Reviewerly/exHarmony huggingface dataset:
From Strings to Streams: A Multi-Period Analysis of QKD over EONs, Showcasing Multi-Band vs. Multi-Fiber Solutions

Quantum key distribution over optical networks has been noted as a state-of-the-art approach to next-generation secure communication. We propose two approaches to manage the signal for quantum key exchange between two trusted nodes; in the first approach, the quantum channel (QCh) uses distinct fiber whereas the classic channel (CCh) and public interaction channel (PICh) use another fiber to be transmitted on C-band (C+L band), and in the second approach CCh and PICh are transmitted over C+L band and QCh only uses O-band, both routed on the same fiber. In this context, two variations of the initial approach have been put forth, namely scenario 1 and scenario 2. In scenario 1, the CChs are positioned within the extended C-band, while in scenario 2, they find their place within the extended C+L-band. Concerning the secondary approach, denoted as scenario 3, the CChs+PIChs and QChs are situated in the extended C+L-band and low-loss O-band with consideration of noises over QChs, respectively. We propose a heuristic resource allocation with a time-division multiplexing technique in quan-tum resource allocation to compare the resource utilization of the mentioned approaches. The simulation outcomes demonstrate the advantages of adopting scenario 3 in relation to fiber deployment. This is highlighted by an 86 % (34 %) enhancement in fiber efficiency when compared to scenario 1 (2), all while employing an equal count of quantum transmitters and receivers over the initial eight years. As per the specifications of scenario 3, during the concluding year, there has been a notable 44 % surge in quantum transmitter and receiver utilization for both scenarios 1 and 2. This upsurge is accompanied by a substantial reduction in fiber utilization, amounting to a 117 % (34 %) reduction concerning scenario 1 (2).
Detecting Disaster Trending Topics on Indonesian Tweets Using BNgram

People on social media share information about natural disasters happening around them, such as the details about the situation and where the disasters are occurring. This information is valuable for understanding real-time events, but it can be challenging to use because social media posts often have an informal style with slang words. This research aimed to detect trending topics as a way to monitor and summarize disaster-related data originating from social media, especially Twitter, into valuable information. The research method used was BNgram. The selection of BNgram for detecting trending topics was based on its proven ability to recall topics well, as shown in previous research. Some stages in detection were data preprocessing, named entity recognition, calculation using DF-IDF, andhierarchical clustering. The resulting trending topics were compared with the topics obtained using the Document pivot method as the basic method. This research showed that BNgram performs better in detecting trending natural disaster-based topics compared to the Document pivot. Overall, BNgram had a higher topic recall score, and its keyword precision and keyword recall values were slightly better. In conclusion, recognizing the significance of social media in disaster-related information can increase disaster response strategies and situational awareness. Based on the comparison, BNgram was proven to be a more effective method for extracting important information from social media during natural disasters.
Information-Theoretic Security in BB84 QKD

This paper demonstrates ways to improve the post-processing phase of BB84-based Quantum Key Distribution (QKD) while maintaining Information-Theoretic security. Maximum entropy in the distilled bits can be achieved provided that we follow certain rules on which bits are sacrificed based on the properties of the generator matrix. We use standard decoder techniques to distill bits and establish Information-Theoretic security and provide insights into how those techniques can be adapted to provide greater bit throughput. Finally, we compare popular coding schemes in BB84 post-processing as a basis for code selection and discover at low initial Bit Error Rates (BER), a Polar 4/5 or Hamming* give the best performance and for higher initial BER, the LDPC 3/4 and Hamming* (a modified Hamming code) give the best performance [1] [2] [9] [10].
The European Union takes its first steps in regulating the relationship between Artificial Intelligence and Intellectual Property

The “Fourth Industrial Revolution” has meant a ma-jor boost for Artificial Intelligence. Among its multiple implications from a legal perspective, its impact on the management and protection of intellectual property ri-ghts stands out. Faced with this reality, the European Union (EU) has adopted an active stance in mana-ging these relationships. This paper stems from this complicated reality, to address the different initiatives originated in the EU in this field. Not only to expose the underlying objectives and achievements, but also to present the lines that guide their work and future prospects.
Penggunaan Pajum Pada Siswa Kelas II SD Negeri Merauke Materi Penjumlahan dengan Teknik Menyimpan

