Reviewerly/exHarmony huggingface dataset:
Multi Model CNN Based Gas Meter Characters Recognition

In the realm of computer vision, the identification and extraction of text from photographs of natural scenes is a very difficult task. Convolutional neural networks (CNNs) have been demonstrated to be highly successful in recognizing characters and words from photographs due to their exceptional ability to recognize the structural patterns within them. This makes CNNs one of the most suitable approaches for solving recognition problems, such as text recognition in natural scene images. In this study, we aim to recognize numerical texts from images and employ three models for this task: CNN models, a combination of CNN-LSTM models, and a combination of CNN-GRU models. The gas meter images comprising the dataset for this study were captured by our team using multiple phones at different times. Our goal is to recognize the counter part of the gas meter digits, which is why we first preprocess the images and prepare the data for detection. We perform the detection using the EAST algorithm. On the gas meter dataset, after the detection stage, we apply data augmentation, normalize image cropping, and perform image equalization. Our results show that the accuracy achieved by the CNN, CNN-LSTM, and CNN-GRU models in recognizing numerical texts from images is 72.9%, 96.6%, and 97.63%, respectively. These findings suggest that the CNN-LSTM and CNN-GRU models are highly effective in recognizing numerical texts in images, with the CNN-GRU model exhibiting the highest accuracy. Overall, these results demonstrate the potential of using deep learning models for recognizing numerical texts in images, particularly the combination of CNN and Gated recurrent unit (GRU) models.
Lossless Watermarking in Encrypted Triangular Mesh Models Based on Optimized Vertex Estimation and Error Histogram Shifting

A lossless watermarking approach is presented for inserting additional data into the encrypted triangular mesh models. These models can be practical when observing a shape from different angles is important. Three-dimensional models can also be utilized in deep fake and hologram technologies. The proposed method is based on optimized vertex estimation. For this purpose, central vertices are predicted in the hexagons. Then estimation error analysis is carried out to develop sharp error histograms. Additional data is inserted by performing estimation error histogram shifting. Various evaluations have been carried out to determine the efficiency of our approach. According to the obtained experimental results, the proposed scheme indicates better performance than the other related schemes in the current literature.
Leveraging the Power of Object Detection Models in Identifying Litter for a Significant Reduction in Environmental Pollution

The growing concern of litter pollution in natural environments has escalated into a significant issue that demands immediate and efficient resolution. Recent studies have used deep learning models to solve the problem of litter pollution, but these approaches have faced challenges in accurately detecting litter in real-world environments. Therefore, this paper has proposed a litter detection model and analyze its performance on the TACO dataset, which contains real-world outdoor environment images. The paper evaluates three distinct deep learning models (YOLOv4, YOLOv5, Faster R-CNN) and identifies the best performing model. The performance of the selected model is then enhanced through adjustments of hyperparameters, use of several preprocessing techniques and data augmentation techniques. The experimental results showed that YOLOv5x achieved 88% mAP@.5 and 71.4% mAP@.75 on testing dataset which outperformed the state-of-art studies. The findings of this paper provide valuable insights into the solution of litter pollution and can inform future research in this area.
IoT for Monitoring System for Cattle Growth Productivity

Internet of things technology is currently being widely applied in various fields, one of which is in the field of animal husbandry. The livestock sector is an activity that plays an important role in national development. One of the farms that is very profitable for the lower middle class is raising cattle. Like PT IPB, located in Lampung Province, can increase the income and welfare of breeders, preserve the environment, and contribute to the country’s foreign exchange. The obstacle at PT IPB is in the process of monitoring the growth of cattle which is still done manually. This affects the estimated weight of the cow and the determination of the selling price of the cow. If it is wrong to predict the selling price of cattle, then the company can suffer losses. For this reason, this research will develop an Internet of Things-based information system that can monitor the growth of cattle from the beginning of arrival until the cows are ready to be sold.
Application of Augmented Reality for The Introduction of The Lampung Script

