Reviewerly/exHarmony huggingface dataset:
UavTinyDet: Tiny Object Detection in UAV Scenes

Due to the limited battery life, object detection using drones is often carried out with a large field of view and high flight altitude. In this case, the objects of UAV images tend to be tiny in size and are therefore not easy to be detected, which remains an open challenge. Although feature pyramid networks can improve this situation, it is still ineffective for predicting extremely small objects considering that information about tiny objects may have already vanished in feature maps. Moreover, current label assignment strategies of CNN-based detectors are usually not suitable for tiny objects due to their tiny size. In this paper, we propose a new method called Expanded Feature Pyramid (EFP) to take full advantage of the low-level information of the feature pyramid, which can better represent tiny objects with less information lost. Based on YOLOv5, to mitigate the deterioration of its performance by introducing many low-quality positive samples for tiny objects, we propose a novel prediction-aware loss for classification and localization, allowing the detector to select more high-quality positive samples which have higher IoU with ground truth objects and focus on training on them. Experiments on the VisDrone-DET dataset demonstrate our method achieves competitive performance among various object detectors. In addition, for the enrichment of VisDrone-DET, we construct a UAV dataset named UAV-OUC-DET with larger image size and abundant small objects and tiny objects. Experiments on the UAV-OUC-DET also demonstrate the effectiveness of our method.
A Face Morph Detection Method Based on Convolutional Neural Networks and Occlusion Test

Face morphing is by far the greatest threat to effective automatic boarder control systems. However, there is a lack of consideration and examination of a morphed face image frame as a blend of two images; hence, one image obstructs the other. To address this, we proposed a combination of an occlusion detection method, FSG-FD and a VGG19 neural network architecture to detect these face morphs. First, we designed our algorithms for each of our proposed systems; one for FSG-FD and the other for the VGG19 architecture. Then, we blended the two methods into a joined model by pre-training the neural network and increasing its convolutional layers by an additional three layers. The experiments showed that our algorithm achieved higher accuracy than other methods at detecting face morphs.
The Comparison of Image Enhancement Methods for A Realtime Under Water Vision System of The Break Water Implementation

The implementation of computer vision in the field of underwater image is currently very popular. However, Indonesia's vision system for breakwater work has not yet been implemented. The work of the vision system is still manual using divers [1]. In addition, the application of computer vision in underwater images has a big obstacle because the resulting image in an underwater environment has poor color and contrast. This poor color and contrast are due to a density nearly 1000 times denser than air. This density directly affects the light transmission, which affects the resulting image, which has less color and contrast than the image taken through the air. So it takes image processing such as Image Enhancement and Image Color Restoration [2]. Another issue is building a real-time system. If the system that is built is not real-time, this makes the possibility of data reading errors being significant, and the movement of information is slow [3]. This research presents methods regarding image enhancement and color restoration built in the Graphical User Interface (GUI). The methods applied in this research are Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Multi-Scale Retinex with Color Restoration (MSRCR). Those methods are applied to the images produced by the two cameras and run on the single board processor and laptop for the comparison. The result by using 71 underwater images shows that for Underwater Color Image Quality Evaluation (UCIQE) value of the Histogram Equalization (HE) Method yields 20%, meanwhile for Contrast Limited Adaptive Histogram Equalization (CLAHE) yields 8%, and the Multi-Scale Retinex with Color Restoration (MSRCR) method yields 72%.
Data Integrity Verification in Network Slicing using Oracles and Smart Contracts

The fifth-generation (5G) wireless networks are expected to provide various services compared to the 4G and previous generations of networks. The Quality of Service requirements can be quite different in terms of latency, bandwidth, reliability, and availability. 5G technology allows the fragmentation of the network into small pieces, known as network slices. This network slicing is done by specific tools and the configuration must be protected from attacks that may be performed by malicious users. Thus in this paper, a solution to protect and prevent these failures from happening is addressed. For this solution to be carried out, a study was conducted on the Blockchain technology, as well as the use of Oracles in order to implement an integrity verification system, a system capable of assuring 5G network slices' configuration integrity through a complete architecture involving Blockchain, Smart Contracts and Oracles.
Identification of the effect of different feature samples on Handwritten Japanese Character Recognition using YOLOv4

