Reviewerly/exHarmony huggingface dataset:
Exploration of Emotion Perception in Serious Interactive Digital Narrative

The procedural process of children's emotional involvement in the interactive digital narrative conforms to children's emotional attachment to the story and enhances the mediating nature of learning through forced interactivity. This study explores how compelling arcs influence learners' preference for serious story content by using a combination of natural language processing methods and statistical analysis methods. By analyzing 474 Chinese short serious stories, the emotional trajectory of each story is generated. Then, the obtained trajectories are combined into clusters of serious story emotional groupings through supervised learning. The study results found that the emotional arc in serious stories can be divided into six basic shapes, and the serious story with the highest preference is the "N"-shaped emotional arc. Emotional ups and downs characterize the emotional narrative aspect of this type of serious story as the story progresses but with an apparent emotional uptick towards the end of the story. Based on experimentally derived emotional topology and narrative generation methods, this paper proposes the design strategies for future emotional arcs to apply to serious interactive digital narratives.
ANALISIS KEKERINGAN MENGGUNAKAN METODE PALMER DROUGHT SEVERITY INDEX (PDSI) SERTA DAMPAKNYA TERHADAP SISTEM IRIGASI SUBAK PADA DAERAH ALIRAN SUNGAI (DAS) PALUNG LOMBOK

Drought is a natural event that is very influential on the availability of water reserves in the soil, both needed for agricultural purposes and human needs. The Irrigation Areas (DI) conducted by the study are Pandanduri-Swangi, Rutus, Terara and Temusik which are located in the Palung River Basin. This Drought Analysis includes Analysis of Rainfall, Regional Rainfall, Evaporation, calculation of the drought of the Palmer Severity Index (PDSI) method. Then do a comparative study of the Planting Pattern, water management and water turn schedule. In this study, the calculation of drought was carried out at 17 Rainfall Stations (ARR) with input data coming from 6 Climate Stations (CR) conducted by Thiessen Polygon analysis. The results of the drought index for each Rainfall Station (ARR) are then analyzed using the ArcGIS Software isohyet method with the Inverse Distance Weighted (IDW) interepolation to obtain the drought value of the area in each Irrigation Area (DI) analyzed. In this study it was found that: i) Pandanduri-Swangi Irrigation Area which has an area of ​​4,593 hectares of irrigation has a fairly long level of drought between July and October. ii) The relationship between the drought index to the existing Planting Area where the correlation obtained is for the Pandanduri-Swangi, Rutus, Temusik and Terara Irrigation Areas (48%, 61%, 72%, and 73%). iii) Drought index for Debits The availability of existing water correlations is: i) For Pandanduri-Swangi, Rutus, Temusik and Terara Irrigation Areas are 40%, 48%, 71%, and 30%.
Credit Card Fraud Detection Using Hybrid Approach of Machine Learning

Abstract: The usage of internet banking and credit cards is growing at an exponential rate. As more people use credit cards, online banking, and debit cards, the probability of becoming a victim of fraud of various kinds also increases. In recent times, there have been a number of instances in which users of credit card companies have, as a result of a lack of understanding, given their card information, personal information, and one-time password to an unidentified fraudulent caller. As a direct consequence of this, fraudulent activity will occur on the account. Fraud is a problem for the same reason that it is tough to track down a con artist who used a phone identity sim or made the call that utilized an internet provider: it is difficult to find them. Therefore, in order to detect fraudulent activity, this research makes use of supervised methodologies and algorithms, and the results are quite accurate. Customers lose trust in an organization when it engages in activities that are fraudulent or illegal, which in turn has a huge negative impact on the organization. Additionally, it has an effect on the total income and turnover of the company. The isolation forest technique is used in this study to classify data sets acquired from professional survey firms in order to detect fraud activities.
Marginal Inference queries in Hidden Markov Models under context-free grammar constraints

The primary use of any probabilistic model involving a set of random variables is to run inference and sampling queries on it. Inference queries in classical probabilistic models is concerned by the computation of marginal or conditional probabilities of events given as an input. When the probabilistic model is sequential, more sophisticated marginal inference queries involving complex grammars may be of interest in fields such as computational linguistics and NLP. In this work, we address the question of computing the likelihood of context-free grammars (CFGs) in Hidden Markov Models (HMMs). We provide a dynamic algorithm for the exact computation of the likelihood for the class of unambiguous context-free grammars. We show that the problem is NP-Hard, even with the promise that the input CFG has a degree of ambiguity less than or equal to 2. We then propose a fully polynomial randomized approximation scheme (FPRAS) algorithm to approximate the likelihood for the case of polynomially-bounded ambiguous CFGs.
On compound mixed concepts

