Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Motion Sickness Level in Autonomous Vehicles According to Look-Ahead Distance

In this study, the motion sickness level (defined in the ISO standards) for autonomous vehicles is analyzed as a function of the look-ahead distance. A standard linearized vehicle model is used to design linear quadratic regulators (LQR) for the automated steering. The dynamics of the otolith organ of the human vestibular system is integrated to the linearized control oriented model. This integration enabled to evaluate the motion sickness level with an H2 norm performance index. Several automated steering controllers are designed with the same performance indices for various vehicle speeds and various look-ahead distance values. The H2 norm of the motion sickness dose values (MSDV) of each controller are used to demonstrate the effect of the look-ahead distance on the motion sickness level. A 3 degrees-of-freedom nonlinear vehicle model with yaw plane dynamics and the semi-empirical Magic Formula tire model is used for the simulation studies by which the analytical results, obtained by linear analysis, are validated.
A Reference Generating Voltage Wide Range Low Power Current Comparator

A low Power wide current range current mode comparator is presented in this paper. The current comparator proposed is based on the concept of Threshold Inverter Quantization(TIQ)[8] which accepts one current input. The circuit's output stage gives a rail-to-rail voltage depending on the difference of the two currents. A voltage against the second current called the reference current is generated internally as a result of MOS transistor sizes. Simulation of the proposed design is carried out in Symica DE at 90nm technology and is compared against an existing[9] TIQ comparator. An average power dissipation of 1.098mW was observed with an operating speed of about 6.7GHz for 90nm at an input current difference of ±1mA
Reachability in Large Graphs Using Bloom Filters

Reachability queries are a fundamental graph operation with applications in several domains. There has been extensive research over several decades on answering reachability queries efficiently using sophisticated index structures. However, most of these methods are built for static graphs. For graphs that are updated very frequently and are massive in size, maintaining such index structures is often infeasible due to a large memory footprint and extremely slow updates. In this paper, we introduce a technique to compute reachability queries for very large and highly dynamic graphs that minimizes the memory footprint and update time. In particular, we enable a previously proposed, index-free, approximate method for reachability called ARROW on a compact graph representation called Bloom graphs. Bloom graphs use collections of the well known summary data structure called the Bloom filter to store the edges of the graph. In our experimental evaluation with real world graph datasets with up to millions of nodes and edges, we show that using ARROW with a Bloom graph achieves memory savings of up to 50%, while having accuracy close to 100% for all graphs.
A Review on Container-Based Lightweight Virtualization for Fog Computing

Innovation in the concept of virtualization acts as a vigorous role in cloud computing. Virtualization helps in splitting the system resources in the form of the multiple and isolated execution environments. Hypervisor-based virtualization is related to the huge requirement of resources and operative overhead due to an additional stage of abstraction. In this review, we first were given an overview of virtualization innovation and Container-based virtualization. Container-based virtualization provides performance enhancements and reduced starting time and hence it is more beneficial than Virtual Machine (VM). Container virtualization does not imitate the hardware but only isolate the processes and it is more light-weight than VM. Container-based virtualization may be successfully implemented in “Fog Computing” framework as containers are suitable to the resource-constrained environment and also enables the distribution of different type of applications on cloud and fog Nodes. This paper describes the possible application of lightweight virtualization to Fog Nodes in the form of case studies.
A Novel Feature Extraction-based Human Identification Approach using 2D Ear Biometric

Biometric is the study of physiological or behavioral characteristics of human being to authenticate his/her identity. In literature, there are a limited number of studies, which have used ear biometric for human authentication or recognition, of them, some are either scale invariant or shape invariant. In addition, time complexity for the entire recognition process is also reported high. To address these issues simultaneously, a novel algorithm has been proposed in this study, which helps in efficient feature extraction. In this extraction, edges from the ear template are searched, and are fitted with appropriate lines. Thereafter, the ear template with fitted line segment is folded in two dimensions (horizontal and vertical). Angles formed in the folded ear template are used to extract different statistical information. To recognize a human, three main features, namely the number of edges, the number of bifurcation points, and the angle information are considered in this study. The experiment is carried out using a total of 1200 ear images obtained from 120 people. Two classifiers, namely minimum distance (MD) and K-Nearest Neighbor (K-NN) are used for recognition. Results reveal that the use of proposed feature extraction technique helps to obtain higher classification accuracy in human identification.
Analogizing of Evolutionary and Machine Learning Algorithms for Prognosis of Breast Cancer

