Miracle-dz/UniFAR-FTUs huggingface dataset:
Secured Smart E-Vehicle Tracking and Calamity Detection System Using Cloud- A Review

A capable automobile safety arrangement is put into action for calamity detection, by means of an embedded architecture having a Global Positioning System (GPS), Wi-Fi Module and cloud. Now a day’s vehicle population is growing exponentially, so is the number of thefts and accident associated with vehicles and in most cases, the owner could retrieve his/her vehicle from the location. With more than one billion vehicles in use in the world, a smart e-vehicle tracking and calamity detection system using cloud is always a boon to the owners. The system sends a predefined message if theft and accident happens. The user can track the exact location of their vehicle with the help of Google earth on an android mobile application using GPS location identifier, The object’s present position is find out and send, along with other important parameter conveyed by vehicle delta ports via cloud server with the help of Wi-Fi module which is associated to computer or smart phone. Since the key intermediate of communication is Internet the term IOT is employed here. The proprietor of the vehicle has an option of disconnection the fuel connection to the engine if he/she wants to take instantaneous action against the attacker. Vehicle also contains the tracking feature, tracking application and will get image of thief driving the vehicle. The most significant feature of this paper is calamity detection. It follows the same method as in melt recognition i.e. when calamity dose takes place the accelerometer reading and Piezoelectric sensor reading with activate the system start sending coordinates of the calamity location to the law enforcement authorities ,hospitals & family member’s so they can take an immediate action.
Novel Weighted Hybrid Approach in Recommendation Method

: In modern E-Commerce it is not easy for the customers to find the best goods of their interest as there are millions of products available online. Recommendation systems are one of information filtering systems forecasting the items that may be additional interest for user within a big set of items on the basis of user’s interests. This System utilizes the Collaborative filtering, which offers a few recommendations to users on the basis of matches in behavioral and useful examples of users and furthermore demonstrates comparable affection and behavioral examples with those users. The paper presents an approach for Recommendation System to generate meaningful recommendations to a collection of users for items or products that might interest them. This approach uses weighted hybrid recommendation system which combines content based recommendation system and knowledge based recommendation system in order to increase the overall performance of the system. The main idea is using multiple recommendation techniques to suppress the drawbacks of the traditional techniques or an individual technique in a combined model. The paper presents a system to improve the accuracy of recommendation in big data application.
A traffic balance scheme of group emotion recognition by using the service function chain

Most researches of emotion recognition focus on single person information. However, everyone's emotions will affect each other. For example, when the teacher is angry, the student's nervousness will increase. But the facial expression information of the light single is already quite large. It means that group emotion recognition will encounter a huge traffic bottleneck. Therefore, there is a vast amount of data collected by end‐devices that will be uploaded to the emotion cloud for big data analysis. Because different emotions may require different analytical methods, in the face of diverse big data, connecting different emotion clouds is a viable alternative method to extend the emotion cloud hardware. In this paper, we built a software defined networking (SDN) multi‐emotion cloud platform to connect different emotion clouds. Through the advantages of the splicing control plane and the data plane, the routing path can be changed using software. This means that the individual conditions of different students can be handled by a dedicated system via service function (SF). The load balancing effect between different emotion clouds is achieved by optimizing the SFC. In addition, we propose a SFC‐based dynamic load balancing mechanism which eliminates a large number of SFC creation processes. The simulation results show that the proposed mechanism can effectively allocate resources to different emotion clouds to achieve real‐time emotion recognition. This is the first strategy to use SFC to balance the emotion data that the teachers can change teaching policy in a timely manner in response to students' emotions.
Reflection on modern methods: risk ratio regression—simple concept yet complex computation

Abstract The risk ratio (RR) is the ratio of the outcome among the exposed to risk of the outcome among the unexposed. This is a simple concept, which makes one wonder why it has not gained the same popularity as the odds ratio. Using logistic regression to estimate the odds ratio is quite common in epidemiology and interpreting the odds ratio as a risk ratio, under the assumption that the outcome is rare, is also common. On one hand, estimating the odds ratio is simple but interpreting it is hard. On the other, estimating the risk ratio is challenging but its interpretation is straightforward. Issues with estimating risk ratio still remain after four decades. These issues include convergence of the algorithm, the choice of regression specification (e.g. log-binomial, Poisson) and many more. Various new computational methods are available which help overcome the issue of convergence and provide doubly robust estimates of RR.
A REFERENCE ARCHITECTURE OF INTERNET OF THINGS ECOSYSTEM

Summing up the experience of building Virtual Education Space, this paper presents a reference architecture known as Virtual Physical Space (ViPS). ViPS is being developed as a Cyber-Physical-Social-Space. Our goal is for the reference architecture to be adaptable for a variety of Internet of Things ecosystems in different domains such as smart cities, smart environment and agriculture, and intelligent medicine. This paper presents the first version of ViPS's general reference architecture and its core components.
On the exponent of convergence of negatively curved manifolds without Green's function

