Reviewerly/exHarmony huggingface dataset:
Bright-Dark Channel Defogging Algorithm Based on SLIC Super-pixel Segmentation

In order to solve the problem of block effect and distortion in the distant view after using dark channel prior algorithm to remove fog, this paper presents a bright and dark channel defogging algorithm based on SLIC super-pixel segmentation. Firstly, the irregular region obtained by SLIC super-pixel segmentation is used to replace the fixed window to obtain the bright and dark channel map; Secondly, the weighted atmospheric light is obtained by weighting global atmospheric light and pixel-level atmospheric light; Then the transmittance of the bright area is corrected; Finally, the defogging image is obtained based on the atmospheric scattering model. The outcomes demonstrated that the block effect at the edge of the image and the distortion at the perspective of the image can be basically solved after using the proposed algorithm, which is superior to other contrast algorithms in subjective evaluation and objective analysis.
Class Distribution Pyramid Experts for Long-tailed Visual Recognition

Deep long-tailed visual recognition is a challenging task due to its extreme imbalance in classes. Existing methods improve the overall performance by making a trade-off between head and tail classes. We conduct experiment find that in this way, not only the performance of the head classes is compromised, but also the performance of the tail classes is not maximized. Based on these observations, we propose Class Distribution Pyramid Experts(CDPE), a clean and simple framework for end-to-end training.T each expert are calculated by insta hrough the idea of divide and conquer, we maximize the representation of classes at different quantitative levels by different experts and further propose a novel instance-aware aggregation network that dynamically integrates the predictions of different experts. We conducted extensive experiments on CIFAR10/100-LT, ImageNet-LT and iNaturalist2018 datasets to demonstrate our method’s effectiveness, practicability, and state-of-the-art performance. We hope this simple method will motivate people to rethink the divide-and-conquer strategy in long-tailed visual recognition.
Software and Hardware Platform Technology of Embedded Device Based on Heterogeneous SoC Chip in substation

In order to meet the demand of high-precision harmonic sampling in electronics power grid, and further improve the performance of secondary equipment such as substation measurement and control, PMU and broadband measurement, the application scenarios and performance requirements of high-performance hardware platform are analyzed, and the technical status of domestic chips in mass production is investigated. The hardware platform scheme of heterogeneous SoC chips is adopted, and the dual-system software architecture based on Linux and RTOS system is adopted, We have designed a large-capacity real-time data parallel processing and layered drive technology scheme, fully developed the computing potential of heterogeneous multi-core chips, improved the performance of hardware platform based on domestic chips, and completed the development and testing of the hardware platform of broadband measurement device prototype. Finally, the feasibility of the hardware platform technology scheme based on heterogeneous SoC chips is verified through the comparison of various performance tests.
An Evaluation of User Experience in a Virtual Semiconductor Laboratory Utilizing HMDs during Covid-19 Pandemic

Virtual reality (VR) technology has been increasingly adopted for creating engaging educational environments, such as virtual laboratories. This study evaluated the user experience of a virtual semiconductor laboratory environment, utilizing Reverb G2 HMD VR headsets, through a user experience questionnaire (UEQ-S) and a psychometric analysis of its 26 items. Results showed that most participants found the virtual laboratories enjoyable, understandable, creative, easy to learn, and valuable. The psychometric analysis revealed relatively consistent and reliable construct related to user experience. The Kruskal-Wallis H test demonstrated that participants perceived the VR headsets to be easier to use when they found the virtual laboratories more enjoyable. However, there were no significant differences in user experience scores based on preferred VR headset, and user experience did not predict headset preference. These findings suggest that VR technology presents a promising solution for addressing practical challenges in education and fostering student engagement, although further research is needed to optimize the user experience and enhance student motivation.
Face Recognition Using Deep Learning

Today, people‘s daily life is more and more dependent on face recognition technology, and the research on face recognition technology is becoming more and more thorough, making great achievements in all directions. In this paper, face recognition technology is based on a neural. The network studied by Convolution Neural Network is considered one of the best types in image classification. The model implementation is based on Python and uses a data set of 540 face images for training and testing. And the technique used in this project also yielded good results in terms of accuracy, the accuracy of these results was 100% for the 16-person dataset, 97% for the 30-person dataset, and 97% for the 2-person data set.
Medical Image Encryption Algorithm Based on Improved Logistic Chaotic System and 3D Space

