Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Flexible Multi-Resolution Image Compression using Deep Learning Based Autoencoders on Overlapping Image Patch

Compressing images using a deep-learning-based autoencoder usually can only work for images of a fixed size. This causes problems since the images might be in various sizes. This paper proposes a novel approach that can be applied in real-life based on a patching algorithm for a given image. This algorithm aims to crop the image into smaller ones that can fit into our autoencoder model so that a single autoencoder model can be used for images with diverse sizes. The proposed approach was tested on a few autoencoder types (Vanilla, Deep Convolutional, and Variational Autoencoder), and it was found that different types of autoencoder impact the quality of the recreated images based on PSNR (Peak signal-to-noise ratio), MSE (Mean squared error), and SSIM (Structural Similarities). In our research we found that for PSNR and MSE, Variational Autoencoder got highest score among other design, with average of 21 PSNR and 1300 MSE Score and Deep Convolutional Autoencoder gives the best results in SSIM score among all other types of autoencoders with SSIM Score rate average at 0.5, when implemented in our system.
Improving Batik Pattern Classification using CNN with Advanced Augmentation and Oversampling on Imbalanced Dataset

In image classification task, imbalanced dataset is a problem that often occurs. Batik pattern data also suffers this problem, mainly because of the poor quality of available images and rarity of certain patterns. In this research, we employed a novel ad- vanced augmentation and oversampling techniques on the imbalanced dataset to address this issue. This approach enhanced the diversity of the images, encompassing variations in color, contrast, wrinkles, and warps that may be present in batik garments. We employed two CNN models, DenseNet169 and VGG-16, along with three different training methods for our study. These methods included training without oversampling and advanced augmentation, training with oversampling, and training with both oversampling and advanced augmentation. The results showed that the best accuracy was achieved with DenseNet169 with our oversampled and augmented dataset, with an accuracy of 84.62%. Additionally, VGG-16 also performed well on said dataset, achieving an accuracy of 82.56%.Our results suggested that by using our oversampling & advanced augmentation on the dataset,the model performance improved compared to plain data and oversampled data.
Study of Cross-Site Request Forgery on Web-Based Application: Exploitations and Preventions

Cross-Site Request Forgery (CSRF) is a prominent web exploit that continues to pose significant security risks, even on highly ranked websites. This research focuses on identifying the underlying vulnerability, understanding the techniques employed, and proposing effective preventive measures. The hybrid method which is systematic report review method and lab-based scenarios method is conducted to identify CSRF exploitation and prevention mechanisms. Using a scenario-based testing approach, this study investigated the cause and effect of CSRF attacks, uncovering numerous techniques for exploiting this vulnerability. Based on the report review, 35 CSRF vulnerability reports from the Hacker one Bug Bounty Platform are analyzed. The result shows there is a lack of awareness and implementation of primary defenses, resulting in a high violation of end-user data integrity. The result also extensively describes some of the profound causes and effects of CSRF attacks and uncovers a broad range of techniques for exploiting this vulnerability. Furthermore, this study provides actionable recommendations to enhance system security and raise awareness among developers and users. The findings from this research serve as a valuable resource for improving security practices and mitigating CSRF attacks.
Sentiment analysis of the Indonesian community toward face-to-face learning during the Covid-19 pandemic

This research aims to analyze how the opinions of the Indonesian people in the learning system in the middle of the covid-19 pandemic. The research method is carried out by performing the K-NN method to determine the accuracy level of the data used. The research data method is taken through public comments on Twitter social media using scrapping techniques with appropriate keywords. The data will then be processed through training and testing and classified using the K-NN method. After the data is classified, the accuracy, F1-Score, Recall, and Precision level will be tested using Confusion Matrix. The result showed that KNN performed well, with above 70% of the F1-score for each class. According to the confusion matrix, accuracy also showed promising results with 82%. Future research may include oversampling the class with fewer numbers. K-Fold cross-validation can also be used to see the general performance of the model. The same method may be used to find sentiment towards a political policy that is taken; whether a policy gets a good or bad response, if the response is bad, you can see the causal factors that cause the negative sentiment. In this way, you can find out the will of the public.
A reversible and lossless secret image sharing scheme with authentication for color images

