Reviewerly/exHarmony huggingface dataset:
Moroccan Public Administration in the Era of Artificial Intelligence: What Challenges to Overcome?

The digital era has brought many benefits to the public system in the worldwide. It helped improving the quality of services offered to the citizen/costumer. Who is not any more obligated to be mobilized for beneficiating from services, which he could get on the internet in a few minutes. However, the development of the Artificial Intelligence and the significant increase of its users, make the Moroccan public system under new forms of pressure. The potential difficulties are due to the situation of the structure, which is not well prepared to join this new era, because of the sluggish process of digitalization.This article aims to explore the possible challenges that the Moroccan public administration may face for embracing the Artificial Intelligence solutions. The slow progress in adopting digital technologies has disadvantaged the public administration, while the world is moving so fast towards more complicated and more sophisticated AI solutions. The objective of this paper is to highlight the imperative obligation for the public system in Morocco to get over all the challenges in order to join the new era. The success of this process will automatically lead to a modern administration with less procedural complexity and more transparency. This work will also demonstrate how the change management can be a real lever for both of policymakers and civil servants, during the transition to the use of artificial intelligence solutions.This work is an anchor to a subsequent empirical study whose objective is assessing the organizational capacity of the Moroccan public administration to be completely digitalized, and start looking for adopting the solutions offered by artificial intelligence.
Solving The Magic Square Problem Using Selection Hyper-heuristics

Selection hyper-heuristics are high-level optimisation strategies the aim of which is to increase the generality of optimisation solvers. These have recently attracted substantial attention and effort from researchers in numerous areas including, but not limited to, operational research, artificial intelligence, engineering and computer science. A Magic Square is a matrix of nxn dimension, which includes all the numbers in $\{$1, 2, $\ldots, n^{2}\}$ such that the sum of each row, column and main diagonal is equal. With large values of n, it’s shown that finding the optimal configuration is hard. This is defined as a minimisation problem that aims to optimise a measure of error that describes how far a given configuration is from being a magic square. In this study, we present the results of testing twenty hyper-heuristics on various instances of the magic square problem. The empirical results reveal that hyper-heuristics that utilise a reinforcement learning selection method outperform all other hyper-heuristics in this study. In particular, with the increase in the complexity of the problem, a hyper-heuristic utilising simulated annealing as solution acceptance method in addition to the reinforcement learning proves to be promising.
Face Detection using Artificial Intelligence

Face detection has received significant attention in many applications, including surveillance systems and facial recognition technology. To ensure security and improve the user experience, accurate and fast facial detection is crucial. This paper presents a new method using advanced deep learning techniques for face detection. This work introduced deep learning Pipelines using Keras TensorFlow and models based on Convolutional Neural Networks (CNN) VGG16, VGG19, Resnet, and Inception. Our model produced classification accuracies of 99.38%, 99.08%, 87.10%, and 99.77%, respectively, for VGG16, VGG19, Resnet, and Inception.
On the Required Iteration Number of the Representant Swarm Intelligence Algorithms Based on Coefficient of Variation

Determination of the required parameters for different optimization algorithms used in specific environments was and remains a hot topic of the informatics sciences. Selection of the right tool for the problem set is hard and careful investigation is paramount. This scientific study investigates the effectiveness of swarm intelligence-based optimization methods, namely Artificial Bee Colony, Grey Wolf optimizer, Multiverse optimizer and Salp Swarm Algorithm methods on four well known search space functions: Sphere, Rosenbrock’s Saddle, Rastring’s function and Ackley’s function. Coefficient of variation of the cost functions was used to evaluate quantitatively the performance of the analysed searching spaces. Ranking of the algorithms was done for each of the optimization problem wherein the SSA algorithm demonstrated a higher degree of favourability across the majority of the search spaces investigated.
IMPLEMENTASI WEB SCRAPING PADA PORTAL BERITA ONLINE

This report discusses the use of web scraping on several well-known online news portals in Indonesia, such as CNBC Indonesia, CNN Indonesia, Kompas, Merdeka, Suara, Jawapos, JPNN, Republika, and Inews. Web scraping is used as a method to collect data from web pages automatically and efficiently. The web scraping process is carried out using the Visual Studio Code application with the Python programming language and the beautifulsoup library. This web scraping method is divided into several stages, starting from opening the scraping template, determining the website from which the data will be collected, exploring and navigating the site to identify the important elements that you want to retrieve, to running the web scraping and generating the desired data. The results of web scraping are in the form of media, titles, subtitles, news URLs, content, news dates, news editors, news journalists, and news locations. Through the implementation of web scraping, this research concludes several important things. First, web scraping helps simplify the process of collecting data from various news sources and combining them into one easily accessible place. Second, with web scraping, data analysis can be done more quickly and efficiently than manual collection methods. Finally, the results of web scraping can be used to see trends and patterns and analyze public sentiment towards news and certain topics, providing valuable insights for people's understanding of current issues.
Error analysis of precision measurement with monocular vision

