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The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: "Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.". This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognized to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research. After discussing the new context, this paper will try to review recent advances in the exploration of ionosphere parameter time series in terms of chaos theory and the use of machine-learning algorithms. [ABSTRACT FROM AUTHOR]

Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement. [ABSTRACT FROM AUTHOR]

With the development of science and computer technology, social networks are changing our daily lives. However, this leads to new, often hidden dangers in areas such as cybersecurity. Of these, the most complex and harmful is the Advanced Persistent Threat attack (APT attack). The development of personality analysis and prediction technology provides the APT attack a good opportunity to infiltrate personality privacy. Malicious people can exploit existing personality classifiers to attack social texts and steal users' personal information. Therefore, it is of high importance to hide personal privacy information in social texts. Based on the personality privacy protection technology of adversarial examples, we proposed a Supervised Character Resemble Substitution personality adversarial method (SCRS) in this paper, which hides personality information in social texts through adversarial examples to realize personality privacy protection. The adversarial examples should be capable of successfully disturbing the personality classifier while maintaining the original semantics without reducing human readability. Therefore, this paper proposes a measure index of "label contribution" to select the words that are important to the label. At the same time, in order to maintain higher readability, this paper uses character-level resemble substitution to generate adversarial examples. Experimental validation shows that our method is able to generate adversarial examples with good attack effect and high readability. [ABSTRACT FROM AUTHOR]

With the development of computer science technology, theory and method of image segmentation are widely used in fish discrimination, which plays an important role in improving the efficiency of fisheries sorting and biodiversity studying. However, the existing methods of fish images segmentation are less accurate and inefficient, which is worthy of in-depth exploration. Therefore, this paper proposes an atrous pyramid GAN segmentation network aimed at increasing accuracy and efficiency. This paper introduces an atrous pyramid structure, and the GAN module is added before the CNN backbone in order to augment the dataset. The Atrous pyramid structure first fuses the input and output of the dilated convolutional layer with a small sampling rate and then feeds the fused features into the subsequent dilated convolutional layer with a large sampling rate to obtain dense multiscale contextual information. Thus, by capturing richer contextual information, this structure improves the accuracy of segmentation results. In addition to the aforementioned innovation, various data enhancement methods, such as MixUp, Mosaic, CutMix, and CutOut, are used in this paper to enhance the model's robustness. This paper also improves the loss function and uses the label smoothing method to prevent model overfitting. The improvement is also tested by extensive ablation experiments. As a result, our model's F1-score, GA, and MIoU were tested on the validation dataset, reaching 0.961, 0.981, and 0.973, respectively. This experimental result demonstrates that the proposed model outperforms all the other contrast models. Moreover, in order to accelerate the deployment of the encapsulated model on hardware, this paper optimizes the execution time of the matrix multiplication method on Hbird E203 based on Strassen's algorithm to ensure the efficient operation of the model on this hardware platform. [ABSTRACT FROM AUTHOR]

In recent years, cellular neural networks (CNNs) have become a popular apparatus for simulations in neuroscience, biology, medicine, computer sciences and engineering. In order to create more adequate models, researchers have considered memory effects, reaction–diffusion structures, impulsive perturbations, uncertain terms and fractional-order dynamics. The design, cellular aspects, functioning and behavioral aspects of such CNN models depend on efficient stability and control strategies. In many practical cases, the classical stability approaches are useless. Recently, in a series of papers, we have proposed several extended stability and control concepts that are more appropriate from the applied point of view. This paper is an overview of our main results and focuses on extended stability and control notions including practical stability, stability with respect to sets and manifolds and Lipschitz stability. We outline the recent progress in the stability and control methods and provide diverse mechanisms that can be used by the researchers in the field. The proposed stability techniques are presented through several types of impulsive and fractional-order CNN models. Examples are elaborated to demonstrate the feasibility of different technologies. [ABSTRACT FROM AUTHOR]

