Reviewerly/exHarmony huggingface dataset:
SRODNet: Object Detection Network Based on Super Resolution for Autonomous Vehicles

Object detection methods have been applied in several aerial and traffic surveillance applications. However, object detection accuracy decreases in low-resolution (LR) images owing to feature loss. To address this problem, we propose a single network, SRODNet, that incorporates both super-resolution (SR) and object detection (OD). First, a modified residual block (MRB) is proposed in the SR to recover the feature information of LR images, and this network was jointly optimized with YOLOv5 to benefit from hierarchical features for small object detection. Moreover, the proposed model focuses on minimizing the computational cost of network optimization. We evaluated the proposed model using standard datasets such as VEDAI-VISIBLE, VEDAI-IR, DOTA, and Korean highway traffic (KoHT), both quantitatively and qualitatively. The experimental results show that the proposed method improves the accuracy of vehicular detection better than other conventional methods.
A Transformer-Based DeepFake-Detection Method for Facial Organs

Nowadays, deepfake detection on subtle-expression manipulation, facial-detail modification, and smeared images has become a research hotspot. Existing deepfake-detection methods on the whole face are coarse-grained, where the details are missing due to the negligible manipulated size of the image. To address the problems, we propose to build a transformer model for a deepfake-detection method by organ, to obtain the deepfake features. We reduce the detection weight of defaced or unclear organs to prioritize the detection of clear and intact organs. Meanwhile, to simulate the real-world environment, we build a Facial Organ Forgery Detection Test Dataset (FOFDTD), which includes the images of mask face, sunglasses face, and undecorated face collected from the network. Experimental results on four benchmarks, i.e., FF++, DFD, DFDC-P, Celeb-DF, and for FOFDTD datasets, demonstrated the effectiveness of our proposed method.
An Expert System for Breast Cancer Prediction (ESBCP) using Decision Tree

Objectives: Breast cancer is one of the major concerns in present day scenario.Detecting breast cancer at early stage increases the chances of survival.The objective of this research is to propose suitable feature selection method to improve the efficiency of breast cancer prediction at early stages to increase the survival rate.Methods: In this work, an expert intelligent technique has been proposed named "Expert System for Breast Cancer Prediction (ESBCP)" to detect breast cancer.To validate the results, the proposed system determines accuracy, precision, F-measure, and recall.The proposed model introduced a feature selection technique named -Undiluted Feature Set (UFS) to select the most relevant and promising features.The experimental work was carried out using Python 2.8 version in a Windows environment, taking a dataset on breast cancer from the UCI machine learning repository.There were 699 occurrences in the dataset with nine attributes and two classes.The proposed work utilized a decision tree and a new feature selection technique based on a heuristic search and the Stochastic Hill method.The experimental results were evaluated using the 10-fold Cross-Validation (CV).Findings: The experimental findings showed that the suggested model -ESBCP can accurately detect breast cancer at an early stage.As per the result, with simple decision tree the accuracy recorded 93.42 percent whereas ESBCP obtained 94.01 percent.It may seem that the improvement of 0.59 percent is very small, but for a large population even this mere change can have a greater impact.Novelty: The suggested model ESBCP and the feature selection technique -UFS have a lot of potential in the fields of medical research and bioinformatics in terms of classification capability and predictive power.
Four-Directional Ambisonic Spatial Decomposition Method With Reduced Temporal Artifacts

For the creation of convincing virtual acoustics of existing rooms and spaces, it is useful to apply measured Ambisonic room impulse responses (ARIRs) as a convolution reverb.Typically, tetrahedral arrays offering only first-order resolution are the preferred practical choice for measurements, because they are easily available and processed.In contrast, higher order is preferred in playback because it is superior in terms of localization accuracy and spatial clarity.There are a number of algorithms that enhance the spatial resolution of firstorder ARIRs.However, these algorithms may introduce coloration and artifacts.This paper presents an improvement of the Ambisonic Spatial Decomposition Method by using four directions simultaneously.The additional signals increase the echo density and thereby better preserve the diffuse sound field components during the process of enhancing measured firstorder ARIRs to higher orders.An instrumental validation and a series of listening experiments compare the proposed Four-Directional Ambisonic Spatial Decomposition Method to other existing algorithms and prove its similarity to the best algorithm in terms of enhanced spatial clarity and coloration while producing the least artifacts.
A Sustainable Fashion Industry Business Model Revolution Based on the Metaverse: Practices and Reciprocal Processes

