Reviewerly/exHarmony huggingface dataset:
SIFT-based blockchain model for preventing the spread of forgery image

The detection of forgery images has become a hot issue in the field of communication. It is of great importance on researching how to prevent the uploading of forged images from the source of propagation. Blockchain can effectively protect the original features of images through distributed ledgers, consensus algorithms, asymmetric encryption, and smart contracts. Therefore, this paper proposes an idea of preventing the forgery images from the very beginning on the basis of blockchain, which aims at storing the original images into the blockchain through interaction with smart contracts. Our method not only provides safe and traceable copyright protection to the owners of the original images but also effectively suppresses the uploading of forgery images. Firstly, image features are extracted by Scale-invariant feature transform algorithm(SIFT) for image to be uploaded, the features are further matched by Fast Library for Approximate Nearest Neighbors(FLANN) with with the stored images in the blockchain. Secondly, the similarity between images is calculated in order to determine the originality of uploaded images, the ones with high similarity scores are refused to upload into the blockchain. At the same time, the copyright owner of the infringed image will be informed immediately. Finally, a large number of simulation results show that the method proposed in this paper can effectively authenticate the image copyright and prevent the spread of forgery images.
Network Traffic Classification for Detecting Multi-Activity Situations

Network traffic classification is an active research field that acts as an enabler for various applications in network management and cybersecurity. Numerous studies from this field have targeted the case of classifying network traffic into a set of single-activities (e.g., chatting, streaming). However, the proliferation of internet services and devices has led to the emergence of new consuming patterns such as multi-tasking that consists in performing several activities simultaneously. Recognizing the occurrence of such multi-activity situations may help service providers to design quality of service solutions that better fit users' requirements. In this paper, we propose a framework that is able to recognize multi-activity situations based on network traces. Our experiments showed that our solution is able to achieve promising results despite the complexity of the task that we target. Indeed, the obtained multi-activity detection performance is equivalent or often surpasses state-of-the-art techniques dealing with only a single activity.
K3BO: Keypoint-Based Bounding Box Optimization for Radiance Field Reconstruction from Multi-View Images

Novel view synthesis has recently been approached with Neural radiance fields, for high quality rendering. While those methods initially addressed the whole scene with a single global function, many state-of-the-art methods decompose the scene by spatially encoding feature vectors. High speed and quality are achieved, but reconstruction stability is still fragile. This usually requires human supervision through data pre-processing, preventing a robust, fully automatic chain for volumetric reconstruction of the visual scene. We observe that stability and quality can be improved by interpreting the scene beforehand, in order to properly set its bounding box. We propose a simple and robust approach to fit the scene volume bounds based on sparse point clouds processed by a Structure From Motion (SfM) pipeline. The benefit of this method is shown over multiple scenes and two state-of-the-art radiance field reconstruction methods.
Predicting the Fluid Flow Around an Airfoil Using Machine Learning Methods

The aim of this paper is predicting pressure distribution around the NACA0012 airfoil by using machine learning models. The pressure distribution model is produced to be used in a simulation environment that is designed to illustrate the behaviour of the fluid around the airfoil in various atmospheric conditions. The model should work for all of these different conditions and finding the solution for all by using CFD (Computational Fluid Dynamics) algorithm is not time and cost efficient. Thus, instead of solving it for all conditions, a training set is formed with certain cases and the distribution for other conditions are predicted with minimum 91.8% accuracy by applying artificial neural networks, decision tree and K-nearest neighbours methods.
Recovering JPEG Compression Loss via Deep Learning-Based Super Resolution Techniques

This study investigates the use of JPEG, a lossy compression algorithm, for efficient utilization of channel bandwidth. To reduce losses caused by the removal of high-frequency components in compressed images, super-resolution models are developed using deep learning methods. Specifically, the SRCNN, VDSR, and SRDenseNet models are trained from scratch using compressed images. The effects of different compression ratios on images are analyzed in terms of their impact on the recovery of high-frequency components by the super-resolution models. The performance of the models is evaluated using PSNR and SSIM metrics, considering various compression ratios.
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN CANVA TERHADAP HASIL BELAJAR PESERTA DIDIK DI SEKOLAH DASAR