We extend to a certain class of compound concepts the binary model classically used for the representation of simple concepts. This class consists of concepts that are determined or modified by a single feature. The treatment of such a mixed composition shows the need to differentiate between exceptional and non exceptional modifiers. In the first case, typicality is easily retrieved from the components of the composition, while, in the second case, it is necessary to isolate the characteristic features of the initial concept that contradict the modifier. The distinction between exceptional and non exceptional modifiers plays a key role in the evaluation of resemblance with mixed compositions.
The way it made me feel – Creating and evaluating an in-app feedback tool for mobile apps

Mobile apps are becoming increasingly important in everyone's daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users' expectations, needs, and problems regarding an app in any situation. By continuously capturing and analyzing user feedback, developers can evaluate the level of user acceptance. There are various feedback channels, such as app stores, social networks, and within the app, which can be used to capture user feedback. As we already have experience with feedback from app stores and social networks, we wanted to investigate inapp feedback approaches and thus conducted a mapping study to understand the state of the art of these approaches.We analyzed 36 publications and derived requirements for in-app feedback tools. Based on that, we defined requirements for an in-app feedback tool to describe its prototypical realization. Then we performed an evaluation regarding user acceptance of our tool with 33 participants. The evaluation showed a high rate of acceptance for the tool among the participants. The results also highlighted improvement areas for our tool, such as optimizing the rate of requests for feedback. We plan to address these aspects in future work and to continue improving our tool.
Perception of hate speech by the public and experts: Insights into predictors of perceived hate speech toward migrants

Online hate speech is a matter of concern for social media platforms, regulators, researchers, and the public. Despite its widespread prevalence and contentious nature, little research has been done on the perception of hate speech and its psychosocial predictors. To address this gap, we conducted a study on the perception of hate speech towards migrants in online comments, analyzing the differences between a public group (NPublic = 649) and an expert group (NExperts = 27), and exploring the correlation between the proposed hate speech indicators and perceived hate speech in both groups. Additionally, we explored various predictors of hate speech perception, including demographic and psychological variables such as human values, prejudice, aggression, impulsiveness, social media behavior, attitudes toward migrants and migration, and trust in institutions. Our results show that the public and experts have differing sensitivities towards hate speech, with the expert group perceiving comments as more hateful and emotionally harmful compared to the general population, who tend to agree more with anti-migrant hateful comments. The proposed hate speech indicators, and especially their total score, have a strong correlation with both groups' perceptions of hate speech. Psychological predictors, such as the human values of universalism, tradition, security, and subjective social distance, were significant predictors of online hate speech sensitivity. Our findings emphasize the need for public and scholarly discussions, more robust educational policies, and intervention programs with specific measures to counter hate speech online.
A Data Mining Based Model for Detection of Fraudulent Behaviour in Water Consumption

Abstract: Drinking water fraud is a major issue for water delivery businesses and authorities. This behaviour results in a significant loss of income and is responsible for the bulk of non-technical losses. Finding appropriate criteria for detecting fraudulent behaviour has been a prominent focus of research in recent years. Intelligent data mining methods can be used by water delivery companies to detect fraudulent activity and reduce losses. This study looks into the usage of two classification techniques (SVM and KNN) to discover suspicious water fraud clients. The main purpose of this research is to assist the Yarmouk Water Company (YWC) in Irbid, Jordan, in overcoming their earnings shortfall. The SVM-based technique uses customer load profile attributes to expose known abnormal behaviour.
PARTICUL: Part Identification with Confidence measure using Unsupervised Learning

In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities of all images in the training set in order to mine for recurring patterns in the feature space of a pre-trained convolutional neural network. We propose new objective functions enforcing the locality and unicity of the detected parts. Additionally, we embed our detectors with a confidence measure based on correlation scores, allowing the system to estimate the visibility of each part. We apply our method on two public fine-grained datasets (Caltech-UCSD Bird 200 and Stanford Cars) and show that our detectors can consistently highlight parts of the object while providing a good measure of the confidence in their prediction. We also demonstrate that these detectors can be directly used to build part-based fine-grained classifiers that provide a good compromise between the transparency of prototype-based approaches and the performance of non-interpretable methods.
SARA: A Voice Assistant Using Python

