Reviewerly/exHarmony huggingface dataset:
A New Method for Image Encryption Using DNA Sequences and Hyper Chaos

In the decade of the significant progress in new communication sciences, digital images with a large amount of information are sent and received many times in different communication networks. Due to the high probability of manipulation and theft of some high-value digital images, as well as digital images that are of high value to the owner, they should be encrypted before transmission to be more secure. Several tools such as chaos functions, DNA coding, evolutionary algorithms, frequency domain transformations and substitution boxes have been used for image encryption. In this research, a new algorithm based on hyper chaotic map and DNA sequences is introduced to improve the security of the state-of-the-arts algorithms. The experimental analyzes show the efficiency of the proposed algorithm.
Distributed photovoltaic short‐term power forecasting using hybrid competitive particle swarm optimization support vector machines based on spatial correlation analysis

Abstract In order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spatial correlation (SC), for realizing short‐term PV power prediction tasks. Firstly, the spatial correlation analysis is conducted on the distributed PV stations. The k‐means clustering method based on morphological similarity distance improvement and mutual information function is used to select the best reference station and the best delay, which generates strongly correlated PV sequences. Then, a hybrid algorithm of particle swarm optimization (PSO) and sine cosine algorithm (SCA) in a competitive framework (HCPSO) is proposed, aiming to fuse the fast convergence capability of PSO algorithm with the global search capability of SCA algorithm, while enabling the algorithm to effectively handle high‐dimensional optimization problems based on a competitive mechanism. Finally, the HCPSO algorithm is combined with SVM algorithm, which expands the applicable scenarios of the SVM model and effectively improves the accuracy of PV short‐term prediction.
Governance and digital transformation in schools with 1:1 tablet coverage

There is an increasing trend of 1:1 coverage of tablets in schools in Scandinavia. Several studies have reported on pedagogical possibilities and challenges, but less is reported about how this change is perceived and practised by other stakeholders. We interviewed public school owners and leaders in Norway and found school owners used various models to support their schools, and school leaders held varied views on their roles in promoting the implementing 1:1 coverage in schools. Considering these findings, we discuss whether establishing overall national guidelines might help school owners and school leaders to effect digital transformation in schools and whether such guidelines would support or counteract school leaders’ autonomy and ability to adapt the digital transformation to their local context.
Detecting and tracking the position of suspicious objects using vision system

Vision-based object tracking is crucial for both civil and military applications. A range of hazards to cyber safety, vital infrastructure, and public privacy are posed by the rise of drones, or unmanned aerial vehicles (UAV). As a result, identifying and tracking suspicious drones/UAV is a serious challenge and that has attracted strong research attention recently. The focus of this research is to develop a new virtual coloured marker based tracking algorithm for estimating the posture of the detected object. After detection, the developed algorithm initiates by determining the coloured area of the detected object as reference-contour. Followed by a Virtual-Bounding Box (V-BB) created over the reference-contour by meeting the minimum area of contour criteria. Additionally, a Virtual Dynamic Crossline with a Virtual Static Graph (VDC-VSG) was developed to track the movement of V-BB, which is considered as virtual coloured marker, helps to estimate the pose of the detected object during observations. Moreover, V-BB helps to avoid ambient illumination-related difficulties during tracking process. A significant number of aerial sequences, including benchmark footage, were tested using the proposed approach, and the outputs were highly encouraging, with satisfactory results. Potential applications of the proposed method includes object detection and analysis applied to the field of security and defence.
Area reconnaissance with heterogeneous groups of autonomous systems

In the last years Unmanned Vehicles for different environments (UxVs) have been recognized as relevant game changers and key technologies for a wide range of military and civilian applications. Even parallel deployment of heterogeneous autonomous assets as swarms or cooperating teams is no longer science fiction, but a realistic operational scenario. Their effectiveness can be significantly increased by optimization of number, capabilities and application strategy of assigned assets, particularly in time-critical tasks (e.g. the search for missing persons, disaster relief and modern warfare operations). Especially, the movements of the vehicles must be carefully managed. However, the use of small UxVs is often accompanied by limitations like short mission time and limited sensor coverage, which in turn can be compensated by the intelligent assignment of vehicles with increased autonomy in cooperating groups. An important basis for efficient and effective autonomous reconnaissance, especially in swarming or teaming scenarios, is movement optimization considering the capabilities of the heterogeneous vehicles equipped with different sensor systems operating in a combined mission. For this purpose, algorithms have been developed at Fraunhofer IOSB that enable appropriate planning and dynamic processing in very different situations for heterogeneous groups of cooperating vehicles. In the following, two exploration methods for reconnaissance missions are described in more detail and their possibilities are discussed and compared.
Fast three selected frequencies phase unwrapping method in phase-shifting profilometry

