Reviewerly/exHarmony huggingface dataset:
Pengembangan E-Restaurant Menggunakan Metode Scrum untuk Meningkatkan Kualitas Pelayanan

The culinary industry is a business that is experiencing very fast growth. Technology simplifies all activities in management so that data presentation is faster and more accurate. The application of technology helps increase the effectiveness of the business processes being carried out. The restaurant business in using technology is needed to simplify the ordering process, submit data, process payments, make reports more innovative and efficient. This application was developed using agile method as needed which has many changes. This development simplifies the process of customer ordering and payment transactions for satisfaction with the service system provided so as to improve performance more effectively and efficiently.
Predictive Machine Maintenance Using Tiny ML

Abstract: Anomaly detection (AD) is detection of pattern in data in expected behaviour. In an industrial environment, any equipment and system that breaks down are affecting productivity. Therefore, Tiny Machine Learning (Tiny ML) is introduced to address this problem. Tiny ML undergo anomaly detection to detect if any equipment did not act expected behaviour and notify the user if an anomaly detection has been detected. Anomaly detection is an unsupervised learning algorithm. It has aim to identify the patterns of data that do not follow the expected behaviour. Tiny Machine Learning (Tiny), a rapidly evolving edge computing concept that links embedded systems (hardware and software) and machine learning, with purpose of realizing ultra-low-power or low-cost and efficiency and privacy also brings machine learning inference to battery-powered intelligent devices. By using TensorFlow Lite Micro, the Tiny ML can be trained to undergo anomaly detection. How is the machine learning process has exported for TensorFlow, then TensorFlow and final TensorFlow L Micro order to upload the machine learning algorithm with Tiny ML. The paper highlight the state the art of the current work on Tiny Machine Learning.
The Role of Artificial Intelligence in Managing the Diversity of Human Resources: A Proposed Research Model

The aim of this study is to examine the appropriate literature and emphasize the significance of taking into account the role of Artificial Intelligence (AI) in managing diversity in human resources.AI has the potential to revolutionize the way organizations manage diversity in their human resources.It can help identify biases in hiring, provide diversity training, monitor diversity metrics, and reduce bias in performance evaluations.The researchers reviewed various related literature and came up with propose framework/model that is predicting the role of AI in managing diversity in human resources.If the empirical validation of the model is successful, practitioners and other stakeholders will gain a valuable understanding of the crucial role played by AI, based on the findings.
A CNN-BASED FRAMEWORK FOR COMPARISON OF CONTACTLESS TO CONTACT-BASED FINGERPRINTS

The success of new contactless 2D fingerprint technologies, which are more hygienic and less prone to distortion, depends on the ability to compare their images with traditional contact-based fingerprints.Biometric recognition using convolutional neural networks (CNN) has shown promise, but little work has been done to apply this approach to fingerprint matching.To address this gap, we have developed a CNN-based framework that uses fingerprint minutiae, ridge maps, and specific regions of the ridge map to train a multi-Siamese network.The resulting deep fingerprint representations are then compared using a distance-aware loss function, and concatenated for greater accuracy.We have tested this approach on two publicly available databases of contactless 2D fingerprints and their contact-based counterparts.
INCLUSION OF THE TOPIC «THE SIMPLEST FUNCTIONAL EQUATIONS» IN THE MODEL PROGRAMS FOR STUDYING THE SUBJECT «ALGEBRA AND THE BEGINNINGS OF ANALYSIS»

Formulation of the problem. Analysis of the issue of including the topic "The simplest functional equations" in the model curricula for studying the subject "Algebra and the beginnings of analysis" for specialized classes with in-depth study of mathematics. A model curriculum studies the approximate sequence of achieving the expected learning outcomes, the content of the subject or integrated course, and the types of students’ educational activities. The inclusion of this topic aims to start a creative understanding of functional relationships existing in real systems and processes, in particular, ecological, economic, and social ones. Materials and methods. Theoretical method of analysis of methodical and educational literature on the researched issue; comparative analysis to understand different views on the problem; systematization and generalization to create recommendations for the content of the proposed topic, as well as formulating conclusions and integrating the pedagogical experience of authors who teach relevant disciplines in educational institutions of various levels. Results. The possible content of the topic "The simplest functional equations" in the model programs for studying the subject "Algebra and the beginnings of analysis", examples for an explanation by the teacher, and confirmation by students are proposed. For some examples, different approaches to their solution are proposed; convenient tables are provided for students to find partial solutions to some types of functional equations. Conclusions. The authors believe that the topic "The simplest functional equations" will be useful and acceptable for studying within the scope of the subject "Algebra and the beginnings of analysis" by students of specialized classes with an in-depth study of mathematics. Within the eleven-year school education, of course, it was impossible to find time to study this topic due to the saturation and density of the material necessary for study. But in the twelve-year New Ukrainian School, marked by a deeper differentiation of specialized education, the topic "The simplest functional equations" can strengthen the fundamentality of mathematical education in classes with in-depth study of mathematics, computer science, etc. Further research in this direction may concern the method of solving the simplest recurrent equations.
CLUE HOG: An Immersive Competitive Lock-Unlock Experience using Hook On Go-Go Technique for Authentication in the Metaverse

