Miracle-dz/UniFAR-FTUs huggingface dataset:
Topic Modeling for Short Texts via Word Embedding and Document Correlation

Topic modeling is a widely studied foundational and interesting problem in the text mining domains. Conventional topic models based on word co-occurrences infer the hidden semantic structure from a corpus of documents. However, due to the limited length of short text, data sparsity impedes the inference process of conventional topic models and causes unsatisfactory results on short texts. In fact, each short text usually contains a limited number of topics, and understanding semantic content of short text needs to the relevant background knowledge. Inspired by the observed information, we propose a regularized non-negative matrix factorization topic model for short texts, named TRNMF. The proposed model leverages pre-trained distributional vector representation of words to overcome the data sparsity problem of short texts. Meanwhile, the method employs the clustering mechanism under document-to-topic distributions during the topic inference by using Gibbs Sampling Dirichlet Multinomial Mixture model. TRNMF integrates successfully both word co-occurrence regularization and sentence similarity regularization into topic modeling for short texts. Through extensive experiments on constructed real-world short text corpus, experimental results show that TRNMF can achieve better results than the state-of-the-art methods in term of topic coherence measure and text classification task.
Cryptanalysis and Improvement of User Authentication Scheme Based on Rabin Cryptosystem

With the emergence of various methods for user authentication, smartcards have appeared as a convenient method of authentication based on possession factor. Besides, Rabin Cryptosystem has the same security strength is seen in using factoring problems such as RSA. In 2016, Ranjan et al. introduced Rabin Cryptosystem based user authentication scheme. Rabin Cryptosystem calculates only square modulo in encryption, it is more efficient than the RSA. Furthermore, they insisted their scheme provides resistance of replay attack and denial of service attack, and mutual authentication. However, unfortunately, we discover that there are some vulnerabilities in Ranjan et al.’s scheme that might cause serious problems. In this paer, we briefly review a Ranjan et al.’s scheme and reveal the possibility of attacks to consider in the user authentication scheme like offline password guessing, user/server impersonation attacks. Also, their scheme does not support a user anonymity and a session key agreement process. Next, we describe our Rabin Cryptosystem based user authentication improvement; then, we demonstrate our proposed scheme shows more secure compared to Ranjan et al.’s scheme and more suitable to the real environment.
Performance Analysis of Switched-Boost DC to DC Converter for Distributed Generation system

This paper presents performance analysis of switched boost dc-dc converter for distributed generation system in CCM (continuous conduction mode) operation, which combine the conventional switched-boost circuit with the switched-capacitor and switched inductor switching structure. Compared with the conventional boost converter, which uses convenient duty ratio, the presented topology is utilized to uplifting in voltage gain. The proposed topology exhibits advantages of the continuous current at the input and boost voltage conversion ratio without use of high duty cycle (approx less than 1) and transformer. Proposed converter supports in producing high output voltage gain by operating in different modes. The simulation result brought out from MATLAB 2016 b.
Design and Implementation of the Washout Filter for the Stewart-Gough Motion Platform

Motion simulators are gaining popularity for training, quality-assurance and gaming purposes. Washout-filter is considered to be the core component of such motion platforms. This work presents the theoretical background of washout filter for Stewart-Gough flight Simulator followed by its design implementation on MATLAB/Simulink. All six degree of freedom flight simulation results are comprehensively discussed and evaluated separately as well as in co-ordination cases on mechanical structure of hexapod in SimMechanics environment of MATLAB/Simulink.
An IoMT Based Non-Invasive Precise Blood Glucose Measurement System

The paper represents IoMT based non-invasive precise blood glucose measurement system based on optical detection along with optimized regression model. A system for performing the light absorbance at 940 nm wavelength with prediction model is designed and the technique is validated through measurements using human fingertip. In this work, optimized computation method is analyzed to obtain the blood glucose concentration values from each sample. For system validation, the obtained glucose concentrations are compared with referenced blood glucose concentrations using SD-check one touch glucometer. This represent the mean absolute difference (MAD), mean absolute relative difference (MARD), average error (AvgE) and RMSE 5.82 mg/dl, 5.20%, 5.14% and 7.50 mg/dl respectively. Tested samples are taken for a Clarke Error Grid analysis. All samples are found over zones A and B. This result of performance is an improvement over those determined previously using techniques for non-invasive blood glucose measurement systems.
Efficient Star Shaped Search Pattern Based Image De-noising

