Reviewerly/exHarmony huggingface dataset:
PENGARUH SOCIAL MEDIA TERHADAP PERILAKU PENCARIAN INFORMASI KESEHATAN PADA REMAJA

Technological developments have led to an increase in social media users. Social media is a technology that can search and share information in the form of text, images, audio, video, and location. Information seeking behavior starts from a person's need to obtain information. The type of information that is sought after by the public regarding health. This study aims to determine the effect of social media on health information seeking behavior in adolescents. The study used a correlational analytic study, with a cross sectional research design. The research was conducted at SMK X Semarang City. The population is youth of SMK X Semarang City as many as 227 people. The sampling technique was by means of cluster random sampling as many as 43 people. Collecting data using a questionnaire. Test the validity and reliability test using Cronbach Alpha. Bivariate analysis using Spearman's rho. The results showed that the sig value of 0.000 &lt;0.05 means that there is a significant relationship. The correlation coefficient value of 0.554 means a strong level of influence. So that social media affects the behavior of seeking health information in adolescents, and it often occurs in adolescents as many as 18 people (41.9%).
Using a Computational Cognitive Model to Understand Phishing Classification Decisions

Numerous studies of human user behaviours in cybersecurity tasks have used traditional research methods, such as self-reported surveys or empirical experiments, to identify relationships between various factors of interest and user security performance.This work takes a different approach, applying computational cognitive modelling to research the decision-making of cybersecurity users.The model described here relies on cognitive memory chunk activation to analytically simulate the decision-making process of a user classifying legitimate and phishing emails.Suspicious-seeming cues in each email are processed by examining similar, past classifications in long-term memory.We manipulate five parameters (Suspicion Threshold; Maximum Cues Processed; Weight of Similarity; Flawed Perception Level; Legitimate-to-Phishing Email Ratio in long-term memory) to examine their effects on accuracy, email processing time and decision confidence.Furthermore, we have conducted an empirical, unattended study of US participants performing the same task.Analyses on the empirical study data and simulation output, especially clustering analysis, show that these two research approaches complement each other for more insightful understanding of this phishing detection task.The analyses also demonstrate several limitations of this computational model that cannot easily capture certain user types and phishing detection strategies, calling for a more dynamic and sophisticated model construction.
Drug Review System Using Machine Learning by Comparing Linear Support Vector Machine with Naïve Bayes Classifier to Measure Accuracy

In comparison to Naive Bayes (NB) Classifier, innovative Linear Support Vector Machine (LSVM) is used to forecast enhanced drug review systems for boosting Accuracy. Materials and Methods: In this research, two groups are compared, novel Linear Support Vector Machine (N = 10),Naive Bayes (N = 10) was generated according to total sample size using the g power software by taking into account alpha of 0.05, enrollment ratio of 0.1, 95% confidence interval, and power of 80%. Result: The accuracy rate of Linear Support Vector Machine (SVM) is 97.75 % whereas results of (NB) accuracy rate are 89.34%. There is a significant difference in accuracy rate (P = 0.045) with pre-test power of 80 % in SPSS Statistical analysis. Conclusion: The Linear Support Vector Machine (SVM) performs significantly better in terms of accuracy for predicting the enhanced drug review system when compared to the (NB) Classifier.
Presentation of a Novel Approach to Combine Multisensor Data Using the Differential Entropy

Methods using data from different sensors are increasingly used, with the rise of machine learning especially. This article presents a technique based on differential entropy, a concept in information theory that measures the randomness of a distribution. This method can take input data from different sensors. For example, by combining SAR and optical images, one can hope to combine the quality of optical images with the availability of SAR images, capable of seeing at night and through clouds. Examples of this technique using both optical and SAR images are presented in this article, showing the advantages and drawbacks of this approach. The different points to be studied will then be discussed.
A Comprehensive Study of Solar Energy Components by Using Various Models on Horizontal and Inclined Surfaces for Different Climate Zones

