Reviewerly/exHarmony huggingface dataset:
SOCIAL CONFLICTS IN WILLIAM FAULKNER’S NOVEL LIGHT IN AUGUST

This study aims to analyze the social conflict in William Faulkner’s novel. It is done to reveal the types of conflicts found in the novel and causes of conflict in this novel. In conducting this research, the research used qualitative methods. This research is classified as library research because the data collection is done by reading novels and reading several books and other references related to the topic. The data were analyzed based on the type of conflict and its causes and then presented descriptively. In other words, the researcher analyzed the data according to the topic and then present the result in a narrative. In analyzing the data, the researcher used a mimetic approach. The results of the research show that there are two categories of social conflict in the novel, namely: conflict between individuals and individuals and conflict between individuals and group
Sistem Informasi Penjualan Berbasis Web Pada Pucabranded.Store

Pucabranded.store is a business engaged in the sale of quality used branded shoes, which is located at Jalan Deli Tua, Medan. The current problem at the pucabranded.store store is in selling, promoting and ordering still manually, this is less effective because there is no detailed information about shoes on pucabranded.store for consumers. The ordering process that is currently running on t pucabranded.store is still using via Instagram or coming directly to the place, it is still not efficient. Recording and checking of transaction data, customer data, goods data, is still done manually in written document archives, data loss is still likely to be vulnerable to occur. The method used here is more directed to the research process with the Waterfall Method. In the construction of a system like this, to make it easier to sell, make transactions easily, make it easier for buyers to purchase goods online if they are far away, increase income and expand sales promotions on pucabranded.store.
DeepQ Feature Selection and Recognition of Handwritten Prediction Using Convolutional Neural Networks

Purpose: Deep learning (DL) is referred to as the "hot subject" in pattern recognition and machine learning. The unmatched potential of deep learning allows for the resolution of the majority of complex machine learning issues, and it is evident that it will receive attention within the framework for mobile devices. There are tools for pattern recognition that can be used by smart applications of the next generation to make huge changes. Design/Methodology/Approach: The deep feature convolutional network (DEEPQ-CNN) extracts the high representation and features of the hierarchical image from the relevant training data when data-driven learning is enabled. In addition, the DEEPQ-CNN characterization strategies are adapted to a couple of datasets in the boundaries and construction. The running time of the neural network and the network's weight are essential requirements for mobile computing. Finding/Results: In the proposed system, the design of the image processing module is based on the characteristics of a mobile device's convolutional neural network. However, the use of mobile devices for data collection, processing, and construction is described. Last but not least, the computing conditions and mobile device data features are taken into account. Original Value: For optical character recognition (OCR), specific datasets support the lightweight network structure. CNN is utilized to approve the proposed framework when examinations are made with the results of past methods to perceive the optical person.
Analisa Prediksi Harga Emas Dengan Kemungkinan Terjadinya Resesi Menggunakan Metode SVR

Gold is a resource that has a high value and has the advantage of a stable selling price. This can be proven by the choice of gold which is often used as a long-term investment tool. It can be seen that the impact of Covid-19 and the Russia-Ukraine war is considered to be the causes of recession that will affect the economy and end with the changes of gold selling price. This research was conducted on the basis of the large number of people who are now starting to be interested in investing in gold. However, this is quite a question for gold investors because of economic changes from the impact of Covid-19 and the Russia-Ukraine war. People are certainly worried, especially for those who have investments in the form of gold. The purpose of this research is to provide an analysis in the form of predictions of gold prices in 2023, an advice on managing gold in the future. The method used is the Support Vector Regression method using a polynomial kernel and supported by the Mean Absolute Percentage Error measurement. From the past research that has been done, the prediction results for gold prices in 2023 with an error value of 4.8% where this value is in the very good category. From this research, several suggestions are also given in managing gold during a recession
Implementasi Metode Convolutional Neural Network Pada Pengenalan Aksara Bali Berbasis Game Edukasi

