Reviewerly/exHarmony huggingface dataset:
Algoritma Genetika Untuk Penjadwalan Karyawan Ira Stationary

Employee scheduling is an activity plan for time sharing that contains a schedule for carrying out planned activities in the form of a table. This study aims to create an employee schedule model using a Genetic Algorithm, which is a heuristic method inspired by the process of natural selection, the strong will survive and reproduce, the stages of the Genetic Algorithm are population initialization, fitness value, selection, crossover, and mutation. The study results show an optimal model consisting of at most two shifts with a maximum of two holidays a week and not consecutively.
Multiple Regression: Methodology and Applications

Multiple regression is one of the most significant forms of regression and has a wide range of applications. The study of the implementation of multiple regression analysis in different settings contributes to the development of relevant theories and the improvement of models. In this paper, four different kinds of regressions are discussed individually by referring to different articles. The four kinds of regressions discussed are multivariable/multiple linear regression, multivariate multiple linear regression, multinomial logistic regression, and multivariate non-linear regression. As for multivariable/multiple linear regression, examples in the manufacturing industry and medical field show that it can be applied in more fields. Multivariate multiple linear regression is more accurate than multivariable/multiple linear regression and can be used with more than a variable. Multinomial logistic regression is relatively mature and accurate, and can help people well solve the problem with non-linearity and multiple independent variables. It does not require the variables to obey a multivariate normal distribution, and is more widely used as well. Multivariate non-linear regression, however, cannot be used properly without powerful professional knowledge. This paper investigates the theoretical development and model applications of multiple regression to demonstrate the flexibility and broadness of the adoption of multiple regression analysis.
ANALYSIS THE IMPLEMENTATION OF CLOUD BASED ACCOUNTING SYSTEM AT CV ANEKA JAYA BITUNG

This study aims to analyze the implementation of a cloud based accounting system at CV Aneka Jaya Bitung, before using a cloud based accounting system, the transition process to a cloud based accounting system, and the benefits after implementing a cloud based accounting system in the purchasing and selling business processes. This study uses a qualitative method with the object of research is CV Aneka Jaya Bitung. The data in this study were obtained through interviews, observation and documentation. Interviews were conducted with 5 employees using cloud based accounting systems, in this case the Odoo application. The results of this study indicate that before the implementation of the cloud-based accounting system, problems occurred, including inaccurate recording and a more time-consuming process in both stock recording and transaction recording. In addition, the authors found obstacles during the transition process to a cloud based accounting system such as lack of employee knowledge, lack of employees who handle the transition process, system complexity, network connectivity, missing and piling data. After implementing the cloud based accounting system, the company will benefit from the aspects of performance, information and data, economics, control and security, efficiency, and service.
Improving Arabic Named Entity Recognition with a Modified Transformer Encoder

This article investigates the use of a transformer encoder for Arabic Named Entity Recognition (NER).The classic transformer that was originally proposed for machine translation adopts the absolute sinusoidal position embedding which is aware of distance but unfortunately is not aware of the directionality.However, in the NER task, both distance and orientation are crucial.Therefore, in this study, instead of using absolute sinusoidal position encoding, we employ relative positional encoding and incorporate the directionality information in our NER model.More specifically, our proposed model uses Bidirectional Long Short-Term Memory (BiLSTM) for encoding every input token.Then, the output of the encoder is fed to the multi-head attention where both the distance and directionality information are incorporated.The decoder layer with a simple fully connected layer takes as input, the result of the attention layer, and the prediction layer with Conditional Random Fields (CRF) predicts the tag of each token.We validate our proposed approach on two merged public datasets, namely, ANER corp and AQMAR.Our experiment results demonstrate significant improvements when compare to the vanilla Transformer with absolute sinusoidal position encoding while achieving a state-of-the-art result on a merged two Arabic public datasets.
Autoencoder based image quality metric for modelling semantic noise in semantic communications

Semantic communication has attracted significant attention as a key technology for emerging 6G communications. Though it has lots of potentials specially for high volume media communications, still there is no proper quality metric for modelling the semantic noise in semantic communications. In this paper, an autoencoder based image quality metric is proposed to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder-decoder model and calculate its latent vector space. Once it is trained, semantically received image is inserted to the same autoencoder to generate the corresponding latent vector space. Finally, both vector spaces are used to define the Euclidian space between two spaces to calculate the mean square error between two vector spaces which is used to measure the effectiveness of the semantically generated image. Results indicate that the proposed model has a correlation coefficient of 89.1% with the subjective quality assessment. Furthermore, the proposed model is tested as a metric to evaluate the image quality in conventional image coding. Results indicate that the proposed model can also be used to replace conventional image quality metrics such as PSNR and SSIM.
Efficient Decoding Algorithm for Binary Quadratic Residue Codes Using Reduced Permutation Sets

