Reviewerly/exHarmony huggingface dataset:
A Reinforcement Learning Approach for Trajectory Planning of Intelligent Vehicles

Intelligent vehicles have received significant attention from numerous research academics and technology businesses, following the rapid growth of artificial intelligence and machine learning. In dynamic environments, motion planning is critical for autonomous vehicles. However, handling these issues of active obstacle avoidance in dynamic environments and real-time optimal trajectory planning are current challenges for traditional motion planning algorithms. To address these issues, this paper proposes a reinforcement learning method for real-time near-optimal trajectory planning of intelligent vehicles in the presence of moving obstacles. To achieve real-time trajectory planning, a dual heuristic programming (DHP) algorithm is adopted. Furthermore, for achieving active obstacle avoidance in dynamic scenarios, the potential field function is incorporated into the designed actor-critic network. Simulation results illustrate that the proposed method has an excellent capacity to follow a specified reference trajectory. Also, the proposed approach can wonderfully plan a real-time collision-free path and has excellent capabilities in the aspect of the position error and the smoothness of the planned trajectory compared with the RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm.
A Multi-UAV Cooperative Ground Target Tracking System Based on a Two-Layer State Fusion Structure

UAVs (Unmanned Aerial Vehicles) have been used in many missions, such as disaster management, traffic monitoring, target tracking, etc. However, an individual UAV has a limited field of view and is prone to lose target in a tracking task. In this paper, we propose a system framework for cooperative tracking of ground moving targets by multi-UAVs to achieve better performance in long time target tracking. Firstly, the UAV-based algorithm AutoTrack is adopted to track targets. Secondly, we propose a two-layer state fusion estimation architecture and use an extended Kalman filtering method to fuse the information and obtain accurate target localization. Finally, experiments on tracking targets from three videos taken under different situations are implemented. Results show that the proposed system performs better than a single UAV. The fps of the tracking algorithm is 25.1 which meets the need of realtime tracking. By applying the fusion estimation, the position error of x and y direction are reduced by 94.2% and 90%.
Movement Tracking Detection of Break Dance Based on Deep Learning

To accurately detect the movements of break dance, a movement detection strategy based on improved SSD is proposed. Among them, in order to reduce the calculation amount of traditional SSD, MobileNet_V2 network is used to replace the traditional VGG backbone network, and then the mutex loss function is introduced to weaken the interference of overlapping movements on detection. Finally, the test is carried out in the data set. The results show that after optimization by Loss function, the detection of the model is more accurate in the case of overlapping targets. The accuracy of the model on the test set is 93.4%, and the recall rate is 91.6%, which indicates that the proposed detection network model has a good effect on movement tracking capture, and it can be used in the movement tracking detection of break dance.
Android applications as an additional tool in inorganic chemistry teaching: A short-review

The concept of mobile access to information is present in all aspects of society today and it constitutes the digital need of the individual. The aim of this paper is to provide a brief overview and systematization of various easily accessible android applications for mobile phones and tablets that would promote proper understanding of chemical concepts, as well as facilitate the acquisition of knowledge in chemistry by students in both primary and secondary schools. Mobile learning has many advantages such as diversity, entertainment, communication, interactivity, but also learning completely adjusted to the needs of the individual regardless of place and time. The use of these tools has a great impact on a different approach in teaching chemistry and contributes to improving of the final learning outcomes. That is why it is important for every teacher to integrate technology into pedagogy and use it to promote student-centered learning.
Moderna's Vaccine Using the K-Nearest Neighbor (KNN) Method: An Analysis of Community Sentiment on Twitter

The COVID-19 is still in Indonesia. The government has made efforts to stop the COVID-19 virus, by moving vaccination program. There are various types of vaccines, one of which is moderna vaccine or MRNA-1273 that applied intramuscularly. The vaccination programs using modern vaccines creates different opinions in public, especially among Twitter users. The opinion uploaded will be the data on Public Sentiment Analysis on Twitter About Moderna Vaccines Using K-Nearest Neighbor Method research. In this study, TF-IDF method is used for weighting the words and KNN for classifying the sentiment into two groups of sentiments, namely positive and negative. The tools used in this research are Rapid miner to collect tweet data and Python for sentiment classification and evaluation. From the test results Based on 50 training data when k = 3 it is known that the accuracy value is 80%, precision is 80%, recall is 100% and F-Measure is 89%.
Examining the Acceptance of Blockchain by Real Estate Buyers and Sellers

