Reviewerly/exHarmony huggingface dataset:
Effect of Composition Variation on The Crystal Size and Band Gap of Thin Film Nano Photocatalyst Fe<sub>2</sub>O<sub>3</sub>/CuO/MnO<sub>2</sub>

Abstract Commercial activities in the field of laundry services, cause problems in the water environment, hurt air quality, and were dangerous for life. Efforts that are now the leading option for most people are using photocatalyst technology on semiconductor materials. MnO 2 semiconductor is used as an auxiliary component for the development of CuO-Fe 2 O 3 photocatalyst to increase the level of degradation of organic contaminants in the environment. This study aimed to determine the crystal size and band gap of Fe 2 O 3 /CuO/MnO 2 nanocomposite. This type of research is a laboratory experiment, and the characterization tools used include X-Ray Diffraction and Uv-Vis Spectrophotometer. Synthesis of Fe 2 O 3 /CuO/MnO 2 nanocomposite with various compositions of Fe 2 O 3 /CuO/MnO 2 respectively, 0.9:2.9:1, 1:3:0.8, 1.1:3.1:0.6, 1, 2:3.2:0.4, and 1.3:3.3:0.2 with the sol-gel method has been successful. Thin layer growth was carried out by the spin coating method with a sintering temperature of 400° C. This research results show that composition variation affects the crystal size and band gap of Fe 2 O 3 /CuO/MnO 2 nanocomposite. The crystal sizes of the nanocomposites obtained were 25.18 nm, 24.48 nm, 25.55 nm, 23.57 nm, and 27.52 nm. The band gap energy values of the nanocomposites were 1.55 eV, 1.44 eV, 1.44 eV, 1.45 eV, and 1.46 eV.
Development of semi-automatic multilingual terminological resources

The text suggests a methodology for semi-automatic creation of terminological resources for the Bulgarian language for computer-aided translation systems. For this purpose, the technical characteristics of computer-assisted translation systems and their components (translation memory, terminological base, machine translation) are described and analyzed. The results of a survey aimed at translators in Bulgaria and their opinion regarding this type of technology are described. The proposed methodology does not claim to be exhaustive, but takes into account the processes that translators follow in their work. Through the proposed methodology, an English-Bulgarian terminological base in the field of computer terminology was also created.
The Effect of Milling Time on Band Gap Value in Thin Layers of Nano Photocatalyst MnO<sub>2</sub>/CuO/Fe<sub>2</sub>O<sub>3</sub>

Abstract Waste from laundry washing discharged into waterways without pre-treatment can cause problems that hurt water quality because they contain oxidizing agents such as bleach for clothes. This study aims to determine the effect of milling time variations on the value of the band gap in thin film MnO 2 /CuO/Fe 2 O 3 and their photocatalytic activity capabilities. This study’s milling time variations were 5 hours, 10 hours, and 15 hours. They were grinding MnO 2 /CuO/Fe 2 O 3 materials using a High Energy Milling (HEM-E3D) tool to obtain the nano size of these particles. Synthesis of MnO 2 /CuO/Fe 2 O 3 nanocomposites using the sol-gel method and the spin coating method to manufacture thin films using a glass substrate to be sampled in testing photocatalyst activity. Characterization testing uses a spectroscopy (Uv-Vis) tool to obtain a band gap value. The band gap values were 5 hours milling at 1.87 eV, 10 hours milling at 1.72 eV, and 15 hours milling at 1.64 eV. Here it shows that the longer the milling time, the smaller the band gap energy obtained. Shrinking the energy gap will experience a good photocatalyst process. The results of this study show the effect of the catalyst in degrading pollutants with the best degradability of 50.67%.
Time-optimal attitude planning for spacecraft with movable parts using second order cone programming

The objective of this paper is to plan a time-optimal attitude trajectory in a relatively short time for spacecraft with movable parts under complex pointing constraints. The constrained attitude planning problem is first established, then the constraints on attitude maneuvers are described. The time-optimal attitude planning for rigid spacecraft body is decoupled into geometric level and dynamic level through the addition of the path scalar. To obtain a given safe path in quaternion space, RRT*-Smart path planning and spherical and quadrangle (SQUAD) interpolation are carried out at the geometric level. The time-optimal parameterization for a given path is reformulated as a second order cone programming (SOCP) problem at the dynamic level through a nonlinear change in variables and the addition of various convexity-preserving extensions. In order to overcome the issue of high system dimension caused by the movable parts, a decoupled attitude planning method is designed to generate the quaternion trajectory of the spacecraft body and the rotation angle trajectory of the movable parts respectively. In comparison to GPOPS, the simulation results demonstrate that the proposed method can plan a suboptimal solution in milliseconds. Furthermore, the performance indicator for energy consumption is better.
Colorized iVAT Images for Labeled Data

