Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Nanovest Investment Application Using Naive Bayes Algorithm

Various applications provide simple ways for individuals interested in investing in crypto assets or stocks - both domestic and international - to do so. One of the companies in this industry, Nanovest, has launched the Nanovest investment application. Since its release in 2022, numerous positive and negative responses have been on Google Play, the App Store, and Twitter. However, Nanovest faces two main problems regarding the use of its application. First, they often receive complaints submitted to the operational team, indicating dissatisfaction or problems faced by users. Second, Nanovest has never conducted formal research regarding user experience in using their application. This indicates a lack of understanding of the perspectives, needs and challenges faced by users. This study tries to find out how the public responds to the Nanovest application through a sentiment analysis. This study used tweet and review data from January 1, 2022, to February 17, 2023. The data underwent sentiment analysis, employing the Naïve Bayes algorithm, and were classified into positive and negative sentiments. The findings revealed that 96.07% of the sentiments expressed towards Nanovest were positive, while 22.11% were negative, with these percentages calculated based on the total number of sentiments detected in the data. To evaluate the model's performance, a 10-fold cross-validation approach was utilized alongside the Naïve Bayes algorithm, resulting in an impressive accuracy rate of 94.8391%. This positive sentiment suggests that users are highly favorable towards the crypto assets and global stock investment services offered by the Nanovest application. Nevertheless, 3.93% of users still expressed dissatisfaction with the app due to some flaws that existed when Nanovest was initially launched. Based on the results that have been obtained and analyzed for the development team, it is recommended to make three improvements, namely reducing application size to minimize memory usage, increasing overall application performance, and increasing access speed across all features to allow application users to access more efficiently. It is recommended for the product team and stakeholders to consider developing the Candlestick chart feature into the application. This also increases the competitiveness of the Nanovest application against other applications.
Knowledge Management System Evaluation Using DeLone McLean Model: A Case Study of IT Service Desk Bank XYZ

Bank XYZ is one of the biggest banks in Indonesia. It has IT Service Desk function under Information Technology Division that specifically handles complaints or problems related to IT. Knowledge management in IT service desk can help to increase the availability of information and knowledge for the IT service desk team who must provide explanations to users. IT Service Desk of Bank XYZ has built knowledge management system using open-source platform since 2017. It is called SDKPedia which was developed inhouse by IT Service Desk team and never been evaluated since it was built. The objective of this study is to evaluate SDKPedia as knowledge management system used in IT Service Desk Bank XYZ. Evaluation was carried out based on Delone and McLean assessment criteria. Survey is distributed to IT Service Desk worker and 31 valid feedback is used in this study. To determine the indications that have a substantial impact and result in a net advantage for SDKPedia, the PLS-SEM algorithm is utilized. Service quality is the only exogen latent variable that affected intension of use. While the other two exogen latent variable are system quality and information quality, did not have significant impact for intention to use or user satisfaction. Considering the findings of this study, several improvements can be made by the IT Service Desk manager to make the quality of SDKPedia better. The points that need more attention are information quality and system quality.
Semi-Supervised Learning Approach for Bladder Cancer Diagnosis

&lt;p&gt;Recent studies have made great strides in reducing the labeling burden in deep learning algorithms by requiring that only a subset of the dataset be labeled. These are called semi-supervised learning algorithms (SSL). In this thesis we explore a type of semi-supervised learning algorithm that focuses on a technique called consistency regularization and self-ensembling to leverage the unlabeled portion of the dataset. Consistency regularization has been used as a driving force in SSL to great success in datasets of natural images, but it has not been used in image pathology where the dataset consists of cell patterns. We successfully implement an SSL algorithm based on the VGG-16 neural network, which combines techniques introduced by state-of-the-art algorithms such as FixMatch and the Π model. Our study shows that our algorithm can leverage the unlabeled portion of a dataset to improve a model’s accuracy by up to 19% from the baseline. &lt;/p&gt;
An Optimal BDCNN ML Architecture for Car Make Model Prediction