The Quadratic Residue (QR) codes have a rich mathematic structure.Unfortunately, their Algebraic Decoding (AD) is not generalizable for all QR codes.In this study, an efficient hard decoding algorithm is proposed to generalize the decoding of the binary systematic Quadratic Residue (QR) codes.The proposed decoder corrects t erroneous bits or less, in the received word, based on a reduced set of permutations derived from the large automorphism group of QR codes.This set of permutations is applied to the received word to move the error positions and trap all of them in redundancy.Then, to evaluate the proposed method, we applied it to many binary QR codes of moderate code length starting with 17 until 113 with reducible and irreducible generator polynomials.The proposed decoder was validated by inserting all possible error patterns, that have t or less erroneous positions, as input of the proposed decoder and the output is always a correct codeword.The complexity study, in terms of the number of operations used, reveals that the light permutation decoding LPD algorithm significantly decreases decoding complexity without performance loss.So, it is qualified to be a good competitor to decode QR codes with lower lengths but is the best for QR codes with higher lengths.
Application of Learning Cloud Computing Technology (Cloud Computing) to Students in Higher Education

The development of internet-based computing technology is currently more directed at application systems that are easy to use and do not require a lot of energy. With the existence of cloud computing in the current digital era, it has been felt amid society in everyday life, such as by using email and social media. In general, the purpose of this research is to understand the application of cloud computing for students to calculate data from learning materials under the teacher's material so that later it can help build an effective and efficient learning system. The results of this study can later help students understand the application and function of cloud computing technology, especially in learning. This type of research is a case study from a qualitative descriptive approach by specifying interview instruments and also from questionnaires. The results of the research described above prove that most users use cloud computing applications based on all the conveniences that this application proposes compared to conventional learning systems in the classroom. The facilities provided by this cloud computing application have become popular and attractive to students
Detecting Arbitrary-oriented Objects in Remote Sensing Imagery with Segmentation-Aware Mask

Arbitrary-Oriented object detection in remote sensing images is a hot topic in recent years. Currently, most arbitrary-oriented object detectors adopt the oriented bounding box (OBB) to represent targets in remote sensing imagery. However, OBB representation suffers from suboptimal regression problems caused by the ambiguity of the angle definition. In this paper, we propose a novel framework to Learning Segmentation-aware Mask for arbitrary-oriented object Detection (LSM-Det) in remote sensing imagery. LSM-Det predicts the mask of the object, and then converts the mask prediction into a minimum external OBB to achieve arbitrary-oriented object detection. Moreover, we designed a segmentation-aware branch to select high-quality predictions via the output matching score. Our method achieves superior performance on multiple remote sensing datasets. Code and models are available to facilitate related research.
Virtual Reality in Healthcare: Exploring the Patients’ Behavior through the Lens of Extended Stimulus, Organism, and Response Framework

Virtual reality (VR) is a recently emerged tool in healthcare. However, there is dearth of research since literature lacks studies on factors that enhance patients' visits to VR-health facilities. The current study has proposed a theoretical framework to engulf this gap, employing the stimulus, organism, and response (SOR) framework to investigate patients' attachment, patient’s cognitive and affective response, authentic experience, and intention to visit VR-health facilities. The study has found significant influences of the patients' authentic experience on their cognitive and affective responses. It shows that patients' authentic experience plays a significant role in VR health. The results further verified that patients' cognitive and affective responses have significant mediating effects to predict the attachment and intentions to VR visit. The patients' intention to visit health facilities reveal that attachment to the VR predicts VR-health. Likewise, cognitive response shows a powerful impact compared to the affective response on the intention to visit health facilities over virtual reality. This research brings to fore why prospective patients visit healthcare facilities using virtual reality.
LiDAR-Based Obstacle Detection and Avoidance for Navigation and Control of an Unmanned Ground Robot Using Model Predictive Control

