Reviewerly/exHarmony huggingface dataset:
Perceptual Quality Assessment of Retouched Face Images

Nowadays, it is a common practice to retouch face images before sharing them on websites, social media, and even identification cards. In response, increased criticisms have appeared about taking photo retouching to an extreme. This naturally leads to the necessity of designing perceptual quality assessment methods that can measure how much a retouched face image has strayed from reality. However, such an issue has seldom been considered. In this paper, we conduct both subjective and objective studies to advance this field. Firstly, we construct a benchmark database (termed SZU-RFD) via subjective experiments. SZU-RFD consists of 200 high-quality images with Asian faces and 1,600 retouched images generated by three popular photo-editing tools under different settings. Secondly, considering that retouching usually distorts the image texture, we propose a novel no-reference (NR) quality assessment method, named TANet, for retouched face images by taking the textural artifact into account. Specifically, a texture enhancement module is embedded into the shallow layer to help the network focus on textural information, and a multi-task learning strategy is applied to improve the performance of the main task with the assistance of an auxiliary task, i.e., texture recognition. Extensive experiments on the constructed SZU-RFD show that our proposed TANet correlates well with subjective perceptual judgments and is superior to 19 mainstream NR image quality assessment methods in evaluating retouched face images.
Increasing Literacy and Numerization by Holding A 1st Primary Level Ranking Competition in Tambusai Village, Rumbio Jaya District, Kampar Regency

Literacy has a broad scope, not only talking about reading and writing letters, but also the ability to capture information with logical and critical thinking, ultimately being able to use it effectively to achieve certain goals. Reading is a process carried out to obtain a message and then convey it using written words. The reading literacy movement for students is an activity designed so that when students read, they are not only proficient at reading. However, students can also understand the content of the reading. The aim of this research is to improve students' reading skills. The method for implementing community service KKN activities is carried out through several stages, including the following: (1) Contacting cooperation partners with the Tambusai Village head to ask for permission to carry out KKN activities, (2) Discussing the work program that our group has prepared and linked it to the problem. what happens to children in the Tambusai Village environment, (3) Contacting elementary schools to carry out activities, (4) Measuring children's learning abilities by helping teachers carry out teaching activities, (5) Carrying out 1st ranking competition activities at Tambusai village elementary schools. The result of this service is children's reading ability by holding competitions that foster students' enthusiasm for reading and learning in order to win the competition.
PERANCANGAN DESIGN UI/UX PADA WEBSITE TOKO MISTER SHOP ID MENGGUNAKAN METODE DESIGN THINKING

This research aims to help people find the items they need more easily and to make the purchasing process smoother. To create this website, the author will use a program called Figma. Design thinking is the approach that will be used to design this website. This approach focuses on finding solutions based on what customers need. This website will hopefully provide better options for customers who wish to purchase additional parts for Vespa motorbikes. By using design thinking, this design will understand what customers need, will create a website appearance that is easy to understand, and will add features that make the purchasing process simpler.Keywords: New Media, Social Media, Instagram, Media Exposure, Followers Satisfaction, Automotive.
Deep Fraud Net: A Deep Learning Approach for Cyber Security and Financial Fraud Detection and Classification

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To tackle these obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions.
State of the Art in Intent Detection and Slot Filling for Question Answering System: A Systematic Literature Review

A Question Answering System (QAS), also known as a chatbot, is a Natural Language Processing (NLP) application that automatically provides accurate responses to questions posed by humans in natural language. Intent Detection and Classification are crucial elements in NLP, especially in a task-oriented dialogue system. In this paper, we conduct a systematic literature review that will perform a comparative analysis of different techniques or algorithms that are being implemented for intent detection and classification with slot filling. The goals of this paper are to identify the distribution, methodology, techniques or algorithms, and evaluation methods, that can be used to develop and construct a model of intent detection and classification with slot filling. This paper also reviews academic documents that have been published from 2019 to 2023, based on a four-step selection process of identification, screening, eligibility, and inclusion, for the selection process. In order to examine these documents, a systematic review was conducted and four main research questions were answered. The results discuss the methodology that can be used for the implementation of intent detection and classification with slot filling, along with the techniques, algorithms and evaluation methods that are widely used and currently implemented by other researchers.
Strategy Indicators for Secure Software Development Lifecycle in Software Startups Based on Information Security Governance

