Reviewerly/exHarmony huggingface dataset:
Copy-Move Forgery Detection Using an Equilibrium Optimization Algorithm (CMFDEOA)

Image forgery detection is a new challenge. One type of image forgery is a copy-move forgery. In this method, part of the image is copied and placed at the most similar point. Given the existing algorithms and processing software, identifying forgery areas is difficult and has created challenges in various applications. The proposed method based on the Equilibrium Optimization Algorithm (EOA) helps image forgery detection by finding forgery areas. The proposed method includes feature detection, image segmentation, and detection of forgery areas using the EOA algorithm. In the first step, the image converts to a grayscale. Then, with the help of a discrete cosine transform (DCT) algorithm, it is taken to the signal domain. With the help of discrete wavelet transform (DWT), its appropriate properties are introduced. In the next step, the image is divided into blocks of equal size. Then the similarity search is performed with the help of an equilibrium optimization algorithm and a suitable proportion function. Copy-move forgery detection using the Equilibrium Optimization Algorithm (CMFDEOA) can find areas of forgery with an accuracy of about 86.21% for the IMD data set and about 83.98% for the MICC-F600 data set.
Superupervised Enumerable Learning for Sentiment Analysis of Social Media Tweets

People express their views on goods, services, governments, and events via words and phrases on social media. Sentiment analysis is a technique used in natural language processing to gather positive and negative feedback on a piece of writing on social media. Academics have been forced to research sentiment analysis by exponential growth in business and e-commerce companies. This study examined emotions in two distinct domains: one for electronics product evaluations and another for film reviews. Due to the enormous volume of data gathered for analysis, it is referred to as massive data. This vast data is multidimensional and includes useless information. Furthermore, this data complicates the analysis since it reduces the effectiveness of machine learning categorization algorithms. As a consequence, the data must be cleansed and pertinent features identified. Numerous methods have been used to eliminate noise from data and identify essential components. There are two methods for determining suitable features: feature selection and feature extraction. The research is split into three stages: the first step involves converting unstructured data to structured data and choosing relevant and feature information. Second, machine learning classification algorithms are employed in conjunction with feature selection approaches, and third, machine learning algorithms are used in conjunction with ensemble learning methods
Features Optimization of Gray Level Co-Occurrence Matrix by Artificial Bee Colony Algorithm for Texture Classification

Gray Level Co-occurrence Matrix (GLCM) is one of the most popular texture analysis methods. The fundamental issue of GLCM is the suitable selection of input parameters, where many researchers depended on trial and observation approach for selecting the best combination of GLCM parameters to improve the texture classification, which is tedious and time-consuming. This paper proposes a new optimization method for the GLCM parameters using Artificial Bee Colony Algorithm (ABC) to improve the binary texture classification. For the testing, 13 Haralick features were extracted from the UMD database, which has been used with the multi-layer perceptron neural network classifier. The experimental results proved that, the proposed method has been succeeded to finding the best combination of GLCM parameters that leads to the best binary texture classification accuracy performance.
Free-Viewpoint Augmented Reality Navigation for Laparoscopic Surgery Based on Virtual Markers And SLAM

Minimally invasive surgery (MIS) has been widely applied in the field of surgery due to its clinical benefits such as low invasiveness and low risk of infection [1]. The surgeon's understanding of the anatomy is a key factor that influences intraoperative complications in MIS [2]. In laparoscopic cholecystectomy (LC), for example, complications due to incomplete exposure of anatomical structures such as the cystic duct and common bile duct account for 92.9% of all LC complications [3]. The key step in LC is to reveal Calot's triangle properly, which is more difficult in cases of heavy inflammation of the gallbladder, encapsulation of surrounding tissues and anatomical variation. This may result in misinterpretation of the anatomy by the surgeon and thus increases the incidence of bile duct injury (BDI) of medical origin [4]. Video see-through augmented reality (VST-AR) navigation [5], a new technology is introduced to address these issues. As a new research hotspot in the field of laparoscopic augmented reality navigation, this technology visualizes the surgical target and key anatomical structures by means of a video transparency overlay to enhance intraoperative perception and improve the safety of the surgery. The core technical issue of VST-AR is the registration, which refers to the matching of a virtual 3D organ model reconstructed preoperatively by CT or MR to the laparoscopic image. The registration is divided into two phases: initial registration and tracking. This study proposes a VST-AR navigation framework based on virtual markers and SLAM. The concept of virtual markers is introduced to achieve rapid non- invasive registration of the virtual scene to the real scene. Based on the patient's body surface features, the proposed navigation framework achieves dynamic tracking of the laparoscope pose and free viewpoint transparency of hidden anatomical structures. In the case of laparoscopic cholecystectomy, for example, the key anatomical structures, such as the gallbladder and the cystic duct, are overlaid onto their corresponding positions in the liver, so as to support the surgeon with the refinement of the operation.
3D RECONSTRUCTION SYSTEM BY MEANS OF UNIQUE CAMERA, STRUCTURED LIGHT AND MATHEMATICAL MODELS

