Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN SISTEM APLIKASI MANAJEMEN PROPERTI BAGIAN PENYEWAAN UNIT (STUDI KASUS: PT. SATRAND GROUP INDONESIA)

PT. Satrand Group Indonesia is a daily rental company, daily apartments, transit rentals, transit apartments, grand kamala lagoon daily rentals, grand kamala lagoon daily apartments, east center daily rentals, east center daily apartments, Gading Nias daily apartments, Gading Nias daily rentals, apartments Bekasi daily rental, Bekasi daily rental, Bekasi lodging. In carrying out its activities, this company still uses the process of ordering units directly and through contact admins. So that customers who will rent are required to come and see or make sure the unit is available or not directly to place an order. The purpose of this research is to build a unit rental management information system at PT. Satrand Group Indonesia uses the waterfall method. This research has resulted in a unit rental management information system at PT. Satrand Group Indonesia which makes it easy for customers to place orders, makes it easier for customers to get information about units and makes it easier to make reports.
Neoclassicism in Bangla Word Formation Processes

In this paper, I examine the notion of neoclassical word formation in Bangla and attempt to account for all the morpho-phonological interactions of both neoclassical and native Bangla word formation processes under the framework of Rule-based Phonology. The paper has both descriptive and theoretical goals. Bangla has a vast lexicon and a rich morphological system. The abundance of the classical Sanskrit language entities makes it interesting to explore the word formation processes and observe the morphophonological interactions in light of Neoclassicism. The words and affixes in Bangla have been divided into: a) Tadbhava and b) Tatsama c) Deshi (Native) and d) Videshi (Foreign). I study the different word formation processes in Bangla affixes. A rule-based account has been given to account for the morphophonological changes and bring out the essence of Neoclassicism in the Bangla lexicon and contribute towards the study of Bangla phonology and morphology.
IMPROVING READING COMPREHENSION SKILL OF THE SEVENTH GRADE STUDENTS’ AT MTS MUHAMMADIYAH PADANGPANJANG BY USING VISUALIZATION STRATEGY

Thisresearch aimsatexaminingthe effectiveness of using visualization strategyas a medium in teaching reading comprehension. Meanwhile, the purpose of thisresearch is to knowif there is an effect ofof visualization strategytowardstudents’ reading comprehension skill. The modelof this research was pre-experimental research in a design group which is divided into three stages: pre-test, treatment, and post-test in learning process. The sample of this research wasVII Tahfidz grade at MTs Muhammadiyah Padangpanjang. Istruments tocollectthe data were pre-test and post-test through students’ performence with a writtentest and measured using an assessment rubric. Furthermore, the criteria forsuccsessweredeterminedonsixindicators:mainidea,fact,reference,vocabulary, inference, and purpose. As a quantitative research, the researcheranalyzed the data by using the T-test. The result showed that there was asignificant effect of students score by using visualization strategy in the readingcomprehensionoflearning process. The total score of the students in the pre-testwas 875 and the mean was 48,61. Then, the total score of the students in the post-test was 1.525 and the mean was 84,72.Then,Tcountishigher than Ttable (-13,5405 &gt;-1,69092). It means there is an improvement in visualization strategytoward reading comprehension in descriptive text. So, the visualization strategyforteachingreadingtoVIITahfidzstudentsofMTsMuhammadiyahPadangpanjang was effective.
Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection

