Reviewerly/exHarmony huggingface dataset:
A cloud service composition method using a trust‐based clustering algorithm and honeybee mating optimization algorithm

Summary One of the most critical issues in using service‐oriented technologies is the combination of services, which has become an important challenge in the present. There are some significant challenges in the service composition, most notable is the quality of service (QoS), which is more challenging due to changing circumstances in dynamic service environments. Also, trust value in the case of selection of more reliable services is another challenge in the service composition. Due to NP‐hard complexity of service composition, many metaheuristic algorithms have been used so far. Therefore, in this paper, the honeybee mating optimization algorithm as one of the powerful metaheuristic algorithms is used for achieving the desired goals. To improve the QoS, inspirations from the mating stages of the honeybee, the interactions between honeybees and queen bee mating and the selection of the new queen from the relevant optimization algorithm have been used. To address the trust challenge, a trust‐based clustering algorithm has also been used. The simulation results using C# language have shown that the proposed method in small scale problem acts better than particle swarm optimization algorithm, genetic algorithm, and discrete gbest‐guided artificial bee colony algorithm. With the clustering and reduction of the search space, the response time is improved; also, more trusted services are selected. The results of the simulation on a large‐scale problem have indicated that the proposed method is exhibited worse performance than the average results of previous works in computation time.
Comparison of daily diffuse radiation models in regions of China without solar radiation measurement

Considering diffuse solar radiation is essential for thermal environment construction and solar energy applications. Due to lack of solar radiation measurement in China, developing appropriate models for data estimation is becoming more and more important. In areas that lack solar radiation measurement, using estimated global solar radiation values or extraterrestrial solar radiation is a key problem. In this study, data obtained between 2000 and 2017 from 17 stations were selected to develop and verify 18 diffuse solar radiation models and three global solar radiation models. The models were evaluated using the mean bias error, mean absolute bias error, root mean squared error, and Nash-Sutcliffe efficiency coefficient. The results showed that the diffuse fraction models based on measured and calculated global solar radiation performed better than diffuse coefficient models. Models containing both clearness index and relative sunshine duration and four parameters (clearness index, relative sunshine duration, mean air temperature and relative humidity) obtained the best performance among all of models. Choosing a precise and suitable global radiation model is essential for estimating daily diffuse radiation in areas without global radiation measurement, especially in areas with low clearness index. A simplified model based only on clearness index can be applied to calculate daily diffuse radiation in Northern China. In areas with lower clearness index and those in Southern China, global radiation models that include multiple parameters (clearness index, temperature difference, and atmospheric pressure) are the optimal options.
What do we loose when machines take the decisions?

This paper concerns the technical issues raised when humans are replaced by artificial intelligence (AI) in organisational decision making, or decision making in general. Such automation of human tasks and decision making can of course be beneficial through saving human resources, and through (ideally) leading to better solutions and decisions. However, to guarantee better decisions, the current AI techniques still have some way to go in most areas, and many of the techniques also suffer from weaknesses such as lack of transparency and explainability. The goal of the paper is not to argue against using any kind of AI in organisational decision making. AI techniques have a lot to offer, and can for instance assess a lot more possible decisions—and much faster—than any human can. The purpose is just to point to the weaknesses that AI techniques still have, and that one should be aware of when considering to implement AI to automate human decisions. Significant current AI research goes into reducing its limitations and weaknesses, but this is likely to become a fairly long-term effort. People and organisations might be tempted to fully automate certain crucial aspects of decision making without waiting for these limitations and weaknesses to be reduced—or, even worse, not even being aware of those weaknesses and what is lost in the automatisation process.
The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications

In this paper, we present the Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking (BRKGA-MP-IPR), a variant of the Biased Random-Key Genetic Algorithm that employs multiple (biased) parents to generate offspring instead of the usual two, and is hybridized with a novel, implicit path-relinking local search procedure. By operating over the standard unit hypercube, such path-relinking mechanism leverages the population representation of the BRKGA and thus provides complete independence between the local search procedure and the problem definition and implementation. This approach contrasts with traditional path-relinking procedures that are tied to the problem structure. Having both BRKGA and IPR operate over the same solution space not only makes the intensification/diversification paradigm more natural but also greatly simplifies the development effort from the perspective of the practitioner, as one only needs to develop a decoder to map unit random-key vectors to the solution space of the problem on hand. Apart from such key benefits, extensive computational experiments solving real-world problems, such as over-the-air software upgrade scheduling, network design problems, and combinatorial auctions, show that the BRKGA-MP-IPR offers performance benefits over the standard BRKGA as well as the BRKGA with multiple parents.
Sentiment Analysis in Healthcare: A Brief Review

