Search results for: graph embedding

Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

Procedia PDF Downloads 41

Authors: Redouane Tlemsani, Redouane, Belkacem Kouninef, Abdelkader Benyettou

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In this paper, our system is a Markovien system which we can see it like a Dynamic Bayesian Networks. One of the major interests of these systems resides in the complete training of the models (topology and parameters) starting from training data. The Bayesian Networks are representing models of dubious knowledge on complex phenomena. They are a union between the theory of probability and the graph theory in order to give effective tools to represent a joined probability distribution on a set of random variables. The representation of knowledge bases on description, by graphs, relations of causality existing between the variables defining the field of study. The theory of Dynamic Bayesian Networks is a generalization of the Bayesians networks to the dynamic processes. Our objective amounts finding the better structure which represents the relationships (dependencies) between the variables of a dynamic bayesian network. In applications in pattern recognition, one will carry out the fixing of the structure which obliges us to admit some strong assumptions (for example independence between some variables).

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, networks

Procedia PDF Downloads 588

Authors: Cinna Soltanpur, Mohammad Ghamari, Behzad Momahed Heravi, Fatemeh Zare

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Low-density parity-check (LDPC) codes have been shown to deliver capacity approaching performance; however, problematic graphical structures (e.g. trapping sets) in the Tanner graph of some LDPC codes can cause high error floors in bit-error-ratio (BER) performance under conventional sum-product algorithm (SPA). This paper presents a serial concatenation scheme to avoid the trapping sets and to lower the error floors of LDPC code. The outer code in the proposed concatenation is the LDPC, and the inner code is a high rate array code. This approach applies an interactive hybrid process between the BCJR decoding for the array code and the SPA for the LDPC code together with bit-pinning and bit-flipping techniques. Margulis code of size (2640, 1320) has been used for the simulation and it has been shown that the proposed concatenation and decoding scheme can considerably improve the error floor performance with minimal rate loss.

Keywords: concatenated coding, low–density parity–check codes, array code, error floors

Procedia PDF Downloads 333

Authors: Sonia Arradaza-Pajaron

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College students are expected to engage in an on-the-job training or internship for completion of a course requirement prior to graduation. In this scenario, they are exposed to the real world of work outside their training institution. To find out their readiness both emotionally and academically, this study has been conducted. A descriptive-correlational research design was employed and random sampling technique method was utilized among 265 randomly selected third year college students of PIT, SY 2014-15. A questionnaire on Emotional Intelligence (bearing the four components namely; emotional literacy, emotional quotient competence, values and beliefs and emotional quotient outcomes) was fielded to the respondents and GWA was extracted from the school automate. Data collected were statistically treated using percentage, weighted mean and Pearson-r for correlation. Results revealed that respondents’ emotional intelligence level is moderately high while their academic performance is good. A high significant relationship was found between the EI component; Emotional Literacy and their academic performance while only significant relationship was found between Emotional Quotient Outcomes and their academic performance. Therefore, if EI influences academic performance significantly when correlated, a possibility that their OJT performance can also be affected either positively or negatively. Thus, EI can be considered predictor of their academic and academic-related performance. Based on the result, it is then recommended that the institution would try to look deeply into the consideration of embedding emotional intelligence as part of the (especially on Emotional Literacy and Emotional Quotient Outcomes of the students) college curriculum. It can be done if the school shall have an effective Emotional Intelligence framework or program manned by qualified and competent teachers, guidance counselors in different colleges in its implementation.

Keywords: academic performance, emotional intelligence, college students, academic success

Procedia PDF Downloads 355

Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Gyumin Hwang, Jihong Lee

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This paper studied the CSS-based indoor localization system which is easy to implement, inexpensive to compose the systems, additionally CSS-based indoor localization system covers larger area than other system. However, this system has problem which is affected by reflected distance data. This problem in localization is caused by the multi-path effect. Error caused by multi-path is difficult to be corrected because the indoor environment cannot be described. In this paper, in order to solve the problem by multi-path, we have supplemented the localization system by using pattern matching method based on extended database. Thereby, this method improves precision of estimated. Also this method is verified by experiments in gymnasium. Database was constructed by 1 m intervals, and 16 sample data were collected from random position inside the region of DB points. As a result, this paper shows higher accuracy than existing method through graph and table.

