Reviewerly/exHarmony huggingface dataset:
Short Term Forecasting Method: Covid 19 and Capital Market in Indonesia

This study aimed to predict the short-term confirmed Covid 19 and Jakarta Composite Index (JCI) cases in Indonesia. The prediction uses ARIMA and SutteARIMA methods, and the data processed with R software. Researcher using time series data from April 2nd, 2020 (the date of covid 19 detected in Indonesia) to September 30th, 2020. We are fitted the data with the data from October 1st to October 10th, 2020. Based on the fitted data, we could forecast the cases from October 11th to October 31st, 2020. We applied the Mean Absolute Percentage Error (MAPE) to predict accuracy measures to evaluate forecasting methods. Based on forecasting with ARIMA and SutteARIMA methods, the SutteARIMA method is more suitable than ARIMA to calculate the daily forecasts of negative Covid 19 in Indonesia with MAPE value of 0.156 (smaller than 0.21 compared to MAPE value of ARIMA). At the same time, the ARIMA method is more suitable than SutteARIMA to calculate the daily forecasts of positive Covid 19 and JCI in Indonesia. The MAPE value of 0.06 (smaller than 0.104 compared to MAPE value of SutteARIMA for positive Covid 19) and MAPE value of 0.012 (smaller than 0.021 compared to MAPE value of SutteARIMA for JCI Indonesia).
Melanoma Detection using Convolutional Neural Network with Transfer Learning on Dermoscopic and Macroscopic Images

Background: Melanoma is a skin cancer that starts when the melanocytes that produce the skin color pigment start to grow out of control and form a cancer. Detecting melanoma early before it spreads to the lymph nodes and other parts of the body is very important because it makes a big difference to the patient's 5-year life expectancy. Screening is the process of conducting a skin examination to suspect a mole is melanoma using dermoscopic or macroscopic images. However, manual screening takes a long time. Therefore, automatic melanoma detection is needed to speed up the melanoma detection process. The previous studies still have weakness because it has low precision or recall, which means the model cannot predict melanoma accurately. The distribution of melanoma and moles datasets is imbalanced where the number of melanomas is less than moles. In addition, in previous study, comparisons of several CNN transfer learning architectures have not been carried out on dermoscopic and macroscopic images.&#x0D; Objective: This study aims to detect melanoma using the Convolutional Neural Network (CNN) with transfer learning on dermoscopic and macroscopic melanoma images. CNN with Transfer learning is a popular method for classifying digital images with high accuracy.&#x0D; Methods: This study compares four CNN with transfer learning architectures, namely MobileNet, Xception, VGG16, and ResNet50 on dermoscopic and macroscopic image. This research also uses black-hat filtering and inpainting at the preprocessing stage to remove hair from the skin image.&#x0D; Results: MobileNet is the best model for classifying melanomas or moles in this experiment which has 83.86% of F1 score and 11 second of training time per epoch.&#x0D; Conclusion: MobileNet and Xception have high average F1 scores of 84.42% and 80.00%, so they can detect melanoma accurately even though the number of melanoma datasets is less than moles. Therefore, it can be concluded that MobileNet and Xception are suitable models for classifying melanomas and moles. However, MobileNet has the fastest training time per epoch which is 11 seconds. In the future, oversampling method can be implemented to balance the number of datasets to improve the performance of the classification model.
Information Security Risk Assessment (ISRA): A Systematic Literature Review

Background: Information security is essential for organisations, hence the risk assessment. Information security risk assessment (ISRA) identifies, assesses, and prioritizes risks according to organisational goals. Previous studies have analysed and discussed information security risk assessment. Therefore, it is necessary to understand the models more systematically. Objective: This study aims to determine types of ISRA and fill a gap in literature review research by categorizing existing frameworks, models, and methods. Methods: The systematic literature review (SLR) approach developed by Kitchenham is applied in this research. A total of 25 studies were selected, classified, and analysed according to defined criteria. Results: Most selected studies focus on implementing and developing new models for risk assessment. In addition, most are related to information systems in general. Conclusion: The findings show that there is no single best framework or model because the best framework needs to be tailored according to organisational goals. Previous researchers have developed several new ISRA models, but empirical evaluation research is needed. Future research needs to develop more robust models for risk assessments for cloud computing systems. Keywords: Information Security Risk Assessment, ISRA, Security Risk
Image buffering in application specific processors