Sentiment analysis is one of data mining types that estimates the direction of personality’s sentiment analysis within natural language processing. Analyzing the text computational linguistics are used to deduce and analyze mental knowledge of Web, social media and related references. The examined data quantifies the global society’s attitudes or feelings via specific goods, people or thoughts and expose the contextual duality of the knowledge. Sentiment analysis used in different approaches such as products and services reviews. Also is used in healthcare, there is a huge volume of information about healthcare obtainable online, such as personal blogs, social media, and on the websites about medical issues rating that are not obtained methodically. Sentiment analysis provides many benefits such as using medical information to achieve the best result to increase healthcare quality. In this paper, sentiment analysis methods and techniques are presented that used in the medical domain.
A prediction models for estimating global solar radiation and evaluation meteorological effect on solar radiation potential under several weather conditions at the surface of Adrar environment

Under weather conditions, the relationship between solar radiation and climatology of desert region is identified. Six models of global solar radiation are selected and studied during period of four years. This paper is based on solar radiation measurements in Research Unit in Renewable Energy in Saharan Medium (URER-MS) at various weather conditions. The Studies have shown that solar radiation can be calculated in terms of ambient temperature. Consequently, from temperature measurements, we know how much solar energy is received per unit area to estimate the solar radiation available at a given point on the Earth's surface. In addition, it was concluded that the impact of dust depending on varies quality of the soil, and the obtained results show that the model (M4) presents the best performance with an R2 value of 0.87.
Realised volatility connectedness among Bitcoin exchange markets

This paper examines the system of Bitcoin exchanges with respect to their common dynamics. We employ connectedness measures based on the daily realised volatility of Bitcoin prices, for which the results reveal that Coinbase is the clear leader of the market, while Binance ranks surprisingly weak. The positions of specific exchanges within the network of connectedness seem to be driven by these exchanges' own characteristics, from which trading in USD rather than USDT (Tether) stands out. Our findings suggest that safer asset withdrawal matters more to the volatility connectedness among Bitcoin exchanges than does trading volume.
Pursuing pleasance: Interrogating energy-intensive visions for the smart home

In recent years, household sustainability has received increasing attention in the field of Human-Computer Interaction. This has largely been driven by the argument that through interaction design, we can create ‘smart homes’ that enable households to be more responsible with precious resources, such as food, water and energy. However, the assumption that home automation technologies lead to improved sustainability may not hold. As a contribution to this discussion, this paper demonstrates how home automation devices promote a lifestyle vision which may undermine intended energy savings. Drawing on a qualitative content analysis of smart home articles, we identify and interrogate the industry vision of ‘pleasance’. We highlight seven qualities that underpin this vision: aesthetic experience, fun and cool, customisation and control, convenience and simplicity, peace of mind, extension and expansion, and effortless energy-saving. We analyse how energy-intensive pleasance is embedded within two commercially available types of devices: smart thermostats and networked lighting. We show how these devices and the pleasance vision that they mobilize may encourage householders to use more energy. The paper concludes with design suggestions on how HCI community can re-envision pleasance through the examples of thermostats and networked lighting to achieve energy reductions and sustainability outcomes.
DAGN: A Real-Time UAV Remote Sensing Image Vehicle Detection Framework

Real-time small object detection from the remote sensing images taken by unmanned aerial vehicles (UAVs) is a challenging but fundamental problem for many UAV applications because of the complex scales, densities, and shapes of objects that are the result of the shooting angle of the UAV. In this letter, we focus on real-time small vehicle detection for UAV remote sensing images and propose a depthwise-separable attention-guided network (DAGN) based on YOLOv3. First, we combine the feature concatenation and attention block to provide the model with the excellent ability to distinguish important and inconsequential features. Then, we improve the loss function and candidate merging algorithm in YOLOv3. Through these strategies, the performance of vehicle detection is improved, while some detection speed is sacrificed. To accelerate our model, we replace some standard convolutions with depthwise-separable convolutions. Compared to YOLOv3 and other two-stage state-of-the-art models that are applied to Vehicle Detection in Aerial Imagery (VEDAI) data sets, DAGN has a detection accuracy of 0.671, which is 5.5% better than that of YOLOv3, and it achieves the same results as two-stage methods. In addition, DAGN achieves real-time detection using GeForce GTX 1080Ti.
Aseismic Refinement of Orogenic Gold Systems