Keywords: chirp spread spectrum, indoor localization, pattern-matching, time of arrival, multi-path, mahalanobis distance, reception rate, simultaneous localization and mapping, laser range finder

Procedia PDF Downloads 219

Authors: Min Kyung An

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In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks

Procedia PDF Downloads 191

Authors: Imran Badshah

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Pedestrian service time reflects the performance of the facility, and it’s a key parameter to analyze the capability of facilities provided to serve pedestrians. The level of service of pedestrians (LOS) mainly depends on pedestrian time and safety. The pedestrian time utilized by taking a service is mainly influenced by the number of available services and the time utilized by each pedestrian in receiving a service; that is called a delay time. In this paper, we analyzed the simulated pedestrian service time with different delay times. A simulation is performed in AnyLogic by developing a model that reflects the real scenario of pedestrian services such as ticket machine gates at rail stations, airports, shopping malls, and cinema halls. The simulated pedestrian time is determined for various delay values. The simulated result shows how pedestrian time changes with the delay pattern. The histogram and time plot graph of a model gives the mean, maximum and minimum values of the pedestrian time. This study helps us to check the behavior of pedestrian time at various services such as subway stations, airports, shopping malls, and cinema halls.

Keywords: agent-based simulation, anylogic model, pedestrian behavior, time delay

Procedia PDF Downloads 184

Authors: Sreedevi P. Chakyar, Jolly Andrews, V. P. Joseph

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A compact method for measuring the relative permittivity of a dielectric material at different temperatures using a single circular Split Ring Resonator (SRR) metamaterial unit working as a test probe is presented in this paper. The dielectric constant of a material is dependent upon its temperature and the LC resonance of the SRR depends on its dielectric environment. Hence, the temperature of the dielectric material in contact with the resonator influences its resonant frequency. A single SRR placed between transmitting and receiving probes connected to a Vector Network Analyser (VNA) is used as a test probe. The dependence of temperature between 30 ^oC and 60 ^oC on resonant frequency of SRR is analysed. Relative permittivities ‘ε’ of test samples for different temperatures are extracted from a calibration graph drawn between the relative permittivity of samples of known dielectric constant and their corresponding resonant frequencies. This method is found to be an easy and efficient technique for analysing the temperature dependent permittivity of different materials.

Keywords: metamaterials, negative permeability, permittivity measurement techniques, split ring resonators, temperature dependent dielectric constant

Procedia PDF Downloads 379

Authors: H. Parveen Begam, M. A. Maluk Mohamed

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Grid computing is an environment that allows sharing and coordinated use of diverse resources in dynamic, heterogeneous and distributed environment using Virtual Organization (VO). Security is a critical issue due to the open nature of the wireless channels in the grid computing which requires three fundamental services: authentication, authorization, and encryption. The privacy and anonymity are considered as an important factor while communicating over publicly spanned network like web. To ensure a high level of security we explored an extension of onion routing, which has been used with dynamic token exchange along with protection of privacy and anonymity of individual identity. To improve the performance of encrypting the layers, the elliptic curve cryptography is used. Compared to traditional cryptosystems like RSA (Rivest-Shamir-Adelman), ECC (Elliptic Curve Cryptosystem) offers equivalent security with smaller key sizes which result in faster computations, lower power consumption, as well as memory and bandwidth savings. This paper presents the estimation of the performance improvements of onion routing using ECC as well as the comparison graph between performance level of RSA and ECC.