The investigation for well-organized picture De-blurring techniques are still valid challenge, because of their complexities in functional analysis and statistics. In this research, a series of salt-and-pepper (SAP) noise reduction method based on star shaped search pattern that can be applicable for monochrome images is presented. A great benefit of the proposed De-blurring is robotical detection of noise position based on star shaped search pattern and replacement of noisy pixels. The proposed De-blurring is also named as star shaped search pattern-salt & pepper- image de-noising (SSSP-SP-ID). The SSSP-SP-ID system is customizable and furthermore it is easy and fast. The investigation shows that the SSSP-SP-ID De-blurring algorithm have better outcomes compared to other state-of-the-art algorithms, when the noise density (ND) is moderate or high. The performance of SSSP-SP-ID De-blurring algorithm is measured using quantitative performance measures such as peak signal-to-noise ratio (PSNR), structure similarity index measure (SSIM) and image enhancement factor (IEF).
Accurate Replication of Simulations of Governing Equations of Processes in Industry 4.0 Environments with ANNs for Enhanced Monitoring and Control

Complex governing equations of physical phenomena like the Navier-Stokes' or Maxwell's equations can be numerically solved to yield detailed information on the characteristic variables of a process in the process domain interior, when the values at the boundary are known. This cannot be achieved in real time making it unamenable to achieve true benefits under Industry 4.0 where measured variables are available instantaneously at process boundaries but information in the domain interior is unobtainable for monitoring, control and optimization functions. The Universal Approximation Theorem provides a unique capability to Artificial Neural Networks - the ability to replicate the functionality of arbitrarily complex functions - including those represented by the above governing equations. A trained ANN can in principle replicate this functionality with high accuracy in milliseconds - hence can serve as the method of choice in Industry 4.0 frameworks to acquire characteristic process variables within the domain interior when boundary values are known from sensory inputs. This is however a concept still to be proven. This work intends to demonstrate this principle through numerical experimentation on a physical example that can be easily generalized.
Compressive Sampling Stepped Frequency Ground Penetrating Radar Using Group Sparsity and Markov Chain Sparsity Model

We investigate an implementation of a compressive sampling (CS) stepped frequency ground penetrating radar. Due to the small number of targets, the B-scan is represented as a sparse image. Due to the nature of stepped frequency radar, smaller number of random frequencies can be used to obtain each A-scan (sparse delays). Also, the measurements obtained from different antenna positions can be reduced to a smaller number of random antenna positions. We also use the structure in the B-scan, i.e. the shape of the targets, which can be known, for instance, when detecting land mines. We demonstrate our method using radar data available from the Web from the land mine targets buried in the ground. We use group sparsity, i.e. we assume that the targets have some non-zero (and presumably known) dimension in the cross-range coordinate of the B-scan. For such targets, we also use the Markov chain model for the targets, where we simultaneously estimate the model parameters using the EMturboGAMP algorithm. Both approaches result in improved performance.
$k$-Means-Lite++: The Combined Advantage of Sampling and Seeding

The $k$-means-lite algorithm was recently introduced to address the poor scalability of the popular $k$-means algorithm. This paper modifies $k$-means-lite by applying D2 sampling, the state-of-the-art seeding technique employed by $k$-means++, to each sample in the former. K-means-lite estimates its final centroids by applying standard $k$-means to the combination of all centroids obtained from initial application of $k$-means to a few (say, five) samples. Although $k$-means-lite achieves drastic efficiency gain, the five-sample implementation trades off some of the standard algorithm's accuracy. One way to improve its accuracy is to increase the number of samples. But it may be difficult to determine a priori what performance level is attainable, or the number of samples required to attain such performance. These would vary per problem. In this paper, we show experimentally that without increasing the sample size or number of samples in k- means-lite, our modified algorithm, named $k$-means-lite++, addresses this problem and is even more accurate than the standard $k$-means. For the cases tested, $k$-means-lite++ matches the accuracy of $k$-means++, which is widely considered the state-of-the-art $k$-means algorithm. The extra cost of sample seeding is compensated for by faster convergence. So, although drastically more accurate, $k$-means-lite++ has efficiency that is comparable to $k$-means-lite's. Thus, the proposed algorithm may also be viewed as a solution to scalability issues associated with $k$-means++'s sequential sampling.
Design of a Highly Biomimetic and Fully-Actuated Robotic Finger