Liver cancer, one of the rapidly escalating forms of cancer, remains a principal cause of mortality globally.Its death rates can be attenuated through vigilant monitoring and early detection.This study aims to develop a sophisticated model to assist medical professionals in the classification of liver tumours using biopsy tissue images, thereby facilitating preliminary diagnosis.The study presents a novel, bio-inspired deep learning strategy purposed for augmenting liver cancer detection.The uniqueness of this approach rests in its two-fold contribution: Firstly, an innovative hybrid segmentation technique, integrating the SegNet network, UNet network, and Al-Biruni Earth Radius (BER) procedure, is introduced to extract liver lesions from Computed Tomography (CT) images.The algorithm initially applies the SegNet to isolate the liver from the abdominal image in a CT scan.Since hyperparameters significantly influence segmentation performance, the BER algorithm is hybridized with each network for optimal tuning.The method proposed herein is inspired by the pursuit of a common objective by swarm members.Al-Biruni's methodology for calculating Earth's radius sets the search space, extending beyond local solutions that require exploration.Secondly, a pre-trained AlexNet model is utilized for diagnosis, further enhancing the method's effectiveness.The proposed segmentation and classification algorithms have been compared with contemporary state-of-the-art techniques.The results demonstrated that in terms of specificity, F1-score, accuracy, and computational time, the proposed method outperforms its competitors, indicating its potential in advancing liver cancer detection.
Analisis Spasial Temporal Indeks Kekeringan dan Sebaran Hotspot dengan Pemanfaatan Data Satelit pada Kabupaten Ogan Komering Ilir dengan Metode Keetch Byram Drought Index (KBDI)

Frequent droughts cause forest and land fires during the long dry season. Therefore, it is necessary to overcome the problem of drought. However, currently, information about drought is still lacking even though it is very much needed. This is the background for researching the analysis of the drought index. This study will analyze spatially and temporally the drought that occurred in Ogan Komering Ilir Regency. Due to the limited data from the observation station, data from the Climate Forecast System Reanalysis (CFSR) was used and calculated using the Keetch Byram Drought Index (KBDI) method, then visualized using the Geographic Information System (GIS) application to map the distribution of drought index, hotspot distribution, and land cover. Based on this analysis, it can be seen that in Ogan Komering Ilir Regency, extreme drought often occurs in September in areas dominated by shrubs and dry forests. After being corrected, the distribution of the drought index, the distribution of hotspots, and land cover showed a strong correlation.
An Efficient Blockchain-Based Verification Scheme with Transferable Authentication Authority

Abstract In some situations, the transfer of authentication authority is necessary for user authentication. In traditional authentication, a trust mechanism based on a trusted party establishes trust between two unfamiliar entities. However, a trusted party's failure or malicious behavior could undermine the trust system. Additionally, traditional authentication methods often involve complex and time-consuming procedures and may incur high costs. With the advancement of technology, more decentralized authentication methods and blockchain-based identity verification solutions have been proposed to address these limitations and enhance the security and efficiency of trust establishment. There are still cases where the number of transfers of their authentication authority should be limited for authentication in blockchain networks. Meanwhile, an authentication scheme consisting only of efficient operations such as hash functions is desirable. However, to the best of our knowledge, blockchain networks have no authentication schemes that consist of only hash functions and allow the transfer of authentication authority. In this paper, we propose an efficient authentication scheme based on only a hash function that can limit the number of times authentication authority can be transferred by using hash chains and Hashcash techniques in a blockchain network. As a result, our scheme has the advantage of reducing the computational complexity and gas costs in smart contracts. Furthermore, we deployed the smart contracts on the Goerli test network and completed authentication, proving the solution's effectiveness. On the Goerli testnet, our smart contract consumed 304.6kgas when deployed and 29.9kgas when performing authentication.
Differentially Private Knowledge Graph Neural Network for Recommeder System

