Reviewerly/exHarmony huggingface dataset:
EGIC: Enhanced Low-Bit-Rate Generative Image Compression Guided by Semantic Segmentation

We introduce EGIC, an enhanced generative image compression method that allows traversing the distortion-perception curve efficiently from a single model. EGIC is based on two novel building blocks: i) OASIS-C, a conditional pre-trained semantic segmentation-guided discriminator, which provides both spatially and semantically-aware gradient feedback to the generator, conditioned on the latent image distribution, and ii) Output Residual Prediction (ORP), a retrofit solution for multi-realism image compression that allows control over the synthesis process by adjusting the impact of the residual between an MSE-optimized and GAN-optimized decoder output on the GAN-based reconstruction. Together, EGIC forms a powerful codec, outperforming state-of-the-art diffusion and GAN-based methods (e.g., HiFiC, MS-ILLM, and DIRAC-100), while performing almost on par with VTM-20.0 on the distortion end. EGIC is simple to implement, very lightweight, and provides excellent interpolation characteristics, which makes it a promising candidate for practical applications targeting the low bit range.
The Effectiveness of Online Problem-Based Learning in Improving Students' Reading Comprehension with Different Learning Styles

Reading is a fundamental skill for conveying information. Nowadays, various information is easily accessible quickly from many sources worldwide, so reading comprehension skills and mastering technology have been crucial for the challenges. Educators need a strategy to improve these skills. This study aims to measure the effectiveness of Problem-Based Learning through Google Classroom in improving the reading comprehension of students with different learning styles. This study used a quasi-experimental with a 2×4 factorial design on the tenth-grade students at SMAN 1 Sindang, Indramayu. The VARK questionnaire and reading comprehension test were carried out by one-sample Kolmogorov-Smirnov test and two-way ANOVA with Tukey's. The study exposed the result of Problem-Based Learning through Google Classroom and the influence of the student's different learning styles on the reading comprehension learning outcomes. This research indicated that the strategy was effective, but the student's different learning styles did not affect their learning outcome. Thus, there was no interaction between the teaching strategy and students' learning styles in improving their reading comprehension learning outcomes. This study offered a new perception of the importance of considering student's learning styles in organizing the appropriate materials, learning activities, and assessments to improve reading comprehension. The contribution of this study to the ELT in advance was a new awareness of the effectiveness of multi-mode digital settings in fulfilling the student's needs. Thus, it is confirmed that recent education is designed to accommodate students anywhere and anytime.
Design of Sentiment Analysis Framework of Digital Media Short Text Based on Multi-pattern Sentiment Lexicon

Along the continuous advancement of the network and the rise of digital media, the amount of data produced by the exponential explosion. And how to use these data to provide personalized services for users is one of the current research focuses. To address the issue of insufficient coverage in the current sentiment lexicon and the difficulty of constructing sentiment lexicon in specific fields, this study proposes a multi-modal emotional thesaurus. Semi-supervised learning is used to solve the problem of insufficient coverage of emotional thesaurus, and a semi-supervised classification algorithm is realized by using a large number of unlabeled sample data combined with a small number of labeled sample data. Optimized learning is used to solve the problem of difficult construction of emotional thesaurus in specific fields, the corresponding specific emotional thesaurus is constructed by adaptive adjustment of emotional word score, and finally the improved emotional thesaurus is used to build a digital media short text sentiment analysis framework. For testing, the NLPCC dataset was used in this study, Experiments show that the framework constructed in this study requires 87 iterations, a Recall value of 0.912, a F1 value of 0.753, and an average accuracy of 83.39%, all of which are better than the sentiment analysis framework without the use of multi-pattern sentiment lexicon. In the simulation experiment, the recognition accuracy reached 85.88%, which was 16.85%, 11.57% and 6.72% higher than the test scenarios using a single emotion thesaurus selected in this study. The above results show that the digital media short-text sentiment analysis framework built in this research based on multi-pattern sentiment lexicon can carry out short-text sentiment analysis more accurately and efficiently, so as to accurately analyze users’ needs and provide customized services precisely.
Reinforcement Learning-based Algorithms for Music Improvisation and Arrangement in Sensor Networks for the Internet of Things