In this paper we prove that for every complete n-dimensional Riemannian manifold without Green’s function and with its sectional curvatures satisfying K ≤−1, the exponent of convergence is greater than or equal to n − 1. Furthermore, we show that this inequality is sharp. This result is well known for manifolds with constant sectional curvatures K = −1.
Feedforward Neural Network-Based Architecture for Predicting Emotions from Speech

We propose a novel feedforward neural network (FFNN)-based speech emotion recognition system built on three layers: A base layer where a set of speech features are evaluated and classified; a middle layer where a speech matrix is built based on the classification scores computed in the base layer; a top layer where an FFNN- and a rule-based classifier are used to analyze the speech matrix and output the predicted emotion. The system offers 80.75% accuracy for predicting the six basic emotions and surpasses other state-of-the-art methods when tested on emotion-stimulated utterances. The method is robust and the fastest in the literature, computing a stable prediction in less than 78 s and proving attractive for replacing questionnaire-based methods and for real-time use. A set of correlations between several speech features (intensity contour, speech rate, pause rate, and short-time energy) and the evaluated emotions is determined, which enhances previous similar studies that have not analyzed these speech features. Using these correlations to improve the system leads to a 6% increase in accuracy. The proposed system can be used to improve human–computer interfaces, in computer-mediated education systems, for accident prevention, and for predicting mental disorders and physical diseases.
Design and Implementation Intelligent Behavior of Non Playable Character on Hajj Simulation

Along with the rapid development of technology, education in Indonesia has begun to develop a method of learning-based technology. Hajj learning can be made as alternative media learning using simulation-based virtual environment. Of course, to strengthen the simulation non playable character (NPC) for the Hajj congregation are necessary to give more effect to the real user who is running the simulation of hajj. NPC in the simulation must have intelligent behavior that have adapted to the needs of the system. Design NPC behavior using a method designed finite state machine (FSM) where the behavior was modeled in some limited states.
Investigating Effects of Visual Anchors on Decision‐Making about Misinformation

Cognitive biases are systematic errors in judgment due to an over‐reliance on rule‐of‐thumb heuristics. Recent research suggests that cognitive biases, like numerical anchoring, transfers to visual analytics in the form of visual anchoring. However, it is unclear how visualization users can be visually anchored and how the anchors affect decision‐making. To investigate, we performed a between‐subjects laboratory experiment with 94 participants to analyze the effects of visual anchors and strategy cues using a visual analytics system. The decision‐making task was to identify misinformation from Twitter news accounts. Participants were randomly assigned to conditions that modified the scenario video (visual anchor) and/or strategy cues provided. Our findings suggest that such interventions affect user activity, speed, confidence, and, under certain circumstances, accuracy. We discuss implications of our results on the forking paths problem and raise concerns on how visualization researchers train users to avoid unintentionally anchoring users and affecting the end result.
Research on abnormal detection of one-class support vector machine based on ensemble cooperative semi-supervised learning

In order to promote the accuracy of anomaly detection model under the condition of only a small number of labeled samples and large number of unlabeled samples, abnormal detection of One-class Support Vector Machine(SVM) based on ensemble cooperative Semi-supervised Learning is proposed. A kind of One-class SVM model which bring supervision with a small number of abnormal samples can classify samples with max interval. The semi-supervised learning methods easily suffer from the low accuracy because the mistake labeled sample are chosen as training sample set. Refer to the semi-supervision method of Tri-training, the K-Nearest Neighbour(KNN) and Naive Bayes classifier are used to uses to assist the One-class SVM based on ensemble cooperative Semi-supervised learning method which can classify the large number of unlabeled samples as accurate as possible. The weight is also given after ensemble cooperative Semi-supervised Learning. Then the proposed semi-supervised One-class SVM would be trained with the result and used to classify test samples. The experimental results on UCI dataset show that the proposed algorithm achieves higher classification accuracy with less labeled samples and it improves generalization performance and reduces the labelling cost.
COGEM: A Toolbox for Computational Genomics in Matlab

Motivation The Matlab programming language is widely used for both teaching and research in engineering, computer science, and mathematics. Despite its many strengths, it has never been a dominant language in computational genomics or bioinformatics more generally. Results Here, we introduce COGEM, a long-term project to develop computational genomics functionality in Matlab. The initial release provides functions for manipulating genomic intervals, stranded or unstranded, with or without numerical data associated. It includes features for both text and binary file input and output, conversion between BAM, BED and BEDGRAPH formats, and numerous functions for manipulating intervals, including shifting, expanding, overlapping, intersecting, unioning, finding nearest intervals, piling up intervals, and performing unary and binary numerical and logical operations on sets of intervals. The toolbox is well-suited to the analysis of high-throughput sequencing data. We demonstrate its functionality by creating a ChIP-seq peak-calling algorithm by chaining together a series of commands, and find it capable of analyzing genome-scale data in reasonable time. Availability The current toolbox and reference manual is available as supplementary material, and updated versions will be maintained at www.perkinslab. ca online.
A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches

This document presents the required layout of articles to Medical data mining has become one of the prominent issues in the field of data mining due to the delicate lifestyle opted by the people which are leading them towards various chronicle health diseases. Heart disease is one of the conspicuous public health concern worldwide issues. Since clinical data is growing rapidly owing to deficient health awareness, various techniques and scientific methods are opted for analyzing this huge data. Several data mining techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, Naive Bayes and Artificial Neural Network (ANN) are introduced for the prediction of health disease. These techniques help to mine the relevant and useful amount of data, form the medical dataset which helps to provide beneficial information to the medical institutions. This study presents various issues related to healthcare and various machines learning algorithms which have to withstand to provide the best possible output. A comprehensive review of the literature has been summarized to put lights on the previous work done in this field.
A New Demagnetization Fault Recognition and Classification Method for DPMSLM

This paper investigates a new method for the demagnetization fault recognition and classification in double-sided permanent magnet synchronous linear motors that are used in linear motion applications. This method is based on time–time-transform (TT) coupled with extreme learning machine (ELM), which are especially suitable for the industrial occasions such as motor batch demagnetization inspection before delivery and periodic maintenance. First, a finite element analysis model with demagnetization faults is built to extract three lines (up-line, center-line, and down-line) magnetic flux density signals. Second, TT is first applied to conduct magnetic signals waveform transformation, and digital picture processing technology is innovatively used to extract the pixel rate of its diagonal elements contour surfaces as the fault feature. Then, machine learning algorithm called ELM is utilized as a classifier to obtain the unique fault labels that can represent the demagnetization occurred positions, sides, and severity types in detail. The validity and superiority of ELM is verified through comparison with back propagation neural network, and probabilistic neural network. Finally, prototype motor experimental platform is designed to confirm the correctness and effectiveness of this proposed method.
Use of the Laryngeum Mask for Intubation (Fastrach): Initial Experience in the Specialty Hospital “Dr. Bernardo Sepulveda G”, Centro Medico Nacional Siglo XXI IMSS Mexico

There is unanimous agreement that if planning is done for the management of the airway, the vast majority of complications can be prevented or solved. The American Society of Anesthesiology in recent years has developed an algorithm to follow in case of presenting a difficult and unexpected airway. In this algorithm, various techniques or mechanisms other than direct laryngoscopy are mentioned, which may at a certain moment resolve a pressing situation. The laryngeal mask for intubation (Fastrach) is a device that has increased its popularity by playing an important role in the management of the anatomically difficult airway allowing adequate oxygenation. It offers advantages such as simplicity and versatility in its placement, as well as being a traumatic for the patient. We decided to carry out this project to obtain an initial experience in the management of the airway with this type of devices whose successful results in other countries have not been corroborated in our hospital. Objective: To demonstrate that the laryngeal mask for intubation (Fastrach) is a useful device for the management of the airway in patients undergoing general anesthesia. Material and method: We studied 50 patients of both genders and ages between 22 and 85 years, physical status ASA I, II or III, scheduled for elective surgery and general anesthetic technique. The number of attempts of Fastrach placement and orotracheal intubation through it, the time in seconds, the hemodynamic and SpO2 values, and finally the complications during and after the procedure (oral bleeding, impossibility to place Fastrach and/or IOT, odynophagia and/or dysphonia). Results: A success rate was obtained for the placement of Fastrach and I.O. T. 94% where 54% was the first attempt, 32% the second and 4% the third attempt. The complications presented were minimal and reached 2%. Conclusion: The use of the laryngeal mask for intubation (Fastrach) as an alternative to direct laryngoscopy for orotracheal intubation in patients with easy airway is feasible and has been shown to be useful, safe and effective.
Coopera: An Environment for Teaching and Learning Internet Cooperation

The Coopera project aims at providing an environment for cooperative work based on a sophisticated object sharing paradigm that should, first remove part of the burden of file management from user, and provide awareness on what is happening in the project. We are trying to adapt paradigms commonly used by professional software engineers with computer illiterate people. This project is conducted by a team composed of pedagogical scientists, computer scientists, psychologist and teachers. This paper reports mainly on the more advanced experiment that concerns primary school children
A Study on Implementation of different Data Mining Techniques on Healthcare