This research is Classroom Action Research (CAR). The aim of the research is to improve Mathematics learning outcomes regarding Addition of Two Numbers using Saving Techniques for grade II students at SD Negeri 2 Merauke for the 2022/2023 academic year by using the Addition Board (Pajum) teaching aid until they reach the Minimum Completeness Criteria (MCC). The research is said to be successful if all students have reached the MCC 65. The results of the research show that there has been an increase, that Pajum teaching aids make learning material more interesting and simple. So it shows that there was an increase in the percentage of learning outcomes in Mathematics subjects before the action of 12 (43%) students who completed the first cycle, there was an increase to 18 (64%) students and after the second cycle it increased to 28 (100%) students who completed it. In accordance with the aim of this research, namely the use of Pajum teaching aids can improve learning outcomes about Adding Two Numbers with Saving Techniques for grade II SD Negeri 2 Merauke for the 2022/2023 Academic Year until they reach the MCC.
Sentiment Analysis Applied to Arabic Tweets Using Machine Learning and Deep Learning*

Natural language processing is a field of study that treats multiple tasks, such as text generation, language modeling, and classification. The extraction of valuable information from text documents allows for better decision making in political decisions and analyzing public opinion; those techniques are also deployed in support systems. This work focuses on Arabic sentiment analysis for Twitter comments in Modern Standard Arabic and Moroccan dialectal Arabic. The process starts by extracting data from Twitter using APIs. This dataset is used to train various machine learning and deep learning models. The data must be properly trained, which requires the right pre-processing. We used emojis to annotate the data, and word sequences are used to create word vectors, and these vectors are then passed through dense layers to generate word embeddings, which is specifically tailored to our dataset. Sentiment analysis is undertaken to extract opinions from Arabic text using both Deep Learning (DL) and Machine Learning (ML) models. Enabling a comparative analysis of performance between Deep Learning and Machine Learning algorithms applied to Arabic textual data. The results will be presented above and evaluated on the basis of many factors and indicators. This work has shown very satisfactory and promising results, which is very interesting and encouraging to keep working on this subject.
Determination of fluctuated threshold of input data in CNN-based Solar Irradiance Prediction Method Using All-sky Image

In Japan, the synchronized frequency restoration reserve, the frequency restoration reserve, the replacement reserve etc. are traded to respond for sudden changes in demand and fluctuations in renewable energy. These reserves are required to react in 5 to 15 minutes. To estimate the amount of electricity to be traded, it is necessary to predict the photovoltaic generation power. Therefore, all-sky images are analyzed using a convolutional neural network to predict solar irradiance 5 to 15 minutes ahead. The authors have reported that the prediction accuracy of solar irradiance is improved by training data with rapidly fluctuations of solar irradiance and the other data separately and creating respective solar irradiance prediction models. In this paper, the threshold value that judges whether solar irradiance fluctuates rapidly or not is changed and determined for suitable to predict solar irradiance 5 to 15 minutes ahead. As a result of verification, the threshold value was determined to be 100 W/$\mathrm{m}^{2}$ because it predicted rapidly fluctuating data with high accuracy at all prediction times. The prediction accuracy for 5, 10 and 15 minutes ahead were improved by 16.7%, 4.1% and 7.5%, respectively, compared to the conventional method that creates a solar irradiance prediction model using all data.
Data Security Patterns for Critical Big Data Systems

With the words' current growth in technologies and digitalization, protecting data in massive data-driven systems has become a new challenge to overcome. Therefore, many measures, algorithms, and protocols are used and still under development to minimize risks of Data loss, manipulation, sniffing, or spying… In order to gain more in terms of security, privacy, and integrity levels. As important as this issue is, combined efforts from different disciplines (Network Security, Security Operations Center Analysis (SOC Analysis), Machine Learning (ML), and Deep Learning (DL) algorithms) are necessary to enhance Data protection. In this paper, we will expose some natural threats of Big Data, based on its definition, ethics, and suggestions to avoid these threats, or at least limit their effects. We will also show works related to big data security based on ML and shed light on Artificial Neural Networks (ANN) applications for data classification and threat detection. Then focus on Convolutional Neural Networks (CNN) to optimize parameters. We will try to find solutions for CNN limitations of hyperparameters tuning and rotating image feature extraction. Furthermore, we will discuss state of art results to understand the matter in a deeper sense and expose high-accuracy solutions with improvement path suggestions. Our work is going to come up with some natural threats, compare some existing methods, expose, and discuss results, and then suggest optimal ways to help increase data security, especially through improving CNN limitations.
Enhancing Syntactic Resolution in Biomedical Data Processing with OpenCL: A Use Case Study