Lampung Province has a very strategic location, situated at the southern tip of Sumatra Island, making it the only gateway for those who want to enter Sumatra Island. Lampung script is a traditional script used by the Lampung community to write the Lampung language. This script has its own uniqueness and is continuously preserved by the Lampung people. However, with technological advancements, the use of Lampung script has decreased, especially among the younger generation who are more familiar with digital technology. To preserve the Lampung script and increase interest in learning the Lampung language, an augmented reality (AR) based Lampung script recognition application has been developed for learning purposes. Augmented reality (AR) is a technology that can display the real world with virtual objects in real-time. This application utilizes AR technology to create a more interactive and engaging learning experience for users. It is developed to facilitate the community, especially children and the younger generation, in learning the Lampung script. By using AR technology, users can directly view the Lampung script on their smartphone or tablet screens, making it easier for them to understand the form and writing style of the Lampung script. This application uses marker-based tracking AR method, which uses markers or special recognition patterns/images that will be scanned by the AR device's camera. Users can easily scan different markers if they want to learn different Lampung scripts. It is hoped that the use of marker-based AR method in this application can enhance users' interest and motivation in learning the Lampung script. Additionally, this application also serves as an attractive alternative for a more interactive Lampung script learning experience.
AI Generated Content in the Metaverse: Risks and Mitigation Strategies

The Metaverse has introduced a vast virtual environment where Artificial Intelligence Generated Content (AIGC) plays a crucial role in content creation. However, the increasing popularity of AIGC has raised concerns regarding its potential misuse and the need for effective detection methods to ensure content integrity. Security and privacy threats in the Metaverse have garnered significant attention, but the specific risks of AIGC in this context remain understudied. This paper aims to analyze the potential risks of AIGC in the Metaverse and explore mitigation techniques. By addressing these challenges, the paper contributes to a comprehensive understanding of AIGC's implications in the Metaverse.
A Comprehensive Approach to SMS Spam Filtering Integrating Embedded and Statistical Features

Modern society relies heavily on mobile phones to communicate. One of the most valuable mobile phone services is SMS (Short Message Service), which simplifies communication greatly. There have been spammers who have misused this platform by sending inappropriate messages to users, provoking them and costing them money. Due to imbalanced data, unclear semantics, and the inability to extract sufficient features from short messages, SMS spam can be difficult to filter. While spam messages have been filtered so far using various methods, their accuracy is still a work in progress. This study uses embeddings and TF-IDF to provide more information from short text messages while improving SMS spam filtering accuracy. The proposed approach was tested on a real dataset. Experiments analyzing evaluation parameters demonstrate that this model is effective.
Distilling Knowledge from CNN-Transformer Models for Enhanced Human Action Recognition

This paper presents a study on improving human action recognition through the utilization of knowledge distillation, and the combination of CNN and ViT models. The research aims to enhance the performance and efficiency of smaller student models by transferring knowledge from larger teacher models. The proposed method employs a Transformer vision network as the student model, while a convolutional network serves as the teacher model. The teacher model extracts local image features, whereas the student model focuses on global features using an attention mechanism. The Vision Transformer (ViT) architecture is introduced as a robust framework for capturing global dependencies in images. Additionally, advanced variants of ViT, namely PVT, Convit, MVIT, Swin Transformer, and Twins, are discussed, highlighting their contributions to computer vision tasks. The ConvNeXt model is introduced as a teacher model, known for its efficiency and effectiveness in computer vision. The paper presents performance results for human action recognition on the Stanford 40 dataset, comparing the accuracy and mAP of student models trained with and without knowledge distillation. The findings illustrate that the suggested approach significantly improves the accuracy and mAP when compared to training networks under regular settings. These findings emphasize the potential of combining local and global features in action recognition tasks.
Scientific and Technical Information Resources Semistructured Data Ontological Model

Creation of ontological model of scientific and technical information resources semistructured data with dynamic integration has been described. The model of dynamic search of scientific and technical information resources semistructured data has been highlighted. The approach to build the structural ontology of the different semistructured data sources integration system has been analyzed. The main components of the scientific and technical information system have been defined. The simplified architectural model of the functioning of the semi-structured data integration scientific and technical system using the ontological model of data transformation has been shown.
Expert System for Identification of Early Illness Due to Cellulitis Complications with the Case Based Reasoning Method

Cellulitis is an infectious disorder that affects the dermis and subcutaneous tissue of the skin. The topic of this study is the fact that cellulitis is a typical and potentially dangerous bacterial skin infection. People frequently hesitate to visit a doctor owing to the time and expense needed, and a lack of knowledge about the seriousness of this bacterial infection causes public carelessness. If neglected, it can result in a number of medical consequences, including gangrene, sepsis, bone infections, lymphadenitis, and bloodstream infections. We'll suggest an expert system as a means of early disease identification for conditions brought on by cellulitis complications using a case-based reasoning approach. Patients can immediately consult with this expert system application, perform early disease detection of cellulitis complications, and become more watchful. 30 samples from the patient consultation data made up the test data that were used. 90% accuracy is achieved after analysis, with just 3 out of 30 data points between the expert system and actual data. With this level of precision, the case-based reasoning expert system is extremely suitable for use in the early detection of diseases brought on by cellulitis complications.
An Incentive Mechanism for Manufacturing Industry Chain Members to Share Data