Breast cancer has proven to be a serious disease caused in women according to medical science. This study focuses on prediction of breast cancer in three different datasets, namely: Wisconsin breast cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Prognosis Breast Cancer (WPBC) datasets. The comparative study has been done between evolutionary algorithms and machine learning algorithms. Evolutionary algorithms include Particle Swam Optimization (CPSO) and Genetic Algorithm for Neural Network (GANN) whereas machine learning algorithms include KNN and C4.5 for predicting the breast cancer. The results are obtained after performing the experiment on different algorithms on the basis of their accuracy and standard deviation which may help people in medical science for better prediction of their disease and hence enabling appropriate treatment.
A Special Event-Based K-Nearest Neighbor Model for Short-Term Traffic State Prediction

Recently, short-term traffic state prediction for urban transportation networks has become a popular topic. However, due to the uncontrollable and unpredictable elements of special events, it is difficult to get abundant data and desired predictions under such condition. As k-nearest neighbor (KNN) has a competitive advantage over other approaches, it could predict traffic state based on a small correlative part of data. Thus, a special event-based KNN (SEKNN) model is proposed for the short-term traffic state prediction with three key points presented in this paper. First, the evolution of the traffic states is redefined as a multipart object, state unit, which includes the benchmark state and the trend vector. Second, to select the nearest neighbors, the state distances of the state units are designed to be compatible with the benchmark states and the trend vectors by fusing the Euclidean distance and the cosine distance. Finally, the prediction results are forced to adjust the benchmark states based on the prediction function using the Gaussian weighted method. The proposed SEKNN is implemented in the district of the Beijing Workers’ Stadium (257 links), where special events occur frequently. The results show that the proposed model performs significantly better under special events than the other traditional machine-learning approaches and state-of-the-art deep-learning approaches.
Efficient Approach for Heuristic Algorithm

Partitioning algorithms like other Physical design steps comes under the category of NP-complete mathematical problems with the disadvantage that time required to solve them increases with the size of the problem. Heuristic algorithms are used for such problems such as Kernighan Lin algorithm. In this paper, a novel GA based algorithm is proposed which improves the efficiency without degradation of efficiency, area, propagation, delay etc. The complexity issue is resolved as GA reduces the complexity globally searching for the solution.
Measuring Service Cohesion Using Latent Semantic Indexing

As low coupling, high cohesion is a service-oriented design and development principle that should be kept in mind during all stages. High cohesion increases the clarity and ease of comprehension of the design that simplifies maintenance and achieves service granularity at a fairly reasonable level. However, unlike coupling that only measures the degree of structural and behavioral dependency to the other services, cohesion metrics need to evaluate the degree of semantic relationships between operations within a service in order to measure functional relatedness. Latent semantic indexing (LSI) is one of the techniques in the field of information retrieval which is widely used to measure the degree of semantic relatedness between a document and a given query and also used to measure the cohesion of a text. In this paper, we propose an approach to automatically measure the strength of conceptual cohesion of a service based on LSI technique. Finally, it has been evaluated theoretically based on a set of cohesion principles. Keywords-Servivce cohesion; Latent semantic indexing; Software metric.
Effort Estimation of Agile Development using Fuzzy Logic

Effort Estimation is one of the most important factors for a successful project. Mostly it is done in the starting of the project life cycle. The vital activity in Agile project organization is a prediction of Software development this is because a project that is available in the beginning is not sufficient and moreover it is not clear because of the confliction. Getting accurate Effort Estimation for any software project has been a challenge always. In this paper fuzzy logic model is used to find Effort Estimation of Agile software development. In this model Fuzzy method is used to take the raw data of actual figures and output is given as the estimated effort available from industry. This way is more suitable when data is not available for analyzing by orthodox method. Basic idea is taken from Human where action and Inaction are not obvious, they are not clear. In this paper we will use three input variable to calculate Efforts estimation i.e User soty, Team Expertise and Complexity. In this manner, it is quite difficult to discover goals in more exact and clear simple way.
A GPU/FPGA-Based K-Means Clustering Using a Parameterized Code Generator

The K-means algorithm is a method used for the unsupervised learning task of data clustering. This work presents a K-means specific domain code generator capable of generating code for GPUs and FPGAs. To increase efficiency, the code is parameterized and specialized for Nvidia GPUs and Intel/Altera CPU-FPGA HARP v.2 platform. Furthermore, the generator is modular and can be extended to other FPGA and GPU platforms. Another contribution of this work is to simplify the use of high performance FPGAs for programmers, once our generator does not require hardware knowledge in order to provide a high performance accelerator at the software level. The generator also simplifies GPU programming. In comparison to an Intel XEON CPU, our experiments show a 55x speed-up for the GPU execution time and a 13.8x speed up for the FPGA. With regard to energy, the FPGA was up to 10 times more efficient than the evaluated GPUs (Nvidia K40 and 1080ti).
Decentralized Time-Delay Compensator Design