In the current medical detection means, medical images account for the vast majority of medical information, which is the main source of information for disease screening and diagnosis, and also an important means to assist clinical disease diagnosis and treatment. In order to improve the security of medical images, a medical image encryption algorithm with improved Logistic chaotic system is proposed. The encryption algorithm is based on Logistic chaotic sequence and three-dimensional matrix. The two-dimensional image is mapped to the three-dimensional space, and each layer in the three-dimensional space is scrambled and encrypted by using the sorted position index, and then the binary sequence of the three-dimensional space is mapped to the two-dimensional space to obtain the encrypted ciphertext image. Through the analysis of histogram, sensitivity and information entropy, the experimental data show that the medical image encryption algorithm has a good encryption effect.
Tracking Control of Adaptive MIMO Nonlinear Systems Using Fuzzy Logic Control and Extreme Learning Machine

This paper presents two different control techniques for tracking an output reference signal to any unknown parameter vector. The two techniques are implemented to perform adaptive control approach for MIMO nonlinear systems. The first technique is fuzzy logic control (FLC). In FLC the appropriate approximation can be established based on the number of membership functions. The designed FLC-based controller will make the system outputs follow the reference input signals. The second technique is called an extreme learning machine (ELM). The ELM is a newly developed learning algorithm for generalized single-hidden-layer neural networks, where the output weights are calculated computationally and the hidden node parameters are produced at random. This work focuses on decreasing the control energy in conjunction with reducing the overall MSE, which makes the output tracks the reference input smoothly.
Smart Surveillance System Using Deep Learning

With the increase in the prevalence of armed robberies and attacks in public places, an effective and reliable surveillance system has become indispensable to fulfill various security aspects and improve the quality of human life. The most common effective video surveillance system used is called closed-circuit television (CCTV), but it can be expensive, and it requires a large amount of memory. In addition to these problems, the need for manpower to detect unauthorized activities can lead to several security problems. An intelligent surveillance system (ISS) that may overcome most of the mentioned problems is proposed in this paper. This system is built with more affordable hardware components that provide automated security services more than what regular surveillance systems can offer to the user with better quality using the assistance of Artificial Intelligence (AI), Deep learning (DL), and Image Processing (IP). The obtained overall detection accuracy of 83.1% at 22.2ms helps to inform the user in a short time during the incident. Also, the classification Mean Average Precision (mAP) of 0.98, 0.95, and 0.94 for detecting Knives, Guns, and Pistols, respectively, can reduce the rate of disturbing the user with false alerts.
A Cloud Computing Model for Libyan Higher Education Institutions

Cloud computing is now changing the IT landscape and playing a significant economic role for companies that develop software. Users of educational resources now have more flexibility and access because of cloud computing. Students, faculty members, and support staff all use cloud computing in academic institutions. Internet-based computing, known as "Cloud Computing," allows users to share IT resources securely and efficiently. The use of cloud computing in universities has many benefits, such as access to files, e-mail, databases, educational resources, and search applications as requested by faculty, administrators, staff, and students at a university alike. Moreover, cloud computing significantly reduces the complexity and cost of IT in universities. This paper discusses cloud computing, its structures, types, and services it provides, and then examines the possibility of implementing cloud services in universities and discovers the various opportunities and benefits of cloud services. The paper presents a proposed model for cloud computing for Libyan universities, which was applied to the University of Benghazi and produced satisfactory results and facilitated a lot of university work. Through the application of the proposed model, effective management of the technological needs of the Libyan university has been achieved, such as connecting programs and providing a platform for development, data storage and computing.
Virtual Reality Semiconductor Laboratory: Usability Testing to Improve User Experience (UX) in Virtual Environments

The Virtual Reality (VR) Semiconductor Laboratory, a computer-based virtual lab designed for training engineering students in semiconductor concepts, was evaluated to enhance user experience (UX) in virtual environments. A sample of 97 participants, including undergraduate, Master's, and PhD students, participated in the study. The User Experience Questionnaire (UEQ) was developed to collect data from users to evaluate their experience with the VR lab. The results of the study showed that the participants had a positive experience with the VR learning environment. They found the experience to be enjoyable, easy to understand, and helpful in completing learning tasks. The majority of participants rated the experience as very easy or easy in terms of learning and understanding the material, enjoyment, ease of using the interface, comfort with body movement during learning, and assistance with tasks. Additionally, most participants rated the experience as very easy in terms of task completion and comfort with the VR experiment. The study found high reliability in terms of usability, indicating that the UEQ is a valid and reliable tool for assessing virtual laboratory usability. These findings suggest that virtual laboratories can be effective learning and training tools in various fields, such as science and engineering. This research highlights the potential of VR technology in improving the training and learning experience for students.
Getting Smarter with Fatrix: A Facial Recognition Access Control System

