Reviewerly/exHarmony huggingface dataset:
Multi-label Comment Classification Using GloVe-RNN Framework

Multi-label comment classification using GloVe embedding is addressed using deep neural networks in this paper. Online web media have opened a platform for users to express their thoughts on various contemporary issues and set no limit for their opinions. Unfortunately, the freedom of expression often leads to the usage of explicit language, which may hurt the readers. GloVe embedding vectors are computed in the front-end. Long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent neural network (GRU) models have been used for the classification phase. The performance is evaluated on the Kaggle comment classification dataset. The evaluation of results shows that recurrent neural network-based models outperform convolutional neural network-based models.
Analysis of Subword based Word Representations Case Study: Fasttext Malayalam

Representation learning has played a crucial role in various natural language processing tasks since the advent of deep neural networks. Recent research shows that word representation learnings perform more accurately by incorporating subword information. With increased parameter sharing, subword-based representations learn more semantic and syntactic knowledge, thus gaining the ability to generate reliable representations for rare and out-of-vocabulary words. This paper reviews some popular techniques involved in subword-based representations and the publicly available pre-trained subword embedding models for Indian languages. The greater morphological richness and higher inflections make Indian languages differ from other languages commonly studied in NLP. Selected fastText models in Malayalam are analyzed in-depth in this paper. Our experiments show that intrinsic and extrinsic evaluations of some of the currently available models produce low accuracy for Malayalam compared to high-resource languages like English. It is hypothesized that the data quality, morphological richness, and higher inflections play a significant role in the low accuracy.
Construction and Application of Knowledge Graph for railway rolling stock failure tree

In railway companies, the development of trains and technologies are constantly evolving, and trains have become under high surveillance, thanks to special equipment that prevents incidents on the track. In parallel, the data have grown rapidly, and obtaining information and knowledge from this data can be difficult. Therefore, in this paper we propose an approach for building a knowledge graph for the failure tree of railway rolling stock. First, extracting data. Second, using TigerGRaph, a knowledge graph is constructed. Then, we use the GSQL for making queries and reasoning of the knowledge graph. Finally, the knowledge graph is used to share knowledge and build a corporate knowledge base
How CMO are reacting to Blockchain, NFT and Metaverse: the case of SIAE - Italian Society of Authors and Editors

The music industry in the last years has suffered numerous changes due to the role that technology has in structuring the music ecosystem, these new technologies so as different spaces and opportunities for consumption increase the challenges that organizations have in protecting intellectual property - IP. The study adopting the case study method aims to discover the concrete impact that digital ledger technologies such as blockchain, NFT, and new space as Metaverse(S) could have in defense of IP and on Collective Management Organizations - CMO. The study evidence as the new technologies offers new weapons to CMO but lead reflections on the sustainability of their business model and their skills in knowledge management and innovation.
Optimal Power Control for Over-The-Air Federated Edge Learning Using Statistical Channel Knowledge

Over-the-air federated edge learning (OTA-FEEL) is an efficient distributed machine learning framework in terms of radio resource requirements. Delicate power control is needed to combat the distortion of the local models caused by channel fading. This paper develops the optimal power control policy under fading channels, to minimize the optimality gap of OTA-FEEL by exploiting the statistical channel state information (CSI), i.e., the mean and variance of the fading. We reveal that the optimal power control policy takes a form where the variance of the effective channel between the server and devices should be minimized when its mean is given. Based on this structure, we propose to iteratively minimize the variance using the Lagrange-duality method and then optimize the mean of the effective channels using a one-dimensional search. A closed-form expression for the optimal power control is derived. Simulations confirm the benefit of the use of the statistical CSI, and the superiority of the proposed optimal power control policy to the existing approaches in the convergence of OTA-FEEL.
Prediction of Survival COVID-19 Patients by Using Backpropagation Neural Network Algorithm

