Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Anomaly Detection System using Genetic Algorithm, Feature Selection and Classification

Networks are dangerous environments with containing numerous security vulnerabilities and those vulnerabilities are likely to be used while attacking systems with the intent of stealing valuable information or stopping the services. A system should be protected from already-known types of attacks and also have ability to detect unknown types of attacks to prevent abduction of the information. Unknown types of attacks may give harm to the system by stopping the services that runs effective and stable. For that purpose, it has become necessary to develop a flexible and adaptable system which can collect instant data from the network, distinguish between harmless and harmful behaviors and take measures against them. The main goal of this work is to explain a network anomaly detection system that is developed using genetic algorithm and Weka classification features to fulfill the purposes stated above. The Genetic Algorithm is used to generate various individuals with the aim of determining which attributes of the individual are providing a better result about learning the behavioral pattern of the network traffic. Furthermore, Weka classifiers are applied to the train and test datasets to calculate the best fitness value, and to decide on individual's attributes that are more effective about finding the anomaly occurring in a given instant.
Hexagonal QAM-Based Four-Dimensional AMO-OFDM for Spectrally Efficient Optical Access Network Transmission

A novel spectrally efficient optical transmission in a passive optical network (PON) with hexagonal quadrature amplitude modulation (QAM)-based adaptive modulated optical (AMO) orthogonal frequency division multiplexing (OFDM) is proposed and experimentally demonstrated. With the help of hexagonal QAM and 4-dimensional (4D) modulation, adaptive bit loading is effectively processed to enhance the total data rate. In this approach, some subcarriers are aggregated to transmit integer bits. To employ hexagonal QAM-based 4D AMO-OFDM, the signal to noise (SNR) ratio is calculated using the measured error vector magnitude of the training signal. In our experiment, hexagonal QAM-based 4D AMO-OFDM transmission with a total data rate of 21 Gbps is demonstrated over a 20 km standard single mode fiber for two optical network units (ONUs). The experimental results show that the total data rate obtained using the proposed method is increased by 14% compared to the conventional square QAM-based AMO-OFDM.
Classificatin of Brain Tumors by Machine Learning Algorithms

Brain tumors are one of the most important causes of death among cancer types. Early and accurate diagnosis of brain tumor plays a key role in the successful implementation of the treatment. Nowadays, new technologies that increase the success rate of neurosurgery and prevent complications continue to develop. Magnetic resonance (MRI) technique is one of the most popular methods used to examine brain tumor images. There are many possible techniques and algorithms for the classification of images. The main purpose of machine learning and classification algorithms is to learn automatically from training and finally make a wise decision with high accuracy. In this study, the performances of tumor classification methods for the classification of MR brain image features as n/a, multifocal, multicentric and gliomatosis were analyzed. In the classification process, the statistical properties of the input images were analyzed and the data were systematically divided into various categories. These data were tested with KNN (k nearest neighbor), RF(random forest), SVM(support vector machines) and LDA(linear discriminant analysis) machine learning algorithms. SVM (support vector machines) algorithm with 90% accuracy rate was found to be better compared to other algorithms.
“Detection, identification and tracking of objects during the motion”

This paper intends to introduce discovery of various objects, object classification, object tracking algorithms including analysis and a comparison of different techniques used for different stages of tracking. The purpose of tracking objects is segmenting a region of interest from a video scene and continuing tracking movement, positioning, and match. Detection of the object and classification are the steps preceding the object tracking in a sequence of images. Object detection is performed to control the existence of objects in the video and find that object, and then the detected object can be classified into many categories such as people, vehicles, floating trees, trees and other moving objects. In this paper is elaborated the autonomous tracking system with the aim of opposing movement in the event of a demonstration of human tracking, as such may enable the former to be distributed as it comprises more than one control center, key component parts of the system include:The camera and its 'interface, arduino viewing boards. , PC - Controlled Center.
Content Similarity Analysis of Written Comments under Posts in Social Media