Abstract To systematically study the measurement error of the calibration coefficient method when the camera’s optical axis is not perpendicular to the measurement plane, this paper first discusses the principle of error measurement with 2D imaging model, and then derives the distribution relationship of physical points corresponding to evenly distributed pixels in the pixel coordinate system based on the projection transformation matrix of the vision system. Theoretical analysis reveals an equivariance series in the expression of physical point intervals. Considering rotational symmetry of the coordinate axis, this paper analyzes the geometric relationship when the calibration plane rotates around the <?CDATA ${X}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="italic">X</mml:mi> </mml:math> axis and derives the mathematical expression for dimensional measurement. Finally, an experimental platform with both industrial and telecentric lenses is proposed and designed. Experimental results demonstrate that the measurement results of the expression derived in this paper are consistent with the traditional coordinate transformation method and the maximum deviation of the measurement is less than 0.06 mm, validating the theoretical analysis presented in this paper and providing theoretical guidance for subsequent practical application.
Automatic Protection of Web Applications Against SQL Injections: An Approach Based On Acunetix, Burp Suite and SQLMAP

Web application security is a critical concern in the digital world. One of the most common and dangerous vulnerabilities in web applications is SQL injection. SQL injection is an attack technique that allows a hacker to exploit security weaknesses in web applications that use SQL queries to interact with the database. This article presents a practical, step-by-step approach to detecting and exploiting the SQL injection vulnerability. The solution combined the use of Acunetix Web Vulnerability Scanner for detection, Burp Suite for capturing http requests containing parameters vulnerable to SQL injection, and SQLMAP as an automatic SQL Injection operating tool. The test was performed in a practical way using the real Damn Vulnerability Application and simulating SQL injection attack scenarios on each available security level: low, medium, and high. The results obtained showed very high performance of the solution at the different security levels, although the security mechanisms have been strengthened at these levels.
Deploying Computational Intelligence AI and Visual Quality Optimization Tools In Embedded Image Watermarking to Enhance the Agricultural IoT PAYLOAD Features

In this work, we propose an agricultural IoT platform, which helps the farmers and all the stakeholders in the food value chain, such as agribusiness professionals, marketers or even the consumer each at its level of use, to send, authenticate and protect these products from the producer to the final consumer.This solution is composed of a photo camera, QrCode generators, QrCode identification readers to read the content of the QrCode, i.e. details about the agricultural products, and an intelligent object recognition system to ensure that the QrCode is appropriate for the agricultural product being identified. To optimize the data transmission, captured from the recipient in terms of execution time, bandwidth, storage space and energy consumption, the proposed solution deploys SPIHT visual coding [1], Foveal coding [2], artificial intelligence for decision making, CoAP communication protocol [3], ECC security encryption [4] and Watermarking [5]to ensure the security of the QrCode during its transmission.The use of this solution will allow to battle against the fraud and the theft of the property of the agricultural products by sending the photo of the agricultural product and its QrCode with full security and optimizing their sending. time and virtual.
A Survey on Automatic Signal Detection Using Deep Learning

Cognitive radio (CR), a potential radio technology, has a number of applications for maximizing the utilization of the useable electromagnetic spectrum. It can efficiently locate and exploit the unused spectrum for improved communication performance, which can help solve the recent surge in the use of wireless sensor networks (WSNs). While performing signal detection and extracting features from the transmitted signal with traditional methods is complicated. Deep learning, on the other hand, is a formidable technique that has seen significant success in a variety of applications. Therefore, deep learning for automatic signal recognition can remove all of this complexity because it does not require prior knowledge or human interaction. This article highlights existing research on the capabilities and fundamental uses of deep learning and the challenges in signal detection. Then, for future research, numerous open research paradigms will be discussed.
Effects of term weighting approach with and without stop words removing on Arabic text classification

