Reviewerly/exHarmony huggingface dataset:
‘FOR EVERMORE’: AN EXAMINATION OF MUSICAL EKPHRASES OF EDGAR ALLAN POE’S “THE RAVEN”

This article analyses the transfer of poetic language into music, focusing on Edgar Allan Poe’s celebrated poem “The Raven” (1845). After a theoretical study on poetic language and theoretical questions regarding transmediation, I look into different pieces of instrumental music directly inspired by Poe’s lines. To this end, I draw on groundbreaking research regarding media transformation by authors such as Lars Elleström, whose work provides the theoretical framework, and, most especially, Siglind Bruhn, who has written about the relation between poetry and music, and who coined the term “musical ekphrasis”. Finally, I argue that these composers transmediate Poe’s “The Raven” by using musical devices similar to those employed by Poe in his poem. Particularly important for this analysis will be compulsive repetition and variation as strategies of the musical ekphrasis, and the re-presentation of the uncanny in music. This article analyses the transfer of poetic language into music, focusing on Edgar Allan Poe’s celebrated poem “The Raven” (1845). After a theoretical study on poetic language and theoretical questions regarding transmediation, I look into different pieces of instrumental music directly inspired by Poe’s lines. To this end, I draw on groundbreaking research regarding media transformation by authors such as Lars Elleström, whose work provides the theoretical framework, and, most especially, Siglind Bruhn, who has written about the relation between poetry and music, and who coined the term “musical ekphrasis”. Finally, I argue that these composers transmediate Poe’s “The Raven” by using musical devices similar to those employed by Poe in his poem. Particularly important for this analysis will be compulsive repetition and variation as strategies of the musical ekphrasis, and the re-presentation of the uncanny in music.
Multimodal Sensor Data Fusion and Ensemble Modeling for Human Locomotion Activity Recognition

The primary research objective of this study is to develop an algorithm pipeline for recognizing human locomotion activities using multimodal sensor data from smartphones, while minimizing prediction errors due to data differences between individuals. The multimodal sensor data provided for the 2023 SHL recognition challenge comprises three types of motion data and two types of radio sensor data. Our team, ‘HELP,’ presents an approach that aligns all the multimodal data to derive a form of vector composed of 106 features, and then blends predictions from multiple learning models which are trained using different number of feature vectors. The proposed neural network models, trained solely on data from a specific individual, yield F1 scores of up to 0.8 in recognizing the locomotion activities of other users. Through post-processing operations, including the ensemble of multiple learning models, it is expected to achieve a performance improvement of 10% or greater in terms of F1 score.
Survey Analysis on smart features selection for machine learning techniques mainly applied to EEG.

This research presents a survey for analyzing and classifying the EEG signal based on feature selection approaches. Moreover, The increasing complexity of high-dimensional medical datasets necessitates efficient feature selection methods for early disease detection and safeguarding public health. Intelligent feature selection represents an advanced stage in machine learning and innovative computer applications, as it reduces the number of features required for accurate classification. Generally, The main goal of feature selection is to improve the predictive model's performance and reduce the computational cost of modeling. This paper contains a survey of considerable research several on feature selection. The main measures to analyze this paper are Accuracy, precision, Recall, and F1-score assessment. In order to evaluate performance used stander dataset are EEG Bonn University. The results have proven that they have achieved the highest accuracy rate of around 99% compared with different techniques.
A Secure and High Capacity Image Steganography Approach Using Huffman Coding and RSA Encryption

Steganography is the practice of hiding data, such as images, videos, or text, within a cover image without it being detectable by the human eye. Several factors, such as the capacity, security, and robustness of the technique, are essential when transferring information using this method. In this study, we propose a new approach to image steganography that improves the Least Significant Bit (LSB) technique by utilizing images of 24 bits in each pixel. Improving the capacity and security of LSB-based steganography requires a combination of techniques, such as indirect embedding, embedding in multiple channels, and applying cryptographic and compression techniques. This approach conducts by encrypting each compressed secret message bit with the most significant bit of the red channel and then saving the output bit (hidden bit) in the least significant bit of the (green/blue) channel according to the row value (odd/ even). To further security, the suggested approach uses multi-level encryption; employs RSA to encrypt the secret message before applying the Huffman compression and encrypting the hidden bit by (XOR/XNOR) in the embedding method based on the row value of the pixel. Meanwhile, it is planned to use the Huffman coding technique to shorten the length of the encrypted message that will be inserted in the cover image. For the color photos in this work, the standard images were acquired from a standard dataset (USC-SIPI). The suggested approach performs better when measured in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), and comparison to findings from related prior efforts.
Phishing Attacks Detection by Using Support Vector Machine