Local fractional calculus (fractal calculus) plays a crucial role in applications, especially in computer sciences and engineering. One of these applications appears in the theory of chaos. Therefore, this paper studies the dynamics of a fractal complex logistic map and then employs this map to generate chaotic sequences for a new symmetric image encryption algorithm. Firstly, we derive the fractional complex logistic map and investigate its dynamics by determining its equilibria, geometric properties, and chaotic behavior. Secondly, the fractional chaotic sequences of the proposed map are employed to scramble and alter image pixels to increase resistance to decryption attacks. The output findings indicate that the proposed algorithm based on fractional complex logistic maps could effectively encrypt various kinds of images. Furthermore, it has better security performance than several existing algorithms. [ABSTRACT FROM AUTHOR]

In the context of a smart city, the collected data may be used with machine learning techniques to forecast unforeseen occurrences. Social goods are commodities and services that for-profit businesses, government agencies, or private enterprises may offer. Additionally, intelligent environments contain a variety of sensors, including motion, touch, door, and light sensors. [Extracted from the article]

The objective of this paper is to manifest the intellectual and cognitive structure of CORINE Land Cover (CLC) research applications. Data from the Web of Science (WoS) was used to delimit publication on CLC during the period from 1985 until 2019 (29th April), retrieving a total of 873 documents. Through author citations, the origins and the most influential papers were identified. The main lines of research were identified from word co-occurrences extracted from the titles, keywords, and abstracts of the papers. In the view of both structures, it can be concluded that CORINE land cover constitutes a relatively young set of scientific data, with a constant expansion and a strongly interdisciplinary structure. The development of this application is dependent on the knowledge of such research areas as geography, remote sensing, ecology, forestry, agriculture, engineering, optics, and/or computer science. We believe that this information could be very useful for CLC users, as it reflects a large-scale analysis of the research lines of CLC and illuminates how research has changed over time in diverse areas of applications. Moreover, this study is intended to offer a useful tool for the CLC scientific community, showcasing the main research lines and the most noteworthy papers. Finally, the methodology used in this study can be replicated in many other fields of science to explore its intellectual and cognitive structure. [ABSTRACT FROM AUTHOR]

Log messages from enterprise-level software systems contain crucial runtime details. Engineers can convert log messages into structured data through log parsing, laying the foundation for downstream tasks such as log anomaly detection. Existing log parsing schemes usually underperform in production environments for several reasons: first, they often ignore the semantics of log messages; second, they are often not adapted to different systems, and their performance varies greatly; and finally, they are difficult to adapt to the complexity and variety of log formats in the real environment. In response to the limitations of current approaches, we introduce IPLog (Intelligent Parse Log), a parsing method designed to address these issues. IPLog samples a limited set of log samples based on the distribution of templates in the system's historical logs, and allows the model to make full use of the small number of log samples to recognize common patterns of keywords and parameters through few-shot learning, and thus can be easily adapted to different systems. In addition, IPLog can further improve the grouping accuracy of log templates through a novel manual feedback merge query strategy based on the longest common prefix, thus enhancing the model's adaptability to handle complex log formats in production environments. We conducted experiments on four newly released public log datasets, and the experimental results show that IPLog can achieve an average grouping accuracy (GA) of 0.987 and parsing accuracy (PA) of 0.914 on the four public datasets, which are the best among the mainstream parsing schemes. These results demonstrate that IPLog is effective for log parsing tasks. [ABSTRACT FROM AUTHOR]

Since architect Nicholas Negroponte first proposed a vision of responsive architecture smart environments have been widely investigated, especially in the fields of computer science and engineering. Despite growing interest in the topic, a comprehensive review of research about smart environments from the architectural perspective is largely missing. In order to provide a formal understanding of smart environments in architecture, this paper conducts a systematic literature review of scholarly sources over the last decade, focusing on four related subjects: (1) responsive architecture, (2) kinetic architecture, (3) adaptive architecture and (4) intelligent buildings. Through this review, the paper identifies and examines interactive and collective behaviors in smart environments, thereby contributing to defining the properties of creative, smart spaces in the contemporary digital ecosystem. In addition, this research offers a means of systematically characterizing and constructing smart environments as interactive and collective platforms, enabling occupants to sense, experience and understand smart spaces. [ABSTRACT FROM AUTHOR]