Keywords: grid computing, privacy, anonymity, onion routing, ECC, RSA

Procedia PDF Downloads 374

Authors: F. Tang, S. Boskovich, A. Raheja, Z. Aliyazicioglu, S. Bhandari, N. Tsuchiya

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Calpoly Autonomous Transportation Experience (CATE) is a driverless vehicle that we are developing to provide safe, accessible, and efficient transportation of passengers throughout the Cal Poly Pomona campus for events such as orientation tours. Unlike the other self-driving vehicles that are usually developed to operate with other vehicles and reside only on the road networks, CATE will operate exclusively on walk-paths of the campus (potentially narrow passages) with pedestrians traveling from multiple locations. Safety becomes paramount as CATE operates within the same environment as pedestrians. As driverless vehicles assume greater roles in today’s transportation, this project will contribute to autonomous driving with pedestrian traffic in a highly dynamic environment. The CATE project requires significant interdisciplinary work. Researchers from mechanical engineering, electrical engineering and computer science are working together to attack the problem from different perspectives (hardware, software and system). In this abstract, we describe the software aspects of the project, with a focus on the requirements and the major components. CATE shall provide a GUI interface for the average user to interact with the car and access its available functionalities, such as selecting a destination from any origin on campus. We have developed an interface that provides an aerial view of the campus map, the current car location, routes, and the goal location. Users can interact with CATE through audio or manual inputs. CATE shall plan routes from the origin to the selected destination for the vehicle to travel. We will use an existing aerial map for the campus and convert it to a spatial graph configuration where the vertices represent the landmarks and edges represent paths that the car should follow with some designated behaviors (such as stay on the right side of the lane or follow an edge). Graph search algorithms such as A* will be implemented as the default path planning algorithm. D* Lite will be explored to efficiently recompute the path when there are any changes to the map. CATE shall avoid any static obstacles and walking pedestrians within some safe distance. Unlike traveling along traditional roadways, CATE’s route directly coexists with pedestrians. To ensure the safety of the pedestrians, we will use sensor fusion techniques that combine data from both lidar and stereo vision for obstacle avoidance while also allowing CATE to operate along its intended route. We will also build prediction models for pedestrian traffic patterns. CATE shall improve its location and work under a GPS-denied situation. CATE relies on its GPS to give its current location, which has a precision of a few meters. We have implemented an Unscented Kalman Filter (UKF) that allows the fusion of data from multiple sensors (such as GPS, IMU, odometry) in order to increase the confidence of localization. We also noticed that GPS signals can easily get degraded or blocked on campus due to high-rise buildings or trees. UKF can also help here to generate a better state estimate. In summary, CATE will provide on-campus transportation experience that coexists with dynamic pedestrian traffic. In future work, we will extend it to multi-vehicle scenarios.

Keywords: driverless vehicle, path planning, sensor fusion, state estimate

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Authors: Akrem Sellami, Imed Riadh Farah

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Hyperspectral imagery (HSI) typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image. Hence, a pixel in HSI is a high-dimensional vector of intensities with a large spectral range and a high spectral resolution. Therefore, the semantic interpretation is a challenging task of HSI analysis. We focused in this paper on object classification as HSI semantic interpretation. However, HSI classification still faces some issues, among which are the following: The spatial variability of spectral signatures, the high number of spectral bands, and the high cost of true sample labeling. Therefore, the high number of spectral bands and the low number of training samples pose the problem of the curse of dimensionality. In order to resolve this problem, we propose to introduce the process of dimensionality reduction trying to improve the classification of HSI. The presented approach is a semi-supervised band selection method based on spatial hypergraph embedding model to represent higher order relationships with different weights of the spatial neighbors corresponding to the centroid of pixel. This semi-supervised band selection has been developed to select useful bands for object classification. The presented approach is evaluated on AVIRIS and ROSIS HSIs and compared to other dimensionality reduction methods. The experimental results demonstrate the efficacy of our approach compared to many existing dimensionality reduction methods for HSI classification.