The Library of private university has an RFID access control system. Such systems are popular door-entry solutions that can manage access in and within buildings and commercial properties. However, it has many security issues ranging from network and hardware attacks to loss of data integrity, privacy, and confidentiality. Besides, the system implements ID cards to store the confidential information of users and the cards can be stolen and misused by unauthorized users. In this paper a robust facial recognition access control system entitled FATRIX is proposed. The system can provide smart user authentication using facial features and machine learning algorithms plus data analytics to visualize the number of students in the building and the peak hours in which the highest number of students visit the library.
Image Encryption using Modified RC4 Algorithm

In this paper, we propose a new method for image encryption based on a modified RC4 stream cipher algorithm that scrambles image pixels before applying the XOR encryption function. We compare the performance of the proposed modified RC4 algorithm with that of the standard RC4 algorithm by analyzing histograms, calculating the correlation coefficient between adjacent pixels, and measuring information entropy. The results demonstrate that the proposed algorithm outperforms the standard RC4 algorithm in terms of resistance to statistical attacks.
Radar Target Recognition of Individuals Based on XGBoost

Radar target individual recognition refers to identifying individual targets by analyzing the target echo signals received by the radar. Due to the complexity and diversity of radar target individual characteristics, it is difficult to describe the changing patterns of these characteristics accurately with mathematical expressions. To address this issue, this paper proposes a radar target individual recognition method based on the XGBoost model, which processes the radar target signals, constructs feature vectors based on frequency spectra, and performs classification recognition. Experimental results demonstrate that the XGBoost-based radar target individual recognition method achieves good performance on this problem.
Analysis of Mobile Accelerometer Sensor Movement Using Machine Learning Algorithm

Currently, many mobile phones come equipped with sensors that enable the delivery of advanced features and services to users. One type of sensor that is commonly embedded in mobile phones is the accelerometer. By applying machine learning algorithms to analyze the movement patterns of a mobile device through its accelerometer data, valuable insights can be gained. This analysis can classify different types of movement, such as walking, running, or driving. These insights can be applied in various settings, such as providing better fitness tracking apps and developing new methods for user authentication. The purpose of this research is to explore and examine the movement patterns of mobile devices through the application of machine learning techniques to accelerometer sensor data.
Informedness and Effectiveness in Search: Heuristic Simplicity and Algorithm Complexity

Suppose that you are the boss of a company and you want to accomplish a specific task through two groups of employees. Where, one group involves old and expert employees and the other consists of young and inexperienced ones. In this case, we think that your best choice is to establish a teamwork from both groups to ensure this mission will be done rapidly with high quality, of course, if the team members could work in consistence with each other. In this research, we are interested in investigating the benefit of applying this concept in search with two groups, one consists of a set of informed algorithms with different complexity based on their search mechanism, and the other group includes a collection of heuristic functions has varying degree of simplicity based on their informedness. The main goal of our study is the distinguishing of work teams that should be used to improve the efficiency of exploring the search space of a sliding-tile puzzle. The actual computational time of our experiments proved that choosing a proper teamwork can rapidly find optimal solutions by professionally exploiting the heuristic informedness and reducing the algorithm complexity.
Application of Classification and Regression Tree and Spectral Clustering to Breast Cancer Prediction: Optimizing the Precision-Recall Trade-Off

The Receiver Operator Characteristic (ROC) test is often used to evaluate classification performance. However, it calls for special consideration when applied to the class-imbalanced data. The Precision-Recall Curve (PRC) is dependent on the class imbalance ratio. It summarizes the trade-off between the true positive rate and the positive predictive value for a predictive model using different probability thresholds. In this work, PRC is used to enhance the precision and recall of the Classification And Regression Tree (CART) algorithm to diagnose breast cancer; this enhanced the CART recall’s capacity to predict wrongly classified samples by 10%, from 89% to 99%, when compared to the standard CART algorithm. On the other hand, when evaluating the performance of the improved CART on unseen data, it achieved a recall of 95%. Thus, the recall of the CART decreased by 4% from 99% to 95%. This drawback can be attributed to the small size of the training data, which has been labeled using one of the clustering methods, spectral clustering, which achieved the best Silhouette in separating data into two clustering with a score of 0.43.
A framework for improving the process of potential faults detection at the design coupling