This paper presents our solution to the 2023 3DUI Contest challenge. Our goal was to provide an immersive VR experience to engage users in privately securing and accessing information in the Metaverse while improving authentication-related interactions inside our virtual environment. To achieve this goal, we developed an authentication method that uses a virtual environment's individual assets as security tokens. To improve the token selection process, we introduce the HOG interaction technique. HOG combines two classic interaction techniques, Hook and Go-Go, and improves approximate object targeting and further obfuscation of user password token selections. We created an engaging mystery-solving mini-game to demonstrate our authentication method and interaction technique.
Applications of Interactive Style Transfer to Virtual Gallery

Since the outbreak of the epidemic in recent years, many events have been held online to avoid spreading illness, such as the virtual gallery. Therefore, we propose an interactive style transfer virtual gallery system. Users can perform style transfer on 2D objects and 3D objects, where 2D objects include pictures and paper jams, and 3D objects include frames and 3D artworks. The results show that it not only makes the viewing process more interesting but also deepens the impression of the artwork through the process of choosing different styles.
User Motion Accentuation in Social Pointing Scenario

Few existing methods produce full-body user motion in virtual en-vironments from only the tracking from a consumer-level head-mounted-display. This preliminary project generates full-body motions from the user's hands and head positions through data-based motion accentuation. The method is evaluated in a simple collaborative scenario with one Pointer, represented by an avatar, pointing at targets while an Observer interprets the Pointer's movements. The Pointer's motion is modified by our motion accentuation algorithm SocialMoves. Comparisons on the Pointer's motion are made be-tween SocialMoves, a system built around Final IK, and a ground truth capture. Our method showed the same level of user experience as the ground truth method.
Template-Based Contrastive Distillation Pretraining for Math Word Problem Solving

Since math word problem (MWP) solving aims to transform natural language problem description into executable solution equations, an MWP solver needs to not only comprehend the real-world narrative described in the problem text but also identify the relationships among the quantifiers and variables implied in the problem and maps them into a reasonable solution equation logic. Recently, although deep learning models have made great progress in MWPs, they ignore the grounding equation logic implied by the problem text. Besides, as we all know, pretrained language models (PLM) have a wealth of knowledge and high-quality semantic representations, which may help solve MWPs, but they have not been explored in the MWP-solving task. To harvest the equation logic and real-world knowledge, we propose a template-based contrastive distillation pretraining (TCDP) approach based on a PLM-based encoder to incorporate mathematical logic knowledge by multiview contrastive learning while retaining rich real-world knowledge and high-quality semantic representation via knowledge distillation. We named the pretrained PLM-based encoder by our approach as MathEncoder. Specifically, the mathematical logic is first summarized by clustering the symbolic solution templates among MWPs and then injected into the deployed PLM-based encoder by conducting supervised contrastive learning based on the symbolic solution templates, which can represent the underlying solving logic in the problems. Meanwhile, the rich knowledge and high-quality semantic representation are retained by distilling them from a well-trained PLM-based teacher encoder into our MathEncoder. To validate the effectiveness of our pretrained MathEncoder, we construct a new solver named MathSolver by replacing the GRU-based encoder with our pretrained MathEncoder in GTS, which is a state-of-the-art MWP solver. The experimental results demonstrate that our method can carry a solver's understanding ability of MWPs to a new stage by outperforming existing state-of-the-art methods on two widely adopted benchmarks Math23K and CM17K. Code will be available at https://github.com/QinJinghui/tcdp.
A Lightweight Identification Protocol Based on Lattices

