Search results for: target detection.

Authors: Y. Bentoutou

Abstract:

On-board Error Detection and Correction (EDAC) devices aim to secure data transmitted between the central processing unit (CPU) of a satellite onboard computer and its local memory. This paper presents a comparison of the performance of four low complexity EDAC techniques for application in Random Access Memories (RAMs) on-board small satellites. The performance of a newly proposed EDAC architecture is measured and compared with three different EDAC strategies, using the same FPGA technology. A statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard Alsat-1 is given for a period of 8 years

Keywords: Error Detection and Correction; On-board computer; small satellite missions

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Authors: Thuy Thi Nguyen, Nguyen Dang Binh, Horst Bischof

Abstract:

In this work, we present a novel active learning approach for learning a visual object detection system. Our system is composed of an active learning mechanism as wrapper around a sub-algorithm which implement an online boosting-based learning object detector. In the core is a combination of a bootstrap procedure and a semi automatic learning process based on the online boosting procedure. The idea is to exploit the availability of classifier during learning to automatically label training samples and increasingly improves the classifier. This addresses the issue of reducing labeling effort meanwhile obtain better performance. In addition, we propose a verification process for further improvement of the classifier. The idea is to allow re-update on seen data during learning for stabilizing the detector. The main contribution of this empirical study is a demonstration that active learning based on an online boosting approach trained in this manner can achieve results comparable or even outperform a framework trained in conventional manner using much more labeling effort. Empirical experiments on challenging data set for specific object deteciton problems show the effectiveness of our approach.

Keywords: Computer vision, object detection, online boosting, active learning, labeling complexity.

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Authors: Chandra Prayaga, Lakshmi Prayaga, Aaron Wade, Kyle Rank, Gopi Shankar Mallu, Sri Satya Harsha Pola

Abstract:

In today's tech-driven world, where connectivity shapes our daily lives, maintaining physical and emotional health is crucial. Athletic trainers play a vital role in optimizing athletes' performance and preventing injuries. However, a shortage of trainers impacts the quality of care. This study introduces a vision-based exercise monitoring system leveraging Google's MediaPipe library for precise tracking of bicep curl exercises and simultaneous posture monitoring. We propose a three-stage methodology: landmark detection, side detection, and angle computation. Our system calculates angles at the elbow, wrist, neck, and torso to assess exercise form. Experimental results demonstrate the system's effectiveness in distinguishing between good and partial repetitions and evaluating body posture during exercises, providing real-time feedback for precise fitness monitoring.

Keywords: Physical health, athletic trainers, fitness monitoring, technology driven solutions, Google's MediaPipe, landmark detection, angle computation, real-time feedback.

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Authors: Jawad H AlKhateeb, Jianmin Jiang, Jinchang Ren, Stan S Ipson

Abstract:

Efficient preprocessing is very essential for automatic recognition of handwritten documents. In this paper, techniques on segmenting words in handwritten Arabic text are presented. Firstly, connected components (ccs) are extracted, and distances among different components are analyzed. The statistical distribution of this distance is then obtained to determine an optimal threshold for words segmentation. Meanwhile, an improved projection based method is also employed for baseline detection. The proposed method has been successfully tested on IFN/ENIT database consisting of 26459 Arabic words handwritten by 411 different writers, and the results were promising and very encouraging in more accurate detection of the baseline and segmentation of words for further recognition.

Keywords: Arabic OCR, off-line recognition, Baseline estimation, Word segmentation.

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Authors: Serkan Solmaz, Jean-Baptiste Gouriet, Nicolas Van de Wyer, Christophe Schram

Abstract:

