Reviewerly/exHarmony huggingface dataset:
CAT: Learning to collaborate channel and spatial attention from multi‐information fusion

Abstract Channel and spatial attention mechanisms have proven to provide an evident performance boost of deep convolution neural networks. Most existing methods focus on one or run them parallel (series), neglecting the collaboration between the two attentions. In order to better establish the feature interaction between the two types of attentions, a plug‐and‐play attention module is proposed, which is termed as ‘CAT’—activating the Collaboration between spatial and channel Attentions based on learned Traits. Specifically, traits are represented as trainable coefficients (i.e. colla‐factors) to adaptively combine contributions of different attention modules to fit different image hierarchies and tasks better. Moreover, the global entropy pooling is proposed apart from global average pooling and global maximum pooling (GMP) operators, which is an effective component in suppressing noise signals by measuring the information disorder of feature maps. A three‐way pooling operation is introduced into attention modules and the adaptive mechanism is applied to fuse their outcomes. Extensive experiments on MS COCO, Pascal‐VOC, Cifar‐100, and ImageNet show that our CAT outperforms the existing state‐of‐the‐art attention mechanisms in object detection, instance segmentation, and image classification. The model and code will be released soon.
PMOD-Net: point-cloud-map-based metric scale obstacle detection by using a monocular camera

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A deep ensemble network model for classifying and predicting breast cancer

Abstract Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta‐learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F‐score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.
A density‐based time‐series data analysis methodology for shadow detection in rooftop photovoltaic systems

Abstract The majority of photovoltaic (PV) systems in the Netherlands are small scale, installed on rooftops, where the lack of onsite global tilted irradiance (GTI) measurements and the frequent presence of shadow due to objects in the close vicinity oppose challenge in their monitoring process. In this study, a new algorithmic tool is introduced that creates a reference data‐set through the combination of data‐sets of the unshaded PV systems in the surrounding area. It subsequently compares the created reference data‐set with the one of the PV system of interest, detects any energy loss and clusters the distinctive loss due to shadow, created by the surrounding objects. The new algorithm is applied successfully to a number of different cases of shaded PV systems. Finally, suggestions on the unsupervised use of the algorithm by any monitoring platform are discussed, along with its limitations algorithm and suggestions for further research.
Organizational innovation and the role of strategic planning: the case of the Dubai hotel industry

Purpose In today's dynamic and competitive environment, innovation is a key requirement for hotels to survive, be profitable and sustain their competitive advantages. However, because the study of innovation in the hospitality industry has only developed recently, little is still known about its determining factors. Given this, this study aims to theorize and test with empirical data the effects of two key dimensions of strategic planning (i.e. participation and flexibility) on innovation. Design/methodology/approach Using a fully standardized questionnaire, data were gathered from a sample of 150 hotels in Dubai, by a professional market research firm, through face-to-face interviews. The study hypotheses are tested with partial least squares structural equation models. The study has three limitations: first, its data are cross-sectional and the analyses are post hoc; second, it uses single informants; and third, the sample was primarily from a single setting, i.e. Dubai. Findings The study argues that participation and flexibility in strategic planning are positively related. It also argues that flexibility is positively related to innovation and that flexibility mediates the effects of participation in strategic planning on innovation. Empirical support is found for all the examined relationships. Originality/value The study takes place in a unique setting (i.e. Dubai, United Arab Emirates) where research on organizational innovation has been rather limited to date. The findings have important implications for the stream of literature in this field and for practitioners and sustaining competitive advantages of hotels.
Mapping Canada’s Green Economic Pathways for Battery Minerals: Balancing Prospectivity Modelling With Conservation and Biodiversity Values