The technological breakthrough of the metaverse makes it accessible as an online social environment where customers can socialize and reside in the form of avatars, as well as able to possess virtual property in the same manner they own physical property. The incorporation of the fashion industry into the metaverse can contribute to the development of its sustainability by revolutionizing the design, production, and communication stages of the supply chain, enabling circular economy business models, and minimizing environmental damage while satisfying customer needs. Through a review of relevant theoretical literature and an analysis of practices, this paper intends to illustrate a mechanism, describe how digital fashion based on the metaverse benefits industry sustainability, consumers, and the environment, and serve as a reference for practitioners seeking to advance their business strategies.
Central Kurdish Sentiment Analysis Using Deep Learning

Sentiment Analysis (SA) as a type of opinion mining and as a more general topic than polarity detection, is widely used for analyzing user's reviews or comments of online expressions, which is implemented using various techniques among which the Artificial Neural Network (ANN) is the most popular one.This paper addresses the development of an SA system for the Central Kurdish language (CKB) using deep learning.Increasing the efficiency and strengthening of the SA system relies on a robust language model.In addition, for creating and training a robust language model, collecting a large amount of text corpus is required and we have created a corpus of size 300 million tokens for CKB.Also, to train the SA model, we collected 14,881 comments on Facebook, then they are labeled manually.The combination of Word2Vec for the language model and Long Short-Term Memory (LSTM) for the classifier are used to create an SA model on the CKB SA dataset.These deep learning-based techniques are the most well-known methods in this field which have received high performance in SA for various languages.The performance of the proposed method for 3 classes SA is %71.35 accuracy.This result is superior to the best-reported result for CKB..
Human Face Recognition and Age Estimation with Machine Learning

Face Recognition (FR) applications are becoming more and more common these days. Face recognition, techniques, tools, and performance are all shown in this work, along with a literature review and gaps in many areas. Some of the most common uses of the FR include medical and government sectors as well as educational institutions. The FR technique can identify an appropriate individual through a camera. Online courses, online FDPs, and Webinars are becoming more interactive nowadays. Using Machine Learning, it is possible to quickly and securely determine a student's unique id to administer virtual online tests. The paper is an analysis of Machine learning and deep learning algorithms as well as tools such as Matlab and Python. The paper covers a survey of different aspects such as face detection, face recognition, face expressions, and age estimation. Hence, this is helpful for researchers to choose the right direction for their research. Future face recognition research is also considered in the paper which is now trending in face recognition systems. Data from recent years are used to evaluate the performance.
Effects of Image Quality on the Accuracy Human Pose Estimation and Detection of Eye Lid Opening/Closing Using Openpose and DLib

The application of computer models in continuous patient activity monitoring using video cameras is complicated by the capture of images of varying qualities due to poor lighting conditions and lower image resolutions. Insufficient literature has assessed the effects of image resolution, color depth, noise level, and low light on the inference of eye opening and closing and body landmarks from digital images.This study systematically assessed the effects of varying image resolutions (from 100 × 100 pixels to 20 × 20 pixels at an interval of 10 pixels), lighting conditions (from 42 to 2 lux with an interval of 2 lux), color-depths (from 16.7 M colors to 8 M, 1 M, 512 K, 216 K, 64 K, 8 K, 1 K, 729, 512, 343, 216, 125, 64, 27, and 8 colors), and noise levels on the accuracy and model performance in eye dimension estimation and body keypoint localization using the Dlib library and OpenPose with images from the Closed Eyes in the Wild and the COCO datasets, as well as photographs of the face captured at different light intensities.The model accuracy and rate of model failure remained acceptable at an image resolution of 60 × 60 pixels, a color depth of 343 colors, a light intensity of 14 lux, and a Gaussian noise level of 4% (i.e., 4% of pixels replaced by Gaussian noise).The Dlib and OpenPose models failed to detect eye dimensions and body keypoints only at low image resolutions, lighting conditions, and color depths.Our established baseline threshold values will be useful for future work in the application of computer vision in continuous patient monitoring.
Optimization of Interval Type-2 Fuzzy Logic System By using A New Hybrid Method of Whale Optimization algorithm and Extreme Learning Machine

The problem of searching for the best values ​​of the fuzzy logic parameters (T1FLS) is consider complex problems, and for type-2 fuzzy logic system (T2FLS) the problem is more complex, in special case interval type-2 fuzzy logic system (IT2FLS). The Researchers have used many methods and algorithms to solve this problem, and among the most important algorithms used in this field are the)Meta-heuristic) algorithms. Because Meta-heuristic algorithms have a high capacity in the practical field, so we used one of the modern algorithms in this field, which is the Whale Optimization algorithm (WOA). We are used the (WOA) algorithm together with the Extreme Learning Machine (ELM) algorithm as a hybrid algorithm to find the best parameters ​​for the IT2FLS. Whereas, the (WOA) algorithm was used to estimate the values of the antecedent for the system, and the (ELM) algorithm was used to find the values ​​of the consequent parts in the system. The simulation results show that the proposed algorithm is effective for a system (IT2FLS).
The Classification of Mushroom Types Using Naïve Bayes and Principal Component Analysis

