Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Introducing Redundancy in a Probabilistic Forwarding Protocol

This paper is concerned with the problem of broadcasting information from a source node to every node in an ad-hoc network. Flooding, as a broadcast mechanism, involves each node forwarding any packet it receives to all its neighbours. This results in excessive transmissions and thus a high energy expenditure overall. Probabilistic forwarding or gossiping involves each node forwarding a received packet to all its neighbours only with a certain probability p. In this paper, we study the effect of introducing redundancy, in the form of coded packets, into a probabilistic forwarding protocol. Specifically, we assume that the source node has $k$ data packets to broadcast, which are encoded into $n$ 2: $k$ coded packets, such that any $k$ of these coded packets are sufficient to recover the original $k$ data packets. Our interest is in determining the minimum forwarding probability $p$ for a “successful broadcast”, which we take to be the event that the expected fraction of network nodes that receive at least $k$ of the $n$ coded packets is close to 1. We examine, via simulations and analysis of a number of different network topologies (e.g., trees, grids, random geometric graphs), how this minimum forwarding probability, and correspondingly, the expected total number of packet transmissions varies with the amount of redundancy added. Our simulation results indicate that over network topologies that are highly connected, the introduction of redundancy into the probabilistic forwarding protocol is useful, as it can significantly reduce the expected total number of transmissions needed for a successful broadcast. On the other hand, for trees, our analysis shows that the expected total number of transmissions needed increases with redundancy.
An Adaptive Rate ECG Acquisition and Analysis for Efficient Diagnosis of the Cardiovascular Diseases

The aim of this paper is to develop an intelligent event-driven Electrocardiogram (ECG) processing module in order to achieve a computationally efficient solution for diagnosis of the cardiac diseases. The suggested method acquires the signal with an event-driven A/D converter(EDADC). The output of EDADC is passed through the activity selection and interpolation blocks. It allows focusing only on the important signal parts and resampling it uniformly by using the Simplified Linear Interpolator. Later on, the signal is de-noised. The autoregressive (AR) method is used to extract the classifiable features of the de-noised signal. Afterwards, the output is classified by employing different robust classification techniques such as support vector machines (SVMs), K- Nearest Neighbor (KNN) and Artificial Neural Network (ANN). The event-driven feature enables to adapt the system processing load according to the signal temporal variations. This interesting feature of the devised system aptitudes a drastic reduction in its processing activity and therefore in the power consumption as compared to the counter traditional ones. A comparison of the performance of different classifiers is also made in terms of accuracy. Results show that the proposed system is a potential candidate for an automatic diagnosis of the cardiac diseases.
Deep Learning for Ligand-Based Virtual Screening in Drug Discovery

Due to the time and cost problems with traditional drug discovery, new methods must be found to increase the declining efficiency of traditional approaches. Virtual Screening (VS) is one possible solution to solve this problem. VS of databases has become an attractive method for pharmaceutical research. It plays a crucial role in the early stage of the drug discovery and development process. It aims to reduce the enormous search space of chemical compounds. As the number of ligands in the databases is increasing rapidly, this step should be both fast and effective in order to distinguish between active and inactive ligands. Deep learning algorithms can be used for screening big databases of molecules and classifying the ligands as drug-like and non-druglike against a particular protein target and therefore speed up the VS process. In this paper, we propose a fast compound classification method based on a deep neural network for Virtual Screening called (DNN-VS) using the Spark-H2O platform in order to label small molecules from huge databases. Experimental results have shown that the proposed approach outperforms state-of-the-art machine learning techniques with an overall accuracy more than 99%.
An Effective Face Recognition Framework With Subspace Learning Based on Local Texture Patterns

Currently, there are mainly two kinds of methods for face recognition, i.e. subspace learning methods and local texture methods. Generally, the first are sensitive to compound changes including illumination, face expression and pose, but have low-dimensional features. Relatively, the second have high-dimensional features but better robustness to compound changes. Making full use of their advantages, this paper proposes a face recognition framework with subspace learning based on local texture patterns. Then, two face recognition methods, which adopt discriminant analysis from two different local texture patterns, are derived from the proposed framework. Lastly, the experimental results on AR database and CAS-PEAL-R1 database show that two proposed face recognition methods gain higher average optimal correct recognition rates than several traditional methods, and much lower dimensional features than traditional local texture methods, which verify the effectiveness of two methods derived from the proposed framework. Therefore, the proposed framework is effective and worth extending.
LDPC Code Design via Masking Technology and Progressive Optimization

