Reviewerly/exHarmony huggingface dataset:
Neural Network Based Data Encryption: A Comparison Study among DES, AES, and HE Techniques

With the improvement of technology and the continuous expansion and deepening of neural network technology, its application in computer network security plays an important role. However, the development of neural networks is accompanied by new threats and challenges. This paper proposes to encrypt the weight data using encryption algorithms and embed image encryption algorithms to improve protected data security further. The purpose is to address the feasibility and effectiveness of using modern encryption algorithms for data encryption in machine learning in response to data privacy breaches. The approach consists of training a neural network to simulate a model of machine learning and then encrypting it using Data Encryption Standard (DES), Advanced Encryption Standard (AES), and Homomorphic Encryption (HE) techniques, respectively. Its performance is evaluated based on the encryption/decryption accuracy and computational efficiency. The results indicate that combining DES with Blowfish offers moderate encryption and decryption speeds but is less secure than AES and HE. AES provides a practical solution, balancing security and performance, offering a relatively swift encryption and decryption process while maintaining high security. However, Fernet and HE present a viable alternative if data privacy is a top priority. Encryption and decryption times increase with file size and require sufficient computational resources. Future research should explore image encryption techniques to balance security and accurate image retrieval during decryption. Advanced privacy-preserving approaches, such as differential privacy and secure multi-party computation, may enhance security and confidentiality in digital encryption and decryption processes.
EmoStory: Emotion Prediction and Mapping in Narrative Stories

A well-designed story is built upon a sequence of plots and events. Each event has its purpose in piquing the audience's interest in the plot; thus, understanding the flow of emotions within the story is vital to its success. A story is usually built up through dramatic changes in emotion and mood to create resonance with the audience. The lack of research in this understudied field warrants exploring several aspects of the emotional analysis of stories. In this paper, we propose an encoder-decoder framework to perform sentence-level emotion recognition of narrative stories on both dimensional and categorical aspects, achieving MAE=0.0846 and 54% accuracy (8-class), respectively, on the EmoTales dataset and a reasonably good level of generalization to an untrained dataset. The first use of attention and multi-head attention mechanisms for emotion representation mapping (ERM) yields state-of-the-art performance in certain settings. We further present the preliminary idea of EmoStory, a concept that seamlessly predicts both dimensional and categorical space in an efficient manner, made possible with ERM. This methodology is useful in only one of the two aspects is available. In the future, these techniques could be extended to model the personality or emotional state of characters in stories, which could benefit the affective assessment of experiences and the creation of emotive avatars and virtual worlds
A Comparison of the Classification Performances of the DINO Model, Artificial Neural Networks and Non-Parametric Cognitive Diagnosis

The purpose of this study was to compare the attribute (ACR) and pattern-level (PCR) classification rates of the Deterministic-Input, Noisy-Or Gate (DINO) model, Artificial Neural Networks (ANNs), and Non-parametric Cognitive Diagnosis (NPCD) from datasets of simulation in various conditions such as the number of attributes, sample size, the number of items and missing data rate. A further purpose was to examine the similarities between the classification rates of the DINO model ANNs, and NPCD on the PISA 2015 collaborative problem-solving (CPS) datasets in various numbers of attributes and sample sizes. For the purpose of the study, simulation datasets were generated on the basis of the complex Q matrix structures and the DINO model. The conditions for the sample size factor for the real datasets were determined by simple random selection among the participants in the PISA 2015 administration. As a result, it was found that there was a similarity between the DINO model and NPCD classification rates in both simulation and real datasets. In addition, regarding the increase in sample size in both simulation and real datasets, no consistency was found in the increase or decrease of the classification rates of ANNs and NPCD and the similarities of these rates.
A Platform for improving the capability of young faculties in universities based on knowledge management

At present, universities introduce a large number of for their own developing needs, their needs for rapid improvement of teaching and research abilities are becoming more and more urgent while the scale of young faculties is expanding. Based on the need for the growth of teaching and research abilities of young faculties in today's universities. The paper proposes a multi-functional improvement platform for young faculties that includes training and management of young teachers, summary and sharing of teaching experience, research forums, knowledge management, and other functions. This platform can effectively help young faculties to fully understand the teaching profession, communicate with experienced teachers, obtain high-quality teaching resources, and improve their scientific research ability. The growth of young faculties can be greatly accelerated, as well as their teaching ability and scientific research ability can be consolidated by utilizing the platform.
A FEW ASPECTS REGARDING THE ACTIVITY OF TEACHER, COMPOSER AND CONDUCTOR GHEORGHE DANGA