The main objective of this research is to analyze the monthly average daily of global (H), beams (B) and diffuses (D) solar irradiance on a horizontal surface at four selected sites (El-Kharga, Hurghada in Egypt and Dammam, Hail in Saudi Arabia) during the period time from 1980 to 2020.The empirical models between (H/H o ) and meteorological parameters along with the values of (MBE), (RMSE), MPE, R 2 and the t-Test statics are discussed.The results in this study indicate good agreement between observed and calculated values of total solar energy and diffuse solar fraction.The results for south facing surfaces of the (RMSE) for different slope at different models in the present research are discussions.Nine different models between isotropic and anisotropic used to estimate the diffuse solar radiation on a tilted surface at selected sites in this study.The absolute relative values of RMSE for the southfacing surface ranges from 7 to 41.3 at El-Kharga and Hurghada sites, Egypt in the present study for Koronakis and Stevenand Unsworth (SU) models respectively.The values of (RMSE), for the south-facing surface ranges from 9.3 to 39.7 at Dammam and Hail sites, Saudi Arabia in the present research for Koronakis and Klucher models respectively.For west-facing surface the values of RMSE range from 11.2 to 47.3 for Badescu and Koronakis models at El-Kharga and Hurghada sites, Egypt respectively, while values of RMSE range from 6.5 to 38.5 for Klucher and Reindl et al. models at Dammam and Hail sites, Saudi Arabia.The models Koronakis, Klucher and Stevenand Unsworth (SU) models are given the most accurate estimate for the south-facing surface, and Badescu, Koronakis, Klucher and Reindl et al. models are good performs better estimated for the west-facing surface.
Forecasting the Bitcoin Closing Price in Times of the Pandemic Using Box-Jenkins Methodology

The epidemic has impacted every financial product, including the stock market, commodities market, and even cryptocurrency. The epidemic of COVID-19 has struck devastation on the economy. The recent decline in the bitcoin market should be regarded as a blip on the radar. Bitcoin is a decentralized digital currency that should be considered an asset class with a long-term growth forecast. In 2020, the most popular cryptocurrency, Bitcoin, has become a precious asset. When we look at Bitcoin’s previous pricing, we can see that it has increased by more than 500% in just 6 months. In this scenario, providing recent estimates for the closing price of Bitcoin shortly using the Box-Jenkins model for the period 2021–2022 becomes even more relevant. As a consequence of the five-step iterative approach, the empirical data indicated that the price of bitcoin is projected to rise shortly in both static and dynamic forecasts.
GCN2CAPS: Graph Convolutional Network to Capsule Network For Wide-Field Robust Graph Learning

Graph Neural Networks (GNNs) have achieved remarkable performance in extracting structure-aware node representations for graph signal data. However, existing GNNs overly emphasize the consistency of neighbor nodes in the limited receptive field and severely overlook the wide-field information. In this paper, we propose a novel GCN2Caps that transfers multi-field node representations from graph convolutional network (GCN) to capsule network for wide-field graph learning. Specifically, multi-field GCN is employed to extend the receptive field and extract multi-field features. To perform multi-field interactions, GCN2Caps then explores the inherent relationships between multi-field features and generates the wide-field features through the capsule mechanism. On the basis of the wide-field features, a wide-field min-cut graph constraint is introduced to the loss function to execute complementary constraints on the original graph structure. Extensive experiments on three real-world datasets demonstrate that GCN2Caps significantly outperforms stateof-the-art baselines on semi-supervised node classification and the robustness of GCN2Caps is further validated.
Automatic Model Evaluation using Feature Importance Patterns on Unlabeled Data

Recent studies have shown that the estimated efficacy of a classification model in a specific training set can be very different from the same model efficacy after deployment, or when the model is evaluated in a dataset with a different distribution from the training set. This situation is known as distribution shift or dataset shift, and an emerging strategy for this problem is to estimate the efficacy of the classification model in unlabeled data with unknown distribution (i.e., aka AutoEval approaches). Most of the recent works study how distribution shift affects Deep Learning Models applied to Computer Vision tasks (i.e., unstructured data). However, distribution shift also can affect the efficacy of a model on tabular/structured data. In this work, we proposed and analyzed AutoEval approaches on tabular data. We proposed an AutoEval method based on the use of feature importance, which are typically used as model explanations, to detect patterns of correct and incorrect classifications that can be used to estimate model efficacy. We conducted experiments using six real-world datasets related to three different subjects. Our results indicated that the proposed method outperforms all baselines, with a reduction up to 89% in the gap between estimated and real efficacy, in comparison with standard 10-fold cross-validation (CV) error estimation. Besides, we evaluated our AutoEval approaches as indicators to model selection in the feature selection task. In this task, compared to CV, our proposed method achieved gains up to 35%.
Measuring Content Preservation in Textual Style Transfer

Style transfer in text, changing text that is written in a particular style such as the works of Shakespeare to be written in another style, currently relies on taking the cosine similarity of the sentence embeddings of the original and transferred sentence to determine if the content of the sentence, its meaning, hasn't changed. This assumes however that such sentence embeddings are style invariant, which can result in inaccurate measurements of content preservation. To investigate this we compared the average similarity of multiple styles of text from the Corpus of Diverse Styles using a variety of sentence embedding methods and find that those embeddings which are created from aggregated word embeddings are style invariant, but those created by sentence embeddings are not.
Monitoring Charging Battery Consumption Level for Electric Power Wheelchair Based IoT

