Reviewerly/exHarmony huggingface dataset:
Enhanced Blockchain Transaction: A Case of Food Supply Chain Management

Distributed ledger technology is an immutable data storage and transparent system, which is a constituent component that empowers the Food supply Chain Management (FSCM).Due to the immutable data feature, the scalability of blockchain technology is quite a challenge.In essence, blockchain data may grow rapidly as compared to the existing approach, and such circumstances is an issue that need to be addressed.Also, high volumes of data process for each transaction verification couple with long period of time for computation, lead to high overhead and resource consumption.This study discusses improved methods to reduce the amount of data generated by system.The proposed method employs the header verification system for transaction to reduce data growth and data processing time.During transaction, this method will verify only the header file information with using Merkle tree.This system is able to store each transaction of a node and form blockchain. Subsequently, each node is capable to verify valid transactions only by use of header data without maintaining full data of blockchain.The expected outcome of the scheme is the significant decrease of data growth in blockchain.
An Advanced Cubature Information Filtering for Indoor Multiple Wideband Source Tracking With a Distributed Noise Statistics Estimator

Acoustic vector sensor (AVS) is an effective tool to tracking acoustic sources. However, for the problem of tracking multiple wideband sources using distributed AVS array (DAVS), there are still unsolved issues which include measurements-to-targets association and targets tracking under incorrect or unknown statistics of measurement noise. Joint probabilistic data association (JPDA) is an effective algorithm to solve data association between measurements and targets and JPDA based cubature information filter (MTCIF) is designed for nonlinear system. Meanwhile, noise statistics estimator (NSE) based on modified Sage-Husa maximum posterior (SHMP) is constructed to cope with incorrect or unknown statistics of measurement noise. Then, a two-step distributed information fusion based on weighted average consensus (WAC) is built for DAVS to improve the stability and accuracy of state estimator and NSE. Numerical simulations demonstrate the effectiveness of the proposed algorithms.
LiftTiles

This paper introduces LiftTiles, a modular and reconfigurable room-scale shape display. LiftTiles consist of an array of retractable and inflatable actuator that is compact (e.g., 15cm tall) and light (e.g., 1.8kg), while extending up to 1.5m to allow for large-scale shape transformation. Inflatable actuation also provides a robust structure that can support heavy objects (e.g., 10 kg weight). This paper describes the design and implementation of LiftTiles and explores the application space for reconfigurable room-scale shape displays.
Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.
Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints

This paper presents a modified genetic algorithm using target-bundle-based encoding and tailored genetic operators to effectively tackle cooperative multiple task assignment problems of heterogeneous unmanned aerial vehicles. In the cooperative multiple task assignment problem, multiple tasks including reconnaissance, attack, and verification have to be sequentially performed on each target (e.g. ground control stations, tanks, etc.) by one or multiple unmanned aerial vehicles. Due to the precedence constraints of different tasks, a singular task-execution order may cause deadlock situations, i.e. one or multiple unmanned aerial vehicles being trapped in infinite waiting loops. To address this problem, a target-bundled genetic algorithm is proposed. As a key element of target-bundled genetic algorithm, target-bundle-based encoding is derived to fix multiple tasks on each target as a target-bundle. And individuals are generated by fixing the task-execution order on each target-bundle subject to task precedence constraints. During the evolution process, bundle-exchange crossover and multi-type mutation operators are customized to generate deadlock-free offspring. Besides, the time coordination method is developed to ensure that task-execution time satisfies task precedence constraints. The comparison results on numerical simulations demonstrate that target-bundled genetic algorithm outperforms particle swarm optimization and random search methods in terms of optimality and efficiency.
Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 1: Methodology)