The problem of designing linear time-invariant stabilizing decentralized time-delay output feedback compensators for a class of linear time-invariant decentralized retarded time-delay systems is considered. For this purpose, a recently proposed approach to design centralized output feedback compensators by using the continuous pole placement algorithm is generalized to design decentralized controllers. A design example is also presented to demonstrate the approach.
Use of Machine Learning in the Area of Image Analysis and Processing

This report tries to investigate the effective use of the machine learning in the area of image analysis and processing. The field of healthcare taking a large datasets of image like X-ray, Ultrasound, MRI, CT scan, Echo-cardiograpgy with the known classification, Take images as input and then process the images, in case of process the images following steps are involved formatting of images if not suitable as per the required format and then Segmentation, Feature extraction and Classification and then train the model with given data of images with known classification (that is called data sets) after that model can predict the classification of the given new images that is called test data. And then compare the result with the already given model result with different-different machine learning algorithm. And try to improve the result with existing result and target is to achieve 100 % accuracy. To improve the result is depend on many things like in image processing it depend on how well had done segmentation and then feature extraction is again main factor to improve the result, how many feature taken during developing model and then classification, it also depend on machine learning algorithm used as per the given data sets.
Consensus Control of Mobile Agents with Obstacle Avoidance using Collision Cone Approach

In this study, a cooperative control problem with obstacle avoidance for a team mobile agent system is investigated. The agents are modelled with double integrator dynamics considering the center coordinates in 2D space. The underlying topology of the focused system is captured by an undirected graph. Firstly, basic consensus controller structures are explained. Then obstacle avoidance problem which is based on collision cone approach is examined. Finally, both controller algorithms are combined and simulated with using second-order agent model. Simulation studies have been conducted with MATLAB program. The obstacle is selected a circle stationary object. The results show that the agents use the collision avoidance algorithm to prevent obstacle and consensus algorithm to reach consensus point after passing the obstacle.
Design and Analysis of Multi Band antenna for S and C Band

In this paper a rectangular Microstrip Patch multiband antenna has been designed. The parameters of the micro strip patch antenna have been optimized to obtain the multi band characteristics at 2.379 GHz, 2.45 GHz, 3.75 GHz, 4.46 GHz, 5 GHz and 6.25 GHz. All the six frequencies can cater to wireless networking applications. The frequencies between 2 GHz – 4 GHz fall in the S Band of the Radar Frequency Bands according to the IEEE standards, and similarly 5 GHz and 6.25GHz fall in the C band. The designed antenna uses the inset feed line technique for feeding the patch of the antenna. The antenna has been implemented on RT Duroid as the substrate which has 2.2 as its dielectric constant. The S11 parameters obtained for all the six frequencies have been found to be below - 10 dB mark which makes the antenna compatible to various wireless applications. The four L shaped slots were cut on the surface of the patch to increase the surface current of the patch of the antenna thereby increasing the gain of the antenna as well. The antenna design has been simulated using the CST software. All the six frequencies obtained were found useful for various wireless applications.
Insecurity of a Secure Certificate-Based Signature Scheme

Certificate-Based Signature (CBS) not only provides the solution of certificate revocation problems arise in the traditional public key cryptography but also solve key escrow problem arise in ID Based signature scheme. Recently, Hung et al’s design an efficient short certificate based signature scheme (SCBS) using bilinear pairing publish in "Information Technology and Control", T. 45, Nr. 3 in 2016 and proves that it is safe against type 1 and type 2 adversaries under the computational Diffie-Hallmen (CDH) assumption. In this paper, we show that Hung et al’s scheme is not protected against an adversary who has no need of secret value and partial private key to forge the legal signature.
Emotional Voice Conversion Using Dual Supervised Adversarial Networks With Continuous Wavelet Transform F0 Features

