Miracle-dz/UniFAR-FTUs huggingface dataset:
Biometric verification system based on Density and classifier based score level fusion

Selecting the most accurate rules under the fusion strategies depends on many factors such as quantity of training data, quality of given training data, understanding reliability of ground truth for training data, modeling tools etc. In our work we have adopted score level fusion scheme as it contains rich source of input data and has proximity features, we have explored promising rules such as density based likelihood features-support vector machines((LF-SVM)), likelihood features random subspace of AdaBoost (LF(RS-ADA)) and classifier based (support vector machines(SVM), random subspace of AdaBoost (RS-ADA), dempster-shafer (DS).
EK-Healthcare: Effectiveness of IoT in the Medical Field with Enhanced Features

Internet of Things (IoT) is the combination or collection of different devices, wearable, appliances and other items having sensors, actuators and software etc. which makes them able to connect with each other through the Internet and exchange or share data. The medical/healthcare industry is leading the list to adopt the IoT. Usage of the IoT-based body sensors is becoming more popular and common between people for continuous monitoring and medical healthcare to perform real-time actions in case of emergencies. In this paper, we propose an application method called EK-healthcare model in which patients' data can be sent through mobile phone SMS as well as through Internet to get emergency services. This paper incorporates new and enhanced features in our existing research [1] so that the patients can get emergency services in case there is no Internet access. The results show that incorporating the enhanced features reduces the response time, increases the security and is reliable.
A model based on eye movement data and artificial neutral network for product styling evaluation

The product styling evaluation method is usually subjective, and the lack of objectively quantitative analysis and data support. To make up these disadvantages and to obtain the objective assessment for product styling, a styling evaluation model based on eye-tracking technology and BP (back propagation) neural network was proposed, whose inputs were multiple eye movement data. This pilot study took the aircraft passenger seats as example. The experiment in this paper used RED desktop eye tracker to collect the eye movement data of 14 participants for evaluating eight aircraft passenger seats' styling, meanwhile, the subjective ratings were obtained with ratings scale via verbal self-report. A mathematical model for evaluating aircraft passenger seat styling was set up. The best number of the neurons in the hidden layer was determined by the training error. Finally, the model was verified. The result suggested that the model based on BP neural network could effectively calculate the subjective evaluation ratings on aircraft passenger seat styling, and this method provide objective support for evaluation. This model assists the designers and managers choose the optimal product design proposal and capture the user's preference.
Inferring, Learning and Modelling Complex Systems with Bayesian Networks. A Tutorial

Bayesian networks, BN, are a formalism for probabilistic reasoning that have grown increasingly popular for tasks such as classification in data-mining. In some situations, the structure of the Bayesian network can be given by an expert. If not, retrieving it automatically from a database of cases is a NP-hard problem; notably because of the complexity of the search space. In the last decade, numerous methods have been introduced to learn the networks structure automatically, by simplifying the search space or by using a heuristic in the search space. Most methods deal with completely observed data, but some can deal with incomplete data. In this tutorial we will present, besides BN, other popular classification methods, i.e. Multilayer Perceptrons Network (MLP) and K-nearest neighbor (KNN) an analyze their performance in the context of medical diagnosis.
Single-Document Summarization Using Sentence Embeddings and K-Means Clustering

This paper proposes a novel method for extractive single document summarization using K-Means clustering and Sentence Embeddings. Sentence embeddings were processed by K-Means algorithm into a number of clusters depending on the required summary size. Sentences in a given cluster contained similar information, and the most appropriate sentence was picked and included in the summary for each cluster by a ridge regression sentence scoring model. Experimental ROUGE score evaluation of summaries of various lengths for the DUC 2001 dataset demonstrated the effectiveness of the approach.
Adaptive Evaluation of Virtual Machine Placement and Migration Scheduling Algorithms Using Stochastic Petri Nets

More and more mobile applications rely on the combination of both mobile and cloud computing technology to bring out their full potential. The cloud is usually used for providing additional computing resources that cannot be handled efficiently by the mobile devices. Cloud usage, however, results in several challenges related to the management of virtualized resources. A large number of scheduling algorithms are proposed to balance between performance and cost of data center. Due to huge cost and time consuming of measure-based and simulation method, this paper proposes an adaptive method to evaluate scheduling algorithms. In this method, the virtual machine placement and migration process are modeled by using Stochastic Reward Nets. Different scheduling methods are described as reward functions to perform the adaptive evaluation. Two types of performance metrics are also discussed: one is about quality of service, such as system availability, mean waiting time, and mean service time, and the other is the cost of runtime, such as energy consumption and cost of migration. Compared to a simulation method, the analysis model in this paper only modifies the reward function for different scheduling algorithms and does not need to reconstruct the process. The numeric results suggest that it also has a good accuracy and can quantify the influence of scheduling algorithms on both quality of service and cost of runtime.
Statistical Topic Modeling for Urdu Text Articles

