Reviewerly/exHarmony huggingface dataset:
Towards Privacy Preserving and Efficiency in Fog Selection for Federated Learning

Federated learning (FL) is an emerging trend related to the concept of distributed Machine Learning (ML). It focuses on a collaborative training process locally conducted on the dataset of the client devices in order to preserve the users’ privacy. Nonetheless, this solution still suffers from many challenges dealing with privacy, security, and performance. In this work, we introduce a novel policy-based FL approach for improving privacy, security, and performance in federated learning. Our proposed solution ensures reliability, communications security, and heterogeneous privacy (i.e., the users have different privacy attitudes and expectations). In addition, it guarantees performance in terms of the dataset’s quality and scalability. To prove the effectiveness of our model, we perform a security and performance evaluation by assuming a threat model with attackers having different behaviors.
Finding Solution for Practical Economic Load Dispatch Problem Using Dragonfly Algorithm in Comparison with Novel Bat Algorithm

Aim: The goal of this research is to create a novel BAT algorithm (BA) that can achieve lower total generation costs than the Dragonfly algorithm. Materials and Methods: The analysis included a total of 20 samples. Group 1 includes the novel BAT algorithm technique, while group 2 includes the Dragonfly algorithm. Groups 1 and 2 are statistically examined with 20 samples, each with an 80 % G power. Results: The Dragonfly algorithm had a mean cost of 337629.5 USD while the novel BAT algorithm technique had a mean cost of 329117 USD. The significance obtained in developing a revolutionary novel BAT algorithm is 0.002. Conclusion: The novel BAT algorithm performed significantly better than the Dragonfly algorithm in reducing the total generation cost.
An Overview of ICT Technology Advancement

Information and Communication Technology, precisely referred to as ICT, is a wider umbrella term that covers different types of technologies—both hardware and software technologies—associated with the communication systems, techniques, transmission media, software programs, protocols, sensors, and standards.
Augmented Bricks an Onsite AR Immersive Design to Fabrication Framework for Masonry Structures

Abstract The Augmented Bricks research project aims to develop an immersive design to fabrication framework for the assembly of masonry building components by incorporating robotic fabrication and augmented reality (AR) technologies. Our method incorporates two main phases: firstly, the design phase in which users’ gestures and interactions are being identified in AR for the immersive design and simulation process; secondly, an innovative robotic assembly phase in which users can control a robotic arm for assembly by interacting with the AR user interface (UI). Our framework is validated by the design and assembly of four brick-based columns. Our findings highlight that the proposed design to fabrication framework offers a novel, intuitive design inspiration and experience beyond the traditional design methods. It returns the task of assembling parametric structures with high-tech equipment back to the designers, allowing them to master and participate in the entire design to the fabrication process. The impact of this practice-based research will allow architects and designers to modify and construct their designs more simply and intuitively through the AR environment.
Impact of Advanced and Futuristic Technologies on Cybersecurity

The threat surface expands with the expansion and utilization of more technologies and technological ecosystems because the new technologies that are evolving are used in the business much faster to achieve the competitive-edge in the market and to capture as much share of the market as possible.
Quantum Computing

Quantum computing is a newly emerging technology in the field of computer science and technology. The quantum computing is based upon the quantum mechanics of an atom’s particles. The theory behind quantum computing is quantum theory of mechanics in which multiple states of a particle of an atom is used.
Geometrical Feature Identification of Cuneiform Signs on Micro-Survey Reconstruction

According to the increasing possibilities offered by technological innovation, new ways of visualizing and exploring data are now accessible in the context of 3D digital documentation. Surveying small artifacts and accurately representing their geometry, however, remains challenging. This can be ascribed to the still high cost of the most popular equipment for that purpose (active sensors) as well as the requirement for professional expertise when employing less expensive methods (passive sensors). This project aims to provide a low-cost configuration for an image-based approach to the demand for a digital survey and 3D representation of sub-millimeter morphological features. A portable USB microscope was used as equipment. The subject of the experiments is a cuneiform tablet belonging to the collection of Ghent University (Belgium) and datable to the end of the 3rd millennium B.C. More specifically, the study seeks to examine the geometry of the wedge-shaped impressions on the clay artifact. Going beyond the traditional practices of text analysis, an accurate representation of the cuneiform signs can be helpful for researchers in conducting comparative geometric studies. This goal can be enriched by recognizing from the analysis the type of stylus used to validate a manufacturing identity and a chronological classification. It is desirable that the data collected can then be shared, on the one hand, by enabling descriptive assessments even between different collections around the world and, on the other hand, by improving the promotion and interaction with the contents of a future exhibition.
INSL: Text2SQL Generation Based on Inverse Normalized Schema Linking