Balinese script or also known as hanacaraka, is the writing used by Balinese people to write their language. In general, this script is used to write everyday language and literary language. Balinese script in the past was not only used for writing literature or sacred texts but also for writing everyday language. Balinese script plays an important role in literary writing. The sacred text of the Vedas uses Balinese script in the Sanskrit language. In preserving the Balinese script, itself, Balinese script lessons are mandatory for students from elementary school to high school. In addition to studying at school, interesting learning is certainly needed to attract students' interest. One way is by way of game applications or educational games. This Balinese script recognition application receives input in the form of Balinese script writing characters from the user, then it will be processed by preprocessing and continued with the classification training process using the Convolutional Neural Network (CNN) and Backpropagation methods. The result is a web-based application that can recognize Balinese script writing with the CNN classification method with an accuracy rate of 81.3% and gets a positive response from respondents who have tested the application.
Application of Stemmer Algorithm to Determine Dhomir Mustatir in a Kalimah Fi’il

The nahwu subject is one of the subjects that form the basis of the world of Islamic boarding schools. Nahwu becomes the foundation for students in studying the yellow book. In the science of nahwu, there are several basic chapters that must be understood by students, one of which is the Dhomir Dhomir chapter, which is one of the chapters in the science of nahwu that must be studied for students in Islamic boarding schools. In the dhomir chapter, students will learn about a pronoun consisting of 14 dhomirs. Dhomir learning is usually done using the word-by-word memorization method, in this memorization, there are still many students who have difficulty finding changes in the form of one word into another so that it will hinder the learning process of the students. Based on this problem, the writer takes the title of applying a stemmer algorithm to determine dhomir mustatir in a sentence of fi'il. This study produces an application that can determine a pronoun or dhomir in a fi'il sentence with 14 applicable dhomir rules. From the test results obtained a percentage of 97% of the level of system compatibility with the form of word changes or dhomir.
Learning Semantic in English Conversations

This paper amis to analyze the learning semantic in English conversation, learning semantic which is a part of applied linguistic that focuses on the meaning, In addition there are manythigs toknow about semantic in English conversation what the important is, when the communication will not be able to deliver well which are about culture, accent, country. They are some aspects that we have to study about semantic in English conversation, in this paper, the writer use method of the research by using qualitative that is about the people habbit and when they have to communicate each othe. Because English is an international language, all of people should be able to communicate in English to express or mention something, to study abroad, to write an international paper, to join a conference, they need this language to do it.
Job Satisfaction of Volunteer Firefighters in Daejeon

Volunteer firefighters receive regular education or training to allow them to provide a suitable response in case of disasters such as social disasters. However, volunteer firefighters are limited to assisting the work of firefighters but are not involved in active duty. Therefore, this study examines the current status of volunteer firefighters and conducts and analyzes satisfaction surveys through questionnaires. Volunteer firefighters undergo regular education and training. Because they have long-term residence in the region as well as expertise, their services should be actively utilized in the field of disaster prevention and response. Moreover, because the satisfaction level of the convocation allowance is low, it is necessary to make the recruitment allowance realistic. The ratio of personnel working in firefighting and technology-related fields is low; thus, it is necessary to expand and allocate practical firefighting-related subjects during education and training. Through this, it is judged that the satisfaction of volunteer firefighters can be increased and the organization can be actively utilized in the case of a disaster to provide more help to the country and the people.
Evaluation of Media-Based Social Interactions in Virtual Environments

The evaluation of users’ experiences in virtual environments is an important task for researchers in the fields of human-computer interaction and extended reality. It can be used to understand and enhance the quality of users’ mediated interactions and communications. In a constantly evolving world, where people are growing with technology, it is important to understand, evaluate and enhance the use of immersive media. In the research agenda of this Ph.D. thesis, the challenge of developing multi-user experiences in virtual environments and setting evaluation metrics for researchers are considered. This Ph.D. thesis showcases an interest in how to enhance trust formation in media-based social environments. The findings of this Ph.D. are expected to help create new open-source tools to facilitate the understanding of individuals and groups in extended reality applications.
A Case Study of Digital Transformation of Rural Teachers from the Perspective of Social Ecosystem Theory

The digital transformation of rural teachers is key to the digital transformation of rural education. Using the method of typical case study, this research clarifies how rural teachers achieve digital transformation and why they can achieve digital transformation from the perspective of social ecological systems theory. The research results show that the benign interaction among the macro system such as the era background and national policies, the micro system such as the education administration and schools, and the individual micro system of rural teachers promotes the digital transformation of rural teachers. The macro and meso systems of the ecosystem provide a digital transformation environment for rural teachers. The physiological and psychological characteristics of rural teachers' individual micro systems provide internal motivation for their digital transformation.
Research on Chinese Diabetes Question Classification with the Integration of Different BERT Models

