Reviewerly/exHarmony huggingface dataset:
Computing optimum solar tilt angles for photovoltaic applications through python code simulation

Abstract Carrying out the dual objectives of computing the optimized tilt angles for multiple locations in Pakistan along with recommending a generalized approach for the estimation of optimum tilt angle for any location in country, this study was conducted. Python code was developed for the optimization purpose of daily, monthly, seasonally, biannually and annual solar tilt angle. Detailed analysis was presented for one location (Karachi) and a comparative analysis of optimum tilt angle conditions has been conducted for all investigated sites. Daily optimum tilt angles were found to vary from −12.0° to 55.5°. Similarly, monthly optimized tilt angles were found to vary from −11.5° (in June) to 55.0° (in December). Furthermore, seasonal tilt angles were found as 46.0 °, 2.0 °, 0.5 ° and 45.0 ° in winter, spring, summer and autumn respectively. Finally, annual optimum tilt angle (fixed throughout the year) was found as 25.5° (closer to the latitude value of Karachi). It has been found that daily adjustment of tilt angle causes a gain of 6.85% in annual energy yields when compared with fixed angle throughout the year. Similarly, energy gains were found as 0.15%, 1.24% and 6.83%, when tilt angle was adjusted daily as compared to monthly, seasonal and biannual adjustment cases respectively. The optimum tilt angle for three pairs of locations having comparable latitudes were compared and it was found that the sites having comparable latitudes do not have any marked difference in their optimum tilt angle values.
Preliminary Site Investigation based on RGB Electromagnetic Energy of Landsat-7 Images in Wadi Fayidah, Saudi Arabia

In this paper, geostatistical analysis of digital image processing data efficiently contributed to the preliminary site investigation and geotechnical mapping of Wadi Fayidah, Saudi Arabia. 3D modeling, clustering, and chart pattern changes were used to analyze the spectral electromagnetic energy reflected values in red, green, and blue (RGB) ranges on false color composite Landsat-7 images. Therefore, from upstream to downstream, a series of measurements were carried out on a 70km dendritic drainage pattern at 78 stations. Wadi Fayidah was found to have a dominant structural lineament of 56° to 84°. Furthermore, as a preliminary engineering geology mapping, Wadi Fayidah has 9 lithofacies that may differ in engineering geological properties.
Shape and Satellite Studies of Highly Charged Ions X-ray Spectra Using Bayesian Methods

High-accuracy spectroscopy commonly requires dedicated investigation into the choice of spectral line modelling to avoid the introduction of unwanted systematic errors. For such a kind of problem, the analysis of χ2 and likelihood are normally implemented to choose among models. However, these standard practices are affected by several problems and, in the first place, they are useless if there is no clear indication in favour of a specific model. Such issues are solved by Bayesian statistics, in the context of which a probability can be assigned to different hypotheses, i.e., models, from the analysis of the same set of data. Model probabilities are obtained from the integration of the likelihood function over the model parameter space with the evaluation of the so-called Bayesian evidence. Here, some practical applications are presented within the context of the analysis of recent high-accuracy X-ray spectroscopy data of highly charged uranium ion transitions. The method to determine the most plausible profile is discussed in detail. The study of the possible presence of satellite peaks is also presented.
Komponent kulturowy w nauczaniu słownictwa na zajęciach z języka obcego na poziomie akademickim na przykładzie języka włoskiego –watykańskie kulinaryzmy jako kulturemy i elementy socjolektalne

Meals are commonly known to be a crucial element of life, culture and interpersonal relationships in the Italian Peninsula. In Italian dictionaries of phraseology, idiomatic expressions representing names of culinary items are the vast majority of dictionary items. This is a sign of the importance of meals, also considered as feasting together and the Italian food centrism. As the cultural component is an integral element of language education at an academic level, Italian culinary items as components of idiomatic and fixed expressions in Vatican culinary items related to culturally rooted habits and secular Catholic rituals will be analyzed. The study presented in the paper aims at: 1. clarifying the sociolect specifics of the Vatican’s clergy and kitchen staff members; 2. determining the importance of food in the socio-cultural reality in the Vatican as a type of a culture code and an element of the regional/national culture; 3. emphasizing the essence of semantic and lexical problems related to food in academic intercultural didactics, e.g. a specialization proseminar in linguistics or translation studies or a university instructor’s own thematic seminar on culinary matters in the Italian Peninsula.
The Design of Intelligent Building Lighting Control System Based on CNN in Embedded Microprocessor