Written comments to the posts on social media are an important metric to measure the followers' feedback to the content of the posts. But the huge presence of unrelated comments following each post can impact many parts of people engagement as well as the visibility of the actual post. Related comments to a post’ s topic usually provide readers more insight into the post content and can attract their attention. On the other hand, unrelated comments distract them from the original topic of the post or disturb them by worthless content and can mislead or impact their opinion. In this paper, we propose an effective framework to measure the similarity of given comments to a post in terms of the content and distinguish the related and unrelated written comments to the actual post. Toward that end, the proposed framework enhances a novel feature engineering by combining a syntactical, topical, and semantical set of features and leveraging word embeddings approach. A machine learning-based classification approach is used to label related and unrelated comments of each post. The proposed framework is evaluated on a dataset of 33,921 comments written under 30 posts from BBC News agency page on Facebook. The evaluation indicates that our model achieves in average the precision of 86% in identifying related and unrelated comments with an improvement of 9.6% in accuracy in comparison with previous work, without relying on the entire article of the posts or external web pages' content related to each post. As a case study, the learned classifier is applied on a bigger dataset of 278,370 comments written under 332 posts and we observed almost 60% of the written comments are not related to the actual posts' content. Investigating the content of both group of related and unrelated comments regarding the topics of their actual posts shows that most of the related comments are objective and they discuss the posts' content in terms of topics whereas unrelated comments usually contain subjective and very general words expressing feedback without any focus on the subject of the posts.
An ab initio Study of Cr and Mn Doped MAX Phase TiMSiB

MAX phases, which are widely studied experimentally and theoretically, have general formula of M(n+1)AXn, where M is transition metals, A is A group element and X is C and N. In this study, the structural, mechanical and electronic properties have been researched for hypothetical MAX phase TiMSiB, where M is Cr and Mn by applying ab initio calculations. Initially, structural optimizations have been completed and the outcomes crosschecked with the studies of Ti2SiB in literatures [1]. After optimization process have been achieved, the mechanical stability has been proved. Moreover, Young, Bulk, Shear modules and Poisson ratio for the compounds have been estimated by using elastic constants. Also, to understand the anisotropic behavior of the materials, directional dependent two and three-dimensional mechanical properties have been plotted. Finally, electronic band structures and corresponding density of state (DOS) have been plotted.
On the Role of Score, Genre and Text in Helpfulness of Video Game Reviews on Metacritic

Evaluating others' reviews of products via helpfulness votes is a crucial aspect of purchase decision-making in online marketplaces. Previous work studied key determinant factors of review helpfulness, such as product metadata and review text. However, understanding the extent to which review helpfulness depends on product context, rather than the inherent textual value of a review, remains an open question. In this work, we study how genre, score and review text relate to the helpfulness of 319 017 video game reviews on Metacritic via correlational analyses and prediction experiments. We find significant, genre-dependent correlations between score and helpfulness. This underlies the strength of text vs. score features in our helpfulness prediction models, which achieve up to 0.64 F1. This work is thus a stepping stone towards causal disentanglement of product context from text in review helpfulness estimation.
Topics in the Russian Twitter and Relations between their Interpretability and Sentiment

Topic modelling is a technique widely used today to detect hidden topicality of text corpora, including those from social media. But, for many quite widespread online languages, like, e.g., Russian, topic modelling is still used rarely. For the Russian Twitter, only a handful of works exists, and these works lack substantial discussion on topic interpretability. Also, the impact of various properties of texts upon the modelling results remains widely unexplored. We partly cover these gaps by assessing a mid-range text corpus of a conflictual Twitter discussion in two respects. In continuation to our earlier study that applied three topic modelling algorithms (LDA, WNTM, and BTM) and assessed their quality via automated means, we here juxtapose automated assessment to human coding and link the human evaluation of topic quality to sentiment of the topics. We show that human coding disagrees with the results of the objective metrics in the number of interpretable topics, showing slightly higher interpretability for the LDA algorithm, but inter-coder reliability is much higher for BTM. We discuss a range of coding issues true for all the three topic models. We also find that interpretability of a topic by the human coders is linked to presence of negative keywords among the topic descriptors, with the strongest linkage shown by BTM.
Model-Based Design for a Self-Balancing Robot using the Arduino Micro-Controller Board

Self-balancing robot control is a common way to challenge students in control-oriented courses to stabilize an unstable, non-linear dynamic system. This paper presents an application of model-based design on a self-balancing robot using the Arduino Due micro-controller board. The system consists of a set DC-motors with quadrature encoders, a nine degree of freedom (9-DOF), a Bluetooth module, a motor controller and a micro-controller. By utilizing cheap off-the-shelf hardware and applying model-based design techniques and simulations an accessible learning environment is created. Both the PID and LQR controllers will be discussed for their educational value.
Performance Analysis of Underlaid Full-Duplex D2D Cellular Networks