In the manufacturing industry chain, the production of products involves many enterprises and generates large-scale traceability data. In order to encourage enterprises to actively sharing data, the paper designs an incentive mechanism. In this paper, we first establish an evolutionary game model of enterprise data sharing incentives, and then simulate several different evolutionary paths using simulation. The incentive value given by the blockchain platform is reasonable when all enterprises participate in data sharing.
Community-Based QoE Enhancement for User-Generated Content Live Streaming

Live user-generated content (UGC) has increased significantly in video streaming applications. Improving the quality of experience (QoE) for users is a crucial consideration in UGC live streaming, where a user can be both a subscriber and a streamer. Resource allocation is an NP-complete task in UGC live streaming due to many subscribers and streamers with varying requests, bandwidth limitations, and network constraints. In this paper, to decrease the execution time of the resource allocation algorithm, we first process streamers’ and subscribers’ requests and then aggregate them into a limited number of groups based on their preferences. Second, we perform resource allocation for these groups that we call communities. We formulate the resource allocation problem for communities into an optimization problem. With efficient aggregation of subscribers and streamers at the core of the proposed architecture, the computational complexity of the optimization problem is reduced, consequently improving QoE. This improvement occurs because of the prompt reaction to the bandwidth fluctuations and, subsequently, appropriate resource allocation by the proposed model. We conduct experiments in various scenarios. The results show an average of 41% improvement in execution time. To evaluate the impact of bandwidth fluctuations on the proposed algorithm, we employ two network traces: AmazonFCC and NYUBUS. The results show 4%, and 28% QoE improvement in a scenario with 5 streamers over the AmazonFCC and the NYUBUS network traces, respectively.
Trust Management Enhancement for the Internet of Things: a Smart Contract Approach

The Internet of Things (IoT) has witnessed tremendous growth in recent years, connecting billions of devices and enabling the exchange of vast amounts of data. Trust management plays a crucial role in establishing reliable and secure interactions among IoT devices and entities. This paper proposes a Smart Contract for Trust Management (SCoTMan) model that employs social relationships and adaptive weight adjustment to achieve efficient trust management in the IoT, leveraging the benefits of blockchain technology. Blockchain facilitates trust management by providing a global perspective on trust values among devices and enhancing trust propagation and updates. The model has been implemented and tested on the Hyperledger Fabric platform, utilizing IoT sample devices. Various experiments have been conducted to evaluate and validate the efficiency of the model. The model demonstrates improved performance in terms of trust convergence, and global storage cost, which is defined in this paper.
Transforming Big Data: A Novel Arithmetic Compression Method Based on Symbol Frequency

The article proposes a new method for arithmetic compression of large volumes of data. The used compression approach is based on dividing the original data into slices depending on the frequency of occurrence of symbols. The resulting cuts are subjected to arithmetic compression, which ensures an effective reduction of the volume of data, while maintaining the quality and reliability of information. The new method studies the statistical information on the frequency of occurrence of characters in the data set, which allowed setting the optimal place for dividing the data into slices. The main advantage of the proposed method lies in its ability to adapt to the specific characteristics of the data set, providing optimal partitioning and compression. This makes it possible to achieve a higher degree of stability with traditional arithmetic embossing methods while maintaining data quality. An additional advantage is the possibility of applying the method to various information types of data, which makes it a universal tool for optimizing the storage and transmission of large volumes.
Performance Analysis and Comparison of Knowledge Distillation Among Diverse Teacher and Student Models

With the advancements in deep learning, the development of large-scale models has become essential for many machine learning tasks in various fields such as computer vision and natural language. Such large-scale models demand extensive computational power and memory, posing challenges for practical deployment on mobile and edge devices. To address these issues, knowledge distillation, a technique for reproducing the performance of large-scale models with a smaller model, has been proposed. This paper investigates the effectiveness of knowledge distillation across various combinations of teach-student model pairs for image classification to deepen the understanding of knowledge distillation and the relationship between large- and small-scale models.
Dynamic Knowledge Enhanced Neural Fashion Trend Forecasting with Quantile Loss