We take on the challenge of classifying car photos, from the most general car type to the precise make, model, and year of the vehicle for a given input. Analyzing pre-existing datasets, we find that the CompCars-SV are a great place to begin our classification project. We demonstrate that convolutional neural networks can obtain a classification accuracy of more than 90% on the most difficult task. Due to a skewed mix between training and testing, this impressive result isn't really typical of how people do in the actual world. Using an ML system for car detection, we automatically generate a vehicle-tight bounding box for each picture, which we disseminate to the full dataset together with the existing (but limited) type-level annotation. We have designed and implemented car classification algorithms to analyze this car dataset, two of which take advantage of the hierarchical nature of car annotations. According to our research, a more precise classification of car type at a finer resolution now achieves an accuracy of 99.25%. It serves as a baseline benchmark for future research. Focusing on "vehicle" tasks, this work intends to bring attention to the vision community's lack of attention to these tasks compared to other objects. The important reason getting higher accuracy is extraction of binary descriptor (BD) feature using edge detection before training the CNN. This step reduced the size of the car dataset; hence network took less time to get trained. From the result outcomes shown it is clear that the presented network architecture having 31 layers of 2d convolutional layer, batch normalization, maxpool, ReLU, fully connected layer and Softmax classifier layer, has given higher accuracy. Numerous relevant car-related issues and solutions have yet to be carefully examined and researched, according to our findings. Car model categorization, model verification, and attribute prognosis are just a few examples of how the dataset might be put to use.
Ethereum Smart Contract Deployment for a Real Estate Management System (REMS) Implemented in Blockchain

The real industry is crucial sector of the world wide financial system, with significant economic, social and environmental implications. However, real estate transactions are often slow, complex and costly and can be prone to fraud errors, which can lead to significant financial losses and legal disputes. This paper proposes the adoption of blockchain technology in real estate cadastral systems as a solution to the challenges encountered in managing property ownership and transactions. We have described the implementation of our prototype called REMS (Real Estate Management System) and we have presented the deployment of smart contracts in Ethereum platform. Based on measurement, benchmarks and other observation of the system, we have evaluated the server usage of the blockchain network and decided whether Blockchain-as-a-Service (BaaS) should be involved or not in our system. The study results demonstrate the successful implementation of a real estate management system (REMS) using blockchain technology and Ethereum's smart contracts. This study is important because it confirms that similar solutions can be implemented in other areas of public administration, where the structure of the work is similar, i.e. where we deal with issuing documents to citizens.
Optimizing mixing in the Rudner-Levitov lattice

Here we discuss the optimization of mixing in finite linear and circular Rudner&#x2013;Levitov lattices (Su&#x2013;Schrieffer&#x2013;Heeger lattices with a dissipative sublattice). We show that the presence of exceptional points in the systems&#x2019; spectra can lead to drastically different scaling of the mixing time with the number of lattice nodes, varying from quadratic to logarithmic. When operating in the region between maximal and minimal exceptional points, it is always possible to restore logarithmic scaling by choosing the initial state of the chain. Moreover, for the same localized initial state and values of parameters, a longer lattice might mix much faster than the shorter one. Also we demonstrate that an asymmetric circular Rudner&#x2013;Levitov lattice can preserve logarithmic scaling of the mixing time for an arbitrarily large number of lattice nodes.
An Overview and Comparison of Selected State-of-the-Art Algorithms Inspired by Nature

Optimization is essential in various fields such as finance, transportation, energy, and health care. However, solving real optimization problems, especially nondeterministic polynomial, requires considerable computational resources. Metaheuristics provide fast and cost-effective solutions to these problems. In this paper, eight state-of-the-art nature-inspired metaheuristic algorithms that have demonstrated excellent performance are compared in detail. In addition, a novel tournament procedure has been proposed to produce a quality ranking of selected metaheuristic algorithms, which are compared based on their optimization results, even if they were not originally tested with the same set of test functions, but only partially. The selected algorithms are evaluated using thirty-two test functions, which is a representative sample size. The evaluation also showed that while one algorithm produced the best overall results, this does not mean that this algorithm is the best for solving each function. This also highlights the need for further research in metaheuristic algorithms.
Unifying Behavioral and Feature Modeling for Testing of Software Product Lines

Existing software product line (SPL) engineering testing approaches generally provide positive testing that validates the SPL’s functionality. Negative testing is commonly neglected. This research aims to unify behavioral and feature models of an SPL, enable testing before and after variability binding for domain-centric and product-centric testing, and combine positive and negative testing for a holistic testing view. This study suggests behavioral modeling with event sequence graphs (ESGs). This heterogeneous modeling strategy supports bottom-up domain testing and top-down product testing with the feature model. This new feature-oriented ESG test creation method generates shorter test sequences than the original ESG optimum test sequences. Statechart and original ESG test-generating methods are compared. Positive testing findings are similar. The Statechart technique generated 12 test cases with 59 events, whereas the ESG technique created six test cases with 60 events. The ESG technique generated 205 negative test cases with 858 events with the Test Suite Designer tool. However, the Conformiq Designer tool for the Statechart technique does not have a negative test case generation capability. It is shown that the proposed ESG-based holistic approach confirms not only the desirable (positive) properties but also the undesirable (negative) ones. As an additional research, the traditional ESG test-generating approach is compared to the new feature-oriented method on six SPLs of different sizes and features. Our case study results show that the traditional ESG test generation approach demonstrated higher positive test generation scores compare to the proposed feature-oriented test generation approach. However, our proposed feature-oriented test generation approach is capable of generating shorter test sequences, which could be beneficial for reducing the execution time of test cases compared to traditional ESG approach. Finally, our case study has also shown that regardless of the test generation approach, there has been found no significant difference between the Bottom-up and Top-down test strategies with respect to their positive test generation scores.
Efficient and Lightweight Visual Tracking with Differentiable Neural Architecture Search