The history of music talks about many teachers, composers, conductors, but, like any science, it cannot fully cover what was written nor all the personalities in the world of music. In this sense, the current article offers details about one of the titans of Romanian choral music, a great self-taught, who studied only two years at the music academy in Bucharest, but being gifted by God with genius abilities, composed and conducted time for over 30 years with an incomparable lightness. I wanted to bring to light the personality of the great maestro Gheorghe Danga, to reveal more unknown aspects about the course of his artistic life , and to make him much better known in the nationally and internationally in the world of music and beyond.
Navigating the Data Economy: A Comprehensive Review of Evolution, Impact, and Future Trends

The data economy has become a revolutionary force in today's digital landscape, transforming how firms run, decisions are made, and societies function. This review examines the many facets of the data economy and provides information on its technological foundations, historical development, and the economics of data. It traces the theoretical underpinnings and investigates how data has developed into a crucial economic asset. The work provides an analysis of the technological developments driving the data economy, highlighting the significance of artificial intelligence, cloud computing, and data analytics. Examining the changing environment of data management procedures and storage options, the importance of new technologies is highlighted. In addition, the paper explores the practical uses of AI and data analytics, offering instances of effective data-driven decision-making in action. A close examination is conducted of monetization tactics, particularly those involving data marketplaces and targeted advertising. Legal frameworks controlling data security and privacy, and new developments such as edge computing and blockchain are also discussed. FAME (Federated Decentralized Trusted Data Marketplace for Embedded Finance) project is highlighted as a novel project that addresses the shortcomings of existing centralized cloud-based data markets. By summarizing the most important discoveries, the review ensures that it is relevant for scholars, practitioners, policymakers, and stakeholders negotiating the complex terrain of datadriven decision-making
A Survey on SQL Injection Attacks Types &amp; their Prevention Techniques

In the world of connectedness even more than ever before we are using digital technologies in daily life from bank transactions to social networking. While all of this data is residing in databases and with increasing usage, the importance of confidentiality and integrity of the data seems to be a challenging and most pressing issue of today’s world. SQL injection are commonly used attacks to gain access to unauthorized data and one such example out of many where an attacker breach the corporate system and stole 130 million credit card details and even after two decades, SQL injection is still one of top ten security threats by open web application security project (OWASP) moving forward with more digitalization in the future it will always be a serious concern since it impacts the core principle of information security such as confidentiality, integrity and availability. In this research project I will be discussing the types of SQL injection mainly in-band, inferential and out of band attacks and keeping scope over in-band SQL injection to further discuss prevention techniques using traditional methods and lastly discussing some of the prevention techniques using advanced algorithms of machine learning and deep learning in the literature.
Higher-Order Multi-Scale Physics-Informed Neural Network (Homs-Pinn) Method and its Convergence Analysis for Solving Elastic Problems of Authentic Composite Materials

The limitations of prohibitive computation and Frequency Principle remain difficult for deep learning methods to effectively resolve multi-scale problems. In this work, a novel higher-order multi-scale physics-informed neural network (HOMS-PINN) framework is developed to compute the elastic problems of real composite materials with high-contrast and discontinuous material parameters, which inherits the advantages of higher-order multi-scale method and physics-informed neural network. In the numerical framework of HOMS-PINN method, two types of network structures including single neural networks and independent neural networks are designed to mesh-free solve lower-order and higher-order microscopic unit cell (UC) functions, and macroscopic homogenization equations of multi-scale composites, which are then assembled into higher-order multi-scale solutions for multi-scale elastic problems by using automatic differentiation technology. Moreover, transfer learning is introduced to accelerate the training process of HOMS-PINN method when computing the multi-scale elastic problems of composite materials with high-contrast mechanical parameters. Furthermore, the error estimate of the proposed HOMS-PINN method is demonstrated under some assumptions. Finally, numerous numerical experiments including high-contrast composites, multi-inclusion composites and three-dimensional composites are presented to validate the accuracy and effectiveness of HOMS-PINN method. This study offers a robust HOMS-PINN computational framework that enables the simulation and analysis of large-scale mechanical problems of authentic composite materials.
Conditions of local parameter identifiability for systems of differential equations with an infinite-dimensional parameter