Handwritten Japanese character recognition has been one of the major tasks in the computer vision field. Previously, various approaches to distinguish characters had proposed, ranging from traditional machine learning, end-to-end model, images retrieval and object detection model. Most research focuses on model performances in ideal conditions with a similar feature distribution and produces an excellent result. In the character recognition task, one of the most efficient ways is to utilize an end-to-end object detection model to avoid sequences and robust against different feature distributions. YOLOv4 is one of the end-to-end object detection models that applies various concepts such as a bag of freebies and bag of specials to increase the robustness of the model by improving the context generalization. In this research, our target is to identify the effect of different feature distributions applied to ideal datasets using YOLOv4 on Japanese character recognition tasks. The result of classification reveals both F1-score and Map accuracy of mAP@50 in the ideal dataset achieve 99%. While in the combined dataset, the performance degradation is 12% lower compared to the ideal dataset. YOLOv4 backbone has proven to be robust generalize different feature distributions.
Crowd Counting in Public Places Using MultiScale Convolutional Neural Network

Crowd of people in public places is a serious problem that needs attention because uncontrolled crowd conditions will cause problems, especially with the Covidl9 pandemic which requires people not to congregate. This research uses the Multi Scale Convolutional Neural Network method to overcome the main problems in crowd images, namely object scale variations, difficulty distinguishing between people objects and the background, as well as overlapping between people objects. The Multi Scale CNN implementation in this research uses the feature extractor layer from VGG16 as the low level feature extractor layer (frontend layer) and the Inception-Restnet-A module from Inception-Resnet-v2 as the high level feature extractor (backend layer). The datasets used to train the model are the ShanghaiTech and UCF_QNRF datasets which already contain the location information of the people in the image. Prior to the training process, ground-truth was made by conducting a convolution process using a Gaussian filter at the point where people are. Then, the Multi Scale CNN model will be trained with these 2 datasets. In the trained model, the input image will be convoluted to produce a density map. The results of the crowd calculation are obtained by adding up all the density map values. The use of Multi Scale CNN is proven to provide a good accuracy value with the MAE loss value being 78.0 for the ShanghaiTech Part A dataset and 10.75 for the ShanghaiTech Part B dataset.
Setchain: Improving Blockchain Scalability with Byzantine Distributed Sets and Barriers

Blockchain technologies are facing a scalability challenge, which must be overcome to guarantee a wider adoption of the technology. This scalability issue is due to the use of consensus algorithms to guarantee the total order of the chain of blocks (and of the transactions within each block). However, total order is often overkilling, since important advanced applications of smart-contracts do not require a total order among all operations. A much higher scalability can potentially be achieved if a more relaxed order (instead of a total order) can be exploited. In this paper, we propose a distributed concurrent data type, called Setchain, which improves scalability significantly. A Setchain implements a grow-only set object whose elements are not ordered, unlike conventional blockchain operations. When convenient, the Setchain allows forcing a synchronization barrier that assigns permanently an epoch number to a subset of the latest elements added. Therefore, two operations in the same epoch are not ordered, while two operations in different epochs are ordered by their respective epoch number. We present different Byzantine-tolerant implementations of Setchain, prove their correctness and report on an empirical evaluation of a prototype implementation. Our results show that Setchain is orders of magnitude faster than consensus-based ledgers, since it implements grow-only sets with epoch synchronization instead of total order. Moreover, since the Setchain barriers can be synchronized with the underlying blockchain, Setchain objects can be used as a sidechain to implement many smart contract solutions with much faster operations than on basic blockchains.
Personal Community-member Information Management System with Facial Identification Functions

This paper presents an implementation method of a system to manage “personal” community-members' information with an individual face recognition function for the purpose of judging a person’s name and related information. Recently, it is pointed that there are not a few people who cannot identify the face of his/her acquaintance (a.k.a. face-blindness, prosopagnosia). Even if you do not have a face-blindness symptom, you may have an experience when you happen to meet someone and you cannot recall his/her name though you recognize the face of him/her. In this research, a design and an implementation method for a system is presented, which calculates the similarity between the input face photo and a user’s personal photo database and indicates the name and related information of the identified person to the user. The feature of this research is that it emphasizes the protection of privacy such as personal relationships, and the database to be referred to is constructed as a time series for each individual. The system is implemented by OpenCV for the purpose of implementing the goal of storing photos and related information on a person-by-person basis. To show the feasibility of the system, we conducted several preliminary experiments to identify the input image’s person from a user’s personal photo database of famous people.
Improving Maintainability in GraphQL Authorization using Dynamic Authorization