Several studies have shown that changes in incoming solar radiation and variations of the diffuse fraction can significantly modify the vegetation carbon uptake. Hence, monitoring the incoming solar radiation at large scale and with high temporal frequency is crucial for this reason along with many others. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis (LSA SAF) has operationally disseminated in near real time estimates of the downwelling shortwave radiation at the surface since 2005. This product is derived from observations provided by the SEVIRI instrument onboard the Meteosat Second Generation series of geostationary satellites, which covers Europe, Africa, the Middle East, and part of South America. However, near real time generation of the diffuse fraction at the surface level has only recently been initiated. The main difficulty towards achieving this goal was the general lack of accurate information on the aerosol particles in the atmosphere. This limitation is less important nowadays thanks to the improvements in atmospheric numerical models. This study presents an upgrade of the LSA SAF operational retrieval method, which provides the simultaneous estimation of the incoming solar radiation and its diffuse fraction from the satellite every 15 min. The upgrade includes a comprehensive representation of the influence of aerosols based on physical approximations of the radiative transfer within an atmosphere-surface associated medium. This article explains the retrieval method, discusses its limitations and differences with the previous method, and details the characteristics of the output products. A companion article will focus on the evaluation of the products against independent measurements of solar radiation. Finally, the access to the source code is provided through an open access platform in order to share the expertise on the satellite retrieval of this variable with the community.
Live video-streaming evaluation using the ITU-T P.1203 QoE model in LTE networks

Nowadays, video-streaming services have become the most consumed service by end-users in mobile networks. This has led telecommunication operators to develop a series of networking mechanisms that allow to provide end-users with the demanded levels of quality. In order to evaluate the degree of user satisfaction, there are numerous methodologies that range from Quality of Service measurements (QoS), which are based on network parameters, to estimations of the Quality of user Experience (QoE), which is centered on users' perception. In this context, the ITU-T has recently released the ITU-T Rec. P.1203, which presents an objective model for estimating QoE in video-streaming services. In this paper, we present a functional implementation of the ITU-T Rec. P.1203 and its validation, as well as the guidelines for its application. As a practical case study, we evaluate the performance of a live video streaming service over an LTE network in terms of QoE using the ITU-T Rec. P.1203 model. The QoE outcomes from two different test-benches, real and emulated, and two TCP-based video transport protocols, DASH and RTMP, are compared. In addition, several propagation models are also tested in the emulated environment to find out the one that best matches real conditions based on QoE. From the results, we obtain that Nakagami-m with m = 5 is the most suitable propagation model to be used in the emulation of outdoor LTE scenarios and that the number of stalling events is the factor with the greatest importance in the QoE estimation provided by the model.
Robustness to spontaneous emission of a variational quantum algorithm

We study theoretically the effects of dissipation on the performances of a variational quantum algorithm used to approximately solve a combinatorial optimization problem, the Maximum Independent Set, on a platform of neutral atoms. We take a realistic model of dissipation with spontaneous emission, and show numerically that the detrimental effects of these incoherent processes are spontaneously attenuated by the variational nature of the procedure. We furthermore implement an alternative optimization scheme recently proposed, where one uses the expected shortfall of the mean energy as the objective function.
An Overview of Quality Metrics Used in Estimating Software Faults

Software reliability is highly affected by software quality attributes and measurements. Faults, bugs, and errors are shown not only in the development process but also in end-user period hereby it is required to detect these issues earlier. These are detected by software quality and object-oriented metrics which are commonly used in the fault detection process. CK, MOOD and QMOOD metrics are the most common metrics applied in this area. In this paper is to aim to provide information about popular software quality metrics and their usage in terms of software fault prediction studies. For this purpose, in this work, these three metrics were analyzed separately and their acquisition methods were showed. Moreover, in order for the software to survive with errors, to remove and minimize errors, a number of recommendations are presented in the conclusion section.
The mediation effect of TQM practices on the relationship between entrepreneurial leadership and organizational performance of SMEs in Kuwait

Small and medium enterprises (SMEs) play a vital role for the economic development of most countries, particularly in developing economies such as Kuwait.There are, however, challenges and problems that have to be addressed in an effort to achieve a better performance and ensure longer-term success.In the pursuit of entrepreneurial leadership (EL), SMEs frequently adopt total quality management (TQM) practices as their key strategies to enhance their competitive advantage.However, there is a dearth of studies that investigate the relationship of these concepts with the performance SMEs in developing countries.This paper seeks to examine the association between determinants of organizational performance, such as entrepreneurial leadership (EL) and TQM practices, of Kuwait's SMEs.This study uses a quantitative method.A survey questionnaire is administered to collect data from 500 SME owners and CEOs in Kuwait, and 384 are found to be usable.The outcomes of this study show beneficial and considerable influence of entrepreneurial leadership and TQM practices on organizational performance.The association of entrepreneurial leadership with organizational performance of SMEs is fundamentally mediated by TQM practices.
A Community-Based Collaborative Filtering Method for Social Recommender Systems