Unmanned Ground Vehicles (UGVs) have, as of late, been utilized in a wide assortment of utilizations because of their flexibility, diminished expense, and quick response, among other benefits.Search and Rescue (SAR) is quite possibly the most conspicuous zones for the work of UGVs instead of a monitored mission, mainly due to its impediments on the expenses, human resources, and view of the human administrators.An ongoing way of arranging to utilize numerous helpful UGVs for the SAR mission is proposed in this study.This study aims to introduce the initial moves towards a Model Predictive Control (MPC) based peril evasion calculation for UGVs representing the vehicle elements through high constancy models and uses just surrounding data about the environment as given by the available onboard sensors.In particular, the paper presents the MPC definition for peril evasion utilizing a Light Detection and Ranging (LiDAR) sensor and applies it to a contextual of the effect of model constancy on the calculation's presentation, where execution is estimated principally when to arrive at the objective point.The Robot Operating System (ROS) is used to drive the sensors and visualize the data in RVIZ.This study presents MPC development for navigating Husky A200 by adjusting the longitudinal, lateral, and yaw motion command behaviors.The proposed algorithm for Husky A200 is tested indoors and compared the results with the simulation results plotted using MATLAB and GAZEBO.A novel simulator package is developed for the Husky using RVIZ and GAZEBO.The efficiency of the proposed MPC design is tested through simulation and compared with real world experiments, the real-time longitudinal movement follows the simulation results closely.For MPC's short-term optimization, an optimized control signal from a linear framework is utilized for a linear quadratic controller.According to the Husky position and orientation, applying a transformation to convert the map coordinate system to the Husky coordinate system.Transforming the map coordinate system helped in computing the errors because the initial vector considers position and orientation as zero.
Design and Development of Rule-based open-domain Question-Answering System on SQuAD v2.0 Dataset

Human mind is the palace of curious questions that seek answers.Computational resolution of this challenge is possible through Natural Language Processing techniques.Statistical techniques like machine learning and deep learning require a lot of data to train and despite that they fail to tap into the nuances of language.Such systems usually perform best on close-domain datasets.We have proposed development of a rule-based open-domain question-answering system which is capable of answering questions of any domain from a corresponding context passage.We have used 1000 questions from SQuAD 2.0 dataset for testing the developed system and it gives satisfactory results.In this paper, we have described the structure of the developed system and have analyzed the performance.
Machine learning based Quality of Experience (QoE) Prediction Approach in Enterprise Multimedia Networks

In this paper, we discuss quality of experience in multimedia networks.We present an architecture and a survey of machine learning methods to predict the quality of experience in an enterprise multimedia network environment.Our approach is based on subjective methods.It consists of the use PRTG (Paessler Router Traffic Grapher) for QoS (quality of service) data collection and Google Forms for the different users of the network MOS (Minimum Score Opinion) parameters collection.We then implement different supervised machine learning schemes using the data collected, and finally analyze their performance.We compare two classes of algorithms namely regression algorithms and classification algorithms.The Random Forest Classifier in the second class algorithm give the best results.
Trust Based Recommendation System Using Knowledge Graph (KGTRS)

Recommender systems has proven its importance to the solution of exponentially increasing data on the internet, which provides users with more personalized services of information for better development of online services.The issue of trust has emerged into our day-to-day life since early 90s, which focuses on improvement of recommender systems.The Google Knowledge base gathers information from a variety of sources.In the presented work trust is incorporated using knowledge graph in order to improve the recommendation system that provide the opportunity to build trust with potential site visitors looking for a product/service/information.To enhance the user belief and usre acceptance trust based recommendation system is incorporated.Trust based recommender systems using knowledge graph improve the accuracy of the recommender system and user experience.They are also capable of handling some challenges of recommender systems such as cold-start problem.
An Interactive Voicebot Using Rasa Framework For Migrant Workers

During the pandemic, one thing that came to light and finally received longoverdue attention was the plight of migrant workers in India.This section of society lacks majorly in terms of resources.To help them, this paper focuses upon one such resource called the MiWo Voicebot.This unique voice bot is an AI Voice Bot that focuses on migrant workers and the problems they face.It focuses on areas such as their constitutional rights, labor laws, organizations, and NGOs that can help these workers.The voice bot can be extended to be multilingual which can cater to the needs of a very diverse section of the workers.MiWo aims to increase the quality of life these workers have and give them an option to know how to access their rights or get help when required from the right place.
A Logistic Regression Model for Hate Speech Recognition

People feel entitled to express their opinions on social media, and many of these opinions may be hostile to others.Hate speech recognition is essential in today's world, where it is important to keep track of what is posted in public for all to see.This paper discusses ways to detect hateful language on any topic using data mining, natural language processing, and machine learning techniques on the social media site Twitter.A Logistic Regression Model for Hate Speech Recognition is proposed.The research conducted provides details of all the hate commenters as well as an overview of the topics on which the hate is being projected the most.
Convolution Neural Network-based Mosquito Classification System

