Reviewerly/exHarmony huggingface dataset:
Natural Language Processing using Federated Learning: A Structured Literature Review

Federated learning (FL) addresses privacy concerns and data distribution challenges in machine learning, as it enables decentralized training on local devices without the need to share raw data. This can be particularly relevant for cross-company collaboration, where data privacy is paramount and shared learning can bring mutual benefits. Since text data is highly sensitive in many real-world use cases, there is great benefit in combining FL with natural language processing (NLP). Given the increasing amount of locally generated text data and the tightening of privacy requirements, this paper aims to analyze previous research on the use of federated learning for NLP. To this end, we conducted a structured literature review based on 76 publications to determine the NLP tasks that were addressed using FL, the types of text data and machine learning approaches used, and the setting of the FL-architectures. In this way, we have shown the current state of the art, identified potential challenges, and highlighted possible research directions.
Adversarial Spam Generation Using Adaptive Gradient-Based Word Embedding Perturbations

In recent years, artificial intelligence (AI) and machine learning (ML) have become extremely promising in almost every aspect of our lives, including in cybersecurity. For instance, intrusion detection systems (IDS) and spam filters use machine learning algorithms to constantly monitor networks for abnormal behavior. However, the security of AI/ML-based solutions remains largely unknown and a cause for concerns. This study examines the possibility of cheating AI/ML-based cybersecurity solutions such as spam filters. In particular, we developed an Adaptive Gradient-based Word Embedding Perturbations (AG-WEP) framework for automatically generating adversarial spam examples. AGWEP smartly chooses the optimal perturbations across all features of the word vectors to minimize the degree of modifications to real spam messages. The experimental study suggests the adversarial model is effective to generate meaningful adversarial examples to fool a CNN-based spam classifier.
Platform Construction to Protect Portraits of Others

As the times change, online video activities and video sharing have become commonplace. However, publicly sharing the images of others can involve risks of violating privacy and portrait rights, especially in today's era of widespread high-resolution cameras that can capture detailed facial features. Therefore, protecting the portrait rights of others has become crucial. In order to balance the protection of portrait and the preservation of image quality, we propose a deep learning-based person tracking system. This system can record and track the time and location of people in videos or video streams. Through image synthesis techniques, we can either place the necessary people in virtual environments or hide people in real videos. In this way, we ensure the portrait and privacy of others while maintaining the authenticity and quality of the image. This approach is not only applicable for individuals protecting their own images but can also be applied to respect the privacy of others, making online video activities safer and more respectful of people rights.
A Blockchain Model in the Hospitality Industry to Increase Traceability and Transparency of Transactions

The hotel industry is a service industry that involves many parties to provide satisfactory service to hotel customers. Customers find it difficult to ensure all the services they receive are worth the money they incur. In the current hospitality system, customers cannot track the food ingredients they consume according to the standards they pay for, hotel customers cannot know hotel facilities, and the security system is supported by quality equipment. The above difficulties can be solved with a transparent supply chain system. Blockchain technology, with its advantages, has the potential to be adopted in the supply chain of hotel products to make it more transparent and easier to track. Supply chain management is greatly aided by blockchain technology, which is a distributed database that permits safe, transparent, and tamper-resistant record keeping from the point of origination of the items to the point of consumption by the hotel guest. We propose a model to transform hotel industry supply chain business processes into transparent and accountable workflows. In addition, an application was developed to enable every stakeholder in the supply chain to contribute to data collection and monitor the status of each stage in the industrial hotel supply chain. The research method is divided into three stages, namely understanding the hotel industry's business processes, model construction, and application prototyping development. The experimental results show several obstacles, ranging from technological readiness to the standardization of systems by hotel stakeholders that support the implementation of blockchain technology.
Functional Correctness Analysis for Judiciary Office’s Case Administration Application using PHPUnit

Information technology is growing rapidly in the current administration of government to become one of the benchmarks for the realization of e-government. In accordance with presidential instruction number 3 of 2003 and law number 14 of 2008 concerning public information disclosure, the use of information technology is a national priority in government. However, in practice there are still many obstacles regarding software quality testing procedures used in government. The aim of the study was to evaluate the quality of the software for the administrative management information system for the stages of case handling through measurement using functional correctness with an assessment of 1 aspect and 23 methods contained in the application as a testing medium. The research method used is ISO/IEC 25023/2016. The results showed that the application obtained the highest score of 1 which means no errors were found in the code and the lowest was 0.5 with several errors in the code found. From these results obtained recommendations on the Functional Suitability aspect so that it can be used to evaluate software.
Enhancing Federated Learning Efficiency with Generative Model-Based Data Augmentation for Non-IID Data