The problem of parameter identification (determining parameters of a system by observing solutions or functions of them) is one of the main problems of the applied theory of differential equations. The property of local identifiability plays the most important role in solving this problem. The presence of this property means that one can uniquely determine values of parameters of a system in a neighborhood of a particular parameter by observing solutions. Earlier the case of a finite-dimensional parameter was mostly studied in this issue. The problem of local parameter identifiability in the case of an infinite-dimensional parameter is less studied. In this paper we propose a new method for obtaining sufficient conditions for local parameter identifiability in the case of an infinite-dimensional parameter. Under these conditions an infinite-dimensional parameter belonging to certain classes is locally identifiable by observing a solution at a finite set of points. For system with linear dependence on a parameter we establish the genericity of the mentioned conditions.
Semi-Supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks

Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the large number of human annotations of quality. In this work, we propose a semi-supervised learning (SSL) framework exploiting many unlabelled and very limited numbers of authentically distorted labelled videos. Our main contributions are twofold. Leveraging the benefits of consistency regularization and pseudo-labelling, our SSL model generates pairwise pseudo-ranks for the unlabelled videos using a student-teacher model on strong-weak augmented videos. We design the strong-weak augmentations to be quality invariant to use the unlabelled videos effectively in SSL. The generated pseudo-ranks are used along with the limited labels to train our SSL model. Our primary focus in SSL for NR VQA is to learn mapping from video feature representations to quality scores. We compare various feature extraction methods and show that our SSL framework can lead to improved performance on these features. We present a spatial and temporal feature extraction method based on predicting spatial and temporal entropic differences. We show that these features help achieve robust performance when trained with limited data, providing a better baseline to apply SSL. Extensive experiments on three popular VQA datasets demonstrate that the proposed semi-supervised VQA method improves on the performance of existing methods in terms of correlation with human opinion by approximately 15-20%. The source code will be made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">"https://github.com/Shankhanil006/Learning-Pseudo-Rank"&gt;</uri> .
Efektivitas Program Indonesia Pintar terhadap Partisipasi Sekolah di Kawasan Barat dan Timur Indonesia

Smart Indonesia Program (PIP) is a government program that aims to make the 12-year compulsory education program a success. The PIP program has been running since 2015. However, the Badan Pusat Statistik (BPS) notes that there are still school-age residents who are not attending school, even though the percentage of children not attending school has decreased. This study aims to measure the implementation of PIP and other factors on school participation in two regions, namely the West Indonesia Region (KBI) and the East Indonesia Region (KTI) in 2021. This research uses data from the 2021 National Socio-Economic Survey (Susenas) in the Indonesian Region West (KBI) and Eastern Indonesia Region (KTI). Data analysis used logistic regression method. The results showed that all independent variables, PIP, gender, marital status of head of household, classification of area of residence, per capita expenditure, other social assistance had a significant effect on school enrollment in the Western Regions of Indonesia (KBI) and Eastern Indonesia (KTI). . The biggest tendency for children to participate in school is the PIP.
Method for Establishing a Spatial Database of Traffic Signs with Machine Learning

Traffic signs are part of road equipment whose existence is very important, in addition to functioning as warnings, prohibitions, orders, or instructions for road users, traffic signs are also a means to reduce accidents and regulate driver behavior. Because of the importance of this function, it is necessary to collect accurate sign data in a spatial database. Currently, there have been many database developments for the management of traffic signs, but data collection is still done manually, by means of surveyors recording groups of traffic signs and entering them into the database. The difficulty faced is the time and accuracy of the surveyors when it comes to selecting groups of signs, this is due to the large number of groups/sub-groups of signs. This problem needs to be solved with the help of a sign group detection tool with an image recognition approach. This study aims to develop an image recognition method to extract photo geotagging information on traffic signs into spatial data and attributes of traffic sign groups. The object of the signs that are sampled are signs that are on roads with the status of provincial roads in the Special Region of Yogyakarta. The results showed that the machine learning-based image recognition accuracy reached 88.66%, further research is needed to improve accuracy by paying attention to the geotagging photo capture variable.
Bayah Dome Geopark Information System Based On Website Using Prototype Method

