Reviewerly/exHarmony huggingface dataset:
Efﬁcient and Scalable Object Localization in 3D on Mobile Device

&lt;p&gt;Two-Dimensional (2D) object detection has been an intensely discussed and researched ﬁeld of computer vision. With numerous advancements made in the ﬁeld over the years, we still need to identify a robust approach to efﬁciently conduct classiﬁcation and localization of objects in our environment by just using our mobile devices. Moreover, 2D object detection limits the overall understanding of the detected object and does not provide any additional information in terms of its size and position in the real world. This work proposes an object localization solution in Three-Dimension (3D) for mobile devices using a novel approach. The proposed method works by combining a 2D object detection Convolutional Neural Network (CNN) model with Augmented Reality (AR) technologies to recognize objects in the environment and determine their real-world coordinates. We leverage the in-built Simultaneous Localization and Mapping (SLAM) capability of Google’s ARCore to detect planes and know the camera information for generating cuboid proposals fromanobject’s2Dboundingbox. Theproposedmethodisfastandefﬁcientforidentifyingeveryday objects in real-world space and, unlike mobile ofﬂoading techniques, the method is well designed to work with limited resources of a mobile device.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
STIMULASI PERKEMBANGAN BAHASA ANAK USIA DINI MELALUI MEDIA PEMBELAJARAN INTERAKTIF DI DENALI DEVELOPMENT CENTER

This activity aims to provide an overview of early childhood language development from learning stimulation activities through interactive video learning media at the Denali Development Center (DDC). Interactive video was chosen because it is considered very suitable for the characteristics of early childhood who tend to get bored easily, have low focus and concentration, and tend to be active and find it difficult to sit still. Based on the results of observations that have been made at DDC found problems where some children aged 1-3 years, experience speech delays where children cannot pronounce vocabulary correctly when communicating, the vocabulary mastered by children is still small, children cannot compose simple sentences when communicating, children have not been able to provide answers to questions that are given quickly and straightforwardly and the child has not been able to listen properly. From the results of the learning activities that have been carried out during the service period, gradually the child experiences significant language development where the child is quiet and never makes a sound or gives a slow reaction, this child begins to make sounds such as laughter or bubbling. And other children who cannot pronounce words correctly, gradually these children can say some words well and their meanings can be understood and the vocabulary they master also develops. Therefore providing stimulation to children is very important to reach this stage of development. Children who receive less stimulation tend to have problems and obstacles in their development. &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
PEMBERDAYAAN SOSIAL EKONOMI MASYARAKAT DI DAERAH RAWAN BANJIR (Studi Kasus Banjir di Kecamatan Periuk Kota Tangerang)

Empowerment of the environment in society is very essential in the regional and national development agenda. Communities, governments, academics and business actors have an important role as drivers of innovation in national environmental preservation programs. In an effort to implement this function, several strategic studies are needed so that all initiator components can jointly protect the environment from potential natural releases including natural disasters such as floods. Based on this, community service research involving members of the Tangerang City DPRD, the Environment Service, the Public Works and Public Housing Service, the Social Service, the Regional Disaster Management Agency, Officials in the Periuk sub-district and Periuk Sub-District underwent a Focus Group Discussion on social impact economic consequences due to flooding in Periuk District. As for some assistance with problems, namely as follows: 1.) Officials in the Tangerang City area require intensive coordination and supervision to overcome flood problems and their economic impacts, (2.) The need for knowledge for the community regarding the socio-economic impacts that can be experienced as a result of flooding . Therefore, the results of community service research are as follows: (1) succeeded in formulating a policy package that has been approved by the executive and legislative components in the Periuk District and Tangerang City, (2.) The community feels helped by the existence of discussion and presentation facilities material organized by the Business Faculty of Buddhi Dharma University.
A Method for Demonstrating Superluminal Communication Using Conscious Intent to Influence a Quantum-Entangled Link

It is fair to say that Bell’s Theorem leaves a nasty taste in the mouths of many physicistsbecause test results seem to suggest that non-local, i.e., superluminal, communicationof some sort is going on all around us and maybe not just at the quantum level. ButBell’s Theorem has been aptly demonstrated to date, and Quantum Mechanics (QM) is,if not the most successful theory devised by man, certainly one of them. To some, andespecially to Einstein, one of the least favorable aspects of QM is its statistical nature.This paper embraces QM’s statistical nature and employs quantum entanglement toshow that superluminal communication may be possible albeit, most likely, initially, ata very low bit rate.
Interpretable Neural Networks for Video Separation: Deep Unfolding RPCA with Foreground Masking