A convolutional neural network (CNN) was designed and built on an embedded building lighting control system to determine whether the application of CNN could increase the accuracy of image recognition and reduce energy consumption. Currently, lighting control systems rely mainly on information technology, with sensors to detect people’s existence or absence in an environment. However, due to the deviation of this perception, the accuracy of image detection is not high. In order to validate the effectiveness of the new system based on CNN, an experiment was designed and operated. The importance of the research lies in the fact that high image detection would bring in less energy consumption. The result of the experiment indicated that, when comparing the actual position with the positioning position, the difference was between 0.01 to 0.20 m, indicating that the image recognition accuracy of the CNN-based embedded control system was very high. Moreover, comparing the luminous flux of the designed system with natural light and the designed system without natural light with the system without intelligent control, the energy savings is about 40%.
Comparison Study of the Top 5 Leading Cryptocurrencies based on General Consensus Protocol: Bitcoin, Ethereum, Tether, XRP and Bitcoin Cash

Bitcoin has had a tremendous impact on the monetary system around the globe today since its launch in 2009 by its founder, Satoshi Nakamoto. Since then, over three thousand cryptocurrencies have risen to compete with traditional fiat currencies. While many experts believe that cryptocurrencies are more comparable to assets like gold, few others believe that cryptocurrencies could replace traditional fiat currencies as a medium of payment, just as Satoshi envisioned when he published the Bitcoin protocol. As such, there exist few papers in the literature discussing the potential for Bitcoin to become a major payment currency. Nevertheless, there is a lack of research in evaluating whether Bitcoin is set to dominate the market as a payment currency, or whether another cryptocurrency would take the lead. In addition, the issue of strict regulation capping the potential of cryptocurrencies has been well-studied in the literature and, hence, this paper tries to evaluate the top five leading cryptocurrencies (based on their market cap value as of July 2020) from a different perspective. Particularly, the evaluation is based on five benchmark evaluation factors: Speed, Activity, Decentralization, Users, and Community. For this preliminary study, we assign all these benchmarks an equal weight. Our conclusion shows that Ethereum could be more suitable as a future payment currency rather than Bitcoin.
Sparse robust subspace learning via boolean weight

Unsupervised feature selection is a hot topic in the field of machine learning, which is more convenient and important because it does not require labeled data. Currently, unsupervised feature selection algorithms based on ℓ2,1-norm are very mature and have certain robustness to outliers, but they still have some problems that cannot be ignored. For example, the sparsity of ℓ2,1-norm is not ideal, they cannot achieve row sparsity but only element sparsity. Second, fine-tuning meaningless regularization parameters increases cost and makes it easy to fall into suboptimal solutions. To release the above problems, we propose an unsupervised feature selection algorithm via Sparse Robust Subspace Learning (SRSL), which combines reconstruction term and variance term so that the model simultaneously preserves reconstruction information and enhances separability. What is more, using the ℓ2,0-norm constraint on transformation matrix makes our model have a row-sparse property. In addition, we design the boolean weight so that the model not only eliminates outliers fundamentally to enhance robustness, but also achieves the effect of anomaly detection. To solve this NP-hard problem, we carefully design an optimization algorithm, which has strict convergence guarantees and obtains a closed-form solution. Experimental results on several real-world datasets demonstrate that our algorithm outperforms other comparison algorithms in both clustering and anomaly detection applications.
A Cognitive Network Perspective for Public Administration and Policy

Abstract This article introduces the concept of cognitive social structures to public administration and policy scholars. Cognitive social structures are the perceptions, or mental maps, actors hold of the relationships that exist among the members of their network. These perceptions and their accuracy have implications for actor behavior, decisions, and outcomes. The literature on network cognitions has developed in the fields of psychology and organization studies but has yet to be applied in public administration and policy. We provide an overview of cognitive social structure research. We then demonstrate the theoretical and practical implications cognitive social structures hold for our field by examining applications in two research areas—implementation and leadership. We argue that using a network cognition perspective can help researchers and practitioners to better explain and pursue important organizational and interorganizational phenomena. We hope this article fosters new dialogue on the role of network cognitions in public administration and policy.
Improving unsupervised keyphrase extraction by modeling hierarchical multi-granularity features