To realize a national Geopark and a World Geopark, it is necessary to socialize and disseminate information that can be accessed by the people of Lebak Regency in particular and the wider community in general to introduce all the geological heritage that has been determined by the Minister of Energy and Mineral Resources. Nowadays, digital information system technology has become an undeniable need, and has even become a source of information that can be accessed by all people in the world. The Bayah Dome Geopark management body in Lebak Regency does not yet have a website-based information system, so it is necessary to create a website-based Bayah Dome Geopark information system so that the Bayah Dome Geopark is known nationally and internationally.This research uses a Research and Development approach. Research procedures include: (a) analysis, (b) design, (c) implementation and (d) evaluation. Stage (a) analysis consists of two steps, namely literature study and field survey. The literature study examines concepts and theories relating to Geoparks and the Bayah Dome Geopark, while a field survey was carried out to collect data relating to the Bayah Dome Geopark. Stage (b) design is carried out by designing a website with UML (Unified Modeling Language) which is a program design based on the results obtained in the analysis stage which includes: use case diagrams, activity diagrams using the prototype system development method. In stage (c) implementation, the results of the design are outlined and implemented into a website.
Interactive Multimedia as Information Media Parasitic Infection with Multimedia Development Life Cycle Method

This study focused on the problem of lack of interactive multimedia-based educational information media that is interesting as a provider of information about parasitic infections in humans. This parasitic infection is very dangerous, especially for children who still lack knowledge about it. The aim of this research is to provide educational information media to the wider community, especially at the Seberang Padang Health Center, about parasitic infections in a way that is as interesting as possible and easy to use. Interactive multimedia is a solution to this problem. This research uses the Multimedia Development Life Cycle Method as a guide in building interactive multimedia with the stages of Concept, Design, Material Collecting, Assembly, Testing and Distribution. The results of the alpha test showed that all features of this interactive multimedia can function properly. A beta test was conducted on 30 respondents from the public who accompanied patients to the Seberang Padang Health Center. As a result, this interactive multimedia received a rating of 4.54 out of 5. Therefore, this interactive multimedia is very good and suitable for use as an educational medium for information about parasitic infections. With this interactive multimedia-based educational information media, it is hoped that the wider community can better understand and be aware of parasitic infections and preventive measures that can be taken to avoid these infections.
Sentiment Analysis of 2024 Presidential Candidates Election Using SVM Algorithm

Elections for presidential candidates are held every 5 years with various candidates, especially on Twitter, arguments about political matters often occur that many Twitter users participate in discussions about the election for presidential candidate. Therefore, this study focuses on sentiment analysis to infer user responses to the presidential election and validate it by looking for a correlation between electability survey results and Twitter sentiment data using Pearson Correlation. In sentiment analysis model, the 10-Fold Cross Validation method is used to find the best model from a dataset with a division of training data and test data with 90:10 split. Then the alphabetic data will be converted into numeric data using the TF-IDF weighting method. To validate the best model, Confusion Matrix is used to get the best f1-score. The model is using Support vector machine algorithm with the Gaussian RBF (Radial Basis Function) kernel. The results of the analysis are compared with the results of the news portal electability survey which contains the 3 candidates using Pearson Correlation. This study produces the best fold for each data on each presidential candidate with the f1-score to find the best model for each fold. In the Peason Correlation result, the higher positive sentiment of each presidential candidate, the higher electability survey data. For further research, research can be discuss about hyper tuning parameters and using other kernels on Support vector machine algorithm.
Comparison of ANN Backpropagation Algorithm and Random Forest Regression in Predicting the Number of New Students

Higher education institutions are educational units located at a higher level after high school or vocational school. Catholic University Darma Cendika Surabaya (UKDC) faces challenges in managing the admission of new students due to variations in the number of prospective students applying to each department, which is also influenced by changing trends in interests and job needs in Indonesia. The use of Artificial Neural Network with Backpropagation and Random Forest Regression algorithms for comparing the prediction of new student admissions in the following year will be beneficial for the administration of Catholic University Darma Cendika Surabaya (UKDC) to gain a clearer understanding of the dynamics of admissions and to support decision making in the future development of the university. The predicted number of students joining Catholic University Darma Cendika Surabaya (UKDC) in the 2024 period using Artificial Neural Network is 219 students with a Mean Squared Error (MSE) of 0,1046 and Root Mean Square Error (RMSE) of 0,32.