Intelligent Malware - Trends and Possibilities

In recent months and years, with more and more computers and computer systems becoming the target of cyberattacks. These attacks are gaining strength and the sophistication of the approach in terms of how to attack. Attackers and Defenders are increasingly using artificial intelligence methods to maximize the success of their actions. For a successful defence, we must be able to anticipate future threats that may come. For these reasons, our research group is engaged in creating experimental software with artificial intelligence to test the possibilities and capabilities of such malware in the event of its deployment. This software has not only malware capabilities but also antimalware and can be used on both sides. This article introduces the reader to the main principles of our design, which can serve as a future platform for cyber defence systems.

Employing Texture Features of Chest X-Ray Images and Machine Learning in COVID-19 Detection and Classification

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient; as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival.

Proposal of a Relational Database (SQL) for Zoological Research of Epigeic Synusion

In recent years, developments in the field of molecular biology and genetics have led to the increase in biological information stored in databases. The same increase in the volume of information occurred in the field of zoology, but the development of databases was not addressed in this area. We prepared a relational database and its diagram in the Microsoft SQL Server Management Studio (SSMS) database program. Our results represent experience with construction of a new database design for the zoology field with a focus on research of epigeic groups. The structure of the database will help with meta-analyzes with the objective to identify zoological and ecological relationships and responses to anthropic intervention.

3D Reconstruction Human Body From Anthropometric Measurements Using Diversity Control Oriented Genetic Algorithm

3D digitalization of the human body has been studied extensively for various applications in anthropology, ergonomics, healthcare, entertainment and fashion industries. There are different methods and approaches to reconstruct the 3D body model namely using RGB cameras, depth cameras, scanning systems or anthropometric measurements of the human body. Generally, most of existing approaches have to tackle issues relating to security of personal data, the impact of the surrounding environment, cost of 3D scanning systems and complication of anthropometric measurements. This study proposes a method using simple body measurements and given body shapes to digitalize the human body. The effectiveness of proposed method is evaluated and demonstrated based on two datasets: a synthetic dataset generated from a parametric model and a real dataset on Vietnamese collected by Viettel Military Industry and Telecoms Group (Vietnam).

Mixed-Integer Programming Model for Ranking Universities: Letting Universities Choose the Weights

Regardless of the shortcomings and criticisms of world university rankings, these metrics are still widely used by students and parents to select universities and by universities to attract talented students and researchers, as well as funding. This paper proposes a new mixed-integer programming model for ranking universities. The new approach alleviates one of the criticisms -- the issue of the ``arbitrariness'' of the weights used for aggregation of the individual criteria (or indicators) utilized in the contemporary rankings. Instead, the proposed model uses intervals of different sizes for the weights and lets the universities themselves ``choose'' the weights to optimize their position in the rankings. A numerical evaluation of the proposed ranking, based on the indicator values and weights from the Times Higher Education World University Ranking, is presented.

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. In this paper, we compare several reinforcement learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a specific goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario and minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word applications are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics.

Three Steps to Improve Jellyfish Search Optimiser

This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.

Dog Face Detection Using YOLO Network

This work presents the real-world application of the object detection which belongs to one of the current research lines in computer vision. Researchers are commonly focused on human face detection. Compared to that, the current paper presents a challenging task of detecting a dog face instead that is an object with extensive variability in appearance. The system utilises YOLO network, a deep convolution neural network, to~predict bounding boxes and class confidences simultaneously. This paper documents the extensive dataset of dog faces gathered from two different sources and the training procedure of the detector. The proposed system was designed for realization on mobile hardware. This Doggie Smile application helps to snapshot dogs at the moment when they face the camera. The proposed mobile application can simultaneously evaluate the gaze directions of three dogs in scene more than 13 times per second, measured on iPhone XR. The average precision of the dogface detection system is 0.92.

The Classification of Documents in Malay and Indonesian Using the Naive Bayesian Method Uses Words and Phrases as a Training Set

Malay Language and Indonesian Language are two closely related languages, sharing a lot in common in the meanings of words and grammar. Classifying the two languages automatically using a tool is a challenge because the two languages are very similar. The classification method that is widely used today is the Naive Bayesian method. This method needs to be implemented in a particular way to increase the level of classification accuracy. In this study, a new method was used, by using a training set in the form of words and phrases instead of just using a training set in the form of words only. With this method, the level of classification accuracy of the two languages is increased.

Phase Transition as an Emergent Phenomenon Analysed by Violation of Structural Invariant (M, BM)

When modeling complex systems, we usually encounter the following difficulties: partiality, large amounts of data and uncertainty of conclusions. The most common approach used for modeling is the physical approach, sometimes reinforced by statistical procedures. If we assume emergences in the complex system, a physical approach is not appropriate at all. Instead, we build here the approach of structural invariants. In this paper, we show that another plane can be built above the plane of physical description, which is responsible for violation of structural invariants. Main attention is concentrated (in this article) on the invariant matroid and bases of matroid (M, BM) in combination with Ramsey graph theory. In addition, the article introduces a calculus that describes the emergent phenomena using two quantities - the power of the emergent phenomenon and the complexity of the structure of the considered complex system. We show the application of the method for modeling phase transition in chemistry.