Indonesia is one of tropical countries with high humidity which makes it possible for various plants and microorganisms to grow properly. One of the microorganisms that shall grow well in Indonesia is be considered as fungi or mushrooms. They have several types including poisonous and edible mushrooms that shall be consumed by human beings. The purpose of this research is to make it easier to classify between the types of poisonous mushrooms and edible mushrooms which can be consumed by using the Naïve Bayes algorithm to get the accurate classification results. In this research, the Naive Bayes algorithm is used to classify the types of mushrooms by utilizing the Principal Component Analyst technique which serves to reduce the number of features applied in the dataset. The data collection technique used in the research is by documenting the official website of the UCI Machine Learning Repository whereas the Mushrooms dataset consists of 22 features and 1 class are applied. After classifying using Naïve Bayes with Principal Component Analyst, then the researcher is evaluating using the 10-Fold Cross Validation technique whereas the results obtained are pc = 10 and the classification result is be considered as 84%.
Improving Extractive Text Summarization Performance Using Enhanced Feature Based RBM Method

Text summarization is the process of creating a short, accurate and fluent summary of a longer text document. As plenty of digital data is available online, automatic text summarization methods greatly needed to help and understand the lengthy &amp; complex documents quickly by discovering the relevant information. This paper proposes the text summarization method for short news articles and long scientific papers using unsupervised neural network model. The proposed method works in four main steps: input document pre-processing, feature extraction, feature enhancement and final summary generation. We have extracted combination of various statistical and linguistic features from input document, which helps in improving the quality of sentence selection. Further Restricted Boltzmann Machine (RBM) model is used to capture &amp; enhance the discriminative, abstract features in an unsupervised way to improve the overall performance without losing any significant information. Sentences are scored based on enhanced feature set and top sentences are selected for final extractive summary. Performance of the proposed method is evaluated using Rouge score and compared with TextRank, LexRank, LSA &amp; Luhn baseline methods and the results demonstrates that proposed methodology performs better compared to other methods.
A Deep Learning-Based Encrypted VPN Traffic Classification Method Using Packet Block Image

Network traffic classification has great significance for network security, network management and other fields. However, in recent years, the use of VPN and TLS encryption had presented network traffic classification with new challenges. Due to the great performances of deep learning in image recognition, many solutions have focused on the deep learning-based method and achieved positive results. A traffic classification method based on deep learning is provided in this paper, where the concept of Packet Block is proposed, which is the aggregation of continuous packets in the same direction. The features of Packet Block are extracted from network traffic, and then transformed into images. Finally, convolutional neural networks are used to identify the application type of network traffic. The experiment is conducted using captured OpenVPN dataset and public ISCX-Tor dataset. The results shows that the accuracy is 97.20% in OpenVPN dataset and 93.31% in ISCX-Tor dataset, which is higher than the state-of-the-art methods. This suggests that our approach has the ability to meet the challenges of VPN and TLS encryption.
Web Based Student Registration and Exam Form Fill-up Management System for Educational Institute

Registration of new students' academic information is essential for every educational institute to continue their education at every semester level and go through their whole student life.And this registration information is used when they do their form fill up of consecutive semesters.Nowadays, almost all educational institutes are using paper based registration and form fill up systems which is prone to many human errors and very time consuming for both students, teachers as well as other related administrative bodies.In this paper, we developed a web based application for academic purposes to control and save student registration and form fill up data that will be helpful for students, teachers and admin authority to make the process easier, less time consuming and error free.There are four main types of users who can use this system: student, department authority, students' hall authority and administrator.The student can submit their registration and form fill up information by using a web form.Moreover, he/she can download their admit card and registration form after the approval of the concerned authority.The students also can be able to do other module activities.The hall and department authority can use the system to approve the students' registration, semester examination form and to provide the students' attendance data.In addition, the department and hall authority has a choice to see all students' academic information.Moreover, the system administrator controls the system by managing (add, delete, update) student, hall and department authority, exam or registration date, subjects of a particular semester, notice board of the institute, module and programme data.The administrator can also add and remove the running and passed student data.The students also can pay their semester fees by using an online banking system.
3D-Model-Based Augmented Reality for Enhancing Physical Architectural Models