The process of learning any new technology requires acquiring the best knowledge about the information of that technology. The better the knowledge humans get about digital technology, the more they become efficient in implementing technological development. In developing the musical rhythm and tuning, the application of programming technologies helps improve the quality. In constructing networking sites and sensing technologies, algorithmic learning processes help in effective development. This development occurs by making the systematic process of transforming a data processing language and data interpreter. Thus, it helps in performing programming effectively in the present as well as future purposes. Therefore, it reflects all the benefits of machine learning. Thus, the preference for machine learning increases technological impact. This development of the programming used in the computer makes humans learn about something easily and get the best information. The effectiveness of the technological development by the algorithm used in the data processing implements the best way to improve the technological language transformation from human language to computer operating language. There is a transnational perspective of the average beat commonness of each part of the music. “Reinforcement algorithms-based learning” incorporated with sensor networks has proposed compelling opportunities for improving “music improvisation” and interpretation.
A Case Study of Parent-Child Interaction in Their Reading Activity

Parents play an important role in the character-shaping of their children, and it is mostly processed during the parent-child interaction. Children can be deeply influenced at home in terms of mother tongue, ideology, habit, or personal behavior. Hence, parent-child reading activity has been used as a popular mode that offers an opportunity to affect children purposely. However, many parents are confused about the way to interact with their children, either verbally or not. This study tries to look into this problem by observing a real case. The conversation between a parent and a child is audio recorded, transcribed, and analyzed. The result shows that equal power distribution can be found in this particular case, and there are two identities that the child can find herself, i.e., teacher and student. In addition, the mother adopts a number of strategies to help fulfil her expectation, such as reading material selection, teaching ideological lessons, child’s language development, evoking imagination, and praising strategy. The findings may provide implications for parents who are confused about how to interact with their children properly and effectively.
Analyzing Spectator Emotions and Behaviors at Live Sporting Events using Computer Vision and Sentiment Analysis Techniques

It makes the reflection of humans’ emotions and intentions from watching live sports events. Watching the event keeps people entertained and changes their mindset from being stressed to joyful. Watching sports events encourages the athletes and the sports persons to participate. Reflection of the live sports event consists of many crowds as the event’s audience. This crowd’s emotions and intentions directly impact the changes in the event’s performance. It provides positive energy to the demotivated sports participants, making them perform better in the event. This study reflects the understanding of the facial emotions of the spectators from the live event. Then, they are decoded in the computer programming language, and an outcome is provided. It understands the emotions and sentiments of the people that affect the event’s environment. The representation by the computer analysis makes the understanding of the changes provided by the spectators of the live event. The effect of the audience’s emotions and behaviors in the crowd are computed by the utilization of computer software analysis and the effect of those reactions in the event. The collection of data is taken from the secondary sources of data collection, including the collection of information from the article and the journal based on the topic. The gathered data is analyzed by comparing them with their reaction and expressions in the live sports event.
Detection of Structured Query Language Injection Attacks Using Machine Learning Techniques

This paper presents a comparative analysis of various machine learning classification models for structured query language injection prevention. The objective is to identify the best-performing model in terms of accuracy on a given dataset. The study utilizes popular classifiers such as Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Based on the tests used to evaluate the performance of the classifiers, the Naïve Bayes gets the highest level of accurate detection. The results show a 97.06% detection rate for the Naïve Bayes, followed by LogisticRegression (0.9610), Support Vector Machine (0.9586), RandomForest (0.9530), DecisionTree (0.9069), and K-Nearest Neighbor (0.6937). The code snippet provided demonstrates the implementation and evaluation of these models.
Sexual Harassment in Eka Kurniawan’s Novel Lelaki Harimau

A reflection of social life with all its dynamics and problems could be expressed or pictured through a novel, a literary genre written based on social reality, the author’s experiences, observations, feelings and ideologies. Themes may be varied depending on the situational elements. Sexual harassment is one frequently exposed, and always discussed because such can occur anywhere and at any time and in this research becomes the focus of analysis. Sexual harassment is a crime that can harm other people or even cause trauma to victims and generally women are the most frequent victims due to the domination of men in the social life. The study is intended to reveal the problem of sexual harassment committed by the major characters in the novel Lelaki Harimau by Eka Kurniawan through a literary psychology approach as the behavior of the perpetrators of sexual harassment is related to personality structure. Psychology and literature are two interrelated disciplines; literary psychology uncovers the psychological aspects in literary works. Descriptive qualitative research method is applied for the whole study to analyze social phenomena without using mathematical calculations. The results of the research using the six data selected show that there are three forms of sexual harassment along the plot, verbal sexual harassment, non-verbal sexual harassment and physical sexual harassment.
A FUZZY CONTROLLER FOR A MOBILE ROBOT WITH OBSTACLE AVOIDANCE