Efficiency of the cooling process for cryogenic propellant boiling in engine cooling channels on space applications is relentlessly affected by the phase change occurs during the boiling. The effectiveness of the cooling process strongly pertains to the type of the boiling regime such as nucleate and film. Geometric constraints like a non-transparent cooling channel unable to use any of visualization methods. The ultrasonic (US) technique as a non-destructive method (NDT) has therefore been applied almost in every engineering field for different purposes. Basically, the discontinuities emerge between mediums like boundaries among different phases. The sound wave emitted by the US transducer is both transmitted and reflected through a gas-liquid interface which makes able to detect different phases. Due to the thermal and structural concerns, it is impractical to sustain a direct contact between the US transducer and working fluid. Hence the transducer should be located outside of the cooling channel which results in additional interfaces and creates ambiguities on the applicability of the present method. In this work, an exploratory research is prompted so as to determine detection ability and applicability of the US technique on the cryogenic boiling process for a cooling cycle where the US transducer is taken place outside of the channel. Boiling of the cryogenics is a complex phenomenon which mainly brings several hindrances for experimental protocol because of thermal properties. Thus substitute materials are purposefully selected based on such parameters to simplify experiments. Aside from that, nucleate and film boiling regimes emerging during the boiling process are simply simulated using non-deformable stainless steel balls, air-bubble injection apparatuses and air clearances instead of conducting a real-time boiling process. A versatile detection algorithm is perennially developed concerning exploratory studies afterward. According to the algorithm developed, the phases can be distinguished 99% as no-phase, air-bubble, and air-film presences. The results show the detection ability and applicability of the US technique for an exploratory purpose.

Keywords: Ultrasound, ultrasonic, multiphase flow, boiling, cryogenics, detection algorithm.

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Authors: O. Siriporn, S. Benjawan

Abstract:

This paper represents four unsupervised clustering algorithms namely sIB, RandomFlatClustering, FarthestFirst, and FilteredClusterer that previously works have not been used for network traffic classification. The methodology, the result, the products of the cluster and evaluation of these algorithms with efficiency of each algorithm from accuracy are shown. Otherwise, the efficiency of these algorithms considering form the time that it use to generate the cluster quickly and correctly. Our work study and test the best algorithm by using classify traffic anomaly in network traffic with different attribute that have not been used before. We analyses the algorithm that have the best efficiency or the best learning and compare it to the previously used (K-Means). Our research will be use to develop anomaly detection system to more efficiency and more require in the future.

Keywords: Unsupervised, clustering, anomaly, machine learning.

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Authors: Andrey V. Timofeev

Abstract:

This paper introduces an original method for guaranteed estimation of the accuracy for an ensemble of Lipschitz classifiers. The solution was obtained as a finite closed set of alternative hypotheses, which contains an object of classification with probability of not less than the specified value. Thus, the classification is represented by a set of hypothetical classes. In this case, the smaller the cardinality of the discrete set of hypothetical classes is, the higher is the classification accuracy. Experiments have shown that if cardinality of the classifiers ensemble is increased then the cardinality of this set of hypothetical classes is reduced. The problem of the guaranteed estimation of the accuracy for an ensemble of Lipschitz classifiers is relevant in multichannel classification of target events in C-OTDR monitoring systems. Results of suggested approach practical usage to accuracy control in C-OTDR monitoring systems are present.

Keywords: Lipschitz classifiers, confidence set, C-OTDR monitoring, classifiers accuracy, classifiers ensemble.

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Authors: Mahmood Ahmad, Ghulam Murtaza, Sonia Khiljee, Muhammad Asadullah Madni

Abstract:

A new reverse phase-high performance liquid chromatography (RP-HPLC) method with fluorescent detector (FLD) was developed and optimized for Norfloxacin determination in human plasma. Mobile phase specifications, extraction method and excitation and emission wavelengths were varied for optimization. HPLC system contained a reverse phase C18 (5 μm, 4.6 mm×150 mm) column with FLD operated at excitation 330 nm and emission 440 nm. The optimized mobile phase consisted of 14% acetonitrile in buffer solution. The aqueous phase was prepared by mixing 2g of citric acid, 2g sodium acetate and 1 ml of triethylamine in 1 L of Milli-Q water was run at a flow rate of 1.2 mL/min. The standard curve was linear for the range tested (0.156–20 μg/mL) and the coefficient of determination was 0.9978. Aceclofenac sodium was used as internal standard. A detection limit of 0.078 μg/mL was achieved. Run time was set at 10 minutes because retention time of norfloxacin was 0.99 min. which shows the rapidness of this method of analysis. The present assay showed good accuracy, precision and sensitivity for Norfloxacin determination in human plasma with a new internal standard and can be applied pharmacokinetic evaluation of Norfloxacin tablets after oral administration in human.

Keywords: Norfloxacin, Aceclofenac sodium, Methodoptimization, RP-HPLC method, Fluorescent detection, Calibrationcurve.