Structured Query Language (SQL) is a query language widely used in databases, Text2SQL automatically parses natural language into SQL, which has great potential to facilitate non-expert users to query and mine structured data using natural language. Current research focuses on improving the matching accuracy of SQL clause tasks, but ignores the correctness of SQL syntax generation, and SQL generation involving multi-table joins still suffers from a large number of errors. Therefore, a neural network-based Text2SQL approach is proposed. To implement a practical Text2SQL workflow, the model associates natural language queries with an inverse normalized database schema, called INSL (Inverse Normalized Schema Link Generation Network). Through theoretical analysis and experimental validation on the public dataset Spider, INSL can effectively improve the quality of Text2SQL tasks.
Aircraft Target Detection Algorithm Based on Improved YOLOv5s

Aiming at the characteristics of multi-scale, diversity and complex background of aircraft targets, in order to improve the average detection accuracy of YOLOv5s algorithm, an improved aircraft target detection algorithm based on YOLOv5s model is proposed, Firstly, replace conv module in the backbone network of YOLOv5s with RepVGGBlock to reduce the number of parameters; Secondly, a small target detection head is added to enhance the recognition ability of small targets; Finally, GAM_Attention mechanism is introduced in front of each detection head to improve the detection accuracy. The research shows that the RG-YOLOv5s (Repvggblock GAM_Attention you only look once) algorithm proposed in this paper improves the average accuracy by about 1% and reaches 97.3% when IOU = 0.5, which is more suitable for the detection of aircraft remote sensing targets.
Software Engineering Methodology for Smart Healthcare Security and its Application in Bangladesh

In the modern day, producing new software requires utilizing the most cutting-edge tools and techniques.Due to the growing complexity of technology, effective software development increasingly depends on well-managed development processes.A software development project has its own distinctive features.There is no one software process model that can manage all the many types of software development projects because there are so many distinct sorts of them.Whatever paradigm is utilized, there will always be limitations.The goal of the healthcare system security and its application is to provide the best possible clinical treatment and to receive the best possible assistance for patient care and security.The healthcare structures need to support good health, honest economic contribution, and top-notch services.The healthcare business has quickly expanded over the past ten years, especially in developing nations.Implementing medical software programs is one of the most significant future challenging scenarios in the healthcare industry.Utilizing healthcare data structures will expand the availability of fitness services, improve patient satisfaction with care, and reduce clinical errors.We are providing a fresh approach to software engineering for healthcare programs called SDLC as a result.This approach is intended to address the softwareimprovement issues and challenging circumstances that exist outside the healthcare area.The need to create durable, steady, and high-performing healthcare equipment is the main driver behind the development of a new software engineering methodology for the industry, including SDLC.
Optimal Deployment of Large-Scale Wireless Sensor Network Based on Topological Potential Adaptive Graph Clustering

This paper proposed an optimal deployment algorithm for large-scale wireless sensor networks based on graph topology. The algorithm mines the intrinsic property of sensor measurement data as node quality through auto encoder and defines topological potential to describe the relationship between sensor nodes as the distribution of the topological potential field, thereby dividing the large-scale graph into several subgraphs. Next, singular value-QR decomposition is used to find the crucial nodes in each sub-network, and achieve the optimal deployment of large-scale sensor networks. We experiment with this algorithm on the CIMIS dataset. The results show that the mean square error of this algorithm is only 0.02116.
Unsupervised Anomaly Detection Method Based on DNS Log Data

In order to solve the problem of network attack by malicious code using Domain Name System (DNS), on the basis of analyzing the characteristics of malicious code lines and abnormal operation behaviors, this paper proposes an unsupervised abnormal IP detection method based on DNS log data. Through the construction of DNS fingerprint characteristics, it is used to demonstrate the DNS behavior characteristics of IP to the greatest extent. The detection model is constructed by using isolated forest and local outlier factor algorithm, and the anomaly score of IP is obtained. The experimental results show that the detection method designed in the paper can well detect the attack exceptions and operation exceptions in the network environment. With the help of whitelist, the accuracy of the method can reach more than 90% after selecting the appropriate anomaly score threshold.
Cognitive Infocommunication and Carbon-Carbon Interaction

