Miracle-dz/UniFAR-FTUs huggingface dataset:
Students’ Feedback and Communication Habits using Moodle

eLearning has managed to penetrate into most classrooms nowadays and Moodle is one of wider accepted LMS. After years of its application in everyday teaching practice we were inspired to analyse the student communication and feedback habits using Moodle. In this paper, we will present the experiences and results of a questionnaire regarding Moodle and eLearning. Firstly we analyse which communications capabilities of Moodle. Additionally, we present an analysis of students’ feedback habits using Moodle regarding the grading of teachers, teaching assistants and the course in general. Finally, we also investigate student’s general opinion on eLearning and experiences with technical problem when using Moodle.
Journaling the Linux ext2fs Filesystem

This paper describes a work-in-progress to design and implement a transactional metadata journal for the Linux ext2fs filesystem. We review the problem of recovering filesystems after a crash, and describe a design intended to increase ext2fs’s speed and reliability of crash recovery by adding a transactional journal to the filesystem.
Java Parallel Secure Stream for Grid Computing

The emergence of high speed wide area networks makes grid computing a reality. However grid applications that need reliable data transfer still have difficulties to achieve optimal TCP performance due to network tuning of TCP window size to improve the bandwidth and to reduce latency on a high speed wide area network. This paper presents a pure Java package called JPARSS (Java Parallel Secure Stream) that divides data into partitions that are sent over several parallel Java streams simultaneously and allows Java or Web applications to achieve optimal TCP performance in a gird environment without the necessity of tuning the TCP window size. Several experimental results are provided to show that using parallel stream is more effective than tuning TCP window size. In addition X.509 certificate based single sign-on mechanism and SSL based connection establishment are integrated into this package. Finally a few applications using this package will be discussed. keywords: Java Parallel Stream Security SSL X.509 Grid
A radio transmitter fingerprinting system ODO-1

This paper presents a new method for the capture, analysis, and classification of radio transmitter transients. This method involves the use of a capturing subsystem consisting of an Icom IC-R7000 communications receiver and a Sound Blaster 16 sound card running on a PC. The radio transients are sampled at 44,100 samples per second and have 16 bits accuracy. Once the transmitter transient has been captured, a genetic algorithm selects the critical features from the wavelet coefficients for classification. The selected wavelet coefficients are considered to be fingerprints, and are presented to a back propagation neural network for transmitter classification. The capturing and analysis system, ODO-1, is able to classify both transients of the same model type as well as individual transmitters with 100% accuracy on a small data base of transmitter fingerprints.
Search Strategies in Unstructured Overlays

Unstructured peer-to-peer networks have a low maintenance cost, high resilience and tolerance to the continuous arrival and departure of nodes. In these networks search is usually performed by flooding, which generates a high number of duplicate messages. To improve scalability, unstructured overlays evolved to a two-tiered architecture where regular nodes rely on special nodes, called supernodes or superpeers, to locate resources, thus reducing the scope of flooding based searches. While this approach takes advantage of node heterogeneity, it makes the overlay less resilient to accidental and malicious faults, and less attractive to users concerned with the consumption of their resources and who may not desire to commit additional resources that are required by nodes selected as superpeers. Another point of concern is churn, defined as the constant entry and departure of nodes. Churn affects both structured and unstructured overlay networks and, in order to build resilient search protocols, it must be taken into account. This dissertation proposes a novel search algorithm, called FASE, which combines a replication policy and a search space division technique to achieve low hop counts using a small number of messages, on unstructured overlays with nonhierarquical topologies. The problem of churn is mitigated by a distributed monitoring algorithm designed with FASE in mind. Simulation results validate FASE efficiency when compared to other search algorithms for peer-to-peer networks. The evaluation of the distributed monitoring algorithm shows that it maintains FASE performance when subjected to churn.
The Design and Implementation of Micro-Blog User Interest Search Engine Base on Cloud Computing Technology