Text classification is a technique for grouping articles into pre-defined categories. Prior to categorization, text documents have to be set up and organised in a form that is compatible with the data mining techniques. That has produced many term weighting techniques have been developed in the literature to improve the functionality of text categorization systems. This paper compares techniques for text classification based on when stop words are eliminated from the documents once and when they are not. We used an Arabic data set of 322 papers grouped into six subjects (health, politics, science, education, agriculture and finance) to research which is the influence of prior weighting of features approaches on classification outcomes with regard to accuracy, recall, precision, and F-measure values. Each document subset includes 50 documents, except the health one that includes 61. We carried out four experiments and checked results getting that only for precision, the term frequency feature weighting approach with stop word removal are more or less the same than for binary method. Moreover the conclusion for accuracy, recall and F-measure is that Feature weighting approach with stop word outperforms the Binary one. Additionally, if the phrase for weighting is the same for both methods, to remove stop word increases classification accuracy for all the experiments.
Quantum Communication Technologies: Future Trends And Prospects For Innovation

Humanity’s social, economic, and technological progress could not have been made without communications infrastructure. The need to attain the sophistication of long-distance communication has grown steadily since the dawn of civilization. The modern global communications system is the single greatest infrastructural project in human history. The global telecommunications grid which uses satellites, deep-sea fiber-optic systems, Wi-Fi, Bluetooth, microwave and other radio, and optical-frequency communications networks are tremendous in coverage, unparalleled in connection, and ubiquitous in application. As we approach the middle of the 21st century, this system’s next major update will incorporate quantum communications technology. Quantum communication, in tandem with quantum computing and quantum sensing, constitutes one of the three pillars of second-generation quantum technology and promises an array of novel applications. Considering the reliable transmission of quantum information often requires the transport of individual particles of light photons through a world inundated by trillions upon trillions of photons, quantum communication systems are an engineering marvel. To provide a technological alternative for existing public-key encryption protocols, which are now susceptible to the threat posed by future quantum computing systems, nascent commercial quantum communication networks aim to realize the potential for cryptographic security provided by the laws of quantum mechanics. However, a number of obstacles must be overcome before quantum communications can be widely adopted. This study focuses on the current state of quantum communication, the promise of its prospects in the near and far future, the obstacles it faces, and the breakthroughs and applications developed by the United Arab Emirates.
ANALISIS KLASTER INFRASTRUKTUR SEKTOR KESEHATAN DI WILAYAH KABUPATEN WONOGIRI TAHUN 2021 MENGGUNAKAN METODE ALGORITMA K-MEANS

Equalizing the development of health infrastructure is one way to improve the Human Development Index (HDI). A high HDI indicates the successful improvement of societal well-being in a region. This research discusses the utilization of the k-means algorithm to cluster sub-districts in Wonogiri Regency based on the similarity of health infrastructure using five metrics: the number of general hospitals, inpatient community health centers, outpatient community health centers, private clinics, and integrated health posts (posyandu). Three clusters were identified based on the distribution of health infrastructure: Cluster 1 consists of the following sub-districts: Paranggupito, Giritontro, Batuwarno, Karangtengah, Wuryantoro, Manyaran, Ngadirojo, Sidoharjo, Jatiroto, Bulukerto, Puhpelem, Slogohimo, Jatisrono, and Jatipurno. Cluster 2 consists of the following sub-districts: Giriwoyo, Tirtomoyo, Nguntoronadi, Eromoko, Kismantoro, Purwantoro, and Girimarto. Cluster 3 consists of the following sub-districts: Pracimantoro, Baturetno, Selogiri, and Wonogiri. By analyzing the distribution of health infrastructure, significant differences were observed among the clusters. In enhancing the HDI of Wonogiri Regency, the findings of this research can be utilized for government program planning to promote equitable development of health infrastructure.
Directional router and controllable non-reciprocity transmission based on phase and pathway coherence

Abstract A multi-channel quantum router with four nodal cavities is constructed by two coupled-resonator waveguides and four single cavities. We can achieve directional routing by adjusting the probability of photon exiting from the specified port to close to 100% based on multiple pathways between the photon from the incident port to the outgoing port in this hybrid system. Under the effect of phase difference between two classical light fields, the mutual interference between different pathways can be adjusted to destructive interference or constructive interference, which lays the foundation for the increase and decrease of the routing probability. The influence of different parameter values on single photon routing probability is also studied. By studying the analytic formula of probability amplitude, we get the physical mechanism of exiting ports being closed under certain parameter conditions and the phase relationship between the backward transmission and the original direction transmission of photons. Furthermore the non-reciprocal transmission and directional routing beyond chiral coupling can also be realized, which provides new possibilities for the study of quantum routers and new insights for the study of photon transmission characteristics.
PENGEMBANGAN SISTEM INFORMASI KELURAHAN TEGALHARJO KOTA SURAKARTA DENGAN WORDPRESS CONTENT MANAGEMENT SYSTEM