As diabetes has become one of the major public health challenges worldwide, the importance of automatic question-answering services for diabetes in daily healthcare is gradually being highlighted. To address this challenge, this paper aims to improve the accuracy of classifying Chinese diabetes questions. In order to achieve this goal, we utilized BERT pre-training models to extract text feature vectors, combined them with convolutional neural networks to extract local features, and employed pooling and fully connected layers for classification. Additionally, we also tested different BERT models and obtained the best results using voting fusion technology. The final accuracy improved by 2% compared to a single model. Experimental results demonstrate that the model can effectively classify Chinese diabetes questions, providing robust support for the implementation of automated diabetes question-answering services.
Physical image properties influence image memorability in a category-dependent manner

Recent studies have shown that image memorability (the estimated probability that an image would be remembered) is a consistent image-specific trait. Several studies have recently found that during naturalistic encoding (when participants freely view images and are not aware of any task that may follow) physical stimulus properties such as image size, contrast, and presentation duration all influence memory such that images with higher levels of these physical properties are associated with higher memory and higher image memorability. Specifically, these studies used 160 images of faces, people, indoors and outdoors from LaMem dataset that were carefully selected to be balanced for memorability scores (as available from LaMem) across the physical stimulus manipulations they performed. During naturalistic encoding, image memorability was significantly affected by physical manipulations with higher levels of each physical property (bigger size, higher contrast, longer duration) leading to higher image memorability. Since the memorability of images was influenced by these physical manipulations to different degrees, here we were interested to examine whether image category contributed to this variability. Image memorability was modulated according to visual category and the specific physical modulation (size, contrast, presentation duration). Memorability of images from all categories was significantly influenced by the strongest vs weakest physical conditions (size of 24° vs 3°, contrast of 60 vs 7.5 RMS, or presentation duration of 2000 vs 250ms), and interestingly, images of people were influenced the most across all physical manipulations. Furthermore, images of people were also the most influenced between the two moderate levels of physical properties (size of 12° vs 6°, contrast of 30 vs 15 RMS, or presentation duration of 1000 vs 500ms), while face images the least. While multiple factors are likely to contribute to image memorability, our results suggest that the contribution of physical image properties to image memorability is likely category-dependent.
Memorable Scenes Attract Attention in Visual Search

Scene images are complex arrangements of multiple items, yet we can have memory for them. However, not all scene images can be remembered with equal performance; it is possible to have outstanding memory for some scenes, but poor for others. This characteristic is called memorability. This study examined the hypothesis that scenes with high memorability are more salient, thus attracting attention more quickly and difficult to disengage, resulting in the scene being remembered. To investigate this, we conducted a visual search task using the search asymmetry paradigm with natural scenes as the search item. Search asymmetry is the dramatic change in search efficiency when the roles of target and distractor are interchanged, and can be explained by the difference in the speed of disengaging from distractors. In Experiment 1, participants searched for a scene that was instructed every trial. There were two conditions: one in which the high memorability scene was among various low memorability scenes, and vice versa. The result showed that a high memorability scene among low memorability scenes could be found more efficiently than vice versa, suggesting that search asymmetry was observed. In Experiment 2, scene color was removed and gray scale images were presented, but we replicated the results of Experiment 1. To exclude the possibility that the high memorability scenes were found earlier because participants easily learned them when they were the target, in Experiment 3, the number of times that each scene was presented as a target increased and participants could train the low memorability scenes as well as the high memorability scenes. The results again showed the search asymmetry as Experiments 1 and 2. These results suggest that attention was less likely to disengage from high memorability scenes when searching for low memorability scenes, resulting in less search efficiency for low memorability targets.
Behaviourally relevant image structure linked with visual sampling and perception of materials

