Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Computer Interface for Communication and Control

The last six years have seen a fast growth in research and techniques in the development of detection in human brain response, analysis of human brain activity, translation of brain state into action and putting these techniques to appropriate uses to help paralyzed people with spinal cord injury causes because of several reasons such as motor vehicle accident, falls and sports injuries. Brain Computer Interface (BCI) is a communication and control mechanism that has a significant difference to other studies in Human Computer Interface (HCI) as it does not rely on any kind of muscular response to send a message to the external world. Current BCIs are error prone and slow with error rates of 10 to 30% and have transfer rates of 10-25 bits/min using different types of non-invasive methods for recording brain signals. This thesis defines BCI technology and will display how brain signals can be recorded using different methods and will show the reason for choosing Steady State Visual Evoked Potential (SSVEP) rather than other methods. This thesis will also focus on the development of virtual telephone keypad based on SSVEP which is linked to the user telephone number file that is created according to the user selection history. Dynamic programming technique with specifically modified Longest Common Subsequence (LCS) algorithm is also used in this thesis in order to compare the user selection and all records in the file from at least the first three digits of the desired phone number and display matching telephone numbers of the user. Saving user selection history in the file and displaying a matching list of the user from at least three digits help the user to easily and in a quicker way reach his/her desired number faster with minimal number of selections as compared with the previous research.
Assessing product image quality for online shopping

Assessing product-image quality is important in the context of online shopping. A high quality image that conveys more information about a product can boost the buyer's confidence and can get more attention. However, the notion of image quality for product-images is not the same as that in other domains. The perception of quality of product-images depends not only on various photographic quality features but also on various high level features such as clarity of the foreground or goodness of the background etc. In this paper, we define a notion of product-image quality based on various such features. We conduct a crowd-sourced experiment to collect user judgments on thousands of eBay's images. We formulate a multi-class classification problem for modeling image quality by classifying images into good, fair and poor quality based on the guided perceptual notions from the judges. We also conduct experiments with regression using average crowd-sourced human judgments as target. We compute a pseudo-regression score with expected average of predicted classes and also compute a score from the regression technique. We design many experiments with various sampling and voting schemes with crowd-sourced data and construct various experimental image quality models. Most of our models have reasonable accuracies (greater or equal to 70%) on test data set. We observe that our computed image quality score has a high (0.66) rank correlation with average votes from the crowd sourced human judgments.
Document Clustering Approaches using Affinity Propagation

Document clustering as an unsupervised approach extensively used to navigate, filter, summarize and manage large collection of document repositories like the World Wide Web (WWW). Recently, Document clustering is the process of segmenting a particular collection of texts into subgroups including content based similar ones. The purpose of document clustering is to meet human interests in information searching and understanding. Nowadays all paper documents are in electronic form, because of quick access and smaller storage. So, it is a major issue to retrieve relevant documents from the larger database. This work will study the key challenges of the clustering problem, as it applies to the text domain. Also will discuss the key methods used for text clustering, and their relative advantages.
Mechanical Properties of Alginate Beads Hosting Hepatocytes in a Fluidized Bed Bioreactor

Fluidized bed bioartificial liver has been proposed as a temporary support to bridge patients suffering from acute liver failure to transplantation. In such a bioreactor, alginate beads hosting hepatocytes are in continuous motion during at least six hours. After having shown in vitro the functionality of such a device, the present study aims at analyzing the potential mechanical alterations of the beads in the bioreactor, perfused by different surrounding media. Compression experiments are performed and coupled for analysis with Hertz theory. They provide qualitative and quantitative data. The average value of the shear modulus, calculated for the different cases studied varied from 2.4 to 10.4 kPa, and could therefore be considered as a quantitative measure of the beads mechanical properties. From the compression experiments and the estimated values of the shear modulus, we could now evaluate the effect of different operating conditions (fluidization, presence of cells, surrounding medium) on the mechanical behavior of alginate beads. On the one hand, the motion during six hours in the bioreactor does not alter the beads significantly. On the other hand, the presence of different substances in the fluid phase might change their mechanical strength. These results can be considered as new encouragements to use such a device as a bioartificial organ.
A Novel Wireless Sensor Network Localization Approach: Localization based on Plant Growth Simulation Algorithm