A design of low-density parity-check (LDPC) code via the masking technology and progressive optimization is proposed. The code generated by this method has lower computational complexity, and their parity-check matrices can effectively refrain from the girth −4 phenomenon. The proposed method has the superiorities such as better girth-length characteristic and more flexible trim in the length and rate of the code. Experiment results indicate that the error-correction performance of the new code should be better than or as good as the capability of the code which is constructed without been masking. Futhermore, under the condition of short code length, the performance is better than that of LDPC codes via the randomized construction methods.
A Novel Sea Ice Classification Method from Hyperspectral Image Based on Bagging PCA Hashing

Hyperspectral imagery has evident advantage for sea ice classification due to enormous spectral bands. At the meantime, hashing is promising in representing a high-dimensional feature with extremely low bit binary codes while maintaining the classification performance. In this paper, a novel sea ice classification framework based on PCA Bagging hashing is proposed to improve the accuracy of hyperspectral image classification. First, local binary pattern(LBP) features are extracted from selected spectral bands. PCA bagging is employed to generating hashing codes of the extracted features. Then, a piece of short but strong code is obtained to represent the features of each pixel. Finally, these hashing codes are fed into the extreme learning machine for classification. The experimental results in real-world dataset demonstrate that the proposed framework is superior to two closely related methods.
Fault Detection and Location in Power Transmission Line Using Concurrent Neuro Fuzzy Technique

In power systems, power transmission lines are an important part of an electrical grid. Thus, it is important to anticipate upcoming faults and their location by predicting them using a powerful artificial intelligence technique to improve power transmission line reliability and sustainability. This paper compares the results of concurrent neuro-fuzzy (CNF) technique applied in different power transmission lines (PTL), to predict the detection faults and their location over two long and short PTL (735 kV, 600 km and 400 kV, 120 km), CNF was used for detecting, locating and classifying faults in PTL. The results show that the utilization of this technique for such task could be time saving for the technical team and could improve the transmission line yield.
Ranking-based Collaborative Clustering for Heterogeneous Information Network

At present, the improved social network analysis methods are mainly based on homogeneous networks. However, the actual social networks are heterogeneous in nature. Heterogeneous social networks can better reflect the composition of the system and the associated relationships than the homogeneous social network model. The previously proposed ranking clustering provides a new idea, however, the algorithm only completes the clustering results of specific target types and cannot cover the complete heterogeneous network type. By introducing collaborative clustering algorithm and combining it with ranking, we propose a ranking collaborative clustering algorithm. Firstly, based on ranking clustering, the ranking distribution matrix is obtained, and then collaborative clustering is used to complete different types of clustering, and the relationship between different types and the same types is fully utilized. The experimental results on the real twitter and foursquare datasets show that the ranking collaborative clustering algorithm has better performance on the modularity compared to the ranking clustering.
A Bias-Reduced Method for TDOA Localization with Receiver Position Errors

This paper proposes a bias-reduced version for the classic two-stage weighted least squares (TSWLS) algorithm using time difference of arrival (TDOA) in the presence of receiver position errors. The proposed method also has two stages. Through introducing an augmented matrix and imposes a quadratic constraint in weighted least squares (WLS) estimator, we construct a constrained optimization model. Subsequently Lagrange multiplier method is applied to obtain the stage-l solution. The stage-2 processing is the same as the original TWLS algorithm. Simulation results show the superiority of the proposed algorithm and demonstrate that the proposed method is effective in reducing the localization bias and can achieve the Cramer-Ran lower bound (CRLB) accuracy under the moderate noise levels.
Droop Design Methodology for Proportional Power Balancing in VSC MTDC Systems

In multi-terminal DC grid droop control is commonly used to regulate the DC voltage. Generally, droop parameters are selected on the basis of steady-state analyses. This paper presents a methodology to design the droop value based on the concept that voltage response time depends on converter end capacitors and the number of terminals responsible for controlling the DC voltage. The effect of proposed droop is investigated on number of droop controlled terminals in use. A five terminal HVDC system is considered to demonstrate the efficacy of the proposed methodology. The said system is analysed for both ideal and non-ideal DC grid. The study shows that with proposed droop, converters can be utilised to their maximum limit.
Real-Time Monitoring & Protection of Power Transformer to Enhance Smart Grid Reliability

Due to deregulation of the power system, grid reliability becomes more complicated. Even, day by day small-scale and renewable power generation is inserted in a distribution system effectively due to people awareness. A power transformer is one of the most important equipment in the grid to reliably and efficiently transmit power to the consumers. Asset management and protection are the best concepts for enlargement of transformer lifespan as well as to increase grid reliability. This article reflects on electrical and other parameter based power transformer asset management. Voltage, current, and power based inrush detection and Power Differential Protection (PDP) are applied to protect the transformer. Various data such as load history, losses and temperature will be monitored in real time to enhance the working capability of the transformer. The proposed method adopts various electrical and nonelectrical parameters for condition monitoring. The proposed scheme is successfully tested on SkV A laboratory transformer using Arm CORTEX-M4 processor. A fitness function estimated from the collected data in the processor will reflect the condition of the transformer. The hardware result confirms the effectiveness of the scheme for monitoring and protection of transformer.
Optimal association of wireless devices to cellular and Wi-Fi base stations