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Authors: H. Özkan

Abstract:

In this study, a new and fast algorithm for Ascending Aorta (AscA) and Descending Aorta (DesA) segmentation is presented using Computed Tomography Angiography images. This process is quite important especially at the detection of aortic plaques, aneurysms, calcification or stenosis. The applied method has been carried out at four steps. At first step, lung segmentation is achieved. At the second one, Mediastinum Region (MR) is detected to use in the segmentation. At the third one, images have been applied optimal threshold and components which are outside of the MR were removed. Lastly, identifying and segmentation of AscA and DesA have been carried out. The performance of the applied method is found quite well for radiologists and it gives enough results to the surgeries medically.

Keywords: Ascending aorta (AscA), Descending aorta (DesA), Computed tomography angiography (CTA), Computer aided detection (CAD), Segmentation

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Authors: Asrul H. Yaacob, Nazrul M. Ahmad, Nurul N. Ahmad, Mardeni Roslee

Abstract:

The proliferation of web application and the pervasiveness of mobile technology make web-based attacks even more attractive and even easier to launch. Web Application Firewall (WAF) is an intermediate tool between web server and users that provides comprehensive protection for web application. WAF is a negative security model where the detection and prevention mechanisms are based on predefined or user-defined attack signatures and patterns. However, WAF alone is not adequate to offer best defensive system against web vulnerabilities that are increasing in number and complexity daily. This paper presents a methodology to automatically design a positive security based model which identifies and allows only legitimate web queries. The paper shows a true positive rate of more than 90% can be achieved.

Keywords: Intrusion Detection System, Positive Security Model, Web application Firewall

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Authors: Junichiro Kawaguchi

Abstract:

Space debris problems have emerged and threatened the use of low earth orbit around the Earth owing to a large number of spacecrafts.. The robots should be sophisticated enough to access automatically the debris articulating the attitude and the translation motion with respect to the debris. This paper presents the idea of using the torpedo-like third unsophisticated and disposable body, in addition to the first body of the servicing robot and the second body of the target debris. The third body is launched from the first body from a distance further than the size of the second body. This paper presents the method and the system, so that the third body is launched from the first body. The third body carries both a net and an inflatable or extendible drag deceleration device and is built small and light. This method enables even micro to nano satellites to perform contactless and multiple debris removal even via a single flight.

Keywords: Ballute, Debris Removal, Echo satellite, Gossamer, Gun-Net, Inflatable Space Structure, Small Satellite, Un-cooperated Target.

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Authors: Yi Yu, Gaoxi Xiao

Abstract:

Community structures widely exist in almost all real-life networks. Extensive researches have been carried out on detecting community structures in complex networks. However, many aspects of how community structures may affect the dynamics and properties of complex networks still remain unclear. In this work, we examine the impacts of community structures on the epidemic spreading and detection in complex networks. Extensive simulation results show that community structures may not help decrease the infection size at steady state, yet they could indeed help slow down the infection spreading. Also, networks with strong community structures may expect to have a smaller average infection size when equipped with a number of sparsely deployed monitors.

Keywords: Complex network, epidemic spreading, infection size, infection monitoring.

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Authors: Tarun Chand, Michael Jurczyk

Abstract:

With the increasing need and popularity of IoT devices and how integrated they are becoming in our daily lives and industries; these devices make for a very lucrative target for malicious actors. And since these devices have such limited resources, the implementation of robust security features is a tradeoff to be made for the actual functionality the device was intended for. This makes them an easy target with high returns. Several frameworks for the secure firmware update of these devices have been recently proposed in the literature. They focus on methods such as blockchains and distributed file systems to secure firmware updates, but do not go into the details of the actual implementation of these frameworks and the lower-level interactions among these methods used. This work integrates some of these security measures into one overall framework and details the actual lower-level implementation of this framework in a virtual dockerized testbed running on AWS.

Keywords: Blockchain, Ethereum, Geth, IPFS, secure IoT-firmware update, virtual testbed development

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Authors: V. G. Rajesh, M. V. Rajesh

Abstract:

In rotating machinery one of the critical components that is prone to premature failure is the rolling bearing. Consequently, early warning of an imminent bearing failure is much critical to the safety and reliability of any high speed rotating machines. This study is concerned with the application of Recurrence Quantification Analysis (RQA) in fault detection of rolling element bearings in rotating machinery. Based on the results from this study it is reported that the RQA variable, percent determinism, is sensitive to the type of fault investigated and therefore can provide useful information on bearing damage in rolling element bearings.

Keywords: Bearing fault detection, machine vibrations, nonlinear time series analysis, recurrence quantification analysis.

