Reviewerly/exHarmony huggingface dataset:
Raw Bayer Pattern Image Synthesis on Histogram of Gradients for Pedestrian Detection

In recent days, there has been a vast implementation and emergency for many pedestrian detection methods. In the current scenario, the complexity of pedestrian detection has increased, and its identification is a challenging task due to the variety of scenes and the complexity of most pedestrian identification systems. The availability of challenging public datasets has fueled most of the progress made in recent years. To keep up with the rapid pace of innovation, the proposed work is a pedestrian detection approach based on the Bayer pattern and gamma correction that efficiently classifies pedestrians and non-pedestrians. The proposed method uses a Support Vector Machine (SVM) to ensure that the gradients recovered from Bayer pattern pictures are robust enough to be employed in pedestrian detection algorithms based on Histograms of Oriented Gradients (HOG). Benchmark datasets from MIT, Caltech, and INRIA were used to test the proposed technique. In comparison to other previous techniques, it is inferred that our proposed technique outperforms them by maintaining good accuracy.
Strengthening the Role of Cadres with Digital Literacy in iPosyandu Application-Based Recording and Reporting

Posyandu is part of the Community-Sourced Health Efforts (UKBM), a health information system in maternal and child health services. Currently, health recording and reporting are still in paper form. Therefore, an iPosyandu application facilitates and accelerates cadres in recording and reporting. This activity was carried out in March 2023 to 145 cadres in Pagedangan District, Tangerang Regency, Banten. The method used was training on recording and reporting based on the iPosyandu application, where cadres were given a questionnaire on the use of the iPosyandu application. The results of this activity showed that most of the cadres had the characteristics of age 31-40 years (41.4%), the last education was high school (55.2%), the occupation was taking care of the household (97.9%), and the experience of being a cadre for 1-5 years (51%), and most of the cadres serving in the posyandu had middle strata (41%). Most cadres agreed to use the iPosyandu application because it can make reports quickly (83.7%), and cadres felt that the iPosyandu application had all the functions based on cadres' abilities (87.1%). In addition, there is a relationship between the last education of cadres and the use of iPosyandu applications (p-value &lt; 0.005; r value &gt; 1). This community service activity concludes that cadres have good digital literacy in using the iPosyandu application, so the system for recording and reporting the results of posyandu activities can run well. Keywords: iPosyandu, cadres, digital literacy, recording, reporting.
A Cerebellum Model Enhanced Gradient Neural Network for Solving Time-Variant Linear Equations Applied to Robot Control

Recurrent neural networks (RNNs) are powerful alternatives to solving time-variant linear equations (TVLEs) arising in science and engineering. However, existing RNN solutions to time-variant problems including TVLEs remain less satis-factory. For instance, gradient neural networks (GNNs) inevitably exhibit non-vanishing errors, and both Getz-Marsden dynamic inverters (GMDIs) and zeroing neural networks (ZNNs) involve matrix inversion and time-derivative information of coefficients. To tackle this situation, this paper proposes a cerebellum model enhanced GNN (CM-GNN) solver by borrowing inspirations from the design principle of GMDIs and the biological function of the human cerebellum. The CM-GNN solver consists of a conventional GNN and a cerebellum model for error compensation, with no matrix inversion and time derivatives of coefficients involved. This paper first details the design of the CM-GNN solver. After that, the proposed CM-GNN solver and a conventional GNN solver are comparatively applied to i) a same TVLE as a numerical example, and ii) a same path-tracking task of a Franka Emika Panda robot. Both numerical and experimental results demonstrate that the proposed CM-GNN solver outperforms the conventional GNN solver in terms of the solution accuracy and the path-tracking accuracy.
The Simulation Analysis of Financial Market Risk Based On Machine Learning

The financial sector is essential for advancing the growth of the country's economy, and the risk it assumes also represents the biggest unintentional risk in the financial market. To ensure that financial markets operate as intended, the risk present in the current financial market should be thoroughly investigated using blockchain technology (BT). Machine learning (ML) and random forests are used to analyze the risk of the financial markets (RF). The clustering approach is first described, and an RF classification model example is shown. The obtained data sets are split into training and test sets, the corresponding rules are created, and the decision tree's (DT) branches are built using the optimization principle. The process of building the branches of DT is then repeated till it can no longer be done. According to the findings, the regional economy's three major industries account for 4.5%, 61.9%, 4.2%, 4.4%, and 4.9% of the region's GDP, respectively. The secondary industry accounts for 55.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , 53%, 57.2%, 46.9%, and 55.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of the regional GDP, while the tertiary industry takes up 21%, 4.8%, 64.3%, 61.9%, 52%, and 55.7%. This demonstrates that as the industrial structure under BT develops, the economic focus gradually moves from the primary sector to the tertiary industry; BT can increase the financial industry's efficiency while lowering operational expenses and reliance on the media. The financial capabilities of BT, meanwhile, can offer a solid foundation for company growth. The use of BT in the supply chain provides a theoretical framework for encouraging corporate cooperation.
Benefits of Using Distributed Ledger Technology in Managing Assets of Firm