Keywords: dimensionality reduction, hyperspectral image, semantic interpretation, spatial hypergraph

Procedia PDF Downloads 287

Authors: Sanjeev Kumar Sharma, Arnab Mondal, Ranjit Kumar Upadhyay

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Excitable cells often produce different oscillatory activities that help us to understand the transmitting and processing of signals in the neural system. We consider a FitzHugh-Rinzel (FH-R) model and studied the different dynamics of the model by considering the parameter c as the predominant parameter. The model exhibits different types of neuronal responses such as regular spiking, mixed-mode bursting oscillations (MMBOs), elliptic bursting, etc. Based on the bifurcation diagram, we consider the three regimes (MMBOs, elliptic bursting, and quiescent state). An analytical treatment for the occurrence of the supercritical Hopf bifurcation is studied. Further, we extend our study to a network of a hundred neurons by considering the bi-directional synaptic coupling between them. In this article, we investigate the alternation of spiking propagation and bursting phenomena of an uncoupled and coupled FH-R neurons. We explore that the complete graph of heterogenous desynchronized neurons can exhibit different types of bursting oscillations for certain coupling strength. For higher coupling strength, all the neurons in the network show complete synchronization.

Keywords: excitable neuron model, spiking-bursting, stability and bifurcation, synchronization networks

Procedia PDF Downloads 98

Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: M. Fernanda Salgado, Alfonso Tierra, Wilbert Aguilar

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The present project consists in applying the informed search algorithm A star (A*) to solve traveler problems, applying it by urban public transportation routes. The digitization of the information allowed to identify 26% of the total of routes that are registered within the Metropolitan District of Quito. For the validation of this information, data were taken in field on the travel times and the difference with respect to the times estimated by the program, resulting in that the difference between them was not greater than 2:20 minutes. We validate A* algorithm with the Dijkstra algorithm, comparing nodes vectors based on the public transport stops, the validation was established through the student t-test hypothesis. Then we verified that the times estimated by the program using the A* algorithm are similar to those registered on field. Furthermore, we review the performance of the algorithm generating iterations in both algorithms. Finally, with these iterations, a hypothesis test was carried out again with student t-test where it was concluded that the iterations of the base algorithm Dijsktra are greater than those generated by the algorithm A*.

Keywords: algorithm A*, graph, mobility, public transport, travel planning, routes

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Manojkumar Sabanayagam

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The Riemann hypothesis is an unsolved millennium problem and the search for a solution to the Riemann hypothesis is to study the pattern of prime number distribution. The scope of this paper is to identify the solution for the critical strip and the critical line axis, which has the non-trivial zero solutions using complex plane functions. The Riemann graphical plot is constructed using a linear complex variable function (X+iY) and is applicable only when X>1. But the investigation shows that complex variable behavior has two zones. The first zone is the transformation zone, where the definition of the complex plane should be a non-linear variable which is the critical strip zone in the graph (X=0 to 1). The second zone is the transformed zone (X>1) defined using linear variables conventionally. This paper deals with the Non-linear function in the transformation zone derived using cosine and sinusoidal time lag w.r.t imaginary number ‘i’. The alternate complex variable (Cosθ+i Sinθ) is used to understand the variables in the critical strip zone. It is concluded that the non-trivial zeros present in the Real part 0.5 are because the linear function is not the correct approach in the critical strip. This paper provides the solution to Reimann's hypothesis.

Keywords: Reimann hypothesis, critical strip, complex plane, transformation zone

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Authors: Gajaanuja Megalathan, Banuka Athuraliya

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Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes.

Keywords: arima model, ANN, crime prediction, data analysis

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Authors: Omaima N. Ahmad AL-Allaf

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Over communication networks, images can be easily copied and distributed in an illegal way. The copyright protection for authors and owners is necessary. Therefore, the digital watermarking techniques play an important role as a valid solution for authority problems. Digital image watermarking techniques are used to hide watermarks into images to achieve copyright protection and prevent its illegal copy. Watermarks need to be robust to attacks and maintain data quality. Therefore, we discussed in this paper two approaches for image watermarking, first is based on Particle Swarm Optimization (PSO) and the second approach is based on Genetic Algorithm (GA). Discrete wavelet transformation (DWT) is used with the two approaches separately for embedding process to cover image transformation. Each of PSO and GA is based on co-relation coefficient to detect the high energy coefficient watermark bit in the original image and then hide the watermark in original image. Many experiments were conducted for the two approaches with different values of PSO and GA parameters. From experiments, PSO approach got better results with PSNR equal 53, MSE equal 0.0039. Whereas GA approach got PSNR equal 50.5 and MSE equal 0.0048 when using population size equal to 100, number of iterations equal to 150 and 3×3 block. According to the results, we can note that small block size can affect the quality of image watermarking based PSO/GA because small block size can increase the search area of the watermarking image. Better PSO results were obtained when using swarm size equal to 100.