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Authors: Andreas Theissler, Ian Dear

Abstract:

In the automotive industry test drives are being conducted during the development of new vehicle models or as a part of quality assurance of series-production vehicles. The communication on the in-vehicle network, data from external sensors, or internal data from the electronic control units is recorded by automotive data loggers during the test drives. The recordings are used for fault analysis. Since the resulting data volume is tremendous, manually analysing each recording in great detail is not feasible. This paper proposes to use machine learning to support domainexperts by preventing them from contemplating irrelevant data and rather pointing them to the relevant parts in the recordings. The underlying idea is to learn the normal behaviour from available recordings, i.e. a training set, and then to autonomously detect unexpected deviations and report them as anomalies. The one-class support vector machine “support vector data description” is utilised to calculate distances of feature vectors. SVDDSUBSEQ is proposed as a novel approach, allowing to classify subsequences in multivariate time series data. The approach allows to detect unexpected faults without modelling effort as is shown with experimental results on recordings from test drives.

Keywords: Anomaly detection, fault detection, test drive analysis, machine learning.

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Authors: Mehran Yazdi, Mehdi Seyfi, Amirhossein Rafati, Meghdad Asadi

Abstract:

Detection and tracking of the lip contour is an important issue in speechreading. While there are solutions for lip tracking once a good contour initialization in the first frame is available, the problem of finding such a good initialization is not yet solved automatically, but done manually. We have developed a new tracking solution for lip contour detection using only few landmarks (15 to 25) and applying the well known Active Shape Models (ASM). The proposed method is a new LMS-like adaptive scheme based on an Auto regressive (AR) model that has been fit on the landmark variations in successive video frames. Moreover, we propose an extra motion compensation model to address more general cases in lip tracking. Computer simulations demonstrate a fair match between the true and the estimated spatial pixels. Significant improvements related to the well known LMS approach has been obtained via a defined Frobenius norm index.

Keywords: Lip contour, Tracking, LMS-Like

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

Abstract:

Surrogate model has received increasing attention for use in detecting damage of structures based on vibration modal parameters. However, uncertainties existing in the measured vibration data may lead to false or unreliable output result from such model. In this study, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The kriging technique allows one to genuinely quantify the surrogate error, therefore it is chosen as metamodeling technique. Enhanced version of ideal gas molecular movement (EIGMM) algorithm is used as main algorithm for model updating. The developed approach is applied to detect simulated damage in numerical models of 72-bar space truss and 120-bar dome truss. The simulation results show the proposed method can perform well in probability-based damage detection of structures with less computational effort compared to direct finite element model.

Keywords: Enhanced ideal gas molecular movement, Kriging, probability-based damage detection, probability of damage existence, surrogate modeling, uncertainty quantification.

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Authors: Sarah Odofin, Zhiwei Gao, Sun Kai

Abstract:

Operations, maintenance and reliability of wind turbines have received much attention over the years due to the rapid expansion of wind farms. This paper explores early fault diagnosis technique for a 5MW wind turbine system subjected to multiple faults, where genetic optimization algorithm is employed to make the residual sensitive to the faults, but robust against disturbances. The proposed technique has a potential to reduce the downtime mostly caused by the breakdown of components and exploit the productivity consistency by providing timely fault alarms. Simulation results show the effectiveness of the robust fault detection methods used under Matlab/Simulink/Gatool environment.

Keywords: Disturbance robustness, fault monitoring and detection, genetic algorithm and observer technique.

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Authors: S. Liangwongsan, A. Oonsivilai

Abstract:

Nowadays, hard disk is one of the most popular storage components. In hard disk industry, the hard disk drive must pass various complex processes and tested systems. In each step, there are some failures. To reduce waste from these failures, we must find the root cause of those failures. Conventionall data analysis method is not effective enough to analyze the large capacity of data. In this paper, we proposed the Hough method for straight line detection that helps to detect straight line defect patterns that occurs in hard disk drive. The proposed method will help to increase more speed and accuracy in failure analysis.

Keywords: Hough Transform, Failure Analysis, Media, Hard Disk Drive

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Authors: K. Idir, H. Merouani, Y. Tlili.

Abstract:

An effective method for the early detection of breast cancer is the mammographic screening. One of the most important signs of early breast cancer is the presence of microcalcifications. For the detection of microcalcification in a mammography image, we propose to conceive a multiagent system based on a dual irregular pyramid. An initial segmentation is obtained by an incremental approach; the result represents level zero of the pyramid. The edge information obtained by application of the Canny filter is taken into account to affine the segmentation. The edge-agents and region-agents cooper level by level of the pyramid by exploiting its various characteristics to provide the segmentation process convergence.