In many digital image-processingapplications, which are implementedin field programmable gate arrays,the currently processed image's frames are stored in external dynamic memory.The performance of such an application dependson the dynamic memoryspeed and the necessaryrequests quantity during algorithm’sruntime. This performance is being optimized through field programmable gate arrays -implemented buffer memory usage.But there is no common method for the formal buffer memory synthesis with preset throughput, input and output data sequenceorderand minimizedhardwarecosts.In this article,the featuresof image input and processing based on Field Programmable Gate Arrayareconsidered.The methods of building buffer circuits in field programmable gate arrays, due to which the intensity of data exchanges with external memory is reduced, are analyzed. Themethod of synthesizing pipeline circuits with specified performance characteristics and the data sequence order is given, which is based on the mapping of the spatial synchronous data flows into the structure implemented in the field programmable gate arrays.A method of designing buffer schemes is proposed, which is based on the mapping of spatial synchronous data flows into local memory in the form of chains of pipeline registers.The method helpsto organize the data flow of at the input of built-in pipeline units of image processing, in which the data follow in a given order, andto minimize the amount of buffer memory.The method ensures the use of dynamically adjustable register delays built into the field programmable gate arrays, which increases the efficiency of buffering.Thismethod was tested during the development of an intelligent video camera. The embedded hardware implements a video image compression algorithm with a wide dynamic range according to the Retinexalgorithm. The same time it selects characteristic points in the image for the further pattern recognition.At the same time, multiple decimation of the frame is performed. Due to themultirate buffering of the image in the field programmable gate arrays,it was possible to avoid using of external dynamic memory.
Classification of Review Text using Hybrid Convolutional Neural Network and Gated Recurrent Unit Methods

Consumer reviews are opinions from buyers to sellers based on service satisfaction or product quality. The more consumer reviews cause the process of analyzing manually will be difficult. Therefore, an automated sentiment analysis system is needed. Each review will be grouped into a sentiment class which is divided into positive and negative classes. This study aims to classify review texts using the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) methods. The research stages in this study include collecting data on Tokopedia review texts, extracting hidden information from review texts using CNN, conducting learning on review texts using GRU. A total of 1000 review texts were divided into 80% training data and 20% test data. The review text is converted into matrix using One Hot Encoding algorithm and then extracted using CNN. The CNN process includes the convolution calculation, the calculation of the Rectified Linear Unit (ReLU) activation function, and the pooling stage. The extraction results in the CNN process are continued in the GRU process. The GRU process includes initializing parameters, GRU feed forward, Cross-Entropy Error calculation, GRU feedback, and updating weights and biases. The optimal weight is obtained when the error value in the training is less than the expected minimum error or the training iteration has reached the specified maximum iteration. Optimal weight is used for validation test on test data. The implementation of review text classification using the hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) method was made using the python programming language. The accuracy of the validation test is 88.5%
Small object detection method based on YOLOv5 improved model

Small object detection is an indispensable and challenging part of object detection. This paper proposes a small object detection method based on YOLOvS improved model. By adding shallow feature extraction networks in FPN layer and PAN layer, feature fusion was carried out with the first C3 layer to extract more details of small objects. The detection output part was extracted in the new fusion layer, and the detection output part of 32 times of the original network was deleted. Up-sampling is performed directly behind the SPPF layer, and the detail features are amplified and fused with the features of the previous layer. The experimental results show that the mAP@0.5 value and mAP@0.5:0.95 value of the improved model for small object detection reach 0.39 and 0.22, respectively, which are 6 and 5 percentage points higher than the original YOLOv5 algorithm. Recall rate increased from 0.34 to 0.39; The accuracy rate increased from 0.43 to 0.48; Classification loss, location loss and confidence loss decreased by 1 percentage point, 4 percentage point and 9 percentage point respectively. The improved model has higher accuracy and faster speed in small object detection.
Tensor decomposition based networks for nuclei segmentation and classification

Nuclei segmentation and classification for Haematoxylin & Eosin stained histology images is a challenging task because of many issues, large intra-class variability among nuclei, overlapping nuclei etc. Most of the existing methods require large computation to improve the accuracy. Moreover, the learned features in deep networks are high dimensional compared to the input microscopy images. Thus, the discriminative features may lie in low dimensional subspaces, which can be recovered by tensor analysis methods such as Tucker decomposition. Therefore, we propose two novel modules, Tucker Decomposition and Convolution Combined (TuCo) module and Tucker Decomposition and Deformable Convolution Combined (TuDe) module, for nuclei segmentation and classification. They are designed to extract the nuclear geometric information and low-rank features, and can be plugged into existing backbones. Thus, we design TuCo-Net and TuDe-Net. Experimental results validate the effectiveness of the proposed networks. The code and optimized models will be available at https://github.com/dark-ai/TuCo-Net-and-TuDe-Net.
Dynamic transfer learning with progressive meta-task scheduler