Abstract Knowledge Graphs (KGs) have been instrumental in mitigating the challenges of cold start and data scarcity in recommender systems, serving as a kind of auxiliary information. However, recent studies reveal that KGs, while beneficial, may also inadvertently furnish malicious attackers with more comprehensive background knowledge, escalating the risk of user privacy data breaches within these recommender systems. Current frameworks, which amalgamate KGs with privacy-preserving techniques, predominantly concentrate on enhancing privacy preservation, often at the expense of recommendation efficacy. In this research, we introduce a framework, named Differential Privacy Knowledge graph Neural network for Recommender systems (DPKNRec), designed to harmonize the trade-off between differential privacy noise and recommendation performance. Firstly, we formulate an integrated KG convolutional neural network adept at mining interrelationships between KG entities and item correlations effectively. Secondly, we incorporate a differential privacy technique to impose uniform Laplacian noise on feature embeddings and obfuscate genuine user-item interactions through pseudo-interaction item sampling, thereby augmenting user data security. Lastly, we evaluate our model using three distinct datasets-movies, music, and books. Empirical results from three benchmarks indicate that DPKNRec markedly supersedes contemporary benchmarks.
Deep Kernel Learning based Gaussian Processes for Bayesian Image Regression Analysis

In neuroimaging applications, different types of regression models have been widely adopted to study the complex associations between images and clinical variables, including scalar-on-image regression, image-on-scalar regression, and image-on-image regression.There are many challenging problems in model interpretations, statistical inferences and predictions in those type of models.To address those issues, we propose a general Bayesian modeling framework for the image regression problems by integrating deep neural networks (DNN) and Gaussian processes (GP) with kernel learning.The proposed framework consists of two levels of hierarchy.At level 1, we assume images as realizations of different GPs and project them on lower dimensional Euclidean spaces using a kernel expansion approach.We adopt a novel DNN based approach to covariance kernel learning of the GPs which provides efficient and accurate image projections.At level 2, we specify the associations between the projected images and other predictors using Bayesian DNNs.We develop efficient variational inference algorithms for posterior computation.We compare the performance of the proposed method with the state-of-the-art methods via extensive numerical experiments on synthetic images from the benchmark datasets as well as analysis of the fMRI data in the large-scale imaging studies.
Fuzzy C-Medoids Clustering Of CUB Data to Handle Likert-Type Scales Uncertainty

In investigating customer satisfaction (CS) with products or services the most popular approach still relies on interviews or questionnaire to obtain consumer's opinions and responses are usually measured by means of Likert-type scales.However, Likert-type data are inherently imprecise and uncertain.Thus, to obtain reliable analysis using such data, an a-posteriori correction must be adopted.Fuzzification procedure is the most common a-posteriori way to deal with uncertainty of Likert-type data, but it requires the subjective definition of the membership function.To overcome this problem, while a cluster analysis is conducted using Likert-type data as segmentation variables, a new approach based on CUB model and Fuzzy C-Medoids Clustering of Mixed Data algorithm (FCMd-CUB) is theoretically and empirically presented in this paper.Advantages of the suggested method are discussed in the conclusion section.
Validation of Two-sample Location-scale Model Using Empirical Likelihood Based Statistics

One of the most popular semi-parametric relationships between two distribution functions is the locationscale model.Our goal is to develop a goodness-of-fit testing procedure for such location-scale models.For this purpose we use the plug-in approach by the estimation of the structural parameters and transformation of one of the samples.Further we propose to use the empirical likelihood test for the difference of two distribution functions and probabilityprobability plots.The effect of the estimation procedure on the limiting chi-square distribution is analysed by some simulation study.For practical applications we use the location-scale appropriate bootstrap resampling method to approximate the limiting distribution of the plug-in test statistic.
Pengembangan Media Pembelajaran Google Spreadsheet di Dayah Darul Ihsan Aceh Besar

