Reviewerly/exHarmony huggingface dataset:
MATHEMATICAL MODELING AND SIMULATION OF SOCIAL MEDIA ADDICTION TIKTOK USING SEI1I2R TYPE MODEL

TikTok is an entertainment application that can be used to watch and share various short videos such as educational information, dance, fashion, and even promote a product, as well as shop online. If it is used continously, it may provide negative impact. TikTok users may become stressed if it does not meet the creator’s expectations such as getting the most likes or viewers from users. Furthermore, they also can addicted to online shopping and buy something that they do not need due to lost self-control and be unable to consider risks in making decisions resulting in stress or depression. This study aims to build a mathematical model of TikTok addiction Type SEI1I2R. The results of the study obtained two equilibrium points, namely the free equilibrium point for TikTok addiction T1 and be stable when R0 &lt; 1 and the equilibrium point for addiction to TikTok T2 and be stable when R0 &gt; 1. The simulation shows that when R0 &lt;1, even though most of the population have TikTok application, they are not interested in content and shopping via TikTok. This causes the population that creates TikTok accounts decrease and hence the addiction to content and shopping on TikTok slowly decrease and then disappear in the population. Meanwhile, when R0 &gt; 1, many people think that content and shopping on TikTok are very interesting so that the population that creates TikTok accounts is increasing so that there is an increase in the population creating content and shopping on TikTok. Therefore, TikTok addiction will remain in the population and will increase with time.
PENINGKATAN HASIL BELAJAR TEMATIK DENGAN MODEL PEMBELAJARAN EXAMPLE NON EXAMPLE DI KELAS IV SD N 223/II RANTAU EMBACANG MUARA BUNGO

The results showed that there was an increase in thematic learning outcomes that were pursued through the example non example learning model. This research was conducted in two cycles. This is indicated by the increase in learning outcomes based on the teacher's observation sheet, namely in the first cycle the student observation results at the first meeting were 70.31%, the second meeting was 71.87%, the third meeting was 75% and already in the second meeting. categorized as good and in the second cycle the increase in the first meeting was 78.12%, the second meeting was 79.68%, the third meeting was 84.37% and was categorized as very good. Student observation sheets, namely in the first cycle the results of student observations at the first meeting were 60%, the second meeting was 71.66%, the third meeting was 75% and was categorized as good and in the second cycle the increase in the first meeting was 75%, the second meeting was 78.33%, the third meeting is 83.33% and has been categorized as very good. Completeness in the final test is the number of students who completed before the action as many as 15 students (60% of all students). After being given action. in the first cycle, the number of students who completed learning increased to 16 students (64% of all students), and in the second cycle the number of students who completed the study increased to 20 students (80% of all students). Based on the results of the research above, students must focus on the learning process using example non example, this is an alternative model that can be used by teachers in the learning process to be able to improve learning outcomes and student activity in the classroom.
MOTIVASI MAHASISWA MENGIKUTI PERKULIAHAN DENGAN SISTEM E-LEARNING

The Covid-19 pandemic that has occurred since 2019 has caused changes, especially in the education sector. Educational activities that were initially carried out face-to-face turned into an online system as an effort to prevent the spread of the COVID-19 virus. The purpose of this study is to determine the motivation of students in attending lectures during the COVID-19 pandemic. The type of research is quantitative with a descriptive research design. The population in this study was students of the Nursing Study Program at Mitra Indonesia University as many as ± 387 students with sampling techniques using snowball sampling, with a total sample of 94 responders. The results of the study found that the type of lecture media most often used was zoom meetings, which were 61 respondents (64.9%), the motivation of students in attending lectures was mostly not good, namely as many as 50 respondents (53.1%). This research can be a foundation for educators to increase student motivation during the COVID-19 pandemic by utilizing various digital platforms or modifying the learning process with blended learning methods.
A Secure Authentication Scheme for Smart Home Based on Trusted Execution Environment

With the rapid growth of the smart home, remote control of devices within a smart home environment has become a primary and essential function. However, there are significant security vulnerabilities in the process of remote control. This paper proposes a secure authentication scheme based on a trusted execution environment for smart home remote control. The proposed scheme generates a session key between smart home devices and users. This session key ensures that a user can securely control devices remotely and resist various well-known attacks. We also utilize the Real-or-Random model to demonstrate our scheme is provably secure. Besides, our work has lower computation and communication costs than other related methods.
Software and Hardware Cooperative Implementation of the Rafflesia Optimization Algorithm

