper presents a power electronics perspective for the power distribution grid, considering innovative features, and including a power quality perception. Throughout the paper are presented relevant concepts for a concrete realization of a smart grid, supported by the integration of power electronics devices as the interface of the mentioned technologies. Aiming to support the innovative power electronics systems for interfacing the mentioned technologies in smart grids, a set of developed power electronics equipment was developed and, along with the paper, are shown and described, supporting the most important contributions of this paper., This work has been supported by FCT -Fundação para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has been supported by the FCT Project newERA4GRIDs PTDC/EEI-EEE/30283/2017.

The general aim of a cognitive Cyber Physical System of Systems (CPSoS) is to provide managed access to data in a smart fashion such that sensing and actuation capabilities are connected. Whilst there is significant funding and research devoted to this area, focus remains purely on creating bespoke systems. This paper presents a novel approach, based on a set of components to leverage Situational Awareness and Smart Actuation in large manufacturing industries with the focus on enabling predictive maintenance for asset and abnormal situation management. This paper presents a novel generic platform, named AtiCoS, that combines case-based and common-sense reasoning, as the enabling methodologies for enhancing CPSoS with cognitive capabilities.

The power consumption in data centers continues increasing, thereby pushing the converter to achieve high efficiency and high power density. This paper presents an asymmetrical half-bridge flyback converter for data center applications. Compared with an active clamp flyback converter, it functions as an isolated buck converter, and is more preferable for voltage step-down applications. The operating principle and the design considerations are included in this paper. An interleaving winding layout with uniformed current distributions is used for the planar transformer design. The built prototype satisfies an industrial standard low-profile sixteenth brick requirement, and achieves 94.9% peak efficiency and 468W/inch3 power density.

In this paper, we propose an explainable approach for hate speech detection from the under-resourced Bengali language, which we called DeepHateExplainer. In our approach, Bengali texts are first comprehensively preprocessed, before classifying them into political, personal, geopolitical, and religious hates using a neural ensemble method of transformer-based neural architectures (i.e., monolingual Bangla BERT-base, multilingual BERT-cased/uncased, and XLM-RoBERTa). Subsequently, important (most and least) terms are identified using sensitivity analysis and layer-wise relevance propagation (LRP), before providing human-interpretable explanations11To foster reproducible research, we make available the data, source codes, models, and notebooks: https://github.com/rezacsedu/DeepHateExplainer. Finally, we compute comprehensiveness and sufficiency scores to measure the quality of explanations w.r.t faithfulness. Evaluations against machine learning (linear and tree-based models) and neural networks (i.e., CNN, Bi-LSTM, and Conv-LSTM with word embeddings) baselines yield F1-scores of 78%, 91%, 89%, and 84%, for political, personal, geopolitical, and religious hates, respectively, outperforming both ML and DNN baselines22Read an extended version of this paper: https://arxiv.org/abs/2012.14353.

Pocket Beach (PB) planview geometry analysis is one of the most important steps needed for PB characterization and dynamic evaluation. With modern technology, quantitative morphology of a PB can be obtained with a high degree of confidence. In this paper we used Unmanned Aerial Vehicle (UAV) photogrammetry-based mapping technology combining drone and Global Positioning System (GPS) for 2D and 3D models’ reconstruction of Isola Bella bay PBs located along the coast of the town of Taormina (eastern Sicily). The aim of this paper is to show how very high spatial resolution data can be obtained and analyzed for the retrieval of geomorphological features and geometric parameters of PBs. A computer-assisted visual interpretation approach was adopted to manually digitize, delineate and extract the planform characteristics of the PB. The planview geometry analysis was conducted on orthophotos and digital surface models of 1.81 cm and 1.69 cm spatial resolutions, acquired during two flight surveys. The mapping approach adopted in this paper shows that information on planform geometry of PBs can be obtained faster using modern remote sensing technologies allowing time series analysis of planview parameters. &nbsp