&lt;p&gt;This paper presents two deep unfolding neural networks for the simultaneous tasks of background subtraction and foreground detection in video. Unlike conventional neural networks based on deep feature extraction, we incorporate domain-knowledge models by considering a masked variation of the robust principal component analysis problem (RPCA). With this approach, we separate video clips into low-rank and sparse components, respectively corresponding to the backgrounds and foreground masks indicating the presence of moving objects. Our models, coined ROMAN-S and ROMAN-R, map the iterations of two alternating direction of multipliers methods (ADMM) to trainable convolutional layers, and the proximal operators are mapped to non-linear activation functions with trainable thresholds. This approach leads to lightweight networks with enhanced interpretability that can be trained on few data. In ROMAN-S, the correlation in time of successive binary masks is controlled with a side-information scheme based on L1-L1 minimization. ROMAN-R enhances the foreground detection by learning a dictionary of atoms to represent the moving foreground in a high-dimensional feature space and by using reweighted-L1-L1 minimization. Experiments are conducted on both synthetic and real video datasets, for which we also include an analysis of the generalization to unseen clips. Comparisons are made with existing deep unfolding RPCA neural networks, which do not use a mask formulation for the foreground. The models are also compared to a 3D U-Net baseline. Results show that our proposed models outperform other deep unfolding models, as well as the untrained optimization algorithms. ROMAN-R, in particular, is competitive with the U-Net baseline for foreground detection, with the additional advantage of providing video backgrounds and requiring substantially fewer training parameters and smaller training sets.&lt;/p&gt;
Effect of Roof Inclination on Solar Panel’s Energy Output in a South-Western Nigerian City

This work sets up four photovoltaic(PV)-based solar power systems with adjustable solar panel inclination angle stands. It also varies the inclination angle of the solar panel every five days while recording daily energy delivered by the panels. The study finally compares the output energy recorded for different angles. This was done with a view to studying the effect of building roof inclination on the energy deliverable by solar panel installed on it. The set up consists of PV-based solar power systems with adjustable inclination angle stands for four 10 W solar panels which are on the same axis. Each solar panel was connected to a 42 Ah battery through multimeter and charge controller. A 25 W load was used to discharge the battery at night to allow fresh charge from the panels during the day. The multimeter was used to monitor the daily energy delivered. The solar panels were subjected to 0°, 25°, 50° and 75° inclination angles and faced to South direction at the Renewable Energy Laboratory, Osun State University, Osogbo, Nigeria. Data were recorded daily for 20 days by subjecting all panels to the considered angles for five days. The results obtained showed that the average energy delivered by panels installed at the stated inclination angles are 35.35 Wh, 32.25 Wh, 24.1 Wh and 13.95 Wh, respectively. This means that, for a specific building energy need, the steeper the roof (intended for panel installation), the more the amount of solar panel required to meet such energy. The results also showed various expressions that can be used to estimate daily average sun hour (ASH) based on roof inclination. For example, the estimated daily (ASH) for Osogbo and environs can be taken to be 4.22 hour, 3.15 hour and 1.82 hour for 25°, 50° and 75° roof inclinations, respectively.
Cartoon physics: a graphic novel guide to solving physics problems

"Cartoon physics: a graphic novel guide to solving physics problems." Contemporary Physics, ahead-of-print(ahead-of-print), p. 1 AcknowledgmentThe authors would like to express their gratitude to Lembaga Pengelola Dana Pendidikan (LPDP/ Indonesia Endowment Fund for Education) and the Ministry of Education, Culture, Research, and Technology in Indonesia for supporting the publication of this review. They also express their deepest gratitude to their mentor, Winny Liliawati, at Universitas Pendidikan Indonesia for her mentorship and assistance on the current manuscript.Additional informationFundingThis work was funded by the Lembaga Pengelola Dana Pendidikan (LPDP) Indonesia.
Constraint-Based Multi-Agent Reinforcement Learning for Collaborative Tasks

In order to be successfully executed, collaborative tasks performed by two agents often require a cooperative strategy to be learned. In this work, we propose a constraint-based multi-agent reinforcement learning approach called Constrained Multi-agent Soft Actor Critic (C-MSAC) to train control policies for simulated agents performing collaborative multi-phase tasks. Given a task with n phases, the first n -1 phases are treated as constraints for the final task phase objective, which is addressed with a centralized training and decentralized execution approach. We highlight our framework on a tray balancing task including two phases: tray lifting and cooperative tray control for target following. We evaluate our proposed approach and compare it against its unconstrained variant (MSAC). The performed comparisons show that C-MSAC leads to higher success rates, more robust control policies, and better generalization performance.
Application of data mining techniques for VAT-Registered Business compliance