Abstract The quality of the infocommunication plays a key role and influences the smoothness of the output resulted by the Human-Computer Interaction. To maximise the quality of the Human-Computer Interaction, high levels of synergy are needed between both actors. To achieve the required level and quality of infocommunication, there is a need for a better understanding of the underlying processes. These processes might not be limited to Human and Computer only but might show some similarity with other carbon-based Agents. Inspired by different fields of biology, the aim of this research is to show how the connection and communication between entities create a synergy that supports them bilaterally. This provides the opportunity to translate a similar result into the Human-Computer Interaction in order to improve the outcome of the infocommunication.
A Multi-task Learning Model for Emotion-Cause Extraction Based on Emotion Classification

Emotion-Cause joint extraction is to extracting both the emotion and its corresponding cause from the given text, which has a wide range of application scenarios. Previous work only considered emotion extraction, cause extraction and emotion-cause relation classification tasks. This paper proposes a multi-task learning model based on emotion classification to perform emotion-cause joint extraction in a unified model, which obtains semantic features of different granularity based on Bert and attention mechanism. We also introduce focus loss function to deal with the sample imbalance problem. Experimental results show that the emotion classification subtask can effectively extract the emotional features of documents. Compared with the existing models, the experimental results show that our model outperforms the state-of-the-art model on emotion-cause joint extraction.
Enhancing video game localization through dubbing <b>Enhancing video game localization through dubbing</b> , by Laura Mejías-Climent, Cham, Switzerland, Palgrave Macmillan, 2022, 247 pp., €99.99(hardcover), €85.59(e-book), ISBN 978-3-030-88291-4, ISBN: 978-3-030-88292-1(e-book)

"Enhancing video game localization through dubbing." Perspectives, ahead-of-print(ahead-of-print), pp. 1–2 Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.
A Deep Learning Model for Detection Cancer in Breast

It is exceedingly difficult to identify and classify breast cancer. In reality, a tumor or cancer is a complicated process that involves several changes to the mammography images. Additionally, distinct tissues are used to describe various sections of the image that exhibit variable and high appearance. The primary advantage of this method is image classification for cancer prediction and performance improvement. On an open-source dataset, we trained and tested the application of our research. Python 3 will be used to create this project. The Jupiter IDE will be used to deploy the project. The overall goal of this project is to provide the highest level of performance and efficiency.
Image encryption based on combined between linear feedback shift registers and 3D chaotic maps

Protecting information sent through insecure internet channels is a significant challenge facing researchers. In this paper, we present a novel method for image data encryption that combines chaotic maps with linear feedback shift registers in two stages. In the first stage, the image is divided into two parts. Then, the locations of the pixels of each part are redistributed through the random numbers key, which is generated using linear feedback shift registers. The second stage includes segmenting the image into the three primary colors red, green, and blue (RGB); then, the data for each color is encrypted through one of three keys that are generated using three-dimensional chaotic maps. Many statistical tests (entropy, peak signal-noise ratio (PSNR), mean square error (MSE) and correlation) were conducted on a group of images to determine the strength and efficiency of the proposed method, and the result proves that the proposed method provided a good level of safety. The obtained results were compared with those of other methods, and the result of comparing confirms the superiority of the proposed method.
The possibilities of an interactive multifunctional cloud portfolio for the individualization of education in primary school

Problem statement . One of the priorities of the modern school is informatization and individualization of education. The implementation of these tasks can be facilitated by an electronic portfolio as a means for evaluating the individual educational results of students, optimizing work with information, and supporting the design of a non-linear structure for presenting materials in different formats. The research is aimed at studying the possibilities of an interactive multifunctional cloud portfolio for the individualization of education in a primary school. Methodology . Theoretical and methodological analysis and generalization of legal acts, literature on the application of information and communication technologies in education, processing of test results and electronic content of the portfolio are applied. Experimental work was carried out on the basis of Gymnasium No. 1 in Kirovo-Chepetsk. The experiment involved 52 students from grades 5-10 (girls - 52%, boys - 48%). Pearson’s chi-squared test for statistical data processing was used. Results . A model of an interactive multifunctional cloud portfolio has been developed for the individualization of education in a primary school. This model determines the structure of an interactive multifunctional cloud portfolio, including the sections “My individual educational route” (personal development of the student), “My lessons” (class activities), “My studios” (extracurricular activities), “My affairs” (educational work). The Google Classroom service was used to implement the model. Statistically significant differences in qualitative changes in the pedagogical system were revealed. Conclusion . The possibilities of an interactive multifunctional cloud portfolio for the individualization of education in a primary school are summarized: changing the nature of interaction between participants in educational relations; formation of universal educational activities, communication skills; personal development of students, etc.
Machine Learning Research on Breast and Lung Cancer Detection