Abstract Buying and selling real estate is time consuming and labor intensive, requires many intermediaries, and incurs high fees. Blockchain technology provides the real estate industry with a reliable means of tracking transactions and increases trust between the parties involved. Despite the benefits of blockchain, its adoption in the real estate industry is still in its infancy. Therefore, we investigate the factors that influence the acceptance of blockchain technology by buyers and sellers of real estate. A research model was designed based on the combined strengths of the unified theory of technology acceptance and use model and the technology readiness index model. Data were collected from 301 real estate buyers and sellers and analyzed using the partial least squares method. The study found that real estate stakeholders should focus on psychological factors rather than technological factors when adopting blockchain. This study adds to the existing body of knowledge and provides valuable insights to real estate stakeholders on how to implement blockchain technology.
HUMAN AS PHILOSOPHER AND NOTARY

A person becomes a human being if he or she is capable of communication based on the disclosure of his or her inner essence, which he or she not only experiences, feels, but also expresses, writes down, and tells others about its content. The well-known saying that “someone else's soul is twilight” captures the complexity of self-knowledge that philosophy calls for. The fact is that the essence of a person is indeed in the twilight of the bodily wrapper that hides its manifestations. Therefore, it is quite possible to argue that cognition is the process of declassifying the content that is hidden behind material bodily forms. Hence the well-known definition that consciousness is the internal state of matter. The organs of perception perceive universal forms of material existence through movement subordinated to internal forces, which, by revealing and projecting outward not only aloud but also through writing, can reveal information about them and their inherent energy potential. M. Mamardashvili defined philosophy as consciousness out loud, but it is always desirable not only to voice any reflections on the “last and ultimate” foundations of existence, as Heidegger defined its essence, but also to write them down, thereby preserving them for posterity, as well as for contemporaries, who thus have the opportunity to join the discussion and, thus, to some extent, to solve problems that concern people but which must be constantly addressed by each new generation.
Rainwater Harvesting

Abstract: One of the severe issues that is well recognized on the planet is the water scarcity. Overexploitation of groundwater and surface water resources is the outcome of population growth, urbanization, and industrial expansion. Due to uneven rainfall, the traditional water sources, such as wells, rivers, and reservoirs, are unable to supply all of the water needed .While a new water source is being investigated by the rainwater gathering system. Utilizing rainwater is the study goal, which is closely related to the idea of protecting the environment. This study examines the Rain Water Harvesting (RWH) system as a substitute for the BBDITM H-block as a source of water. By taking into account nearly all technological aspects, the development system satisfies social requirements and maybe implemented in both urban &amp; rural areas.
CLASSIFICATION OF ROASTING RATES IN COFFEE BEANS BY DIGITAL IMAGE PROCESSING USING THE NAIVE BAYES CLASSIFIER (NBC) METHOD

Coffee is the bean of the coffee plant and is the source of coffee drinks. Coffee beans must pass through the coffee roasting stage or also called coffee roasting, from this stage of the process the coffee will be roasted and this process also has its own level. At this stage of the roasting process coffee shop business people often do not know this process, then in this case used Local Binary Pattern (LBP) method. LBP is a simple and very efficient texture operator by labeling pixels by doing thresholding on each pixel neighbors and considers the result as a binary number. This method is used to obtain the texture ektrasi of an image. While for the classification method using the Naive Bayes Classifier (NBC) method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by summing the frequencies and combinations of values from a given dataset. The algorithm uses Bayes' theorem and assumes all the independent or non-interdependent attributes given by the values on the class variables. From the test results by comparing training data and testing data obtained an accuracy rate of 81%. For an image-based developed system with display recognition difficulties, this performance is good.
GAMBARAN VERBAL BULLYING YANG TERJADI PADA SISWA KELAS TINGGI DI SD INPRES 5/81 SAMAELO KECAMATAN BAREBBO KABUPATEN BONE

This research is a qualitative research that aims to describe the behavior of verbal bullying that often occurs in the upper grade at SD Inpres 5/81 Samaelo, Barebbo District, Bone Regency. The research subjects were the high grade students and high grade teachers at SD Inpres 5/81 Samaelo. Data collection was carried out using interviews and documentation techniques. Data analysis techniques include: data reduction, data presentation, and drawing conclusions. Results of the study: the forms of verbal bullying that often occur in high grades at SD Inpres 5/81 Samaelo, Barebbo District, Bone Regency are mocking friends with inappropriate names, mocking friends who can't do the questions given by the teacher, shouting at friends, forbidding other friends join the group, and make fun of friends.
Generalizing French Schwa Deletion: the Role of Indexed Constraints*