With the increasing concern for public privacy protection, Secret Image Sharing (SIS) has garnered significant attention among researchers. The goal of SIS is to split a secret image into multiple shadows, rendering it unrecoverable unless a specific threshold of shares is obtained. However, these shadows typically appear as meaningless noise-like patterns, making them susceptible to attacks and raising suspicion when transmitted over traditional internet channels. Moreover, traditional SIS schemes often restrict image formats to binary, limiting their applicability. To address these limitations, we propose a reversible and lossless secret image sharing scheme with authentication specifically designed for color images. Our scheme generates noise-like shadows by combining original secret images and authentication images, which are then embedded into color images using an enhanced steganography method. During the recovery process, participants can extract the shares losslessly and complete the authentication phase without the need for a trusted third party. The authentication results can be easily verified by visual inspection. Ultimately, the qualified shadows are utilized to reconstruct the secret image, ensuring secure transmission of confidential image data. The efficacy and security of our proposed method are demonstrated via theoretical analysis and experimental results.
Emotion Detection in Textual Data using Deep Learning

Technological advancement is something we can no longer ignore, deep learning being one of those advancements. With deep learning, emotions can be extracted and analyzed in textual data to be utilized in various sectors, involving human behavior analysis. The main objective of this paper is to investigate and discover basic emotional categories that are typically exhibited by humans, such as sadness, joy, anger, fear, and disgust. Sectors focused on business can utilize emotion detection for various applications, such as to create personalized services, and in medical scenarios to help develop specialized mental treatments. This paper enables other researchers to enrich their knowledge and develop a more accurate and efficient deep learning model. To create an emotion detection model, analysis regarding the best performing model has to be done. This paper discusses the difference in performance across three different RNN models, namely LSTM, BiLSTM, and GRU. The RNN based models, in theory, share the same architecture and will be evaluated using the ISEAR dataset to create an emotion detection model. The result of this paper concluded that the simplest RNN model, GRU, achieved the highest score across four scoring metrics: accuracy, recall, precision, and F1 score. The GRU model achieved an accuracy of 60.26%, BiLSTM with 59.3%, and LSTM with 57.65%. Hence, based on the results, the GRU-based models are a strong choice for emotion detection when tested on the ISEAR dataset.
Face Shape Classification Using Swin Transformer Model

Face shape classification plays a crucial role in various applications, including facial recognition and personalized beauty rec- ommendations like hairstyles. In some cases, it is tricky to establish one's genuine facial shape, making it difficult to further process this information. This paper attempts to harness the advantages of the Swin Transformer to classify face shapes with higher accuracy. We also compared the test results with augmentation to evaluate the improvement. Our proposed method obtained 86.34% accuracy with augmentation, which is decent for further processing in various applications that require face shape recognition.
Gestive: Evaluation of Multi-Class Classification Methods for Gesture Recognition to Improve Presentation Experience

Gesture recognition is able to transform how we interact with technology, including during presentations. We aim to classify slide-control gestures and create a model that allows users to add their personalized gestures to to control presentation com- mands. To avoid wasting time on capturing and training the model, we aim to construct a model with a short training time and high accuracy. We assessed the efficiency of various multi-class classification methods for gesture recognition in presentation settings. We compared K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, Linear Discriminant Analysis (LDA), and Random Forest. The results demonstrated that KNN offers the shortest training time while maintaining high accuracy. The findings of this study provide a basis for future research on gesture recognition technology and its imple- mentation to improve presentation experience.
Implementation Graph Database Framework for Credit Card Fraud Detection

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth’\ Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%.
Development of 3D Scanner Application with Stereo Camera for 3D Object Reconstruction