GraphQL is the new way to build an API that Facebook has developed. The rising trend of GraphQL has intrigued developers as it solves the REST API problems like under-fetching and over-fetching. Still, at the same time, it also brings additional security issues like authorization. GraphQL authorization is one of the most challenging and hot issues. As a state-of-the-art method to solve GraphQL Authorization uses hard-coded authorization rules, it increases the burden for a developer to develop in a maintainable way. This paper proposes a platform that alleviates the authorization problem in GraphQL in a maintainable way. This platform will showcase how to solve this problem in a microservices system. Authorization services will decouple the application, increasing the separation of concern between the application and security contexts. The experiment on the proposed system tested with an identical system shows that dynamic authorization will increase software maintainability, proved by the class coupling decreasing by 21%. Although the proposed design uses GraphQL technology, the approach is helpful for any distributed computing technology.
Congestion Monitoring System Using Infrared Sensor

Social-distancing is one of the measurements to prevent and reduce COVID-19 infection risk. An easy and effective way to keep a distance in a confined space is to limit the number of people in that area at a time. In this paper, we develop a congestion monitoring system using an existing motion sensor to indicate the occupation in the recreation area where a number of students will get together during break time. For the first phase, an infrared sensor, HC-SR501, and Raspberry Pis have been manipulated to collect motion data in order to generate graph visualization together with a heat map of the density of congestion. In the next phase, video images will also be used to identify the occupancy for higher accuracy.
Integrated Ticketing Website for Cultural Events in Covid-19 Endemic Transition Era Using Heuristic Evaluation Method

During this endemic transition era of the Covid-19 as today, all activities to do must still comply health protocols as an effort to prevent the return of high cases of Covid-19. By allowing the return of offline cultural events, it is hoped that it will help the welfare of cultural actors after experiencing difficulties during the Covid-19 pandemic. Innovation is required to develop a system for managing events without using any physical contact (contactless). One type of technology that can be used for is QR code which is used to access the venue for cultural events. There are several important aspects related to the usability of UI/UX design in making a digital system. The purpose of this paper is to develop the "Dewishinta" system as a solution for managing cultural events that are effective, environmentally friendly, and real time using an integrated ticketing system. The method chosen to be used in the usability testing of this system is Heuristic Evaluation with ten usability aspects and a total of 24 evaluation points which are tested to get severity rating values. As a final result of the test, there are 13 usability problems finding in the cosmetic problem category which means it do not really need improvements because not affect the comfort of use with an average usability validation value of 92%. For target market validation data, an average validation of 98.25% from 200 user testing respondents state the feasibility of implementing the system as a solution to existing problems.
Movies Analysis on DBpedia and Wikidata Using Community Detection and Centrality Algorithms

The entertainment industry, especially movies, has a wide spectrum of markets. The popularity of the movie can be seen from several aspects, such as actors who play, genres, directors, producers, and opinions. This research aims to conduct movie analysis using community detection and centrality algorithms. The dataset used was taken from DBpedia and Wikidata using Federated SPARQL Query. We detect the community using the Speaker-Listener Label Propagation Algorithm (SLPA). In addition, the study observes the dataset using two centrality algorithms, Closeness and Betweenness. The findings showed that Lincoln is the most popular movie. This movie belongs to the genre of found footage, thriller movie, horror movie, science fiction movie, speculative fiction movie, and pseudo-documentary. The famous actor is Desmond Llewelyn, who played in popular film.
Automated Library System Mobile Robot using A-Star Algorithms

Currently, libraries have realized many aspects of information technology such as book purchases, book collections, RFID, databases and other technologies that improve library management and service levels. One of the technologies used is library robots. Library robots are the integration of automation and information in daily library applications, which can increase the level of library automation. One of the automation systems that need to be applied in the library management system is a robotics system that can arrange books into bookshelf automatically in order to facilitate an automatic and structured book return system. An example is in a library where books must be arranged according to category or title, so that readers can easily find books. But if someone has finished reading a book, sometimes someone forget to put the book back in its place. Placing books in the wrong place can make it difficult for someone to find the book, this will also increase the work of the librarian. For this reason, library management system need an automated mobile robot system to solve the above problem using RFID to recognize book title and A-Star Algorithm to mobile robot move. The a-star algorithm will be used to calculate the closest path for the mobile robot when finding the location of the bookshelf to place books, so that it will shorten the time the mobile robot returns book to the bookshelf. Based on the results of the A-Star algorithm calculation in the case of finding the closest route in the library area, get accurate results and get the closest path from start to finish by considering the final result of the calculation f(n) = g(n) + h(n). Therefore, the A-Star algorithm is recommended to find the closest route calculation which in this case is used to find the fastest route in returning books to the shelves in the library.
Javanese Script Text Image Recognition Using Convolutional Neural Networks