Dynamic transfer learning refers to the knowledge transfer from a static source task with adequate label information to a dynamic target task with little or no label information. However, most existing theoretical studies and practical algorithms of dynamic transfer learning assume that the target task is continuously evolving over time. This strong assumption is often violated in real world applications, e.g., the target distribution is suddenly changing at some time stamp. To solve this problem, in this paper, we propose a novel meta-learning framework L2S based on a progressive meta-task scheduler for dynamic transfer learning. The crucial idea of L2S is to incrementally learn to schedule the meta-pairs of tasks and then learn the optimal model initialization from those meta-pairs of tasks for fast adaptation to the newest target task. The effectiveness of our L2S framework is verified both theoretically and empirically.
Detection of Tumor for Breast Cancer Patient Using Regression Tree Compared with Linear SVM Algorithm

The paper aims to detect the tumor in breast cancer images using a Regression Tree and compare it with a Support Vector Machine to evaluate accuracy values.The regression tree algorithm is applied to 10 sample images from a dataset of more than 250 images. For the same input images taken accuracy, values are evaluated. The dataset taken consists of breast cancer images in this research study a Novel Regression Tree and Linear Support Vector Machine is evaluated with a total of 20 sample sizes. Based on the statistical analysis the significance value for calculating accuracy was found to be p&lt;0.05(significant two-tailedtests). Detection of breast cancer was performed by using a Regression Tree and Support Vector Machines which achieved the accuracy of 95% and 86% respectively. By the above two results, we conclude that the performance of the Novel Regression tree is significantly better than the Linear Support Vector Machine in terms of accuracy.
Improving the Accuracy of Object Detection Models using Patch Splitting

The classification and afterwards also the localization of objects in images were among the initial breakthroughs that stimulated research into artificial neural networks. Improving the detection accuracy along with other metrics has been a continuous research objective and analysis subject. One particular issue during the training of such models is the varying resolution of input images and it's effects on the training/detection results as most models accept arbitrary resolutions and resize them internally to a specific resolution. This article examines the benefits of automatically splitting images into patches before the detection stage and afterwards joining the patches along with the detection results into the original image. An accuracy test is conducted in a real environment.
Risk prediction of enterprise credit financing using machine learning

For the credit financing risk of small, medium-sized and micro enterprises, a risk prediction model of enterprise credit financing management based on antagonistic neural network and least squares support vector machine is proposed. The data samples are processed combined with antagonistic neural network, and the risk prediction of enterprise credit financing is realized by using least squares support vector machine model. Compared with many classification models, the effectiveness of the least squares support vector machine model is verified. The results show that the accuracy rate of the least squares support vector machine model is 90.15%, the recall rate is 85.63%, and the prediction error rates of the model in default and non default are 6.48% and 3.09% respectively, which is better than BP neural network, random forest algorithm and Gaussian naive Bayesian algorithm. The least squares support vector machine model can effectively and accurately predict the enterprise credit financing risk, provide scientific and efficient risk early warning for the enterprise credit financing control, and provide technical support for preventing and reducing the occurrence of loan default.
Electromagnetic dipole moments of the tau-lepton at the ILC and CLIC

We quantify the anomalous magnetic moment and electric dipole moment of the -lepton through the process + -→ + -, within the ranges of energies and luminosities affordable at the future International Linear Collider (ILC) and the Compact Linear Collider (CLIC).The tau-lepton is a key particle in various Beyond the Standard Model (BSM) models and is considered a laboratory for many experimental or simulation aspects in searches for new physics.In particular, the taulepton anomalous couplings to bosons in the + -and + -vertices, have made the tau-lepton one of the most attractive particles for new physics searches.
ANALYSIS OF USER SATISFACTION LIBRARY ONLINE CATALOG AT UPN VETERAN EAST JAVA USING EUCS METHOD

The development of information technology in this digital era has prompted numerous changes in many aspects of life, notably in the field of libraries, that are necessary to satisfy the demands of librarians to locate information easily, quickly, and precisely. OPAC (Online Public Access Catalog), often known as Online Catalog, is a website-based catalogue system used to search for information on library materials so they may be accessible whenever and wherever. One of the academic libraries that have used the Online Catalog is Universitas Pembangunan Nasional "Veteran" East Java. The purpose of this study is to determine what factors influence online catalogue user satisfaction using the End-User Computing Satisfaction (EUCS) model approach with two additional independent variables, System Speed and System Reliability. End-User Computing Satisfaction is one of the methods used to evaluate the overall system for user satisfaction. Proportional stratified random sampling was employed as the sample collecting method in this study, with a total of 303 respondents. The research data obtained from the questionnaire results were processed using SmartPLS 3.2.9 software. Based on the results obtained, accuracy, format, and ease of use are the three factors that have a positive and significant effect. In addition, the most influencing factor for Online Catalog user satisfaction is the format.
Real-time military person detection and classification system using deep metric learning with electrostatic loss

