Reviewerly/exHarmony huggingface dataset:
Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents

Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of network CyOps, a good simulator is difficult to achieve. This work presents a systematic solution to automatically generate a high-fidelity simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through representation learning and continuous learning, CyGIL provides a unified CyOp training environment where an emulated CyGIL-E automatically generates a simulated CyGIL-S. The simulator generation is integrated with the agent training process to further reduce the required agent training time. The agent trained in CyGIL-S is transferrable directly to CyGIL-E showing full transferability to the emulated “real” network. Experimental results are presented to demonstrate the CyGIL training performance. Enabling offline RL, the CyGIL solution presents a promising direction towards sim-to-real for leveraging RL agents in real-world cyber networks.
Parameter Identification of a Multilayer Perceptron Neural Network using an Optimized Salp Swarm Algorithm

Effort estimation in software development (SEE) is a crucial concern within the software engineering domain, as it directly impacts cost estimation, scheduling, staffing, planning, and resource allocation accuracy.In this scientific article, the authors aim to tackle this issue by integrating machine learning (ML) techniques with metaheuristic algorithms in order to raise prediction accuracy.For this purpose, they employ a multilayer perceptron neural network (MLP) to perform the estimation for SEE.Unfortunately, the MLP network has numerous drawbacks as well, including weight dependency, rapid convergence, and accuracy limits.To address these issues, the SSA Algorithm is employed to optimize the MLP weights and biases.Simultaneously, the SSA algorithm has shortcomings in some aspects of the search mechanisms as well, such as rapid convergence and being susceptible to the local optimal trap.As a result, the genetic algorithm (GA) is utilized to address these shortcomings through fine-tuning its parameters.The main objective is to develop a robust and reliable prediction model that can handle a wide range of SEE problems.The developed techniques are tested on twelve benchmark SEE datasets to evaluate their performance.Furthermore, a comparative analysis with state-ofthe-art methods is conducted to further validate the effectiveness of the developed techniques.The findings demonstrate that the developed techniques surpass all other methods in all benchmark problems, affirming their superiority.
National Crisis Management System Development Direction

Recently, as the U.S. and China's hegemony competition has intensified, a new Cold War is being formed in East Asia where the world's military power is concentrated, and the security crisis is increasing, such as the continuous increase of North Korea's nuclear threat. This study examined the national crisis management system of the previous regimes in Korea and analyzed the laws and regulations related to the national crisis management, the organizational system, the control tower, and the consultative body to suggest the direction of the development of the national crisis management system that can cope with the future crisis situation. As a result, in the legal system field, the enactment of the “National Crisis Management Basic Act” which is the most basic of the national crisis management system is essential, and the grading of the national crisis alarm and the supplementation of the national crisis management stage were required. The organizational system and control tower suggested a plan to strengthen the function of the national emergency preparedness while controlling the crisis management in the disaster field by organizing the national crisis management execution control tower in the prime minister's office while the national security office serves as the control tower for the national crisis management. In addition, the councils operated according to individual laws and regulations were proposed to unify into the National Crisis Management Council so that they could cope efficiently if a large-scale crisis occurs simultaneously. Like the “Umbrella on a Clear Day,” the crisis is not predictable, and what is expected, prepared, and not known is the difference between heaven and earth. The crisis situation that will come in the future should be recognized as a scale and form beyond the imagination of mankind, and should be supplemented and developed by law, system and organizational system.
Cloud forensics-enabled chain of custody: a novel and secure modular architecture using Blockchain Hyperledger Sawtooth

The exchange of digital information has significantly emerged in the last decade. The increased number of cyber threats over the cloud increases the rate of utilisation of cloud forensics protocols. The lifecycle of cloud computing is getting more affected by the increased number of malicious attacks as more users are sharing, accessing, manipulating, scaling, and reusing data storage. In a cloud forensics environment, it poses a serious issue to provide a confidentiality, integrity, reliability, and trustworthiness platform. This paper overpasses the rift by enabling a novel, secure, and transparent cloud forensics chain-of-custody investigation processes using blockchain. A Hyperledger Sawtooth provides a secure cloud forensics chain-of-custody investigation architecture is proposed. A private block-based ledger network is set up by a group of people who want to exchange and digitally sign on different parts of a forensics investigation. On the other side, chain codes are implemented to automate transactions of a chain of custody.
Comparison of Periodic Pattern Mining Algorithms on Temporal Datasets