Recommender systems have become indispensable for recommending items of interest to users and have been successfully deployed in a wide range of real-world applications. In this paper, we exploit the community influence of users in social networks to improve the recommendation accuracy. Depending on the community in which the user is located, the user's preferences are defined more accurately. Specifically, we propose a community-based collaborative filtering method for social recommender systems, which makes full use of the rich link/community structure within a social network. We first group users in a social network into overlapping communities. Then we explicitly incorporate the community preference into the latent factor model. Compared with seven state-of-the-art methods on four real-world datasets, our method achieves the best performance.
Which variables are essential for renewable energies?

Essential Variables are defined as a minimal set of variables that explain the state of the system. They are crucial for predicting its developments, and support metrics that measure its evolution. The variables should be relevant to meet requirements of stakeholders and be technically and economically feasible for systematic observation. A definition of Essential Renewable Energies Variables is proposed linked with their identification in several domains in renewable energy using a bottom-up and user-driven approach, and spanning over several years of documented interaction with stakeholders. Lists of variables are proposed in hydropower, solar, wind, and marine energies. It does not comprise the variables relating to social and economic aspects supporting decision making in investment nor those relating to civil engineering that are needed to erect a plant or farm.
Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics

As the penetration of photovoltaic power in the electrical grid increases, reliable forecasts of solar irradiance are becoming ever more critical. For forecasting horizons beyond a few hours, Numerical Weather Prediction models (NWP) are stapled to yield the best results. For potential users of NWP forecasts, choosing which model to use can be challenging, as a large palette of NWP models and configurations is available. To tackle this issue, several studies have conducted systematic comparisons between available models and configurations. Most of these studies, however, only evaluate forecast skills with a small number of metrics, such as the Root Mean Square Error (RMSE) or the Mean Average Error (MAE). To better understand the nature and specificities of various NWP models, we developed an evaluation approach that combines multiple-criteria analysis, Fourier analysis and classification of daily profiles of irradiance. In this paper, we present this novel approach and demonstrate its strengths by applying it to a practical use-case, using 108 different NWP forecasts models based on different parametrizations of the mesoscale NWP Weather Research and Forecasting model (WRF) in Singapore. Our approach allowed a clearer overview of the forecasts’ skills, as well as better discrimination between models. It also significantly improved our understanding of the nature of the NWP errors. In particular, WRF forecasts in Singapore were found unfit to timely resolve irradiance at an hourly scale, but better adapted to predict daily profiles of irradiance. Furthermore, the proposed multiple criteria approach was applied to a sensitivity analysis of WRF physical schemes, providing a better insight into the impact of each model. This work shows that it is critical to go beyond RMSE when evaluating forecasts and that more holistic approaches such as the one proposed here should be preferred.
MobileCount: An Efficient Encoder-Decoder Framework for Real-Time Crowd Counting

In this work, we propose a computation-efficient encoder-decoder architecture, named MobileCount, which is specifically designed for high-accuracy real-time crowd counting on mobile or embedded devices with limited computation resources. For the encoder part, MobileNetV2 is tailored in order to significantly reduce FLOPs at a little cost of performance drop, which has 4 bottleneck blocks preceded by a max pooling layer of stride 2. The design of decoder is motivated by Light-weight RefineNet, which further boosts counting performance with only a $$10\%$$ increase of FLOPs. In comparison with state-of-the-arts, our proposed network is able to achieve comparable counting performance with 1/10 FLOPs on a number of benchmarks.
EXPLOITATION OF MCDA TO LEARN THE RADIAL BASE NEURAL NETWORK (RBFNN) AIM PHYSICAL AND SOCIAL VULNERABILITY ANALYSIS VERSUS THE EARTHQUAKE (CASE STUDY: SANANDAJ CITY, IRAN)