Indexed constraints (like cophonologies) increase a grammar’s fit to seen data, but do they hurt the grammar’s ability to generalize to unseen data? We focus on French schwa deletion, an optional process whose rate of application is modulated by both phonological and lexical factors, and we propose three indexed constraint learners in the Maximum Entropy (MaxEnt) framework. Using data from Racine (2008), we test the ability of four learners to capture existing patterns and generalize to unseen data: three learners and a control MaxEnt learner without indexed constraint induction. The Indexed constraint learners indeed lead to better fit to the training data compared to the control. The resulting grammars are tested on a different schwa deletion dataset from Smith &amp; Pater (2020). It is shown that indexed constraints do not lead to a drop in generalization to these data, and one of the indexation learners produces a grammar that predicts Smith &amp; Pater’s data quite closely. We conclude that indexed constraints do not necessarily hurt a grammar’s ability to generalize to unseen data, while allowing the grammar to achieve a closer fit to training data.
Total Dissolved Solids, Phenol, and Barium Removals from Oilfield Produced Water Using Kapok Fibers and Ultrafiltration Membrane

Oilfield-produced water treatment using raw Kapok fiber (RKF) and a modified surface of Kapok fiber followed by an ultrafiltration membrane was conducted to reduce total dissolved solids (TDS), phenol, and barium. Variables considered in the experiment were contact times (30, 60, 90 min.), the flow rate of samples (5, 6, and 7 L/min), and trans-membrane pressure of ultrafiltration membrane (0.25, 0.35, and 0.50 bar). Raw Kapok fiber and modified surface Kapok fiber were used to investigate the effect of Kapok fiber on total dissolved solids, phenol, and barium removals from produced water. The results showed that raw KF decreased the TDS, phenol, and barium by 51.81%, 62.63%, and 54.20%, respectively. Treatment of raw Kapok fiber column filtrate using ultrafiltration membranes (UF) showed the removal of TDS, barium, and phenol achieved 73.15%, 42.44%, and 79.45%, respectively, at a flow rate of 5 L/min, TMP of 0.25 bar, and contact time of 90 min. Modifying the Kapok fiber surface using sodium hydroxide solution (5 wt%) and hot water (98.5 °C) reduced the TDS, phenol, and barium to 57.32%, 65.83%, and 79.08%, respectively. Further, at the same operating condition, the modified surface of Kapok fiber followed by UF decreased 94.31% TDS, 84.20% phenol, and 56.23% barium, respectively. The results show that modification of the Kapok fiber surface followed by UF can be used to remove the TDS, phenol, and barium from produced water.
Ethics of Big Data: A Socio-Economic Perspective

With the development of information technology, artificial intelligence, and robots, capitalism has increasingly assumed a form of platform capitalism which is ‘reconceptualizing our economy’ to the extent that it is ‘forcing us to rethink economics’. Capitalism and various institutions controlled by this system have been the subject of criticism in the past and need was expressed to reform them. But today it has become most obvious that there is need for ‘replacing it with a more caring and provisioning sustainable system’.
Implementation of Generative Adversarial Network (GAN) Method for Pneumonia Dataset Augmentation

As a communicable disease, the majority of pneumonia cases are brought on by bacteria or viruses, which cause the lungs' alveoli to swell with fluid or mucus. Pneumonia may arise from this and further making breathing challenging since the lungs' air sacs are unable to contain enough oxygen for the body. Pneumonia may generally be diagnosed clinically (by a physician based on physical symptoms) as well as through a photo chest radiograph, CT scan, and MRI. In this case, the lower cost of a chest radiograph examination making it as one of the most popular medical imaging tests. However, chest radiograph photo readings have a disadvantage, where it takes a long time for medical staff or physicians to identify the patient's illness since it is difficult to detect the condition. Therefore, an identification of chest radiograph imagery into various forms using machine learning becomes one way to address this issue. This research focuses on building a deep neural network model using techniques from the Generative Adversarial Network algorithm. GAN is a category of machine learning techniques using two models to be trained simultaneously, one is a generator model to generated fake data and the other is a discriminator model used to separate the raw data from the real data set images. The dataset used is Chest X-Ray images obtained from repo GitHub and repo Kaggle totaling 5,863 with normal data 1583 images and pneumonia data 4273 imagesThe results showed that the use of the Generative Adevrsarial Network method as augmentation data proved to be more effective in improving the generalization of neural networks, this can be seen from the results the result of the accuracy value obtained is 97%.
Sexuality, Relationships and Factors Leading to Teenage Pregnancy among Students in Glan – Padidu National High School

This study aimed to find out the context of sexuality, relationships and factors leading to teenage pregnancy among students of Glan – Padidu National High School. The researcher used descriptive-quantitative research design. The respondents of the study were nine (9) students who were identified using purposive random sampling method. The data showed that respondents’ parents were mostly elementary level, earning P5,000 below monthly and most were self – employed. As to context of sexuality and relationships, most of the respondents engaged in sex when they were 14 – 16 years old, with partners 20 years old and above. Because of physical attraction, expression of love, and curiosity they engaged in such activity. Various factors lead students to get involve in premarital sex. Home factors such as parents lack of supervision; environmental factor such as TV show, movies and peer pressure; and emotional factor such as to satisfy the sexual needs. With this, it is recommended to create an effective program such as parental seminar, no to teenage pregnancy campaign, true love waits seminar and development of an IEC material to at least lessen the growth of statistics if not totally eliminate pre – marital sex among students at a very early stage.
The Use of Gamification in Teaching Writing Skills to Junior High Students | Istikhdām Al Tal’īb Fī Ta’līm Mahārah Al Kitābah Lada Thullāb Al Madāris Al Muthawassithah