Keywords: image watermarking, genetic algorithm, particle swarm optimization, discrete wavelet transform

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Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

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Authors: E. Cuevas, M. Feigel'man, L. Ioffe, M. Mezard

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The origin of continuous energy spectrum in large disordered interacting quantum systems is one of the key unsolved problems in quantum physics. While small quantum systems with discrete energy levels are noiseless and stay coherent forever in the absence of any coupling to external world, most large-scale quantum systems are able to produce thermal bath, thermal transport and excitation decay. This intrinsic decoherence is manifested by a broadening of energy levels which acquire a finite width. The important question is: What is the driving force and mechanism of transition(s) between two different types of many-body systems - with and without decoherence and thermal transport? Here, we address this question via two complementary approaches applied to the same model of quantum spin-1/2 system with XY-type exchange interaction and random transverse field. Namely, we develop analytical theory for this spin model on a Bethe lattice and implement numerical study of exact level statistics for the same spin model on random graph. This spin model is relevant to the study of pseudogaped superconductivity and S-I transition in some amorphous materials.

Keywords: strongly correlated electrons, quantum phase transitions, superconductor, insulator

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Authors: Juliet Udoudom

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Inappropriate complement selection constitutes one of the major features of non-standard complementation in the Nigerian users of English output of sentence construction. This paper investigates complement clause choice in Written Educated Nigerian English (ENE) and offers some results. It aims at determining preferred and dispreferred patterns of complement clause selection in respect of verb heads in English by selected Nigerian users of English. The complementation data analyzed in this investigation were obtained from experimental tasks designed to elicit complement categories of Verb – Noun -, Adjective – and Prepositional – heads in English. Insights from the Government – Binding relations were employed in analyzing data, which comprised responses obtained from one hundred subjects to a picture elicitation exercise, a grammaticality judgement test, and a free composition task. The findings indicate a general tendency for clausal complements (CPs) introduced by the complementizer that to be preferred by the subjects studied. Of the 235 tokens of clausal complements which occurred in our corpus, 128 of them representing 54.46% were CPs headed by that, while whether – and if-clauses recorded 31.07% and 8.94%, respectively. The complement clause-type which recorded the lowest incidence of choice was the CP headed by the Complementiser, for with a 5.53% incident of occurrence. Further findings from the study indicate that semantic features of relevant embedding verb heads were not taken into consideration in the choice of complementisers which introduce the respective complement clauses, hence the that-clause was chosen to complement verbs like prefer. In addition, the dispreferred choice of the for-clause is explicable in terms of the fact that the respondents studied regard ‘for’ as a preposition, and not a complementiser.

Keywords: complement, complement clause complement selection, complementisers, government-binding

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Authors: Sopheak Sorn, Kwok Yip Szeto

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Reliability is one of the important measures of how well the system meets its design objective, and mathematically is the probability that a complex system will perform satisfactorily. When the system is described by a network of N components (nodes) and their L connection (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, we address the network design problem for a random point set’s pattern in two dimensions. We make use of a Voronoi construction with each cell containing exactly one point in the point pattern and compute the reliability of the Voronoi’s dual, i.e. the Delaunay graph. We further investigate the communicability of the Delaunay network. We find that there is a positive correlation and a negative correlation between the homogeneity of a Delaunay's degree distribution with its reliability and its communicability respectively. Based on the correlations, we alter the communicability and the reliability by performing random edge flips, which preserve the number of links and nodes in the network but can increase the communicability in a Delaunay network at the cost of its reliability. This transformation is later used to optimize a Delaunay network with the optimum geometric mean between communicability and reliability. We also discuss the importance of the edge flips in the evolution of real soap froth in two dimensions.