Visual perception in RGBD (Red-Green-Blue and Depth) camera technology allows the camera to provide color image and depth structure information simultaneously from the surface of the scanned object. Based on this technology, data from an RGBD camera can be processed to reconstruct the scanned object in a three-dimensional (3D) model. However, devices that are prebuilt to perform this task with good accuracy are expensive and are only intended for 3D scanning tasks. On the other hand, some multi-purpose depth cameras can be used for 3D scanning but require some tweaks to ensure a good and accurate reconstructed model. A method has been proposed to optimize the camera and reconstruction system to compile RGBD information from recorded close-up objects and process them into a full-scale 3D digital model. This process involves developing the appropriate recording method along with settings and adjustments for scanning and model reconstruction of close-range objects. As a result, information from multi-purpose RGBD camera can be reconstructed in three-dimensional form that accurately represents real-life objects. The reconstructed model can be utilized for applications that rely on real-life objects as their main basis. The proposed method enables the use of inexpensive multi-purpose RGBD cameras for 3D scanning.
Time-Based Steganography Image with Dynamic Encryption Key Generation

The rapid advancement of technology has necessitated stronger protection measures for data exchange. Encryption, coupled with robust keys, is an effective method to safeguard sensitive information. This study aims to improve the security of key levels by implementing a combination of steganography and encryption. The approach involves using two keys, one in the form of a normal string and the other based on the insertion time. These keys are combined to create a new, stronger encryption key. The secret messages are then encrypted using this new key and inserted using the Least Significant Bit method in steganography. This integrated approach aims to provide a higher level of protection for data during transmission and storage.
Designing Personalized Integrated Healthcare Monitoring System through Blockchain and IoT

Personalised Integrated Healthcare Monitoring System (PIHMS) has received major attention from the healthcare industry. With large population and strong growth of Indonesian middle class, PIHMS is certainly a big market for major healthcare providers. Although Indonesia has strong growth of middle-class, ironically, it is not supported with adequate physician ratio. Indonesian Ministry of Health (MoH) reported in 2020 the ratio of physicians per 1.000 patients is 0,62, far below WHO standard. As consequence, many Indonesian patients prefer to go abroad to have better healthcare services. The article supports the government's initiative to develop PIHM design by implementing IoT devices and blockchain networks. The use of PIHMS is essential to reach large patients that spread in remote areas, that suits to Indonesian condition. The article proposes PIHM based on blockchain solution (MedDApp) that enables to utilise IoT devices to carry out remote monitoring and medical consultation. The article examines the use of blockchain architecture to support developing data interchange between patients, healthcare providers and third parties such insurances and ministry of health (MoH). Data gatherings from respective stakeholders from Data and Information Centre division (Pusdatin), MoH and state-own hospitals to support developing smart health care architecture (MedDApp). The MedDApp design is expected to provide secure access between patients with healthcare professionals and third parties (national insurance companies and researchers in MoH). The outcome of the article is expected to be used as a reference for major healthcare providers to provide better services for their large patients.
Implementation of Augmented Reality in Android-based Application to Promote Indonesian Tourism

Indonesia is known for its iconic tourism industry. However, there is a significant popularity difference between each destination. This imbalance has been a problem that the Indonesian Ministry of Tourism has been trying to solve through the 'Wonderful Indonesia" campaign. Unfortunately, some did not perceive it as a success. With the breakthrough of Augmented Reality (AR) that is seen as a great factor in enhancing user experience in the tourism sector, this research aims to design and develop an immersive android-based application that implements Augmented Reality and Audio Guidance to promote and create branding for Indonesian tourism spots. This research uses a methodology divided into 4 phases; Analysis Requirement, Research and Collect Data, System Design and Development, and Testing. A survey was conducted in the Analysis Requirement phase to find people's knowledge and preference for tourism spots in Indonesia. Another survey was conducted in the Testing phase to test the application's usability and whether the aim has been achieved. The main findings indicate that most local tourist respondents stated that the application developed is an effective way for them to explore and educate themselves about Indonesian tourism sites. Therefore, this application might be a sustainable answer and a new approach compared to what the Indonesian government has done in the past years.
Attention is Everything You Need: Case on Face Mask Classification

Automated face mask classification has surfaced recently following the COVID-19 mask wearing regulations. The current State-of-The-Art of this problem uses CNN-based methods such as ResNet. However, attention-based models such as Transformers emerged as one of the alternatives to the status quo. We explored the Transformer-based model on the face mask classification task using three models: Vision Transformer (ViT), Swin Transformer, and MobileViT. Each model is evaluated with a top-1 accuracy score of 0.9996, 0.9983, and 0.9969, respectively. We concluded that the Transformer-based model has the potential to be explored further. We recommended that the research community and industry explore its integration implementation with CCTV.
Non-parametric style transfer: Correlation-aware exact distribution matching