3D digital profiles are key information in many areas of knowledge, from medical to agricultural or industry. And in order to achieve reliable digitized surfaces we can have in hand techniques using laser, digital cameras, structured light associated to image processing tools. To choosethe best technique we face some challenges based on the application itself, but on the cost of equipment, on the robustness and on the computational cost and programming complexity. This work aimed to address a reliable, robust and accessible methodology using just one camera and a light projector to, with mathematical models, generate digital 3D surfaces. The mathematical model was designed using the geometry of the system (camera, projector and object) with additional routine to adjust the optimum values of the variables involved. We validated the proposed method with known objects and presented a 3D reconstruction of objects that are difficult to achieve manually, such as a callote and the back of human-being. The results showed that the mathematical model created conditions to provide reliable outcomes from a low cost devices (webcam and regular light projector) usage, with correction of warping caused by perspective.
MOTION TRAJECTORY BASED HUMAN HAND TRACKING FOR SIGN LANGUAGE RECOGNITION

Sign language recognition is an important area of research with uses in a range of industries, including education, communication, and human-computer interaction.Trajectory-based human hand tracking is a popular technique for sign language recognition because it allows capturing dynamic hand movement during signing.This paper presents an overview of different trajectory-based human hand tracking methods, including template matching, featurebased tracking, and model-based tracking.This framework aims to accurately identify various one-handed dynamic isolated characters and interpret their intended meaning.Emphasis is placed on designing a system that can effectively recognize and interpret a wide variety of sign gestures, enabling effective communication and understanding between sign language users and individuals who do not know sign language.
Multilingual Multiword Expression Identification Using Lateral Inhibition and Domain Adaptation

Correctly identifying multiword expressions (MWEs) is an important task for most natural language processing systems since their misidentification can result in ambiguity and misunderstanding of the underlying text. In this work, we evaluate the performance of the mBERT model for MWE identification in a multilingual context by training it on all 14 languages available in version 1.2 of the PARSEME corpus. We also incorporate lateral inhibition and language adversarial training into our methodology to create language-independent embeddings and improve its capabilities in identifying multiword expressions. The evaluation of our models shows that the approach employed in this work achieves better results compared to the best system of the PARSEME 1.2 competition, MTLB-STRUCT, on 11 out of 14 languages for global MWE identification and on 12 out of 14 languages for unseen MWE identification. Additionally, averaged across all languages, our best approach outperforms the MTLB-STRUCT system by 1.23% on global MWE identification and by 4.73% on unseen global MWE identification.
Enhancing Life and Career Skills for Students in Khon Kaen Wittayayon School, Khon Kaen Province

The objective of this research was to enhance the life and career skills of the students at Khon Kaen Wittayayon School in Khon Kaen Province by utilizing participatory action research methodology that involved the development of three aspects: (1) changes in the expected and unexpected outcomes from the implementation, (2) learning gleaned from the implementation process for the researchers, co-researchers, and school, and (3) the theoretical knowledge grounded in the specific context of Khon Kaen Wittayayon School. The target group for the development of the research was students in Grades 10-12 at Khon Kaen Wittayayon School, with a total of 268 individuals. The research involved 2 cycles and was comprised of 10 steps. The results of the study indicated the following: (1) there were improvements in both expected and unexpected outcomes, both in cases in which the jointly developed development guidelines had been followed and in cases, in which the expected life and career skills had been developed; (2) the researchers, co-researchers, and school learned together about the effectiveness of collaborative work, which had helped everyone to see the inefficiency of the individual work that had been carried out in the past; and (3) the theoretical knowledge gained from the implementation, which explained the relationships between expected changes and the driving forces behind those changes, the barriers to change, and the strategies for overcoming those barriers.
Pupils’ Motivation and Reading Performance