Communication devices such as cellular phones are now capable of using different communication technologies, such as 3G/4G and WiFi. The high data rate requirements of current user applications have lead to the important engineering problem of optimally associating a communication device with different networks using different communication technologies so as to maximize data rates. In this paper, we study the problem of optimally associating a group of users to a single base station and a single WiFi access point so that the sum throughput of all the users is maximized. Compared to prior work, we find that under realistic models for throughputs which are achievable on WiFi, the above association problem is a fractional programming problem, namely a “maximising sum of ratios” problem. Although the general sum of ratios problem is hard to solve, for the above association problem, we suggest heuristic algorithms that are shown to have a sum throughput close to the maximum. We also suggest a novel upper bound to the maximum sum throughput which has applications in performance evaluation.
Education 4.0 - Artificial Intelligence Assisted Higher Education: Early recognition System with Machine Learning to support Students' Success

Education 4.0 is being empowered more and more by artificial intelligence (AI) methods. We observe a continuously growing demand for adaptive and personalized Education. In this paper we present an innovative approach to promoting AI in Education 4.0. Our first contribution is AI assisted Higher Education Process with smart sensors and wearable devices for self-regulated learning. Secondly we describe our first results of Education 4.0 didactic methods implemented with learning analytics and machine learning algorithms. The aim of this case study is to predict the final score of students before participating in final examination. We propose an Early Recognition System equipped with real data captured in a blended learning course with a personalized test at the beginning of the semester, an adaptive learning environment based on Auto Tutor by N. A. Crowder theory with adaptive self-assessment feedback. It is obvious that focusing on students' success and their experiences is a win-win scenario for students and professors as well as for the administration.
Linkage of Co-seismic Failure Stress and Gravity Changes using Statistical Approach for the 2016 Mw7.8 Kaikoura earthquake, New Zealand

The objective of the paper is to establish a statistical relationship between Coulomb failure stress and gravity changes triggered by the 2016 Kaikoura Mw 7.8 earthquake, New Zealand. The changes were generated using PSGRN/PSCMP program at a layered gravitational half-space. The analysis assumed that the evolving stress level in the area is stable before the Kaikoura earthquake. The study area is extended over the main ruptures and surrounded regions in the South Island; the change patterns were estimated at six depths of 0 km, 5 km, 10 km, 15 km, 20 km and 25 km. Due to the stress and gravity models, the change patterns of the two models were numerically estimated and statistically analyzed using different methodologies viz.; Descriptive statistics, Regression, Correlation and ANOVA. These statistical methods were served at 95% confidence interval to expose the linkage between the two models under the umbrella of SPSS 23 program. The different results can be summarized as: (1) Descriptive analysis result revealed that the positive Coulomb stress change is ~17 MPa and located at 20 km depth, whereas the negative change (~59 MPa) observed at the depth of 10 km. Likewise, the maximum gravity change −7.67 mGal- was observed at 15 km and the minimum change value of 4.69 mGal occurred at 0 km depth, worth mentioning the epicenter located at ~15 km depth, i.e., the maximum gravity change happened near the epicenter and decreased away from the epicenter. (2) According to the regression model, if all variables are held constant, a unit change in the stress at 5 km depth is associated with 0.943 increase in the stress at 0 km. Also, we found that a unit change in gravity change associated with 0.930 increase in the gravity at 0 km. (3) The correlation results mostly showed positive strong correlations of stress or gravity models at adjacent depths and vice versa. (4) The final result from ANOVA method was obtained from two steps: the first, comparing the stress or gravity changes at different depths utilizing the omnibus F-test and post hoc results. The results demonstrate existing of significant difference in both stress and gravity models. Secondly, further analysis of the mean difference were done to provide specific group details. The research is a scientific basis for utilizing statistical analysis in this area, and it can be applied for similar research in other areas over the world.
Energy-Efficient Motion Planning for Electrical Drives