Nowadays, powered wheelchairs are also one of the most common modes of transportation vehicle for people with physical disabilities. It is vital to have frequent monitoring battery level given that the movement of power wheelchair requires sufficient and long-lasting battery level. Therefore, this study aims to apply Internet of Things (IoT) for data monitoring and controlling battery conditions of electric powered wheelchairs, which is divided into monitoring the wheelchair position, state of charge, voltage, current, remaining charge capacity and battery charge level. In addition, this study can be divided into two parts: to develop a system that can charge the battery automatically, and can also monitor the output voltage and percentage state of charge battery AA, 7.2 V, Ni-Cd, 2000 mAH by using Blynk application. The charging process will terminate automatically when the battery is fully charged. This procedure is critical because once the battery is fully charged, the charging current must be dissipated, resulting in heat damaging the battery. Hence, the condition of the battery will be measured by using Blynk application while the user is informed through mobile phone via Blynk notify when the battery is fully charged (100%) and output voltage is achieved 9.0 V. The user can determine the status of the wheelchair at any time, the user’s family could also comprehend the operation status of an elderly user or one with limited mobility, and the user is informed in advance when the wheelchair needs to be adjusted.
Unified Recurrence Modeling for Video Action Anticipation

Forecasting future events based on evidence of current conditions is an innate skill of human beings, and key for predicting the outcome of any decision making. In artificial vision for example, we would like to predict the next human action before it happens, without observing the future video frames associated to it. Computer vision models for action anticipation are expected to collect the subtle evidence in the preamble of the target actions. In prior studies recurrence modeling often leads to better performance, the strong temporal inference is assumed to be a key element for reasonable prediction. To this end, we propose a unified recurrence modeling for video action anticipation via message passing framework. The information flow in space-time can be described by the interaction between vertices and edges, and the changes of vertices for each incoming frame reflects the underlying dynamics. Our model leverages self-attention as the building blocks for each of the message passing functions. In addition, we introduce different edge learning strategies that can be end-to-end optimized to gain better flexibility for the connectivity between vertices. Our experimental results demonstrate that our proposed method outperforms previous works on the large-scale EPIC-Kitchen dataset.
MGGD: R Package for Mixture Generalized Gamma Distribution

This paper is interested in creating an R language package for a mixture generalized gamma distribution as Suksaengrakcharoen and Bodhisuwan [1] introduced. In order to be easy for practical use and be highly beneficial to the applications of engineering and medical science, the R-language package consists of four functions: (1) a probability density function (2) a distribution function (3) a random generation, and (4) a parameter estimation function.
Optical Remote Sensing Image Deblurring Based on Deep Unfolding

Due to the atmospheric turbulence, defocusing, noise and other factors, the optical remote sensing image acquisition may become blurred. Therefore, it is critical of deblurring the images by algorithm. In recent years, neural network algorithms have shown excellent performance in optical re-mote sensing images deblurring. However, neural network algorithms have some limitations at the same time. They lack interpretability and need large amounts of training samples. The traditional deblurring algorithms are interpretable, but the performance is not as good as the neural network algorithms. In order to obtain an interpretable deblurring algorithm with good performance, this paper proposes a deblurring algorithm based on deep unfolding method, which is the combination of traditional algorithms and neural networks. It can achieve good performance and be interpretable at the same time. We demonstrate the effectiveness of the algorithm on remote sensing datasets with PSNR values and visual deblurring images. The experiments show the proposed algorithm has better deblurring results.
A Matching Method for Large-Scale Heterogeneous Remote Sensing Images with Rotation and Scaling Transformation

The automatic registration of multi-modal remote sensing data (such as optical and SAR) is a challenging task because of the significant non-linear radiation difference between these data, as well as the transformation of rotation and scaling. In order to solve the above problems, this paper proposes a two-step strategy. Firstly, the improved HOPC algorithm is introduced to obtain matching point pairs, and then a weighted strategy is used to calculate the global affine transformation matrix. In the first step, an improved Harris operator is used to extract the points of interest, and the matching accuracy is maintained while the amount of calculation is reduced. Then the matching loss calculated by the HOPC algorithm is used to calculate the contribution weight of the image block, which is utilized to calculate the global transformation matrix. The results show that the method in this paper has strong robustness to complex nonlinear radiation differences, and the two-step strategy greatly improves the accuracy of matching, which is better than similar matching algorithms in performance.
Differences Between Research Projects in Computer Science Funded by Japanese and American Agencies