Natural Language Processing (NLP) is a branch of Artificial Intelligence to help computers manipulate and interpret human languages. In NLP, text mining is a technique to derive useful information from text. Topic Model (TM) is a statistical model to extract topics from a large collection of unlabeled text using NLP and machine learning techniques. Several effective TM are available to fulfill the needs of various languages like English, German, Arabic etc. However no compelling TM is available for poor resource South Asian language Urdu. In this research study, our focus is to work on existing TM like Latent Dirichlet Allocation (LDA) to overcome the issues of Urdu language in text mining. We studied and analyzed LDA as an unsupervised model for the Urdu topic identification. Hence, we studied LDA deeply for Urdu topic identification at two levels: Variational Bayes (VB) based LDA for Urdu (VB-ULDA) with stemmer and without stemmer. Experiments are performed on a self-created massive number of Urdu documents in four different corpora. Experimental study shows that VB-ULDA outperformed in the identification of topics from Urdu text documents as compared to existing Urdu LDA (ULDA) in terms of accuracy and efficiency and results also reveal the high impact of stemming algorithm in Urdu topic identification.
A Review on Video Summarization Approcahes : Recent Advances and Directions

With the advancement of technology and easy accessibility of internet, an enormous amount of audio visual content is generated daily. This multimedia content takes a lot of space and time to be processed which decreases the performace of various video processing based applications like video searching, indexing, video recommendation and many more. The field of video summarization deals with the techniques for reducing the length of the video. Various state of the art techniques exists in this area and a lot of progress has been already done over the past years, but researchers are still working to generate more efficient and robust techniques, and to produce more accurate and sematically strong summarized videos The aim of this paper is to discuss about basic approaches for summarization, why it is so important in current scenario while keeping focus on the methods and recent approaches that have been introduced in last seven years in this area.
A Novel Technique for Image Super Resolution Based on Sparse Representations and Compact Entity Extraction

A novel method of image super resolution using sparse representation has been discussed in this paper. The main purpose is to acquire the super-resolved image from the down scaled and blurred images. With the small number of elements from a huge set of vectors, sparse signal model approximates signals and this large dataset is called a dictionary. For construction of high and low-resolution dictionaries from the condensed atoms extracted from the training image patches, the Orthogonal Matching Pursuit approach has been used. The blurred and down-scaled version of the image is super resolved using the above-mentioned dictionaries. The outcomes are compared both instinctively by the visual assessment of the resulting super-resolve images by means of the proposed scheme and the bi-cubic interpolation method, and by comparing the Peak Signal-to-Noise Ratio (PSNR) obtained by the two approaches. Both the comparison metrics, i.e. visual quality of acquired super resolved images and PSNR measures show that the proposed approach is superior to the existing state of the art Bi-Cubic interpolation.
Energy Consumption and Completion Time Tradeoff in Rotary-Wing UAV Enabled WPCN

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled wireless-powered communication network (WPCN), where a rotary-wing UAV is employed as a hybrid access point (AP) to serve multiple ground users (GUs). Specifically, the GUs harvest radio frequency (RF) energy from the signal sent by the UAV, which is then used by the GUs to power their uplink information transmission to the UAV. In practice, the mission completion time and energy consumption are two important indexes to evaluate the performance of UAV-enabled communication. To complete the mission as soon as possible, the UAV should fly above the ground users it serves at maximum speed, but this leads to more propulsion energy being consumed. Our objective is to reveal the energy-time tradeoff, characterized by the boundary of the so-called “Energy-Time” region. We first derive the mathematical form of the tradeoff, the UAV trajectory, user scheduling and mission completion time, as well as the time allocation, all of which need to be jointly optimized. To this end, we propose two communication protocols: (i) fly-hover-communicate and (ii) path discretization. For each protocol, we first find the two extremes, where minimum energy consumption and minimum mission completion time are achieved. We then complete the boundary for minimizing the energy consumption for given mission completion time. Moreover, because of the nonconvexity of the problem, we propose an algorithm to obtain a locally optimal solution based on the successive convex approximation (SCA) technique for both designs. Finally, the simulation results are provided to validate the effectiveness of our study.
Hardware Implementation of Asymmetrical Cascaded H-Bridge 41 Level Inverter using PIC Microcontroller