Electrification of Canada’s energy and transport sectors is essential to achieve net-zero emissions by 2050 and will require a vast amount of raw materials. A large proportion of these critical raw materials are expected to be sourced from as yet undiscovered mineral deposits, which has the potential to accelerate environmental pressures on natural ecosystems. Herein we overlay new prospectivity model results for a major source of Canada’s battery minerals (i.e., magmatic Ni ± Cu ± Co ± PGE mineral systems) with five ecosystem services (i.e., freshwater resources, carbon, nature-based recreation, species at risk, climate-change refugia) and gaps in the current protected-area network to identify areas of high geological potential with lower ecological risk. New prospectivity models were trained on high-resolution geological and geophysical survey compilations using spatial cross-validation methods. The area under the curve for the receive operating characteristics (ROC) plot and the preferred gradient boosting machines model is 0.972, reducing the search space for more than 90% of deposits in the test set by 89%. Using the inflection point on the ROC plot as a threshold, we demonstrate that 16% of the most prospective model cells partially overlap with the current network of protected and other conserved areas, further reducing the search space for new critical mineral deposits. The vast majority of the remaining high prospectivity cells correspond to ecoregions with less than half of the protected areas required to meet national conservation targets. Poorly protected ecoregions with one or more of the five ecosystem services are interpreted as hotspots with the highest potential for conflicting land-use priorities in the future, including parts of southern Ontario and Québec, western Labrador, and northern Manitoba and Saskatchewan. Managing hotspots with multiple land-use priorities would necessarily involve partnerships with both Indigenous peoples whose traditional lands are affected, and other impacted communities. We suggest that prospectivity models and other machine learning methods can be used as part of natural resources management strategies to balance critical mineral development with conservation and biodiversity values.
Humanization in educational environments; aspects necessary for living in school today

While it is true, quality has always been measured through statistics and percentages, often forgetting the training of the whole person. Now, when we talk about issues such as humanism and education supported by technology, we can fall into extremes: it is common to label technology as the provider of all the evils of today's civilization; or the opposite assumption; to argue that incorporating technologies into education would be the panacea for all the problems it faces and finally, to assume that the subject of the humanities is more typical of actions and attitudes of the past than as a current need, giving vitality to any educational process. However, and fortunately for the educational community, today there are global efforts to make the educational task a human action that minimizes inequities and the abysmal differences that exist in countries like ours. Fernando Reimers says: “Equal educational opportunities must be the priority objective of educational policies, the aim of education must be to contribute to creating just societies. This requires improving the learning environments of the poor, but not only along the paths that have been started over the last decade. This aspect pointed out by Reimers is precisely the central aspect that all educational action must contemplate, the educational policies that are implemented in our country must guarantee, above all, the construction of a just and egalitarian society, strengthening the cultural wealth that we have, with respect to to the diversity of people, their past, their present and especially their future.
The Group Nature of Academic Dishonesty &amp; Diffusion of Responsibility in Online Student Chat Groups

Opportunities for academic dishonesty have changed since the COVID-19 pandemic, as courses moved to virtual formats and online chat groups became an essential means of communication. Prior explanations of academic dishonesty tend to overlook the fact that it is often committed in groups, discounting the role that group based mechanisms play in facilitating this form of deviance. The current study integrates group dynamics into an explanation of academic dishonesty in online student chat groups with a specific consideration of assessing group size and the role of diffusion of responsibility. Using hypothetical vignettes administered to a sample of university students, findings suggest that the involvement of others contributes to an individual’s willingness to participate in academic dishonesty; however, the size of the group is not related to the decision to engage and does not diffuse responsibility for participation. In total, the results affirm the importance of considering the group context but raise additional questions regarding why groups serve as an important inducement to engage in academic dishonesty.
Improving fraud detection via hierarchical attention-based Graph Neural Network

Fraud has seriously influenced the social media ecosystems, and malicious users pursue high profit by disseminating fake information. Graph neural networks (GNN) have shown a promising potential for fraud detection tasks, where fraudulent nodes are identified by aggregating the neighbors that share similar feedbacks and relations. However, crafty fraudsters can trivially get around such detection via seemingly legitimate feedbacks once connected to legitimate users. In this paper, we leverage Relational Density Theory and propose a Hierarchical Attention-based Graph Neural Network (HA-GNN) for fraud detection, which incorporates weighted adjacency matrices across different relations against camouflage. This is motivated by the fact that there are dense connections between fraudsters who collectively participate in fraud activities. Specifically, we design a relation attention module to reflect the tie strength between two nodes, while a neighborhood attention module to capture the long-range structural affinity associated with the graph. We generate node embeddings by aggregating information from local/long-range structures and original node features. Experiments on three real-world datasets demonstrate that our approach achieves 3.21 – 9.97% RUC improvement compared with the state-of-the-arts.
Property-Based Quality Measures in Ontology Modeling