Resilience is a feature that is gaining more and more attention in computer science and computer engineering. However, the definition of resilience for the cyber landscape, especially embedded systems, is not yet clear. This paper discusses definitions provided by different authors, on different years and with different application areas the field of computer science/computer engineering. We identify the core statements that are more or less common to the majority of the definitions, and based on this we give a holistic definition using attributes for (cyber-) resilience. In order to pave a way towards resilience engineering, we discuss a theoretical model of the life cycle of a (cyber-) resilient system that consists of key actions presented in the literature. We adapt this model for embedded (cyber-) resilient systems. [ABSTRACT FROM AUTHOR]

Definition: Nature is composed of elements at various spatial scales, ranging from the atomic to the astronomical level. In general, human sensory experience is limited to the mid-range of these spatial scales, in that the scales which represent the world of the very small or very large are generally apart from our sensory experiences. Furthermore, the complexities of Nature and its underlying elements are not tractable nor easily recognized by the traditional forms of human reasoning. Instead, the natural and mathematical sciences have emerged to model the complexities of Nature, leading to knowledge of the physical world. This level of predictiveness far exceeds any mere visual representations as naively formed in the Mind. In particular, geometry has served an outsized role in the mathematical representations of Nature, such as in the explanation of the movement of planets across the night sky. Geometry not only provides a framework for knowledge of the myriad of natural processes, but also as a mechanism for the theoretical understanding of those natural processes not yet observed, leading to visualization, abstraction, and models with insight and explanatory power. Without these tools, human experience would be limited to sensory feedback, which reflects a very small fraction of the properties of objects that exist in the natural world. As a consequence, as taught during the times of antiquity, geometry is essential for forming knowledge and differentiating opinion from true belief. It not only provides a framework for understanding astronomy, classical mechanics, and relativistic physics, but also the morphological evolution of living organisms, along with the complexities of the cognitive systems. Geometry also has a role in the information sciences, where it has explanatory power in visualizing the flow, structure, and organization of information in a system. This role further impacts the explanations of the internals of deep learning systems as developed in the fields of computer science and engineering. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940–2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artificial intelligence in energy processes and systems. The article also discusses the possible directions for the future development of neural networks. [ABSTRACT FROM AUTHOR]

There are many industrial products in our life, and the actions of those products are controlled by embedded control software (ECSW). Recently, many troubles have been caused by ECSW. To avoid those troubles, it is necessary to clarify the causes of the troubles and take countermeasures. However, the results of those tasks depend on the skills of the analyst. This paper proposes an analytic method that clarifies the causes of troubles by applying fault tree analysis (FTA) to the ECSW. The characteristics of the proposed method are as follows: Preparation of fault tree templates (FTTs) corresponding to instructions of the ECSW, and definition of the FT development rules by combining FTTs according to the back-tracing of the instruction execution process. By complying with the proposed method strictly, when an analyst who has studied computer science and safety engineering for 2–3 years conducts FTA, the analyst can obtain an appropriate result of FTA. This indicates that the safety level of ECSW will improve. As a result of applying the proposed method to existing ECSWs, we find that we can obtain the result of FTA at the appropriate level. [ABSTRACT FROM AUTHOR]

We report a new learning approach in science and technology through the Qui-Bot H2O project: a multidisciplinary and interdisciplinary project developed with the main objective of inclusively increasing interest in computer science engineering among children and young people, breaking stereotypes and invisible social and gender barriers. The project highlights the social aspect of robotics applied to chemistry, at early ages. We successfully tested the project activities on girls between 3 to 13 years old. After taking part in the project, the users rated their interest in science and technology to be higher than before. Data collected during experiences included background information on students, measurements of the project's impact and students' interest in it, and an evaluation of student satisfaction of this STEM activity. The Qui-Bot H2O project is supported by the actions of territorial public administrations towards gender equality and the contributions of humanistic and technological universities and entities which specialize in education and business. [ABSTRACT FROM AUTHOR]