Design testing is a critical process because it includes a mechanism to ensure that software systems are designed in such a way that their subsequent implementation is correct, especially with regard to the coupling of those software components. A number of studies have been conducted to introduce methods and techniques to improve the software testing process, which will positively affect its quality. However, the focus on testing couplings is still not done enough, which can help bugs propagate unpredictably faster. As a result, this paper proposed a simplified framework that allows software developers to track the likelihood of potential faults appearing, through a structured mechanism that encourages focus on this dilemma, as well as being supportive and complementary to various validation and testing processes by focusing on design coupling tests.
Data Center Tree Topology Comparison Using POX Controller

Nowadays Data Center Networks (DCN) deploys the Software Defined Networking (SDN) as administration and management solutions. This architecture gives centralized management and monitoring that helps to simplify the administration and reduce the data center maintenance costs. Furthermore, redundancy is the major issue of reachability and reliability which are both impacted by data center topology. This paper investigates the performance of deploying an SDN POX controller in two different DCN tree topologies. The paper considers the binary and fat-tree topology with a different number of servers. This comparison of a tree topology is based on the traditional three-layer hierarchy of core, aggregation, and access layers. The comparison of binary and fat-tree topology was conducted by the Mininet emulator. The paper compares the performance of two tree topologies based on a POX controller with a different number of servers. This evaluation compares two three topologies of DCN using performance metrics of throughput, delay as reachability metric, and overhead as reliability metric. The results show that the fat-tree topology outperforms the binary tree based on performance metrics.
OwnBook: Developing Mobile Application For Badminton Court Booking System

As a badminton lover, I decided to create a mobile application-based system based on the experience of the existing online booking system in order to solve the problem of booking badminton courts every time. The requirements of the majority of users are studied and the functionalities and pages of the OwnBook application are designed based on the total demand analysis through inquiry and research. To develop and implement this paper, Unified Modeling Language (UML), SQLite, Android Studio, and the JAVA programming language are used. The OwnBook badminton booking system will address the problems of the present system, allowing users to book badminton courts more easily.
Medical Named Entity Recognition in Arabic Text using SVM

There is a huge amount of digitalized medical documents which are valuable for many applications. The text in a medical document is needed to collect knowledges and information about diseases and are used to make medical decisions. An automatic processing to extract information and recognizing named entities in these texts is very useful to facilitate many medical tasks. However, the text is not structured and is written in natural language, which makes this task a challenge. In fact, recognizing entities in Arabic language is not well studied in this field. In our work, we propose a Named Entity Recognition (NER) model to recognize entities such as: of disease, diagnosis, treatment, and symptom. For that, we perform a classification task using a dataset of 27 real medical documents based on a Support Vector Machine (SVM). We have pre-processed the text and selected candidate words to be classified. Then, we have represented each word by multiple features such as: FastText embedding, Part of Speech (PoS), Term Frequency-Inverse Document Frequency TF-IDF embedding, and non medical word matching. To classify the word, we take into account the surrounding words to capture its context. We have used PCA to reduce the size of TF-IDF vectors and we don’t use a hand-crafted supplemental resource. Thus, we have classified each word into its entity class by the SVM model. Evaluation results show that our model is stable and provides high and balanced results. In addition, we outperform a state-of-the-art model by an F1-score of +6.56% although the model requires a big manual effort for preparation.
Decision support system of staff member promotion based on cloud computing (Case Study University Of Benghazi)

The scientific council at Benghazi University suffered from issues related to the scientific promotion process for faculty members. Promotion is still done manually, without a computerized system. Therefore, it faces obstacles in obtaining basic information, issuing promotion decisions, making managerial procedures, lack of flexibility, and the accumulation of papers. There are also unclear biases in decision-making and mechanisms of evaluating promotion criteria. Thus, they need to keep up with the international universities in the promotion process to be successful in administrative business today. Benghazi University needs an information system that can support decision-making, which is a major problem often occurs in the process of promotion of faculty member.The use of decision support systems is a solution to reduce subjectivity in decision-making. This work presents an E-Gate that can assist in the promotion of educational staff. It is a system able to provide the best alternative in promotion educational staff. With the decision support system, the promotion process is carried out via the E-Gate using the AHP method according to several criteria. It is stored in the cloud computing because many enterprises are adopting the cloud-computing platform to examine the benefits of self-service, user guide, and high flexibility. The time is right for the educational universities to derive benefits of the cloud, as they are obliged to deliver better ubiquitous services. The study also developed Search Engine to verify the submitted research.