With the wide application of machine learning, data privacy has been widely concerned. As a distributed machine learning method, federated learning has the characteristic of protecting user data privacy. However, the data between clients is often non-independent and identically distributed (non-iid), which leads to the efficiency of federated learning is greatly reduced. In this paper, we propose a data argumentation method using generative models to solve the problem of data non-iid problem by synthesizing high-quality data at the client. Specifically, each client pre-trains generative models locally according to local data, and then the client synthesizes the missing data according to the distribution of the local data, that is, performs local data argumentation. We take the original non-iid distribution and the normal data argumentation methods in machine learning as two control groups. Multiple experiments show that the data argumentation method using generative model improves the performance of federated learning to some extent.
Down to Earth: Investigating Barefooted Experiences in Seated Virtual Reality Scenarios

Including tactile feet perception in the experience of virtual worlds has received little attention. Examining this aspect could be particularly useful in seated virtual reality (VR), where users are limited in their ability to get in contact with the virtual world due to spatial constraints. This paper investigates whether tactile feet perception increases perceived presence and physical awareness in seated VR scenarios. Specifically, we aimed to examine whether barefoot explorations create a superior experience compared to settings where shoes are worn. Therefore, a comparative study was set up (N=20) consisting of two virtual scenarios (forest/beach) and three different conditions (barefoot, shoes, reference condition). Results showed that body perception and perceived presence were significantly higher in barefoot conditions compared to shoe conditions. Some aspects of perceived presence were influenced by the type of virtual scenario (forest vs. beach). This can be explained through the nature of the employed scenarios and their visual presentations. The knowledge gained by the results of the current study helps to extend different use cases for seated VR experiences (environments with little space, people with physical limitations) with new design elements.
The Design Methods of Augmented Reality in a Historical Site

Augmented reality (AR) technology has the potential to enhance the visitor experience at historical and cultural sites by providing rich, interactive information about the site's history and culture. In our project to develop an AR guide app for a historical and cultural park, we identified three key design methods for creating engaging and informative AR content. The first method is the use of small interactive 3D models, such as 3D maps of a historical site at different points in time, which can help visitors understand the evolution of the site over the years. The second approach is the overlay method, which involves superimposing images of historical objects or buildings onto the present-day environment to give visitors a sense of what the site looked like in the past. The third method is the replacement of current scenes with historical ones, allowing visitors to experience the appearance and location of historical buildings as they once were. These design methods can serve as a useful reference for creating AR guides at other historical and cultural sites around the world. As AR technology continues to advance, it has the potential to revolutionize the way we learn about and engage with history and culture.
Some Thoughts on Using Artificial Intelligence to Improve Data Quality

With the rapid development of artificial intelligence, massive multi-modal data continues to accumulate and precipitate, how to efficiently use rich data resources, improve data quality, and enhance data empowerment has become a key and challenging issue facing all fields. This paper aims to improve the data quality by using artificial intelligence to study the abnormal data processing methods in the field of communication network. The research focuses on how to locate data outliers and identify noisy data to provide protection for multi-source data aggregation; On the basis of obtaining the location of the missing value of the data, how to accurately and automatically fill the necessary data to maintain data integrity; How to accurately calibrate noisy data on the basis of knowing that the data deviates from the normal state to ensure that the abnormal data is handled correctly.
Research on Anomaly Detection Method for the Executive Motor of Six-Rotor Unmanned Aerial Vehicle

In order to achieve real-time anomaly detection for the motors of multi-rotor UAV, this study proposes a anomaly detection method based on improved multivariate state estimation technique. The anomaly detection method proposed in this study includes two sections: offline and online. The offline section includes data preprocessing, memory matrix (MM) construction, and anomaly detection threshold calculation. The online section includes obtaining estimated states based on improved multivariate state estimation technique and anomaly detection based on residuals. By analyzing the flight logs of DJI Matrice600 and collecting monitoring data of the executive motor, a training set, a validation set, and a testing set were obtained. The effectiveness of the improved method was verified based on the validation set, and the results showed that estimated states obtained by the improved multivariate state estimation technique is closer to the actual states compared to traditional methods. Based on the test set, the anomaly detection method proposed in this study was tested. The experimental results showed that the proposed anomaly detection method based on improved multivariate state estimation technique can detect the operating status of the six-rotor unmanned aerial vehicle’s executive motor. Compared with the actual fault information recorded by the unmanned aerial vehicle’s executive motor, abnormal states can be detected 22 time points in advance. Compared to traditional anomaly detection method, the anomaly detection method proposed in this study can effectively reduce the error detection rate and improve the detection efficiency by 10%.
Empowering Web 3.0: Data Privacy Solutions for the Internet of Things