Over the last decade, Siamese network architectures have emerged as dominating tracking paradigms, which have led to significant progress. These architectures are made up of a backbone network and a head network. The backbone network comprises two identical feature extraction sub-branches, one for the target template and one for the search candidate. The head network takes both the template and candidate features as inputs and produces a local similarity score for the target object in each location of the search candidate. Despite promising results that have been attained in visual tracking, challenges persist in developing efficient and lightweight models due to the inherent complexity of the task. Specifically, manually designed tracking models that rely heavily on the knowledge and experience of relevant experts are lacking. In addition, the existing tracking approaches achieve excellent performance at the cost of large numbers of parameters and vast amounts of computations. A novel Siamese tracking approach called TrackNAS based on neural architecture search is proposed to reduce the complexity of the neural architecture applied in visual tracking. First, according to the principle of the Siamese network, backbone and head network search spaces are constructed, constituting the search space for the network architecture. Next, under the given resource constraints, the network architecture that meets the tracking performance requirements is obtained by optimizing a hybrid search strategy that combines distributed and joint approaches. Then, an evolutionary method is used to lighten the network architecture obtained from the search phase to facilitate deployment to devices with resource constraints (FLOPs). Finally, to verify the performance of TrackNAS, comparison and ablation experiments are conducted using several large-scale visual tracking benchmark datasets, such as OTB100, VOT2018, UAV123, LaSOT, and GOT-10k. The results indicate that the proposed TrackNAS achieves competitive performance in terms of accuracy and robustness, and the number of network parameters and computation volume are far smaller than those of other advanced Siamese trackers, meeting the requirements for lightweight deployment to resource-constrained devices.
Assessment of Fine-Tuned GPT2 for Lyric Generation

&lt;p&gt; &lt;/p&gt; &lt;p&gt;Over the years in the field of natural language processing, huge improvements have been made thanks to deep learning. More specifically, in the sub-field of language modeling, which is a task to train a model to learn the distribution of the words in a certain language, models such as GPT2 are famous allowing realistic news articles to be written. In this thesis we assess the feasibility of using a fine-tuned GPT2 model as a tool for lyric generation. More precisely, we compare the characteristics between generated lyrics, conditioned on a specific artist or genre of music, and the original lyrics. We find that the models were able to learn the characteristics and styles of each genre and each artist. &lt;/p&gt;
Analysis of algorithms as a teaching experience

Teaching analysis of algorithms to students in Computer Science degrees, using the approach popularized by Knuth in his series of books ``The Art of Computer Programming" and later by Sedgewick and Flajolet in the book ``An Introduction to the Analysis of Algorithms", is not a simple task since, in general, these students are more interested in the implementation of an algorithm than in the corresponding theoretical aspects. This approach concentrates on precisely characterizing the performance of algorithms by determining their best, worst and average case performance using a methodology based on symbolic tools such as recurrence relations and generating functions.The most difficult aspect is to understand the average case since this corresponds to studying the algorithm as its possible inputs vary: this represents the most important goal since generally students have no difficulty in understanding the best and worst cases, corresponding to particular input configurations.A compromise that has been successful over the years consists in teaching students the analytical aspects of the problem and then organize a simulation of the algorithm with a system of symbolic computation in order to precisely check the theoretical results.
Working conditions of nursing professionals in the context of COVID-19

Introduction: the COVID-19 pandemic has increased the number of hospitalizations and is responsible for increasing the workload of nursing professionals with a deficiency in human resources and personal protective equipment. Objective: to analyze aspects related to the working conditions of Nursing professionals in the context of COVID-19. Methods: documentary research recorded under narratives in the databases of the Federal Nursing Council, the Regional Nursing Councils of Espírito Santo and Pernambuco, the Federal Public University and Social Media. Results: are presented and analyzed according to the categories: a) nursing work conditions, threatening the worker’s health and life; b) the autonomy of nurses in the full exercise of their profession and the cultural aspect of the dominant ideology; c) the Federal Nursing Council and the Regional Nursing Councils of Espírito Santo and Pernambuco as disciplining, normalizing, managing and controlling bodies for the professional practice of nursing. Conclusion: the narratives found in this study demonstrate the precarious working conditions, exacerbated by the pandemic, and the role of nursing in coping with COVID-19.
An IoT-Based Bed Fall Prediction System Using Force Sensitive Resistor