With the development of edge technology in the fields of transportation, wireless sensor networks, and the internet of things, more and more intelligent optimization algorithms are implemented in hardware structures. The Rafflesia optimization algorithm (ROA) is a novel intelligent optimization algorithm proposed recently. To observe its performance on hardware, this paper implements the ROA algorithm in a cooperative way of software and hardware, referred to as the FROA algorithm. To convenient testing and accelerated computing, the initialization module, fitness module, and update module of the FROA algorithm are deployed on the advanced RISC machine (ARM) platform and the field programmable gate array (FPGA) platform, respectively. In the experimental part, we test the FROA algorithm on nine different benchmark functions. The results are compared with those implemented in software. The experimental results demonstrate the effectiveness and superiority of the FROA algorithm.
Data Privacy and Algorithmic Inequality

Abstract This paper develops a foundation for a consumer's preference for data privacy by linking it to the desire to hide behavioral vulnerabilities. Data sharing with digital platforms enhances the matching efficiency for standard consumption goods, but also exposes individuals with self-control issues to temptation goods. This creates a new form of inequality in the digital era—algorithmic inequality. Although data privacy regulations provide consumers with the option to opt out of data sharing, these regulations cannot fully protect vulnerable consumers because of data-sharing externalities. The coordination problem among consumers may also lead to multiple equilibria with drastically different levels of data sharing by consumers. Our quantitative analysis further illustrates that although data is non-rival and beneficial to social welfare, it can also exacerbate algorithmic inequality.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Deep Learning Based Bengali Image Caption Generation

Generation of captions from images in English is having a predominant record of explorations. On the contrary, moderate rates of analysis has been observed in the generation of image captioning in the language of Bengali. We render a judicious framework on the generation of captions from images using InceptionV3, VGG16 and Convolutional Neural Network (CNN). The coalescence of the aforementioned has been done with respect to BanglaLekhaImageCaptions Dataset and Flickr8k dataset. Having less datasets on the Bengali language, we aim at the creation of a hybrid model that would work on datasets of different linguistics. The usage of two datasets has been incorporated to check the preciseness and the quality of the caption texts spawned by the model using the Bilingual Evaluation Understudy Algorithm and BARTScore. The implementation has harvested a better performance for the Flickr8k dataset consisting of eight thousand images.
Utilization of Data Augmentation Techniques to Enhance Learning with Sparse Datasets

Neural network-based object detection has many important applications but requires a vast amount of training data. In applications where training data may be scarce, data augmentation techniques can be used to expand the training set. This paper explores the performance of such techniques on You Only Look Once Version 5 (YOLOv5).
Examining the Hydra: Simultaneously Shared Links in Tor and the Effects on its Performance

Tor is a popular privacy-enhancing technology that allows anonymous communication using onion routing. However, such technologies are only helpful if used; therefore, performance is an important aspect. One of the main performance bottlenecks of Tor is the cross-circuit interference (CCI) problem. Tor multiplexes multiple circuits over a single Transport Layer Security (TLS) 1.2 connection if they share a path segment (link). Therefore, they have the same congestion window, which can yield unfair bandwidth allocation. However, there has been little work in understanding this problem in more depth. This paper investigates the number of simultaneously shared links in the current Tor network, which are the root cause of CCI. We developed a novel shared links simulator called SALSA to investigate this problem. Our results show that 3.7 % of active links are shared, and the involving Onion Routers (ORs) have the most common bandwidth capabilities. Additionally, we show that the internal circuits and exit policy influence the CCI problem. Furthermore, we model the number of shared links when the demand grows further and show that the number of shared links can go up to 16 %. Finally, we run Shadow simulations with a 25 % down-scaled Tor network and show that a network without shared links is faster.
Knowledge Condensation Model to Support Expertise Location in Software Development

inding relevant expertise is a critical need in software organizations since developers use it to support their knowledge needs. Developers accumulate expertise from the technologies they used, and the problems solved within projects (e.g., bug solving, requirements, architecture design). However members of the organizations do not benefit from this expertise, so knowing how to model it then could grant access to the best practices, training practices, and data across applications. In this work, we present a model that implements the knowledge condensation concept to capture exprtise during the software developing process, which aims to classify, retrieve, and share valuable knowledge among stakeholders in an unsuitable form for its recovery. The model consists of three modules: formal grammar, semantic knowledge, and expertise tools. In the formal grammar module, an approach is made to formalize the description of how developers store and share their knowledge. An architectural knowledge model is proposed in the semantic knowledge module. Finally, in the module of expertise tools, two prototypes were developed using the elements from the semantic knowledge module.
Integration of STEAM Method with Constructivism Approach in Graphic Design Subject for Information Technology Vocational High School: A Systematic Reviews