Abstract Orogenic Au deposits have contributed the majority of Au recovered globally throughout history. However, the mechanism that concentrates Au to extremely high bonanza grades in small domains within these deposits remains enigmatic. The volume of fluid required to provide extreme Au endowments in localized occurrences is not reflected in field observations (e.g., in the extent of quartz veining or hydrothermal alteration). Detailed optical, scanning and transmission electron microscopy, nanoscale secondary ion mass spectrometry, and 3-D neutron tomography have been used to investigate the processes responsible for development of anomalously high grade ore (upward of 3% Au) found in quartz veins at Fosterville gold mine (Victoria, Australia). Distinct textural settings of visible Au include (1) Au concentrated along pressure solution seams associated with wall-rock selvages, (2) as nano- to microscale dusty Au seams parallel to pressure solution seams, and (3) in microscale tension fractures perpendicular to stylolitic seams. The distribution of Au in arsenopyrite and pyrite hosted within pressure solution seams changes as a function of the extent of deformation. Sulfides in highly deformed pressure solution seams exclusively host Au as nano- to micrometer-sized clusters within features associated with corrosion and brittle failure, whereas sulfides in mildly deformed pressure solution seams have Au bound in the crystal structure. It is proposed that Au supersaturation in fluids introduced during seismic periods led to the deposition of abundant Au nanoparticles in quartz-carbonate veins. Subsequent pressure dissolution of vein quartz and carbonate during interseismic intervals allowed for episodic increase in the Au/quartz ratio and permitted liberation and migration of Au nanoparticles, promoting Au grain growth in favorable textural settings. Galvanic corrosion and brittle fracturing of auriferous sulfides during the interseismic period allowed additional remobilization and/or enrichment of sulfide-hosted Au. Repetition of this mechanism over the time scale of deposit formation acted to concentrate Au within the lodes. This Au ore upgrading model, referred to as “aseismic refinement,” provides a new insight for the genesis of ultrarich Au mineralization and, based on textures reported from many Au deposits, may be a globally significant component in the formation of orogenic Au deposits.
A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning

In this article we explore the blending of the four models (Satellite, WRF-Solar, Smart Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the aim of improving GHI and DNI forecasts. Two blending approaches that use the four models as predictors have been studied: the horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The influence on the blending models of adding information about weather types is also studied. The approaches have been evaluated in the same four Iberian Peninsula stations of Part 1. Blending approaches have been extended to a regional context with the goal of obtaining improved regional forecasts. In general, results show that blending greatly outperforms the individual predictors, with no large differences between the blending approaches themselves. Horizon approaches were more suitable to minimize rRMSE and general approaches work better for rMAE. The relative improvement in rRMSE obtained by model blending was up to 17% for GHI (16% for DNI), and up to 15% for rMAE. Similar improvements were observed for the regional forecast. An analysis of performance depending on the horizon shows that while the advantage of blending for GHI remains more or less constant along horizons, it tends to increase with horizon for DNI, with the largest improvements occurring at 6 h. The knowledge of weather conditions helped to slightly improve further the forecasts (up to 3%), but only at some locations and for rRMSE.
The Influence of Agile Methodology (Scrum) on Software Project Management

Software project management has main role in the Software industry. It includes different processes and knowledge areas. The triple constraint of the software project like time, cost and scope is directly dependent on the requirement of the project. Agile methodology is the iterative way for developing the software project for frequent changes, fast delivery and reduce risk. Software project management also plays important role in agile based software project. Agile methodology influence software project management at 10 knowledge areas. In this study we carried out survey from different software companies and it shows that almost every software company uses agile development (Scrum) and has a positive impact on the software project management.
Beyond spontaneous emission: Giant atom bounded in the continuum

The quantum coupling of individual superconducting qubits to microwave photons leads to remarkable experimental opportunities. Here we consider the phononic case where the qubit is coupled to an electromagnetic surface acoustic wave antenna that enables supersonic propagation of the qubit oscillations. This can be considered as a giant atom that is many phonon wavelengths long. We study an exactly solvable toy model that captures these effects, and find that this non-Markovian giant atom has a suppressed relaxation, as well as an effective vacuum coupling between a qubit excitation and a localized wave packet of sound, even in the absence of a cavity for the sound waves. Finally, we consider practical implementations of these ideas in current surface acoustic wave devices.
Efficient feature selection techniques for sentiment analysis