In this paper, an asymmetrical cascaded h-bridge 41 level inverter powered by a single Photo Voltaic (PV) unit using PIC Microcontroller is proposed. The PV unit drives an Interleaved Soft Switched Boost converter (ISSBC) that drives a simple three level inverter and this inverter unit drives a multiple secondary winding transformer. The AC output of the four isolated secondary windings of the transformer are rectified and filtered to deliver four isolated DC voltages in the ratio 1: 3: 9:27. The system incorporates Maximum Power Point Tracking (MPPT) at the front end boost DC-DC converter level. Overall reduced THD is achieved by strategically spacing on the time axis, for each AC cycle, the discrete voltage levels of the 41 level inverter. The results of experimental verifications are provided.
A CiteSeerX-Based Dataset for Record Linkage and Metadata Extraction

Data cleaning constitutes an important problem in information science. Collecting data about the same entities from multiple sources or following distinct methodologies might result in slightly different, inconsistent data. The objective of data cleaning is to produce a fused version combining the differing data, resulting in a cleaner dataset. In this paper we collect document metadata records from CiteSeerX and build a supervised record linker to Crossref. The supervised method is trained using a manually linked dataset containing 512 verified DOIs—to our knowledge, up to now being the largest such dataset for bibliographic record linkage. We experiment using different supervised learning methods, and also prove experimentally that the accuracy of the attached metadata records can improve the performance of automatic metadata extraction systems.
Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation

Power consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a convolutional neural network (CNN)-based architecture with inputs and outputs formed as data sequences taking into consideration an appliance’s previous states for better estimation of its current state. Furthermore, the proposed model endows CNN models with a recurrent property in order to better capture energy signal interdependencies. Using a multi-channel CNN architecture fed with additional variables related to power consumption (current, reactive, and apparent power), additionally to active power, overall performance, robustness to noise and convergence times are improved. The experimental results prove the proposed method’s superiority compared to the current state of the art.
Emotionally Adaptive Driver Voice Alert System for Advanced Driver Assistance System (ADAS) Applications

Human cognitive analysis catalyzes the innovations in Human Machine Interface (HMI) for a variety of applications. In an Automotive Advanced Driver Assistance System (ADAS), the continuous cognitive interaction of the driver with the assistance system plays a crucial role in enhancing the active safety system. Multiple ADAS functionalities uses a variety of driver alerts through visual, audio and vibrational means to provide a numerous safety alerts to the driver. The effectiveness of any alert system is measured through its success rate in mitigating the actions which are against the alert commands. The actions taken by the driver for the alerts depends heavily on the driver’s moods, which are responsible for driver’s perception in understanding the alerts. Even though the voice alerts are considered as the most effective form of human alerts, the static nature of the voice alerts makes them less effective in making the driver to understand the criticality of the alerts when his moods are abnormal or having a reduced driving concentration levels. An adaptive voice alert system with a cognitive driver synchronization makes the alert penetration successful when the driver’s moods are abnormal or having a reduced driving concentration levels. Here in this paper the adaptive voice alert system is designed using the driver’s emotional cognitive features. The emotionally adaptive voice alert system changes the voice alerts as according to the moods of the driver, which are measured by Deep Learning based Emotion Recognition System. The adaptive voice alert system makes the voice enabled HMI effective which improves the vehicle safety.
An Overview of Anomaly Detection in Video Surveillance

As of late, a drastically extending number of perception cameras have been acquainted with screen private and open spaces and districts. Video perception is seen as a fruitful way to deal with ensures our security. In this way, demonstrating action designs also, human practices for discovery or acknowledgment of impossible to miss occasion is a basic innovation which has pulled in exceptional investigate enthusiasm for the most recent couple of years. This paper throws light on numerous videos datasets which are reasonable for diverse applications and different techniques and models for anomaly detection which are created to construct keen vision frameworks.
LI-RMLO: Loop Iteration based Runtime Method Level Offloading for Mobile Applications

Application partitioning is of particular importance to address the long communication delay issue which occurs during offloading from mobile device to cloud. With the launch of mobile edge framework, the connection delay tends to get reduced to a certain extent and paves the way for utilizing neighbourhood competent mobile devices for taking care of intensive computations of fragile devices. While Mobile Edge Computing (MEC) transforms the Mobile cloud computing in one way, the improvisation in shortening the granularity of code to be offloaded is raising on the other side. Method Level Offloading (MLO) is one such solution of code granularity reduction which is discussed in many research works. Our proposed model, Loop Iteration based Runtime Method Level Offloading (LI-RMLO) adopts MLO to offload applications having iterative and independent methods that behaves diverse to smaller and larger inputs. In such cases, the method which is labeled as offloadable never needs to be executed in the remote machine for all runs. Instead our model analyzes the iteration limit of the method at runtime and decides to offload only when the local execution time of the method exceeds its remote execution time. By this adaptation, we achieve an optimal offloading performance and ward off unwanted offloading to the proximate edge nodes.
Updating HD-Maps for Autonomous Transfer Vehicles in Smart Factories