Keywords: Communicability, Delaunay triangulation, Edge Flip, Reliability, Two dimensional network, Voronio

Procedia PDF Downloads 390

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Fatiha Bedjou, Meriem Meddas, Tadjajikt Chekkal

Abstract:

The present work aims to evaluate the antioxidant activity of ethanolic extracts and essential oils of aromatic plants of the Lamiaceae family: Thymus algeriensis and Salvia rosmarinus, and Anacardiaceae: Pistacia lentiscus, Myrtaceae: Eucalyptus polybracetea. The polyphenols were measured using the Folin-Ciocalteu method; the results showed that the essential oils studied as well as the ethanolic extracts are relatively rich in polyphenols. Their antioxidant properties were tested by the synthetic DPPH radical trapping method. The IC50 values were determined according to the graph representing the percentage of inhibition of the DPPH radical by essential oils and by ethanolic extracts, according to our results there is a correlation between the level of polyphenols present in the different essential oils and different ethanolic extracts and their ability to neutralize free radicals. Several combinations were carried out between the essential oils and also between the ethanolic extracts in order to determine the type of interactions existing between the combined substances, the results were represented in the form of isobolograms. Additive and super-additive effects were observed in combinations of essential oils, and super-additive and sub-additive effects were observed for combinations of ethanolic extracts.

Keywords: essential oils, ethanolic extracts, DPPH, combination

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Authors: Skiker Kaoutar, Mounir Maouene

Abstract:

Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from McRae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-worls, resilience to damage

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Authors: Simran Ballani

Abstract:

The purpose of this paper is to call education professionals to implement etiquette and public speaking skills for preschoolers, primary, middle and higher school students. In this paper the author aims to present importance of etiquette learning and public speaking curriculum for preschoolers, reflect on experiences from implementation of the curriculum and discuss the effect of the said implementation on higher education/global job market. Author’s aim to introduce this curriculum was to provide children with innovative learning and all around development. This training of soft skills at kindergarten level can have a long term effect on their social behaviors which in turn can contribute to professional success once they are ready for campus recruitment/global job markets. Additionally, if preschoolers learn polite, appropriate behavior at early age, it will enable them to become more socially attentive and display good manners as an adult. It is easier to nurture these skills in a child rather than changing bad manners at adulthood. Preschool/Kindergarten education can provide the platform for children to learn these crucial soft skills irrespective of the ethnicity, economic or social background they come from. These skills developed at such early years can go a long way to shape them into better and confident individuals. Unfortunately, accessibility of the etiquette learning and public speaking skill education is not standardized in pre-primary or primary level and most of the time embedding into the kindergarten curriculum is next to nil. All young children should be provided with equal opportunity to learn these soft skills which are essential for finding their place in job market.

Keywords: Early Childhood Learning, , public speaking, , confidence building, , innovative learning

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Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

Abstract:

The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

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Authors: Jinen Daghrir, Anis Kricha, Karim Kalti

Abstract:

The amount of incoming administrative documents is massive and manually processing these documents is a costly task especially on the timescale. In fact, this problem has led an important amount of research and development in the context of automatically extracting fields from administrative documents, in order to reduce the charges and to increase the citizen satisfaction in administrations. In this matter, we introduce an administrative document understanding system. Given a document in which a user has to select fields that have to be retrieved from a document class, a document model is automatically built. A document model is represented by an attributed relational graph (ARG) where nodes represent fields to extract, and edges represent the relation between them. Both of vertices and edges are attached with some feature vectors. When another document arrives to the system, the layout objects are extracted and an ARG is generated. The fields extraction is translated into a problem of matching two ARGs which relies mainly on the comparison of the spatial relationships between layout objects. Experimental results yield accuracy rates from 75% to 100% tested on eight document classes. Our proposed method has a good performance knowing that the document model is constructed using only one single document.

Keywords: administrative document understanding, logical labelling, logical layout analysis, fields extraction from administrative documents

Procedia PDF Downloads 188