COVID-19 is a disease caused by a virus and increasing in cases every day. This is because the large number of patients makes it difficult to be treated at the hospital. This is behind the need for survival prediction of COVID-19 patients within 48 days so that the medical team can prioritize patients who are predicted to not survive on that period. In this research, the firefly algorithm is used which aims to select attributes and will perform comparisons for data that is balance or imbalance and combined with data that do feature selection and does not feature selection. The data that will be used are age, asthma, diabetes, gender, COPD, pregnancy, hypertension, obesity, ICU, chronic kidney disease, smoking, heart disease, immune deficiency, pneumonia, and other medical history. In this research, the selected attributes were gender, type of patient, intubation, pneumonia, age, pregnancy, diabetes, COPD (Chronic Obstructive Pulmonary Disease), asthma, hypertension, other diseases, obesity, chronic kidney disease, smokers, contact with COVID patients, and ICU. The prediction model with the highest level of performance is a model with balanced data with a recall value of 0.79, then a precision value of 0.93, then an f score of 0.85, then an accuracy value of 0.86, then a specificity 0,93, then a NPV 0,82 and a geometric mean value of 0.87
Twitter Data Warehouse and Business Intelligence Using Dimensional Model and Data Mining

Sentiment analysis is used to analyze data in text format such as tweets from Twitter social media users. Twitter is one of the most popular social media with more than half a billion users and can generate large volumes of data. It is difficult to operate large-scale data, so the data warehouse can be used as a data storage area that allows the operation of large-scale data. The final step of data warehousing is the application of business intelligence. This research uses dimensional model approach to build data warehouse from Twitter and uses lexicon-based approach to analyze public opinion of Twitter users toward covid-19 vaccine in Indonesia. The results show that the creation of a data warehouse and sentiment analysis have been successfully carried out based on the evaluation of the data warehouse and sentiment analysis. The evaluation carried out on the data warehouse is to examine the components of the dimensional model based on indicators and dimensional modeling rules by Kimball. While the evaluation carried out on sentiment analysis is the confusion matrix with the result of the accuracy of sentiment analysis is 74%.
Fast and Accurate Detection of UAV Objects Based on Mobile-Yolo Network

With the development and popularization of unmanned aerial vehicle (UAV) technology, the UAV devices have been widely used in practice. Aiming at the problems of low accuracy and slow speed in detecting UAV objects, this paper constructs a UAV object dataset and proposes an efficient UAV object detection method based on Mobile-YOLO Network (MYN). Firstly, a UAV data set was constructed, including 3,698 UAV images, in which the proportion of large, medium and small-scale objects was about 3:1:1, providing a data basis for algorithm research and experimental verification. Secondly, we construct a Mobile-YOLO network model for UAV object detection based on YOLOv4, enhancing the detection speed to 51FPS under the premise of high precision. The results show that the Mobile-YOLO network has fewer parameters, faster operation speed and better comprehensive performance.
Deepfake Video Detection Using Spatiotemporal Convolutional Network and Photo Response Non Uniformity

It is important to improve the detection of deepfake videos to differentiate between real and fake videos that cause disinformation in the digital age so that a high level of accuracy is required. The purpose of deepfake video detection is to aid digital content consumers to surmount disinformation and sever real videos from fake ones. Limited by the number and quality of datasets, the time required for detection, and consistent performance evaluation i.e., the detection model cannot detect videos detected with video editing tools. This study provides a solution to this problem by using the Spatiotemporal Convolutional Network (SCN) method and Photo-Response Non-Uniformity (PRNU) analysis. The dataset used will go through pre-processing stages, extract per-frame video, detect face parts, and face cropping. Then the data is spread and modeled using RestNext50 and LSTM. This study produced 10 models using the FaceForensic, CelebDF, and DFDC datasets, and a mixture of these datasets which can then be used to analyze deepfake videos. The test results show that the deepfake detection process is faster and more accurate with an accuracy rate of up to 97.89%.
Performance Comparison of YOLOv4 and YOLOv4-Tiny Algorithm for Object Detection on Wheeled Soccer Robot

This paper aims to describe performance comparison of YOLOv4 with YOLOv4-tiny for object detection on wheeled soccer robot using omnidirectional camera. Wheeled soccer robot mainly detects the ball, the goal, opponents, and its surrounding. Omnidirectional mirror is installed perpendicular to camera position such that the surrounding area which reflected on mirror captured by camera. Captured images are then process following YOLOv4 and YOLOv4-tiny algorithm scheme in order to detect founded object in the field. There are 4 objects to be detected by the robot such as ball, opponents, goal and keeper. There are 1000 images that has been used in training data, validation data, and tested data. Training was built first on the computer, then implemented it to the robot so that data can be collected. The result shows that the YOLOv4 model is better than the YOLOv4-tiny model in terms of mAP parameter based on number of datasets. However, based on frames per second, YOLOv4-tiny produced 7.7 fps compares to YOLOv4 with 2.01fPs. It is said that the YOLOv4-tiny is better 3 to 4 times than YOLOv4 in capturing images implemented on Rapsberry Pi on Wheeled Soccer Robot.
Quadcopter Unmanned Aerial Vehicle (UAV) Design for Search and Rescue (SAR)