This study aims to determine the effect of using Canva-based learning media on student learning outcomes in elementary schools. In this study using a quantitative approach with a research design that is quasi experimental (quasi experimental design). The sample in this study was the VA class, which consisted of 24 students as the experimental class and the VB class, which consisted of 22 students as the control class. Data collection techniques through tests and observations. The tests used are tests using the pre-test and post-test which are used as data on student learning outcomes. Based on the results of the study, in the experimental class, the average pre-test score of students was 50.75 and the average post-test score of students was 84.87. Whereas in the control class, the average pretest score for students was 43.32, and the average post-test score for students was 70.95. The results of the calculation test in the experimental class and control class, it can be concluded that the post test scores are greater than the pretest values, meaning that there is an increase in student learning outcomes. The researcher used an independent sample t test to find the effect of using Canva-based learning media on student learning outcomes in elementary schools. It is known that the calculated t value of 7.851 &gt; 2.069 has a sig of 0.000 &lt;0.05, which means that there is a significant influence on the learning outcomes of students in the experimental class and control class. Then in accordance with the decision making determined that H0 is rejected and Ha is accepted. So, thus the researchers concluded that the effect of using Canva-based learning media on the learning outcomes of students in elementary schools.
Applying Use Case 2.0 Approach to The Development of IoT-Based Rainfall Monitoring System

The use of Internet of Things (IoT) technology for monitoring and controlling environmental conditions or objects is quite popular. However, most of the development of the IoT systems including rainfall monitoring systems, mainly focuses on the implementation perspective, rather than discussing the development approaches or design techniques. The use of suitable development approaches will increase maintainability aspect of the IoT system in the future. Therefore, the aim of this study is to implement and evaluate the use case 2.0 approach in modeling and designing an IoT-based monitoring rainfall system. Data collection was performed through evaluation using object-oriented metrics to measure encapsulation, polymorphism, and reusability properties of the designed system. In modeling the IoT system, collected requirements specifications are organized into user stories. The user stories are then mapped into UML use case diagrams. Each of the use case should be sliced into thinner pieces, taking into account of the basic and alternative flows of the user stories. Moreover, the use case slices are designed, implemented, and evaluated independently. The results of modeling and designing a rainfall monitoring system using the use-case 2.0 are then implemented on the NodeMCU platform and Android-based application. Evaluation results show that the implementation of use-case Reading, Viewing and Searching for Rainfall Data can be run successfully on the target platform. The measurement uses object-oriented metrics on the designed IoT system indicating that the use case slices have an impact on the ease of system modification level.
Implementation of Chatbot for Merdeka Belajar Kampus Merdeka Program using Long Short-Term Memory

Good service can help the organization improve efficiency and effectiveness in operations. Optimal service can also improve the customer experience and provide added value to an organization that provides services. One of the services that can be optimized is the Merdeka Belajar Kampus Merdeka (MBKM) program which is a learning program organized by the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek), especially MBKM services at the Institut Teknologi Telkom Purwokerto (ITTP). The problem is that the MBKM service at ITTP is not optimal due to inaccessibility to anyone and so many programs available. Thus, resulting in not optimal services provided. Therefore, this study aims to implement a Chatbot service in the MBKM program at ITTP. The method used in building a Chatbot service is the Deep Learning Long Short-Term Memory (LSTM) algorithm. LSTM is a type of artificial neural network architecture that matches text data. The results show an accuracy score of 100% and a loss of 0.121%. Meanwhile, the results of the further evaluation are in the form of average weights consisting of precision, recall, and F1-score, respectively of 100%, 100%, and 100%.
DECEITFULNESS OF DR. JEKYLL IN STEVENSON’S THE STRANGE CASE OF DR. JEKYLL AND MR. HYDE