This study addressed a system design to detect the presence of military personnel (combatants or non-combatants) and civilians in real-time using the convolutional neural network (CNN) and a new loss function called electrostatic loss. The basis of the proposed electrostatic loss is the triplet loss algorithm. Triplet loss’ input is a triplet image consisting of an anchor image (xa), a positive image (xp), and a negative image (xn). In triplet loss, xn will be moved away from xa but not far from both xa and xp. It is possible to create clusters where the intra-class distance becomes large and does not determine the magnitude and direction of xn displacement. As a result, the convergence condition is more difficult to achieve. Meanwhile, in electrostatic loss, some of these problems are solved by approaching the electrostatic force on charged particles as described in Coulomb's law. With the inception ResNet-v2 128-dimensional vectors network within electrostatic loss, the system was able to produce accuracy values of 0.994681, mean average precision (mAP) of 0.994385, R-precision of 0.992908, adjusted mutual information (AMI) of 0.964917, and normalized mutual information (NMI) of 0.965031.
Data Transferring and Remote Data Trustworthiness Checking using Personality Based Proxy Arranged System (PB-PAS) in Public Cloud

Abstract: An increasing number of customers might want to store their data to public cloud servers (PCSs) alongside the quick advancement of cloud registering. New security issues must be illuminated so as to help more customers process their data in public cloud. At some point of instance the customer is outfall to get to PCS, owner will designate its intermediary to process his data and transfer them. Then again, remote data respectability checking is additionally a vital security issue in public cloud stockpiling. It ensures that customers verify whether their stored data is placed saved from downloading the entire data. From the security issues, we propose a novel intermediary situated data transferring and remote data honesty checking model in character based public key cryptography: Data Transferring and Remote Data Trustworthiness Checking using Personality Based Proxy Arranged System in Public Cloud (PB-PAS). We give the formal definition, framework model, and security show. On that instance of time, a PB-PAS method is planned to utilize the bilinear pairings. The proposed PB-PAS convention is provably secure in view of the hardness of computational Diffie–Hellman issue. This PB-PAS solution is flexible and fertile. In view of the first customer's approval, the proposed PB-PAS convention can understand private remote data honesty checking, appointed remote data trustworthiness checking, and public remote data respectability checking
Culture Shock In Intercultural Communication Between Foreign Students In Jakarta

Cultural differences can lead to culture shock and anxiety among those who participatein intercultural communication. Migrant students are one illustration of students who havefaced culture shock since choosing to study in Jakarta. This study intends to describe theCulture Shock of Intercultural Communication among Overseas Students in Jakarta. Thepurpose of this study is to find out how the stages of culture shock and communication barriersexperienced by overseas students in Jakarta are in intercultural interactions.The theory applied in this study is the stages in culture shock, namely the honeymoonphase, the crisis phase, the adjustment phase, the bhicultural phase and Anxiety/UncertaintyManagement Theory (AUM). Anxiety/Uncertainty Management Theory (AUM) is a theory thatdiscusses a person's experience of anxiety and uncertainty that arises when dealing withstrangers or meeting people with different cultures from him in Jakarta.This study uses a qualitative approach with descriptive research type, the types of dataused are primary and secondary data, with data collection techniques using interviews,observation, and documentation. Data analysis techniques used are data reduction, datapresentation, conclusion drawing. The validity of the data used is source triangulation.The results of this study indicate that all informants who are overseas students inJakarta experience four stages in culture shock, namely the honeymoon phase, the crisis phase,the adjustment phase, the bicultural phase and anxiety/uncertainty management (AUM). Thesecond informant, Melan, does not experience anxiety at all, because Melan admits that shelikes to mingle with new people. The culture shock and anxiety experienced by overseasstudents in Jakarta did not really cause a sense of hopelessness. Various feelings of discomfortexperienced by overseas students in Jakarta will be eroded by itself with the passage of timewhich makes overseas students adapt to their environment. The obstacles in the interactionprocess of immigrant students come from within, habits, and language. For each individualwho will travel overseas, first seek information from reliable sources regarding the social andcultural situation in the area so as not to experience culture shock