Reviewerly/exHarmony huggingface dataset:
CLINICOPATHOLOGIC DESCRIPTION OF PROSTATE DISEASES IN SANGLAH GENERAL HOSPITAL DENPASAR FROM 2018-2020

Prostate disease is one of the diseases with a high incidence among elderly men. . In establishing the diagnosis of prostate disease, histopathological and laboratory examinations are vital. These histopathological results can be grouped into 3, namely benign lesions, PIN, and carcinoma. This study aims to determine the clinicopathological description of prostate diseases at Sanglah Hospital Denpasar from 2018-2020. The cross-sectional research was conducted using the descriptive and analytic method. Samples were selected based on several inclusion and exclusion criteria. Data were analyzed using SPSS version 26 to obtain a general clinicopathological picture based on age, histopathological diagnosis, the relationship between age and histopathological, diagnosis, PSA level, and the relationship between PSA level with histopathological diagnosis, and grade group carcinoma. The results showed that the prostate disease with the most patients at Sanglah Hospital Denpasar from 2018-2020 occurred between the age of 70-79 years by 39,0% with the dominant histopathological diagnosis being benign prostate 240 (74,3%), followed by PIN 27 (8,4%), and carcinoma 56 (17,3%). There was no relationship found between age, mean age and histopathological diagnosis with p=0,881 and p=0,119. The highest PSA level was &gt; 20ng/mL. It was found that the PSA level was significantly associated with histopathological diagnoses with a value of p=0,000. The most prevalent grade group characteristic was grade group 5 with 36 (64,3%).&#x0D; Keywords: Prostate Disease, Clinicopathologic, PSA Level
Manajemen Layanan Perpustakaan Madrasah dalam meningkatkan Minat Baca Peserta Didik di MTs Negeri 10 Jember

The purpose of writing this research is to describe the management of library services through mobile libraries at MTsN 10 Jember as strengthening students’ interest in reading, describing the management of madrasa libraries through planning, organizing, implementing, monitoring and evaluating processes. This research uses a descriptive qualitative approach with data collection instruments in the form of observation, interviews and documentation. The subjects of this research were students of MTsN 10 Jember, librarians from the Jember Archives and Library Service. The research results show that Madrasah Library Service Management is something that motivates students to write works entitled Love Poems for My Prophet and Longing for Family Heaven. Apart from the madrasa library, there are other benefits gained from using the mobile library, namely increasing students’ knowledge, insight and abilities, teachers taking an active role in accompanying students, teachers are happy to see students enthusiastic about reading.
Can Google Trends(GT) be used to predict tourist arrivals?: FB Prophet Machine Learning(ML) for Predicting Tourist Arrivals

The big problem in tourism is how to provide appropriate preparations to serve tourists so that when the tourist season is low, resources can be saved and when the tourist season is busy, all resources can be provided effectively. Machine learning is a derivative branch of artificial intelligence, one of whose capabilities can be used to carry out data/dataset-based forecasting. This research uses a dataset obtained from GT from 2013-2023 with several keywords combining city names and tourist destination names in Yogyakarta Indonesia, then it will be compared with a dataset of tourist arrivals in the city of Yogyakarta obtained from the Central Statistics Agency. The Machine Learning model that will be used is Prophet Facebook.. This model uses a Bayessian as a backend algorithm. The results obtained from this research are that GTs can be used to predict tourist arrivals with some tweaks on the dataset. However, to get accurate results, various combinations of keywords are needed for the desired destination, and it is recommended to add some column namely max and mean to the dataset to prevent insufficiency of data of some keywords that make prediction result bad. In this research it can be concluded that the use of an additional max column can increase the COERR, MAPE and R2 values. Meanwhile, we found that the GT dataset can be used for forecasting best in time periods under 200 days. Also we found that using the GT dataset alone produces unstable COERR, MAPE and R2 values. Another finding is that the GT dataset that uses the YouTube filter is only suitable for use in Indonesia for the time period above 2018 considering that Indonesian people's access to YouTube has increased massively over that year and tends to decrease below that year. However, the trend shows that the use of searches on YouTube after 2018 tends to increase drastically, beating searches on the Google web.
Cervical Vertebrae Column Radiography with Suspective Fractures of the Corpus and Processus Spinosus Cervicalis at Columbia Asia Hospital Medan