As the diagnosis of these cancer cells at late stages causes greater pain and raises the likelihood of death, the initial-state cancer finding is crucial to giving the patient the proper care and reducing the risk of dying from cancer. The publication offers a chance to research breast and lung cancer detection techniques as well as various algorithms for cancer early detection. With the aid of various image kinds and test results data sets, hybrid approaches are utilized to identify lung and breast cancer based on the size and form of the cells. The basic concept of breast and lung cancer block diagram is also explained in this study, with an emphasis on the difficulties and potential future applications of cancer detection and diagnosis techniques.
Teachers’ Attitudes toward Integrating ICT in Teaching Chemistry in Morocco: Contribution, Constraints and Perspectives

Nowadays, Information and Communication Technologies in Education (ICT) have become essential pedagogical tools in any quality education system.While their integration becomes a necessity, especially in the scientific disciplines in general and chemistry in particular, which were the subject of this study.The current article aims to present the attitudes of physics-chemistry teachers of secondary education facing the integration of the information and communication technologies (ICT) in the teaching-learning process during the sessions of chemistry, on the one hand.On the other hand, it highlights the advantages and obstacles to the integration of ICT in the teaching-learning of chemistry, and identifies the conditions that promote the integration of ICT in teachers' practice.For this, we conducted a questionnaire survey with 41 teachers of secondary education from various high schools in the region Casablanca-Settat.The research results suggest that teachers have positive attitudes toward the integration of ICTE into teaching practices, and the majority of teachers emphasize the benefits of ICTE in chemistry lessons.However, the study revealed certain constraints and obstacles that hinder the integration of ICTE, including the lack of computer equipment in schools, overcrowded classrooms, and the absence of educational software adapted to the curriculum.Furthermore, the study proposes some perspectives that would allow for the implementation of a learning environment integrating ICTE in chemistry courses.
Maximizing the utility of public data

The human genome project galvanized the scientific community around an ambitious goal. Upon completion, the project delivered several discoveries, and a new era of research commenced. More importantly, novel technologies and analysis methods materialized during the project period. The cost reduction allowed many more labs to generate high-throughput datasets. The project also served as a model for other extensive collaborations that generated large datasets. These datasets were made public and continue to accumulate in repositories. As a result, the scientific community should consider how these data can be utilized effectively for the purposes of research and the public good. A dataset can be re-analyzed, curated, or integrated with other forms of data to enhance its utility. We highlight three important areas to achieve this goal in this brief perspective. We also emphasize the critical requirements for these strategies to be successful. We draw on our own experience and others in using publicly available datasets to support, develop, and extend our research interest. Finally, we underline the beneficiaries and discuss some risks involved in data reuse.
A Quantitative Approach to the Study on Length-Frequency Relationship of English Patterns

As a master of lexicology and corpus-driven linguistic studies, pattern grammar shares the features of concern on collocation and approximation to natural language, which provides an optimization idea for the extensively used phrase structure grammar and dependency grammar in natural language processing. This research adopts the methods of quantitative linguistics, taking patterns as the research focus to explore the length-frequency relationship of patterns, verify Zipf law and synergic linguistic model in respect of patterns, and analyze the usage characteristics of patterns in different registers. The results have demonstrated that Zipf's law is also applicable to patterns, and the synergic linguistic model shows a preferable goodness of fit on the description of the pattern length-frequency relations. This study provides bidirectional contributions to pattern grammar and the synergic relationship between length and frequency. In addition, it offers a refinement method for the application of patterns in different registers.
Correction for Carulli et al., Cerebellar plasticity and associative memories are controlled by perineuronal nets

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Supplementary Figures from The Mutational Signature Comprehensive Analysis Toolkit (musicatk) for the Discovery, Prediction, and Exploration of Mutational Signatures

&lt;p&gt;Supplementary Figures and Legends in the order cited in the manuscript&lt;/p&gt;