The development of an appropriate ontology model is usually a hard task. One of the main issues is that ontology developers usually concentrate on classes and neglect the role of properties. This paper analyzes the role of an appropriate property set in providing multi-purpose ontology models with a high level of re-usability in different areas. In this paper, novel quality metrics related to property components are introduced and a conversion method is presented to map the base ontology into models for software development. The benefits of the proposed quality metrics and the usability of the proposed conversion methods are demonstrated by examples from the field of knowledge modeling.
Forecasting the Cumulative COVID-19 Cases in Indonesia Using Flower Pollination Algorithm

Coronavirus disease 2019 (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) on 12 March 2020. Indonesia is reported to have the highest number of cases in Southeast Asia. Accurate prediction of the number of COVID-19 cases in the upcoming few days is required as one of the considerations in making decisions to provide appropriate recommendations in the process of mitigating global pandemic infectious diseases. In this research, a metaheuristics optimization algorithm, the flower pollination algorithm, is used to forecast the cumulative confirmed COVID-19 cases in Indonesia. The flower pollination algorithm is a robust and adaptive method to perform optimization for curve fitting of COVID-19 cases. The performance of the flower pollination algorithm was evaluated and compared with a machine learning method which is popular for forecasting, the recurrent neural network. A comprehensive experiment was carried out to determine the optimal hyperparameters for the flower pollination algorithm and recurrent neural network. There were 24 and 72 combinations of hyperparameters for the flower pollination algorithm and recurrent neural network, respectively. The best hyperparameters were used to develop the COVID-19 forecasting model. Experimental results showed that the flower pollination algorithm performed better than the recurrent neural network in long-term (two weeks) and short-term (one week) forecasting of COVID-19 cases. The mean absolute percentage error (MAPE) for the flower pollination algorithm model (0.38%) was much lower than that of the recurrent neural network model (5.31%) in the last iteration for long-term forecasting. Meanwhile, the MAPE for the flower pollination algorithm model (0.74%) is also lower than the recurrent neural network model (4.8%) in the last iteration for short-term forecasting of the cumulative COVID-19 cases in Indonesia. This research provides state-of-the-art results to help the process of mitigating the global pandemic of COVID-19 in Indonesia.
Multiple Graph Adaptive Regularized Semi-Supervised Nonnegative Matrix Factorization with Sparse Constraint for Data Representation

Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensional data representation. However, the existing methods do not consider the sparse constraint, which can enhance the local learning ability and improve the performance in practical applications. To overcome this limitation, a novel NMF-based data representation method, namely, the multiple graph adaptive regularized semi-supervised nonnegative matrix factorization with sparse constraint (MSNMFSC) is developed in this paper for obtaining the sparse and discriminative data representation and increasing the quality of decomposition of NMF. Particularly, based on the standard NMF, the proposed MSNMFSC method combines the multiple graph adaptive regularization, the limited supervised information and the sparse constraint together to learn the more discriminative parts-based data representation. Moreover, the convergence analysis of MSNMFSC is studied. Experiments are conducted on several practical image datasets in clustering tasks, and the clustering results have shown that MSNMFSC achieves better performance than several most related NMF-based methods.
Simultaneous Schlieren-Shadowgraph Visualization and Temperature Measurement Fields of Fluid Flow Using One Color CCD Camera

Visualizing two variables in the fluid flow requires a complex optical system to fulfill such a purpose. Furthermore, in these applications, measuring the temperature fields of the fluid is sometimes necessary. In this work, we use a color digital camera to record in one snapshot shadowgraph and two direction sensitivity Schlieren images. Furthermore, the Schlieren images obtained are used to measure temperature fields. The optical system, as main components, employs an rgb Light Emitting Diode (LED), two knife edges represented by shortpass and longpass ultra-thin filters, and a color digital camera. The results of this study show that shadowgraph images are contaminated with the crosstalk effect, and correction against it was applied. On the other hand, the reached resolution of the optical system to measure temperature values is approximately one degree.
Outlier-Robust Surrogate Modelling of Ion-Solid Interaction Simulations