Computer science is a dynamic field of study that requires constant review and updating of the curricular designs in academic programs—in general, measuring the impact of plan changes has been little explored in the literature. In most cases, it focuses only on structuring its curricula, leaving aside several factors associated with important events or facts such as student dropout, retention, and inclusion. However, these features provide academic institutions with many opportunities to understand student performance and propose more effective preventive/corrective actions to avoid dropouts. This work focuses on the curricular changes' influence on student gender imbalance, socioeconomic provenance, and dropout. Specifically, we employ three different approaches for our analysis: (i) a longitudinal study of four curricula from informatics engineering to computer science transition at San Pablo Catholic University, (ii) an exploratory analysis for identifying essential features that determines the events mentioned above, and (iii) a survival analysis to estimate the probability that a student will stay (not dropout) before graduate, and calculate the average permanence time per curricula. Our analysis shows that the female student rates decreased, student rates from lower socioeconomic provenance increased, and the dropout rates were reduced with updates towards an internationally standardized curriculum. This is even strongly evidenced when the program changes its name. Finally, the set of techniques employed in this work composes a statistical mechanism that can be replicated/adapted to any other program in computer science aiming to extract valuable insights to support the decision-making process in educational institutions. [ABSTRACT FROM AUTHOR]

Digital Terrain analysis (DTA) and modeling has been a flourishing interdisciplinary field for decades, with applications in hydrology, geomorphology, soil science, engineering projects and computer sciences. Currently, DTA is characterized by a proliferation of multispectral data from new sensors and platforms, driven by regional and national governments, commercial businesses, and scientific groups, with a general trend towards data with higher spatial, spectral or temporal resolutions. Deriving meaningful and interpretable products from such a large pool of data sources sets new challenges. The articles included in this special issue (SI) focuses on terrain analysis applications that advance the fields of hydrology, geomorphology, soil science, geographic information software (GIS), and computer science. They showcase challenging examples of DTA tackling different subjects or different point of views on the same subject. [ABSTRACT FROM AUTHOR]

The popularity and remarkable attractiveness of cryptocurrencies, especially Bitcoin, absorb countless enthusiasts every day. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to use the Blockchain technology of symmetry and asymmetry in computer and engineering science to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. In this study, a collective anomaly approach was used. Instead of detecting the anomaly of individual addresses and wallets, the anomaly of users was examined. In addition to using the collective anomaly detection method, the trimmed_Kmeans algorithm was used for clustering. The results of this study show the anomalies are more visible among users who had multiple wallets. The proposed method revealed 14 users who had committed fraud, including 26 addresses in 9 cases, whereas previous works detected a maximum of 7 addresses in 5 cases of fraud. The suggested approach, in addition to reducing the processing overhead for extracting features, detect more abnormal users and anomaly behavior. [ABSTRACT FROM AUTHOR]

The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of this study is to analyze global research on smart cities based on IoT technology applications. For this, bibliometric techniques were applied to 1232 documents on this topic, corresponding to the period 2011–2019, to obtain findings on scientific activity and the main thematic areas. Scientific production has increased annually, so that the last triennium has accumulated 83.23% of the publications. The most outstanding thematic areas were Computer Science and Engineering. Seven lines have been identified in the development of research on smart cities based on IoT applications. In addition, the study has detected seven new future research directions. The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications. This study contributes to the academic, scientific, and institutional discussion to improve decision making based on the available information. [ABSTRACT FROM AUTHOR]

As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent's local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC's antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient. [ABSTRACT FROM AUTHOR]

New master's degrees in computer engineering, designed according to Spanish government requirements, include both technical and business subjects to provide advanced knowledge compared to undergraduate studies. In spite of acquiring this advanced knowledge, our students are not always aware of the significant added value and the positive elements the master's degree provides for their future professional careers. Moreover, we have observed that students regard the different subjects as isolated blocks of knowledge. To tackle these problems, we have carried out a learning experience based on the creation of a technology-based enterprise (TBE) through the development of a common project (CP). Drawing on project-based learning (PBL) methodology, the CP consists of the identification, development, and final oral presentation of a business model. It aims to utilize sensor data and analysis in different domains. The experience has been highly positive, reducing the gap between studies and business practice, and increasing coordination among teachers. A key issue is that all the subjects of the master's degree are involved in this learning experience. In addition, results show a high level of satisfaction with the master's degree, compared to other similar courses. [ABSTRACT FROM AUTHOR]