Data mining is one of the richest areas of research that is more popular in health organizations. It is the process of pattern discovery and extraction where huge amount of data is involved. The data generated by the health organizations are very vast and complex. This data contains details regarding hospitals, patients, medical claims, treatment cost etc. So, there is a need to generate a powerful tool for analyzing and extracting important information from this complex data. Disease prediction plays an important role in data mining. More data mining classification algorithms like decision trees, neural networks, Bayesian classifiers, Support vector machines, etc are used to diagnosis the heart diseases. The aim of this paper is to summarize some of the current research on predicting heart diseases using different data mining techniques, analyze the various combinations of mining algorithms used and conclude which technique(s) are effective and efficient.
Perception and Treatment Practices of malaria among tertiary institution students in Oyo and Osun States, Nigeria

The knowledge, prevention and practices on malaria treatment measures were assessed among 643 tertiary students in Osun and Oyo States in Nigeria. Data were collected using well-structured questionnaires. The study observed that the respondents are aware of the common symptoms of malaria as 26.4% recognised fever as the most noticeable sign of malaria. On the other hand, the knowledge of the severe symptoms of malaria was poor as only 13.1%, 13.5% and 7.9% were aware that convulsion, coma and anaemia respectively were severe symptoms of malaria. Surprisingly very few (14.6%) believed that dirty environment could cause malaria. The knowledge (23.4%) and the use (22.1%) of insecticide treated bed net was low in the studied population. More respondents used Coartem (20.1%; p= 0.002) for malaria treatment than any other drug and most of them will take prescription (67.5%) from a medical personnel. Also, most of the respondents (60.8%) will not go for malaria diagnostic test before taking malaria drug and the difference was highly significant (p=0.001). It is therefore suggested that more efforts should be invested in enlightening the populace on practises that will influence their behavioural pattern in a way that will lead to effective malaria control. Keywords: Malaria, prevention, practices, symptoms
Cross-Layer Multi-User Selection in 5G Heterogeneous Networks Based on Hybrid Beamforming Optimization for Millimeter-Wave

Lack of coordination between network layers limits the performance of most proposed solution for new challenges posed by wireless networks. To overcome such limitations, cross-layer physical and medium access (PHY-MAC) design for multi-input-multi-output orthogonal frequency division multiple access system in heterogeneous networks (HetNETs) is proposed. In this paper, we formulate an optimization problem for hybrid beamforming, in a multi-user HetNET scenario aiming to maximize the total system throughput. Furthermore, analog beamforming is selected from a codebook containing a limited number of candidates for steering vectors. The proposed problem is non-convex and hard to solve. Thus it is relaxed by transforming it into a subtraction form of two convex funcions. Afterward we apply a group of well-known metaheuristic algorithms to calculate the normalized hybrid beamforming vectors. The optimal solution is obtained using an exhaustive search (ES) algorithm that provides an ideal solution, but with high complexity. In addition, zero-forcing-based approach (ZFA), matched filter (MF), and QR-based approach (QR) are applied to get quick sub-optimal solutions. Hence, we analyze the performance of our systems using the throughput metric. The simulation results show that QR algorithm outperforms ZFA and MF in low and middle signal-tonoise ratio (SNR) regime, while ZFA outperforms QR and MF at higher SNRs. Moreover, QR is close to the optimal solution ES.
A NEW CHAOTIC ATTRACTOR FOR RANDOM NUMBER GENERATION BASED ON MEMRISTIVE CIRCUIT

In this study, the well-known chaotic circuit, Muthuswamy-Chua circuit, is used with a different memristor element and obtained a new chaotic attractor. Equilibrium points, lyapunov exponents, phase portraits and bifurcation diagrams of the new ODE system is analysed mathematically. Obtained chaotic signals are sampled by a low-frequency sampling clock and formed number sequence is numerically tested for randomness. According to test results, the proposed circuit can be used as pseudo random number generator at various applications such as criptology, modulator design and steganography
An Overview of Local Positioning System: Technologies, Techniques and Applications

Positioning system like global position system (GPS) and Local position system (LPS) have become very important in a large number of applications such as monitoring and tracking, etc. Because of the limitations of GPS in indoor environments due to the lack of line of sight (LoS), the use of LPS has become a true necessary to estimate user’s or object position with a good accuracy. In order to choose the best LPS system, a compromise between accuracy, precision, power consumption, coverage and cost should be taken into account. This paper introduces an overview of LPS performance parameters, current technologies, techniques and methods used by LPS. On the other hand, the comparison between LPS technologies and techniques used based on those technologies are also discussed. Furthermore, the LPS’s applications that have been done by previous researches such as human tracking, object tracking, animal tracking and automatic guide vehicle (AGV) tracking will be discussed. We believe this paper would catalyze further investigation by the researcher which is interested on the LPS field.