Phase unwrapping is a key technique in phase-shifting profilometry (PSP). Among various phase unwrapping methods available, temporal phase unwrapping has received considerable attention due to its ability to handle discontinuous objects with high robustness. However, temporal phase unwrapping requires calculation of fringe patterns with multiple spatial frequencies, making this technique difficult to apply in online measurements. To reduce the number of fringe frequencies required for temporal phase unwrapping, three frequency-based phase unwrapping methods were proposed, including three selected frequency phase unwrapping (TSFPU) and heterodyne multiple frequency (HMF) methods. However, the TSFPU algorithm is computationally expensive and results in relatively long processing times, while the HMF algorithm lacks noise robustness. In this article, we propose an improved fast three selected frequency phase unwrapping algorithm to obtain absolute phase maps in PSP. Compared to existing TSFPU methods, our method relaxes the conditions for constructing the relationship between the wrapped phase and the unwrapped phase, making it simpler and faster. Compared to HMF, noise is not accumulated during the unwrapping process in our method. Simulation and experimental results demonstrate that the proposed method can effectively eliminate blurring and noise issues while being highly robust. Compared to TSFPU, our method has a processing speed that is 3.3 times faster and significantly reduces phase error.
Speckle Noise Reduction in Satellite Images

Satellite images play a crucial role in various applications due to their unique characteristics, large coverage area, and availability over time. These are captured by remote sensing instruments and sent over long distances, which can introduce various types of noise into them such as speckle noise, atmospheric noise, and more. Satellite image denoising is relevant because it enhances the quality and utility of satellite data for various image processing tasks. This paper introduces a deep learning model based on the UNet architecture, to remove speckle noise from images. The performance of the model has been evaluated on satellite images as well as standard denoising datasets like Kodak24, CBSD68, and McMaster. When compared with the RDUNet model, the proposed UNet with single and multi-head attention module gives better PSNR and SSIM values.
Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection

Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications.
Performance and Statistical Evaluation of Three Sampling Approaches in Handling Binary Imbalanced Data Sets

The issue of imbalanced data is still extensively investigated because of the existence of this issue in a wide range of cases. Several methods have been proposed to address this issue and improve classification performances, especially for the minor class. However, no single method gives a satisfying result for every case. Hence, a comparative study is required to understand better the correlation between the imbalanced data characteristic and a suitable method. In this study, we statistically evaluated 21 sampling methods on 100 imbalanced data sets with a range of imbalance ratio (IR) values varying from 1.82 to 129.44. Those methods were selected to represent three sampling approaches, i.e., undersampling, oversampling, and hybrid sampling. According to the results, we found that the implementation of sampling methods significantly impacted the classification result. Also, three methods, i.e., RandomUnderSampler (RUS), InstanceHardnessThreshold (IHT), and RandomOverSampler (ROS), give the best performance with the value of balanced accuracy score (BAS) are 0.8599, 0.8460, and 0.8049, respectively. The statistical evaluation shows a significant performance difference between RUS and ROS. Meanwhile, there is no significant difference in performance between RUS and IHT, and ROS and IHT. Furthermore, the results indicated that the imbalance rate and instances per attribute ratio alone could not be used to determine the complexity level of the data set.
Enhancing Online Learning in LCM Moodle through Interactive Adaptive Learning

The article considers the practical experience of introducing interactive adaptive learning in the e-learning environment. The basis of the learning environment is the LCM Moodle learning resource management system. One of the ways to individualize and personalize the learning process is to attract interactive and adaptive technologies for teaching students. The LCM Moodle’s interactive module of activity “Lesson’’ is a series of HTML pages linked by transitions. The main difference between “Lesson’’ and other LCM Moodle modules is that there are elements of adaptability. Using this tool, each student’s choice can be accompanied by appropriate comments from the teacher and the ability to go to different pages of the course depending on the correctness/incorrectness and completeness of the answers. With such planning, “Lesson’’ can provide theoretical material and control tasks to check its mastery for each student automatically, without additional action by the teacher. As a result of completing the tasks, the corresponding score appears in the student’s register of marks. In general, this module of activity is similar to a classroom lesson, when the teacher, presenting new material, from time to time interviews students in order to identify the level of its mastery.
Mathematical Tools and Software System for Purchase Planning and Selection of Building Materials