The perceived material of an object is inherently tied to its environmental affordances, whether we are avoiding a wet, slippery floor or handling a fragile, crystal vase. Moreover, the way we interact with an object also affects how we visually sample it, as our gaze is guided to behaviorally relevant features. We hypothesized that human gaze behavior during object viewing should therefore be guided by the object’s perceived material and, if so, visual sampling of the object should reflect regularities in image structure that perceptually define the material. To test this, we characterised the relationship between human gaze behaviour, image structure, and material perception by combining eye tracking and deep-learning-based gaze predictions for 924 rendered photorealistic object stimuli. These stimuli were complex glossy objects rendered in natural illumination fields with varying reflectance properties, leading to a wide range of material appearances such as plastic, clay, ceramics, fabric, etc. Using DeepGaze IIE to predict fixation patterns on these images, we found that these patterns do indeed differ between stimuli, independent of object shape. This suggests that surface properties affect how we visually sample objects. Further, these differences in gaze patterns correlated with differences in perceived material. Finally, we found that variations in contrast, clarity, size, and colour of specular reflections of the objects predicted differences in both perceived material and gaze behaviour, providing a direct link between image cues, viewing behaviour, and affordance-related information. In a series of follow-up analyses, we then tested these model-based predictions in eye tracking data. Collectively, our results support the notion that both object perception and viewing behaviour are shaped by the affordances of the things we look at, and that both are determined by regularities in image structure caused by the complex yet characteristic ways that light is scattered by materials with different surface properties.
Scene memory for intrinsic and extrinsic boundaries

When viewers study a photograph of a scene, they often remember more of the scene than they actually saw. This “boundary extension” is found for the boundaries of the photograph but not for boundaries of objects within a photograph (e.g., a tray upon which a place setting is arranged). A set of experiments tested memory for boundaries of scenes where the boundary is a naturally occurring aperture within a scene, such as a door or a window. Participants viewed a series of 32 picture 16 of them had naturally occurring (intrinsic) apertures (e.g., French windows) through which another scene (e.g., a garden) could be seen. In the study phase, participated were instructed to memorize the scene to indicate if each scene was all indoor, all outdoor or a combination of indoor and outdoor (e.g., room with a garden seen through the window). In the test phase, participants were given four choices. For the scenes with internal apertures, the inner scene (seen through this aperture) was varied separately from the outer scene (the scene where this aperture was located). Prior research showed that participants were more likely to pick a scene where the outer scene was correct and the inner scene was extended. However, in that study participant were not given the choice to pick the target picture. In the current study, participants were given 4 test choices: target, extended inner scene only, extended outer scene only (inner scene correct), and a version in which both were extended. Participants still showed preference for the scene with only the inner scene extended, followed by the target scene, showing that participant were less likely to extend the boundaries of the overall scene but did tend to extend the boundaries of the inner scene, viewed through the intrinsic aperture.
Long-term memory for objects in real-world scenes: The effects of semantic consistency and task priorities

Understanding the interplay between different cognitive factors in long-term memory is essential to understanding the nature of mental representations of our visual environments. In this study, across three online experiments, we investigated how task priorities and expectations arising from semantic knowledge based on schemas may concur to determine long-term memory for objects in real-world visual scenes. In Experiment 1, participants intentionally memorised a sequence of scenes, 35% of which contained a semantically inconsistent object. In Experiment 2, participants searched for target letters within the same scenes, and this was followed by a surprise memory test. Experiment 3 required intentional memorisation but presented 50% of the scenes with an inconsistent object. Presentation times (10s) and intervals between scenes (2.5s) were the same across experiments. Memory for two objects in each tested scene was examined in a four alternative-forced-choice paradigm, where participants also rated their confidence in each response on a Likert scale. One object, tested as first, was either (50% of the trials) semantically consistent or inconsistent with the scene, and the other object was always consistent. The results showed higher accuracy and confidence in intentional (Experiments 1 and 3) than incidental (Experiment 2) memorisation. Accuracy was higher for consistent than inconsistent objects in all experiments, suggesting that schemas guided retrieval, which was also supported by a bias to select consistent foils in wrong responses. Equating the number of consistent and inconsistent scenes in Experiment 3 did not considerably reduce the consistency advantage, showing that semantic knowledge played a greater role in the effect than any modulations of reliance on schemas due to the short-term task context. Confidence was higher for correct responses for inconsistent than consistent objects, especially in intentional memorisation, suggesting that, if correctly encoded, a memory trace that violates schema expectations has higher distinctiveness and precision.