Keywords: Dual Pyramid, Image Segmentation, Multi-agent System, Region/Edge Cooperation.

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Authors: Hossein Sahoolizadeh, Davood Sarikhanimoghadam, Hamid Dehghani

Abstract:

This paper proposes new hybrid approaches for face recognition. Gabor wavelets representation of face images is an effective approach for both facial action recognition and face identification. Perform dimensionality reduction and linear discriminate analysis on the down sampled Gabor wavelet faces can increase the discriminate ability. Nearest feature space is extended to various similarity measures. In our experiments, proposed Gabor wavelet faces combined with extended neural net feature space classifier shows very good performance, which can achieve 93 % maximum correct recognition rate on ORL data set without any preprocessing step.

Keywords: Face detection, Neural Networks, Multi-layer Perceptron, Gabor wavelets.

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Authors: S. Padmapriya

Abstract:

Vehicular Adhoc Networks (VANETs), a subset of Mobile Adhoc Networks (MANETs), refers to a set of smart vehicles used for road safety. This vehicle provides communication services among one another or with the Road Side Unit (RSU). Security is one of the most critical issues related to VANET as the information transmitted is distributed in an open access environment. As each vehicle is not a source of all messages, most of the communication depends on the information received from other vehicles. To protect VANET from malicious action, each vehicle must be able to evaluate, decide and react locally on the information received from other vehicles. Therefore, message verification is more challenging in VANET because of the security and privacy concerns of the participating vehicles. To overcome security threats, we propose Monitoring Algorithm that detects malicious nodes based on the pre-selected threshold value. The threshold value is compared with the distrust value which is inherently tagged with each vehicle. The proposed Monitoring Algorithm not only detects malicious vehicles, but also isolates the malicious vehicles from the network. The proposed technique is simulated using Network Simulator2 (NS2) tool. The simulation result illustrated that the proposed Monitoring Algorithm outperforms the existing algorithms in terms of malicious node detection, network delay, packet delivery ratio and throughput, thereby uplifting the overall performance of the network.

Keywords: VANET, security, malicious vehicle detection, threshold value, distrust value.

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Authors: B. Rebekka, B. Malarkodi

Abstract:

This paper describes an approach to detect the transmitted signals for 2×2 Multiple Input Multiple Output (MIMO) setup using roulette wheel based ant colony optimization technique. The results obtained are compared with classical zero forcing and least mean square techniques. The detection rates achieved using this technique are consistently larger than the one achieved using classical methods for 50 number of attempts with two different antennas transmitting the input stream from a user. This paves the path to use alternative techniques to improve the throughput achieved in advanced networks like Long Term Evolution (LTE) networks.

Keywords: MIMO, ant colony optimization, roulette wheel, soft computing, LTE.

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Authors: S. Manaseer, F. A. Masooud, A. A. Sharieh

Abstract:

Fault tolerance is critical in many of today's large computer systems. This paper focuses on improving fault tolerance through testing. Moreover, it concentrates on the memory faults: how to access the editable part of a process memory space and how this part is affected. A special Software Fault Injection Technique (SFIT) is proposed for this purpose. This is done by sequentially scanning the memory of the target process, and trying to edit maximum number of bytes inside that memory. The technique was implemented and tested on a group of programs in software packages such as jet-audio, Notepad, Microsoft Word, Microsoft Excel, and Microsoft Outlook. The results from the test sample process indicate that the size of the scanned area depends on several factors. These factors are: process size, process type, and virtual memory size of the machine under test. The results show that increasing the process size will increase the scanned memory space. They also show that input-output processes have more scanned area size than other processes. Increasing the virtual memory size will also affect the size of the scanned area but to a certain limit.

Keywords: Complex software systems, Error detection, Fault tolerance, Injection and testing methodology, Memory faults, Process and virtual memory.