Calculation of building materials is an important part of any construction project. Therefore, there is a need to quickly and accurately calculate the required amount of materials. The ability to input one’s own data into the calculation mechanism frees the user from unnecessary calculations and human error. However, one of the most difficult stages is choosing the best material from among countless alternatives. This article proposes a solution to all these problems by developing a proposed program for calculating building materials, which can save time and costs, as well as reduce expenses on hiring specialists to conduct calculations.
Automatic Hate Speech Detection using Ensemble Method and Natural Language Processing Techniques

The use of social media has exploded in recent years, and sharing information has numerous benefits for society. Hateful content has increased as a result of the increased usage of social media. Hate speech can exist in any sort of content designed to slander, dishonor, or incite hatred toward certain affecting various communities, organizations and individuals. It is critical to distinguish between hate and offensive texts to detect hate and offensive speech in any given text. Since no previous work has been done utilizing both English and Hinglish (Hindi- English code combined) data sets for multi class prediction, an ensemble model has been proposed to categorize any given input sentence into one of the three categories: hatred, offensive, or neither. Data collection, data pre-processing, feature extraction, and text categorization are significant processes needed for the proposed approach to detect hate speech. The data set collected for this model is a publicly available Twitter data set in English and Hindi-English code mix language to which data preprocessing is done. Extraction of n-grams as features is done using the term frequency-inverse document frequency (TFIDF) extraction method. Several single classification methods, such as Decision tree, SVC, Logistic Regression, Random Forest, and Naive Bayes are considered, evaluated and compared, combinations of various single classification models are done to form a better ensemble model with a combination of Random Forest and Support Vector Classifier. When tested against the testing dataset, the proposed ensemble model achieved an accuracy of 90.7.
Cloud Resource Scheduling Strategy Based on Improved PSO Algorithm

The scheduling of cloud computing resources is a key technology in cloud computing. A particle swarm optimization algorithm based on family relationship selection (FS-PSO) is proposed to address the drawback of particle swarm optimization algorithm being prone to falling into local optima. By introducing a family selection strategy, the limitations of the PSO algorithm are compensated for, to improve the optimization efficiency and solving ability of the algorithm. Simulation experiments have shown that the FS-PSO algorithm has stronger adaptability, faster convergence speed, and stronger global search ability. It can effectively improve the utilization of cloud computing resources and has good application value.
A Deep Learning Approach for Detection and Analysis of Anomalous Activities in Videos

The detection and analysis of anomalous activity in videos have become increasingly important in many real-time applications like patient monitoring, passenger monitoring, and public safety. Differentiating between normal and abnormal human activities poses a significant challenge due to their inherent similarities and challenges like occlusion, clutter etc. This proposal aims to develop a system which can identify and distinguish multiple anomalous actions from video sequences which has occluded scenarios, both individually and in groups. It effectively differentiates between various normal and abnormal activities in the given input videos. Toachieve, this proposal employs a combination of Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), and Long-term Recurrent Convolutional Networks (LRCN) models. Created own data sets for the required scenario along with existing large datasets UCF101, HMDB having patient and passenger videos are used to train the model. The experimental results demonstrate that the proposed approach achieves an accuracy of 96.8% in recognizing abnormal actions. This model is potential to use in the applications like hospitals, elder care homes, and public places where accurate monitoring of human activities is crucial and by efficiently detecting and identifying abnormal activities, it can aid in the early recognition of potential risks of unusual behavior, facilitating timely intervention and ensuring the safety and security of individuals in such environments.
Research on Research Performance Evaluation Based on Analytic Hierarchy Process and BP Model

Scientific research performance evaluation is a crucial method to assess the scientific research results of researchers. However, current quantitative evaluation methods often prioritize quantitative indicators such as the quantity and level of scientific research results, which may not fully reflect their real quality. Qualitative evaluation suffers from subjectivity, and can be influenced by factors like human relations and the Matthew effect. To address these limitations, this paper proposes a research performance evaluation model based on the BP model and analytic hierarchy process (AHP). Simulation experiments were conducted to verify the model's effectiveness. The results showed that this model can significantly improve the evaluation speed and accuracy compared to other models, leading to better evaluation outcomes. By taking into account both quantitative and qualitative indicators, the proposed model provides a more comprehensive and objective evaluation of scientific research performance. It is expected to be useful for decision-makers in academic and research institutions, as well as researchers themselves, to identify strengths and weaknesses and optimize their research strategies.
Design of the Blockchain System for the Generation and Verification of Diplomas

