Reviewerly/exHarmony huggingface dataset:
Linear Feedback Shift Registers-Based Randomization for Image Steganography

Steganography involves concealing information by embedding data within cover media and it can be categorized into two main domains: spatial and frequency. This paper presents two distinct methods. The first is operating in the spatial domain which utilizes the least significant bits (LSBs) to conceal a secret message. The second method is the functioning in the frequency domain which hides the secret message within the LSBs of the middle-frequency band of the discrete cosine transform (DCT) coefficients. These methods enhance obfuscation by utilizing two layers of randomness: random pixel embedding and random bit embedding within each pixel. Unlike other available methods that embed data in sequential order with a fixed amount. These methods embed the data in a random location with a random amount, further enhancing the level of obfuscation. A pseudo-random binary key that is generated through a nonlinear combination of eight Linear Feedback Shift Registers (LFSRs) controls this randomness. The experimentation involves various 512x512 cover images. The first method achieves an average PSNR of 43.5292 with a payload capacity of up to 16% of the cover image. In contrast, the second method yields an average PSNR of 38.4092 with a payload capacity of up to 8%. The performance analysis demonstrates that the LSB-based method can conceal more data with less visibility, however, it is vulnerable to simple image manipulation. On the other hand, the DCT-based method offers lower capacity with increased visibility, but it is more robust.
Extracting Descriptive Frames from Informational Videos

Informational videos are becoming increasingly important among all video types. The users spend so much time browsing the informative videos, even if they are not interested in all their topics. Thence, a new method for extracting descriptive frames is presented in this paper that allows users to navigate directly to the topics of their interest in the video. The proposed method consists of three main phases: video preprocessing, video segmentation, and the video separation phase. Firstly, frames are extracted from the videos, resized, and converted to grayscale. Then, the frames are divided into blocks, and the kurtosis moment is calculated for each block. The videos are segmented based on an examination of the differences between the features of the kurtosis moment. Lastly, the informative frames are grouped into a separate video after they are distinguished from the uninformative ones using the clustering technique. The results demonstrated the functional effectiveness of the proposed method. According to the accuracy and F1-Score measures, it has a performance of up to 100%. Moreover, the video is significantly summarized by reducing the duration to less than 1% of its original time.
A DISCRETE PARTICLE SWARM ALGORITHM WITH SYMMETRY METHODS FOR DISCRETE OPTIMIZATION PROBLEMS

Particle Swarm Optimization (PSO) is a commonly used optimization to solve many problems. The PSO, which is developed for continuous optimization, is updated to solve discrete problems and Discrete PSO (DPSO) is obtained in this study. With DPSO, the Traveling Salesman Problem (TSP), which is well-known in the literature as a discrete problem, is solved. In order to improve the results, the swap method, the shift method, and the symmetry method are added to DPSO. The symmetry method is a new and successful method. The variations of the DPSO occurred according to the selected method type (DPSO1 (swap method), DPSO2 (shift method), DPSO3 (swap and shift methods), DPSO4 (symmetry method), DPSO5 (swap, shift, and symmetry methods), DPSO6 (swap, shift, symmetry, and 2-opt methods)). The effect of each method on the performance of the DPSO has been studied in detail. To demonstrate the success of the variations of the DPSO, the results are additionally compared with many well-known and new discrete algorithms in the literature. The results showed that the performance of DPSO has improved with the symmetry method and it has achieved better results than the discrete heuristic algorithms recently proposed in the literature.
MPAI-EEV: Standardization Efforts of Artificial Intelligence based End-to-End Video Coding

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric.
Keeping Safe Distance from Obstacles for Autonomous Vehicles by Genetic Algorithms

Industrial automation and autonomous mobile robots have become increasingly popular in warehouses and factories worldwide. However, some of these industrial robots, such as the Autonomous Guided Vehicles (AGV), usually operate in a workspace with humans and other machines, thus the risk of collisions and accidents cannot be ignored. In this context, the importance of safety measures for AGVs are key for their right performance. This paper proposes a novel approach for optimizing trajectory design in autonomous vehicles, with focus on ensuring safe navigation through occupancy maps. A mathematical tool that uses genetic algorithms to design trajectories that maintain a safe distance from obstacles is developed. The occupancy map is defined using geometric shapes formed by polylines, which enables more efficient calculations during the genetic algorithm search process.
Optimalisasi Sitem Informasi Manajemen Pendidikan Di Madrasah