A 2-dimensional numerical data set X = {x1,…,xn} with associated category labels {l1,…,ln} can be accurately represented in a 2-dimensional scatterplot where color is used to represent each datum's label.The colorized scatterplot indicates the presence or absence of spatial clusters in X and any special distribution of labels among those clusters.The same approach can be used for 3-dimensional data albeit with some additional difficulty, but it cannot be used for data sets of dimensions 4 or greater.For higher dimensional data, the improved Visual Assessment of cluster Tendency (iVAT) image can be used to indicate the presence or absence of cluster structure.In this paper we propose several new types of colorized iVAT images, which like the 2-dimensional colorized scatterplot, can be used to represent both spatial cluster structure and the distribution of labels among clusters.
Face recognition using ensemble statistical local descriptors

The use of data fusion can be of a virtuous help in boosting classification performance. Feature fusion is a data fusion technique that is being considered in this study. The effect of fusing different feature descriptors extracted by using histogram-based local feature extraction algorithms on the performance of the face recognition problem is investigated. Feature fusion/concatenation of more than one generated feature descriptor is applied. The impact of fused two and three feature descriptors on the system performance is evaluated when the training set is limited to only one-shot per person. Extensive experiments are carried out using two well-known face databases. Comparisons are conducted among different algorithms for extraction of the local statistical feature descriptors of the face images. The obtained results show that feature fusion of the descriptors can significantly improve the performance with certain feature descriptors.
Effects of visual cue state and type on the mobile user interface of shopping apps with 360° panoramas

With the development of modern technology, various shopping apps have emerged, and at the same time, the user interface of shopping apps is constantly being optimized to enhance users’ online shopping experience; in shopping apps, an excellent visual cue design can effectively guide users to quickly complete the process of their visual cognitive processing. The purpose of this study was to explore the usability of visual cue and presentation type in the operation of shopping apps in 360° images and to propose design suggestions for future improvements. A 2 × 3 between-subject design was planned to help explore whether different visual cue states (i.e., dynamic and static cues) and different visual cue presentation types (i.e., dot cue, gesture cue, and cartoon cue) may affect users' task performance and their subjective evaluations. The experiment adopted the purposive sampling method. A total of 60 participants were recruited to take part in the experiment. The experimental data were collected pertinent to task completion time, the system usability scale (SUS), subjective evaluation questionnaires created using a 7-point Likert scale, and semi-structured interviews. The generated results revealed that: (1) Different visual cue states may affect users' performance. (2) Different visual cue presentation types significantly affect users' task performance. Static cues improved users' task performance more than dynamic cues in the visual cue presentation type of dot or cartoon. However, the result was the opposite in the gesture condition. (3) Concerning subjective evaluations of the degree of reasonableness, acceptance, and willingness to use in the condition of the visual cue presentation, the dot or gesture cues are significantly better than the cartoon cue. (4) Among the different visual cue presentation types, participants had a greater preference for the gesture cue. (5) In terms of the degree of reasonableness, acceptance, preference and willingness to use, participants preferred the dynamic cue to the static cue in the condition of the visual cue presentation type of the dot or gesture. However, the opposite result was obtained for the cartoon visual cue presentation type. The findings generated from the research can be a good reference for the development of visual cue design for mobile shopping apps.
Data-centric approach for predicting critical metals distribution: Heavy rare earth elements in cretaceous Mediterranean-type karst bauxite deposits, southern Italy

In the last few years, many efforts have been devoted to the factors controlling the distribution of CMs in karst bauxites, residual deposits hosted in carbonate rocks. Most of these efforts regard Mediterranean-type karst bauxite deposits of Cretaceous age occurring in southern Italy. Further, there is an increasing interest in assessing the usefulness of machine learning applications devoted to geochemically based datasets. With this in mind, we explored a data-centric machine learning arrangement aiming to find the proper input, limited to Al2O3, Fe2O3, TiO2, and SiO2, the most abundant major oxides occurring in these ores, for predicting the HREE distribution in southern Italy karst bauxite deposits. Among the machine learning techniques used, Artificial Neural Network (ANN), Support Vector Machine (SVR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) are those that effectively predict HREE concentrations. A predictive model based on just Al2O3, Fe2O3, and SiO2, is one conducing at the worst performance impact suggesting that TiO2 is a relevant input variable in order to predict HREE concentrations in considered karst bauxite deposits. The XGBoost model was found to deliver the highest accuracy in predicting HREE for the validation data records (R2 ~ 0.830, RMSE~7.299, MAE ~ 5.091). Moreover, Fe2O3 is the highest correlated input variable with the output variable and is a significant predictor in our model suggesting iron oxyhydroxides play a relevant role in distributing HREE, likely through a scavenging mechanism at the expense of soil solutions. A further step of our research will involve comprehensive cross-validation studies across multiple areas where Mediterranean-type karst bauxite deposits occur, thus providing a thorough assessment of the model's performance. By addressing these tasks and exploring avenues for improvement, the data-centric approach can advance its potential as a cheap and fast technique to perform a preliminary economic evaluation of potentially HREE abundance, as well as other CMs, in karst bauxite ores benefiting applications reliant on these critical resources.
Hyperspectral placenta dataset: Hyperspectral image acquisition, annotations, and processing of biological tissues in microsurgical training