This paper proposes a novel wireless sensor network (WSN) localization algorithm. Accurate localization is the key technology of WSN. From various aspects many scholars consider localization problem, which is more effective to regard WSN localization problem as an optimization problem. Plant growth simulation algorithm (PGSA) is a kind of new intelligent optimization algorithm, which is intelligent simulation of plant growth in nature way. In addition to the common characteristics of intelligent algorithms, PGSA could be good robustness and guarantee the global optimal solution, etc. In this paper, further enhances the algorithm by adding the plant root of adaptive backlight function to effectively improve the computing speed and localization precision. Comparing this algorithm with simulated annealing algorithm (SAA), simulation results show that this algorithm has a higher and more consistent localization precision and faster computing speed. DOI: http://dx.doi. org/10.5755/j01.eee. 19.8. 5326
Boosting Estimation of RBF Neural Networks for Dependent Data

This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specification testing illustrates the usefulness of the results.
DATA MINING IN EDUCATION FOR STUDENTS ACADEMIC PERFORMANCE: A SYSTEMATIC REVIEW

Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. Higher education, throughout the world is delivered through universities, colleges affiliated to various universities and some other recognized academic institutes. The main objective of higher education institutes is to provide quality education to its students. Indian education sector has a lot of data that can produce valuable information which can be used to increase the quality of education. Good prediction of student’s success in higher learning institution is one way to reach the higher level of quality in higher education system. In this paper we analyzed the potential use of data mining in education section and survey the most relevant work in this area. Data Mining can be used for dropout students, student’s academic performance, teacher’s performance and student’s complaints. As we know large amount of data is stored in educational database, so in order to get required data and to find the hidden relationship, different data mining techniques are developed & used. Various algorithms and data mining techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees (CART and CHIAD), Genetic algorithms, Nearest Neighbor method etc. are used for knowledge discovery from databases and helps in prediction of students academic performance. In future work we can apply different data mining techniques on an expanded data set with more distinct attributes to get more accurate results .
Comparison and evaluation of PnP algorithms of monocular vision

A new method is proposed to compare and evaluate the accuracy and robustness of Perspective-n-Points (PnP) algorithms, which bases on the principle that position and pose of a camera in the world coordinate system is unique when the camera captures an image of a target. First, a world coordinate system, a camera coordinate system and several target coordinate systems are established, and transformational relations from the world coordinate system to each target coordinate system are known. Second, the transformational relations from each target coordinate system to the camera coordinate system are calculated from theoretical simulation and experimental test, and furthermore, the transformational relations from the world coordinate system to the camera coordinate system are obtained. Third, the average and variance of the different transformational relations are calculated, and the value of the variance can be used to evaluate the accuracy and robustness of the algorithms. Finally, the EPnP and LHM algorithms are compared by the proposed method. The comparison results show that the LHM is more accurate and more robust than EPnP, and it consistent with previous comparison method using rotation error and translation error.
Modifying Formative Evaluation Techniques For Distance Education Class Evaluation

Post-secondary classes are usually followed by mandatory summative evaluations, yet changes in teaching and course structure cannot be made with summative evaluations. Formative evaluations in online education can result in candid responses from students if a third-party facilitator is involved in the process. This paper presents a method for conducting formative evaluation in an online class by utilizing the assistance of a colleague. Results of one such evaluation are presented, as are its advantages and disadvantages.
Is the EDA Industry Ready for Cloud Computing?

ABSTRACT Many industries are increasingly adopting cloud computing. There are several electronic design automation (EDA) industry players, large and small companies, who have explored the idea of providing cloud-based very large-scale integration design tools and services. This paper briefly explores the history of EDA solutions and their growth path thus far, starting with standalone computer aided design (CAD) tools, through specialized EDA workstations, to integrated suites of tools and flows as currently provided by EDA vendors. A representative EDA flow and its steps are described to provide a basis for relating individual EDA tools to appropriate workload categories. Each step in the EDA design flow is then mapped to a cloud computing workload category. This mapping provides a basis for a decision on moving particular EDA design flow steps to a cloud computing environment. This paper also lists some capabilities currently offered by the public and private cloud providers as a basis for looking at the challenges and opportunities for migrating EDA solutions to cloud computing.
Heat Transfer and Solidification of Molten Iron in a Pipe