Veterbra Cervicalis is a spine consisting of seven segments which have small segment bodies and large segment openings. The function of the Veterbra Cervicalis is to cover and protect the spinal nerves, which act as support for almost two-thirds of the body's weight. As for the purpose of the work write scientific This in arrange is for show fracture and abnormality on the organ being examined, to obtain image criteria that can be appropriate to the case, for know location fracture the, for straighten up diagnosis in accordance with the case. Aircraft X-ray is Wrong One equipment installation radiology which has an important role in producing X-rays and providing images of objects on X-ray film. Radiographic techniques to show the anatomy of the Cervical Vertebral Column and its abnormalities, including cervical fractures, can theoretically be done with several projections, namely AP (Antero - Posterior), Lateral, Hyperextension and Hyperflexion. After carrying out a radiographic examination of the Cervical Vertebra Column with a suspected fracture in the Anterior part of the Cervical Vertebrae Corpus -5 and Cervical Spinous Process -2 at Columbia Asia Hospital Medan, the author concluded that the projections used were Anterior Posterior (AP) and Lateral projections. In the AP projection carried out at Columbia Asia Hospital Medan, a fracture appeared in the anterior portion of the Cervical Corpus -5, and in the lateral projection, a fracture appeared in the spinous process. Cervical -2.
Augmented Reality (AR) And Virtual Reality (VR) Applications In Tourism: Embracing Emerging Technologies For Improved Tourist Experiences In Malaysian Tourism Industry

This paper investigates the impact of Augmented Reality (AR) and Virtual Reality (VR) on the Malaysian tourism business. AR and VR have emerged as revolutionary forces, transforming a wide range of industries, with tourism at the forefront of this digital transition. Augmented Reality complements real-world experiences by superimposing digital information on top of the physical world, whereas Virtual Reality immerses users in simulated environment. AR and VR have completely transformed the tourist experience, including planning, booking, on-site exploration, and post-trip comments. AR applications enrich the tourist experience by providing interactive maps, historical insights, and real-time translations, whereas virtual reality (VR) technologies enable deep interaction in a variety of fascinating activities and provide a fascinating insight into the destination's attractions.
A Critical Discourse Analysis of the Korean Language Teacher Certificate Training Program: Focusing on the Media Discourses

Drawing upon Fairclough’s critical discourse analysis (CDA), this paper aims to uncover and analyze the media discourse that surrounds the Korean Language Teacher (KLT) certificate training program. A total of 23 articles, ranging from 2006 to 2022, from five different Korean newspapers were analyzed. The textual analysis revealed that the KLT program was framed by themes of employability, self-development, lifelong education concerning gendered populations, government-driven policy, and teaching abroad. The newspaper section where articles on KLT most frequently appeared was society, followed by the economy section. The most cited voices were personal anecdotes from people who took courses, and institutions that offer the courses and recruit students. Although the government’s voices were not actively cited, employees in post-secondary institutions speak in support of the policy. In the socio-political analysis, discourses of fixity and fluidity were co-present, along with the transformation of higher education into a market. This paper argues that media discourses on the KLT primarily frame teacher education not as a pathway for professional development but rather as a means to obtain certificates that offer future job market advantages, both domestically and internationally. In the context of Korea’s national push for the internationalization of its language, it is imperative to re-examine whether the focus on cultivating novice, pre-service teachers and enhancing their development programs has been overshadowed by institutional efforts to recruit students and market these credentials.
REAL-TIME EMBEDDED SYSTEM OF MULTI-TASK CNN FOR ADVANCED DRIVING ASSISTANCE

In this research, we've engineered a real-time embedded system for advanced driving assistance. Our approach involves employing a multi-task Convolutional Neural Network (CNN) capable of simultaneously executing three tasks: object detection, semantic segmentation, and disparity estimation. Confronted with the limitations of edge computing, we've streamlined resource usage by sharing a common encoder and decoder among these tasks. To enhance computational efficiency, we've opted for a blend of depth-wise separable convolution and bilinear interpolation, departing from the conventional transposed convolution. This strategic change reduced the multiply-accumulate operations to 23.3% and the convolution parameters to 16.7%.Our experimental findings demonstrate that the decoder's complexity reduction not only avoids compromising recognition accuracy but, in fact, enhances it. Furthermore, we've embraced a semi-supervised learning approach to heighten network accuracy when deployed in a target domain divergent from the source domain used during training. Specifically, we've employed manually crafted correct answers only for object detection to train the whole network for optimal performance in the target domain. For the foreground object categories, we generate pseudo-correct responses for semantic segmentation by employing bounding boxes from object detection and iteratively refining them. Conversely, for the background categories, we rely on the initial inference outcomes as pseudo-correct responses, abstaining from further adjustments. Semantic segmentation of object classes with widely different appearances can be achieved thanks to this method, which tells the rough position, size, and shape of each object to the task. Our experimental results substantiate that the incorporation of this semi-supervised learning technique leads to enhancements in both object detection and semantic segmentation accuracy. We implemented this multi-task CNN on an embedded Graphics Processing Unit (GPU) board, added multi-object tracking functionality, and achieved a throughput of 18 fps with 26 Watt power consumption.
PIXEL-ORIENTED VISUALIZATION FOR EXPLAINING DATA CLASSIFICATION IN A MULTILAYER NEURAL NETWORK