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Authors: Mamoun F. Abdel-Hafez

Abstract:

In this paper, the detection of a fault in the Global Positioning System (GPS) measurement is addressed. The class of faults considered is a bias in the GPS pseudorange measurements. This bias is modeled as an unknown constant. The fault could be the result of a receiver fault or signal fault such as multipath error. A bias bank is constructed based on set of possible fault hypotheses. Initially, there is equal probability of occurrence for any of the biases in the bank. Subsequently, as the measurements are processed, the probability of occurrence for each of the biases is sequentially updated. The fault with a probability approaching unity will be declared as the current fault in the GPS measurement. The residual formed from the GPS and Inertial Measurement Unit (IMU) measurements is used to update the probability of each fault. Results will be presented to show the performance of the presented algorithm.

Keywords: Estimation and filtering, Statistical data analysis, Faultdetection and identification.

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Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane

Abstract:

Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.

Keywords: MOOC, Massive Open Online Courses, Online learning, E-learning, Blended learning.

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Authors: Levente Varga, Dávid Deritei, Mária Ercsey-Ravasz, Răzvan Florian, Zsolt I. Lázár, István Papp, Ferenc Járai-Szabó

Abstract:

One of the major shortcomings of widely used scientometric indicators is that different disciplines cannot be compared with each other. The issue of cross-disciplinary normalization has been long discussed, but even the classification of publications into scientific domains poses problems. Structural properties of citation networks offer new possibilities, however, the large size and constant growth of these networks asks for precaution. Here we present a new tool that in order to perform cross-field normalization of scientometric indicators of individual publications relays on the structural properties of citation networks. Due to the large size of the networks, a systematic procedure for identifying scientific domains based on a local community detection algorithm is proposed. The algorithm is tested with different benchmark and real-world networks. Then, by the use of this algorithm, the mechanism of the scientometric indicator normalization process is shown for a few indicators like the citation number, P-index and a local version of the PageRank indicator. The fat-tail trend of the article indicator distribution enables us to successfully perform the indicator normalization process.

Keywords: Citation networks, scientometric indicator, cross-field normalization, local cluster detection.

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Authors: Liansheng Ren, Xihua Yang, Guoping Wang, Hong Zhang, Lili Zhao, Zhenguo Mi

Abstract:

The inhibition effect of brazilin to human bladder tumor cell line T24 in vitro and in vivo was studied. The results of the in vitro experiments showed that brazilin has strong inhibition activity on the target cells. The inhibition ratio of 100 μg/mL brazilin and 100 μg/mL mitomycin to the target cells was 90.90 % and 63.24 % respectively, which showed that brazilin has higher inhibition activity than mitomycin under the same concentration. Brazilin could induce cell apoptosis in T24 cells. Significant antitumor activity of brazilin was also showed in the animals experiments. The life extention rate of 200 mg/mL, 300 mg/kg, and 400 mg/kg brazilin intraperitoneally injected into Balb/c-nu-nu nude mice that with human bladder cancer were 51.50 %, 56.90 %, and 58.42 %（P<0.05）. Our study showed that brazilin has significant inhibitory effect on human bladder tumor cell.

Keywords: bladder cancer, brazilin, inhibition, T24 cell line

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Authors: Leili Nosrati, Alireza Nemaney Pour

Abstract:

Although e-mail is the most efficient and popular communication method, unwanted and mass unsolicited e-mails, also called spam mail, endanger the existence of the mail system. This paper proposes a new algorithm called Dynamic Weighted Majority Concept Drift Detection (DWM-CDD) for content-based filtering. The design purposes of DWM-CDD are first to accurate the performance of the previously proposed algorithms, and second to speed up the time to construct the model. The results show that DWM-CDD can detect both sudden and gradual changes quickly and accurately. Moreover, the time needed for model construction is less than previously proposed algorithms.

Keywords: Concept drift, Content-based filtering, E-mail, Spammail.

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Authors: Ahmet Diken, Tahsin Karabulut

Abstract:

The factor which developing countries are in need is capital. Such countries make an effort to increase their income in order to meet their expenses for employment, infrastructure, superstructure investments, education, health and defense. The sole income of the countries is taxes collected from businesses. The businesses should drive profit and return in order to be able to toll. In a world where competition exists, different strategies may be followed by business in developing countries and they must specify their target markets. İn order to minimize cost and maximize profit, SMEs have to concentrate on target markets and select cost oriented strategy. In this study, a theoretical model is suggested that SME firms have to act as cluster between each other, and also must be optimal provider for large scale firms. SMEs’ policy must be supported by public. This relationship can benefit large scale firms to have brand over the world, and this organization increases value added for developing countries.

Keywords: Competitiveness, sustainability, SMEs, developing countries.

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