The government of Surakarta City uses the WordPress CMS platform to share public information. The obstacle faced at this time is the lack of public information disclosure, especially in the Tegalharjo Sub-district. To improve public information accessibility, we conducted a real-life work practice with the goal of helping the government improve how they share and manage public information by adding PPID information to the sub-districts website and performing website maintenance. The methods we used are included, such as needs analysis, planning, preparation, and website maintenance, followed by a performance evaluation stage. The achieved results make it easy for Tegalharjo Sub-district to handle public information and provide the community with easier access to information from PPID of Surakarta City's.
An Investigation into Fuzzy Negation in Semantic Similarity Measures

Machine computation of semantic similarity between short texts aims to approximate human measurements of similarity, often influenced by context, domain knowledge, and life experiences. Logical negation in natural language plays an important role as it can change the polarity of meaning within a sentence, yet it is a complex problem for semantic similarity measures to identify and measure. This paper investigates the impact of logical negation on determining fuzzy semantic similarity between short texts containing fuzzy words. A methodology is proposed to interpret the implications of a negation word on a fuzzy word within the context of a user utterance. Three known fuzzy logical not operators proposed by Zadeh, Yager and Sugeno are incorporated into a fuzzy semantic similarity measure called FUSE. Experiments are conducted on a sample dataset of short text inputs captured through human engagement with a dialogue system. Results show that Yager's weighted operator is the most suitable for achieving a matching threshold of 90.47% accuracy. This finding has significant implications for the field of semantic similarity measures. It provides a more accurate way to measure the similarity of short texts that contain fuzzy words combined with logical negation. Whilst validation of the approach on more substantial datasets is required, this study contributes to a better understanding of how to account for logical negation in fuzzy semantic similarity measures and provides a valuable methodology for future research in this area.
Data-Driven Structure Identification of Takagi Sugeno Fuzzy Models Using a Bounded Error Approach

Takagi Sugeno fuzzy models are a widely used model class for data driven identification of nonlinear systems. Besides good approximation quality, the local model approach allows for good interpretability and the utilization of linear control strategies in dynamic models. However a major challenge is the identification of the model structure, i.e. determining the number of local models and the partitioning parameters. A new method for identification of Takagi Sugeno fuzzy models inspired by bounded error methods is presented. The method finds partitions of the data that are defined by bounding linear inequalities and therefore identifies locally linear system behavior. The error bound as a tuning parameter is used to adjust the compromise between model accuracy and sparsity. The presentation of the method is complemented by two case studies.
Explain Reinforcement Learning Agents Through Fuzzy Rule Reconstruction

There has been a lot of interest in making reinforcement learning (RL) models more explainable, as their applications become more widespread. Currently, most of the explainable RL (xRL) models focus on improving the transparency of the agent's observations, rather than the relationship between the agent's states and actions. This study introduces the Explainable Fuzzy Reconstruction Net (EFRN), which aims to interpret these relationships in RL. The EFRN utilizes the interpretability of Fuzzy Neural Networks (FNNs) to create IF-THEN rules and a generative model to showcase the learned knowledge. The IF-THEN rules can be expressed in a way that is easily understandable for humans, such as “IF A THEN B.” The generative model then visualizes the state as patterns that is easy for humans to comprehend. The results of the study shows that the proposed EFRN maintains the same level of performance as traditional RL methods and significantly improves the explainability of the RL agents both globally and locally.
Dynamic Evaluation of Fuzzy Compositions

Compositions of partial fuzzy relations dealing with undefined values have been extensively studied in partial fuzzy set theory. Their effectiveness in practical classification problems has been addressed. In general, the compositions relate to three sets of objects, truth-valued features and classes. The aim of compositions is to assign a class to an object by using knowledge of the assignment of features to objects and classes to features. In a classical setting, all features of objects (subjects to the classification) are known in advance. Such an assumption may be indeed inconvenient for some practical applications, e.g., because of the costly determination of features. This paper aims at the development of such framework that allows obtaining results from the composition even if some features are unknown and, more importantly, to select the most appropriate feature to be questioned additionally to maximize the accuracy of the result. For this purpose, the concept of dynamic feature selectors is defined. Based on classification results obtained from the composition on mandatory features, a dynamic feature selector recognizes the most promising optional feature whose knowledge may best improve the accuracy of classification. We propose two distinct dynamic feature selectors: (1) based on the sharpness and maximal truth values of the result, the ability to reach maximal values, and (2) based on the distance between the original and updated result of the composition. A practical experiment shows the performance of the proposed solution.