Sentiment analysis is a domain of study that focuses on identifying and classifying the ideas expressed in the form of text into positive, negative and neutral polarities. Feature selection is a crucial process in machine learning. In this paper, we aim to study the performance of different feature selection techniques for sentiment analysis. Term Frequency Inverse Document Frequency (TF-IDF) is used as the feature extraction technique for creating feature vocabulary. Various Feature Selection (FS) techniques are experimented to select the best set of features from feature vocabulary. The selected features are trained using different machine learning classifiers Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree (DT) and Naive Bayes (NB). Ensemble techniques Bagging and Random Subspace are applied on classifiers to enhance the performance on sentiment analysis. We show that, when the best FS techniques are trained using ensemble methods achieve remarkable results on sentiment analysis. We also compare the performance of FS methods trained using Bagging, Random Subspace with varied neural network architectures. We show that FS techniques trained using ensemble classifiers outperform neural networks requiring significantly less training time and parameters thereby eliminating the need for extensive hyper-parameter tuning.
A novel smart meter data compression method via stacked convolutional sparse auto-encoder

With the wide deployment of smart meters in distribution systems, a new challenge emerges for the storage and transmission of huge volume of power consumption data collected by smart meters. In this paper, a deep-learning-based compression method for smart meter data is proposed via stacked convolutional sparse auto-encoder (SCSAE). An efficient and lightweight auto-encoder structure is first designed by leveraging the unique characteristics of smart meter readings. Specifically, the encoder is designed based on 2D separable convolution layers and the decoder is based on transposed convolution layers. Compared with the existing auto-encoder method and traditional methods, the proposed structure is redesigned, and the parameters and reconstruction errors are efficiently reduced. In addition, cluster-based indexes are used to represent the regularity of power consumption behavior and the relationship between electricity consumption behavior and compression effect is studied. Case studies illustrate that the proposed method can attain significant enhancement in model size, computational efficiency, and reconstruction error reduction while maintaining the most abundant details. And grouping compression considering users’ electricity consumption rules can further improve the compression effect.
Adaptive Online Decision Method for Initial Congestion Window in 5G Mobile Edge Computing Using Deep Reinforcement Learning

Mobile edge computing provides users with low response time and avoids unnecessary data transmission. Due to the deployment of 5G, the emerging edge systems can provide gigabit bandwidth. However, network protocols have not evolved together. In TCP, the initial congestion window (IW) is such a low value that most short flows still stay in slow start phase when finishing, and do not fully utilize available bandwidth. Naively increasing IW may result in congestion, which causes long latency. Moreover, since the network environment is dynamic, we have a challenging problem-how to adaptively adjust IW such that flow completion time is optimized, while congestion is minimized. In this paper, we propose an adaptive online decision method to solve the problem, which learns the best policy using deep reinforcement learning stably and fast. In addition, we propose an approach to further improve the performance by supervised learning, using data collected during online learning. We also propose to adopt SDN to address the challenges in implementing our method in MEC systems. To evaluate our method, we build an MEC simulator based on ns3. Our simulations demonstrate that our method performs better than existing methods. It can effectively reduce FCT with little congestion caused.
A Lightweight Blockchain-Based Privacy Protection for Smart Surveillance at the Edge

Witnessing the increasingly pervasive deployment of security video surveillance systems(VSS), more and more individuals have become concerned with the issues of privacy violations. While the majority of the public have a favorable view of surveillance in terms of crime deterrence, individuals do not accept the invasive monitoring of their private life. To date, however, there is not a lightweight and secure privacy-preserving solution for video surveillance systems. The recent success of blockchain (BC) technologies and their applications in the Internet of Things (IoT) shed a light on this challenging issue. In this paper, we propose a Lightweight, Blockchain-based Privacy protection (Lib-Pri) scheme for surveillance cameras at the edge. It enables the VSS to perform surveillance without compromising the privacy of people captured in the videos. The Lib-Pri system transforms the deployed VSS into a system that functions as a federated blockchain network capable of carrying out integrity checking, blurring keys management, feature sharing, and video access sanctioning. The policy-based enforcement of privacy measures is carried out at the edge devices for real-time video analytics without cluttering the network.