During design a mechanical structure such as a robotic finger, modeling parts and designing joints are two timeconsuming steps. This paper presents a method for design and fabrication of a humanoid robotic finger that intends to mimic the human finger in terms of tendon, bones, ligaments and the pulley systems which aims to generate a mechanism rapidly. The method is inspired by the anatomy and biomechanics of human hand, and focuses on bones, joints and actuation systems, with the aim of creating a customised finger that has the similar degrees of freedom and ranges of motion of a human finger. Specifically, we propose to create the personalised 3D models of finger’s bones by adapting a template to the data acquired by scanning the real hand, which are then fabricated by 3D printing. Low cost and easy-to-get materials such as Nylon cables and silicon rubber are used to build the tendon sheaths and the joints. We design a "3 layer cascade cable driven system" built upon eight driven cables and four servo motors for simulating all possible four Degree of Freedom (DOF) of the finger. As a result, we present a highly biomimetic and fully-actuated robotic finger, which can achieve more DOFs and larger ranges of motion compared to existing humanoid robotic hands. Particularly, the experiments validate that our robotic finger is capable of achieving all 11 standard finger gestures as a human fìnger.
EEG-Based Driver Drowsiness Estimation Using Self-Paced Learning with Label Diversity

Drowsy driving is one of the major contributors to traffic accidents. Continuously detecting the driver’s drowsiness and taking actions accordingly may be one solution to improving driving safety. Electroencephalogram (EEG) signals contain information of the brain state, and hence can be utilized to estimate the driver’s drowsiness level. A challenge in EEG-based drowsiness estimation is that when directly applied to a new subject without any calibration, the system’s performance usually degrades significantly. Many efforts have been devoted to reducing the calibration data requirement, but there are still very few approaches that can completely eliminate the calibration process. This paper proposes a self-paced learning approach, which also takes the label diversity into consideration. The model learns from the easiest samples when the training first starts, and then more difficult ones are gradually added to the training process. This training strategy improves the generalization performance of the model on a new subject. Experiments on a simulated driving dataset with 15 subjects demonstrated that the proposed approach can better reduce the estimation error than several other approaches.
Heart Disease Prediction and Analysis using PCO, LBP and Neural Networks

Many diseases have been invited to human body due to the changes that occur in lifestyle. In Asian subcontinent as report of WHO the main cause of death is due to heart or cardiovascular disease. The primary requirement of the system is to reduce the symptoms dataset since there are many symptoms that leads to heart disease so that can take easy decision and treat clinically. In this paper many kinds of heart disease and symptoms responsible for that were discussed. In this paper initially heart local binary pattern and PCA have been used along with artificial neural network for heart disease prediction. Where LBP and PCA are for feature extraction and feature set size reduction and neural network for classification and verifying accuracy of the system. The complete prediction system have been developed with Matlab tool and predicts almost 95% of heart problems.
A Low-Rank Tensor Dictionary Learning Method for Hyperspectral Image Denoising

As a 3-order tensor, a hyperspectral image (HSI) has dozens of spectral bands, which can deliver more information of real scenes. However, real HSIs are often corrupted by noises in the sensing process, which deteriorates the performance of higher-level detection tasks. In this paper, we propose a Low-rank Tensor Dictionary Learning (LTDL) method for HSI denoising. Differing to existing low-rank based methods, we consider a nearly low-rank approximation, which is closer to the latent low-rank structure of the clean groups of real HSIs. Furthermore, the proposed method benefits from multitask learning gain by learning a spatial dictionary and a spectral dictionary shared among different tensor groups. While most existing work usually consider sparse representations, we exploit a simultaneously sparse and low-rank tensor representation model to enhance the capability of dictionary learning, which is inspired from the observation of the low rank structure in HSI tensor groups. Experiments on synthetic data validate the effectiveness of dictionary learning by the LTDL. Experiments on real HSIs demonstrate the superior denoising performance of the proposed method both visually and quantitatively as compared with state-of-the-art methods along this line of research.
R-Mgspline: Retrospective Multi-Gradient Search for Multi-Objective Simulation Optimization on Integer Lattices

We introduce the R-MGSPLINE (Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, each objective can only be observed with stochastic error and the decision variables are integer-valued. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments demonstrate R-MGSPLINE’s empirical convergence to a local weakly efficient point.
Optimal Illumination of Arbitrary Rooms Using Genetic Algorithm

Illuminating rooms is something that is taken as for granted. This paper presents an evolutionary method for solving this problem. The paper aims to find a minimalist arrangement of lamps in user-defined or random rooms such that they are optimally lit. Genetic algorithm is used to explore exponential search space of lamps placement and find those that suit the given criteria. Blender is used as a 3-D tool for this experiment. Results show promising arrangements of lamps that can be used in situations that require an emitter of signals in an obstruction filled environment.
Using Twitter Mentions and a Graph Database to Analyse Social Network Centrality