The identification of periodic patterns is of great significance in revealing hidden temporal patterns and regularities across various domains, including finance, healthcare, and social networks.As the availability of large-scale temporal datasets continues to grow, the selection of an appropriate periodic pattern mining algorithm becomes crucial for efficient and accurate analysis.The objective of this research paper is to conduct a comparative evaluation of various periodic pattern mining algorithms applied to temporal datasets.The algorithms under consideration include Apriori-based methods such as Modified-Apriori and LPP-Apriori, as well as Tree-based approaches such as LPP Breadth, and LPP-FP Growth.We have assessed the performance of these algorithms across different datasets, focusing on metrics such as Execution Time, LPP Count, and memory usage.
Mask recognition in computer vision technology

In order to lessen the strain on employees and the potential for carelessness, intelligent identification is required in China where there is a scarcity of staff to monitor the wearing of masks in public areas. In this work, we use a mask recognition technique to determine which members of the population weren't wearing masks by using the CNN and the VGG16 model. The ideas of data augmentation, dropout, non malicious, and transfer learning are used in the proposed study. This method may be used at hospitals, retail centers, transit hubs, dining establishments, and other community gatherings that require monitoring.
Face Mask and Physical Distance Detection using Deep Learning in Alarm Systems

This COVID-19 Pandemic is having devastating effects on societies and economies around the world. It is a communicable disease that spreads from another person who sneezes, coughs, and is in contact with another person. In this present COVID-19 situation, all must wear masks and maintain physical Distance. This is the most effective way to battle the virus. Our project uses a deep learning approach using the image classification method called CNN and YOLO. This proposed model gives better accuracy and is much more efficient than the existing model. The proposed model is implemented by using Python and OpenCV with TensorFlow. The construction of Face masks and Physical distance detection requires data collection, data splitting, data pre-processing, testing, and implementation. This proposed model gives an accuracy of 99% when a mask and physical distance is detected and it gives 95% accuracy when not following the practice. This model recognizes whether a person is wearing a mask and checks whether people are maintaining a physical distance of 2 meters. If they are not wearing a mask or not maintaining physical distance, then an alarm gives a signal. This model ensures safety and decreases the chances of virus spread. It can be implemented at Hospitals, Educational Institutions, Malls, Airports, etc.
Live Demonstration: Supervised-learning-based Visual Quantification for Image Enhancement

This demonstration showcases a framework of visual quantification for image enhancement where multivariate Gaussian (MVG) models are trained to assess image visibility. The visibility of an image is depicted by statistical features such as the contrast energy of the gray channel, yellow-blue channel, and red-green channel, average saturation, and gradients. The predicted visibility scores are then applied to define adaptive histogram equalization clip parameters for image enhancement. Finally, the hardware architecture is implemented on an FPGA to demonstrate the results for real-time image enhancement.
Improved algorithm of transient chaotic neural network with combinatorial optimization

Transiently chaotic neural network (TCNN) and its improved versions have been proven to have search abilities for combinatorial optimization problem (COP). However, the TCNN may be able to maintain its solving ability while the chaotic dynamics are cut. In this paper, the mechanism of the continuous-time Hopfield neural network (CHNN) and the TCNN for COP are analyzed qualitatively from the view of the energy function. It is believed that the “annealing” progress, i.e., the dynamic relaxation of the energy function, helps the improvement of the TCNN over the CHNN. Another Hopfield network with a linear activation function and annealing strategy for the COP is proposed in this paper. Simulations on the TSP show that the improved network performs as well as TCNN but is much more efficient. The performance of the TCNN is improved when linear activation functions are used. Compared with the traditional sigmoid activation function, the improved network is more suitable for hardware implementation.
Multi-Object Robot Visual Servo Based on YOLOv3

Aiming at the low robustness of image feature extractor in Image-Based Visual Servo (IBVS), a robot visual servo method based on object detection neural network YOLOv3 is proposed. By improving the output layer of YOLOv3 and adding attitude angle of camera, the pixel coordinate and depth information of feature points, the robustness of the IBVS system based on point features is improved while it can cope with multi-type and multi-instance objects, and the problem of the image Jacoby matrix falling into singularity caused by excessive rotation angle error of the optical axis is avoided. The visual servo convergence is accelerated. Meanwhile, the network training data generation algorithm of the desired image is used to replace the traditional manual data annotation, which reduces the cost of data acquisition, and the data enhancement method ensures the generalization performance of the training model.
Deep Knowledge Tracing Model with an Evolved Transformer Structure