Understanding the relationship between funding agencies’ expectations and researchers’ activities in funded research projects will contribute to improved policy-making by funding agencies and better research activity by researchers. Thus, the purpose of this study is to investigate how research project size, international collaboration, and publication in international journals of articles resulting from funded research projects in Japan have progressed over the last decade. We focused on publications resulting from research projects funded by the major grants agencies in Japan and the USA using bibliographic data for computer science papers published from 2011 to 2020. Based on the numbers of authors of published journal articles and conference papers, the results showed that the size of projects funded by the National Science Foundation has been growing. In terms of international collaboration, both countries have increasingly collaborated with China, and researchers involved in research projects funded by the Japan Society for the Promotion of Science have increasingly published in English-language journals with less diverse audiences.
Mitigating Data Imbalance Issues in Medical Image Analysis

Medical images mostly suffer from data imbalance problems, which make the disease classification task very difficult. The imbalanced distribution of the data in medical datasets happens when a proportion of a specific type of disease in a dataset appears in a small section of the entire dataset. So analyzing medical datasets with imbalanced data is a significant challenge for the machine learning and deep learning community. A standard classification learning algorithm might be biased towards the majority class and ignore the importance of the minority class (class of interest), which generally leads to the wrong diagnosis of the patients. So, the data imbalance problem in the medical image dataset is of utmost importance for the early prediction of disease, specifically cancer. This chapter attempts to explore different problems concerning data imbalance in medical diagnosis. The authors have discussed different rebalancing strategies that offer guidelines for choosing appropriate optimal procedures to train the samples by a classifier for an efficient medical diagnosis.
A Software Model of Masking Hidden Message in Steganography Tasks

The authors deal with the issue of developing a software model for disguising a concealed message in HUGO systems in conditions of affected noise in the connection channel using a discrete random Arnold cat map and a Baker map that are repetitive reverse discrete chaotic transforms. All stages of software model development are considered in detail, such as the choice of the development environment, the composition of software modules and their purpose, screenshots of graphical interfaces for various modes of operation of the model. In a software model to assess the random state of a secret message presented by a digital static picture, the authors enter a chaotic coefficient, a quantitative measure of the entropy of the probabilities of unordered pixels. In the designed software model, the authors suggest a procedure for defining the limit value of the chaotic coefficient corresponding to the limit of the random state of the hidden message. Testing of the software model shows the practical indistinguishability of the transformed hide message from the native noise in the connection channel and greatly improves the security of HUGO stegosystems.
Multi-scale Adaptive Dual Attention for Image Defocus Blur Detection

Defocus blur detection aims to accurately differentiate the defocus blurred regions and in-focus clear regions in a natural image. In spite of remarkable advances, the disturbing of background clutter and the homogenization of boundary remains as the two most challenging issues. To address these issues, we propose an end-to-end network with an Adaptive Dual Attention Module (ADAM), which is designed to simultaneously capture effective local boundary detail and global semantic information in the channel and spatial dimensions, and dynamically aggregate the multi-level features. Specifically, ADAM pays attention to the correlation between local and global features associated with blur objects enhancing the synthetical feature representation. Furthermore, we design a Detail optimization Module (DOM), which is subsequently employed to correct the uncertain detection in boundary regions within the low-resolution details. The comprehensive experiments on two common datasets demonstrate our proposed method has superior performance for dealing with defocus blur detection.
Design and User Research in AR/VR/MR

The workshop will explore new techniques and processes for design, prototyping and testing in AR/VR/MR. How do we design and test useful and usable AR/VR/MR applications? How can we modify known design and research techniques and establish new techniques to suit immersive mediums? How might these processes vary based on the industry use case of the AR/VR/MR application? What are the metrics of success? We are interested in evaluation techniques that are qualitative, quantitative, computational/sensor-based, as well as rooted in ergonomics and human factors, and others! The workshop is of interest to conference participants and timely for ISMAR 2022, because as AR/VR/MR becomes more ubiquitous in industry, designers and researchers will need new methods to evaluate the usability and usefulness of immersive applications.
Assessing Batch and Online Learning for Delivery in Full and On Time Predictions

Improving results by optimizing process execution is one objective of major companies. For these corporations, the main point for achieving better results is the good maintenance of supply chain management. The most important supply chain metric is Delivery in Full and On Time (DIFOT). DIFOT measures how well a supply chain delivers value to the customer. In this work, we bring forward an analysis of DIFOT prediction from large Brazilian food company. More specifically, we compare a batch and online learning algorithm for DIFOT prediction and depict why the latter is suitable for this problem. Furthermore, we report a feature drift analysis to identify whether there are considerable shifts along with the dataset timespan. As a byproduct of this research, we make the dataset used in this analysis publicly available for future research in DIFOT prediction.