Accompanying the growth of applications that use Artificial Intelligence, recent research is also growing to explain how these applications work and make them more acceptable to humans. In this context, this paper presents an alternative explanation of the data classification process carried out by an Artificial Intelligence algorithm. The work proposes a pixel-oriented information visualization approach to explain the multilayer perceptron classifier using SHAP. Observing the results obtained in Shapley values, for the Iris dataset composed of four features, the proposed methodology identified one relevant feature, and for the Wine dataset composed of 13 features, the methodology identified six relevant features. The relevant features are those that most influence the classification, this information explains the results as it is possible to understand the reasons for the classifier's successes and errors.
BIMBINGAN BAGI MASYARAKAT DALAM PROSES PENGISIAN DATA E-KTP PADA DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL (DUKCAPIL) KOTA PALANGKA RAYA

Community service activities which are one form of student obligation at the Palangkaraya School of Informatics &amp; Computer Management (STMIK) include completing Integrated Field Work Practices (PKL) before graduating which aims not only to direct students to practice the knowledge they have gained during college but also adopting community service activities, which in this activity take the form of guidance in making E-KTPs for the people of Palangka Raya city at the Department of Population and Civil Registration (DUKCAPIL) of Palangka Raya city. Filling in E-KTP data is a crucial stage in efforts to accurately identify residents. The Department of Population and Civil Registration (Dukcapil) of Palangka Raya City has identified challenges in filling in data faced by the community, resulting in incomplete identity documents. This guidance helps the public understand the process of filling in E-KTP data better, reduces errors in filling in data, and speeds up the process of applying for E-KTP. Apart from that, this guidance program also increases public awareness of the importance of having an E-KTP as an official identity. This PKL activity in the form of community service will be carried out from July 10 to September 1 2023. The target of this community service activity is people who will make E-KTPs in the city of Palangka Raya. The method of this activity is in the form of direct service using the application system available at the Palangka Raya city DUKCAPIL service. The result of this community service is that the people of Palangka Raya city who will make E-KTP are well served
Computational Intelligence-based Data Analytics for Sentiment Classification on Product Reviews

Abstract Computational Intelligence (CI) is a sub-field of Artificial Intelligence (AI) that concentrates on the development of intelligent techniques and models to solve complex problems. When it comes to data analytics, CI techniques can be highly effective in handling large volumes of data, extracting meaningful patterns, and making accurate predictions. An increasing number of online reviews were being posted on the Internet every day with the rapid growth of Electronic Commerce (E-Commerce) and social networks. In the fastest growing research area, Sentiment Analysis (SA) is one among them that helps consumers in making better decisions relating to purchases through proper analysis and understanding of shared sentiments from social media and the web. In recent times, several approaches were modelled for acquiring insights from such datasets. But still, there comes a problem in managing text of large size; hence, precise polarity recognition of consumer reviews was an exciting and ongoing issue. This article introduces a novel Sentiment Analysis on Product Reviews using Enhanced Grasshopper Optimization with Deep Learning (SAPR-EGODL) approach. The objective of the SAPR-EGODL approach lies in the identification and classification of different sentiment types that exist in product reviews. At the initial stage, data preprocessing takes place which transforms the product review data into meaningful data. Next, the SAPREGODL technique employs Multi-Head Attention-based Bidirectional Long Short Term (MHABLSTM) technique for sentiment classification. In this study, the EGO model is exploited for improving the classification accomplishment of the MHABLSTM technique. A sequence of simulations was accomplished on different datasets for examining the advanced sentiment classification results of the SAPR-EGODL technique. The comprehensive relative research exhibited the promising accomplishment of the SAPR-EGODL model compared to current techniques.
Blockchain Based Wireless Sensor Networks for Detecting Nodes