Existing unsupervised keyphrase extraction methods typically emphasize the importance of the candidate keyphrase itself, ignoring other important factors such as the influence of uninformative sentences. We hypothesize that the salient sentences of a document are particularly important as they are most likely to contain keyphrases, especially for long documents. To our knowledge, our work is the first attempt to exploit sentence salience for unsupervised keyphrase extraction by modeling hierarchical multi-granularity features. Specifically, we propose a novel position-aware graph-based unsupervised keyphrase extraction model, which includes two model variants. The pipeline model first extracts salient sentences from the document, followed by keyphrase extraction from the extracted salient sentences. In contrast to the pipeline model which models multi-granularity features in a two-stage paradigm, the joint model accounts for both sentence and phrase representations of the source document simultaneously via hierarchical graphs. Concretely, the sentence nodes are introduced as an inductive bias, injecting sentence-level information for determining the importance of candidate keyphrases. We compare our model against strong baselines on three benchmark datasets including Inspec, DUC 2001, and SemEval 2010. Experimental results show that the simple pipeline-based approach achieves promising results, indicating that keyphrase extraction task benefits from the salient sentence extraction task. The joint model, which mitigates the potential accumulated error of the pipeline model, gives the best performance and achieves new state-of-the-art results while generalizing better on data from different domains and with different lengths. In particular, for the SemEval 2010 dataset consisting of long documents, our joint model outperforms the strongest baseline UKERank by 3.48%, 3.69% and 4.84% in terms of [email protected], [email protected] and [email protected], respectively. We also conduct qualitative experiments to validate the effectiveness of our model components.
Managing Expatriate Employment Contracts with Blockchain

Expatriates, or migrant workers, are employees who work outside their home country and reside in a foreign country for the purpose of work. They are often subject to job fraud, employment contract violations, and poor working conditions. These calamities are mainly due to language barriers, limited legal protection, and feeling inferior in their host countries. Many reports have indicated that minimum working and living standards for expatriates are not as adequately enforced as those for domestic employees. These issues may be elevated with the presence of an employment contract framework, which would enable better enforcement and wider visibility for both workers and employers. Thus, we propose a blockchain-powered framework to represent expatriate employment contracts as digital assets managed by smart contracts. It enables employers to create contracts to which employees agree in a decentralized, tamper-proof, transparent, and traceable manner. This framework facilitates auditability, tracking, and enhanced visibility of expatriate employment contracts and job history verification for both workers and employers. We provide a prototype implementation using the Hyperledger Fabric platform and analyze the framework qualitatively from scalability, efficiency, security, and privacy perspectives.
Sensory reweighting: a common mechanism for subjective visual vertical and cybersickness susceptibility

The malaise symptoms of cybersickness are thought to be related to the sensory conflict present in the exposure to virtual reality (VR) content. When there is a sensory mismatch in the process of sensory perception, the perceptual estimate has been shown to change based on a reweighting mechanism between the relative contributions of the individual sensory signals involved. In this study, the reweighting of vestibular and body signals was assessed before and after exposure to different typical VR experiences and sickness severity was measured to investigate the relationship between susceptibility to cybersickness and sensory reweighting. Participants reported whether a visually presented line was rotated clockwise or counterclockwise from vertical while laying on their side in a subjective visual vertical (SVV) task. Task performance was recorded prior to VR exposure and after a low- and high-intensity VR game. The results show that the SVV was significantly shifted away from the body representation of upright and towards the vestibular signal after exposure to the high-intensity VR game. Cybersickness measured using the fast motion sickness (FMS) scale found that sickness severity ratings were higher in the high intensity compared to the low-intensity experience. The change in SVV from baseline after each VR exposure modelled using a simple 3-parameter Gaussian regression fit was found to explain 49.5% of the variance in the FMS ratings. These results highlight the aftereffects of VR for sensory perception and suggest a potential relationship between the susceptibility to cybersickness and sensory reweighting.
Secure Intrusion Detection by Differentially Private Federated Learning for Inter-Vehicle Networks

Along with providing several benefits, the unprecedented growth of connected and automated vehicles brings worries about damaging cyber attacks. Network-based intrusion detection systems (IDSs) using deep learning methods can effectively mitigate the threats by promptly detecting malicious behaviors. However, the centralized learning mode may cause data leverage. Federated learning has emerged as a new distributed machine learning training paradigm to preserve data privacy by allowing clients to train and validate models locally with their data and then send the model parameters to the central server. First, we propose a new framework named DPFL-F2IDS scheme for an edge inter-vehicle network that transmits Basic Safety Messages, which consists of Differentially Private Federated Learning (DPFL) and F2IDS (Framework for IDS). DPFL can defend against the member inference attacks faced by the standard federated learning, but difficulties still exist in making a tradeoff on the utility metrics and privacy metrics. Second, experiments by centralized learning methods were performed on the VeReMi Extension dataset. Third, the performance of federated learning by different numbers of vehicles and different optimizers is evaluated. DPFL by different noise values is also evaluated. Results clarified that the F1-scores reached 0.9915 and 0.9700 by long short-term memory (LSTM)-based intrusion detection for binary classification and multi-classification in the centralized learning mode, respectively. The utility results achieved by federated averaging (FedAvg) with Adabound optimizer were closer to the centralized learning mode than the classical FedAvg algorithms. The optimal values of the noise multipliers were also found without degrading the model quality and preserving privacy.
Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task