This research paper has explained the scenario of understanding the DLT process where it has accumulated different knowledge and ideas. Objectives have been prepared on getting the entire system verified along with nurturing the factors thoroughly. However, it has again incorporated its aspects of understanding sustainability along with making DLT as an efficient way of data storage. It has again explained the scenario of accelerating the ways to meet the research requirements and the process of developing cloud storage. Issues have been explained along with understanding the problems and issues with DLT and the proper recommended strategies as well.
A Comparative Study of Twitfeel and Transformer-Based Techniques for the Analysis of Text Data for Sentiment Classification

Sentiment analysis is an important task in the field of Natural Language Processing (NLP) with various applications such as understanding customer feedback and monitoring social media sentiment. In recent years, the development of transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) has revolutionized the field of NLP, achieving state-of-the-art results in a wide range of tasks, including sentiment analysis. This work presents a comparative study between traditional techniques versus transformer-based models like BERT highlighting the advantages and limitations of each.
Smart Auto Image Captioning Using LSTM and Densenet Network

The integration of semantic attention increase photo captioning ability. The premise underlying semantic attention-based techniques is that the model should prioritize words or qualities with high semantic value. Prior studies focused on the attribute detector and the captioning network as distinct entities. This hampers the best use of semantic data. Image captioning is the computational process of producing accurate written descriptions of an image's or video's visual content. Captioning images makes it easier to construct contextualized interpretations, allowing people to situate photographs within a larger context. Using photo captions can be useful in a variety of contexts, such as conducting research with an unlabeled image collection or attempting to identify. previously unknown patterns. Subtitles added to the photos using a Deep Learning Model. Recent advances in the fields of deep learning and natural language processing have made it much easier to generate captions for input photographs. This study will use neural networks to generate image captions on their own. The Long Short-Term Memory (LSTM) model, a type of recurrent neural network, used as an encoder, together with a pre-trained lexicon and visual attributes. This enables the extraction of image data and the generation of captions.
The Smart Performance Analysis of Cyber Security Issues in Crypto Currency Using Blockchain

Crypto currency is subject to persistent and dynamic cyber security risks. As with all types of online assets, digital wallets, exchanges and even individual user accounts are often subject to both malicious attacks and other vulnerabilities. Blockchain technology, due to its distributed nature, is more secure and provides a strong platform on which to build digital currency platforms. This paper seeks to examine the performance of Blockchain technology for improving the security of crypto currency transactions. To assess the system's performance in this regard, we conduct a series of tests to evaluate the system's resilience against various types of attacks, including distributed denial of service (DDoS) and other specialized cyber attacks. We assess the performance of various cyber security measures employed by the crypto currency and Blockchain platforms, including multi-signature wallets, distributed identity solutions, and data encryption approaches. The proposed model achieved 86.82 % hit rate, 87.05 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> miss rate and 88.57% fall out. Our results, based on an extensive empirical study of crypto currency transactions and Blockchain records, indicate that Blockchain technology significantly enhances the security of crypto currency transactions compared to other technologies. Our findings offer quantitative evidence that Blockchain-secured crypto currency networks are less vulnerable to attack than conventional payment systems.
Exploring the Mind-Bending Connection Between Virtual Reality and Human Perception: Review

Despite many distinctions in the human perception and virtual reality, they exhibit a connection to each other which makes the experience in the virtual reality immersive and interactive for the user. Technological advancements in the field of simulated environments have led the human to be able to connect and perceive the virtual environment generated by the software but there are still some drawbacks of the virtual reality due to which the humans are still unable to connect to the simulations on a larger scale. This is because the human eye and other senses are still more capable than the senses imitated by the devices used in virtual reality. However, efforts are being continuously made to curb the lack in the technology of the virtual reality, it is still a great help with the existing technology in many fields. This research work represents an understanding of the study of theory of human perception and the technology of the virtual reality along with its types, Comparison and challenges.
Crime Rate Prediction Using Machine Learning