Deep Learning based Knowledge Tracing (DKT) has become a research hotspot in the intelligence education field. Compared to conventional methods, DKT has better predictive performance, but it also has some problems such as poor interpretability, and difficulties in reflecting the causal association between the learning process and test results. In this paper, a new DKT model is proposed based on an evolved Transformer structure (DKT-ETS). The encoder layer is composed of three coding networks with a multi-head self-attention mechanism, while inputs are three types of pre-processed data: process characteristic data, test label data, and answer results data. Output as three matrices of V, Q, K. The decoder layer also uses the attention mechanism, in which the input is the three matrices that come from encoder, and the output is the predicted result. By improving the structure, the new model introduces certain interpretability into the V, Q and K matrices of the attention mechanism. Thus, the causal relationship between the learning process and test results can be reflected a certain extent: the V matrix represents the characteristic information of the testee's learning process; the Q matrix reflects the knowledge point information examined by the current test item; and the K matrix represents the results of the previous tests. DKT-ETS was validated by using the large-scale knowledge tracking data set EdNet, and the results show that its ACC and AUC evaluation indicators have been significantly improved.
Dynamic Path Planning of UAV Based on KF-RRT Algorithm

For the dynamic path planning of UAV, an algorithm based on Kalman Filter and improved Rapid-exploration Random Tree (KF-RRT) is proposed. Firstly, on the basis of the RRT algorithm, the weight coefficient of the target area trend is added, which reduces the time of UAV path planning. Secondly, the prediction function of Kalman Filter is added to predict the motion trajectory of dynamic obstacles in advance. Then, B-spline curve is used for smoothing to plan the feasible path of UAV. Finally, the KF-RRT algorithm in this paper is compared with other RRT algorithms by simulation, which shows that the proposed algorithm is more suitable for the dynamic path planning of UAV.
Research on K-Means Clustering-Based Algorithm for Evaluating Teaching Ability of Vocational College Teachers

Using fuzzy clustering technology, the teaching results in vocational colleges are grouped to select reasonable similarity ratios, form a fuzzy matrix, and use dynamic clustering technology for sorting research. The results provide not only traditional ranking arrangements but also a combination of quantitative assessment and comprehensive evaluation. The assessment level is improved providing a research foundation for formulating assessment standards in the future.
Fault Diagnosis Design Mechanism of Communication System Based on Bayesian Networks

One of the most remarkable features of the information age is the massive volume of information which becomes an instantaneous surge. Thus, how to apply the information efficiently with high quality is a problem worth discussing. This also means that the scientific construction of communication systems is significantly important. To ensure the normal operation of information and meet the communication requirements of people in the new era, a powerful fault diagnosis technology for communication systems is required. Among them, the fault diagnosis mechanism of communication systems based on the Bayesian network is one of the focuses of current research activities. It has the characteristics of high efficiency and real-time performance for automatic fault diagnosis and analysis. This study firstly discusses the design issues of the fault diagnosis mechanism of a communication system based on the Bayesian network, then presents the application disadvantage of the fault diagnosis mechanism, and finally analyzes how to build the fault diagnosis mechanism of a communication system based on the Bayesian network. The result shows the proposed design makes its value in communication service and ensures the stability and high quality of communication.
PROTECTION AGAINST ADVERSARIAL ATTACKS ON AUDIO AND IMAGES IN ARTIFICIAL INTELLIGENCE MODELS USING THE SGEC METHOD

In the modern world, the use of artificial intelligence (AI) is increasingly facing the risk of adversarial attacks on audio and images. This article explores this issue and presents the SGEC method as a means to minimize these risks. Various types of attacks on audio and images are discussed, including label manipulation, white-box and black-box attacks, leakage through trained models, and hardware-level attacks. The main focus is on the SGEC method, which offers data encryption and ensures their integrity in AI models. The article also examines other approaches to protect audio and images, such as dual verification and ensemble methods, access restriction and data anonymization, as well as the use of provably robust AI models.
High Availability Design and Information Technology Architecture Optimization of Data Center Network

With the rapid development of the Internet, enterprise informatization construction is becoming more and more important, and the rationality of data center network design has become the top priority of enterprise informatization construction.This paper mainly analyzes the high-availability design of the data center network and the optimization of the information technology architecture.When designing a data center network, it is necessary to analyze and plan the network architecture in combination with the specific conditions of the enterprise data center, so as to maximize the reliability of the network system, thereby improving the availability of the data center network.The optimization of information technology architecture also needs to comprehensively consider various factors such as system architecture, equipment functions, performance, and cost, so as to establish an information technology architecture suitable for its own development.Experimental results show that the bandwidth of the optimized data center network can reach 11.5TB/s.
Graph Ranking and the Cost of Sybil Defense