Biomedical data is known for its large size, which presents significant computational and storage challenges for its processing. Accelerating biomedical applications will be of height interest to the research community. This paper proposes a novel approach for biomedical syntactic resolution based on OpenCL, an open and versatile parallel computing framework, to accelerate the computational aspects of syntactic resolution in the medical domain. OpenCL's ability to leverage heterogeneous hardware architectures, including CPUs, GPUs, and FPGAs, provides flexibility and cost-saving opportunities. Within biomedical research, Syntactic resolution serves as a subtask in Named Entity Disambiguation (NED), an essential process for mapping ambiguous or variant mentions of biomedical entities to standardized concepts. One of the critical challenges in named entity normalization is achieving proper and fast syntactic resolution in large biomedical vocabulary. Our approach aims to improve the precision and relevance of search results in the context of biomedical named entity disambiguation. Through experimental evaluations, we demonstrate the effectiveness of our biomedical syntactic resolution approach in disambiguating biomedical terms with the application of OpenCL ensuring rapid execution. The results highlight the potential of OpenCL as a valuable tool for enhancing the performance and efficiency of biomedical NLP tasks, opening up new possibilities for accelerating other tasks in the biomedical domain.
A Hybrid Algorithm Based on PSO Algorithm and Chi-Squared Distribution for Tasks Consolidation in Cloud Computing Environment

In order to maximize the effectiveness and performance of cloud computing systems, this study focuses on addressing the challenges of workload balancing and resource utilization in cloud scheduling. Workload balancing plays a crucial role in ensuring that computing workloads are evenly distributed across available resources, thereby reducing the likelihood of resource constraints and enhancing system performance. On the other hand, resource utilization aims to utilize processing power, memory, and network bandwidth to their fullest capacity, resulting in improved efficacy and cost-effectiveness of the cloud infrastructure. To tackle these challenges, we propose a novel optimization technique called CHPSO (Chi-squared Particle Swarm Optimization) in this context. The proposed algorithm demonstrates its effectiveness in optimizing resource utilization compared to other algorithms such as PSO (Particle Swarm Optimization) and CS (Cuckoo Search).
A linear model for estimating power generation on city facade using a city 3D model and solar power generation simulation

Photovoltaic power generation is a promising method of power generation that is not affected by geographical characteristics. However, it is difficult in urban areas to supply sufficient electricity to high-rise buildings through photovoltaic power generation owing to the limited roof area. As a countermeasure, photovoltaic power generation on flat wall surfaces has attracted attention and demonstrated its high potential. In this study, using GIS software, aerial photo LiDAR data, basic map information, and historical weather data, area-level solar power generation in several Japanese cities was simulated as power generation for a smart grid using a simulation system. In Japan ’s six locations (Chofu, Sapporo, Kyoto, Hachiken, Takasaki, and Oyama), power generation on the vertical surfaces of buildings exceeded horizontal surfaces power generation. The total solar power generation was expected to be twice as much as using the top surface only. Furthermore, a city close to the south in Japan obtained more solar power generation. Besides, power generation is strongly influenced by the urban form; power generation potential at the wall surface in residential areas is greater than in central areas. Based on the simulation results, solar radiation, latitude, and the characteristics of cities, a log-linear model to estimate solar power generation was proposed and evaluated. Wall photovoltaic power generation is an effective renewable energy source in a smart grid.
Analyzing Sentiment with Self-Organizing Map and Long Short-Term Memory Algorithms

This research delves into the impact of Chat Generative Pre-trained Transformer, one of Open Artificial Intelligence Generative Pretrained Transformer models. This model underwent extensive training on a vast corpus of internet text to gain insights into the mechanics of human language and its role in forming phrases, sentences, and paragraphs. The urgency of this inquiry arises from Chat Generative Pre-trained Transformer emergence, which has stirred significant debate and captured widespread attention in both research and educational circles. Since its debut in November 2022, Chat Generative Pre-trained Transformer has demonstrated substantial potential across numerous domains. However, concerns voiced on Twitter have centered on potential negative consequences, such as increasedforgery and misinformation. Consequently, understanding public sentiment toward Chat Generative Pre-trained Transformer technology through sentiment analysis has become crucial. The research’s primary objective is to conduct Sentiment Analysis Classification of Chat Generative Pre-trained Transformer regarding public opinions on Twitter in Indonesia. This goal involves quantifying and categorizing public sentiment from Twitter’s vast data pool into three clusters: positive, negative, or neutral. In the data clustering stage, the Self-Organizing Map technique is used. After the text data has been weighted and clustered, the next step involves using the classification technique with LongShort-Term Memory to determine the public sentiment outcomes resulting from the presence of Chat Generative Pre-trained Transformer technology. Rigorous testing has demonstrated the robust performance of the model, with optimal parameters: relu activation function, som size of 5, num epoch som and num epoch lstm both at 128, yielding an impressive 95.07% accuracy rate.
Comparative Analysis of Numerical Integration Solutions Pias Method and Newton Cotes Method Using Python Programming Language