The hard and challenging disaster terrain often hindered victim search processes which commonly use ground-based methods. UAVs were used to avoid this problem altogether, thus decreasing the victim search time. This research mainly focuses on the design of UAVs, specifically quadcopters, which are used for victim search. Our quadcopter UAV is designed to meet the requirements for SAR UAV systems, such as having a real-time response, stable flight, autonomous flight capability, and the ability to maintain its position and altitude. Commercial off-the-shelf components were used in this UAV. On the software side, an open-source autopilot firmware called ArduPilot is used. An observation points arrangement and UAV flight path for victim search operation is also described in this paper. The resulting UAV could take off at a maximum weight of 4kg. This research has not tested the maximum range and velocity of the UAV. Logging data of the UAV shows that the UAV is capable of maintaining altitude and position steadily, even though there are some wind disturbances. UAV is also capable of autonomous flights using 1 and 3 waypoints. UAV is also capable of completing a search mission scenario. Our UAV can also reach 30m height, which is more than enough for monitoring the disaster area.
Learning Media Application about Covid-19 with Augmented Reality Technology

The Covid-19 pandemic has given many changes to aspects of human life. The spread also occurs quickly and to all groups. Therefore, social interaction must be reduced, especially outdoor activities where many people can meet. To reduce the chain of the spread of Covid-19, education is needed regarding coronavirus and how to prevent Covid-19. Education is intended for the wider community including children. Providing education will be carried out through an application containing information about Covid-19 and how to prevent it. Children will be our main target in carrying out this education. They need to get adequate education so that they can easily understand and participate in efforts to prevent the spread of Covid-19 which has been proclaimed by the government. This application will take advantage of the technology Augmented Reality. Augmented Reality is a combination of technology between the real world and the virtual world that is presented in the real world so that it can be felt directly in the real world. The way AR works in the application is by pointing the camera through the application towards the poster provided by the application, then it will display a Covid-19 prevention pop-up containing an animated video. By utilizing AR technology and animated videos on Covid-19 prevention measures, children will find it easier to understand and apply them.
Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08.
Keyword Extraction Based on Random Forest and XGBoost - An Example of Fraud Judgment Document

Fraud is a common crime and its constituent elements are extremely complex. In recent years, telecommunication fraud and Internet fraud are particularly frequent, and the government has taken many measures to enhance the anti-fraud awareness of the general public. So, the study of the characteristics of fraud is of great significance. This paper studied the problem of key word extraction using random forest and XGboost model. The data are selected from the judgment documents about fraud crimes in China Judgments Online from 20l8 to 202l. The two models well extract the keywords that can reflect the characteristics of fraud crimes and certain characteristics of fraud crimes are analyzed by keywords to change over time.
SDformer: Efficient End-to-End Transformer for Depth Completion

Depth completion aims to predict dense depth maps with sparse depth measurements from a depth sensor. Currently, Convolutional Neural Network (CNN) based models are the most popular methods applied to depth completion tasks. However, despite the excellent high-end performance, they suffer from a limited representation area. To overcome the drawbacks of CNNs, a more effective and powerful method has been presented: the Transformer, which is an adaptive self-attention setting sequence-to-sequence model. While the standard Transformer quadratically increases the computational cost from the key-query dot-product of input resolution which improperly employs depth completion tasks. In this work, we propose a different window-based Transformer architecture for depth completion tasks named Sparse-to-Dense Transformer (SDformer). The network consists of an input module for the depth map and RGB image features extraction and concatenation, a U-shaped encoder-decoder Transformer for extracting deep features, and a refinement module. Specifically, we first concatenate the depth map features with the RGB image features through the input model. Then, instead of calculating self-attention with the whole feature maps, we apply different window sizes to extract the long-range depth dependencies. Finally, we refine the predicted features from the input module and the U-shaped encoder-decoder Transformer module to get the enriching depth features and employ a convolution layer to obtain the dense depth map. In practice, the SDformer obtains state-of-the-art results against the CNN-based depth completion models with lower computing loads and parameters on the NYU Depth V2 and KITTI DC datasets.
A Cognitive Study on Semantic Similarity Analysis of Large Corpora: A Transformer-based Approach