Javanese script is one of the traditional Indonesian scripts used on the island of Java, which is currently starting to decline in its usages due to the difficulty in learning the many, complex, and similar forms between characters. This can have an impact on the preservation of the declining Javanese script culture, which if left ignored will cause the loss of one of the characteristics of Indonesia. This study aims to overcome these problems by creating an application that can work well in recognizing images of Javanese script text. The method used in this study is Convolutional Neural Network (CNN) with a new approach to transfer learning using the pre-trained model ResNeXt. The dataset used for model training stage is obtained by merging Javanese script images dataset publicly available on Kaggle website with manually collected images and by applying image augmentation techniques on the collected dataset. The recognition process used in this application is based on Optical Character Recognition (OCR) with preprocessing, segmentation, feature extraction and classification, and post-processing stages. The best CNN model testing results were obtained in an experiment with a combination of using Adam optimizer, learning rate of 0.0001 without a scheduler, and freezing first 4 layers of the 10-layer CNN ResNeXt model, with a testing accuracy value of 98.19% Meanwhile the OCR processes in the mobile application are tested on 11 images captured from Javanese Pepak book achieving an average character error rate of 38.09%.
Flood Vulnerability Mapping in Lamongan District, Indonesia, using Fuzzy Analytical Hierarchy Process - Natural Breaks

Lamongan is one of the districts in East Java Province, Indonesia, which is often hit by floods, as evidenced by the occurrence of floods yearly. The occurrence of floods in Lamongan itself has an impact on hampering residents' activities and can cause material losses and other aspects such as health, economy, and the environment. Flood risk assessment studies are essential for identifying critical or high-risk zones. It is helpful as a flood disaster preparedness and decision support system for the government and the community. This research proposes flood vulnerability mapping in Lamongan based on multi-criteria using the Fuzzy Analytical Hierarchy Process (FAHP) – Natural Breaks method. The multi-criteria used consisted of population density, availability of pumps, Bengawan Solo river level, elevation, history of flooding, and rainfall. The weight of the FAHP results will be classified using a natural breaks algorithm, producing a low, medium, and high flood susceptibility index in 27 sub-districts. This system is built on a mobile device to display the vulnerability map spatially. Based on the GVF calculation, the FAHP-Natural breaks of 0.75 have a better priority and grouping value than the AHP-Natural breaks of 0.69.
Thermal Face Recognition: A Comprehensive Review Of Databases And Methodologies

Face identification is the task of recognizing a human face using a digital system, typically for security purposes. Because the face is such an important feature of the human body, it can be used to individually identify a person. For various applications, concentrating the face for verification or identification is critical. Despite recent breakthroughs in face recognition systems, they continue to have major issues due to the wide range of variations in the person’s face (changes like glasses, head tilt, light, and different emotional modes). Each of these issues has the potential to drastically affect the accuracy of face recognition. This research examines the performance of thermal face detection approaches that were described in the existing literature within last seven years (2016–2022). A comparative table of the works covered is also included. The comparative analysis given PUCV-VTF face database that proposed StyleCLIP, GAN structure achieves a higher detection rate compared with other traditional detection such as DTFA, LBP and moments invariant. Nonetheless, using thermal imaging as an effective and distinctive option has gained greater attention among the proposed options in recent years.
Design of Intelligent Information Model for Modern and Contemporary Transformation of Chinese Literature based on Big Semantic Mining Algorithm

With the development of global economic integration, all countries in the world have set off a wave of "cultural research", and my country, as a cultural power, naturally research work is the top priority. The development of the concept of "cultural studies" has brought a certain impact on the cultures of all countries in the world, which is both an opportunity and a challenge for the development of modern and contemporary literature in my country. The new changes in the style include the renewal of stylistic concepts, the creation of various styles (styles, systems) of new literature, and the refresh of the style of new literature, all of which can be demonstrated in the practice of style change by Vietnamese new literature writers.
The Problems and Countermeasures of Educational Information Management in Colleges under the Background of Smart Campus Technology

Educational informatization in colleges and universities has become one of the main issues in the reform of education work. At the stage when the country is promoting the development of educational informatization, the construction of informatization in colleges and universities has received extensive attention. The focus of the construction of educational informatization in colleges and universities is to optimize and reform education and teaching by means of informatization technology, so as to cultivate innovative, applied and comprehensive talents that are more in line with the needs of current social development. This article analyzes the concepts, problems, and corresponding strategies of college education informatization to ensure the effective development of college education informatization.