In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side. We believe that both the query and passage sides are equally important and should be considered for improved OpenQA performance. In this paper, we propose a contrastive pseudo-labeled data constructed around passages and queries separately. We employ an improved pseudo-relevance feedback (PRF) algorithm with a knowledge-filtering strategy to enrich the semantic information in dense representations. Additionally, we proposed an Auto Text Representation Optimization Model (AOpt) to iteratively update the dense representations. Experimental results demonstrate that our methods effectively optimize dense representations, making them more distinguishable in dense retrieval, thus improving the OpenQA system’s overall performance.
A feature selection for video quality of experience modeling: A systematic literature review

Abstract Quality of Experience (QoE) multidimensional concept is the key for successful delivery of multimedia services. Higher user requirements for new experiences such as augmented reality, virtual reality, and future 6G services set higher requirements for QoE. A more complex QoE space requires the use of data mining methods in order to process the data for better QoE prediction. The increased dimensionality of the QoE space becomes a limiting factor for achieving the desired QoE prediction accuracy. Existing studies considering the QoE multidimensional concept with approaches that overcome the challenge of increased QoE space dimensionality are of great importance for future research. Accordingly, this article aims to review the applications of Feature Selection (FS) methods in video QoE modeling. It provides a comprehensive overview of the existing studies with the categorization and review of applied FS methods with reference to the data collection and data modeling steps. The analysis included 71 studies which provides overview of the FS methods applications in video QoE modeling depending on the input Influence Factor (IF) dimension sizes, type of IFs, QoE prediction methods used and QoE evaluation type. Our review revealed the advantages of using FS methods in video QoE modeling, frequency of application of FS methods with potential of applying more FS methods in a series or a parallel, gives an overview of the achieved dimensionality reduction degree for different methods, and provides insights in opportunities for researchers for applying FS methods on complex multidimensional QoE space. This article is categorized under: Technologies &gt; Data Preprocessing Algorithmic Development &gt; Multimedia
How Instagram as a MALL tool impacts EAP learners’ technical vocabulary learning and perceptions in an electronic context

Mobile-assisted language learning (MALL) technology and the link it creates with language education domains made researchers interested in the plausible effects of MALL tools on English for academic purposes (EAP) students' language development. Likewise, this study investigated the effect of Instagram as a MALL tool on technical vocabulary knowledge of Iranian EAP university students (physical education major) in an electronic course conducted via Adobe Connect as well as their perceptions and attitudes toward Instagram. In a pretest-posttest quasi-experimental research design, 60 EAP university students at the intermediate level were randomly assigned to a control group and an experimental group who practiced technical vocabulary in physical education via different tasks on Instagram. Both groups participated in an online pretest and posttest of technical vocabulary, yet only the experimental group completed a questionnaire about their perceptions and attitudes toward Instagram. The repeated-measures two-way ANOVA on the test scores indicated that the experimental group outperformed the control group in learning technical vocabulary. Further, analysis of the responses to the questionnaire revealed positive perceptions and attitudes toward Instagram as a supplementary MALL tool for learning technical vocabulary. The findings encourage EAP instructors to use Instagram as an accessible MALL application to share their teaching materials with learners.
A Survey on Open-Source Simulation Platforms for Multi-Copter UAV Swarms

Simulation platforms are critical and indispensable tools for application developments of unmanned aerial vehicles (UAVs) because the UAVs are generally costly, have certain requirements for the test environment, and need professional licensed operators. Thus, developers prefer (or have) to test their applications on simulation platforms before implementing them on real machines. In the past decades, a considerable number of simulation platforms for robots have been developed, which brings convenience to developers, but also makes them hard to choose a proper one as they are not always familiar with all the features of platforms. To alleviate this dilemma, this paper provides a survey of open-source simulation platforms and employs the simulation of a multi-copter UAV swarm as an example. The survey covers seven widely used simulators, including Webots, Gazebo, CoppeliaSim, ARGoS, MRDS, MORSE, and USARSim. The paper outlines the requirements for multi-copter UAV swarms and shows how to select an appropriate platform. Additionally, the paper presents a case study of a UAV swarm based on Webots. This research will be beneficial to researchers, developers, educators, and engineers who seek suitable simulation platforms for application development, (not only multi-copter UAV swarms but also other types of robots), which further helps them to save expenses for testing, and speed up development progress.