The Paperlees concept is to reduce the use of paper in schools with the aim of effectiveness and efficiency in school management. All of this can be done with the help of Information and Communication Technology (ICT) which has developed rapidly at this time. The World 4.0 era with increasingly advanced technology turned out to have a positive impact on various lines of life. Even in educational institutions, even though technological advances have also begun to be applied. For example, using Google Sheets for attendance, collecting test scores, and student data and others. However, the use of this technology will fail if there is no special training for teachers or educators. Using Google Sheets for attendance and assessors makes it very easy for teachers and academic departments to collect data in real-time. Based on this fact, writing this article aims to explain what steps were taken by Dayah Darul Ihsan in training educators about Google Spreadsheets. This article combines qualitative data from 3 teaching staff respondents to get objective results about the ease and difficulty of using Google Sheets. Based on field studies, it was found that it was easy and several obstacles in using Google Spreadsheet by teachers. convenience; data connection in real-time between the teacher who teaches the homeroom and the academic division of Dayah. Constraint; the small screens of Android devices, the large number of old teaching staff who have difficulty using the media and the limited access to the WIFI network at Dayah Darul Ihsan. This study is considered new because it describes training on the use of technology at Dayah educational institutions. The findings from this article can be used in developing and optimizing the use of technology in Dayah/Madrasah. Maximum use of technology can improve the quality of education in Aceh and Indonesia in general.
The YOLO model that still excels in document layout analysis

Abstract Document layout analysis can help people better understand and use the information in a document. However, the diversity of document layouts and considerable variation in aspect ratios among document objects pose significant challenges. In this study, we designed the Multi-Convolutional Deformable Separation (MCDS) module as the main structure of the network, using the YOLO model as a baseline. Integration of this module into the Backbone and Neck layers enhances the image feature extraction process significantly. Moreover, we incorporate ParNet-Attention to direct the network's focus toward document objects through parallel networks, thereby facilitating a more exhaustive feature extraction. To optimize the model's predictive potential, the Decouple Fusion Head (DFH) is employed within the Head layer. This technique leverages multi-scale features based on the decoupled head, thereby enhancing the accuracy of predictions. Our proposed model achieves remarkable performance on three distinct public datasets with varying characteristics, namely ICDAR-POD, PubLayNet, and IIIT-AR-13K. Notably, in ICDAR-POD, both IoU$_{0.6}$ and IoU$_{0.8}$ achieve the optimal mean Average Precision (mAP), 96.2 and 94.4, respectively.
Relevansi Teori Multiple Intellegences dengan Undang-Undang Sistem Pendidikan Nasional Tahun 2003

Howard Gardner's idea about the existence of multiple intelligences or multiple intelligences which he outlined in his book "Frames of Mind" has changed the perspective of many people in studying human intelligence. To recognize and develop all the intelligences possessed by each student which varies greatly and all combinations of these intelligences the state has an important role to play, including in formulating various policies in education laws. However, in Indonesia, there are still many teachers who often do not provide opportunities and opportunities for their students to develop their multiple intelligences through suitable learning strategies and methods. One of the factors that may underlie this is the absence of mention of the theory of multiple intelligences in the Indonesian National Education System Law or the 2003 National Education System Law. In this study the authors wish to examine the relevance of the theory of multiple intelligences to the 2003 National Education System Law. This research is a qualitative research that can be categorized as library research. The results of this study explain that there is some relevance between the theory of multiple intelligences and the 2003 National Education System Law.
Hybrid Model Compression for BERT-Based Polyphone Disambiguation in Mandarin Chinese

&lt;p&gt;Polyphone disambiguation has long been a crucial problem for Chinese text-to-speech (TTS) systems. Researchers have greatly improved the performance of neural disambiguation models by leveraging pre-trained BERT models. However, the vast computation of BERT makes its deployment on edge devices extremely hard. To mitigate this problem, we present a hybrid model compression strategy incorporating both network pruning and knowledge distillation for polyphone disambiguation. In contrast to previous methods that compressed the model only through knowledge distillation, our approach can produce compact alternatives by directly pruning an existing BERT, thus remarkably reducing the training cost. Besides, we also provide a new dataset for common Chinese polyphonic characters (CCPC) to facilitate future research. Experimental results demonstrate that our approach can offer efficient disambiguation models with highly comparable performance to the BERT-based baseline.&lt;/p&gt;
Improved Cuckoo Search Algorithm for Hyper-parameter Optimization in Deep Learning and Its Application in Activity Recognition