In the presentation of architectural projects, physical models are still commonly used as a powerful and effective representation for building design and construction.On the other hand, Augmented Reality (AR) promises a wide range of possibilities in visualizing and interacting with 3D physical models, enhancing the modeling process.To benefit both, we present a novel medium for architectural representation: a marker-less AR powered physical architectural model that employs dynamic digital features.With AR enhancement, physical capabilities of a model could be extended without sacrificing its tangibility.We developed a framework to investigate the potential uses of 3D-modelbased AR registration method and its augmentation on physical architectural models.To explore and demonstrate integration of physical and virtual models in AR, we designed this framework providing physical and virtual model interaction: a user can manipulate the physical model parts or control the visibility and dynamics of the virtual parts in AR. The framework consists of a LEGO model and an AR application on a hand-held device which was developed for this framework. The AR application utilizes a marker-less AR registration method and employs a 3D-model-based AR registration. A LEGO model was proposed as the physical 3D model in this registration process and machine learning training usingVuforia was utilized for the AR application to recognize the LEGO model from any point of view to register the virtual models in AR.The AR application also employs a user interface that allows user interaction with the virtual parts augmented on the physical ones.The working application was tested over its registration, physical and virtual interactions.Overall, the adoption of AR and its combination with physical models, and 3D-model-based AR registration allow for many advantages, which are discussed in the paper.
Pengembangan Buku Ajar dengan Model Addie pada Mata Kuliah Manajemen Teknologi Pendidikan

This research aims to develop The Teaching Book of Education Technology Management Course to prepare undergraduate students of Elementary School Teacher Education Study Program at STKIP Muhammadiyah Bangka Belitung in managing integrated Thematic learning of elementary schools. The data collection method used is a questionnaire. Data are analyzed with qualitative and descriptive quantitative descriptive techniques. The results showed that the educational technology management textbook developed following the ADDIE Model includes five steps, namely; planning, development, implementation and evaluation. The composition of the teaching book development has followed all five stages of the ADDIE model. The results of research, validation of expert materials related to educational technology management textbooks developed qualified very decent (86.66%) from media experts are also highly qualified (90%), and so are qualified linguists (86.66% ), the average validation result by students on teaching book products is 90.15% (qualifications are very feasible). In conclusion, the overall teaching book of Educational Technology Management Course developed is valid and feasible for use by students.&#x0D; Keywords: Education Technology Management, ADDIE Model, Textbook Development
Problems of improving information security on the example of CEMI RAS

This article is devoted to the issue of information security in a scientific organization on the example of the CEMI RAS. This topic is very relevant at the moment. This article describes the experience gained at the Institute over many years. The article discusses the problems and their solutions as information technologies develop at the Institute, as well as ways to implement information security software.
On the Personalized Learning Space in Educational Metaverse Based on Heart Rate Signal

The application of the metaverse provides a new perspective for educational innovation and talent training in the new century. By creating multi-channel perceptions such as sight, hearing, and touch through the VR/AR/MR immersive environment, the study found that learners' emotional engagement and cognitive level can be effectively improved in the metaverse. The metaverse integrates information technology, intelligent technology, and digital technology to realize a multimodal human-machine interaction and intelligent evaluation. On this basis, a personalized learning space in the educational metaverse is developed, which is researched by the PPG signal of the learning process, the pre-test, and post-test data to verify each other. In addition, it is verified that learners' learning engagement and learning effects are affected in the personalized learning space of the educational metaverse. A comparison of PPG signal-based metaverse learning and real-world classroom learning is taken.
Team Productivity in Agile Software Development: A Systematic Mapping Study

The culture of agility has been incorporated into the software industry, offering various benefits such as increased team productivity. However, this indicator is addressed in a limited way in the literature and its measurement continues to be a great research challenge. The purpose of this article is to characterize the conceptions of team-level productivity in agile software development (ASD) through a Systematic Mapping Study (SMS) where 616 studies were identified, of which eight were included applying of the protocol described in this text. As a result of the SMS, it is possible to establish the sense and meaning of team productivity in ASD, which is revealed as an abstract, relevant concept, composed of a set of factors, which function as an indicator of compliance with goals and continuous improvement. The review revealed 63 factors that contribute to the measurement of team productivity in ASD, categorized as: meaning, impact, flexibility and high performance. The main contribution of this work is to present a systematic and repeatable way to make an approximation to the definition of team productivity in ASD, identifying factors that can be measured, which were organized into four categories.