Online learning at the IT-Vocational High School (ITVHS) during a pandemic is considered less successful.A factor influencing this is the lack of student feedback.Various methods have been implemented to increase student interest and self-efficacy.One of them is STEAM method with a constructivist approach.STEAM has emerged as a new learning method due to developing STEM to meet the learning needs of students in science, technology, engineering, art, and mathematics.Art elements aim to increase creativity, innovation, and interest, promoting problem-solving skills and other cognitive interests.The constructivism approach makes students more dominant in their own learning.The finding in this article is exploring the use of the STEAM method combined with a constructivist approach, so it can improve several aspects of self in student's achievement.This paper reviews several studies in the field of STEM and STEAM education with a systematic review to evaluate the contemporary implementation of STEAM and how to it in teaching and learning.The meta-analysis will be used as the basis for research on the integration of the STEAM method with student basic competencies using a constructivism approach in online learning.The population in this research will be take in IT-VHS.The integration of this model will be applied to Graphic Design course with the aim of being able to stimulate students in practical activities to be more active, independent, creative, and collaborate well with fellow students.
Determining the Optimal Route for Newspaper Distribution by Using the Sweep Algorithm Method (Case Study: PT Aksara Solopos)

The Vehicle Routing Problem (VRP) is a problem related to the distribution of products using the optimal and involving more than one vehicle by taking into account several obstacles in serving a number of agents according to the request of each related agent.The Capacitated Vehicle Routing Problem (CVRP) is one of the variations of VRP, namely by adding constraint in the form of vehicle capacity used in product distribution.This paper applies the formation of a CVRP model to the problem of the distribution route of the Solopos daily mail and its solution using the Sweep Algorithm method which aims to optimize the distribution route.The Sweep Algorithm is an algorithm consisting of two stages, the first stage is clustering agents, then the second stage is the formation of routes for each cluster with the Nearest Neighbor method.Based on the calculations carried out in solving CVRP problems using the Sweep algorithm, the results obtained that the total vehicle mileage is 216,5 km with a time windows of 6 hours 5 minutes.
THE EFFECT OF OPTIMIZERS ON SIAMESE NEURAL NETWORK PERFORMANCE

Optimizers are approaches or algorithms dependent to enhance the characteristics of the Neural Network (NN) like weights and learning rate in order to decrease the loss rate, On the other hand, Siamese Neural Network (SNN) are two identical sub-networks, they work in parallel and they are sharing parameters and weight, SNN uses for indicate similarity.In this research, we study the effect of optimizers Siamese Neural Network, using Digits handwritten (MINST) dataset, the effects is studied for Adam, Nadam, Adadelta and SGD optimizers with respect to process time and accuracy, the accuracy is 97%, 97%, 79% and 92%.
Development of Chatbot Application to Support Academic Staff Works for Academic Student Services

The use of chatbots nowadays is expanding in some fields in human life.It has been used in customer service, education, etc.In the educational area, a chatbot has been used to help students to learn, or to give information relates to the academic and non-academic area, it even helped students to choose the elective course for the next semester.Even though it has been used for many purposes, not all education institutions implement the chatbot.Seeing that challenge, in this paper, the researchers try to develop a chatbot to improve students' academic service in one of the study programs in Bina Nusantara University, namely Information Systems.The researchers want to give more improvement in the way students communicate with the department, whereas now it's only through social media like Facebook, Instagram, and Line.The methods for analysis and development of this chatbot use the foundation of Decision Support Systems (DSS).Other methods for collecting data are also being used to develop the flow of the Chatbot that is through the questionnaire and interview with some of the stakeholders.The result of the research is the chatbot flow that can be used as the first step to develop a chatbot.
The Effects of Autocratic, Democratic, and Laissez-Faire Leadership Styles on Organizational Performance

Nowadays, it is crucial for leaders to implement an optimal leadership style to enhance the efficiency and effectiveness of their workforce.Through looking at the relationship between leader, follower, and situation, this study aims to investigate the effect of Democratic, Autocratic, and Laissez-Faire leadership styles on organizational performance.This study was conducted by observing the secondary data obtained through previous research studies conducted in this field.Accordingly, a total of 31 studies was extracted from different research engines.The result of the study shows that there is no one optimal way to lead; each leadership style has its advantages and disadvantages.There was a mixed relationship between the three examined leadership styles.In conclusion, the main implication of this study is that it emphasizes the importance of the leader flexibility to adapt in a changing and uncertain environment.
ManiLoco

