Hasil untuk "Computer Science"

Nicolas Boumal

Optimization on Riemannian manifolds-the result of smooth geometry and optimization merging into one elegant modern framework-spans many areas of science and engineering, including machine learning, computer vision, signal processing, dynamical systems and scientific computing. This text introduces the differential geometry and Riemannian geometry concepts that will help students and researchers in applied mathematics, computer science and engineering gain a firm mathematical grounding to use these tools confidently in their research. Its charts-last approach will prove more intuitive from an optimizer's viewpoint, and all definitions and theorems are motivated to build time-tested optimization algorithms. Starting from first principles, the text goes on to cover current research on topics including worst-case complexity and geodesic convexity. Readers will appreciate the tricks of the trade for conducting research and for numerical implementations sprinkled throughout the book.

S. Shalev-Shwartz, Shai Ben-David

B. T. T. Yeo, Fenna M. Krienen, J. Sepulcre et al.

Michael A. Nielsen, Isaac L. Chuang

Nikolay Raychev, I. Chuang

S. Haykin

S. Fortunato

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e.g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

J. Hopcroft, J. Ullman

J. Nocedal, Stephen J. Wright

Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

R. Alur, D. Dill

Stephen P. Boyd, L. Vandenberghe

Fourth Edition, Averill M. Law

K. Price

Ronald Fagin

M. Newman

B. Ganter, Rudolf Wille, C. Franzke

S. Cheryan, V. Plaut, Caitlin Handron et al.

Cheryl Akinyi Genga

Digital transformation is reshaping global operations by integrating technology into business, fundamentally changing how value is delivered. In Sub-Saharan Africa, this shift is altering work processes and job content, impacting the demand for skills and leading to the displacement of certain roles across all industries. Understanding the effects of digital technologies on the future of work in the region is essential for developing effective strategies. It is important to recognise how these changes will affect labour markets and workers' ability to transition to new opportunities. While technology can create new paths and improve access, it also exacerbates existing inequalities. This study aimed to explore the challenges shaping the future of work in Sub-Saharan Africa. A qualitative research approach and inductive thematic analysis were utilised for this study. The findings highlight that the major challenges affecting the future of work are digital skills, followed by Diversity, equity and inclusion- digital divide, gender inequality and discrimination and lack of DEI initiatives and finally, workforce- unemployment and inadequately skilled workforce. In conclusion, while the future of work in Africa presents significant challenges, it also offers great promise. Realising this potential depends on bold and proactive decisions by policymakers, educational institutions, and businesses. Strategic investments made today can empower the next generation of African workers, innovators, and entrepreneurs to thrive in an increasingly digital and competitive global economy.

Mohammad Aldossary, Ibrahim Alzamil, Jaber Almutairi

Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCAN), a new IDS architecture for IoD networks. CLCAN accurately detects complex cyber threats using multi-scale convolutional processing, hierarchical contextual attention, and dynamic feature fusion. Preprocessing methods like weighted differential scaling and gradient-based adaptive resampling improve data quality and reduce class imbalances. Contextual attribute transformation captures the nuanced network behaviors needed for anomaly identification. The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. The model has a remarkable AUC of 0.991. CLCAN can handle datasets of over 118,000 balanced data records in 85 s, compared to 180 s for comparable frameworks. This study pioneers a unified security solution for Drone-to-Drone (D2D) and Drone-to-Base Station (D2BS) communications, filling a crucial IoD security gap. It protects mission-critical drone operations with a strong, efficient, and scalable IDS from emerging cyber threats.

Akbar Asgharzadeh-Bonab, Salar Ghamati, Farid Ahmadi et al.

Forests uniquely deliver different vital resources, particularly oxygen and carbon dioxide purification. Wildfire is the leading cause of deforestation, where massive forest areas are annually lost due to the failure to identify and predict forest fires. Accordingly, early detection of wildfires is crucial to inform operational and firefighting teams to prevent fires from advancing. This study analyzes images taken by unmanned aerial vehicles for wildfire detection. For this purpose, the two-dimensional discrete wavelet transform was first performed on the images. Next, due to its superior ability, a convolutional neural network was utilized to extract deep features from wavelet transform sub-bands. Then, the features obtained from each sub-band were merged to create the final feature vector. Afterward, multidimensional scaling was employed to reduce the extracted non-useful features. Ultimately, the presence or absence of wildfire locations in the images was detected using proper classifiers. The proposed method reaches an accuracy and F1 score of 0.9684 and 0.9672, respectively, from the images of the FLAME dataset, indicating its efficiency in detecting the presence of wildfire locations. Thus, this method can significantly contribute to the on-time and prompt firefighting operations and prevent extensive damage to forests.