The study sought to identify the Pupils' Level of Reading Motivation, and the level of Pupils' midyear reading performance, and find the significant relationship between Pupils' Level of Reading Motivation and Reading Performance of the 198 Grade 5 pupils in Villanueva South District namely; Balacanas Elementary School, Dayawan Elementary School, Don Fernando Elementary School, and San Martin Integrated School.This study utilized the Motivations for Reading Questionnaires (MRQ) adopted from Wigfield et al. (1996) with a 4-point Likert scale and the Reading Performance Level based on the PHIL-IRI Reading Assessment tool.It employed a descriptive statistical tools like Mean, SD, frequency count and percentage.Pearson Product Moment Correlation Coefficient was used to ascertain the relationship between pupils' reading motivation and performance.Results showed that majority of the Grade 5 pupils are motivated both intrinsically and extrinsically.Almost half of the Grade 5 pupils' population are in the Instructional level in reading.There is a significant relationship between the pupils' reading motivation and performance.It can be concluded that pupils read more if they are motivated.So, it can be recommended that teachers and parents will continue to motivate the pupils intrinsically or extrinsically so that pupils will sustain their reading interest.
Automatic Spoiler-Sensitive Video Preview Generation with Reinforcement Learning

With the plethora of video content uploaded to the internet each day, the amount of watchable content far exceeds the amount of time available for us to consume it.That being said, the aim of this research is not to create something that does the watching for us and merely generates a short summary; instead, the goal is to help us select videos that we would enjoy watching the most while preserving the viewing-integrity of the original video.In other words, we wish to be able to generate preview video summaries that do not reveal any essential plot items.To accomplish this task, we developed an algorithm that is able to select a fraction of a video's total frames to create a video trailer/summary while excluding important frames that would detract from the main viewing experience (i.e. it does not include any main plot points, spoilers, plot twists, etc.).This output video would allow us to decide better whether we are interested in a particular video, and at the same time will still allows us to fully enjoy the video if we do choose to watch it since we will have avoided any spoilers.
Students’ Perception of Reciprocal Reading Strategies and Their Reading Performance

Appropriate reading strategies improve the reading comprehension of Grade 7 students.The study aimed to attain the following objectives: To know the students' perception of their teacher's level of practice on Reciprocal Reading Strategies (RRS) in terms of Predicting, Questioning, Clarifying, and Summarizing; To identify the students' level of Reading Performance; and To find if there is a significant relationship between the students' perception of their teachers' level of practice on RRS and their reading performance.The study employed Stratified Quota Sampling because only two sections of Grade 7 from the three big schools in South District, Cagayan de Oro City Division were recommended as the respondents of this study.Descriptive correlational methods of research was used on which a researcher-made questionnaire was utilized as the main instrument.It was validity and reliability tested.Mean and Standard Deviation were used to determine the students' perception of teacher's level of practice on Reciprocal Reading Strategies.Pearson Product Moment Correlation Coefficient was utilized to find the significant relationship between the students' perception of their teachers' level of practice on Reciprocal Reading Strategies.Results revealed that the English teachers are using the Reciprocal Reading Strategies.However, Clarifying is difficult for the students that needs to be addressed by all teachers to improve the reading comprehension.This can also be a good material for further study for future researchers.
Rancang Bangun Media Pembelajaran Algoritma Perograman Berbasis Android

The digital age in the era of society 5.0 creates a technology that currently has significant developments that education needs to provide a new image of Indonesian education, namely by implementing a curriculum that is by the current curriculum. In teaching and learning activities, achieving an optimal result needs to pay attention to the existing learning principles. That is one way to attract the attention of students in the learning process. As well as with current technological advances making a physical book is something that is no longer attractive to today's students where at this time students are more and more often using smartphones. In teaching and learning activities carried out in schools today, informatics subjects still use physical books provided by the government. This causes students to not understand the concepts being taught and students will feel less interested in learning algorithms and programming. This study uses the Hannafin and Peck research method with the software used in product manufacture, namely ISpring Suite 10 and Website 2 APK Build. The results of this study are that the learning media product for Android-based programming algorithms using Blackbox Testing gives the result that the designed product works according to the expected results and is very suitable for use in students’ learning process.
Image Denoising Using Python and Machine Learning