Today's world is heading towards complete digital transformation, and with all its advantages, this transformation involves many risks, the most important of which is phishing. This article proposes a system that extracts features from all parts of the email, initially brought from different data sets, and uses one of the machine learning algorithms (K-means algorithm) to extract the valuable features, as used four methods to calculate the distance in the K-means algorithm. This work used SVM as a classifier to classify emails into phishing and legitimate and tuned its parameters to obtain a high percentage of accuracy. The proposed model gave accuracy equal to 98.8 %.
A Survey of Medical Image Analysis Based on Machine Learning Techniques

Machine learning is a result of the availability and accessibility of a massive amount of data collected via sensors and the internet. The concept of machine learning demonstrates and spreads the fact that computers can improve themselves. Deep learning is causing a paradigm shift in medical image analysis. A medical image is a visual representation of the interior of a body, typically used for diagnostic or therapeutic purposes. Researchers and policymakers interested in healthcare outcomes should read this; this research provides an overview of machine learning at a high level. Computer vision is the field of using computer algorithms to understand and analyze visual data, and machine learning is a key tool for developing these algorithms. This review discovered that there are three varieties of machine learning strategies: supervised, unsupervised, and semi-supervised, and they seem to be gaining traction in risk assessment, disease prognosis, and image-based diagnosis, with increasing success. Convolutional neural networks (CNNs), k-means clustering, random forests, transductive learning, and support vector machines are among the most commonly used algorithms. Image analysis using CNN is the most effective method for medical imaging.
A Survey of Transform Coding for High-Speed Audio Compression

The necessity for audio compression continues to be a critical issue., as it is associated with lowering the data capacity of one of the most widely distributed digital files transmitted between remote parties, audio compression remains a critical topic. The effectiveness of the two sound pressure units in this article has been tested. Because of this industry's importance,, which is reflected in storage capacity and transportation needs, Over the last two decades, sound pressure has been the topic of intense investigation. The rapid expansion of the computer industry is leading to a growing demand for high- quality audio data; As a result, the development of sound compression methods, of which there are two types: lossy and lossless, is critical. The purpose of this study is to explore sound compression techniques and to highlight the suitability and applications of each approach.
A Review of Image Retrieval Based on Ontology model

Online retrieval systems for specified Information is more growth, so the need for efficient retrieval schemes become more significant area to employ user’s need. Ontology based image retrieval consider more precisely method. In image retrieval system, the necessity for effective technique to achieve image matching. In this paper image retrieval techniques and methodologies are reviewed and concern with Ontology based image retrieval, like "Content based image retrieval (CBIR)" with the extracted significant features (such as: color, texture, and shape features) from the image for using in image matching. Other methodologies are "Text based image retrieval (TBIR)" and "Ontology based image retrieval".
Multi Discrete Laguerre Wavelets Transforms with The Mathematical aspects

The Linear approximation, means the order of smoothness of the void function. As for non-linear approximation, when compared to linear approximation, a significant improvement in the arrangement of approximation can be observed.&#x0D; In this paper the theorems of linear approximation and non-linear approximation are demonstrated, Moreover, some hypotheses were orthogonal theorem is proven&#x0D; many important theorems will be proven and proven in formation Multi Discrete Laguerre Wavelets Transform (MDLWT) so that they take their role in signal or image processing, this makes them possess properties like those they have those used for the same purpose and with high efficiency.&#x0D; In second part in this paper, the new wavelet MDLWT was used after extracting the filter high pass filter and low pass filter by extracting the decision trees for the transactions where the new wavelet was used in image processing and mean square error (MSE), Peak signal-to-noise ratio (PSNR), Bits per pixel ratio (BPP) and Compression ratio (CR) were calculated, and the applications were done on 4 samples which good results have been found to prove the efficiency of the new wavelet
GrammarGPT: Exploring Open-Source LLMs for Native Chinese Grammatical Error Correction with Supervised Fine-Tuning