The dataset consists of 101 hyperspectral images of four human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset is instrumental for advancing machine-learning algorithms and automated classification of anatomical structures, particularly the classification of superficial and deep vessels and transparent tissue layers.
Temporal Graph Processing in Modern Memory Hierarchies

Updates in graph DBMS lead to structural changes in the graph over time with different intermediate states. These intermediate states in a DBMS and the time when the actions to the actual data take place can be processed using temporal DBMSs. Most DBMSs built their temporal features based on their non-temporal processing and storage without considering the memory hierarchy of the underlying system. This leads to slower temporal processing and poor storage utilization. In this paper, we propose a storage and processing strategy for (bi-) temporal graphs using temporal materialized views (TMV) while exploiting the memory hierarchy of a modern system. Further, we show a solution to the query containment problem for certain types of temporal graph queries. Finally, we evaluate the overhead and performance of the presented approach. The results show that using TMV reduces the runtime of temporal graph queries while using less memory.
A Comparative Study on Improvement of MapReduce Performance with Skewed Data

Skewness in input data can degrade the performance of MapReduce. There are many approaches to address this problem. This paper compares and contrasts these approaches to observe their performance on syntactic and real-world data. The results show that all of the algorithms studied in this paper can improve the execution time of MapReduce with skewed data. However, there are some limitations to improvement, especially when data is not heavily skewed; the overhead of the algorithms might overcome their benefits.
Selecting a Reduced Set of Features for Supporting the Stance Detection Task

Stance detection is one of the many NLP tasks that is gaining importance with the spreading of information on the internet and social media. Even though there are several studies that conduct stance classification experiments using machine learning models, those that utilize a set of variables including emotional aspects are not that many. In this work, starting from a set of tweets, we describe the procedure of feature extraction and, using two machine learning models (random forest and support vector) we evaluate our results. The set of considered features includes variables measuring emotional, sentiment-based, readability, morality and engagement-related aspects. We examine which features could be considered useful by exploiting the Mann-Whitney test and the recursive feature elimination procedure. Doing this we provide a specific analysis related to the emotional and sentiment-based aspects. We find out also that features taking into consideration both target and message seem to be particularly advantageous. Also regressors measuring lexical aspects like readability and complexity of the text seems to give a good contribution. The best results are obtained using a selected and smaller set of variables that obtains the 50% of F1 score. Although, in general, this is not very high, it is comparable with those obtained by other models taking into consideration mainly lexical aspects of the same data.
Photovoltaic Power 24-hour Predictions Using PDE Component Models Based on the L-Transform Conversion Produced in Node-by-Node Evolved Binomial Networks

Local predictions of day-ahead photovoltaic (PV) power induction are required due to the unavailability or delay in the generation of solar radiation forecasts based on numerical weather prediction (NWP). Ground-level interactions usually include combined unexpectable changes in processes and the parameters (cloudiness level, visibility index, etc.) in local atmosphere. Solar uncertainty is a result of cloud progress and dynamical variability related to the progress of chaotical structures. These complex phenomena can be defined by differential equations and modelled by data statistics. Artificial intelligence (AI) can develop independent statistical models or transform/process data from NWP systems to compute PV supply considering the plant configuration/situation and terrain characteristics. Mesoscale NWP models are usually produced in 6 h to simulate each meteo-variable progress, although the prognoses may be essentially delayed (due to computing costs) and insufficient operational practicability. Autonomous AI models (based on pattern similarity) are more successful if they predict fewer stable conditions adequately represented by historical weather and photovoltaic training samples. The Differential Binomial Network (DBN) employs a new designed neurocomputing approach called differential learning. It allows modelling and complex representation of uncertain chaotic patterns and anomalies in local weather. DBN searches the most valuable 2-input couples in combinatorial nodes to split the general kth order Partial Differential Equation (PDE) of n-parameters into simplified low order PDEs. The determined 2-input subnode PDEs are transformed using operator calculus (OC) to represent the unknown searched output by the Laplace images of node functions. The inverse transformational operation is required to rebuild the original sum sequences. DBN produces and optimises the most applicable self-standing node PDE modules to progressively expand the overall compound summary models. AI self-optimising models are evolved and adapted to spatial record patterns/samples in an algorithmically processing frame with the daily estimated training periods. After final testing based on complementary constraints, they process the last 24 h of data in a sequence to compute the Clear Sky Index (CSI) output in the related prediction time.
Metaverse and Modification Needs of Human Resources Management Practices and Policies: An Overview

