Explore the vast range of titles available.

The Internet of Things work by constantly sensing the physical properties in the vicinity of the user such as ambient light, sounds, motion and temperature. These sensors produce huge volumes of data that has to be efficiently sifted for relevant events required triggering certain actions. In addition, filtering has to be performed to ensure that privacy-sensitive confidential data is not leaked. Efficient and expressive pattern matching is thus a key enabling technology for the full realization of ambient and humanized computing. The bulk of research in this area has focused on the use of specialized hardware and reducing of the memory footprint. Unfortunately, there has been limited work if any on optimizing the core elements of pattern matching- the regular expression language and the compilation process that is responsible for converting patterns into internal data structures. The importance of writing good REs so that on compilation they do not lead to unrealizable data structures is relatively less understood. In the proposed research, we empirically compare different RE processing engines and practically demonstrate that the compilation phase is highly memory intensive and time-consuming as compared to the matching phase -and hence is worth exploring for new techniques and optimizations. As a second important contribution, we propose a novel technique for defining regular expressions by utilizing JavaScript Object Notation. Our evaluation with carefully created patterns shows that the performance of the proposed technique is at par with competing approaches. It is also less ambiguous, extensible, more expressive and much appropriate for defining large and complex patterns.

The objective of the article is twofold: first, to employ the knowledge of the recurrence of low-frequency words in authorial texts; and second, to prevent the misuse of this knowledge. Contrary to the prevailing authorship attribution theory and practice (Evert et al. 2017, Juola 2008), our research has revealed that the personal linguistic profile is not primarily composed of frequent words with grammatical functions. Instead, we have identified that a distinct set of full-meaning words defines an individual’s linguistic profile (Faltýnek 2020, Faltýnek – Matlach 2021). An examination of these meanings reveals an individual’s unconscious language habits and, consequently, their personality settings. Such personal profiling is referred to as “deep content” and “deep sentiment analysis”. The innovation in question has the potential to facilitate a novel form of linguistic personalization in digital communication, one that has not been previously observed or utilized. The main aim of this article is to describe the algorithm to conduct single-person linguistic deep content and deep sentiment profiling and personalization. We will describe technical steps to provide such a form of digital communication processing and to facilitate the adjustment of a text targeted at an individual, described as a System and method for adapting text based data structures to text samples (Patent No.: US11797753B2, Faltýnek et al. 2023). This algorithm can be used to (a) produce a personal linguistic profile (analogically to psychometrics instruments such as NEO-FFI Big Five, Minnesota Multiphasic Personality Inventory (MMPI)), (b) target digital communication to an individual by “translating” a text to their language (i.e. linguistic habits) and stimulate desired feelings to a predetermined content. The algorithm is, however, also designed (c) to be used to avoid procedures (a) and (b) using any kind of digital communication platform by an individual. This algorithm is implemented in the software Cloakspeech (Faltýnek – Benešová – Kučera 2025), which provides personalization of AI-generated texts: AI speaks like a particular person.

Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.

This article reports the findings of a qualitative research project that explored pre-service English teachers' perceptions of and attitudes toward the aspects of culture and intercultural competence addressed in their English classes in the undergraduate programs at three Colombian universities. Findings reveal that pre-service teachers are mainly taught elements of surface culture and lack full understanding of intercultural competence. They also see culture as a separate aspect of their future teaching career. We provide alternatives so that pre-service teachers might overcome limitations of the teaching of culture as preparation for their future teaching career in the foreign language classroom. Este artículo reporta los hallazgos de una investigación cualitativa que indagó sobre las percepciones y las actitudes de los profesores en formación en el área de inglés respecto a los contenidos culturales y la competencia cultural que se abordan en las clases de inglés, en tres universidades colombianas. Los hallazgos revelan que los docentes en formación primordialmente tratan aspectos de la cultura superficial y no tienen total claridad de qué es la competencia comunicativa intercultural. También conciben la cultura como un aspecto desligado de su futura profesión docente. Se sugieren algunas alternativas para que los profesores en formación puedan superar las limitaciones de la enseñanza de la cultura y se preparen para su futura carrera docente en el salón de inglés como lengua extranjera.