This paper investigates the benefits of incorporating underlaid full-duplex (FD) device-to-device (D2D) communications into cellular networks. Toward this end, we provide an analytical performance characterization of underlaid D2D cellular networks where D2D users operate in FD mode under the presence of residual self-interference. In considered networks, the base-stations (BSs) are distributed according to a hexagonal grid, while the locations of cellular and D2D users follow Poisson point processes (PPPs). Based on the stochastic-geometry approach, we develop the approximations of key performance metrics including coverage probabilities and achievable sum-rates of both cellular and D2D links, and such approximations involve quickly commutable integrals. Under a special case in which the number of D2D links is sufficiently large, the obtained approximations can be simplified to closed-form expressions, allowing characterize the sum-rate behaviors under the effects of various system parameters. We show that underlaid D2D communications in cellular network can offer a significant spectral efficiency gain as compared to pure cellular transmission. With a sufficiently low self-interference cancellation level, FD D2D can offer substantial spectral efficiency improvement over the half-duplex (HD) counterpart. Finally, the resulting performance metrics are compared with multi-cell networks operating in standard and fractional frequency reuse modes, and observe that frequency reuse provides improved coverage probabilities of both cellular and D2D links, but substantially reduces the D2D sum-rate performance.
An Advanced Hardware-in-the-Loop Battery Simulation Platform for the Experimental Testing of Battery Management System

Extensive testing of a battery management system (BMS) on real battery storage system (BSS) requires lots of efforts in setting up and configuring the hardware as well as protecting the system from unpredictable faults during the test. To overcome this complexity, a hardware-in-the-loop (HIL) simulation tool is employed and integrated to the BMS test system. By using this tool, it allows to push the tested system up to the operational limits, where may incur potential faults or accidents, to examine all possible test cases within the simulation environment. In this paper, an advanced HIL-based virtual battery module (VBM), consists of one “live” cell connected in series with fifteen simulated cells, is introduced for the purposes of testing the BMS components. First, the complete cell model is built and validated using real world driving cycle while the HIL-based VBM is then exercised under an Urban Dynamometer Driving Schedule (UDDS) driving cycle to ensure it is fully working and ready for the BMS testing in real-time. Finally, commissioning of the whole system is performed to guarantee the stable operation of the system for the BMS evaluation.
Longitudinal Wheel Slip Regulation using Nonlinear Autoregressive-Moving Average (NARMA-L2) Neural Controller*

In this study, the implementation of a nonlinear autoregressive-moving average model ( NARMA-L2) neural network controller to maximize the tra ction of tires during braking scenarios was explored. The proposed controller and system dynamics was done in Simulink. All in all, the neural network controller shows good stability and good response in following the reference trajectory or desired slip ratio. It has experienced the peak worst error of around 2%, its best performance was reached after 89 epochs and it can reach around 99.5% of the reference trajectory or desired slip ratio. Further research should focus on hardware implementation, integration with slip estimation techniques , and, better sets of training data to make the controller more adaptive to different environment and road surface characteristics.
MTRecS-DLT: Multi-Modal Transport Recommender System using Deep Learning and Tree Models

Multi-modal transport recommender systems aim to provide different users with different route choices for more than one mode of transportation. Most existing systems focus on unimodal transportation providing shortest distance or travel time. Knowing that the use of machine learning and deep learning techniques are achieving success in many fields, it has also been applied to improve the transport networks by helping individuals to meet their needs and observe their various preferences. In this paper, we develop a model called MTRecS-DLT (Multi-Modal Transport Recommender System using Deep Learning and Tree Models) for recommending the most appropriate transport mode for different users. We have used the weighted average ensembling method of Convolutional Neural Network (CNN) and Gradient-Boosted Decision Trees (GBDT) that shows promising results. We have extracted context and user features from the training data. Then, CNN has been applied to extract latent features. The proposed model utilizes a weighted average ensembling to combine CNN and GBDT.
Linear Array Antenna Diagnostics Through a MUSIC Algorithm

The problem of detecting defective elements in antenna arrays from near-field measurements by a MUSIC method is addressed. It is shown that, owing to the rank deficiency of the involved correlation matrix, MUSIC is indeed no better than back-transformation or matrix methods. In order to restore MUSIC performance, a rank recovering procedure is required. Therefore, here, we introduce a rank recovering method which is properly tailored to address the pertinent near-field configuration. Numerical examples, obtained for 2D scalar cases and linear array antennas, show the effectiveness of the method.
American Sign Language Character Recognition with Capsule Networks

Sign language is an argument that has vital importance in the life of hard of hearing people for communication with others. With sign language, a person with disabilities can convey his/her opinions and thoughts by using gestures and mimics to another person. Today, understanding and using sign language of people without disabilities along with the people with disabilities is really important to socialize them. With the development of technology, software tools become usable for identification of sign language and various software products are produced.In this study, recognition of sign language characters via a system, which was trained using images of letters in American sign language, is aimed. Capsule networks, which is proposed in the recent period, are used for training and test processes and compared with LeNet which is one of the first successful and currently using a model of deep learning. As a result of the study, it has been seen that capsule networks are useful for sign language character recognition and produced more successful results than LeNet.
NetWord: The Cognitive Network Model of the Neurally Distributed Meanings of Words