Autonomous robots are increasingly used in many fields to perform some special tasks. Autonomous transfer vehicles (ATV) in smart factories are one of the most critical components of industry 4.0. Mapping is the key process for the long term successful operation of ATVs. Although, robotic mapping is enough for the autonomous navigation of an ATV, high definition (HD) maps are required to perform the perfect behavior and ensure better interactions with the environment. HD maps can be created manually, but updating the maps is an open problem for sustainability of the performance. In this study, an HD-map update strategy is proposed for ATVs that operate in smart factories.
A Faster High-Dimensional Bilateral Image Filtering By Efficient Utilization of Color Sparsness Offline

Most of the image filtering algorithms involve nonlinear processing of high-dimensional data. Bilateral, joint bilateral, and non-local means filters are the examples of the same. Real-time implementation of high-dimensional filters has always been a research challenge. In this paper, we have proposed a solution utilizing color sparseness in an image which ensures a faster algorithm for generic high-dimensional filtering. The solution speeds up the filtering algorithm further by psycho-visual saliency lookup-table (LUT) evolved through iterations of batched image data processing. We use the proposed filtering algorithm with our LUT for edge-preserving smoothing and denoising of color images. The results demonstrate competitiveness of our proposed solution with existing fast bilateral algorithms in terms of both accuracy and performance.
Determining Human Voice Source Location Using TDOA

Human being has the capability to determine the sound source location because of sound waves difference received by the ear. The method applied to determine the humans source location in this research was Time Difference of Arrival Estimation (TDOA) at a sampling rate of 6 KHz. The results of tests found that the system can determine the human voice source location in semicircle $(0^{\mathrm{o}}\ -\ 180^{0})$, with the best angular resolution was at $10^{0}$. The results of this study can be applied to the teleconference room.
A systematic mapping study of usability vs security

Usability and security are two of the inevitable but conflicting quality factors in today’s multi-user modern computing environment. Although usability is considered to be an essential component of a secure system, not many studies focused on these two factors together. This study aimed to determine the general trends regarding the usability and security issues. Therefore, a systematic mapping approach was applied with the articles published on these two issues together between years 2006 and 2016 to reveal the research trends in this research area. 179 papers, which were indexed in web of science, were included for the mapping. The results revealed that many researches dealt with usability and security issues. They reported problems of efficiency and learnability from usability perspective and authentication from security perspective. They mainly adopt usability evaluation methods rather than any specific usability-security specific method and a few proposed recommendations or a method for design or improvement of usable security.
Fuzzy Theory Based Single Belief State Generation for Partially Observable Real-Time Strategy Games

As the basic problem of the real-time strategy (RTS) games, AI planning has attracted wide attention of researchers, but it still remains as a huge challenge due to its large searching space and real-time nature. The situation may get worse when the planning in RTS games is implemented under a partially observable environment considering the existence of the fog-of-war. Given the recorded past positions of an agent, it would be helpful if the targets’ next position can be predicted based on the recorded data since this will increase the certainty of the target. Therefore, this paper proposes a fuzzy theory-based single belief state generation method named FTH to do what based on multi-layer information sets extracted from the history position information. Besides, we incorporate the FTH generation method into adversarial hierarchical task network repairing (AHTNR) planning algorithm, which can be used for the prediction of the unit’s position and task planning. Finally, we carry out an empirical study based on the $\mu $ RTS game and validate its effectiveness by comparing its performance with that of other state-of-the-art algorithms.
Battery Chargers in Electric Vehicles

Battery chargers are the energy transmission part of electric vehicles between the grid (or electric energy source) and the vehicle. This energy transmission process has a great effect on the technology of electric vehicles. Charging a vehicle as fast as possible is necessary for an electric vehicle to compete with the internal combustion engine vehicles. The technology has been dealing with this process near past, while charging a car was taking a few hours, this time is reducing day after day. In this paper it is aimed to give some information about the current technologies of chargers (only the chargers with cable not wireless chargers) and it is also aimed to design a trial Simulink model for chargers. When the literature is considered, simulation samples of chargers are little so increasing these samples is important to improve the technology of fast charging.