Many diseases caused on by mosquito bites have spread all over the world in recent decades.The most prevalent diseases are malaria, dengue, and chikungunya.Worldwide, diseases transmitted by mosquitoes are indeed a threat.Due to the extreme significant degree of similarity in appearance between different mosquito species, classifying mosquito is a particularly tough task.We focused on deep learnign using convolutional neural network.Classifying mosquitoes is a particularly difficult pursuit because different mosquito species have an extremely high level of visual analogy.In this paper, we offer a convolutional neural network-based (CNN) technique for classifying mosquito species.We have built mosquito image classification models using CNN on our own mosquito dataset of 1800 mosquito images from three different genera Aedes, Anopheles, Culex.We made use of data augmentation.Results prior to and following augmentation were compared.After augmentation, we obtained accuracy of 84.51%.
Improving English-Bharti Braille Machine Translation Through Proper Name Entity Translation

Machine Translation has been one of the main areas of research in the dawn of Artificial Intelligence.Although getting a human-level accuracy through a machine was considered as a task that is next to impossible, this cliché is on the verge of being broken.The paper discusses the development of an MT system for English-Bharti Braille.The system translates the English test into Braille which can be read by people who are visually impaired.For this a separate module for handling name entities is incorporated into the MT pipeline.It is an attempt to disseminate knowledge to people who have a craving for learning.The results of the system are compared using BLEU metric and have shown improvement over the baseline system.
Scope of IoT in Business Applications for Sustainable Development in India

In the Digital Age, the IoT is entering many industries.It is easy to gather and analyze system data to enhance personal and professional goods.This technological transformation is revealing new IoT weaknesses, notably in business intelligence.Computer hardware, embedded system devices, networking devices, display devices, control devices, and software upgrades have helped IoT progressively promote Business Information Systems.This chapter discusses how network agility, unified Intelligence, and large-scale deployment can revive the Business Internet.It will also be discussed how the Internet of Things (IoT) will shape future business models.It is about connecting billions of devices and using many useable data to construct Business Information Systems with some practical examples.This chapter includes various instances.
AR in Education as Crystal Concept

Despite the present generation has 3-D, our nation utilizes 2-D media in studies.The mixture of AR tech and study material form a newly type of automation system and actions to improve efficiency and raise interest of giving lesson and gaining knowledge for trainer in real-life situations.Augmented Reality is a new, integrated approach features from computer everywhere, laptop, and the public computer.The center offers unique capabilities, including a virtual world, with and vague manual control of point of view and interaction.The research gives a launch to augmented reality (AR) technology, its educational opportunities.Important technology and ways of communicating in the context of studies.
Enhancement of Security in Connection establishment for IoT Infrastructure through Adversarial Neural Cryptography using GANs

As we are moving ahead in this Digital Age of Information, the need for fortification for Digital Assets and Data is also increasing day by day.Soon, in the near future, we will be able to see everything connected and controlled by a single large-scale network called the Internet.This whole infrastructure of connected various machines is coined with a term called the Internet of Things.With this versatile connection of Devices, Sensors, and machines, every digital asset an Individual can own will be at risk.The Increase in number of Assets will lead to increase in the number of Threats and Vulnerabilities which will result in a catastrophic future.To avoid such situations, we are in need of some dynamic strategies that can help individuals to protect themselves from such threats.Here we will be discussing, how these assets can be protected in an IoT environment by implementing Security through Adversarial Neural Cryptography using Generative Adversarial Networks.Fortification of this Cryptographic technique is giving momentous results when implemented in IoT devices during establishing connections, without human interference.
A Review of Monocular Depth Estimation Methods Based on Deep Learning

In applications like autonomous vehicle driving or robot maneuverability, Precise depth estimation from images is vital for understanding the scene and its reconstruction.Traditional depth estimation techniques are based on component correspondences of several viewpoints.Monocular depth estimation from a single image is a challenging task due to the inherent ambiguity in the scene's geometry.Deep neural networks have shown great promise in addressing this problem by capturing complex features from the image and providing accurate depth maps.In this paper, we review the recent advances in monocular depth estimation based on deep learning techniques.We explore the various network frameworks and training methods used to improve the accuracy of depth estimation.We also examine the limitations of current methods and discuss open challenges in this field.Our goal is to provide a comprehensive overview of the current stateof-the-art in monocular depth estimation based on deep learning and to inspire further research in this area.The paper examines a variety of learning strategies, as well as datasets for Monocular depth estimates and challenges.