We present a cognitive architecture based on the network mathematical tool to describe the neurally distributed meanings of words. This network architecture builds on recent empirical findings that the meanings of words are distributed and emerge from a dynamic interaction between several neural elements. The network consists of set of nodes representing words and edges representing features. The specificity of this network lies particularly on its unique architecture consisting of cliques that are semantically differentiated, unlike those obtained from a classical method of clique percolation in graph theory. Each word is distributed in different cliques and serves as a bridge to connect different semantic cliques. Our cognitive architecture provides a prominent way to study the development, the growth and the breakdown of the semantic network in children, adults and people with brain disorders, respectively.
Average Current Control with Asymmetrical Sawtooth or Peak Current Control

Conductance control within SMPS (Switch Mode Power Supply) is typically implemented relying either on the ACC (Average Current Control) or on the PCC (Peak Current Control) technique. An in-depth analysis of the PCC dynamics has been published in the past, highlighting that such technique responds to a feedback control loop scheme covered by Laplace transfer functions properly modelling the limited switching frequency effects. The present paper consists in the dynamic analysis of ACC based on asymmetrical sawtooth, proceeding by similarity with the PCC analysis. It is shown that these ACC and PCC techniques are described by the same differential equations, hence that they pertain to a unique control technique. The bandwidth and stability margins of that closed-loop control technique are subsequently investigated, demonstrating that ACC has a phase margin larger than 60°, while PCC dynamic performance extends beyond ACC with larger bandwidth frequency at a price of lower phase margin. The paper further stresses the differences between Laplace transfer functions relevant to time-discrete and analogue systems, and why they shall be considered separately. Time simulations are finally reported and specific provisions for proper measurement of stability margins on electronic breadboard are indicated.
Sociopolitical Network for Ekphrastic Poetry and Algorithmic Co-authoring

COPO (Cooperative Poetry and Community Politics) on digitalpoetry.net is a sociopolitical network for coauthored ekphrastic poetry and postmodern political commentary. If language is material, or matter, then COPO poems are interference patterns of waves of language because the creation of the poems is not observed. COPO manages pre-relativist experiences and incorporates the poetry written as part of the experience into a random relativist framework. Unlike writing that attempts to represent a visual scene accurately, automatic-ekphrastic poetry reflects subconscious human sentiment or a mental representation that may appear radically different from the actual scene. In doing so, COPO's dataset has the potential to expand the creative authenticity of artificial intelligence.
Mobile robot path planning based on Q-learning algorithm*

Nowadays, reinforcement learning is gaining more and more attention with AlphaGo’s triumph. Path planning algorithm based on Q-learning, a model free reinforcement learning algorithm, was proposed for mobile robots. The algorithm translated sonar sensor information of the environment around the robot, the robot’s pose, and the location of target points to finite states. Afterwards, a reasonable environment model and state space were constructed, with discrete rewarding functions being established. The experimental results validated the effectiveness of the proposed algorithm, as each action of the robot obtained the corresponding rewarding value, which improved the convergence efficiency of the proposed algorithm.
Hierarchically rough structured and self-powered pressure sensor textile for motion sensing and pulse monitoring.

Nowadays, the real-time human motion sensing and pulse monitoring can provide significant basis for health assessment and medical diagnosis. Nevertheless, it is still a big challenge to design a lightweight, flexible and energy-sustainable pressure sensor with high sensitivity and breathability. Here, we fabricated a triboelectric all-fiber structured pressure sensor via facile electrospinning technique. The constructed sensor textile hold a composite structure made up of PVDF/Ag NWs nanofibrous membrane, EC nanofibrous membrane and two layers of conductive fabrics. This wearable device with high shape adaptability exhibited excellent sensing capability due to the introduced hierarchically rough structure on the nanofibers. The sensitivity can reach up to 1.67 V·kPa-1 and 0.20 V·kPa-1 in the pressure range of 0-3 kPa and 3-32 kPa, respectively. The fabricated sensor textile also showed a superior mechanical stability even after a continuous operation of 7200 working cycles. This sensor textile was easily conformable on different desired body parts for dynamic motion sensing and real-time pulse monitoring. It can work in a self-powered manner to detect and quantify various human motions associated with joints, such as elbows, knees and ankles. Additionally, it can be placed on the carotid artery to capture the pulse signals, serving as a reliable way to reflect the state of health. This work has great possibilities to promote the rapid advancement and broad applications of multifunctional pressure sensors and next-generation wearable electronics.