Under the light of increasing air traffic congestion and reduced environmental impacts, the growing need for tailored approaches has arisen. To coordinate tailored approaches, accurate predictions of descent trajectories must be available. The ability of ground based automation for such endeavours however must be improved. Specifically in the ability to predict where descents begin, also referred to as the Top of descent (TOD). This paper attempts to provide a machine learning approach to TOD location prediction, considering the operational capabilities of modern-day air traffic management by working within the confines of parameters available to controllers and considerations of stakeholders. To identify the common TOD clusters and the resultant average TOD locations representing the controllers macroscopic perspective, an unsupervised learning approach is utilised. Specifically, an ensemble method of DBSCAN-K-means is used. This was compared to the more microscopic pilot controllers perspective and generic decision-making formulas to determine the proximity to expected TOD. Of which the microscopic option has proven to be more reliable in representing the individual Flight Management System (FMS) recommendations or pilots’ decisions on aircraft. In the prediction of TOD location, operationally available factors such as proximity to expected TOD, current flight level, approach heading, ground speed, fleet and aircraft type are used to train a decision tree model. A Singapore flight information region (FIR) case study is conducted to illustrate the methodology. Results of the methodology employed on the inbound A380 fleet consisting a total of 79 flights, yielded a maximum prediction range of 31NM range from actual TOD occurrence. This paper has demonstrates the possibility of using a machine learning approach to predict TOD of a flight considering both prior cruise characteristics and operational considerations of pilots. Accepted version

Fifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate.

The Publisher's final version can be found by following the DOI link. Data quality plays a crucial role in the context of smart buildings. Meanwhile, missing data is relatively common in acquired datasets from sensors within the smart buildings. Poor data could result in a big bias in forecasting, control and operational services. Despite the common techniques to handle missing data, it is essential to systematically select the most appropriate approach for such missing values. This paper aims to focus on the lift systems as one of the essential parts in the smart buildings by exploring the most appropriate data imputation methods to handle missing data and to provide its service and allow a better understanding of patterns to issue the correct control actions based on forecasted models. The imputed data is not only investigated statistically but also modelled through machine learning algorithm to explore the impact of selecting inappropriate imputation techniques. Seven imputation techniques deployed on datasets with three level of missing values including 10%, 20% and 30% and the performance of methods examined through the normalized root mean square error (NRMSE) approach. In addition, the interaction between imputation techniques and a machine learning algorithm, namely random forest were examined. Findings from this paper can be employed in identifying an appropriate imputation technique not only within the lift datasets, but smart building context.

This paper presents an experimental study of the softlanding problem in quadrotors using the induced wind modeling approach. The landing phase has been typically one of the critical phases in drone flight. Landing complexity drastically increases when the drone needs to land on sensitive sites, such as platforms or rack of pipes in refineries (for inspection purposes), in which explosive material is running through or there exist flammable/explosive material in the environment. Multirotor unmanned aerial vehicles (UAVs) are usually lightweight platforms and they are significantly disturbed by the aerodynamic ground effect while landing; so, near the ground, those drones are subjected to an external disturbance in proximity to the ground. In this situation, the airflow can be reflected after reaching the ground, disturbing the performance of the rotors significantly. This paper aims to model the induced wind velocity, caused by the propellers to see and consider the ground effect during the landing. The reduction of the total thrust near the ground provides a smooth landing and avoids bumping. The complex wind modeling formulation and limitation of the commercialized autopilots make the implementation a challenging task. Herein we propose how to incorporate the proposed soft-landing algorithm within an existing UAV autopilot. Experimental results show that the proposed approach successfully replicates the wind modeling which leads to a soft-landing. European Union (UE). H2020 779411 Ministerio de Ciencia, Innovación y Universidades (España) RTI2018-102224-B-I00

Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson's paradox through two usage scenarios., 5 pages, 4 figures, accepted as short paper at IEEE VIS 2021

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many different types of service offerings, developer productivity enhancement tools, and new application classes as well as the manifestation of cloud functionality closer to the user at the edge. The notion of utility computing, however, has remained constant throughout its evolution, which means that cloud users always seek to save costs of leasing cloud resources while maximizing their use. On the other hand, cloud providers try to maximize their profits while assuring service-level objectives of the cloud-hosted applications and keeping operational costs low. All these outcomes require systematic and sound cloud engineering principles. The aim of this paper is to highlight the importance of cloud engineering, survey the landscape of best practices in cloud engineering and its evolution, discuss many of the existing cloud engineering advances, and identify both the inherent technical challenges and research opportunities for the future of cloud computing in general and cloud engineering in particular., Comment: author copy/preprint of a paper published in the IEEE International Conference on Cloud Engineering (IC2E 2021)

