Reviewerly/exHarmony huggingface dataset:
AATTENUATION—The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.
Cybersecurity Awareness Platform with Virtual Coach and Automated Challenge Assessment

Over the last years, the number of cyber-attacks on industrial control systems has been steadily increasing. Among several factors, proper software development plays a vital role in keeping these systems secure. To achieve secure software, developers need to be aware of secure coding guidelines and secure coding best practices. This work presents a platform geared towards software developers in the industry that aims to increase awareness of secure software development. The authors also introduce an interactive game component, a virtual coach, which implements a simple artificial intelligence engine based on the laddering technique for interviews. Through a survey, a preliminary evaluation of the implemented artifact with real-world players (from academia and industry) shows a positive acceptance of the developed platform. Furthermore, the players agree that the platform is adequate for training their secure coding skills. The impact of our work is to introduce a new automatic challenge evaluation method together with a virtual coach to improve existing cybersecurity awareness training programs. These training workshops can be easily held remotely or off-line.
Slim Chain and Dependability

The slim chain is proposed in this paper in an effort to address and resolve the scalability issue of blockchain-based crypto computing by managing the volume of the blockchain itself. An adequate balancing of computation across on/off-chain is expected to relieve the spatial and temporal overhead of managing the otherwise exponentially growing size of the blockchain by making the blockchain slimmer as much as the volume migrated and kept off-chain than the conventional, as referred to as a slim chain in this paper. As an initial design, a novel theoretical model is proposed to assure the efficacy of the proposed slim chain with specific respect to the dependability from a single transaction's standpoint in a stochastic manner. It is demonstrated that the dependability can be traced with respect to whether the transaction of concern is to write off the chain or read back from off the chain to on-chain, as the transactions writing off the chain are more dependable than reading from off the chain without loss of practicality, as well as the crypto rate and the IPFS rate as IPFS (Inter-Planetary File System) is an off-chain storage considered in this research.
Servitization in the Era of Blockchain: the Ice Cream Supply Chain Business Case

In the last years, the servitization is emerging as a new business model in the manufacturing sector, pushed by its promises of allowing to pay machines based on their actual use. Servitization is based on the recording of important events related to machine usage, such as the type and time of used tools and the type of materials processed. However, actors involved in the servitization should either trust one each other or rely on a third-party authority to store machine usage statistics in a secure and non-repudiable manner. The paper presents a novel solution adopting Blockchain in the ice cream market to leverage the servitization of ice cream machines, involving machine manufacturers, ice cream refill producers, and ice cream makers. The proposed solution exploits the amount of used refills as the metric of machine usage and ensures that involved actors are aware of every refill procedure. In addition, it ensures the privacy of business-relevant information, e.g., by avoiding that two refill producers are reciprocally aware of the amount of sold refills. While targeting the ice cream supply chain, our solution may represent a reference model for servitization even for other market sectors. Finally, performance results based on a working prototype demonstrate the feasibility of the proposed solution even on resource constrained devices.
Experimental demonstration of optimized quantum process tomography on the IBM quantum experience

We experimentally performed complete and optimized quantum process tomography of quantum gates implemented on superconducting qubit-based IBM QX2 quantum processor via two constrained convex optimization (CCO) techniques: least squares optimization and compressed sensing optimization. We studied the performance of these methods by comparing the experimental complexity involved and the experimental fidelities obtained. We experimentally characterized several two-qubit quantum gates: identity gate, a controlled-NOT gate, and a SWAP gate. The general quantum circuit is efficient in the sense that the data needed to perform CCO-based process tomography can be directly acquired by measuring only a single qubit. The quantum circuit can be extended to higher dimensions and is also valid for other experimental platforms.
3D Hand Gesture Recognition for Drone Control in Unity

In this paper, the gesture-sensing device Leap Motion is utilized to operate a drone in the virtual environment set by game engine Unity. Four swiping gestures and two static gestures, including face up, face down, are tested. From the experimental results, static gestures are more recognizable than dynamic gestures. Among different users, drone reacts to gesture control with accuracy more than 90% on average.
PCA based Facial Recognition for Attendance System

One of the most important issues about authentication from the long period of history is the user authentication. Among all the techniques for user authentication, human face recognition (HFR) is a well-known technique. As the human face recognition goes under the part of biometric confirmation, HFR has been generally utilized in numerous applications like surveillance systems, entrance of houses, mobile face unlock, and network security. This paper introduces a novel method for student attendance system in the classroom using face recognition technique. The human face attendance system is developing based on the current image recognition technology in order to detect the faces of the students in a classroom and mark attendance of the person if he or she is matched with the facial data in the given facial database. This HFR system will independently mark the attendance of the students in a classroom without disturbing the teacher, so that the teacher can enhance their teaching role in different ways. The time can be saved by using the proposed system. The idea is developed by using an open CV.
Humane Visual AI: Telling the Stories Behind a Medical Condition

A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.
Pseudo-LiDAR point cloud magnification