The development of startup software is massive because many services have turned digital. Various applications have emerged that match their functions by taking advantage of the momentum of digital transformation. For example, applications for the Education sector, industrial sector, and economic sector. With their top priority being generating applications, the security factor becomes less of a concern. This is evidenced by the many data breaches that have caused losses to serious companies and public institutions. This is where the role of top-level management emerges in restructuring the company’s strategy, especially software startups, to support producing more secure applications. In this study, the RACI matrix is used to determine who is suitable for implementing a security strategy in information security governance within the organizational structure of the software startup. Where this study starts from stakeholder identification, top-level management roles in software startup, and activities in the secure software development lifecycle. And the results of this study are strategy indicators that are suitable for software startup.
AI and IoT: A Future Perspective on Inventory Management

Abstract: The field of inventory management stands on the brink of a transformative era, heralded by the confluence of Artificial Intelligence (AI) and the Internet of Things (IoT). This paper conducts an in-depth analysis, offering a visionary outlook by leveraging an extensive review of scholarly articles and existing literature on Artificial Intelligence (AI) and Internet of Things (IoT). The fusion of these technologies promises to revolutionize traditional practices by enabling real-time tracking, predictive analytics, and automated replenishment—ushering in unprecedented levels of efficiency and precision in inventory control. Furthermore, the study probes into the horizon of emerging trends, casting light on the progressive strides in machine learning, edge computing, and blockchain technology. Such advancements beckon a reimagined future for inventory strategies. However, this promising future is not without its hurdles. The research underscores critical impediments, including concerns surrounding data privacy, security, and technological constraints
ISLE: A Framework for Image Level Semantic Segmentation Ensemble

One key bottleneck of employing state-of-the-art semantic segmentation networks in the real world is the availability of training labels. Conventional semantic segmentation networks require massive pixel-wise annotated labels to reach state-of-the-art prediction quality. Hence, several works focus on semantic segmentation networks trained with only image-level annotations. However, when scrutinizing the results of state-of-the-art in more detail, we notice that they are remarkably close to each other on average prediction quality, different approaches perform better in different classes while providing low quality in others. To address this problem, we propose a novel framework, ISLE, which employs an ensemble of the “pseudo-labels” for a given set of different semantic segmentation techniques on a class-wise level. Pseudo-labels are the pixel-wise predictions of the image-level semantic segmentation frameworks used to train the final segmentation model. Our pseudo-labels seamlessly combine the strong points of multiple segmentation techniques approaches to reach superior prediction quality. We reach up to 2.4% improvement over ISLE’s individual components. An exhaustive analysis was performed to demonstrate ISLE’s effectiveness over state-of-the-art frameworks for image-level semantic segmentation.
Real-World Image Deblurring via Unsupervised Domain Adaptation