Writing as a language skill is still considered a language skill that is categorized as very difficult. Various kinds of problems arise in learning to write Arabic because writing skills are skills that combine various disciplines. Therefore, teachers must continually innovate in the learning process, such as choosing a learning model or using various learning methods, one of which is gamification-based Arabic learning. Gamification is a modern educational strategy popular in the West, namely educational stimulation by applying game elements and principles in non-game contexts. This article describes Gamification's use in teaching high school students writing skills. The research method used is the descriptive method with the desk research method. The results of the study show that the use of Gamification in teaching writing skills can improve the quality of learning, especially in Arabic, so that students can master writing skills in an easy and fun way, and can lead to the development of teaching writing skills in a social context following their interests, motivations, and needs Student. This study recommends further research to test the effectiveness of Gamification on other Arabic language skills with a more varied research method.
Predicting the Silent Data Error Prone Devices Using Machine Learning

Silent Data Errors (SDEs) are a subset of Defective Parts per Million (DPPM) test escapes that cause unnoticed data corruption. Even at very low levels of DPPM, these are visible at cloud service provider data-center scales. In high-volume manufacturing, some defects manifest as SDEs that are screened at system level test (SLT) which is expensive. Due to subtleness of such defects, semiconductor devices prone to SDEs don’t exhibit evident patterns or anomalies in the test data distributions. So, screening such faulty devices with ATE using statistical kill limits is challenging. To accelerate identification of those faulty devices, ahead of system testing, we propose to use Supervised Machine Learning (ML) approach to learn intrinsic patterns in an industrial test dataset. The experimental results illustrate that the embraced supervised learning framework via an ensemble of feature selection methodologies shows a noticeable performance improvement over traditional supervised and unsupervised methods.
Optimizing Issue Tracking Systems using Deep Learning-based Issue Classification

This research paper aims to investigate the use of text classification for automatic issue tagging in issue-tracking systems. The study focuses on the current state of issue-tracking systems and their limitations in terms of issue tagging, specifically the manual effort required to tag and categorize issues. The research describes the implementation of a text classification model for automatic issue tagging and evaluates its performance in terms of accuracy and loss. The results of this study show that the use of text classification can significantly improve the efficiency and accuracy of issue tagging in issue-tracking systems, while also providing a more efficient and user-friendly experience. The results also provide valuable insights into the design and implementation of issue-tracking systems and demonstrates the potential of deep learning to enhance the accuracy and efficiency of issue-tracking. This research also provides insights for software development teams and managers on how to use text classification techniques to improve the efficiency and effectiveness of their issue-tracking systems.
ODLPIM: A Write-Optimized and Long-Lifetime ReRAM-Based Accelerator for Online Deep Learning

ReRAM-based Processing-In-Memory (PIM) architectures have demonstrated high energy efficiency and performance in deep neural network (DNN) acceleration. Most of the existing PIM accelerators for DNN focus on offline batch learning (OBL) which requires the whole dataset to be available before training. However, in the real world, data instances arrive in sequential settings, and even the data pattern may change, which calls concept drift. OBL requires expensive retraining to solve concept drift, whereas online deep learning (ODL) is evidenced to be a better solution to keep the model evolving over streaming data. Unfortunately, when ODL optimizes models over a large-scale data stream in the PIM system, unbalanced writes are more severe than OBL, due to the heavier weight updates, resulting in the amplification of unbalanced writes and lifetime deterioration. In this work, we propose ODLPIM, an online deep learning PIM accelerator that extends the system lifetime through algorithm-hardware co-optimization. ODLPIM adopts a novel write-optimized parameter update (WARP) scheme that reduces the non-critical weight updates in hidden layers. Besides, a table-based inter-crossbar wear-leveling (TIWL) scheme is proposed and applied to the hardware controller to achieve wear-leveling between crossbars for lifetime improvement. Experiments show that WARP reduces weight updates on average to 15.25% and up to 24% compared to that without WARP, and eventually prolongs system lifetime on average to 9.65% and up to 26.81%, with a negligible rise in cumulative error rate (up to 0.31%). By combining WARP with TIWL, the lifetime of ODLPIM is improved by an average of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{12}.\mathbf{59}\times$</tex> and up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{17}.\mathbf{73}\times$</tex> .