Abstract Current research shows that deep learning algorithm is an effective method for activity recognition. However, the deep learning algorithms often contain some hyper-parameters which may be continuous, integer, or mixed, and are often given based on experience but largely affect the effectiveness of activity recognition. In this paper, Cuckoo Search (CS) algorithm is proposed to optimize hyper-parameters in deep learning models. In order to adapt to different hyper-parameter optimization problems, an improved CS algorithm is proposed. Then, the hyper-parameters in CNN and LSTM are optimized based on the improved CS algorithm on the smart home activity recognition dataset. Recognition results show that hyper-parameter optimization based on improved CS algorithm can improve the performance of the deep learning model.
Information integration across saccades plays a prominent role during goal-directed viewing of everyday scenes

One of the fundamental issues in visual perception is how and how much visual mechanisms integrate visual information across fixations and whether visual operations within each fixation are independent. Here, we examine how humans actively scan the visual environment when performing goal-directed visual search on photographs of complex everyday scenes. We focus specifically on the evidence for information integration and transfer across saccades under these more naturalistic conditions, as revealed by saccades preceded by short intersaccadic intervals. We use COCO-Search18 and COCO-FreeView, two open datasets of eye-movements made by observers performing either category-search (COCO-Search18) with 18 search target categories or free-viewing (COCO-FreeView) on a subset of over 4000 unique images from the MS-COCO dataset. We show that short-latency second saccades, defined as second saccades preceded by an intersaccadic interval less than 125 ms, occur frequently (45 % of saccades on average) in goal-directed visual search with naturalistic images, and much more frequently than in most prior studies. Short-latency second saccades foveate the search target more often than saccades executed after intersaccadic intervals longer than 125 ms (regular-latency saccades), and this advantage for short-latency saccades is substantially larger when they start far away from the target. Consistent with this advantage, short-latency saccades are also elicited more often when they start further away from the target (i.e. the target is more eccentric during the fixation preceding the second saccade). Short-latency saccades show both a target-foveation and a target-discrimination advantage, indicating that information about both target location and target identity is transferred across the first saccade. Short-latency second saccades are much more frequent during goal-directed visual search with the search target present – active searching, the top-down salience of the search-target, and easier-to-find search targets all contribute to the frequency of short-latency saccades. The results show that human searchers use a satisficing strategy when actively searching photographs of complex everyday scenes for a categorically defined target. Two eye-movements are often made when waiting a little longer before the first movement would have likely allowed the target to be foveated with just one eye-movement. Short-latency saccades and information transfer across saccades work towards ensuring that the cost of making additional saccades to distractor stimuli is minimal; this would not be the case if perception began anew at each fixation. Information integration and transfer across saccades plays a prominent role during naturalistic vision.
IMPLEMENTATION OF FACE RECOGNITION AND LIVENESS DETECTION SYSTEM USING TENSORFLOW.JS

Facial recognition is a popular biometric security system used to authenticate individuals based on their unique facial structure. However, this system is vulnerable to spoofing attacks where the attacker can bypass the system using fake representations of the user's face such as photos, statues or videos. Liveness detection is a method used to address this issue by verifying that the user is a real person and not a representation. This journal article focuses on the life sign method of liveness detection, which utilizes facial movements to confirm the user's existence. We implement the latest technology of artificial intelligence from TensorFlow.js using face-api.js and compare it with the GLCM algorithm. However, even with the life sign detection method, there is still a chance of bypassing the system if an attacker uses a video recording. To mitigate this, we propose the addition of an object detection system to detect the hardware used to show video recordings with ml5.js. Our face recognition and expression detection system, using the pre-trained model face-api.js, achieved an accuracy of 85% and 82.5%, respectively, and the object detection system built with ml5.js has high accuracy and is very effective for liveness detection. Our results indicate that face-api.js outperformed GLCM algorithm in detecting spoofing attempts.