This research paper presents a mobile robot based on the MAMDANI fuzzy inference system. The mobile robot is equipped with three pairs of HC-SR04 ultrasonic sensors to detect the distance between the robot and obstacles. A fuzzy logic controller is built into the STM32F4 microcontroller platform to generate the actuation signal for the DC motor mounted on each robot wheel. To perform obstacle avoidance, a sensor capable of detecting the distance between the robot and the obstacle must be installed so that the controller can calculate and determine the appropriate control signal to send to the robot's actuator. In achieving this goal, the robot can perform movements at a constant speed or at a variable speed. When the distance between the robot and the obstacle is too far, the speed is too fast. Then, as the distance to the obstacle increases, the speed decreases. Such a control mechanism can be implemented with certain control methods installed in the controller. The data obtained from the ultrasonic sensors are expressed as linguistic variables that indicate the speed of movement of each wheel in fuzzy sets. A robot with a fuzzy logic controller based on a rule table is designed to achieve uninterrupted motion with obstacle avoidance capability. Keywords: STM32F4 microcontroller, Fuzzy logic, Fuzzy inference system, mobile robot, obstacle avoidance.
Digital Forensics in the Era of Cybercrime: Emerging Trends and Challenges for Forensic Accountants in Nigeria

This study examined the impact of technology on digital forensics and the challenges faced by forensic accountants in the era of cybercrime. The study highlights the four dimensions in which technology has an impact on digital forensics: technical, legal, organizational, and educational. The paper also discusses how emerging technologies such as artificial intelligence and machine learning can be used in digital forensics, along with the challenges they present. Cloud computing is also examined, and the challenges it poses for forensic accountants who need to access and analyze financial data stored or processed in the cloud are discussed. The research used a qualitative research design and incorporated the chain of custody theory in digital forensics. The findings of this study suggest that forensic accountants need to update their knowledge and skills in digital forensics to keep up with the latest trends and technologies in cybercrime in Nigeria. It is recommended that forensic accountants adopt a multidisciplinary approach that integrates accounting principles with digital forensics methods and tools. Additionally, following best practices and guidelines for digital evidence collection, preservation, analysis, and presentation is crucial to ensure the integrity, reliability, and admissibility of digital evidence in court.
Spatial distribution of benthic habitats in Kapota Atoll (Wakatobi National Park, Indonesia) using remote sensing imagery

Abstract. Hamuna B, Pujiyati S, Gaol JL, Hestirianoto T. 2023. Spatial distribution of benthic habitats in Kapota Atoll (Wakatobi National Park, Indonesia) using remote sensing imagery. Biodiversitas 24: 3700-3707. This study aims to classify and map benthic habitats in Kapota Atoll, Wakatobi National Park, using Sentinel-2A satellite imagery, that has undergone both atmospheric and geometric corrections. Sentinel-2A satellite imagery was processed using pixel-based classification method. This study used two machine learning algorithms: support vector machine (SVM) and random forest (RF). This study used nine classified benthic habitat classes, namely five homogeneous benthic classes (sand, live coral, dead coral, rubble, and dense seagrass) and four mixed benthic classes (mixed rubble and sand, mixed sand and rare seagrass, mixed medium seagrass and sand, and a mixture of rubble, dead coral, and seagrass). The RF algorithm produced benthic habitat maps with a higher overall accuracy than the SVM algorithm, which was 65.31% and 63.51%, respectively. These accuracy values have met the standards of accuracy for mapping benthic habitats that apply in Indonesia. The SVM algorithm classification results showed that the benthic habitat of the mixed class of medium seagrass and sand was more dominant than other benthic habitat classes. In contrast, the RF algorithm showed the class dense seagrass was more dominant. The selection of the appropriate algorithm for processing satellite imagery was found to be a significant factor in producing an accurate benthic habitat map.
Career Phases in Research Computing and Data

The Campus Research Computing Consortium (CaRCC) Staff Workforce Development Interest Group serves to support current Research Computing and Data (RCD) professionals, provide information for people looking to become an RCD professional, and to provide information to institutions looking to establish RCD workforce development programs. The goal of this group is to curate information from existing, successful programs in order to develop leading practices for staff onboarding, training, and development. This paper presents the results of the interest group’s early effort to define the phases of an RCD career from beginning to end, which is the first step in defining a framework and a shared vocabulary to better collect and organize information and resources. The paper defines pre-, early-, mid-, and late- RCD career phases, and activities, as well as the transitions in and out of a phase. The intent is to use these definitions as a basis for future work of the interest group.
A SURVEY ON ANOMALOUS MOTION DETECTION IN VIDEO DATA