Recently, the pseudo-LiDAR gains increasing attention since it provides the probability of replacing the expensive LiDAR with the camera in autonomous driving. However, due to the fixed field of view of the camera, the pseudo-LiDAR point cloud suffers from the limited spatial range. In this paper, we present a novel pseudo-LiDAR point cloud magnification algorithm, aiming to extrapolate the narrow baseline and further bridge the gap between LiDAR and camera. To achieve this goal, we design a complete pipeline consisting of the hybrid view synthesis module, stereo depth estimation module, image-depth associated stitching module, and magnified pseudo-LiDAR point cloud transformation module. Considering the artifacts existed in generated views, we propose a region-aware restoration approach to obtain more realistic synthesized results. To the best of our knowledge, this is the first work for the pseudo-LiDAR point cloud magnification, which shows appealing and meaningful applications in 3D spatial perception systems only equipped with cameras. Experimental results on KITTI benchmark demonstrate that our algorithm can effectively magnify pseudo-LiDAR point cloud with a wider field of view, note that we only use two images captured by stereo cameras.
Reducing parameter space for neural network training

For neural networks (NNs) with rectified linear unit (ReLU) or binary activation functions, we show that their training can be accomplished in a reduced parameter space. Specifically, the weights in each neuron can be trained on the unit sphere, as opposed to the entire space, and the threshold can be trained in a bounded interval, as opposed to the real line. We show that the NNs in the reduced parameter space are mathematically equivalent to the standard NNs with parameters in the whole space. The reduced parameter space shall facilitate the optimization procedure for the network training, as the search space becomes (much) smaller. We demonstrate the improved training performance using numerical examples.
Performance Modeling of Microservice Platforms

Microservice architecture has transformed the way developers are building and deploying applications in the nowadays cloud computing centers. This new approach provides increased scalability, flexibility, manageability, and performance while reducing the complexity of the whole software development life cycle. The increase in cloud resource utilization also benefits microservice providers. Various microservice platforms have emerged to facilitate the DevOps of containerized services by enabling continuous integration and delivery. Microservice platforms deploy application containers on virtual or physical machines provided by public/private cloud infrastructures in a seamless manner. In this article, we study and evaluate the provisioning performance of microservice platforms by incorporating the details of all layers (i.e., both micro and macro layers) in the modeling process. To this end, we first build a microservice platform on top of Amazon EC2 cloud and then leverage it to develop a comprehensive performance model to perform what-if analysis and capacity planning for microservice platforms at scale. In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both the microservice platform and the back-end macroservice infrastructures.
A Sequential Structure for Water Inflow Forecasting in Coal Mines Integrating Feature Selection and Multi-Objective Optimization

To construct an accurate and stable approach for water inflow forecasting, a series of advanced and effective techniques, such as variational mode decomposition (VMD), outlier robust extreme learning machine (ORELM) and multi-objective grey wolf optimizer (MOGWO), are appropriately integrated into this study. Considering that the influence of the mode number on the VMD decomposition effectiveness, such an argument is determined by observing the converged centre frequency distribution among the components. Then the characteristic items of water inflow series are extracted by VMD, thus obtaining a series of sub-components. Afterwards, ORELM is applied to predict each component, where the parameters of ORELM are optimized by MOGWO with multi-objective functions including forecasting accuracy and stability. Correspondingly, the aggregation of all components' prediction values is considered as the final results. The experimental results obtained by performing eight various models on real-time data demonstrate that the supplementary modules achieve positive effects on the improvement of prediction accuracy, where the proposed model implements an average performance promotion of 48.43% compared with the contrastive models.
Benchmarking the performance of microservice applications

Application performance is crucial for end user satisfaction. It has therefore been proposed to benchmark new software releases as part of the build process. While this can easily be done for system classes which come with a standard interface, e.g., database or messaging systems, benchmarking microservice applications is hard because each application requires its own custom benchmark and benchmark implementation due to interface heterogeneity. Furthermore, even minor interface changes will easily break an existing benchmark implementation. In previous work, we proposed a benchmarking approach for single microservices: Assuming a REST-based microservice interface, developers describe the benchmark workload based on abstract interaction patterns. At runtime, our approach uses an interface description such as the OpenAPI specification to automatically resolve and bind the workload patterns to the concrete endpoint before executing the benchmark and collecting results. In this extended paper, we enhance our approach with the capabilities necessary for benchmarking entire microservice applications, especially the ability to resolve complex data dependencies across microservice endpoints. We evaluate our approach through our proof-of-concept prototype OpenISBT and demonstrate that it can be used to benchmark an open source microservice application with little manual effort.
Support vector machine based novelty detection and FDD framework applied to building AHU systems