Abstract The versatility of Wireless Sensor Networks (WSNs) and other unique qualities inspired by real-time collaboration between the sensor nodes make them widely used for a range of applications in monitoring and surveillance. As solutions for malicious node identification involve a single, centralized decision-making process, security in WSNs is becoming more and more important. Errors are difficult to prevent with this paradigm, and repeatability and traceability are problems. Therefore, the detecting ability and integrity of the developed detection method cannot be guaranteed by malicious node identification technology in ordinary WSNs. This article provides insights into this innovative idea as well as a thorough investigation of the use of blockchain technology to WSNs (Blockchain-Based WSN), including a full analysis of a blockchain-based method for identifying rogue nodes. The Blockchain-Based WSN architecture, business-specific applications, and use are all included in this study. The Blockchain-Based WSN architecture model for tracking malicious nodes and the smart contract features of malicious node detection is both included in the explanation of harmful node identification based on Blockchain-Based WSN in this paper. The second part of this study investigates the use of blockchain technology to the management of WSN data, which necessitates online information aggregation and may also require auditing, event logging, and storage for information analysis and offline query processing. The traditional WSN data management approaches are discussed first in this study, followed by the blockchain-based WSN data management choices. The benefaction of blockchain to WSN security management is also included in this survey. Before discussing blockchain-based WSN security management solutions, such as those that provide access control, protect data integrity, ensure anonymity, and prolong the life of WSN nodes, it first looks at the security vulnerabilities of centralized WSN models.
Formation of professional motivation of cadets of the modern military institute

The dual content of the process of professional motivation is clarified: as a system of factor mechanisms that determine the professional behavior of subjects; as a characteristic of a labor person, his orientation / non-orientation towards obtaining a specialty and implementing professional activities, professional needs of a person, his internal and external driving factors. The professional motivation of a cadet manifests itself not only as a set of motivating actions that determine the approval of the motives for choosing a military profession, but also the need for long-term performance of professional duties of a serviceman. It is proved that the process of complex motivation of cadets of the military institute is carried out at three levels: weighing — based on the analysis of the surrounding situation, determining the preferences of behavior strategies; social planning — task specification, primary formation of professional motives; transformation of professional labor action — renewal of professional activity practices. Within the framework of training at the military institute, the design of professional motivation is a single, multi-system component that reflects the unity of projects: 1) educational and cognitive development; 2) professional stimulation; 3) service and labor motivation. The types of formation of professional motivation of cadets are specified: 1) a normal stable process from the first to the fifth year (characterized by the consistent formation of abilities and motives for professional activity); 2) deviant process (episodic manifestations of destructive processes mainly in the initial courses, characterized by maladaptation professional practices). The factors of inhibition of the processes of formation of professional motivation of cadets of a modern military institute are revealed: insufficient material allowance of cadets; inconsistency of vocational guidance work in the system of pre-university and university education; reduction of guarantees of social protection and service and labor stability of military professional activity of students in military institutes; disunity of various components of professional motivation of cadets (personal-thinking, as well as practical-effective).
Relationship between the effectiveness of the professional activity of a choreographer teacher and his/her emotional stability

The article discusses the features of the effective professional activity of the choreographer’s teacher and his emotional stability. The results of the performed researches on the influence of emotional stability of the teacher-choreographer on the success of the considered activity are analyzed. The use of correlation analysis made it possible to identify significant connections between the studied characteristics.
On some peculiarities of the renewal of national higher education system in Russia

The purpose of this work is to discuss the problems that arise at the next stage of the renewal of the national system of higher education. Based on the results of the study, the author assesses the planned structural changes in higher professional education in Russia, the degree of their continuity with the existing structure of higher education, identifies the emerging features of the main educational programs of higher education, the degree of their compliance with the new requirements for specialists with higher professional education in various sectors of the national economy. It is shown that the proposed restructuring of the main educational programs of higher education needs a thorough adjustment, since it does not solve the problem of high-quality training of specialists with higher professional education based on the conjugation of the main educational programs of higher education and the sphere of work.
Cancer Text Article Categorization and Prediction Model Based on Machine Learning Approach

Many research studies have been carried out to identify different variations of cancers in the human body throughout the globe. Due to numerous volumes, the primary concern for healthcare researchers is to segregate the article into a well-thought-out manner. Biomedical text classification is a globally volitional realm due to its numerous uses in different fields. This study motivates us to use a dataset related to cancer articles text containing 7570 research papers with categories into three classes. Like the Word2Vec and BERT models, the word embedding technique converts the text into a numeric vector. The text classification-based prediction model considers nine Machine Learning (ML) classifiers. The models are tested with k-fold cross-validation and evaluate the prediction performances by considering the different standard metrics. The model trade-off between bias and variance is controlled by calculating the standard deviation and k-fold mean accuracy to overcome the overfitting problem of the deployed model. The outcome is observed on the Random Forest (RF) classifier in the case of Word2Vec embedding with a mean accuracy of 99.98%. The BERT models also performed well, with about 97.70% mean accuracy and other parameters compared to the Word2Vec model. The other parameters’ evaluation score was near 1.00, promising in multiclass text classification deployed model performances.