Hasil untuk "Cybernetics"

Christian Riekel, Emanuela Di Cola, Manfred Burghammer et al.

Parisa Foroutan, Salim Lahmiri

In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes. It aims to be the first to (i) explore the potential of spatial-temporal graph neural networks family for price forecasting of these markets, (ii) examine the role of attention mechanism in improving forecasting accuracy, and (iii) integrate various sources of predictors for better performance. Specifically, we present three distinct models: Multivariate Time Series Graph Neural Networks with Temporal Attention and Learnable Adjacency matrix (MTGNN-TAttLA), Spatial Attention Graph with Temporal Convolutional Networks (SAG-TCN), and Attention-based Spatial-Temporal Graph Convolutional Networks (ASTGCN), to capture the intricate interplay of spatial and temporal dependencies within crude oil and precious metals markets. Moreover, the effectiveness of the attention mechanism in improving models' accuracies is shown. Our empirical results reveal remarkable prediction accuracy, with all three models outperforming conventional deep learning methods such as Temporal Convolutional Networks (TCN), long short-term memory networks (LSTM) and convolutional neural networks (CNN). The MTGNN-TAttLA model, enriched with a temporal attention mechanism, exhibits exceptional performance in predicting the direction of price movement in the WTI, Brent, and silver markets, while ASTGCN is the best-performing model for the gold market. Additionally, we observed that incorporating technical indicators from the crude oil and precious metal markets into the graph structure has improved the classification accuracy of spatial-temporal graph neural networks.

Alireza Sadeghi-Nasab, Hossein Ghaffarian, Mohsen Rahmani

In this paper, we present a novel framework that considers the expiration period time of the Internet of Things (IoT) data stream to anonymize it. IoT stands among one of most fast-growing technology in the world. Also, anonymity is one of the safeguards in place to protect data privacy. Because of the dynamic nature, vastness, and rapid changes in data streams, traditional approaches cannot be used to anonymize IoT data. The anonymization framework proposed in this paper performs its operation using a new clustering method and Apache Flink flow data processing engine. In this framework, firstly, we cluster received data. Then, if the size of the clusters doesn't meet the K-anonymity threshold, our review will continue to suppress and delete them; otherwise, the data would be anonymized and published. In this way, the framework handles both numerical and categorical data. At the end of the stream, the final remaining data will be merged and anonymized. Implementing and evaluating the framework using Scala and Apache Flink shows that the proposed approach reduces data delay by 12.33–66.62% compared with the other methods. Furthermore, in the end, combining the leftover clusters avoids information loss. In comparison with similar methods, information loss is reduced by 5.68–18.26%. The evaluation results show better performance in terms of data delay and information loss.

Jianwen Liu

In this paper, we present an approach to improve the effectiveness of automatic classification of music genres by integrating emotion and intelligent algorithms. We propose an automatic recognition and classification algorithm for music spectra, which takes into account emotional cues that can be extracted from music to improve classification accuracy. To achieve this goal, we set different weight coefficients, which are continuously adjusted based on the convergence process of the previous iteration. The size of each weighting coefficient is adaptively controlled to reduce the number of iterations of the reconstruction process, thereby reducing the algorithm’s computational complexity and speeding up its convergence. We conducted several experiments to evaluate the effectiveness of our proposed method. The experimental results demonstrate that the automatic classification method of music genres, which integrates emotion and intelligent algorithms, can significantly improve the accuracy of automatic music genre classification. Moreover, our approach reduces the algorithm’s computational complexity, resulting in a faster convergence speed. Our proposed approach provides a promising solution for automatic music genre classification that takes into account emotional cues. The integration of emotion and intelligent algorithms can help achieve higher accuracy and reduce computational complexity, making the proposed method applicable in various scenarios.

Luca Bonatti, Gabriel Gil, Gabriel Gil et al.