Although numerous methods have been formalized for handling the technical aspects of developing domain-specific modeling languages (DSMLs), user needs and usability aspects are often addressed in ad hoc manners and late in the development process. Working in this context, this paper presents the development of the customer journey modeling language (CJML), a DSML for modeling service processes from the end-user's perspective. CJML targets a wide and heterogeneous group of users, making it especially challenging regarding usability. This paper describes how an industry-relevant DSML was systematically improved by using a variety of user-centered design techniques in close collaboration with the target group and how their feedback was used to refine and evolve the syntax and semantics of CJML. We also suggest how a service-providing organization may benefit from adopting CJML as a unifying language for documentation purposes, compliance analysis, and service innovation. Finally, we generalize the experience gained into lessons learned and methodological guidelines.

Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise. Recently, deep convolutional neural networks (D-CNNs) have shown remarkable performance in interpreting CXR in adults. However, there is a lack of evidence indicating that D-CNNs can recognize accurately multiple lung pathologies from pediatric CXR scans. In particular, the development of diagnostic models for the detection of pediatric chest diseases faces significant challenges such as (i) lack of physician-annotated datasets and (ii) class imbalance problems. In this paper, we retrospectively collect a large dataset of 5,017 pediatric CXR scans, for which each is manually labeled by an experienced radiologist for the presence of 10 common pathologies. A D-CNN model is then trained on 3,550 annotated scans to classify multiple pediatric lung pathologies automatically. To address the high-class imbalance issue, we propose to modify and apply "Distribution-Balanced loss" for training D-CNNs which reshapes the standard Binary-Cross Entropy loss (BCE) to efficiently learn harder samples by down-weighting the loss assigned to the majority classes. On an independent test set of 777 studies, the proposed approach yields an area under the receiver operating characteristic (AUC) of 0.709 (95% CI, 0.690-0.729). The sensitivity, specificity, and F1-score at the cutoff value are 0.722 (0.694-0.750), 0.579 (0.563-0.595), and 0.389 (0.373-0.405), respectively. These results significantly outperform previous state-of-the-art methods on most of the target diseases. Moreover, our ablation studies validate the effectiveness of the proposed loss function compared to other standard losses, e.g., BCE and Focal Loss, for this learning task. Overall, we demonstrate the potential of D-CNNs in interpreting pediatric CXRs., Comment: This is a preprint of our paper which was accepted for publication to ICCV Workshop 2021

The informational environment surrounding the Covid-19 pandemic has been widely recognized as fragmented, politicized, and complex [1]. This has resulted in polarized public views regarding the veracity of scientific communication, the severity of the threat posed by the virus, and the necessity of nonpharmaceutical interventions (NPIs) which can slow the spread of infections [2]. This paper describes CovidCommitment.org, an effort toward enhancing NPI adoption through the combination of a social behavioral commitment device and interactive map-based visualizations of localized infection data as tabulated via a 1-hourdrive-time isochrone. This paper describes the system design and presents a preliminary analysis of user behavior within the system.

Nowadays, energy consumption and especially energy saving are important issues. The concern over global warming has increased the need to save energy in many areas of our living community. One way of slowing global warming is to reduce energy consumption and the related emissions. IoT systems can be utilized to monitor living conditions in residential and commercial buildings. These IoT systems continuously produce huge amounts of data. Similarly, various vendor-specific building automation systems generate data on an equal scale. Combining these data sources can be challenging, as protocols and data are seldom in a unified and interoperable format. In this paper, we present the DataSites service, which integrates back-end systems and client-oriented REST APIs. The high-level view of DataSites architecture, how to relay building data to client applications, and the required interfaces are described in this paper. Use of the service is illustrated using two real-life use cases. acceptedVersion

High temporal frequency monitoring of transport infrastructures is crucial for implementing effective maintenance prioritisation strategies and prevent major failures. To this extent, ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards of data quality and accuracy. However, routine and systematic campaigns are required over relatively long inspection times for data collection and implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructures (roads, railways and airfields), with a much higher temporal frequency of investigation and wider inspection catchment areas. Nevertheless, the integration of information from i) satellite remote sensing and ii) ground-based NDT methods is still an area to be explored in civil engineering. Within this framework, this paper aims to review significant stand-alone applications in these two areas of technology for transport infrastructure monitoring. Furthermore, recent advances, main challenges and future perspectives arising from their integration are discussed. Contents of this paper are organised within the context of an invited keynote talk given at the 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing (AGERS – 2021).