This research is to find out the optimization of management information systems. And also to know how the process of data collection. This research method uses a qualitative research method with a descriptive approach. This research was conducted at the Jabalnur Paloh Lada Private Madrasah Aliyah, Dewantara District, North Aceh Regency. This article uses by collecting data related to data collection in madrasas. The sources of informants in this study were the school principal and teaching staff at the madrasah. The results of this study indicate that a management information system or SIM can make it easier for teacher staff, students or student guardians to find out the information in the madrasah.
A Review on Digital Image Watermarking Techniques Using Neural Networks

In the current digital age, the extraordinary development and usage of Internet Technology and Communication networks have made exchange and transmission of digital content much easier and quicker. This has given rise to the threat of privacy, unauthorized circulation, duplicating, copying, and editing of multimedia content. Hence copyright protection and ensuring the authenticity of digital content is still a paramount challenge. Data hiding method such as watermarking has become key player in solving this biggest challenge of the multimedia world. Digital watermarking is used to secure digital contents by embedding a watermark with the digital host image at the transmission end and extraction of a watermark is done from the watermarked image at the receiving end, to authenticate the ownership of the image. Numerous methods of watermarking have been proposed by different authors. The frequency domain methods are very prevalent approaches as they exhibit superior performance in terms of imperceptibility and robustness. Recently, several watermarking algorithms used soft computing techniques like neural networks to preferment these characteristics and attain equipoise between robustness and imperceptibility. The greatest forte of a neural network is its learning and adaptation ability. This ability of the neural network enhances the capabilities of conventional methods used in watermarking by memorizing the actual relation between the watermarked host image and watermark data. This paper gives a review of the current trends in neural network-based Image watermarking techniques to uncover the state-of-the-art approaches and their performance evaluations.
User Preference Recommendation System and Analytics for News Articles

With advancements in technology, the content space has changed and the major problem faced by the digital space is providing personalized content to users. This enables users to get more relevant content, increasing the time spent on the application. This is great for business as higher retention leads to more data being stored which in turn can be analyzed. This gives better business insights to enhance targeted marketing. This would solve the poor merchandising problem faced by businesses. The cycle continues and our project comes into the picture to streamline this process using machine learning. With the advent of digitization, there has been a change in traditional media. Through the means of software, we are able to read, share and interact with news articles on our smartphone devices. This has completely changed the game for News supplying houses because of the abundance of information available on the internet. Instead of orthodox print newspapers, people have started to prefer the convenience of mobile articles (Darvishy in HYPNER: a hybrid approach for personalized news recommendation, 2020) [1]. The written structure of articles has also changed to comply with the fast and to-the-point inclination of the users. Finding niche articles with copious amounts of information is the problem faced by users and even news supply agencies. It is hence critical to analyze user data to carve out the perfect set of information. Users get a better user experience and from a business perspective, the agencies can target very specific content to the user and open up business opportunities.
Perception of school teachers towards integrating ict in senior secondary school curriculum: aftermath of covid-19 pandemic

&#x0D; The study is aimed at determining the perception of school teachers’ towards integrating ICT in senior secondary school curriculum content delivery, aftermath of Covid-19 pandemic. The study adopted a survey research. The Population of the study consisted of all 310 Senior Secondary School teachers (78 males and 132 females) in Owerri municipal Council of Imo state. The sampling technique used is purposive since the population is small. The researchers designed an assessment questionnaire which was used for data collection. It has a reliability co-efficient of 0.83 determined through cronbach alpha. The data collected was analyzed using mean and standard deviation in answering the research questions while the hypothesis was analyzed using independent t-test analysis tested at 0.05 level of significance. The result of the study showed that senior secondary school teachers have positive perception towards integrating ICT in senior secondary school curriculum content. It was therefore recommended among others that the government should speed up the expansion of its existing ICT and remote learning capacity in senior secondary school curriculum, especially for learners with disabilities who may need access to assistive technology. This is based on the fact that senior secondary school teachers have positive perception on the workability of ICT integrated curriculum in senior secondary school.&#x0D;
P‐24: A Novel OLED Full Display Technology with High Image Quality