Semantic similarity analysis and modeling is a fundamentally acclaimed task in many pioneering applications of natural language processing today. Owing to the sensation of sequential pattern recognition, many neural networks like RNNs and LSTMs have achieved satisfactory results in semantic similarity modeling. However, these solutions are considered inefficient due to their inability to process information in a non-sequential manner, thus leading to the improper extraction of context. Transformers function as the state-of-the-art architecture due to their advantages like non-sequential data processing and self-attention. In this paper, we perform semantic similarity analysis and modeling on the U.S Patent Phrase to Phrase Matching Dataset using both traditional and transformer-based techniques. We experiment upon four different variants of the Decoding Enhanced BERT - DeBERTa and enhance its performance by performing K-Fold Cross-Validation. The experimental results demonstrate our methodology's enhanced performance compared to traditional techniques, with an average Pearson correlation score of 0.79.
Big Model and Small Model : Remote modeling and local information extraction module for medical image segmentation

In the past few years, convolutional neural networks (CNN) and Transformer have achieved great success in medical image segmentation, but they each have inevitable drawbacks. Among them, convolution operation is difficult to calculate the relationship between elements at a certain position and elements far away from the position in the feature map. However, Transformer tends to ignore the importance of local information when exploring the correlation between overall elements. In order to allow the network to acquire both the ability to explore local details and compute the correlation between distant elements, this paper proposes TransUNet++ based on TransUNet. Specifically, this paper proposes two modules, Big Model and Small Model, to explore the element relationship between feature maps. Among them, on the basis of the whole feature map as the basic unit, the Big model can not only calculate the correlation between distant elements in the feature map but also extract the detailed information of the local feature map. On the basis of taking 1/4 of the feature map as the basic unit, the Small model not only explores the correlation between distant elements but also extracts the local details of the feature map. We demonstrate on the Synapse multi-organ segmentation dataset(Synapse) and Automated cardiac diagnosis challenge dataset (ACDC) that using either the Big Model or the Small Model alone can improve the experimental results, and using the Big Model and the Small Model in parallel can achieve more optimal experimental results. Among them, in Synapse dataset, we achieved 80.87% dice score and 24.79% HD score, and in ACDC dataset, we achieved 91.41% dice score and 1.08% HD score.
Applications of Reflectance Transformation Imaging (RTI) for Books and Objects on Paper

Reflectance Transformation Imaging (RTI) is a photography technique that documents object surface relief under varying illumination directions. This article presents the technique through a portable, automated RTI dome system, which allows for convenient photographing of objects in the collection and subsequent rendering of interactive, digital RTI images. The paper showcases the possible applications of RTI methods in documenting books and objects on paper. We discuss the applicability of RTI in four principal areas of interest: technology identification, object condition assessment, evaluation of conservation treatments, and dissemination of conservation work results. Each example is supported with RTI images recorded for objects from the collections of the Jagiellonian Library in Krakow, the Central Archives of Modern Records in Warsaw and the Asia and Pacific Museum in Warsaw.
Providing Students With Mobile Access to an Assessment Platform

The growing ubiquity, rich functionality, and relative affordability of mobile devices have been seen as opportune factors for implementing mobile learning solutions that can be used in a variety of contexts and domains. Plenty of successful mobile educational applications have been built. This paper describes an attempt to build on this success. The authors have investigated the use of mobile devices by students accessing assessment and self-assessment quizzes in the context of a university course. Two experiments were conducted with undergraduate students. The results of the first experiment were not successful, and initially, very few students used mobile devices. After several adjustments, during the second experiment, the usage of the system increased. However, the numbers were still much lower when compared to desktop access. This paper reports an investigation into the lack of mobile usage of the developed platform despite the educational affordances brought by mobile devices.
Research on Data Classification Method of Optimized Support Vector Machine Based on Gray Wolf Algorithm

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability machine learning method. Among them, the kernel function and its parameters have a great impact on the classification accuracy. In order to find the optimal parameters to improve the classification accuracy of SVM, this paper proposes a data multi-classification method based on gray wolf algorithm optimized SVM(GWO-SVM). In this paper, the iris data set is used to test the performance of GWO-SVM, and the classification result is compared with those based on genetic algorithm (GA), particle swarm optimization (PSO) and the original SVM model. The test results show that the GWO-SVM model has a higher recognition and classification accuracy than the other three models, and has the shortest running time, which has obvious advantages and can effectively improve the classification accuracy of SVM. This method has practical significance in image classification, text classification, and fault detection.