Irregular areas cannot be solved by ordinary calculus formulas, so it is necessary to use numerical methods such as the Quadrature and Newton-Cotes methods. This research compares numerical integration solutions using the Quadrature method (Rectangular and Trapezoidal) and the Newton-Cotes method (Trapezoidal, Simpson 1/3, Simpson 3/8, and Weddle) with the Python programming language. Manual calculation of the first case study on integrals where the smallest error from the numerical method to the analytical method is achieved by the rectangular method of 0,017. In the second case study of tabular data for manual calculations the author only uses the Simpson 3/8 method with an absolute error for an analytical area of 1.418,583 km2. Whereas in the Python application for the first case study the smallest error was achieved by the Simpson 1/3 and Simpson 3/8 methods with an error of 0, in the sense that these two methods are very accurate to the actual analysis results. In the second case study the smallest error was achieved by the Simpson 1/3 method of 1.039,365 km2. The difference between manual calculations and application results is due to decimal rounding, and the linspace(a,b,n+1) function in the numpy library.
News Website Implementation Using The Fuzzy Analytical Hierarchy Process (FAHP) Method

Technology is developing quite rapidly, especially when disseminating information through internet networks that reach the wider community using websites. Website news is a medium for delivering news information that has an important role in everyday life; thus, a website that has high quality, accuracy, and speed in its presentation is needed. Accessibility is a very basic thing to measure the quality of a website, especially a news website, so that it can provide convenience to users when accessing it. To answer these problems, measurements are needed to observe the priorities of all criteria by utilizing subjective fuzzy concepts. The Fuzzy Analytical Hierarchy Process (FAHP) is a technique used to obtain the weight value of each criterion. This research used four websites, including Kompas.com, CNN Indonesia, and Tempo, were used as research objects. Co and Bisnis.com. From this research, a final result was obtained: a business website that ranked first with the highest score of 0.63 and the last ranking obtaining a score of 0.11 on the Tempo.co website. Problems that often affect website accessibility are the number of elements that are not allowed to be used in the wrong position or the elements used are incomplete, so many empty tables violate WCAG 2.1. A 4.1.1 section 508 (2017) A 4.1.1 is difficult for users to use.
Metrics and Red Teaming in Cyber Resilience and Effectiveness: A Systematic Literature Review

Rising cyber risks have compelled organizations to adopt better cyber-protection measures. This study focused on discovering crucial security metrics and assessing the function of red teaming in enhancing cybersecurity defenses against novel cyber hazards. The PRISMA standard considered nine core research works issued between 2014 and 2023. The inclusion of red teaming best practices can significantly enhance cybersecurity architecture. Accurate simulations of cyber threats during red teaming exercises help identify vulnerabilities, and actively embracing red teaming can amplify an organization's capacity to repel future cyber assaults. Researchers and practitioners can utilize the study's insights to pioneer novel security solutions. Combining red teaming methodologies with relevant metrics is essential for enhancing cybersecurity posture. The study's discoveries grant companies a priceless benefit in navigating the rapidly changing cyber threat environment and reinforcing their cyber protection mechanisms.
Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures

Lung disease is one of the biggest causes of death in the world. The SARS-CoV-2 virus causes diseases like COVID-19, and the bacteria Streptococcus sp., which causes pneumonia, are two sample causes of lung disease. X-ray images are used to detect the lung disease. This study aimed to combine the stages of segmentation and classification of lung disease. This study in segmentation aims to separate the features contained in the lung images. The classification aimed to provide holistic information on lung disease. This research method used the Deep Residual U-Net (DrU-Net) segmentation architecture and the Deep Residual Neural Network (DResNet) classification architecture. DrU-Net is a modified U-Net architecture with dropout added in its convolutional layers. DResNet is a modified Residual Network (ResNet) architecture with dropout added in its convolutional block layers. The result of this study was segmentation using the DrU-Net architecture obtained 99% for accuracy, 98% for precision, 98% for recalls, 98% for F1-Score, and 96.1% for IoU. The classification results of the segmented images using the DResNet architecture obtained 91% for accuracy, 86% for precision, 85% for recalls, and 84% for F1-Score. The performance results of DrU-Net architecture were excellent and robust in image segmentation. Unfortunately, the average performance of DResNet in classification was still below 90%. These results indicate that Dres-Net performs well in classifying lung disorders in 3 labels, namely Covid, Normal, and Pneumonia, but still needs improvement.