Abstract: Artificial intelligence technologies are starting to be actively utilized in human life, thanks to the Internet of Things' debut and widespread distribution. Autonomous gadgets are growing more intelligent in their interactions with humans and with each other. New capabilities led to the development of various solutions for integrating smart devices into the Internet of Things Social Networks. The technique of recognizing a human's natural language is one of the most important trends in artificial intelligence. New insights into this area could lead to new forms of natural human-machine interaction, in which the computer learns to understand and engage with human language. Voice assistant is one of these tools, and it can be integrated into a variety of different intelligent systems. The basics of voice assistant operation are outlined in this paper, as well as the major flaws and limitations. The approach for establishing a local voice assistant without needing cloud services is explained, allowing future applications of such devices to be considerably expanded.
International experience of human capital management in higher educational institutions: the american model

The issues of training qualified specialists have become especially relevant in recent years, since the requirements imposed on higher educational institutions have increased significantly due to recent trends in the world. Universities today are not just knowledge transfer centers, but they are increasingly involved in social issues. Higher educational institutions have a direct impact on raising the standard of living, improving material well-being, career prospects, and the economic growth and prosperity of the state. Higher educational institutions play a key role in the formation, development of human capital, which is the main basic value of any economy, and especially an innovative economy. The purpose of this article is to study the foreign experience of formation and development of human capital, to identify the main trends, to study statistical data on the example of universities in the United States. The choice of American universities was conditioned by a number of factors, namely by the fact that the majority of American universities occupy leading positions in the world rankings. The main input data were the materials presented in the annual reports of the U.S. Department of Education, scientific journals. The authors provide an overview of statistical data on such indicators as the number of startups launched by students and faculty, their success in the market, and the scope of their activities. The conclusions offered in the article on the peculiarities of human capital management in higher educational institutions based on the analysis of the American higher education system can have a positive impact on the development of other higher education systems.
AUDIOVISUAL PRODUCTION MOOCS: ANALYSIS OF ORGANIZATIONAL, CONTENT, AND METHODOLOGICAL CRITERIA

The COVID-19 pandemic has imposed unprecedented changes and challenges to the ways of learning, teaching, and communicating within educational institutions.Prophylactic isolation measures, adopted globally, have discontinued face-to-face teaching and precipitated distance learning.This reality highlighted the importance of online courses as creative initiatives to overcome space and time constraints in a limited pedagogical context.It was in this scope that the Project "Exploration of online methodologies in the development of competencies in the audiovisual area" emerged, proposing the creation of a Massive Open Online Course (MOOC) in the audiovisual area (from the idea to the final product) and the development of the online platform LOOP for the implementation and availability of this and other courses held afterward.The project involves three Portuguese higher education institutions and one Spanish one, motivated to develop innovative proposals, both in research and education.Moreover, this network presents a diversity of training in degree courses, master's, and technological specialization programs, which allows us a privileged contact with the students and helps us establish priorities and needs.This paper focuses on the survey and analysis, at the national and international level, of MOOCs dedicated to the themes of audiovisual production to expose parameters of both organizational and content scope.The methodological approach will be divided into two phases: a) identification of the criteria for analyzing courses through the literature review; b) and selection of the courses to be analyzed.This selection will be conducted through a database search, identifying the keywords in Portuguese, English, and Spanish: audiovisual online course; audiovisual free course; filmmaking course; scriptwriting course; video editing course; and other derivations.It is hoped to present a set of guidelines for developing the course related to the organization and the content.
BAURAN PEMASARAN GAS ELPIJI PADA PT.CITRA KARUNIA ILAHI BANJARMASIN

This study aims to determine the Marketing Mix (Product, Price, Distribution andPromotion) of LPG, to find out what obstacles are faced and what alternative solutionscan be done related to the constraints faced.This research uses a descriptive method. Sources of data used in this study are primarydata and secondary data. Data collection techniques used are through observation,interviews and documentation. The data analysis technique is descriptive qualitativedata analysis technique.The results showed that, the marketing mix carried out by PT. Citra Karunia Ilahi is theselection of products marketed, namely subsidized and non-subsidized LPG GasProducts. Non-subsidized LPG Gas products include 5.5 Kg LPG Gas, 12 KG LPG Gas,and 12 KG LPG Bright Gas, then the application of varying prices, for the price of 5.5Kg refill cylinders is priced at Rp. 65,000, LPG 12 Kg Rp. 142,000, and LPG 12 KgBright gas Rp. 145,000. PT. Citra Karunia Divine distributes Non-subsidized LPG GasProducts to gas stations, minimarkets, subsidized LPG Gas Bases, MSMEs, Institutionsand to the public directly, while the promotion is carried out by means of promotionsthrough banners/banners, and promotions for holidays. The obstacles faced are the lackof purchasing power and public interest in non-subsidized LPG Gas, and the PromotionStrategy is still not intensive, while the Alternative Solution that can be applied is byconducting a tube loan trial program, and using Digital Marketing and Promotionstrategies.
The Role of Organizational Cultural Determinants on Stimulating Innovation in Commercial Companies