The effect of the pipe wall temperature on the heat transfer and the internal solidification phenomena during the pouring of pure molten iron in a pipe is studied in this work. A mathematical model consisting in the motion, mass balance and heat equations is proposed. The Reynolds Stress Model is employed to simulate turbulence. The mathematical model considering transient three-dimensional simulations is numerically solved using the Computational Fluid Dynamics technique. To simplify the mushy zone problem, pure iron considered. Numerical simulations show that a pipe wall temperature of 300 K promotes early solidification and blockage, and yields strong internal gradients of velocity and temperature. Besides, a pipe wall temperature of 1000 K prevents solidification and promotes a more homogeneous flow and temperatures contours of molten iron in the pipe.
computing applications on the cloud RAPPORT: running scientific high-performance

References 73.full. html#ref-list-1 http://rsta.royalsocietypublishing. org/content/371/1983/201200 This article cites 17 articles, 4 of which can be accessed free Subject collections (65 articles) e-science  collections Articles on similar topics can be found in the following Email alerting service here in the box at the top right-hand corner of the article or click Receive free email alerts when new articles cite this article sign up
An adaptive hierarchical fuzzy logic system for modelling of financial systems: Research Articles

In this paper an intelligent hierarchical fuzzy logic system using genetic algorithms for the prediction and modelling of interest rates in Australia is developed. The proposed system uses a hierarchical fuzzy logic system in which a genetic algorithm is used as a training method for learning the fuzzy rules knowledge bases that are used for prediction of interest rates in Australia.A hierarchical fuzzy logic system is developed to model and predict three-month (quarterly) interest rate fuctuations. The system is further trained to model and predict interest rates for six-month and one-year periods. The proposed system is developed with frst two, three, then four and fnally fve hierarchical knowledge bases to model and predict interest rates.A novel architecture called a feed forward fuzzy logic system using fuzzy logic and genetic algorithms is also developed to predict interest rates. A back-propagation hierarchical neural network system is also developed to predict interest rates for three-month, six-month and one-year periods. The results obtained from these two systems are then compared with the hierarchical fuzzy logic system results and conclusions are drown on the accuracy of all systems for prediction of interest rates in Australia. Copyright © 2004 John Wiley & Sons, Ltd.
Hardware-in-the-Loop Test of Battery Management Systems

Abstract The essential task of a battery management system (BMS) is to consistently operate the high-voltage battery in an optimum range. Due to the safety-critical nature of its components, prior testing of a BMS is absolutely necessary. Hardware-in-the-loop (HIL) simulation is a cost-effective and efficient tool for this. Testing the BMS on a HIL test bench requires an electronics unit to simulate the cell voltages and a scalable real-time battery model. This paper describes a HIL system that enables comprehensive testing of BMS components. Hardware and software solutions are proposed for the high requirements of these tests. The individual components are combined to make a modular system, and safety-critical aspects are examined. The paper shows that the system as developed fulfills all the requirements derived from the different test scenarios for BMS systems.
A Novel Hybrid Approach Based on Maximum Entropy Classifier for Sentiment Analysis of Malayalam Movie Reviews

S entiment analysis is an application of Computational Linguistics and Text Mining, in which the hidden emotions in a given text are extracted. In this paper, S entiment Analysis of Malayalam movie reviews is done by classifying the review obtained from user as positive, negative and neutral. A hybrid approach for S entiment Analysis is proposed in this work in which Maximum Entropy Model is used for tagging and certain rules are also incorporated to handle certain special cases. The Maximum Entropy classifier is a probabilistic classifier which belongs to the class of exponential models. It is based on the Principle of Maximum Entropy. It selects the one which has the largest entropy and from all the models that fit our training data. Maximum Entropy Classification finds out in which class the review must belong, given a context so that it maximizes the entropy of the classification system. The rules included ensure that special cases are handled which include negation, intensifiers, dilators etc. The system performed well gi ving a considerable precision rate.
Intrinsically Resilient Energy Control Systems