The use of virtual reality (VR) in laboratory skill training is rapidly increasing. In such applications, users often need to explore a large virtual environment within a limited physical space while completing a series of hand-based tasks (e.g., object manipulation). However, the most widely used controller-based teleport methods may conflict with the users' hand operation and result in a higher cognitive load, negatively affecting their training experiences. To alleviate these limitations, we designed and implemented a locomotion method called ManiLoco to enable hands-free interaction and thus avoid conflicts and interruptions from other tasks. Users can teleport to a remote object's position by taking a step toward the object while looking at it. We evaluated ManiLoco and compared it with state-of-the-art Point & Teleport in a within-subject experiment with 16 participants. The results confirmed the viability of our foot- and head-based approach and better support concurrent object manipulation in VR training tasks. Furthermore, our locomotion method does not require any additional hardware. It solely relies on the VR head-mounted display (HMD) and our implementation of detecting the user's stepping activity, and it can be easily applied to any VR application as a plugin.
Incorporating Signal Awareness in Source Code Modeling: An Application to Vulnerability Detection

AI models of code have made significant progress over the past few years. However, many models are actually not learning task-relevant source code features. Instead, they often fit non-relevant but correlated data, leading to a lack of robustness and generalizability, and limiting the subsequent practical use of such models. In this work, we focus on improving the model quality through signal awareness , i.e., learning the relevant signals in the input for making predictions. We do so by leveraging the heterogeneity of code samples in terms of their signal-to-noise content. We perform an end-to-end exploration of model signal awareness, comprising: (i) uncovering the reliance of AI models of code on task-irrelevant signals, via prediction-preserving input minimization; (ii) improving models’ signal awareness by incorporating the notion of code complexity during model training, via curriculum learning; (iii) improving models’ signal awareness by generating simplified signal-preserving programs and augmenting them to the training dataset; and (iv) presenting a novel interpretation of the model learning behavior from the perspective of the dataset, using its code complexity distribution. We propose a new metric to measure model signal awareness, Signal-aware Recall, which captures how much of the model’s performance is attributable to task-relevant signal learning. Using a software vulnerability detection use-case, our model probing approach uncovers a significant lack of signal awareness in the models, across three different neural network architectures and three datasets. Signal-aware Recall is observed to be in the sub-50s for models with traditional Recall in the high 90s, suggesting that the models are presumably picking up a lot of noise or dataset nuances while learning their logic. With our code-complexity-aware model learning enhancement techniques, we are able to assist the models toward more task-relevant learning, recording up-to 4.8× improvement in model signal awareness. Finally, we employ our model learning introspection approach to uncover the aspects of source code where the model is facing difficulty, and we analyze how our learning enhancement techniques alleviate it.
A STUDY ON SENTIMENT ANALYSIS TECHNIQUES OF TWITTER DATA

Nowadays a huge amount of data is being produced on web platforms .The Internet has become a podium for E-Learning, exchanging ideas and spreading opinions.Social networking platforms like Twitter, Facebook, Google are swiftly gaining marketing popularity as they enable mankind to demonstrate and convey their views about ideas and concepts, to examine discrete nations, or ship thoughts across the world.So dealing with so much data that is being produced daily is a big concern .Also the products that may be intended to spread hate and execution among the people of communities .but thanks to sentiment analysis which enables the system to detect such ill efforts.Much work has been done in the area of sentiment analysis of Twitter data.Twitter is a popular social media platform that has become a valuable source for understanding public opinion on various topics.Sentiment analysis is a natural language processing technique used to determine the sentiment expressed in text, including tweets.This study aims to explore the methods and techniques used in sentiment analysis and apply them to tweets related to a specific topic to gain insights into public opinion.The study also aims to evaluate the accuracy of sentiment analysis algorithms and determine the challenges faced when performing sentiment analysis on tweets.This check focuses substantially on Sentiment analysis of Twitter data.It helps us analyse the information contained in Tweets.Opinions are mostly vague, varied, positive, negative, and sometimes neutral.In this paper, we give a check and a relative inspection of ways for viewpoint mining like machine literacy and wordbook-grounded approaches, jointly with assessment criteria .Using colourful machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we give exploration on twitter data streams.We've also bandied general challenges and operations of Sentiment Analysis on Twitter.