A central part of the evolution of the Internet, web 3.0, is the Internet of Things. The Internet of Things works to improve the quality of life of its users by making everything we interact with smarter. However, with these improvements comes increased risk as more private data becomes available. This paper discusses the major challenges of keeping IoT users’ data private and the emerging solutions to these issues, such as blockchain, trust managers, and legislation.
Blockchain-based Data Sharing Model for Assessment of Lubangsewu Erorejo Tourism Destinations

In 2023, the population of Erorejo, Indonesia, will be 981 people, of which 105 heads of families work as red tilapia (Oreochromis niloticus) freshwater fishermen and tour operators in Lubangsewu. After the COVID-19 pandemic, regional problems emerged, namely, the marketing of freshwater fish decreased by 30-35%, and the number of visitors to Lubangsewu decreased by around 40%. To increase the number of visitors, we developed an initial evaluation model for the Lubangsewu tourist destination, which is easily accessible to all users with guaranteed data security. This research aims to create a decentralized data-sharing model in a blockchain-based distributed network to evaluate the Lubangsewu tourist destination. We used the Hyperledger fabric platform using ten nodes; Then, we tested the blockchain process and proof of work performed to determine that the data-sharing process was successful and that the hash value obtained from the data identification could meet the set targets. The results showed that testing the chaining block process on localhost-based data-sharing assessments and peer-to-peer devices succeeded in forming interconnected chaining blocks. The target in this study is the prefix hash value digit 0. When trying to prove work done on a localhost basis, the seven hash values met the proof of work target, and the average time taken was 67.9 m/s. Meanwhile, eight hash values were obtained in the peer-to-peer device performance proof test, which met the proof of work target with an average longer time of 321.4 m/s so that this model can increase the number of visitors.
A Multi-constraint Hybrid Network for Ultrasound Image Synthesis

Simulating ultrasound (US) through US image synthesis is crucial for preoperative training and learning ultrasound techniques for doctors. Due to the significant modality differences between magnetic resonance (MR) images and US images, the structure of the synthesized US images tends to be blurry and of lower quality. In this paper, we propose a hybrid network combining CNN and Transformer that enhances the synthesis performance by integrating their respective abilities in local and global feature extraction. We also introduce multiple constraints to improve the quality of the synthesized images. Firstly, we introduce a multi-scale perceptor (MSP) to enhance the structural content information of the synthesized images, thereby improving their authenticity. Additionally, we address the blurriness issue of synthesized images by incorporating a gradient difference loss in the loss function, which enhances the structural edge information and further improves the quality of synthesized images. Experimental results demonstrate that our proposed method achieves a PSNR of 14.7251 and an SSIM of 0.8556, outperforming other advanced methods currently available.
Adaptive Video Streaming: An AI-Driven Approach Leveraging Cloud and Edge Computing

Video streaming efficiency remains a major challenge, with increasing demands for high-resolution content and minimal buffering times. We propose a novel solution to enhance user experiences. Our approach combines adaptive streaming, AI algorithms predicting network conditions, and edge computing to optimize task allocation. These technologies aim to reduce latency, lower network traffic, and facilitate seamless transitions in video quality. Our results improve the streaming services and enable real-time video applications in remote or mobile environments, presenting substantial implications for the future of video streaming.
Modeling a Single Camera System based on Predicted Bounding Box and Pan-Tilt Angles

The control schemes that are related to visual servoing systems are often done through trial and error. Based on the classical control concept, it is necessary to have a mathematical model of the plant before designing the controller. Therefore, this research focuses on producing a mathematical model of the pan-tilt system. We propose the combination of the computer vision method with the classic system identification method, namely N4SID to obtain the dynamics model of a pan-tilt system. In this research, the system identification procedure was successful in producing a state-space model of a visual servoing system using a pan-tilt system and a single camera. This study merged traditional system identification techniques with computer vision to create a discrete-time linear state-space model. When the system order value equals three, the approximation with the minimum MSE value is obtained.
Single Line Electrical Drawings (SLED): A Multiclass Dataset Benchmarked by Deep Neural Networks