Grammatical error correction aims to correct ungrammatical sentences automatically. Recently, some work has demonstrated the excellent capabilities of closed-source Large Language Models (LLMs, e.g., ChatGPT) in grammatical error correction. However, the potential of open-source LLMs remains unexplored. In this paper, we introduced GrammarGPT, an open-source LLM, to preliminary explore its potential for native Chinese grammatical error correction. The core recipe of GrammarGPT is to leverage the hybrid dataset of ChatGPT-generated and human-annotated. For grammatical errors with clues, we proposed a heuristic method to guide ChatGPT to generate ungrammatical sentences by providing those clues. For grammatical errors without clues, we collected ungrammatical sentences from publicly available websites and manually corrected them. In addition, we employed an error-invariant augmentation method to enhance the ability of the model to correct native Chinese grammatical errors. We ultimately constructed about 1k parallel data and utilized these data to fine-tune open-source LLMs (e.g., Phoenix, released by The Chinese University of Hong Kong, Shenzhen) with instruction tuning. The experimental results show that GrammarGPT outperforms the existing SOTA system significantly. Although model parameters are 20x larger than the SOTA baseline, the required amount of data for instruction tuning is 1200x smaller, illustrating the potential of open-source LLMs on native CGEC. Our GrammarGPT ranks $$3^{rd}$$ on NLPCC2023 SharedTask1, demonstrating our approach’s effectiveness. The code and data are available at https://github.com/FreedomIntelligence/GrammarGPT .
The Organic Integration of Innovation and Entrepreneurship Education with Ideological and Political Education—A Case Study of the C Programming Course

Innovation and entrepreneurship are important driving forces for contemporary social development and are also a focal point of higher education. The C programming course, as a compulsory subject in the field of computer science and technology, holds significant practical significance and finds widespread application. Integrating innovation and entrepreneurship into the C programming course presents a challenging task as it not only enhances students' professional abilities but also cultivates their awareness and skills in innovation and entrepreneurship. This paper takes the C programming course as an example to explore how ideological and political education can be incorporated into the curriculum to foster students' consciousness and capabilities in innovation and entrepreneurship.
Virtual Reality for Historical Education: Designing an Immersive and Interactive Platform for 3D Digital Heritage and Dynamic Storytelling

This paper focuses on designing and developing a Virtual Reality (VR) platform tailored explicitly for historical storytelling. The study aims to provide tourists with an immersive and interactive learning experience by leveraging VR technology. While using internet history as a case study, the platform encompasses various modules, including a Historical Event Module that enables tourists to engage with significant moments in the history of the internet from a first-person perspective. A Visiting Mode is also implemented to facilitate navigation and offer guidance within the virtual environment. Interactive elements such as manipulation and voice guidance are incorporated to enhance tourists' curiosity and engagement. This study serves as a valuable contribution to the field of VR historical education, offering insights and potential directions for future research in creating immersive and educational experiences.
Hybrid Path Planning for Unmanned Surface Vehicles in Inland Rivers Based on Collision Avoidance Regulations

In recent years, with the continuous advancement of the construction of the Yangtze River’s intelligent waterway system, unmanned surface vehicles have been increasingly used in the river’s inland waterways. This article proposes a hybrid path planning method that combines an improved A* algorithm with an improved model predictive control algorithm for the autonomous navigation of the “Jinghai-I” unmanned surface vehicle in inland rivers. To ensure global optimization, the heuristic function was refined in the A* algorithm. Additionally, constraints such as channel boundaries and courses were added to the cost function of A* and the planned path was smoothed to meet the collision avoidance regulations for inland rivers. The model predictive control algorithm incorporated a new path-deviation cost while imposing a cost constraint on the yaw angle, significantly minimizing the path-tracking error. Furthermore, the improved model predictive control algorithm took into account the requirements of rules in the cost function and adopted different collision avoidance parameters for different encounter scenarios, improving the rationality of local path planning. Finally, the proposed algorithm’s effectiveness was verified through simulation experiments that closely approximated real-world navigation conditions.
Photovoltaic Power Prediction Model Using Pre-train and Fine-tune Paradigm Based on LightGBM and XGBoost

Accurate photovoltaic power prediction is crucial for optimizing the performance of photovoltaic power plants and ensuring the stability of the power grid. However, existing forecasting methods are challenged when predicting the power output of multiple plants with different geographical locations and environmental conditions simultaneously, due to the complexity of the underlying factors that affect power generation. To address this issue, this paper proposes a novel LightGBM-XGBoost photovoltaic power prediction model based on a pre-training and fine-tuning paradigm. The model leverages a large dataset to pre-train and learn rich feature representations within meteorological elements, which are then fine-tuned on specific downstream tasks to improve prediction accuracy and generalization ability. Experimental verification is conducted using data from three photovoltaic generators operating in Guangdong, China. Results demonstrate that the proposed model outperforms other methods when predicting the power output of multiple plants with different conditions simultaneously, highlighting the effectiveness of the proposed approach. Overall, this work contributes to advancing the state-of-the-art in photovoltaic power prediction and has practical implications for improving the control and scheduling of photovoltaic power plants, as well as ensuring the stability and security of the power grid.
On the utility of Colour in shape analysis: An introduction to Colour science via palaeographical case studies