Abstract. Despite years of research on natural hazards and efforts to reduce physical and psychological damage, earthquake as a natural disaster is catastrophic. Though, human is the main axis in dealing with crisis and vulnerability, and since the space of cities encompasses largest population spectrum, managing this space is considered as an essential issue. Accordingly, the vulnerability of the City of Sanandaj was defined by environmental, physical and social criteria. In this regard, with the aim of modeling, and assessing the risk and vulnerability, the MCDA-ANN hybrid model was introduced as a new method for teaching of learning models. In order to determine the final value of each of the criteria, AHP analysis was performed as one of the MCDA methods to solve complex and non-structural problems by creating a functional hierarchy, and after that, a training data base for learning ANN was created randomly based on the AHP classification map. Then, for modeling, the radial base functional neural network (RBFNN) was used as one of the techniques of artificial neural networks. After the modeling, 30% of the points were selected as validation data to determine the accuracy of the model. After the implementation of RBFNN model, the area of AUC curve resulted is 0.922, which indicates the high accuracy of the model in assessing the risk of an earthquake. The results show high vulnerability in urban areas1 and 2 and in downtown Sanandaj that in these zones the physical and social factors dramatically affect the vulnerability of these areas.
A feature clustering-based adaptive modular neural network for nonlinear system modeling

To improve the performance of nonlinear system modeling, this study proposes a feature clustering-based adaptive modular neural network (FC-AMNN) by simulating information processing mechanism of human brains in the way that different information is processed by different modules in parallel. Firstly, features are clustered using an adaptive feature clustering algorithm, and the number of modules in FC-AMNN is determined by the number of feature clusters automatically. The features in each cluster are then allocated to the corresponding module in FC-AMNN. Then, a self-constructive RBF neural network based on Error Correction algorithm is adopted as the subnetwork to study the allocated features. All modules work in parallel and are finally integrated using a Bayesian method to obtain the output. To demonstrate the effectiveness of the proposed model, FC-AMNN is tested on several UCI benchmark problems as well as a practical problem in wastewater treatment process. The experimental results show that the FC-AMNN can achieve a better generalization performance and an accurate result for nonlinear system modeling compared with other modular neural networks.
LFastqC: A lossless non-reference-based FASTQ compressor

The cost-effectiveness of next-generation sequencing (NGS) has led to the advancement of genomic research, thereby regularly generating a large amount of raw data that often requires efficient infrastructures such as data centers to manage the storage and transmission of such data. The generated NGS data are highly redundant and need to be efficiently compressed to reduce the cost of storage space and transmission bandwidth. We present a lossless, non-reference-based FASTQ compression algorithm, known as LFastqC, an improvement over the LFQC tool, to address these issues. LFastqC is compared with several state-of-the-art compressors, and the results indicate that LFastqC achieves better compression ratios for important datasets such as the LS454, PacBio, and MinION. Moreover, LFastqC has a better compression and decompression speed than LFQC, which was previously the top-performing compression algorithm for the LS454 dataset. LFastqC is freely available at https://github.uconn.edu/sya12005/LFastqC.
ECONOMIC GROWTH, ENERGY CONSUMPTION AND HUMAN CAPITAL FORMATION: IMPLICATION FOR KNOWLEDGE-BASED ECONOMY

This study examines the relationship between technology, human capital and economic growth and also attempts to establish their implications on knowledge based economy in Nigeria. The data used for the study are from secondary sources while the new growth model was also adopted. The dependent variable in the model is the level of real output while the explanatory variables are gross capital formation and government expenditure on education. The result of the causality test shows that is a uni-directional relationship running from gross capital formation and real output, human capital formation and real output growths do not Granger cause each other while causality runs from human capital to capital formation and vice versa. The implication of the result; the increase in economic growth has not improve the rate of capital formation in Nigeria. The study concluded that Nigeria has been slow to identify the strands of global knowledge due to the following: weak institutions; limited awareness and disincentives preventing them from taking the root to the knowledge and information based- economy. Based on the findings the study recommended; strategies in which education can be incorporated into the growth system. Research and development should be encouraged as well and polices that promote output through savings. Keywords: Economic Growth, Human Capital Formation, Knowledge and Information Based Economy, Technology.JEL Classifications: O47, O15, G14DOI: https://doi.org/10.32479/ijeep.8165