International audience; Modern manufacturing systems are subject to many disruptions due to increased competition. They are subject to many configuration changes during their life cycle and must cost-effectively make these changes. Intelligent componentbased development enables the rapid design of control systems. This paper presents a new methodology for structuring the control architecture of cyber-physical systems with intelligent components: The Hierarchical Intelligent Component Architecture (HICA). Compared to traditional design methods, this methodology integrates the design of the physical and software architecture simultaneously. This paper also describes the model-driven engineering to realize this architecture using SysML diagrams to implement the code in IEC 61499 standard. Diagram Linkage Table (DTL) is the central element that allows the transition from SysML diagrams to IEC 61499.

Information and communication technologies (ICT) are an indispensable and decisive factor in developing society and the economy. Due to the greater spread of the telecommunications network and more affordable services, these technologies have become more accessible to a larger population. In the past five years, almost all business sectors have made a certain degree of transformation of their business, bringing new digital implications. Simultaneously, all sectors, especially the service ones, are showing readiness for the digital society's challenges. Industries that are predominantly dependent on the digital aspects of doing business are continually looking for faster ways to deliver services with as little cost and downtime as possible. Objective limitations in achieving these efforts emerge in the form of Internet connectivity. The emergence of the 5G network opens up new opportunities to expand and improve existing business models and the emergence of new ones. The aim of this paper is to look at the opportunities that the 5G network brings to digitally dependent industries. The paper will use available data from relevant publishers and publications that provide an overview of forecasts within individual industries related to improvements in supply, productivity, and potential shortcomings. The paper can serve the scientific community as a conceptual design and predictor of the development of individual parts of industries that should be monitored and analyzed through the prism of various commercial applications in the coming period.

Disrupted by the COVID-19 pandemic, the 2019/20 academic year brought an unplanned shift to education online. While teaching itself became more challenging, the main disruption was in testing, where not being in the same room with students whose knowledge was being evaluated posed serious challenges. This paper discusses the approach taken by the lecturers of the course „Fundamentals of electrical engineering” in professional study of electrical engineering at the Zagreb University of Applied Sciences. The paper examines the important role of a properly set up Learning management system for knowledge evaluation and compares the unproctored online approach to the typical proctored exam.

In modern software development, the JavaScript ecosystem of various frameworks and libraries used to develop contemporary web applications presents many advantages. JavaScript is a widely known interpreted programming language, simple to learn and start development, and with numerous third-party libraries and extensions. However, with the rise of highly user-interactive websites and browser-based games, in some cases, JavaScripts executable engine could lack in performance. Therefore, developers could combine several other programming languages to create a polyglot user-interactive interoperable system to develop efficient modern web applications. The interoperability modules offer significant advantages but also present challenges in the execution due to high complexity and longer compilation times. This paper explores WebAssembly, a binary format compilation target with a low-level assembly-like language used for targeting from other programming languages. The binary format allows near-native performance level due to its compactness, as it prioritizes usage of low-level languages. Moreover, as a continuation of our previous research of the GraalVM ecosystem, we analyzed a guest language implementation of a WebAssembly based system, TruffleWasm, hosted on GraalVM and Truffle Java framework. This paper presents the architecture and review of the TruffleWasm within the GraalVM-based ecosystem as well as from performance test results within our academic environment., Comment: 6 pages, 3 figures

Higher education institutions (HEIs) face many challenges in an increasingly complex and turbulent environment. Academic managers are becoming leaders of change in their institutions and their roles are increasingly more challenging. Their managers' decisions and decision-making styles (DMS) directly affect the organization's efficiency, adaptability, employee satisfaction, and student academic performance. Since a DMS of decision maker forms the backbone of effective decision-making (DM), it is extremely important to analyze DMS of an academic managers. Therefore, this paper aimed to present findings of relevant studies on the DMS used in HEIs as well as the consequences of their usage. To emphasize the need of conducting further research on academic managers' DMS in conditions of unstable environment, the systematic literature review (SLR) is conducted and presented in the paper.