In emotional voice conversion (VC) tasks, it is difficult to deal with a simple representation of fundamental frequency (F0), which is the most important feature in emotional voice representation. In order to address this issue, we propose the adaptive scales continuous wavelet transform (ADS-CWT) method to systematically capture F0 features of different temporal levels, which can represent different prosodic aspects, ranging from micro-prosody to sentences. Moreover, in an emotional VC task, each dataset is paired with the labeled emotional voice and neutral voice, which can be regarded as a dual task. Owing to, first, dual supervised learning's ability to improve the training performances by using the leveraging probabilistic connection between the dual tasks to enhance the learning from labeled data and, second, generative adversarial networks’ (GANs’) ability to mitigate the over-smoothing problem caused in the low-level data space when converting the acoustic features, we further present a novel training framework for emotional VC using GANs combined with dual supervised learning, named as dual supervised adversarial networks. In emotional VC experiments, we confirmed the high similarity performance of our method when using limited labeled data for emotional VC. Our method achieves good and consistent performance, in both objective and subjective evaluations.
Query Answering for Kisan Call Centerwith LDA/LSI

With the enhanced storage and processing capacity of the computers in today’s world we have lots of unprocessed data in every domain. This data has gained interest of data scientist of all over the world to get some meaningful information from the raw data. We are here processing kisan call center data. First the preprocessing of data is done which is then converted to a similarity matrix which we save in a database using mongoDb. This prepared data will now be fed to the gensim TI-IDF model for text transformation. After this we have used another two methods of topic modelling to be used with TI-IDF model. We are concerned in getting the query answers efficiently from the data collected by usingLatent Dirichlet allocation (LDA) and Latent Semantic Indexing (LSI) in pipeline to the TI-IDF model. One algorithm which will be used with pipeline to the TI-IDF is LDA. LDA is a technique that automatically discovers topics that these documents contain. Another model which we are exploring is the LSI which also will be used with TI-IDF model for preparing the model for getting query answers efficiently. On these parameter we will be building the model training and finally will compare the models for finding which model works best in terms of efficiency, accuracy and complexity.
Stochastic Double Deep Q-Network

Estimation bias seriously affects the performance of reinforcement learning algorithms. The maximum operation may result in overestimation, while the double estimator operation often leads to underestimation. To eliminate the estimation bias, these two operations are combined together in our proposed algorithm named stochastic double deep Q-learning network (SDDQN), which is based on the idea of random selection. A tabular version of SDDQN is also given, named stochastic double Q-learning (SDQ). Both the SDDQN and SDQ are based on the double estimator framework. At each step, we choose to use either the maximum operation or the double estimator operation with a certain probability, which is determined by a random selection parameter. The theoretical analysis shows that there indeed exists a proper random selection parameter that makes SDDQN and SDQ unbiased. The experiments on Grid World and Atari 2600 games illustrate that our proposed algorithms can balance the estimation bias effectively and improve performance.
Qualitative Analysis of Contamination Severity between NaCl and CuSO4 for Outdoor Insulator

This paper presents a new criterion for insulator flashover condition prediction based on percentage 5th to 3rd harmonic component. These harmonics are extracted from leakage current of insulator employing FFT. In this context, a threshold limit of this ratio has been mentioned. To investigate experimentally, leakage current of an 11 kV glass disc insulator (contaminated at three different levels) has been recorded at different applied voltages. For contamination purpose two different types of salt are taken as contaminants, one is NaCl and other is CuSO4. Above mentioned ratio has been calculated for both types of contamination separately and compared. Results show that contamination due to NaCl is more severe than that of CuSO4.
Application of Meaningful Text Analytics to Online Product Reviews

Online reviews have a significant impact on opinion sharing, thus playing an important role in the purchase process. In this paper we examine the relations between the online review rating score and helpfulness on one hand and attributes measured by the readability tests on the other hand. Furthermore we investigated the interaction between users and, based on our data sets, we found that most of these are between customers and product owners. Lastly we proposed a sentiment analysis algorithm of reviews and we experimentally evaluated it using the review rating scores.
Forecasting Direction of Stock Index Using Two Stage Hybridization of Machine Learning Models

Accurate forecasting of directional changes in stock prices is essential for trading decisions. This study attempts to predict the direction of movement for the next day for S&P BSE Sensex index. Experiments are done in two phases. The first stage provided the direction classification that is whether predicted movement is up or down based on six technical indicators distinctly. Indicators are calculated as per their definition by using daily trading data of open, high, low, close and volume. Stage two takes in the processed discretized data for predictions from stage one. The accuracy of both stages for each model is evaluated and compared. Experimental results show significant improvement of the combined model over single-pass model hence supporting the assumption that conversion from continuous to discrete form based on indicators filter more noise than relevant information and provides an effective mechanism of dimensionality reduction. Random forest provided the best accuracy which is closely followed by support vector and artificial neural network.