World Bank recommends that Indonesia lower the turnover threshold required to be a VAT-Registered Business from Rp. 4,8 billion to Rp. 600 million to increase VAT-Registered Businesses numbers which will also increase VAT revenue. The number of VAT-Registered Businesses will be significantly increased, which will push Directorate General of Taxes to determine the correct audit priority because it is impossible to audit all taxpayers. This study aims to form a prediction model for formal compliance of VAT-Registered Businesses in the Sampit Tax Office towards 1270 VAT-registered Businesses as of December 31, 2019, which are classified as low-risk VAT-Registered Businesses. The prediction model will be useful for determining audit priorities for certain taxpayers. This study uses a qualitative method using the RapidMiner application and decision tree technique in making prediction models for VAT-Registered Business compliance. The model made has Prediction Efficiency of 67,9%, reduction in Examination Effort by 63.67%, and Strike Rate of 85.99%. The model made is used to predict new VAT-Registered Business data which registered in 2020 and predicts 76 VAT-Registered Businesses will be compliant and 7 VAT-Registered Businesses will not be compliant
Machine learning: Classifiying taxpayer’s supervising zone based on the street address using Natural Language Processing algorithm

Assigning taxpayers into their respective Account Representatives is a crucial step to optimize Taxpayers supervision. However, the large number of registered taxpayers and missing data has been a great challenge. A lot of taxpayers only include their street addresses and no additional information such as RT, RW, etc. This will cause additional work to manually search each taxpayer address in the internet and manually assigned, which is not efficient and takes a lot of time. This study will try to solve this problem using Natural Language Processing algorithm. Efficiency and accuracy are the key on creating machine learning model. Choosing the right classifier is crucial to the accuracy. Other than the classifier, managing text data is also challenging, since it cannot be understood directly by computers. Thus, this study will also include how we could transform the text data into arrays of numbers called Bag of Words.
REGULAR EXTENSION OF GRAPHS

Regular graph is a graph in which all vertices have same degree. In this article, we find regular extension of graphs. For that, we introduce a regularizing sequence, which is a new tool for regularization of graphs. The process of constructing a regular graph that contains a given graph as a subgraph is called regularization of a graph. Regular extension of different classes of graphs is determined, and necessary conditions for a graph to be a regular-extendable graph are obtained. Some classes of regular extendable graphs and non-regular extendable graphs are identified. Received: January 18, 2023;Accepted: March 11, 2023
Eye Aspect Ratio based on Histogram Oriented Gradient and Linear Support Vector Machine to Microsleep Detection

Traffic accidents are caused by several factors, especially due to driver fatigue. To minimize accidents caused by human negligence, developing a prototype microsleep detection system to trigger an alarm is necessary. This research uses Histogram Oriented Gradient and Support Vector Machine methods to detect objects. The programming language used is Python, using the dataset from the Idlib face landmark as a marker in the face point area, which will then be calculated based on the eye aspect ratio. This system is implemented using video input captured using a webcam in real-time. The output of this system uses a buzzer to alert the driver. In this study, the test results were obtained well, carried out with 2 test scenarios with a distance of 40cm - 100cm and testing light levels of 33 lux to 226 lux. From these results, the accuracy results were obtained at 88% each.
Sistem Informasi Akuntansi Penggajian Dalam Perspektif Ekonomi Islam pada Dinas Perindustrian dan Perdagangan Kabupaten Langkat

The payroll accounting information system is a function, record document, which is used for the benefit of cost of products and the provision of information to monitor labor costs. The purpose of this research is to find out the accounting information system that has been implemented by the Department of Trade and Industry of Langkat the accounting information system that has been implemented by the agency is in accordance with an Islamic economic perspective. The analytical tool used in this study was a flowchart through drawing procedures. The data collection method used interviews and documentation. The data analysis technique used was descriptive qualitative analysis technique, which is an analytical method by first conducting interviews and then collecting data. , classifying and interpreting data so as to provide a clear, directed and comprehensive picture of the problem under study. The results of the study indicate that the application of the payroll accounting information system for the Department of Trade and Industry of Langkat Regency is still not effective. This is indicated by the absence of segregation of duties between those who input salary data and those who process salary data. &#x0D; Keywords: Payroll Accounting Information System, Islamic economics, Flowchart.
Dual-Channel Relation Mixup Strategy for Social Bot Detection Based on Graph Neural Network

Abstract In social bot detection, accurate labeling needs to be done manually, resulting in large-scale labels are difficult to obtain. However, the limited labels mean that there is less data available for the training set, which may result in over-fitting. Therefore, we proposed a novel data augmentation strategy named Dual-Channel Relation Mixup (DCRM) to address potential over-fitting issues caused by a small training set. In our strategy, we firstly construct two channels that input different account relations, then combine the resulting aggregated representations from both channels before passing them on to the next layer of the “message passing” mechanism. More critically, our DCRM is a general skill that can be equipped with many other backbone models (e.g., GCN, GraphSAGE, GAT, and RGCN) to enhance performance. Extensive experiments on several benchmarks demonstrate that our proposed DCRM strategy is an effective way to enhance the performance of bot detection based on graph neural networks. Furthermore, it also exhibits better generalization ability and stability, especially when working with smaller datasets.
Identification of hybrids between the Japanese giant salamander (Andrias japonicus) and Chinese giant salamander (Andrias davidianus) using deep learning and smartphone images