The amount of data that has to be processed increases along with the demand for labor to understand the data due to substantial reduction in the cost of transmitting and storing video data. An important component of a video surveillance system is anomaly detection. Because manually analyzing and monitoring such a big amount of video would be impossible. For automated video analysis to determine typical and aberrant vehicle motion behavior for traffic management, public services, and law enforcement, automatic tracking is a crucial necessity. Even though a few anomaly detection techniques have been proposed in the past, there are still a limited number of them that can handle illumination variations, smaller object detection and tracking in poses that change, learning the movement of heterogeneous objects, anomalies in sparse and dense conditions, and tracking under occlusion that the human eye can easily detect. This survey study provides an overview of numerous techniques that have been employed to find anomalies in diverse applications.
Pandemic, Return the Nature of Children's Learning

The world shaken by the pandemic of late 2019, the being of Coronavirus Disease (COVID-19) had a profound impact on the educational aspect, including children's studies. The pandemic changed the education system directly children to learn from home (LFH). Work from home (WFH) policies keep parents at home and can keep children study at home. The research reveals a positive view of the impact the COVID-19 pandemic has on children's learning traits. The study uses a qualitative approach with library research methods in seven ways define research ideas, collect information, reinforce the research focus, classify reading, reading journal, review, and make reports. The results of this research suggest that children in general have poor and good potential depending on the parents and the environment make it up. Parents have an important role in the child's learning needs, the mother being the madrosatul ula (first teacher) of her children and the father as the head of the family has the authority to direct matters including education. So it can be concluded that the COVID-19 pandemic has a positive impact on education in returning Nature of Children's Learning.
Off-Record Politeness Strategies and Different Gender’s Characteristics in Woman at Point Zero Novel

Language is a communication tool which is used to exchange information between the speakers and the hearers according to their particular purposes. The indirect message in communication is called off record politeness; it helps the speakers avoiding responsibility of the hearers’ self image. The purposes of the study are explaining off record politeness strategies based four maxim violation. Then, explaining the characteristics of gender differentiate male and female’s off record politeness strategies. Moreover, explaining the relationship among different off record strategies and the gender. The data in this study is male and female’s utterances cointaining off record politeness. The result shows that male frequently used rthetorical questions and female mostly used metaphors strategies. The frequent male’s characteristic in communication is succint language meanwhile female’s characteristic is storytelling. The result found relationship between gender’s characteristics in the use of off record politeness strategies.
English Education Students’ Perception on the Use of Elena in the Post-Pandemic Era

Elena, an e-learning aid based on MOODLE version 2.0 developed by Universitas Negeri Semarang, is used to support the teaching and learning process as a response to the post COVID-19 outbreak. In English Education study program, Elena is mostly used not only for the lecturer to upload the materials, but also for the students to have discussion session, do assignment, exam, and quiz. However, as we shift from full online classes to ofline classes, it is important for all the parties and stakeholders to re-evaluate the use of Elena in teaching and learning process in post pandemic era, especially for students as the main user. A descriptive study was conducted by giving questionnaire and having in-depth interview to 100 students who participated. The results shows that students have positive perception towards the use of Elena in post-pandemic era to support the teaching and learning process, especially in learning language. However, some improvement such as make Elena into application rather than web-based learning aid, improve the user interface, and add live notification synchronized into students’ Telegram bot or MyUNNES should be considered.
The Implementation of Politeness Strategies in Showing Disagreement Among the Communicative Members

Politeness is an essential aspect of interlanguage pragmatic study used to create a harmonious relationship among people who engage in specific interactions. Politeness is implemented differently in showing disagreement regardless of cultural background. This study was used to analyze the implementation of politeness strategies in showing disagreement by the students. In this study, the researchers applied descriptive qualitative research. The subjects in this study were 20 students, all non-native speakers, who joined the CEC club at UIN Salatiga. The data was taken from the students’ utterances of the politeness strategies used to show disagreement. Also, the data was analyzed using the theory of politeness strategies by Brown and Levinson (1987) In collecting the data, the researchers used a discourse completion test (DCT) questionnaire, which gave 5 (five) topics. The results showed that the students applied all politeness strategies with different percentages. Positive politeness is implemented for 73%, bald on record 18%, negative politeness for 5%, and off record 2%. Positive politeness is the most dominant strategy in showing disagreement. Besides, the sub-strategy in positive politeness also applied by the students are exaggerating, intensifying interest in the hearer, avoiding disagreement, presupposing, offering, being optimistic, including both the speaker and the hearer, giving or (asking for reason) and assuming or asserting reciprocity. In conclusion, politeness strategies are essential to convey a different opinion from the interlocutors.