Single-line drawings have diverse applications across various industries, including electrical substations, buildings, power distribution, maintenance, and more. Analyzing and interpreting these diagrams using deep neural networks requires the creation of large datasets, which poses a challenging task. The complexity of these diagrams, combined with the limited availability of publicly accessible datasets, makes dataset creation even more difficult. The quality of the dataset significantly impacts the classification accuracy, as weaker datasets can negatively affect the overall performance of the model. To address these challenges, we introduce Single Line Electrical Diagrams (SLED), a multiclass dataset consisting of 3,078 instances of engineering symbols. These symbols are extracted from intricate technical drawings known as Single Line Diagrams (SLDs). The SLED dataset is meticulously curated by annotating and pre-processing appropriate images to represent various symbol classes present in the diagrams. In this article, we benchmark the SLED dataset using a variant of the You Only Look Once (YOLO) algorithm, specifically YOLO v5, for symbol classification. The results of image classification on this newly generated dataset are promising. However, further improvement can be achieved by addressing class distribution imbalances within the dataset. By making this dataset available to the academic community, we aim to enhance the understanding of the field and shed light on an important yet neglected problem within the industry. We provide a comprehensive analysis of the dataset’s characteristics and demonstrate the performance of deep learning models on recently created datasets. Our conclusions offer insights into the potential directions for future research in this domain.
USB 3.0 Based Heterogeneous Data High-Speed Transfer Subsystem

Development of a high-speed data transfer subsystem to connect a computer and an external equipment is very relevant for a wide variety of areas - industry, medicine, scientific research and many others. At first glance, modern high-speed interfaces provide all the possibilities to complete the task. In practice, developers face a number of difficulties that prevent the full use of high bandwidth, for example, of USB 3.0 interface. The reasons for these difficulties lie both in the lack of information about the practical experience of using these interfaces, and in the well-known technical complexity of high-speed interfaces implementation. The article presents a method for developing a subsystem that provides high-speed transfer of heterogeneous data using the example of a large-format digital inkjet printer. The development results are formulated, information on the use of the subsystem in commercial products is given.
Entangling capacity of operators

Abstract Given a unitary operator U acting on a composite quantum system what is the entangling capacity of U ? This question is investigated using a geometric approach. The entangling capacity, defined via metrics on the unitary groups, leads to a minimax problem. The dual, a maximin problem, is investigated in parallel and yields some familiar entanglement measures. A class of entangling operators, called generalized control operators is defined. The entangling capacities and other properties for this class of operators is studied.
An Evaluation of Number of Pixels Change Rate (NPCR) in Symetric Cryptography Based on Data Encryption Standard (DES)

Data Encryption Standard (DES) is a symmetric encryption algorithm that uses a single key to encrypt and decrypt information. In addition, the cipher text will be hidden inside an image using Stepic. DES Encryption use symmetric encryption to encrypt and decryption to decrypt. It will secure the symmetric encryption and maintain the speed of the encryption and decryption. The result of this study is the lowest error that we got as the Mean Square Error (MSE) is 0.00% and is inversely proportional with the Peak Signal to Noise Ratio (PSNR) and Avalanche with 79.36% and 34.91% in order that is not stable for result of Avalanche Effect (AVA). Inversely proportional to the length of the text that is hidden in the image, the longest text that is hidden, the more changes that we get in the image with the highest Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) with the biggest image size with 43.47% for UACI and 99.87% for the NPCR.
Forecasting the Impact Of The News On Cryptocurrency via Machine Learning Algorithms

In this study, it was investigated which of the machine learning algorithms gave the most optimal results in determining the effect of news about cryptocurrencies on the prediction of Bitcoin (BTC) price. In this direction, three different data sets were created, and these data were tested separately in each machine learning algorithm examined. In this way, it is aimed to determine the effect of the data set on the success of machine learning algorithms. By doing web scraping with the script, we developed to determine the news data we used in the study, 684 news made by BBC, CNBC, Fox, Guardian, NY Post, Over News, Sun, USA Today, and VOA news sources were found and by sentiment analysis of them, 564 news related to cryptocurrencies were taken into account in the study. March 2020 and March 2021 were chosen as the dates of the selected news. (The period when the BTC price rose from 9Ks to 30Ks.). The reason for choosing a past time period is to precisely measure the consistency of the predictions made. While creating the data sets used in machine learning algorithms, high, low, volume, open, and close values in the dollar, Ethereum, and gold values were also taken into account, as well as news sources. In this way, it is aimed to make more realistic estimations by taking market data into account. Machine learning methods in the study; Decision tree, deep learning, random forest, support vector machine, gradient boosted trees, and a generalized linear model was used. The generalized linear model (model II), which gives the most successful result with a rate of 98.73%, is explained in detail in the study.