Biological invasions are recognized as one of the factors causing biodiversity loss. Incomplete reproductive isolation with a closely related species can result in hybridization when a non-native species is introduced into a new habitat. Management of hybrids is essential for biodiversity conservation; however, the distinction between the two species becomes a challenge in cases of hybrids with similar characteristics to native species. Although image recognition technology can be a powerful tool for identifying hybrids, studies have yet to utilize deep learning approaches. Hence, this study aimed to identify hybrids between native Japanese giant salamanders (Andrias japonicus) and non-native Chinese giant salamanders (Andrias davidianus) using EfficientNet and smartphone images. We used smartphone images of 11 native individuals (with 5 training and 6 test images) and 20 hybrid individuals (with 5 training and 15 test images). In our experimental environment, an AI model constructed with efficientNet-V2 showed 100% accuracy in identifying hybrids. In addition, highlighting the regions that influenced the AI model’s predictions using Grad-CAM revealed that salamander head spots are responsible for correctly classifying native and hybrid species. The results of this study revealed that our approach is one of the methods that enable the identification of hybrids, which was previously considered difficult without identification by the experts. Furthermore, since this study achieved high-performance identification using smartphone images, it is expected to be applied to a wide range of low-cost identification using citizen science.
Theories of Materials Evaluation and Adaption: A textbook English in Focus for Junior High School Grade VII

The textbook which is going to evaluate and adapt consists of four English skills to be learnt. They are listening, speaking, reading and writing. However, these materials have too many objectives and need to be reorganized in order to suit the students at Pinrang, South Sulawesi. The chosen unit of the middle school source book is chapter 3 (Growing Up) which contains 24 pages and 39 exercises. I think the materials and exercises are too compact and irrelevant with my students so these have to be adapted to the students’ learning style and situation.
Sentiment Analysis of Linguistic Data in Behavioral Research

A complete science of human behavior requires a comprehensive account of the verbal behavior those humans exhibit. Existing behavioral theories of such verbal behavior have produced compelling insight into language’s underlying function, but the expansive program of research those theories deserve has unfortunately been slow to develop. We argue that the status quo’s manually implemented and study-specific coding systems are too resource intensive to be worthwhile for most behavior analysts. These high input costs in turn discourage research on verbal behavior overall. We propose lexicon-based sentiment analysis as a more modern and efficient approach to the study of human verbal products, especially naturally-occurring ones (e.g., psychotherapy transcripts, social media posts). In the present discussion, we introduce the reader to principles of sentiment analysis, highlighting its usefulness as a behavior analytic tool for the study of verbal behavior. We conclude with an outline of approaches for handling some of the more complex forms of speech, like negation, sarcasm, and speculation. The appendix also provides a worked example of how sentiment analysis could be applied to existing questions in behavior analysis, complete with code that readers can incorporate into their own work.
Mindsponge theory: a way of life

In general, the harmonious combination of the unique perception of the mind, the knowledge of natural sciences, and the set theory logical reasoning can provide us (readers) with precious contemplation moments of who we are and how the world works. To me, "Mindsponge theory" is not only a theory or a worldview, but it also implies a way of life. Through it, I come to know that we experience the outer world through our inner mental realm.
Does Corruption and Institutional Quality Impede Economic Performance in Nigeria?

This study examined the nexus between corruption, institutional quality and economic performance with the motivation to determine the extent to which economic performance is impeded by corruption and institutional quality in Nigeria. The autoregressive distribution lag (ARDL) technique was employed to test the short-run and long-run relationship among the variables of interest. The data used for the analysis were obtained from the World Development Indicators and the Central Bank of Nigeria’s Statistical Bulletin for the period 1970 to 2020. The study found that corruption has a negative and significant impact on economic performance in the long-run. In contrast, institutional quality was found to have a negative but insignificant effect on economic performance. This implies that despite the establishment of institutional structures and enactment of stringent laws to curb the menace of corruption, Nigeria still witnesses a decline in economic performance. As such, Nigeria has weak institutions and regulations to fight corruption. Contrastingly, the study revealed that human capital, trade openness and the working population exerted positive and significant impacts on economic performance. By implication, the Nigerian economy reacts positively to changes in human capital development, trade openness and working population in the long-run. This suggests that, with the availability of appropriate policies and resources, human capital development, trade openness, and working population growth have the potential to enhance Nigeria’s economic performance.