In our culture, crime is a major problem that affects everyone. Many crimes take place every day. The dataset utilized in this study includes data on each year's date and crime rate. This initiative mainly focuses on the crime rate related to robberies. Using historical data, a linear regression technique is used to forecast future crime rates. The programmer accepts a date as input and outputs the year's matching percentage of crime rate.
Exploring the Potential of AI for Urban Crime Prediction in India: A Case Study of Indian Metropolitan Cities

Urban crime prediction is of major interest to police departments and criminologists. This study focuses on the prediction of urban crime for 4 Indian cities (Surat, Delhi, Bangalore, Kolkata). Prediction is made for the total IPC crime rate for selected cities using the time period of 2001 to 2020. Here we have used a novel approach for prediction which uses the neural network time series application of MATLAB. This application has a nonlinear autoregressive tool that solves non-linear time series problems. So, this method can be referred to as NARNN (Nonlinear Autoregressive Neural Network). This method is compared with two classical methods of time-series forecasting (Linear regression, ARIMA). The results of the NARNN gave the highest accuracy for all 4 cities. It gave an accuracy of up to 98% for crime prediction in Delhi. When the crime rate is non-linear or doesn't have any significant trend ANN is strongly recommended. ARIMA is reasonably good when data have trends, while the use of linear regression for prediction is completely impractical.
Securing Digital Payments a Comprehensive Analysis of AI Driven Fraud Detection with Real Time Transaction Monitoring and Anomaly Detection

This investigation seeks to carry out an all-encompassing examination of AI-powered deception identification systems, concentrating on instantaneous transaction surveillance and irregularity detection, as crucial elements of safeguarding digital payments. The investigation embraces a comparative exploration blueprint, examining and amalgamating pertinent articles and research papers issued from 2010 to 2023. This investigation depends on auxiliary data sources, particularly articles and scholarly papers, to offer perspectives into the progressions, efficiency, and obstacles of AI-powered deception identification in the setting of digital payment safety. Through scrutinizing and contrasting the discoveries of current investigations, this examination endeavors to provide valuable perspectives for corporations, monetary establishments, and decision-makers striving to augment the safety of electronic transaction systems.
Enhancing Language Representation with Emotional Intelligence: A Weighted TF-IDF Approach

In the era of vast data availability, harnessing the power of smart automation and computational analysis is crucial for understanding public opinions in real time. Emotional intelligence technology plays a significant role in this intelligence landscape, and its research is pivotal in gaining insightful perspectives. Traditional scientific research often relies on representation classification, which primarily focuses on the semantic knowledge of words while neglecting their emotional dimensions. This study highlights the potential of sentiment analysis as a promising area of investigation and emphasizes the need to develop robust and precise methods that can effectively handle the complexities of natural language and capture the nuances of human emotions and sentiments. To address this, we propose a language representation enhancement technique that integrates emotional knowledge into the TF-IDF algorithm, resulting in weighted language representations. Furthermore, we explore the performance of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in the field of Natural Language Processing (NLP). We find that while both architectures have demonstrated exceptional performance, RNNs, with their ability to predict longer-term dependencies, offer a more comprehensive understanding of language and provide richer information compared to CNNs. Future research should focus on refining these techniques and advancing our understanding of emotional intelligence in computational analysis.
Image Forgery Detection Using CNN

One of the biggest issues nowadays is picture forgeries or manipulation utilising various techniques. change of the original picture is not only done by the pic modification itself, but also who imitate as original to commit crimes. Forensic digital techniques are required to identify image modification and manipulation for such unlawful intentions. Inside the article, the intro of deep learning-based system for detecting picture modification that has produced outstanding results in recent studies. A convolutional neural network that has been shown effective for image processing is used initially. In addition, using a high pass filter the hidden features in picture are gathered comparatively semantic data. Gaussian blurring, median filtering, and other techniques are used to create changed photos for the studies. Boss Base 1.01 pictures with a resolution of 256x256 that were divided into 4 equal sections were resized and given additive white Gaussian noise addition. The performance of the suggested method is tested quantitatively, and image modification is identified.
Reinforced model-agnostic counterfactual explanations for recommender systems

Explanation is an important requirement for transparent and trustworthy recommender systems. When the recommendation model itself is not explainable, an explanation must be generated post-hoc. In contrast to traditional post-hoc explanation methods, counterfactual methods can provide scrutable and actionable explanations with high fidelity. Existing counterfactual explanation methods for recommender systems are either not generalizable or face a huge search space. In this work, we propose a reinforcement learning counterfactual explanation method MACER (Model-Agnostic Counterfactual Explanations for Recommender Systems) which generates item-based explanations for recommender systems. We embed the discrete action space into a continuous space, making it possible to use the process of finding counterfactual explanations as a task of reinforcement learning. This method treats the recommender system as a black box (model-agnostic) and has no requirement on the type of recommender system, and thus is applicable to all recommendation systems.
On the Problem of the Ontology of a Literary Work on the Ontological Dimension of a Work of Art