This study presents the analysis of the Deceitfulness of Dr. Jekyll in Stevenson’s The Strange Case of Dr. Jekyll and Mr. Hyde. The aim of this study is to analyze the qualities and conditions of Dr. Jekyll that form his deceitfulness by using the objective approach. The method used in this research is the library research method. This method is used also as the data collection technique to get information or references dealing with the object discussed. The objective approach is used here to analyze the structural elements of this novel like character, setting, conflict, and theme to reveal the research’s object. The result shows that the novel The Strange Case of Dr. Jekyll and Mr. Hyde tells about Dr. Jekyll’s deceitfulness. The main instance of the deceitfulness of Dr. Jekyll in the novel is when he hides his real personality and when he is being a murderer. There are several reasons behind the actions of Dr. Jekyll: his strict childhood, his wrong ambition, and his bad environment.
Test Automation for Symbol Recognition on the Map

In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.
RoBGP: A Nested Biomedical Named Entity Recognition Method Based on RoBERTa and Global Pointer

Named entity recognition (NER) is one of the fundamental tasks in biomedical text mining, aiming to extract specific types of biomedical entities from texts. Compared to general-domain texts, biomedical texts often contain nested entities and local dependencies among entities, which pose challenges to existing NER models. To address these issues, we propose a nested biomedical NER method based on RoBERTa and Global Pointer (RoBGP). First, we dynamically obtain word-level semantic representations using the RoBERTa-wwm-ext-large model. Second, A BiLSTM model is used to capture long-distance semantic information in a bidirectional manner. Finally, we utilize a global pointer network to recognize all nested entities from innermost to outermost. Experimental results on the Chinese medical dataset CMeEE show that our method achieves the F1 score of 67.69%, outperforming other advanced models by 0.32% to 5.11%, validating the effectiveness of our approach in biomedical NER.
The Research and Improvement of Facial Expression Recognition Algorithm Based on Convolutional Neural Network

As an important branch of facial recognition, facial expression recognition has always been a hot topic in interdisciplinary research and has broad application prospects. Currently, there are numerous facial expression recognition methods based on convolutional neural networks. However, these methods show different effects on different datasets, which makes it difficult to distinguish their advantages and disadvantages. This article presents a comparative study of three classic emotion recognition algorithms based on convolutional neural networks. Furthermore, an image enhancement algorithm utilizing super resolution generative adversarial (SRGAN)and adaptive grayscale normalization is designed based on the dataset and characteristics of Efficient Convolutional Neural Networks (MobileNet). The proposed algorithm improves the facial expression recognition accuracy of MobileNet from 68.03% to 70.04%, which improves the performance of facial expression recognition effectively.
Chinese Text Correction System Based on Ernie

Nowadays, Internet technology is developing at a high speed, which makes the vast number of Internet users constantly producing and receiving huge amounts of information data. Because of the low threshold of Internet use, it leads to the uneven quality of information on the Internet, and the quality of most of the information is decreasing. The quality of information can be improved by proofreading the text for errors. Therefore, it is important to detect and correct typos in text information. With the more widespread application of deep learning in the field of natural language processing, a deep learning and rule-based approach to Chinese text detection and error correction has achieved better results in processing text compared to traditional methods. The main work is as follows: the existing deep learning algorithms and models on text error correction are compiled, briefly introduced and compared, in which the effect of Ernie and Bert-based Chinese error correction processing is mainly compared, laying the foundation for selecting the ernie end-to-end Chinese error correction model. The error correction process of the Ernie model is also introduced, and the prediction results are evaluated and trained on the selected dataset. After experimental validation, the Ernie-based end-to-end model achieves better results with a greater improvement in accuracy. Moreover, the front-end interface design was carried out, and the Chinese text error correction system was successfully built, which can perform functions such as uploading and downloading of files, and support the saving of results.
The Intellectualized Disposal System of Cognitive Domain's Confrontation