Patients with impaired mobility and neurological disorders such as Alzheimer's disease, Parkinson's disease, dementia etc. are vulnerable to bed falls, which can be damaging to their physical and psychological well-being. Existing systems are mostly fall detection based on wearable devices, which can be uncomfortable to wear or ambient devices such as cameras that invade privacy. A bed falls prediction system using force sensitive resistors (FSR) has been proposed in this paper. It is designed to eliminate privacy intrusion and discomfort issues. The system can identify the patient's different on-bed positions and determine the possibility of bed falls. In case of any risky position, the caretaker will be alerted to mobile applications via the Internet of Things (IoT), making patient monitoring more accessible and manageable. This integrated system yields an average of 92% accuracy for 5 different on-bed positions. The bed fall prediction system will facilitate caretakers/nurses to take care conveniently at homes, hospitals and assisted care facilities to ensure patients' health and safety.
Transfer Robustness to Downstream Tasks Through Sampling Adversarial Perturbations

Due to the vulnerability of deep neural networks to adversarial attacks, adversarial robustness has grown to be a crucial problem in deep learning. Recent research has demonstrated that even small perturbations to the input data can have a large impact on the model’s output, exposing them susceptible to malicious attacks. In this work, we propose Delta Data Augmentation (DDA), a data augmentation method for enhancing transfer robustness by sampling extracted perturbations from trained models against adversarial attacks. The main idea of our work is to generate adversarial perturbations and to apply them to downstream datasets in a data augmentation fashion. Here we demonstrate, through extensive experimentation the advantages of our data augmentation method over the current State-of-the-Art in Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks for CIFAR10 dataset.
AN ANALYSIS OF MAXIM FLOUTING IN TV SERIES GAME OF THRONES SEASON 1

This research aims to analyze the types of maxims that are flouted in Game of Thrones Season 1. The descriptive qualitative method is employed in this research. The data of this research are in the form of utterances from the characters in Game of Thrones that contain maxim flouting. The source of the data is the Game of ThronesTV series and its script. The data are collected through watching the TV series, reading the script, and taking notes of utterances where the maxims are flouted. The results show that all the four types of maxims are flouted by the characters of the TV series with the occurrence of 19 times. The type of maxim that is flouted the most by the characters is the maxim of relation. The characters flout the maxim of relation on nine different occasions. Then, the maxim of quality is ranked second with the frequency of six times. After that, the following maxim of quality is maxim of quantity with three cases. Finally, the least flouted maxim of all four is maxim of manner which only appears one time.
USE OF GENERATIVE LEARNING STRATEGY TO IMPROVE STUDENTS' WRITING SKILL IN DESCRIPTIVE TEXT

The aim of this study is to determine whether or not there is an effect on the use of generative learning strategy to improve students’ writing skill in descriptive texts at grade eight of SMP N 6 Tondano. This study involves 30 students consisting 15 students in experimental group and 15 students in control group. This study uses a quantitative research design which is a quasi-experimental research. Data collecting is done by giving a test to write a descriptive text with pre-test and post-test, by students’ in two groups. The mean score of experimental group is 17.73 and control group was 9.6. The data are analyzed using the t-test formula, the results is 5.055. The degrees of freedom is 28 were competency interval 0.05 (95%) is 2.048 and 0,01 (99%) is 2.763. Based on the result, it can be concluded that students’ skill in writing descriptive texts using generative learning increases, but does not increase significantly because from the post-test results on experimental group there are no students’ who are able to achieve score above the KKM. So, the researchers suggest that generative learning strategy should be replaced with other learning strategy.&#x0D;
CHARACTERISTICS OF KANSAI-BEN ACCENT BY AIKO-SENOU CHARACTERIN ANIME OJAMAJO DOREMI (STUDY OF PHONETIC)

The result of speech is the use of language sounds, occurs in the process of high and low and the strength of the sound of speech. The verbal result of a use a language sound, happens on how high and low, and how weak and strong of the pronunciation of the word, it is what differentiate the meaning of the word or sentence. That is what we usually called “accent”. The main goal of this research is to understand the characteristics of Kansai-ben accent in ojamajo-doremi anime. This research focuses on the Kansai-ben accent spoken by the object of the research which is Aiko-senou in Anime Ojamajo Doremi. Descriptive qualitative method is used to analyze and to describe how Kansai-ben accent was used by Aiko Senou then determining the characteristic based on the result that has been analized through the software speech analizer. The finding shows that there are 4 accent characteristics (used by Aiko-senou in Ojamajo-doremi). Dielect is a an utterance whose background is the domicile of the speaker