Ranking functions such as PageRank assign numeric values (ranks) to nodes of graphs, most notably the web graph. Node rankings are an integral part of Internet search algorithms, since they can be used to order the results of queries. However, these ranking functions are famously subject to attacks by spammers, who modify the web graph in order to give their own pages more rank.
Comparison and analysis of the machine learning in the movie subtitle document classification

With the rapid iteration of film and technology, the quantity of movie related subtitle document has been rapidly expanded. However, the issue of classifying these documents is becoming non-negligible. This paper aims to compare different methods of classification of every machine learning algorithm on movie subtitle document and then analyse the result of each method. For the classification of movie subtitle document, this paper mostly does the following features of the work: firstly, retrieve of movie subtitle document and pre-processing step of text document. Secondly, deletion of the irrelevant text and extraction of the related texts, the methods are unsupervised machine learning approach and supervised machine learning approach respectively. Furthermore, analyse each classifier in various model which applied with different approach. Lastly, compare the result of different approach and each model then display the results.
Pengenalan Konsep Perpustakaan dalam Lirik Lagu "Perpustakaan Jalanan" Karya Senartogok

In the lyrics to the song "Library of the Streets", Senartogok uses a standard choice of words, which may sound foreign to ordinary people. Like the words sigil, the mask-cutter, the exclamation mark of anxiety, the face of knowledge, the spirit of the tv, and the heart component wire. Words like that will certainly be difficult to understand for ordinary people who rarely hear these words. The purpose of this study was to find out the concept and meaning of the library contained in the lyrics of the song "Library of the Streets" by Senartogok. The research method uses a qualitative approach, as well as data collection through observation and overall observation of the research object, namely the lyrics of the song "Library of the Streets". Descriptive data analysis with Roland Barthes's semiotic theory analysis. The results of the study show that the lyrics of the song Senartogok provides a form of attitude and criticism regarding how the culture of reading nowadays has begun to be pushed aside by instant information media, such as TV and social media, where the street library points directly at reading sources in open spaces. Media, where books can be presented without any restrictions, is a true remedy for human alienation due to technological developments. The street library that was established has provided a unique warmth, such as the combination of urban flow with the excitement of the desire to open space.
Analisis Sentimen Ulasan Aplikasi Wetv Untuk Peningkatan Layanan Menggunakan Metode Support Vector Machine

Wetv is an online streaming media that has been running since 2019. Wetv has many user reviews from various applications. The rating consists of positive, neutral and negative. The response is used to determine sentiment by using the support vector machine classification method. This study took 12,000 comments from the Google Play Store, this study used preprocessing namely, cleaning, case folding, tokenizing, normalization, stopword removal, and steaming, then to the TF-IDF stage and the final results were tested with a fusion matrix with the Python program, the score results highest from the acquisition test process with accuracy of 0.76%, precision of 0.77%, recall of 0.79%, and f1 score of 0.78, in a dataset of 90% training data and 10% test data. Based on the research results of the Support Vector Machine method which is known to be good in the process of requesting negative responses on WeTV.
Perancangan Aplikasi Layanan Jasa Perbaikan Komputer dan Laptop Toshiba Berbasis Android

In improving customer service and guaranteeing customers who use the Toshiba brand, Toshiba is still opening Authorized Service Partnerships in various regions, one of which is the ASP Toshiba Palembang which provides laptop and computer repair services, as well as pick-up services. Improving services can affect the results achieved. Based on this, it is necessary to take action by creating an Android-based computer and laptop repair service application that will facilitate the ordering process for customers by having the ability to pick up goods and view order status. The result that can be felt by ASP Toshiba Palembang is that it can store data. The system is built using the Java programming language for Android-based mobile managers and messengers, while the admin uses a website based on the PHP programming language, MySQL as a database and Firebase for push notifications. The system development methodology used is an iterative model. The results of this study are to produce an Android-based PC and laptop repair service application that allows the application to improve customer service for ASP Toshiba Palembang, by providing convenience benefits for cutomers in placing orders and ASP Tohiba Palembang can perform transaction data recording of laptop and computer repair orders.