Most deep learning models for image deblurring are trained on pairs of clean images and their blurry counterparts, where the blurry inputs are artificially generated. However, it is impossible for these synthesized blurry images to cover all the real-world blur. Even in two synthetic datasets, the blur type, illumination, and other important image parameters could be different. Consequently, the performance of most existing deblurring models decreases when applied to real-world images and artificial blurry images from a different synthetic dataset. Very few previous deblurring works consider the gap among blurry images from different domains. Inspired by the current success of unsupervised domain adaptation (UDA) on image classification tasks, we develop, UDA-Deblur, a novel deblurring framework that utilizes domain alignment to attenuate effects of the aforementioned gap. In our work, channel attention modules are adopted to exploit the inter-channel relationship for features; multi-scale feature classifiers are designed to discriminate domain difference. UDA-Deblur is trained adversarially to align the feature distributions of the source domain and the target domain. We provide adequate quantitative and qualitative analysis to show the state-of-the-art performance of UDA-Deblur. Firstly, we evaluate the proposed UDA-Deblur on synthesized datasets related to real-life scenarios, which achieves satisfying deblurring results. We further demonstrate that our approach also outperforms prior models on real-world blurry images. For a persuasive comparison, we carefully design experiments on GoPro, HIDE and ReaBlur datasets. More importantly, this is the first work considering real-world image deblurring from a feature-level domain adaptation perspective.
Prediction Based Load Balancing in Cloud Computing Using Conservative Q-Learning Algorithm

Cloud computing is a striking expertise trend that provides Computing assets as a service experiencing a revolution for the IT industry and academics researchers. The potential of emergent cloud computing technology is efficiently hooked by the primary requisite such as resource management. The furthermost vital aspect of asset controlling techniques in Cloud environment depends on scheduling and Load Balancing techniques. Load Balancing strategy is attracting and generating considerable interest in order to utilize resources, thereby increasing the enactment of the cloud datacenter. The energy consumption in the datacenter is customarily due to improper utilization of resources in terms of overloading the Servers or sometimes due to idle Servers. Load is flourishing in the recent era; the Internet based Computing proposals shared resources such as hardware, software, and information on the demand basis. Cloud Computing carry amendments, and the revolution of the Information Technology industries emerged with its popularization and applications. Balancing is one of the best solutions for efficient utilization of resources and is extensively considered to be the most important method to decrease energy utilization. The prime aim of the exploration work is to scrutinize various Load Balancing techniques and propose an energy-aware Load Balancing strategy for ideal utilization of the assets in cloud Computing environment. The proposed Conservative Q-learning algorithm (CQA) for maintaining efficient equilibrium between the work load among virtual machines and optimally lessens the energy ingesting through the Load Balancing algorithms proposed by us.
Two-Level Reinforcement Learning Framework for Self-sustained Personal Robots

As social robots become integral to daily life, effective battery management and personalized user interactions are crucial. We employed Q-learning with the Miro-E robot for balancing self-sustained energy management and personalized user engagement. Based on our approach, we anticipate that the robot will learn when to approach the charging dock and adapt interactions according to individual user preferences. For energy management, the robot underwent iterative training in a simulated environment, where it could opt to either “play” or “go to the charging dock”. The robot also adapts its interaction style to a specific individual, learning which of three actions would be preferred based on feedback it would receive during real-world human-robot interactions. From an initial analysis, we identified a specific point at which the Q values are inverted, indicating the robot’s potential establishment of a battery threshold that triggers its decision to head to the charging dock in the energy management scenario. Moreover, by monitoring the probability of the robot selecting specific behaviours during human-robot interactions over time, we expect to gather evidence that the robot can successfully tailor its interactions to individual users in the realm of personalized engagement.
Bidirectional Long Short-Term Memory for Analysis of Public Opinion Sentiment on Government Policy During the COVID-19 Pandemic

One of the initiatives adopted by the Indonesian government to combat the development of COVID-19 in Indonesia is Community Activities Restrictions Enforcement. Many public opinions emerged, both for and against this policy. There are so many comments every second that it is certainly not easy to analyze them by reading each one by one. This task necessitates computer applications. Therefore, this study was conducted to produce an application that can help analyze public sentiment on the policy through social media, namely Twitter, into three classes: positive, neutral, and negative. The method used in this research is bidirectional long short-term memory (BiLSTM), one of the algorithms of deep learning. This study trains the model using the dataset, which consists of 10,486 tweets. The model receives an f1-score of 76.67 %. Thus, the model can be used to analyze public sentiment when the same policy is enforced. It can determine public acceptance of this policy. Thus, the system created in this research can be used as evaluation material for the government to review the policy when it is implemented in the future. However, this study concentrates on how to develop the sentiment analysis system and does not examine how the community responds to government policy.
FORMATION OF PROJECT CULTURE OF FUTURE TEACHERS IN GLOBALIZATION PROCESSES