In this work we present a lightweight lattice-based identification protocol based on the CPA-secured public key encryption scheme Kyber. It is designed as a replacement for existing classical ECC- or RSA-based identification protocols in IoT, smart card applications, or for device authentication. The proposed protocol is simple, efficient, and implementations are supposed to be easy to harden against side-channel attacks. Compared to standard constructions for identification protocols based on lattice-based KEMs, our construction achieves this by avoiding the Fujisaki-Okamoto transform and its impact on implementation security. Moreover, contrary to prior lattice-based identification protocols or standard constructions using signatures, our work does not require rejection sampling and can use more efficient parameters than signature schemes. We provide a generic construction from CPA-secured public key encryption schemes to identification protocols and give a security proof of the protocol in the ROM. Moreover, we instantiate the generic construction with Kyber, for which we use the proposed parameter sets for NIST security levels I, III, and V. To show that the protocol is suitable for constrained devices, we implemented one selected parameter set on an ARM Cortex-M4 microcontroller. As the protocol is based on existing algorithms for Kyber, we make use of existing SW components (e.g., fast NTT implementations) for our implementation.
An Improved Grey Wolf Optimizer for Numerical Optimization

Aiming at the problems of Grey Wolf Optimizer (GWO), such as slow convergence rate in the late iteration, lack of mechanism to jump out of local optimum. In this paper, an improved Grey Wolf Optimizer (IGWO) is proposed. In this algorithm, a new update equation is used to improve the search speed of the algorithm. Meanwhile, the forgetting mechanism is introduced, which improves the diversification of the population and enables the algorithm to jump out of local optimum. Simulation experiments on the CEC2017 test set show that compared with standard GWO, the results obtained by IGWO have higher accuracy, which proves that the algorithm has better performance in dealing with complex optimization problems.
The Process, Problems and Countermeasures of Modern Enhanced Technology on Agricultural Reshaping

This paper sorts out and analyzes the application of modern emerging enhanced technologies in agriculture, studies the reshaping and significant impact of emerging enhanced technologies on modern agriculture, reflects on the risks and problems arising from the application of emerging technologies in agriculture, and tries to put forward an agricultural management model in the era of modern science and technology.On the basis of clear management objectives, management principles and management paths, combined with typical cases of Chinese domestic agricultural management, this paper tries to construct a management system of Chinese contemporary agricultural enhancement technology.
QUEILITE ACTÍNICA EM TRABALHADORES RURAIS: UMA REVISÃO DE LITERATURA

Rural workers, due to their longer exposure to the sun, have a greater chance of developing skin diseases with great malignant potential, with actinic cheilitis (AC) being one of the most frequent. In view of this, the present work aims to bring the frequency of AC in rural workers, through a literature review. For this, some articles were selected from the Lilacs, Pubmed and Scielo databases, including those published between 2017 and 2022 and excluding those published in periods prior to these, in addition to other literature reviews. AC is classified as a potentially malignant disease. It is described as a degeneration of the epithelial and connective tissue, caused by chronic sun exposure, mainly affecting the lower lip. Histologically, it is characterized by an inflammatory infiltrate and vasodilatation, in addition to keratosis, hyperplasia, atrophy and cellular dysplasia. There is a prevalence in male patients, leucoderma, who are between 40 and 60 years old and who work outdoors. As a prevention and treatment measure, making use of photoprotectors and IPEs is of paramount importance, since in the findings, workers who did not use them, developed AC. With that, the literature shows that rural workers are more predisposed to develop AC because they do not use preventive mechanisms, leaving it to the dentist to create educational methods and, in cases where the disease is installed, curatives, so that they do not evolve into malignant changes.
Implementasi Algoritma Naïve Bayes untuk Sistem Rekomendasi Pemilihan Fakultas di Universitas Amikom Yogyakarta

When someone experiences obstacles in making decisions that must be done, they will usually ask for recommendations from those around them. Similarly, when a prospective student has difficulty determining the faculty when they want to continue their education to the higher education level, they will ask for recommendations from those closest to them who have not provided objective recommendations in accordance with their potential, even though a person's potential is very instrumental in making faculty selection decisions so that they can achieve learning success which can be seen from the achievement index. The same happened to prospective students of Amikom University Yogyakarta. It is necessary to build a system of recommendations for faculty selection. The potential of prospective students explored in this study includes school origin, personality expression, and the values of several subjects that have been obtained in high school. Cluster sampling was chosen to determine respondents where the Faculty as a cluster, respondents were selected from the student population who had taken at least 4 semesters with a grade point average of at least 3.00. The application of the Naïve Bayes algorithm in this study shows good accuracy results so that it can be used on research objects, but it would be more perfect if this system is later developed again into a recommendation for the selection of smaller educational units, namely study programs.
MO-DRISHTI GROUND PERSONNEL TRACKING SYSTEM