The growing demand for photovoltaic (PV)-based buildings and increasing utilization of DC electronic loads, along with decreasing DC appliances prices, have resulted in attraction of DC networks for residential consumers. The main aim of this paper is to present an economic study of a PV system-based DC residential distribution network. This includes designing and simulation of the system that is based on DC residential distribution network. Electrical Transient Analyzer Program (ETAP) software can be used for designing and modelling of the PV system components for a single house and for the distribution grid. The process starts with the PV module, DC/DC converter, battery, and the load. It takes into account all the ratings of the specific components for the whole system. Subsequently, the system efficiency has been evaluated for a common DC home at 24V, 48V, and 110V DC, and compared to the 220V AC through the simulation. Power losses will be through the conversion and distribution. Based on the obtained results using American Wire Gauge AWG-6 for a single house, it is found that the efficiency of a 48V DC system is higher than that of 220V AC system. It is also found that the usage of 48V DC leads to better efficiency compared with that of other system. This states that the efficiency of the system will increase with increasing of the main bus voltage. At the rest of the paper, the installed capacity for a single house is evaluated at 10 KW per house and this is results in a 2 MWp total installed PV system for 200 houses. This installed capacity can produce 4344 GWh per year. The installed PV system could improve the Libyan grid by reducing losses. Moreover, the installed PV capacity saves around 592,956,000 LD yearly and prevents emission of 328.82 million kg, 5,408,890 million kg and, 4963617743 million kg from gas, heavy fuel, and light fuel respectively.

Our paper provides insights into which critical success factors (CSFs) for digitalization projects are seen as important from the companies’ perspective based on an online survey. The results presented in this paper show that CSFs of the dimensions of Corporate organization and Technology are considered to be of particular relevance, as stated by the companies, with Corporate culture, Top management support, and a Unified digital corporate strategy / vision as the three most important CSFs. Therefore, this paper contributes to the CSF research regarding digital transformation and enables the development of practice-oriented recommendations for action and assistance in shaping digital transformation.

This paper presents a model predictive control (MPC) approach to a microgrid at a cotton farm so as to minimize the water pumping operational cost while taking full advantage of renewable energy sources. The reason for using MPC is its ability in handling noise, disturbance, and real-time parameter changes. In this paper, the MPC models of grid-connected are established; moreover, the effectiveness and robustness of the MPC models are analyzed by cotton farm case studies. Simulation results show that the optimal MPC solutions for grid-connected microgrid of a farm are AU$8.4/ML less than a manual control-based strategy. In addition, the MPC solution shows outstanding robustness in controlling the water reservoir level. When the disturbance data of the rainy season in 2016 are added, the system saves 34.5% of the operating cost compared with the baseline. When the rainy season disturbance is added together, the system saves 11.74% of operating costs compared to the baseline.

The integration of renewable energy sources (RESs) into distributed microgrid systems has been widely applied in agriculture, and in particular in cotton farms. Due to the specific irrigation periods and non-irrigation periods during cotton growth, and the inherent intermittent characteristics of RESs, the design of a cotton farm microgrid system becomes challenging. Finding the optimal size of the RESs for a cotton farm microgrid needs to consider not only the energy demand for cotton irrigation but also the investment cost and the payback period. This paper presents an optimization model for cotton farm microgrid design, which explores available RESs and energy storage options to ensure reliable power supply from renewables. Furthermore, the designed microgrid utilizes solar photovoltaic (PV) units and wind turbine generator as RESs together with battery storage and demonstrates the supply and demand relationship between the microgrid and pump loads. By using RES power supply, renewable energy is optimally utilized to satisfy the seasonal loads demand, and the grid power is used as a backup power source. The objectives of optimization include investment cost, operating cost and simple payback period. In order to solve the underlying optimization problem, this paper adopts YALMIP MATLAB Toolbox. A case study is undertaken using historical energy consumption data for a cotton farm in Gunnedah, New South Wales, to verify the applicability of the proposed approach.