This study aims to develop a conceptual framework that explains the modification needs in Human Resources Management Practices and Policies when an organization uses the metaverse as an alternative to virtual offices. Recent discussions in the existing literature broadly conclude that hybrid work offers high employee productivity, well-being, and mental health. However, not all types of work can be done through the metaverse, while some work may be done using the metaverse. Other discussions also offer the metaverse as a place for recreation rather than work. Looking at reviews of current literature and interviews with three senior leaders, we provide detailed insights and strategies for maximizing the metaverse as a virtual office through modifications to human resources management (HRM) policies and practices that could be considered for implementation. Our finding shows that organizations need digital leadership and work ethics to benefit from using the metaverse. In addition, the list of modifications in HRM includes (a) the Gamification of HRM practices and policies, and (b) levering up readiness to change. The specific outcome to be targeted in this study is understanding the future opportunities and challenges of employee management in the workplace.
A Fuzzy-Based System for Estimation of Inland Flooding Risk

In Japan, inland flooding caused by heavy rainfall and overflowing rivers is a serious problem. The inland flooding can cause many damages to people’s lives and property. Also, the evacuation is very difficult. Therefore, it is necessary to identify areas where it may occur and take evacuation action. In order to estimate the inland flooding are needed different information on topography, geology and urban drainage capacity. However, the information may be uncertain and incomplete. Fuzzy theory and Fuzzy Logic (FL) can deal with incomplete information and ambiguity and can be a promising approach for estimation of inland flooding. In this paper, we propose a Fuzzy-based system for estimation of inland flooding risk. The evaluation results show that the proposed system can reduce the inland flooding damage by estimation of inland flooding risk.
City Scale Seismic-Damage Prediction of Buildings Using Quantum Neural Network

In many countries, earthquakes have led to severe economic losses and a large number of casualties. The quick response to earthquakes is critical for saving lives and limiting economic losses, which necessitates precise and real-time estimation of seismic damage in earthquake-affected areas. Such emergency tasks demand fast, accurate, and efficient seismic-damage prediction algorithms, as delayed or inaccurate seismic-damage evaluations can lead to serious consequences. Numerous research works have been carried out to predict the damaged condition of buildings after the earthquake using machine learning techniques to overcome the problems of manual inspection, which is a labor-intensive, time-consuming, and error-prone process. In this study, a quantum neural network (QNN) algorithm is developed and examined its capability to classify the post-earthquake building damage data into ATC-20 damage tag categories (green, yellow, and red). The buildings damaged dataset recorded after the 2015 Nepal earthquake is used to train the model, and its performance is evaluated based on the testing dataset, neither of which is included in the training dataset.
An Improved Data Integrity Verification Scheme for Cloud Storage

In recent years, many cloud data security auditing protocols have been proposed to protect the integrity of cloud data. In 2017, Wang et al. proposed an FSBA scheme on batch auditing. We found that this integrity verification algorithm does not enable public verification. In this paper, we improved this scheme for supporting public verification.
A Real-World Clinical Data Mining of Post COVID-19 Patients

Analysis of real-world clinical data, which includes multiple heterogenous categories of attributes, requires a well-designed data-mining process to obtain meaningful information. We explored the application of several data-mining methods on a real-world dataset of patients diagnosed with COVID-19, with emphasis on cohort selection with maximum data availability, and feature selection based on their correlation with clinical data. Two data mining platforms (Orange data-mining platform coupled with LRNet method, and Waikato environment for knowledge analysis) were used for finding important attributes associated with post COVID-19 symptoms from the multiple modalities of this cohort and evaluating the ability of these attributes to separate patients into clusters. We introduced a dynamic method of inclusion and exclusion, as well as outlier selection, which maximized the knowledge extracted from this real-world dataset. We also demonstrated that a comprehensive first-view of this dataset was only possible by applying multiple methods for dimensionality reduction and feature selection.
User-Oriented Solutions in Cybersecurity and Cloud-to-Things Applications

This paper will present new protocols to guarantee data security in distributed systems and Cloud-to-Things infrastructure. These protocols will be based on the use of unique personal characteristics, specific to the user, which will enhance the security of systems and prevent external cyberattacks. The use of user-oriented solutions is an extremely universal solution applicable in cyberspace both in the processes of data protection as well as their transmission, storage and processing.