This research paper presents an indigenous platform and a wearable tool for asset tracking that aims to monitor the location of ground personnel deployed to their respective bandobast duty.The tool consists of a GPS module and a vibrator attached to the police personnel's vest, which continuously analyzes and monitors their location.If the personnel try to breach a designated perimeter or area drawn over a map by superior officials, the vibrator notifies the personnel and the system notifies the superior over a web platform.The platform enables the superior officials to track the personnel's location in real-time and take appropriate measures if necessary.In addition, the future scope of the project is to incorporate motion analysis and predictive models to predict the number of ground personnel needed to achieve a certain task.This would aid in efficient deployment of ground personnel and enable superior officials to make data-driven decisions.The research paper also highlights the significance of the tool in enhancing the safety and security of police personnel and improving the effectiveness of their operations.The paper concludes that the tool has the potential to revolutionize asset tracking in law enforcement and other industries.
Substation smoking behavior detection based on improved decoupling head

In the complex environment of the substation, small objects such as long-distance cigarettes, cigarette boxes, and lighters have few imaging pixels and lack texture information, making it difficult for convolutional neural networks to extract small object features. In the case of multiple targets, the missed detection rate and false detection rate of small targets are high, and the fusion of model features is insufficient, making it difficult to accurately identify and detect. Aiming at the above problems, a multi-scale small target detection algorithm is proposed. In the prediction part of the network, a more effective decoupling head is designed. In addition, shallow features are introduced to improve the feature pyramid, extract small target features, increase the correlation between multiple targets, and prevent the loss of small target feature information. At the same time, a multi-layer attention mechanism is embedded in the backbone network to increase the regional features of invisible small targets and reduce the missed detection rate. In the post-processing stage, the Focal Loss loss function is introduced to increase the model's learning of positive sample targets and further reduce the rate of missed detection and false detection. The experimental results show that the method achieves a 𝑚𝐴𝑃@. 5: .95 of 0.6350 on the homemade smoking dataset, and 𝑚𝐴𝑃@0.5 achieves 0.9569. For the self-made multi-scene and multi-scale smoking data set, this model has advantages in detection accuracy compared with the current excellent target detection models such as YOLOX and YOLOv5. The experimental results show that the model method can realize the identification and detection of small targets such as cigarettes and lighters under multi-scale targets, which has a certain reference value for anti-smoking measures.
SRI AUROBINDO GHOSH AND THE OUTLINE OF INTEGRAL EDUCATION

The primary objective of this research paper is to analyse the "Integral Education" as proposed by Sri Aurobindo Ghosh.Integral Education is the development of five essential aspects of human personality namely the physical, the vital, the mental, the psychic, and the spiritual through education.As methodology here the researcher has employed the content analysis method.Important aspects of Integral Education have been discussed in this paper.This paper attempts to clarify the concept and features of Integral Education, goals, five phases, principles, philosophical basis and the relevance of Integral Education in the Indian Education.
Sistem Pengarsipan Data Pegawai Menggunakan Metode Mixturemodelling Berbasis Web

Technological developments are increasing very fast along withcurrent development. It encourages every human being, especially tatabusiness as a provider of information to continuously follow developments and make decisions to improve,develop, and update any information presented to provide good service. The general section is part of the institution that is authorized to handle documents andfiling. In the General Section, verything related to archiving documents is archived and updated. The use of Microsoft Excel to handle documents in the General Section raises obstacles which include difficulties in searching for old data, formats in the documentation process that are still inconsistent, and difficulty knowing which documents will be processed. Therefore the researchers conducted this research aiming to produce an application that is able to manage document data so that managing document data is easier. From the research that the writer is doing, it will produce an application using the PHP programming language and SQL Server as the database, where the user of the application is the user. With this application, it can help users in the process of documenting, filing, and preparing document reports. The application can also be a solution in managing, storing, and archiving documents.
Research on path planning of manipulator based on improved RRT* algorithm

An improved RRT* algorithm based on dynamic bias angle and path simplification is proposed to address the problem that the traditional RRT* algorithm requires a large computational effort to obtain a better path. The algorithm uses a dynamic bias angle expansion strategy to enhance path planning guidance while avoiding local optimality, and then performs path simplification to eliminate redundant points and reduce path length. Finally, the improved RRT* algorithm is used to smooth the processed paths using cubic B-sample interpolation to extend the life of the robot arm. The simulation results demonstrate that the path generated by the improved RRT* algorithm is of higher quality, and the robot arm completes the grasping and placement of the object with a better path around the obstacle after the smoothing process is added.