The fully atomistic model, ωFQ, based on textbook concepts (Drude theory, electrostatics, quantum tunneling) and recently developed by some of the present authors in Nanoscale, 11, 6004-6015 is applied to the calculation of the optical properties of complex Na, Ag, and Au nanostructures. In ωFQ, each atom of the nanostructures is endowed with an electric charge that can vary according to the external electric field. The electric conductivity between nearest atoms is modeled by adopting the Drude model, which is reformulated in terms of electric charges. Quantum tunneling effects are considered by letting the dielectric response of the system arise from atom-atom conductivity. ωFQ is challenged to reproduce the optical response of metal nanoparticles of different sizes and shapes, and its performance is compared with continuum Boundary Element Method (BEM) calculations.

V. К. Ivanov

The paper presents the results of the experiments that were conducted to confirm the main ideas of the proposed approach to determining the objects innovativeness. This approach assumed that the product life cycle of whose descriptions are placed in different data warehouses is adequate. The proposed formal model allows us to calculate the quantitative value of the additive evaluation criterion of objects innovativeness. The obtained experimental data make it possible to evaluate the adopted approach correctness.

Thomas Poell, David Nieborg, José van Dijck

This article contextualises, defines, and operationalises the concept of platformisation. Drawing insights from different scholarly perspectives on platforms—software studies, critical political economy, business studies, and cultural studies—it develops a comprehensive approach to this process. Platformisation is defined as the penetration of infrastructures, economic processes and governmental frameworks of digital platforms in different economic sectors and spheres of life, as well as the reorganisation of cultural practices and imaginations around these platforms. Using app stores as an example, we show how this definition can be employed in research.

Vlad Diaconita, Ana-Ramona Bologa, Razvan Bologa

A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.

Oleg Barabash, Nataliya Lukova-Chuiko, Andrii Musienko et al.

Предметом вивчення в статті є процес забезпечення властивості функціональної стійкості інформаційних мереж. Метою є розробка методу протидії DDoS-атакам, що дозволяє ефективно захищати інформаційну мережу, як від атак на всьому часовому інтервалі, так і від повільних атак. Завдання: розробити алгоритми виявлення та блокування DDoS-атак, що описують послідовність дій при застосуванні методу протидії; провести оцінку ефективності запропонованого методу протидії DDoS-атакам. Використовуваними методами є: графовий підхід, математичні моделі оптимізації, методи розв'язання нелінійних задач. Отримані такі результати. Побудовані алгоритми виявлення та блокування DDoS-атак, що описують послідовність дій при застосуванні методу протидії. Алгоритм виявлення атак реалізується на аналізаторі вхідного трафіку, який перевіряється на предмет наявності DDoS-атак. У разі виявлення такої атаки визначається її тип. Після цього реалізується алгоритм блокування, який зчитує з бази даних джерела шкідливого трафіку та перенаправляє його на програмний шлюз, який забирає на себе подальший деструктивний вплив. Висновки. Наукова новизна отриманих результатів полягає в наступному: ми запропонували метод протидії DDoS-атакам, що дозволяє ефективно захищати інформаційну мережу як від атак на всьому часовому інтервалі, так і від повільних атак. Даний метод дозволяє забезпечити функціональну стійкість інформаційної мережі та базується на використанні алгоритмів виявлення та блокування DDoS-атак, а також збору інформації про вхідний трафік із записом до бази даних «Джерела шкідливого трафіку». При виявленні атаки визначається її тип та запускається механізм її блокування, що реалізується в два етапи. На першому етапі виконується пошук джерел шкідливого трафіку, використовуючи зібрану в базі даних інформацію про вхідні пакети. На другому етапі виконується безпосереднє блокування виявлених джерел шляхом відправлення пакетів-відповідей по резервному каналу зв’язку через програмний шлюз, на якому вихідна адреса серверу у пакетах підміняється адресою шлюзу, що дає змогу замаскувати сервер від зовнішнього деструктивного впливу (у разі атаки ззовні). При атаці з внутрішньої мережі відключаються порти комутатора, до яких підключені джерела шкідливого трафіку. Після цього оповіщається системний адміністратор, який негайно приступає до пошуку та знешкодження шкідливого програмного забезпечення.