Data for complex plasma&ndash;wall interactions require long-running and expensive computer simulations of codes like EIRENE or SOLPS. Furthermore, the number of input parameters is large, which results in a low coverage of the (physical) parameter space. Unpredictable occasions of outliers create a need to conduct the exploration of this multi-dimensional space using robust analysis tools. We restate the Gaussian-process (GP) method as a Bayesian adaptive exploration method for establishing surrogate surfaces in the variables of interest. On this basis, we complete the analysis by the Student-t process (TP) method in order to improve the robustness of the result with respect to outliers. The most obvious difference between both methods shows up in the marginal likelihood for the hyperparameters of the covariance function where the TP method features a broader marginal probability distribution in the presence of outliers.
Do Stock Market Volatility and Cybercrime Affect Cryptocurrency Returns? Evidence from South African Economy

The study investigates the effects of stock market volatility and cybercrime on cryptocurrency returns in the South African economy. Daily time series data on four different types of cryptocurrencies (Bitcoin, Ethereum, Tether, and BMB) were employed. The data covers the period from 1 January 2019–31 December 2021. The study employed the dynamic conditional correlation (DCC GARCH) and Bayesian liner regression model to investigate time-varying correlations among the variables. Empirical findings suggest that stock market volatility has a positive impact on the returns of BNB, Bitcoin, and Ethereum. However, it has a negative impact on Tether. Expectedly, cybercrime poses negative impacts on the returns of BNB, Bitcoin, and Ethereum but could be said to have no impact on the returns of Tether. The study concludes that ongoing efforts to reduce cybercrime activities need to be strengthened to further the use of digital currencies.
Quantifying the entanglement of quantum states under the geometric method

Abstract Quantifying entanglement is an important issue in quantum information theory. Here we consider the entanglement measures through the trace norm in terms of two methods, the modified measure and the extended measure for bipartite states. We present the analytical formula for the pure states in terms of the modified measure and the mixed states of two-qubit systems for the extended measure. We also generalize the modified measure from bipartite states to tripartite states.
A Blockchain-based Traceability System to Achieve the Quality Objectives in the Production Process of A Manufacturing Industry

ISO 9001 is a standard for an organization that focuses on quality management. In ISO 9001, the organization must implement seven main clauses in achieving the certification or audit if the company has obtained ISO 9001 certification. One of the clauses is clause 6.2, which is related to quality objectives and focuses on process. The organization can achieve this clause if all SOPs in the organization are appropriately implemented. Therefore, if the SOP is well implemented, the process inside also runs well, and the quality objectives will be in line with the expectations. Manufacturing industry will be the case of the organization for this study. This study proposed a tracing system for the procedure in the SOP that used to achieve clause 6.2, quality objectives in ISO 9001. The database has an important role in system development because the system will store all information in it. Databases with conventional method have various weaknesses in data handling, such as centralized data storage and lack of security. Centralized data will make it difficult for each staff in the organization to access data if the main server is down. This study will focus on blockchain applied to traceability system as database technology because it can minimize the shortcomings of the database with conventional method. The results show that the data traceability system can trace procedures well and the data running well according to its function in storing data with blockchain. Based on these results, the traceability system in this study can trace procedures in achieving quality objectives in ISO 9001.
Blockchain-based Record Keeping System for Building Permit Issuance

The used of centralized system in government are common nowadays. However, by using the centralized database, only one authority that has the system can do everything to the the system. This aspect of centralized system can lead to collusion with the government officials or middleman in the process of issuing building permit. In this paper, we propose a blockchain based system to record all the transactions in the process of issuing a building permit. The purpose is to increase the transparency, data integrity and make it easier to track the transactions with the feature in blockchain. We use a case study of the building permit issuance process in one stop-service local government office in Jakarta. After knowing the process, we determine the participant, asset, transaction and access control of participant in the blockchain system.