Early feed-forward neural methods of arbitrary image style transfer utilized the encoded feature map up to its second-order statistics, i.e., mean and variance (or covariance) of the encoded feature map. Recent methods have begun to utilize feature statistics beyond the 2nd-order statistics, e.g., a higher-order moment, slicing-based method, and histogram matching for better style representation. However, even in the distribution matching of the latest method, considering the channel-correlation and the order of duplicated values in the feature map is missing. This resulted in insufficient style transfer or artifacts on style-transferred images e.g., unexpected color or pattern co-occurrence, skewed color or style pattern into one side, noise between background and foreground, etc. In this work, we show how to make a feature transform layer to consider both channel-correlation and duplicated values in the feature map for exact feature distribution matching in style transfer. Our experimental results prove that the stylized images obtained with our method are more similar to the target style images qualitatively without losing content clearness and superior to the existing methods quantitatively in all possible style measures, e.g., 0.0311 lower sliced Wasserstein distance than the previous method and the most favored choice with 25.54% preference.
Rust's Memory Safety Model: An Evaluation of Its Effectiveness in Preventing Common Vulnerabilities

Ensuring safe program operation is crucial for systems programming, and memory safety is a significant aspect of this. This paper assesses the memory safety strategies used in Rust and C++ programming languages. Rust is a modern language that has become popular due to its focus on safety, performance, and concurrency, whereas C++ is known for its efficiency and low-level program- ming capabilities. The analysis compares and contrasts the memory safety features of both languages, including Rust's ownership and borrowing model and C++’s manual memory management techniques. Additionally, the study examines the development tools available for both languages and compares ease of use and productivity. The analysis shows that Rust has several memory safety advantages over C++. Rust's ownership and borrowing model enables safe memory management without requiring manual intervention. By enforcing strict rules for memory usage, the compiler in Rust prevents typical memory-related errors like null and dangling pointers. However, in contrast to Rust, C++ provides more low-level hardware control but needs manual memory management, exposing it to memory errors such as buffer overflows and memory leaks. Moreover, Rust's memory safety features simplify the development of secure and dependable code, especially for applications with crucial security requirements. In con- trast, C++ demands significant memory management expertise to avoid memory-related errors. In conclusion, Rust offers several memory safety benefits over C++, making it a superior choice for applications with critical safety needs. However, the selection of language ultimately depends on the specific project requirements and the development team's experience and preferences.
Robust Environmental Perception of Trajectory Prediction

Trajectory prediction has always been an important research direction in the field of transportation (Helbing and Molnar in Phys Rev E 51(5):4282, 1995, [1]; Luber et al. in People tracking with human motion predictions from social forces. IEEE, pp 464–469, 2010, [2]; Mehran et al. in Abnormal crowd behavior detection using social force model. IEEE, pp 935–942, 2009, [3]).
Human and Technical Aspect of Content Management

The solution for content moderation is unlikely to be a choice between human moderation or moderation by artificial intelligence, but rather a combination of the two in the future. In 2020, during the COVID-19 pandemic—while big tech companies were also making their employees work from home and giving artificial intelligence more tasks—“Facebook and Google roughly doubled the amount of potentially harmful material they removed in the second quarter of this year compared with the three months through March”, and there were many more complaints about the decisions as a result, making it clear that human content scrutiny will not be unnecessary for some time. The chapter examines the human moderation and the use of artificial intelligence. “After all, a butt is a butt and a nipple is a nipple. But deciding when a nipple is art, porn or protest gets murky even when humans are doing the deciding. Teaching AI software about human sexual desire is a whole other ballgame.”
Robust Environmental Perception of Multi-object Tracking

Multi-object tracking (MOT) is an essential component in a number of fields such as robotics, autonomous vehicles, and surveillance systems. For accurate tracking and predicting of the motion of multiple objects in complex environments, strong sensing capabilities are crucial.