This paper introduces in detail the development process of full screen camera technology and products in the past two years, carefully compares the TFT inside method represented by Samsung Fold3 product and the TFT outside method represented by ZTE Axon30 product, and proposes the FDC (Full Display with Camera) technology developed by BOE that has been used in ZTE Axon40 product. Compared with the one‐ to‐multi array technology launched by Visionox, BOE's one‐to‐one driving method realized by TFT compression design is optimal in both display quality and photographing, which can be really called full screen technology.
Python Classes

In Python everything is an object and as such is an example of a type or class of things. For example, integers are an example of the int class, real numbers are examples of the float class, etc. In this chapter we will look at what a class is in Python, how it is defined, how instances of a class can be created, how attributes (data) and methods (behaviour) can be defined for a class and outline how memory is managed within Python with respect to instance creation and deletion.
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation of anatomical structures and abnormalities from medical images can aid in the early detection and treatment of various diseases. In this paper, we address the local feature deficiency of the Transformer model by carefully re-designing the self-attention map to produce accurate dense prediction in medical images. To this end, we first apply the wavelet transformation to decompose the input feature map into low-frequency (LF) and high-frequency (HF) subbands. The LF segment is associated with coarse-grained features while the HF components preserve fine-grained features such as texture and edge information. Next, we reformulate the self-attention operation using the efficient Transformer to perform both spatial and context attention on top of the frequency representation. Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components. Moreover, we propose a multi-scale context enhancement block within skip connections to adaptively model inter-scale dependencies to overcome the semantic gap among stages of the encoder and decoder modules. Throughout comprehensive experiments, we demonstrate the effectiveness of our strategy on multi-organ and skin lesion segmentation benchmarks. The implementation code will be available upon acceptance. \href{https://github.com/mindflow-institue/WaveFormer}{GitHub}.
COBIT 2019 INFORMATION SECURITY FOCUS AREA IMPLEMENTATION FOR REINSURCO DIGITAL TRANSFORMATION

As information technology (IT) advancement evolves in Indonesia's insurance sector, organizations like ReinsurCo must accelerate their digital transformation (DT) to remain competitively viable. Although DT paves the way for new business models and operational improvements, the implementation often fails due to poor IT governance. Under the supervision of the State-Owned Enterprises Agency (SOE) and the Financial Services Authority (FSA), ReinsurCo must comply with regulations stating that SOEs must independently assess IT maturity to ensure information security. This research utilizes the five stages of Design Science Research (DSR): problem explication, requirement specification, design and development, demonstration, and evaluation. Data was collected through semi-structured interviews and both internal and external document triangulation. The data were then analyzed using the COBIT 2019 Information Security framework, implementing design factors prioritizing information technology governance and management (ITGM) objectives: APO13 Managed Security, DSS05 Managed Security Services, and BAI06 Managed IT Changes. Further analysis and identification were conducted to discover gaps against the seven component capabilities. These identified gaps were mapped into people, process, and technology aspects, which led to the creation of essential improvement recommendations. These recommendations were compiled into an implementation roadmap that can serve as a priority guide for ReinsurCo. This research is expected to provide a knowledge base for prioritizing information security management in supporting DT by implementing the COBIT 2019 Information Security framework. In a practical context, it aids ReinsurCo in controlling its strategic plans to face information security challenges. Furthermore, this study also offers extensive benefits to the insurance industry.
The role of distance education in the modern educational space