To preserve critical energy control functions while under attack, it is necessary to perform comprehensive analysis on root causes and impacts of cyber intrusions without sacrificing the availability of energy delivery. We propose to design an intrinsically resilient energy control system where we extensively utilize Semantic Web technologies, which play critical roles in knowledge representation and acquisition. While our ultimate goal is to ensure availability/resiliency of energy delivery functions and the capability to assess root causes and impacts of cyber intrusions, the focus of this paper is to demonstrate a proof of concept of how Semantic Web technologies can significantly contribute to resilient energy control systems.
The positioning system using the digital mark pattern-the method of measurement of a horizontal distance

We propose a self-positioning system of an autonomous mobile robot using a digital mark pattern and a CCD camera. The mark pattern is put on the ceiling as a landmark. Grey codes are written around this mark pattern. The CCD camera is put on the mobile robot to read codes on the mark pattern. In this system, the horizontal distance from the ceiling mark pattern is measured using the tilt angle of the CCD camera. The problem is a leaning camera platform. To improve this problem, we propose a method of measurement of the horizontal distance using the mark pattern image which is captured by CCD camera. The length and the width ratio of the mark pattern image depend on horizontal distance. Thus, the horizontal distance can be calculated by using the ratio between the length and the width of the mark pattern image.
A privacy preserving authentication framework for safety messages in VANET

In Vehicular ad hoc networks, security and privacy are the major concerns. A single point of failure may cause great havoc in the network for safety related applications. In this paper, we analyse the recent trends in authentication and privacy preservation based on the signature size, verification time and anonymity. Our investigation shows that the id based signatures are suitable for VANET based safety applications due to its reduced signature size. And, the usage of pseudo identities with id based signatures provides a better solution for privacy preservation. Based on this we propose a framework for privacy preserving authentication which includes key generation by the trusted authority, distribution of keys to the registered vehicles, driver authentication, pseudo id generation by the vehicles, id based signature generation and priority based verification for prioritized messages and batch verification for non-prioritized messages.
GESTURAL STRATEGIES FOR SPECIFIC FILTERING PROCESSES

The gestural control of filters implies the definition of these filters and the way to activate them with gesture. We give here the example of several real “virtual instruments” which rely on this gestural control. This way we show that music making is different from algorithm producing and that a good gestural control may substitute to, or at least complement, a complex scheme using digital audio effects in real time implementations [1].
Data mining in higher education: university student dropout case study

In this paper, we apply different data mining approaches for the purpose of examining and predicting students’ dropouts through their university programs. For the subject of the study we select a total of 1290 records of computer science students Graduated from ALAQSA University between 2005 and 2011. The collected data included student study history and transcript for courses taught in the first two years of computer science major in addition to student GPA , high school average , and class label of (yes ,No) to indicate whether the student graduated from the chosen major or not. In order to classify and predict dropout students, different classifiers have been trained on our data sets including Decision Tree (DT), Naive Bayes (NB). These methods were tested using 10-fold cross validation. The accuracy of DT, and NlB classifiers were 98.14% and 96.86% respectively. The study also includes discovering hidden relationships between student dropout status and enrolment persistence by mining a frequent cases using FP-growth algorithm.
FUNCTIONAL DEPENDENCIES IN XML

XML has become a standard for data representation and exchange on the Internet. The functional dependency is the most important part of the research on XML database normalization and there is already some research work on this topic. According to the types of existing complicated functional dependency constraint, the features of the data constraint are analyzed. A definition of XML functional dependency (XDF) is proposed which is based on a DTD path language. Compared with formal definitions already proposed, XFD proposed in this paper can express more XML dependencies, which makes it more suitable for further research work.
Protocol test generation, trace analysis and verification techniques

A survey is presented of recent developments in protocol validation for open systems interconnection. The discussion includes two important components any protocol test system must have: test sequence generator and trace checker, as well as protocol verification techniques. It covers protocol specification techniques and conformance testing, test sequences, two main test selection tools, trace checking, and verification. <<ETX>>
SR-Tree: An Index Structure of Sensor Management System for Spatial Approximate Query

Sensor management plays an important role in the field of Internet of Things. Therefore, the requests of spatial approximate query increase dramatically. Indexing is no doubt a feasible way for efficient spatial approximate search. However, there is a lack of an effective index structure for spatial approximate query. In this paper, we propose a new type of index structure called SR-tree for providing more intelligent retrieval, which is based on R-tree and inverted table. Our index can support for spatial approximate search and work freely either in memory or external memory. The experimental results show that the structure proposed can provide high scalability and fast response time.