The energy consumed over a limited operation time of an electrical drive has a huge impact on the energy consumption in a production line, and therefore, affects directly the economics of a factory or company. Although polynomial interpolation with cubic, quartic or quintic polynomials has been widely used in the literature for motion planning purposes, these polynomials have also some major drawbacks. One of these drawbacks is an insufficient smooth trajectory profile. The other problem is not having adequate equations to solve the problem of using seventh-order polynomials for pre-specified positions, velocities, accelerations and jerks in every specified control points or time intervals during the desired path. Instead, seventh-order or higher-order-polynomials are used to satisfy more boundary conditions to overcome the problem of specifying desired position, velocity, acceleration and jerk at the specified control points. Another method that is used for the trajectory planning, is to use spline interpolation polynomials. A comparison between different methods will be done, and their impact on the overall energy consumption of the system will be discussed.
Construction of Polar Codes Combined with Physical Layer Security on Impulsive Noise Channels

Polar codes are the first proven capacity-achieving codes for any symmetric binary input discrete memoryless channel (B-DMC) and their has been recent interest in their combination with the popular area of physical layer security. Previous work on error-correcting codes and physical layer security has assumed a wiretap channel where each user experiences Gaussian noise, but this paper presents the first results evaluating the performance of polar codes on wiretap channels where both the intended recipient and eavesdropper experience varying levels of impulsive noise. The impulsive noise has a symmetric alpha-stable probability density function (pdf) and density evolution is employed with knowledge of this pdf to construct optimal polar codes for an impulsive environment. Simulation results confirm that the bit error rate (BER) at the eavesdropper is always 0.5 for all signal-to-noise ratios, thus always ensuring secure communication, and we present the very interesting result where this is still achieved when the main channel is more impulsive than the eavesdropper's channel.
Fingerprint Enhancement Using Wavelet Transformation and Differential Support Vector Machine

The security measurement relies on knowledge-based methods such as token (swipe cards (credit cards) and passports to control access to physical and fundamental spaces), passwords (Net banking, ATM Cards). Though ever-present, such techniques are not secured. Passwords like badges and cards might be shared or stolen. Furthermore, they cannot not differentiate between the authorized person and an unauthorized user. Hence, biometric traits like face, retina, palm and fingerprints are used for authorization. The fingerprint is the most famous biometric traits used for security and law-enforcement. Our research work, introduces a novel method for fingerprint image enhancement using DWT, PDE and SVM (matching and classification). Discrete Wavelet transformation method is used to filter the latent fingerprint images in LL, HL, LH and HH bands. The fingerprint minutiae methods are extracted by screening the local point of each ridge pixel in the image using a $3\times 3$ window. PDE algorithm fetches the improved features through minutiae method. The SVM classify the unique features in two phases: (i) Training Section and (ii) Testing Section). The proposed system has given better performance with GAR 0.8%, Peak Signal to Noise Ratio value is 54% and FAR value 1 to 5 per cent. PDR (Partial Difference Rate) and Gaussian filter is used for the improvement phase in Partial and classification fingerprint verification.
Accurate Solution of Volume-Surface Integral Equations at Very Low Frequencies

Electromagnetic (EM) problems with both conducting and penetrable media can be formulated by volume-surface integral equations (VSIEs) in integral equation approach. Traditionally, the electric field integral equation (EFIE) is used to describe the conducting part, but it has a low-frequency breakdown problem because the vector potential and scalar potential are seriously imbalanced at low frequencies and also it belongs to the first kind of integral equation. In this paper, we use the magnetic field integral equation (MFIE) to replace the EFIE for the conducting part and combine the volume integral equations (VIEs) for the penetrable part to form the VSIEs. Both the MFIE and VIEs belong to the second kind of integral equations and the resultant VSIEs will not suffer from the low-frequency breakdown anymore. We present numerical examples to demonstrate the scheme at very low frequencies and good results have been obtained.
Medinsights: Twitter Based Platform for Health Care Analytics

Twitter is a social media platform where the tweets convey opinions, but the interpretation of this unstructured data can be very time-consuming. Medinsights is a data science platform which leverages machine learning and natural language processing technologies on twitter data. This platform helps to analyze the inherent value of the data extracted from tweets such as diseases, treatments, symptoms linked to healthcare domain, which can only be fully exploited through deep data analytics. Medinsights takes particular health-related query on medicine, disease, brand, medical hashtags as input and converts corresponding twitter data into actionable information in order to gain insights and provide diverse recommendations. Medinsights analyses the sentiment, trend, prevalent location to provide meaningful inferences. Medinsights extracts and proposes associated symptoms, diseases, treatments, drugs and brands based on user query from tweets. For this study gradient boosting classifier is used for tweets classification into medical and non medical domain. Word2vec word embeddings with feed forward neural network is used for sentiment analysis. Conditional random field (CRF) is used to extract medical entities from tweets. Medinsights can be helpful to research correlation from tweets and analytics related to healthcare domain.