The article reveals the role of distance education in the modern educational space. The purpose of the article is to clarify the role of distance education in the modern educational space. The research is based on methodological, theoretical and practical levels. The development of distance education is considered from the following directions: online courses, the results of analytical reviews are shown, the possibility of using distance learning in institutions of higher education as a separate approach to learning or together with traditional forms of the educational process; distance universities. The possibilities of distance education, which are now offered by institutions of higher education, are proven, the forms of distance classes are considered, and their diversity is shown, examples of obtaining such education in different countries of the world are given. Features of distance universities are listed: simplified admission to the university; the availability of education is limited only by knowledge of the relevant language, technical capabilities, and funds; exams at leading distance universities are mostly conducted face-to-face, which prevents fraud, although there are additional costs for students. The most common types of distance education, which are the most essential in the educational space of society, are considered.
Pengaruh Iradiasi Gamma dan Asam Akrilat terhadap Sifat Mekanik Polimer Super Water Absorbent (SWA) Berbasis Onggok

The Super Water Absorbent (SWA) polymer is a type of polymer that has exceptional water absorption capabilities while remaining insoluble in water. In this study, the authors conducted research on the modification of SWA by including acrylic acid and acrylamide monomers, followed by a crosslinking process utilising gamma irradiation. This study investigated the impact of acrylic acid and gamma irradiation on the mechanical characteristics of SWA. The experimental findings demonstrated a notable enhancement in the mechanical properties of the hydrogel by the incorporation of acrylic acid. This was evidenced by a substantial rise in the hardness value, which rose from 11.09 mJ to 36.13 mJ.
Quantum Computer Architecture: A Quantum Circuit-Based Approach Towards Quantum Neural Network

According to recent research on the brain and cognition, the microtubule level activities in the brain are in accordance with the quantum mechanical concepts. Consciousness is the emergent phenomenon of the brain’s subsystems and the quantum neural correlates. Based on the global work-space theory and traditional neural networks, investigations in machine consciousness and machine intelligence are reporting new techniques. In this study, a novel approach using circuit-based quantum neural network is proposed and simulated. This approach satisfies all the criteria of quantum computing and is tested for the exclusive OR (XOR) gate’s nonlinear learning. As a result of the use of quantum gates, various quantum circuits, such as quantum adders and subtractors, are also created and included in the designing and simulation of circuit of the quantum neural networks. Moreover, it is also argued that the proposed circuit of quantum neural network may also be tested and implemented on real quantum computer hardware. The present study also stresses the applicability of techniques of machine learning algorithms such as quantum and classical neural networks to various challenges of High Energy Physics.
Digital literacy as a tool for preventing destructive practices in the digital environment: following the materials of the regional scientific and practical conference

Introduction. On February 28, 2023, on the basis of South Ural State University, with the support of the Research Center for Monitoring the Prevention of Destructive Manifestations in the Educational Environment (Chelyabinsk Institute of the Develop- ment of Vocational Education), there was held a regional scientific and practical conference “Preven- tion of destructive practices in the digital environ- ment and measures to improve the digital literacy of students: presentation of research results and the formation of an expert community in the field of social sciences and humanities in the region”. The purpose of the study is to present the key ideas from the reports and the results of the con- ference. Methods. In order to present the key ideas from the reports, the authors use general scientific methods, such as analysis and synthesis, induction, deduction, abstraction. Scientific novelty of the research. In the current discourse of the conference, digital was defined as a new human habitat, which contains the potential for human growth and at the same time anthropo- logical and social risks. Results. In meta-reality, between real and virtual, only monitoring of public opinion on existing and potential social threats is not enough. In this case, sociatria can be a practical response to digital threats and risks. Conclusion. In the current digital environment, constructive practices of individual social subjects and social groups coexist with destructive practices. Sociatria can become a social response to the issue of monitoring, preventing and combating digital threats, and digital literacy is an anthropological response that should be created in the educational environment.
Thesaurus-Based Transformation: A Classification Method for Real Dirty Data

In this paper, we consider a retail store classification problem supported by a real word dataset. It includes yearly sales from several thousand stores with dirty categorical features on product labels and product hierarchies. Despite the fact that classification is a well-known machine learning problem, current baseline methods are inefficient due to the dirty nature of the data. As a consequence, we propose a practical thesaurus-based transformation. It uses an intermediary global approximate classification of products, based on local products hierarchies. Activities for a subset of stores are human-labeled to serve as ground truth for validation, and enable semi-supervision. Experiments show the effectiveness of our approach compared to baseline methods. These experiments are based on datasets and solutions made available for reproducibility purposes.