Social networks are one category of social media that facilitates the formation of communities, sharing of content, and meeting people. Twitter is a popular microblogging and social networking service. Social media marketers within business organisations, are interested in identifying popular social network users, known as influencers who can be targeted for purposes of word-of-mouth branding. For Twitter, influencers are those users who have many followers. Influencers are typically identified through graph mining of social networks data. This type of mining involves the analysis of links between the graph nodes which store data for social network members. Follows relationships are commonly used to analyse Twitter social networks. The purpose of this paper is to demonstrate how mentioned relationships in Twitter data can be used to create a social networks graph database. Centrality measures are then used to analyse the social networks. It is demonstrated that the analysis of social networks based on the mentioned relationships can provide more information about influencers compared to the analysis of social networks based on the follows relationships.
Computationally Efficient Harmonic-Based Reactive Exploration

Although Harmonic Potential Fields constitute a powerful tool for tackling the autonomous robot exploration problem, yet their applicability is limited by the heavy computational load involved in solving the Laplace equation in real time. In this letter, we propose a computationally efficient exploration scheme employing a Fast Multipole accelerated Boundary Element Method, which enjoys both linear complexity w.r. t. the boundary's size as well as linear memory requirements. Furthermore, we devise an adaptive control law for the specified boundary conditions that allows us to tune the robot's behavior without affecting the inherent safety and convergence properties of the underlying potential field. Finally, we validate the performance of the proposed exploration scheme through extensive realistic simulations.
Algorithm for using NLP with extremely small text datasets

A quick analysis of current technological trends displays an increasing global tendency of turning towards data-centric solutions. The ongoing wave of wide spread implementation of machine learning algorithms and artificial intelligence techniques allows us to harness the power of data to derive solutions and innovations. However, one major constraint that these algorithms face is the requirement of large amounts of data to train the models over. This project attempts to solve that issue by implementing a custom algorithm that is capable of generating NLP models that return medium to high accuracy results despite being trained on a very limited number of text data points. The posited algorithm is tested against a custom dataset consisting of answers to a set of questions pertaining to fears faced in day to day life.
Towards Making Regional Issues of Marine Resources a Teaching and Learning Material

Hiroshima Bay has been developing marine resources sustainably by fish farming and oyster culture. Black seabream had been one of these farming. It is recently seemed as a harmful one and a regional issue. The management of marine resources is very important but hard tasks. In order to manage sustainably them, marine data is collected and provided to fishery workers. But it is also useful for local residents and people in general to know the regional issue subjectively. In this paper, we propose an idea to make the regional issue a teaching and learning material (TLM) systematically and efficiently.
Latent Factor Models Fusing User & Item Attributes

Data sparsity, cold-start, and suboptimal recommendation for local users or items have been recognized as the most crucial three challenges in the latent factor model (LFM) for recommender systems. This paper proposes an approach that integrates the User-Item attributes into the classical LFM named UILFM focusing on above challenges. First, for the problem of data sparsity and cold-start, we develop an online learning algorithm to update the weights of user or item attribute for identifying the importance of different attributes. By aggregating the users and items based on their similar attributes, we obtain the local neighbor group which makes it possible for recom- mender to estimate some missing ratings based on adjacent user's ratings towards items and adjacent item's ratings. By introducing the convex mixed-parameters, we combine the estimate ratings with the classical LFM to predict the missing entries of the high-dimensional and sparse (HiDS) matrix for further closing the true ratings and reducing matrix sparsity. Second, for the suboptimal recommendation problem, we propose a new matrix filling (for missing ratings) method based on positive and negative samples, in which when the sparsity of the HiDS matrix is reduced to a threshold, the classical LFM will dominate the filling procedure, instead, the prediction based on neighbors' ratings remains a domination role. This method elegantly solves the suboptimal recommendation problem that the ratings of partial users are extremely sparse and the number of ratings per user are unbalanced. The proposed algorithm is tested by the MovieLens dataset, the results show that it promotes the recommendation accuracy compared with the classical LFM algorithm and the dimensionality reduction approaches as well as the collaborative filtering (CF) algorithms.
Citation Analysis of Research Articles (CARA)

In a research paper multiple citations and authors opinion about them are mentioned. Our primary focus is to identify which opinion is given on which citation and secondly to identify the exact sentence due to which citation has occurred in the citing paper. We have worked on two parallel approaches one is sentence level similarity, in which we try identifying the sentence(s) due to which citation has occurred. Other approach is article level similarity in which we identify the citation category and map anchor text to its citing article. We are able to show that an automated system can be created for identifying whether a sentence (without citation mark) is part of the citation text. In future we wish to extend this work with larger corpus and more sophisticated techniques.