Holo-oligomixis is one of the most important hydrodynamic characteristics of deep lakes in temperate regions, especially those of the Southern Alps. It influences such important lake chemical and biological processes as the oxygenation of deep layers, recycling of nutrients, vertical migration of plankton, and reproduction. Analysis of physico-chemical data from Lago Maggiore over the years 1951 – 2008 has shown that in addition to ever active but relatively inefficient convective mixing, three other mechanisms act to oxygenate this lake’s deep waters in winter. These are conveyor belt currents, cold and well-oxygenated tributary inflows that sink down to depths of equal density, and differential cooling of littoral waters that subsequently slide down the lake flanks. Their common outcome is to cause deep erosion of the hypolimnion. Heat content and thermal stability also are affected and are analyzed here in relation to external driving forces, examining in particular how dynamics may be altered by climate change.

The university curriculum has been urged to incorporate 21st-century digital competence and skills, particularly information literacy, in accordance with recommendations made by numerous organizations, including the Scientific and Cultural Organization (UNESCO) and the International Society for Technology in Education (ISTE). Instructors are then considered true facilitators in this sense. Therefore, recent studies investigated the factors that shape teachers’ competence in developing learners’ information literacy by exploring contextual factors such as the availability of information and communication technologies (ICTs) and internal factors such as their attitudes. This study aims to shift the focus from exploring contextual factors or teachers’ attitudes to exploring instructors’ individual ICT-related factors. Through a bi-phenomenon analysis, this study leveraged teachers’ professional knowledge and ICT-related teaching skills in shaping their competence to develop learners’ information literacy. To do so, 346 university English as a Foreign Language (EFL) lecturers in Iran voluntarily participated and answered the study instruments. The result of the partial least square modeling approach (PLS-SEM) revealed that lectures’ skills in designing the procedure to develop university language learners’ information literacy and integrating psychological factors, such as self-regulation and attitude, as well as the skills to implement the procedure and handle unforeseen challenges effectively cultivate information literacy. The professional knowledge areas of planning, exertion, and ethics were also recognized as preconditioning factors in this manner. In addition to introducing a new conceptual framework to the literature, the findings of this study also make recommendations on how lecturers can upgrade their action-oriented skills and professional knowledge to increase learners’ information literacy. Executive managers should also update their recruitment criteria and evaluate lecturers’ skills and knowledge during recruitment.

In smart cities, the networked control system plays a significant role in transportation systems, power stations or other critical infrastructures, and it is facing many security issues. From this point, this paper proposes a content-based deep communication control approach to guarantee its security. Based on the layer architecture, this approach analyzes the interactive content in depth according to different industrial communication protocols, and implements the access control between two distinct enclaves. For OPC Classic, we acquire the dynamic port provided by OPC server, and open a new connection belonging to this port; for Modbus/TCP, we not only analyze the ordinary function codes and addresses, but also check the register or coil values by using the multi-bit Trie-tree matching algorithm. Besides, the white-listing strategy is introduced to satisfy the special requirements of industrial communication. Our experiment results show that, on the one hand the proposed approach provides OPC and Modbus/TCP defenses in depth; on the other hand it has less than 1 ms forwarding latency and 0 packet loss rate when the rule number reaches 200, and all these meet the availability requirements in the networked control system. In particular, this approach has been successfully applied in several real-world petrochemical control systems.

This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The camera was installed in the vertical direction above the soil surface layer. To maintain uniform illumination conditions, a dark room and LED lighting were utilized. Following the acquisition of soil surface images, soil samples were collected using a metal cylinder to obtain measurements of soil water content and bulk density. Various features were extracted from the images, including color, texture, and shape features, and used as inputs for both a multiple regression analysis and a deep neural network model. The results show that the deep neural network regression model can predict soil water content and bulk density with root mean squared error of 1.52% and 0.78 kN/m3. The deep neural network model outperformed the multiple regression analysis, achieving a high accuracy for predicting both soil water content and bulk density. These findings suggest that in situ soil surface images, combined with deep learning techniques, can provide a fast and reliable method for predicting important soil properties.

Content delivery networks (CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning (DRL) offers a promising solution for intelligent zero-touch network governance. However, the black-box nature of DRL models poses challenges in understanding and making trusting decisions. In this paper, we propose an explainable reinforcement learning (XRL)-based intelligent edge service caching approach, namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence (XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent’s Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental inputs. The results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service (QoS), and space utilization.