In this article, we explore the use of colour for the analysis of shapes in digital images. We argue that colour can provide unique information that is not available from shape alone, and that familiarity with the interdisciplinary field of colour science is essential for unlocking the potential of colour. Within this perspective, we offer an illustrated overview of the colour-related aspects of image management and processing, perceptual psychology, and cultural studies, using for exemplary purposes case studies focused on computational palaeography. We also discuss the changing roles of colour in society and the sciences, and provide technical solutions for using digital colour effectively, highlighting the impact of human factors. The article concludes with an annotated bibliography. This work is a primer, and its intended readership are scholars and computer scientists unfamiliar with colour science.
Experimental results of electromagnetic analysis on smartcard

Abstract— Electromagnetic analysis attack is a form of side channel attack that can be used to reveal secret information of a cryptographic device by examination of electromagnetic radiations. The effectiveness of EMA has a large dependences on the quality of the electromagnetic radiation measured by the measuring system and how the EM traces are simulated during the key recovery procedures. In this paper, we propose the practical procedure for electromagnetic analysis attack methods and the experimental results of EMA on smartcard that implemented AES algorithm, including assessing the influence of measuring system, radiation leakage model on effectiveness of attacks.
HATE SPEECH AND SOCIAL MEDIA

In the 21st century – the time of the Internet, hate speech is present throughout social media. Hate speech existed even before the advent of the Internet. However, what is different about online hate speech is the speed at which it is transmitted on certain social media, such as Twitter and Facebook. In this sense, the question of whether and to what extent online hate speech should, or rather should not be protected was posed. This paper aims to explore whether online hate speech deserves freedom of expression protection. Public officials, civil servants, and politicians often use different social media platforms, especially Twitter, to communicate new political ideas, upcoming events, and even conspiracy theories. In this sense, the aim of the paper is to investigate whether a need to balance between constitutionally protected freedom of expression and the core values of democratic society arose from the development of Internet technology.
2 Unification and selection in Light Verb Constructions. A study of Norwegian

One characteristic of so-called Light Verb Constructions (LVCs) is that they unfold, mostly over a sequence ‘Subject V (P) N’, a content that could in principle be carried by some verb V alone, where the N of the sequence expresses a situational content close to that of V (N thus being some kind of ‘deverbal’ variant of V). A typical role of (the ‘light’ verb) V in the LVC is to connect the Subject to this situational content as some kind of role bearer, and add aspectual content to the situational content expressed by N. In languages where LVCs are seen as constituting a major category, the number of verbs serving as ‘Light Verb’ is low, and LVCs constitute formally recurrent patterns. In Norwegian the picture is different, with many verbs serving as possible Light Verbs, where these verbs select different nouns, and the nouns in turn select different verbs. In illustrating this, we outline a format for representation of LVCs in corpora. We in addition outline a format for representing LVCs in a valence lexicon, and for representing them in a sentence parsing formalism. The unification between the meaning of the Light Verb and the meaning of the noun is formally represented. While a sizable number of LVC selection relations between verbs and nouns have been identified, and a large valence lexicon and parser constitute the frames for the formalizations mentioned, the formalizations have only to very small extents been implemented, being presented here only for formal consideration.
The Development of Thematic Board Educational Game Tools to Train The Literacy Skills of Children 5-6 Years Old

The absence of thematic board educational game tools motivates this research to train the literacy skills of children aged 5-6 years. This study aims to determine the development and feasibility of thematic board educational game tools to improve the literacy skills of children aged 5-6 years. The research method used is Research and Development (R&amp;D) which adopts the Borg and Gall development stages. The results of this research and development are: 1) Development of the thematic board educational game tool through several stages, namely: a) seeing potential and problem identification b) research and data collection, c) product design e) product revision, d) validation and trial phase small scale and large scale. The results of the validation of the material experts prove 2 with a category is very feasible. The average media expert validation assessment is the very feasible category. The assessment of small class trials in the very feasible category, large-scale trials in the very feasible category. The average teacher response questionnaire is in the very feasible category.
Fake account detection in social media using machine learning methods: literature review

With the rapid development of emerging technologies in the industrial revolution 4.0 or 5.0, social media has become one of the social environments to carry out social activities, both socializing and advertising. However, since it is an open platform by nature, cybercrime occurrence in social media is inevitable. Currently, more than a million fake accounts are existing on Instagram, Twitter, and Facebook, intending to increase followers, spread hoaxes, and spam. On one hand, it is difficult to manually eliminate these accounts on social media platforms. On the other hand, research on automatic fake account detection has been carried out for more than a decade. This study provides literature reviews aiming to deliver information about several methods and machine learning algorithms with the performances measured in identifying fake accounts on three well-known social media platforms: Twitter, Instagram, and Facebook.