The purpose of this paper is to identify the determinants of organizational culture that stimulate innovation and the strength of their effect and to provide more methodological evidences that are addressing organizational cultural determinants, which stimulate or imped innovation in Commercial Companies. Based on the responses of the study sample that included 397 employees in (11) Palestinian companies, their age varied from 25 to more than 50 years old with administrative and executive positions, the results highlighted the role of organizational culture in stimulating innovation, thus helping the organization to achieve innovation. The study showed that there was a significant effect of organizational culture dimensions on innovation. Therefore, large- and medium commercial companies must take general, clear, and time-scheduled strategic and structural actions to promote and address the shortcoming in their customer focus levels, continued organizational learning, empowerment and clarity of changing work environment to enhance sharing knowledge to ensure an effective balance. In addition, it is necessary to emphasize interest in the development programs to gather and attract innovative workers.
SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration

Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis. The unsupervised deep learning registration method can quickly achieve high registration accuracy without labels. However, these methods generally suffer from uncorrelated features, poor ability to register large deformations and details, and unnatural deformation fields. To address the issues above, we propose an unsupervised multi-scale correlation iterative registration network (SearchMorph). In the proposed network, we introduce a correlation layer to strengthen the relevance between features and construct a correlation pyramid to provide multi-scale relevance information for the network. We also design a deformation field iterator, which improves the ability of the model to register details and large deformations through the search module and GRU while ensuring that the deformation field is realistic. We use single-temporal brain MR images and multi-temporal echocardiographic sequences to evaluate the model's ability to register large deformations and details. The experimental results demonstrate that the method in this paper achieves the highest registration accuracy and the lowest folding point ratio using a short elapsed time to state-of-the-art.
An Analysis of Machine-Learning Feature-Extraction Techniques using Syntactic Tagging for Cross-site Scripting Detection

Working for a strategy to ensure web application security has become more complex as it is not only to protect against the more challenging cross-site scripting (XSS) attacks but also to assist the open expressiveness of web applications to provide users with interactive services.The feature extractions used in supervised machine learning to detect XSS as an approach to the strategy are now in question of their effective classification.Their lack of preserving structural information may not uphold the property of structured data in input payloads.We apply the concept of syntactic n-grams to a payload text representation.The study of different feature extractions on the representation is to see if syntactic information is maintained.Our purpose is to determine the more effective approach to correctly classify benign and malicious payloads on a real-world dataset.The use of sn-grams that produces the most favourable results of accuracy and precision would only indicate that the extraction is reasonably able to minimize false reports during classification.
Personalized recommendation system based on social relationships and historical behaviors

Previous studies show that recommendation algorithms based on historical behaviors of users can provide satisfactory recommendation performance. Many of these algorithms pay attention to the interest of users, while ignore the influence of social relationships on user behaviors. Social relationships not only carry intrinsic information of similar consumption tastes or behaviors, but also imply the influence of individual to its neighbors. In this paper, we assume that social relationships and historical behaviors of users are related to the same factors. Based on this assumption, we propose an algorithm to focus on social relationships useful for recommendation systems through mutual constraints from both types of information. We test the performance of our algorithm on four types of users, including all users, active users, inactive users and cold-start users. Results show that the proposed algorithm outperforms benchmarks in four types of scenarios subject to recommendation accuracy and diversity metrics. We further design a randomization model to explore the contribution of social relationships to recommendation performance, and the result shows that the contribution of social relationships in the proposed algorithm depends on the coupling strength of social relationships and historical behaviors.
Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection

Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modelling the distribution of features. We further incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble stochastic neural networks (BE-SNNs) and overcome the feature collapse problem. We compare the performance of the proposed BE-SNNs with the other state-of-the-art approaches and show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionMNIST vs NotMNIST dataset, and the CIFAR10 vs SVHN dataset.