Handwriting-Based Gender Classification Using End-to-End Deep Neural Networks

Handwriting-based gender classification is a well-researched problem that has been approached mainly by traditional machine learning techniques. In this paper, we propose a novel deep learning-based approach for this task. Specifically, we present a convolutional neural network (CNN), which performs automatic feature extraction from a given handwritten image, followed by classification of the writer's gender. Also, we introduce a new dataset of labeled handwritten samples, in Hebrew and English, of 405 participants. Comparing the gender classification accuracy on this dataset against human examiners, our results show that the proposed deep learning-based approach is substantially more accurate than that of humans

Deteksi Serangan Denial of Service pada Internet of Things Menggunakan Finite-State Automata

Internet of things memiliki kemampuan untuk menghubungkan obyek pintar dan memungkinkan mereka untuk berinteraksi dengan lingkungan dan peralatan komputasi cerdas lainnya melalui jaringan internet. Namun belakangan ini, keamanan jaringan internet of things mendapat ancaman akibat serangan cyber yang dapat menembus perangkat internet of things target dengan menggunakan berbagai serangan denial of service. Penelitian ini bertujuan untuk mendeteksi dan mencegah serangan denial of service berupa synchronize flooding dan ping flooding pada jaringan internet of things dengan pendekatan finite-state automata. Hasil pengujian menunjukkan bahwa pendekatan finite-state automata berhasil mendeteksi serangan synchronize flooding dan ping flooding pada jaringan internet of things, tetapi pencegahan serangan tidak secara signifikan mengurangi penggunaan prosesor dan memori. Serangan synchronize flooding menyebabkan delay saat mengaktifkan/menonaktifkan peralatan internet of things sedangkan serangan ping flooding menyebabkan error. Implementasi bash-iptables berhasil mengurangi serangan synchronize flooding dengan efisiensi waktu pencegahan sebesar 55,37% dan mengurangi serangan ping flooding sebesar 60% tetapi dengan waktu yang tidak signifikan

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

Offline printed Arabic character recognition

Optical Character Recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Normal OCR problems are compounded by the right-to-left nature of Arabic and because the script is largely connected. This research investigates current approaches to the Arabic character recognition problem and innovates a new approach. The main work involves a Haar-Cascade Classifier (HCC) approach modified for the first time for Arabic character recognition. This technique eliminates the problematic steps in the pre-processing and recognition phases in additional to the character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks. These 61 classifiers were trained and tested on an average of about 2,000 images each. A Multi-Modal Arabic Corpus (MMAC) has also been developed to support this work. MMAC makes innovative use of the new concept of connected segments of Arabic words (PAWs) with and without diacritics marks. These new tokens have significance for linguistic as well as OCR research and applications and have been applied here in the post-processing phase. A complete Arabic OCR application has been developed to manipulate the scanned images and extract a list of detected words. It consists of the HCC to extract glyphs, systems for parsing and correcting these glyphs and the MMAC to apply linguistic constrains. The HCC produces a recognition rate for Arabic glyphs of 87%. MMAC is based on 6 million words, is published on the web and has been applied and validated both in research and commercial use

Application of machine learning in operational flood forecasting and mapping

Considering the computational effort and expertise required to simulate 2D hydrodynamic models, it is widely understood that it is practically impossible to run these types of models during a real-time flood event. To allow for real-time flood forecasting and mapping, an automated, computationally efficient and robust data driven modelling engine - as an alternative to the traditional 2D hydraulic models - has been proposed. The concept of computationally efficient model relies heavily on replacing time consuming 2D hydrodynamic software packages with a simplified model structure that is fast, reliable and can robustly retains sufficient accuracy for applications in real-time flood forecasting, mapping and sequential updating. This thesis presents a novel data-driven modelling framework that uses rainfall data from meteorological stations to forecast flood inundation maps. The proposed framework takes advantage of the highly efficient machine learning (ML) algorithms and also utilities the state-of-the-art hydraulic models as a system component. The aim of this research has been to develop an integrated system, where a data-driven rainfall-streamflow forecasting model sets up the upstream boundary conditions for the machine learning based classifiers, which then maps out multi-step ahead flood extents during an extreme flood event. To achieve the aim and objectives of this research, firstly, a comprehensive investigation was undertaken to search for a robust ML-based multi-step ahead rainfall-streamflow forecasting model. Three potential models were tested (Support Vector Regression (SVR), Deep Belief Network (DBN) and Wavelet decomposed Artificial Neural Network (WANN)). The analysis revealed that SVR-based models perform most efficiently in forecasting streamflow for shorter lead time. This study also tested the portability of model parameters and performance deterioration rates. Secondly, multiple ML-based models (SVR, Random Forest (RF) and Multi-layer Perceptron (MLP)) were deployed to simulate flood inundation extents. These models were trained and tested for two geomorphologically distinct case study areas. In the first case of study, of the models trained using the outputs from LISFLOOD-FP hydraulic model and upstream flow data for a large rural catchment (Niger Inland Delta, Mali). For the second case of study similar approach was adopted, though 2D Flood Modeller software package was used to generate target data for the machine learning algorithms and to model inundation extent for a semi-urban floodplain (Upton-Upon-Severn, UK). In both cases, machine learning algorithms performed comparatively in simulating seasonal and event based fluvial flooding. Finally, a framework was developed to generate flood extent maps from rainfall data using the knowledge learned from the case studies. The research activity focused on the town of Upton-Upon-Severn and the analysis time frame covers the flooding event of October-November 2000. RF-based models were trained to forecast the upstream boundary conditions, which were systematically fed into MLP-based classifiers. The classifiers detected states (wet/dry) of the randomly selected locations within a floodplain at every time step (e.g. one hour in this study). The forecasted states of the sampled locations were then spatially interpolated using regression kriging method to produce high resolution probabilistic inundation (9m) maps. Results show that the proposed data centric modelling engine can efficiently emulate the outcomes of the hydraulic model with considerably high accuracy, measured in terms of flood arrival time error, and classification accuracy during flood growing, peak, and receding periods. The key feature of the proposed modelling framework is that, it can substantially reduce computational time, i.e. ~14 seconds for generating flood maps for a flood plain of ~4 km2 at 9m spatial resolution (which is significantly low compared to a fully 2D hydrodynamic model run time)

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Mathematical linguistics

but in fact this is still an early draft, version 0.56, August 1 2001. Please d