The creation of blockchain systems for generation and verification of diplomas would reduce the possibility of forgeries that may occur, as diplomas are important documents that characterize a person in a professional sense. Through the paper we will describe the importance of the application of blockchain systems in higher education institutions for the generation and verification of diplomas. The design of the smart contract will be made through UML diagrams, respectively class diagrams. We will elaborate some techniques for smart contract testing.
A Comparison of Text Classification Methods: Naïve Bayes and Support Vector Machine for E-Commerce Item Classification

Currently, buying products from the e-commerce industry has already become the norm for many people. It is important to categorize the item that is sold in e-commerce so that the customer can easily find the item they want to buy and have a better experience browsing through the application. We have found a dataset to use for our experiment that we will preprocess so we can use it without any problem. The experiment will consist of looking for the accuracy, precision, recall, and f1-score of each algorithm with the dataset we use and compare it so we can find which algorithm is better. Out of 6 experiments we have done, we found out that SVM outperforms Naive Bayes four times. From that we can conclude that SVM is overall better than Naive Bayes with 96.6% accuracy and 0.967 F1-Score.
Implementing Knuth-Morris-Pratt Algorithm in Detecting The Plagiarism of Document

In the internet dan data era, plagiarism is a big issue. Plagiarism cases are in text, audio, video, and picture file on the internet. Making a copy of other people’s work is a significant problem that might lead to law consequences. Some algorithms might work in detecting plagiarism. Algorithms would match the word or string to the database of publication. Researchers try to implement these algorithms to ease the plagiarism-checking process. This paper presents a plagiarism detection system that uses the Knuth-Morris-Pratt (KMP) algorithm. Users need to upload two documents before comparing. Program load the documents onto the computer. Then, it compares the word within two and presents the similarity to the user. It displays the number of plagiarized sentences in percentage. Based on the testing on 100-sentence documents, the algorithm can match a similar word and display it correctly. In conclusion, KMP works well as a plagiarism detector algorithm.
A Solution to Prevent Resource Conflict and Paging Channel Hijacking in the Internet of Vehicles

With the development of the Internet of Vehicles, the security of the Internet of Vehicles has received more and more attention. However, in the Internet of Vehicles, the traffic topology changes rapidly and the vehicles are always in high-speed motion, so the secure communication between vehicles is more vulnerable to be damaged. Aiming at resource conflicts and channel hijackings that are prone to occur in the Internet of Vehicles, this paper proposes a scheme based on blockchain and time stamps to prevent resource conflicts, and selects the base station with the highest reliability by scoring the base stations, avoids paging channel hijacking, prevents vehicle terminals from accessing malicious base stations, and improves the security of the Internet of Vehicles.
Joint Relation Extraction Model Based on Potential Relation and Entity Boundary Information

Extracting entities and relation from unstructured text is a crucial step in building knowledge graphs. Existing methods have achieved certain effectiveness, but issues such as entity nesting and relation redundancy still persist. Regarding the mentioned problems, a method of Potential Relation and Entity Boundary Information (PREBI) is proposed. At the start, the model uses a pre-trained model for representing the encoded input text, then predicts the relation present in the sentence through a potential relation extraction module, incorporating entity boundary information to jointly model and generate relation triplets. Experiments conducted on the DuIE2.0 dataset demonstrate outstanding performance on Chinese data, with an F1 score of 76.41%.
Detection Approach of Bird Nest from Power Transmission Line Towers with Low-Quality Images

Drone inspection combined with target detection can automatically detect bird nests on power transmission towers. However, the quality of the pictures taken by the drone is low, and the object to be recognized is small, so the recognition effect is not good. In response to this, an end-to-end transmission line tower bird's nest detection model for low-quality images is proposed. Considering the blur of small targets in low-quality images, firstly, a feature pyramid and a path aggregation structure are added between the backbone network and the classification head, and then the channel attention mechanism and the spatial attention mechanism are added to effectively improve ability of the network extraction of small target features. The experimental results demonstrated that the proposed model outperforms several other commonly used object detection methods in various indicators.