The article is devoted to the search for the nature of the ontology of an art work on the example of a literary work. Tradition viewed a work of art as the discovery of a higher truth. Analytical philosophy deprived literature of the status of truth in general, and thereby deprived it of any ontological dimension. Heidegger’s attempt to return this dimension to literature through its relationship with being did not find continuation in philosophy. The author proposes to consider a literary work as a model of the world, primarily the social world. This approach makes liken a literary work to a mathematical theory. A mathematical theory is built as a correlation of simple elements; in a literary work such elements are characters (sometimes things). A mathematical theory is built on clearly defined axioms, a literary work is also built on axioms, but they do not have an explicit expression within the work and cannot always be perceived intuitively. The author shows that the mathematical theory never fully meets the specified conditions, and the mathematicians themselves note that in the future they will largely lose their strength. Thus, a literary work is of the same nature as a mathematical theory, only it presents a more complex case. The problem of truth serves as the way to find the ontological dimension. Analysis shows that in mathematics it is solved not by reducing a hypothesis to axioms, but by recognizing a theory through its effectiveness or applicability. For a work of art, such a criterion is its perception by its readers. In both cases, the ontological dimension is in the mind of the reader
Hybrid Images for Personalized Media Streaming Optimization

Efficiently transmitting media while maintaining or improving the quality of experience (QoE) poses a constant challenge. On the media playback side, there exists a variety of factors affecting the resulting QoE perceived by end users, such as device characteristics, viewing conditions, and individual visual acuity. Understanding the characteristics of playback-side context can provide opportunities for optimizing media delivery tailored to each user. — We present a novel approach to estimating viewers’ visible frequency ranges by creating hybrid images in a quantitatively controlled manner. The proposed approach, which can be applied to adaptive bitrate (ABR) streaming optimization, combines low- and high-frequency components of two different images providing multiscale image perception. The proposed method is designed to adjust the hybrid image size and the cutoff frequencies of lowpass and highpass filters of hybrid image synthesis according to the given frequency range under test. This method can be used to estimate the minimum video resolution for maintaining maximum QoE, in any viewing conditions and devices. — Our experimental results reveal a strong correlation between the minimum resolution obtained from hybrid image perception tasks and the traditional just noticeable difference (JND) of real images of different resolutions when presented side-by-side. These JND tasks exhibit a wider spread both in the measured minimum resolution and elapsed time to complete the tasks, potentially due to the complexity of the cognitive process and the variability caused by contents and subjects. In contrast, the hybrid image technique achieves more stable results with much less effort from subjects.
Atheneum: The Blockchain That Manages Theatrical Releases

Managing theatrical releases is a complex, crucial business task. Proper management is a crucial factor in success. Unfortunately, this management is currently ad-hoc and non-optimal. — Blockchain is a secure distributed ledger that may offer a collegial, efficient, cost-effective solution. — Atheneum is a permissioned blockchain that manages the titles and releases of all studios. It hands the distribution rights to the distributors. The smartcontracts enforce that release dates and territories comply with the business rules. Atheneum implements a Decentralized Autonomous Organization (DAO) to streamline the enrolment and revocation of entities. — Using open-source Hyperledger Fabric, Atheneum perfectly illustrates how blockchain technology can benefit the media industry outside of cryptocurrencies and non-fungible tokens (NFT).
Analysis of the Impact of Learning with Online Media During the Covid-19 Pandemic on the Mental Health of Adolescent Through Literature Module

Online learning and working from home for educators are changes that must be made by lecturers to continue teaching students. Distance education has the aim of improving the quality of education and the relevance of education as well as increasing equitable access and expansion of education. Distance education that is held with good quality assurance and in accordance with the needs of stakeholders is one of the mechanisms for expanding access to higher education. Subjects and Methods: Systematic review using the database : Google Scholar. The search results that meet the criteria are then analyzed for articles. Results: Online learning has several impacts on students, namely (1) online learning still confuses students to become passive, less creative, and productive, (3) accumulation of information/concepts on students is less useful, (4) students experience stress, (5) increased students' language literacy skills. Conclusion: Online learning has several impacts on students. This can be prevented, one of them is by socializing literacy about the impact of using social media on mental health and academic performance through literacy modules with the aim that learning can be sought to be well received by students without reducing the essence of education itself.