Abstract: Image denoising is a fundamental task in image processing and computer vision, aimed at reducing noise while preserving important image details. Traditional denoising techniques often struggle to effectively remove noise while preserving fine details, especially in high-noise scenarios. In recent years, machine learning approaches have gained popularity for image denoising tasks, leveraging their ability to learn complex patterns and features directly from data. The denoising process begins with the acquisition of a noisy image, which is then preprocessed to enhance its features and reduce artifacts. The preprocessed image is fed into the trained deep learning model, which employs convolutional neural networks (CNNs) to learn the underlying noise patterns and predict the corresponding denoised image. The model is trained using a combination of loss functions, such as mean squared error (MSE) and perceptual loss, to optimize the denoising performance while preserving image details. To evaluate the proposed approach, extensive experiments are conducted on various benchmark datasets and compared against state-of-the-art denoising methods. Quantitative metrics, such as peak signalto-noise ratio (PSNR) and structural similarity index (SSIM), are used to measure the denoising performance. The results demonstrate that the proposed method outperforms existing techniques in terms of denoising quality, preserving fine details, and handling different noise types and levels.
PUBLIC KEY ENCRYPTION SCHEME BASED ON KEYWORD SEARCH

This study explores the concept of a Public Key Encryption Scheme (PKES) with an emphasis on enabling keyword search functionality. The aim of this research is to develop a cryptographic framework that allows users to securely search over encrypted data without revealing sensitive information. The study investigates various methods and techniques employed in PKES to achieve efficient and privacy-preserving keyword search capabilities. The research highlights the increasing importance of data privacy and the need for secure information retrieval over encrypted data. Traditional encryption methods hinder search functionality, making it challenging to perform keyword searches on encrypted data. To address this limitation, the study focuses on PKES, which facilitates search operations while preserving the confidentiality of the underlying data. Experimental research design was used for this research. To accomplish the objectives, the study employs a combination of cryptographic algorithms and data transformation techniques. Public key encryption algorithms are utilized to secure the data, ensuring that only authorized users can access the information. Additionally, searchable encryption methods, such as Searchable Symmetric Encryption (SSE) and Index-Based Encryption (IBE), are explored to enable efficient and secure keyword searches on encrypted data. The results of the research demonstrate the feasibility and effectiveness of the proposed PKES with keyword search. The developed framework allows users to encrypt their data and store it securely, while still being able to search for specific keywords without decrypting the data. The experiments conducted on different datasets showcase the efficiency of the implemented techniques, providing a practical solution for secure data retrieval. In conclusion, the study presents a robust and privacy-preserving Public Key Encryption Scheme that incorporates keyword search capabilities. The research contributes to the field of cryptography by addressing the limitations of traditional encryption methods and offering an effective solution for secure information retrieval. The findings highlight the significance of PKES in safeguarding sensitive data while allowing users to perform keyword searches conveniently
Quantitative 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy.

2616 Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 µm section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and failure to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire, intact lung tissue samples. Herein, we present analysis methods for comprehensive 3D assessment of intact NSCLC tissue. Methods: Human NSCLC tissue, stored frozen in optimal cutting temperature (OCT) compound, was fixed in 4% paraformaldehyde, stained with nuclear (TOPRO-3) and general protein (Eosin) fluorescent dyes, and optically cleared using a modified iDISCO protocol with ethyl cinnamate as the refractive index matching solution. Twenty tissue samples measuring 1-5 mm 3 in volume were imaged at 2 µm/pixel resolution with a hybrid open-top light-sheet microscope. Smaller regions of interest (ROIs) with interesting pathologic features were re-imaged at higher resolution, 0.17 µm/pixel. Visualization and analysis was performed using Aivia software, custom spatial analysis software and python scripts. Results: All NSCLC tissue samples were successfully imaged in 3D at both low and high resolution. Computational approaches, such as machine learning, were used to segment and classify tumor cells and lymphocytes within the high resolution ROIs. In one example we quantified a 0.0045 mm 3 ROI containing 1x10 9 voxels. We segmented 1,474 unique objects, and classified 683 (46%) of the objects as lymphocytes and 309 (20%) of the cells as tumor cells. We found that on average the lymphocytes were within 12 µm of the nearest tumor cell. Additionally the tissue was broken into separate lymphocyte dense (58% of volume) and tumor dense regions (42% of volume). Further analysis is in progress to quantify more features, such as tertiary lymphoid structures, stromal cells, and fibrosis over the full 20 sample dataset. Conclusions: We successfully implemented 3D machine learning analysis pipelines using images from intact NSCLC tissue samples measuring up to 5 mm 3 . The imaging was performed using our custom hybrid open-top light-sheet microscope. This novel method enables us to identify and quantify key histological features such as tumor cells and lymphocytes within the tumor microenvironment. In addition, machine learning driven algorithms clustered cells into distinct tissue regions in 3D which enables a more precise assessment of disease state and heterogeneity. Further investigation is ongoing to link these results to patient outcomes.