In recent years, the confrontational behavior in cognitive domain have gradually developed from fragmented and local behavior such as guided propaganda and agitation to widely influencing global confrontational behavior. The impact of confrontational behaviors act in cognitive domain is even worse. Vicious cognitive domain events occurred in our society are more frequent these years. Problems created by cognitive domain's confrontation are worthy to explore and research from the perspective of artificial intelligence and system science. In view of this, we try to build an intellectualized disposal system to give a solution for these problems. The main work is as follows: (1)We build the framework required for the disposal of cognitive domain's confrontation based on the OODA loop and confrontational strategy theory. We analyze the characteristics and methods of cognitive domain's confrontation from actual cases. (2)We construct an intelligent solution to the problems of cognitive domain confrontation hierarchically, and analyze its engineering feasibility based on existing artificial intelligence methods such as natural language processing. (3)We presents the design of the Intellectualized Disposal System of Cognitive Domain's Confrontation(IDSoCDC), which is introduced in this paper.
Swift Concurrent Semantic Segmentation and Object Detection on Edge Devices

We propose a real-time network optimized for joint semantic segmentation and object detection on edge devices. Our architecture builds on the latest YOLO series network and incorporates lightweight segmentation sub-networks for multi-task learning. Specifically, we leverage layers two to four of the YOLO network, which contain substantial semantic information at varying resolutions, to segment objects of diverse sizes. We introduce the Parallel Aggregation Pyramid Pooling Module (PAPPM) to efficiently generate buffered semantic segmentation feature maps by utilizing single-point addition and residual learning. This approach reduces computational complexity and memory usage without compromising accuracy. We also propose a novel Progressively Iterative Learning (PIL) approach to learn the weights for the backbone, neck, and multi-task heads, respectively, without catastrophic forgetting. Our approach achieves state-of-the-art performance on benchmark datasets, demonstrating the effectiveness of our proposed techniques.
MATCHING GAME TO ENRICH STUDENTS’ VOCABULARY ACHIEVEMENT

This study is to prove whether the use of the Matching Game in teaching vocabulary can achieve students’ vocabulary or not. In doing this research, the writers use quantitative research through post-test only with an experimental group and a control group. The experimental class is taught in test after treatment and the control class is taught by conventional method. This study is conducted on students in the second grade of SMPN 2 East Siau. The sample is taken from one class of junior high school. After conducting research, it can be concluded that the use of Matching Games is very effective in improving a student's English vocabulary skills. It also further supports that the selection of appropriate and educational games greatly affects the output of each learner, of course, by taking into account demographic factors and the background of each student in order to increasingly provide games that match their interests.
FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction

Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list’s quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of corresponding recommendation algorithms. Experimental results on two commonly used datasets demonstrate the effectiveness of the proposed algorithm in enhancing list ranking quality, reducing prediction errors, and maintaining recommendation diversity and accurate user preference classification. This research highlights the potential of integrating heuristic methods with complex network theory and fuzzy techniques to enhance recommendation system performance with interpretability in mind.
DIGITALIZED BRIEFS: REFRAMING THE INFLUENCE OF THE DIGITAL ENVIRONMENT ON READERS' ENGAGEMENT WITH FLASH FICTION

The proliferation of digital platforms has redefined readers' engagement patterns, especially with concise narratives like flash fiction This research delves into a thorough analysis of the distinctive characteristics of flash fiction, a form that is currently gaining popularity and widespread acceptance due to the enjoyment it gets from the digital environment. Embracing the essence of brevity, flash fiction thrives on conciseness and magnetizes its audience within a limited word count. With the advent of digital platforms and technological advancement, the boundaries of traditional storytelling have expanded, providing a fertile and dynamic environment for the growth of flash fiction. The research employs an interdisciplinary approach, interweaving threads from psychology, media analysis, and literary criticism to investigate the specific aspects of the digital milieu that are crucial to the heightened engagement and enjoyment felt by flash fiction enthusiasts. This study seeks to shed light on the fundamental mechanisms that contribute to the magnetic pull of flash fiction in the digital domain by analyzing a variety of dimensions, including interaction, accessibility, social connection, and creativity. A fundamental component of this exploration is to unravel the core processes that elevate the appeal of flash fiction in the digital world. By dissecting these aspects, the research illuminates how the combination of these factors works in unison to enhance the charm of flash fiction in the digital age.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0049/a.php" alt="Hit counter" /&gt;&lt;/p&gt;