Forecasting fashion trends based on time series and style analysis is an innovative and attractive approach. Still, the dynamic nature of fashion trends and the influence of various factors make this forecast challenging. Additionally, it’s important to note that previous research in this field has primarily focused on specific elements, such as seasonal clothes, which may not be suitable to the evolving requirements of the fashion industry. So, we extended the Knowledge Enhanced Recurrent Network. (KERN) model, which leverages deep recurrent neural networks for time series analysis to forecast fine-grained elements for a specific user group. Due to the low flexibility and the lack of management of sequences with variable memory lengths in the KERN method and the increasing improvement of memory the efficiency of this model, we propose a Dynamic Knowledge Enhanced Recurrent Network model (DKERN) along with the use of the Quantile loss function, which is an improvement on this method. We have tried to help the fashion industry by improving forecasting fashion trends by using various analytical techniques. The proposed approaches have shown improvements ranging between 0.2% to 8.2% compared to previous works in terms of periods, datasets, and different evaluation metrics.
Advanced Color Pseudo Anomaly to Enhance Learning of Convolutional Neural Network Models

Advanced Vision Inspection Systems (VIS) enable automation of anomaly detection in the production lines of smart factories. Incorporating a pseudo-anomaly insertion step in the training process allows a target VIS to effectively differentiate normal and abnormal examples for specific product. However, prior research has not extensively explored the fundamental nature of abnormalities in the process of product defect detection. In this work, we introduce a novel Color Anomaly Insertion (CAI) scheme that adds anomalies into specific images considering color of anomaly as a key attribute. CAI transforms the color of anomalies into dark/bright and challenging forms. Overall anomaly insertion in CAI is done in two steps: first, anomaly patterns are extracted, and then their colors are transformed into desired color by altering RGB channels.
Versatile-VTON: A Versatile Virtual Try-on Network

Virtual Try-on Network, an AI technology for virtual clothing try-ons in online stores, offers users an intuitive experience with lucrative commercial potential. Most of VTON researches have focused on single garments, and have limitation in application to trying on various types of clothing. This is an inevitable issue due to limited provision of only top images in the dataset. Although recent datasets used in VTON include various types of clothing images and labels, including tops, bottoms, and dresses, it is not sufficient to give usability of virtual try-on of clothing items other than tops compared to the available data. We introduce Versatile-VTON to give more usability and better quality of VTON. Also, we show the feasibility of the proposed method by presenting quantitative measurements and conducting ablation study experiments.
Brain Floating Point Precision for Object Detector

As the size of the deep neural network (DNN) model increases, the memory usage and computational time also increase. Deeper neural network models require huge memory size and high computational power. For edge devices, it is not feasible to run larger models with full precision. We present the usage of half precision data format which accelerates the computational time of DNN models and reduces the size of the final hardware while losing little accuracy.
Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention

Deep neural networks have made a revolution in different areas of Artificial Intelligence such as image classifi-cation and object detection. However, most of these models are heavy and time-consuming at the inference stage. If we desire to use real-time applications, it’s necessary to lighten them up. However, this could negatively affect the performance of models. One suggested approach for enhancing the capabilities of smaller models involves a technique called knowledge distillation. In this method, a less advanced model, referred to as the student model, learns from and emulates the more advanced teacher model to enhance its performance. This method has been proven as an effective technique to compress the model and increase its accuracy. Previous methods mostly focus on proposing feature transformation and loss functions between the same level’s features to improve the effectiveness. In this paper, we investigate the effect of connections between different layers of teacher and student networks and reveal their great importance. We have employed three different loss functions—attention-driven distillation, hierarchical context loss, and non-local loss—to enhance the classifier’s overall performance. Extensive experiments on the CIFAR-100 dataset show that the proposed method has a great performance improvement. For the benefit of the research community, we will make the code for this study available on GitHub.
A large input-space-margin approach for adversarial training

It is shown that machine learning models are vulnerable to adversarial attacks. Therefore, different defense methods such as adversarial training have been proposed to improve models’ robustness against these attacks. Some recent approaches proposed to structurally improve the robustness of the models. For example, large margin methods try to increase a margin, empty of instances, along decision boundaries that structurally increase necessary change to modify a training instance to an adversarial one. However, nonlinear large-margin models, maximize the margin in a high dimensional space although adversarial examples are generated with a little change in the original space. In this paper, a novel mixed approach is proposed, called LIM (Large Input Margin) to improve the robustness of the model by minimizing both structural and empirical risks. Specifically, both training and adversarial example generation are done based on a loss function to maximize the margin in the original feature space even in a non-linear model. The proposed method is evaluated with FGSM and PGD attacks on MNIST and CIFAR10 datasets. The experimental results show that LIM method outperforms the state-of-the-art defense methods significantly and improves adversarial robustness against FGSM and PGD attacks on both datasets.