The growing role of education in the modern socio-economic situation leads to qualitative changes in the professional field of the teacher. Along with the traditional functions of a teacher (teaching, education, development, etc.), in the conditions of improvement of education, another new professional function is actualized - projecting (forecasting, projecting and organizing the content and procedural aspects of education and the socio-cultural environment). It is the project culture in the educational process that becomes the basis of innovative transformations that ensure the improvement of the quality of education, the activation of self-knowledge, the understanding of the ways and possibilities of one's own self-realization, and the self-change of the individual. Project culture assumes that the teacher will focus on innovative processes, avoid templates in his activities and when choosing pedagogical methods, means and forms. The article reveals the essential characteristics of the formation of the project culture of future teachers in the conditions of globalization processes. Pedagogical design allows you to preserve the basic values of education and at the same time take into account changes in the socio-cultural situation. As a result of project activity, a project is born. It can be defined as a step-by-step realization of the image of the future. The project culture of the subjects of the educational process becomes the basis of innovative transformations that ensure the improvement of the quality of education, the activation of self-knowledge, the understanding of the ways and possibilities of one's own self-realization, and the self-change of the individual. The formation of the project culture of the future teacher in his professional training is impossible without familiarizing students with the essence of project activities, without inclusion in various forms of educational projects, without the development of project-pedagogical thinking, readiness to implement project activities. The essential characteristics of the formation of the project culture of the future teacher are: the conditioning of the formation of the teacher's project culture by the demand of society and the school; the conditioning of the effectiveness of the formation of the teacher's project culture with a high level of reflexive management of this process; the dependence of the formation of the project culture on the level of development of the individual's professional freedom, his creative self-realization in pedagogical activity, in the choice of his strategy and tactics.
Personalized Information Presentation based on Detailed Individual Values for Persuasive Agents

Research has demonstrated the effectiveness of personalization in persuasive agents, recommendation agents, and nudge agents. Ultimate personalization targets the presentation of information tailored to individual’s nuanced understanding and preferences, instead of relying on broad attributes such as personality traits, age, or gender. In this paper, we propose a method for persuading people to accept controversial technologies, such as a fully automated vehicle, by presenting personalized positive counter-propositions to address their concerns about the technology.
EXPECTED IMPACT OF DECENTRALIZATION USING BLOCKCHAIN BASED TECHNOLOGIES

The anticipated impact of decentralization through blockchain-based technologies is poised to bring transformative changes across various domains. By leveraging the inherent features of blockchain, such as transparency, security, and decentralization, the financial landscape is expected to witness increased inclusivity. Blockchain's ability to facilitate peer-to-peer transactions globally, without the need for traditional banking intermediaries, holds the promise of reducing transaction costs and enhancing financial accessibility, particularly for the unbanked and underbanked populations. The implementation of smart contracts, self-executing agreements governed by code, is expected to automate and streamline complex financial processes, reducing the reliance on centralized authorities. Furthermore, decentralized autonomous organizations (DAOs) could revolutionize governance structures, allowing for more democratic decision-making processes. Global access to capital is likely to expand through decentralized fundraising mechanisms, democratizing investment opportunities and fostering innovation on a global scale. The focus on privacy and data ownership in blockchain-based systems is expected to empower individuals with greater control over their personal information, challenging the conventional model of centralized data control. Additionally, the resilience and immutability of blockchain contribute to the integrity and reliability of financial records. As decentralized technologies continue to evolve, their impact is not limited to the financial sector but extends to various industries, promoting economic empowerment and reshaping traditional business models. While the potential benefits are substantial, addressing regulatory challenges and ensuring user education will be critical for the successful integration of decentralized technologies into mainstream practices.