The increasing energy consumption of heating, ventilation and air conditioning (HVAC) systems is one of the main concerns in the building sector. Fault detection technologies are now indispensable for energy efficiency and performance improvement. In this paper, a methodology for the robust and reliable fault detection and diagnosis is presented as a two-stage framework composed by an offline stage where the models are built and an online stage that is constantly receiving new samples. The system includes a novelty detection scheme developed using one-class support vector machines (OC-SVM) and a classifier built using SVM. The proposed strategy is applied to a dataset for a single-zone constant air volume air handling unit. The experimental results show that the novelty detection stage adds robustness layer to the typical classification scheme.
Fuzzy Kinodynamic RRT: a Dynamic Path Planning and Obstacle Avoidance Method

Path planning is the essential capability for autonomous navigation of UAV (Unmanned Aerial Vehicle) in unknown environments. In this paper, a Fuzzy logic inferencing system has been designed to achieve obstacle avoidance in a dynamic environment. We introduce Fuzzy-Kinodynamic RRT, method which generates dynamic path based on the traditional rapidly exploring random tree (RRT) algorithm. A set of simple Fuzzy rules are proposed for simple 2D and 3D path planning cases. It is an optimized path planning method which uses Kinodynamic RRT algorithm [1] [2] to do global path planning and utilizes Fuzzy logic to avoid obstacles. A set of heuristics Fuzzy rules are proposed to lead the UAV away from un-modeled ground-based obstacles and to guide the UAV towards the goal. In addition, the designed Fuzzy rules can augment traditional RRT for dealing with new obstacles in the environment. Various simulations are conducted in 2D and 3D environment and the results illustrate the effectiveness of the algorithm in simple dynamic environment.
On Transferability of Histological Tissue Labels in Computational Pathology

Deep learning tools in computational pathology, unlike natural vision tasks, face with limited histological tissue labels for classification. This is due to expensive procedure of annotation done by expert pathologist. As a result, the current models are limited to particular diagnostic task in mind where the training workflow is repeated for different organ sites and diseases. In this paper, we explore the possibility of transferring diagnostically-relevant histology labels from a source-domain into multiple target-domains to classify similar tissue structures and cancer grades. We achieve this by training a Convolutional Neural Network (CNN) model on a source-domain of diverse histological tissue labels for classification and then transfer them to different target domains for diagnosis without re-training/fine-tuning (zero-shot). We expedite this by an efficient color augmentation to account for color disparity across different tissue scans and conduct thorough experiments for evaluation.
Ways of sociotechnical integration of scientists and volunteers in citizen science

Abstract The given research is directed on the analysis of the scientist and volunteer’s relations at realization of joint projects of the citizen science, which have recently been developed increasingly due to information-communication technologies introduction in the process. The authors conducted a substantial analysis of more than 200 projects; thus, special attention is paid to the projects presented on Zooniverse site. The site serves as the main base for the paper’s authors since it is the world’s largest and most popular platform for constructing communication of scientists and volunteers. The continuous research covers the period from May 24, 2019 to September 24, 2019, showing what changes occurred to the projects, specifying in speed of their ending, progress of the volunteers’ involving, the most popular themes of research, etc. The authors generate the cloud of citizen science’s objectives from scientists and volunteers, generalizing motivation and objectives of the given interaction. The perspective opportunities of the citizen science are noted, allowing solving not only research problems, but also the contemporary global problems.
A Variable Bulk Arrival and Static Bulk Service Queueing Model for Blockchain

This paper proposes a novel embedded Markovian queueing model of the M(1,n)/Mn/1 type in order to establish a theoretical foundation to design a blockchain-based system with specific interest in Ethereum. The model assumes variable bulk arrivals of transactions in Poisson distribution, i.e., M(1,n), where n the number of slots across all the mined transactions and static bulk service of transactions in exponential time, i.e., Mn, for posting in the current block, namely, VBASBP. The primary performance measurements to be taken are the average number of slots no matter how many transactions are mined under the assumption of the maximum number of slots per block as specified by n; the average waiting time per slot; and the throughput in terms of the average number of slots to be processed per time. The variable bulk arrival rate is assumed to vary linearly proportional to the size of the transactions in a multiple of per slot, and the static bulk service is assumed to take place when the number of slots in the mined transactions reaches at n, i.e., a bulk processing of multiple transactions in multiple slots for posting in a block. Numerical simulations are conducted on Matlab to demonstrate the efficacy of the model.
Single-atom maser with an engineered circuit for population inversion

We present a blueprint for a maser with a single three-level transmon superconducting artificial atom. The system can be pumped coherently via a two-photon process, and to achieve high population inversion, the relaxation rate of the metastable state is increased via an auxiliary low-$Q$ cavity coupled to a transition between the transmon excited states. We show numerically that such a maser can operate both in the intermediate-coupling regime with super-Poissonian photon statistics and in the strong-coupling regime, where the statistics is sub-Poissonian. For the former the maser exhibits thresholdless behavior, and for the latter there is a well-defined pumping threshold. A useful side effect of the auxiliary resonator is that it allows to overcome the photon blockade effect for the pump, which would otherwise prevent a high photon population. Finally, we observe the bistability of the steady-state Wigner function and the self-quenching effect for some parameters.