With the rapid development of Internet and the explosive growth of Internet information, massive data processing received more concerns. Micro-blog, which is an important representative pattern of the Internet development in the future, has become the essential tool of communication and marketing to all of us. Processing and using the massive data resulting from micro-blog activities has becomes a hot topic. In this paper, we propose a method to design and implement the User Interest Based Search Engine, a search engine can be used to search for the same interest micro-blog users. We at first crawl massive micro-blog data from micro-blog websites, and store this data in HBase. Then we process the massive data and build indices using MapReduce. Finally, we build a search engine web site based on Solr, and we propose a rank algorithm for searching. By employing this User Interest Based Search Engine, we can accurately search other users with the same interests as ourselves.
An analysis and comparison of the effects of computer-assisted instruction versus traditional lecture instruction on student attitudes and achievement in a college remedial mathematics course

Remedial mathematics courses are often taught at post-secondary institutions in response to the substantial number of students entering college who lack the skills and motivation necessary to be successful in college level mathematics courses. Much of the research on remedial mathematics instruction shows that it has been only moderately successful in improving the students' achievement in mathematics or their attitudes towards mathematics (Adelman, 1995; Burley, 1994; Wieschenberg, 1994). The purpose of this study was to compare the effectiveness of web-based computer-assisted instruction (CAI) and traditional lecture-based instruction in a college remedial mathematics course. The remedial mathematics course was taught to two groups of college students, which included a treatment group that received CAI, and a control group that received traditional lecture instruction without the use of computers. The ALEKS (Assessment and Learning in Knowledge Spaces) web-based software program, was used in conjunction with lecture-based instruction for the treatment group. Mathematics achievement and student attitudes towards mathematics were assessed by means of pre- and post-tests administered at the beginning of the semester and again at the end of the semester. Student withdrawal rates and passing rates for the course were also compared for the two groups. The statistical analysis of the data compiled in this study included the use of paired and independent group t-tests, analysis of covariance, and z-tests. The paired t-test results showed that there were statistically significant achievement gains within the treatment and control groups as a result of the instruction that each group received. However the independent t-tests and the Analysis of Covariance (ANCOVA) revealed that there was no statistically significant difference in achievement gains between the two groups when the study ended. The paired t-test results on the attitude assessment showed no statistically significant difference within each group after the study was completed. In addition, the independent t-tests and the ANCOVA results showed no significant change in attitudes between the two groups when the experiment had been completed. The z-test analyses also showed no significant difference in the withdrawal or passing rates between the treatment and control groups.
A genetic algorithm for server location and storage allocation in multimedia-on-demand network

We propose a genetic algorithm (GA) to design a non-hierarchical and decentralized multimedia-on-demand (MOD) network architecture. To optimize the MOD network resource based on cost analysis, including server installation cost, program storage cost, and transmission cost, both server location and storage allocation were considered. In applying the proposed algorithm to the problem considered, many of components are devised to improve solution quality and computational efficiency: genetic representation; evaluation function; genetic operators; procedure. The results of extensive computational simulations showed that the proposed algorithm provides high quality solutions within reasonable computation times.
Scalable data management for web applications

Figure 4.2: The parent class of all subtransactions classes. tions, which operate on a single data item each. The application must provide the primary keys of all accessed data items when it issues a transaction. Concretely, a transaction is implemented as a Java object containing a list of subtransaction instances. All subtransactions are implemented as subclasses of the SubTransaction abstract Java class. As shown in Figure 4.2, each subtransaction contains a unique class name to identify itself, a table name and primary key to identify the accessed data item, and input parameters organized as attribute-value pairs. Each subtransaction implements its own data operations by overriding the run() operation. The return value of the run() operation specifies whether this subtransaction is able to commit. The execution of run() also generates the data updates and the results for read data operations, which are stored in the Output attributes. The bytecode of all subtransactions is deployed at all LTMs beforehand. A Web application issues a transaction by submitting the names of included subtransactions and their parameters. LTMs then construct the corresponding subtransaction instances to execute the transaction. In the first phase of 2PC, LTMs load the data items of each subtransaction and execute the run() operation to decide on their votes and generate proposed data updates. If an agreement to commit is reached, LTMs apply the updates. We assign data items to LTMs using consistent hashing [57]. To achieve a balanced assignment, we first cluster data items into virtual nodes, and then assign virtual nodes to LTMs. As shown in Figure 4.1, multiple virtual nodes can be assigned to the same LTM. To tolerate LTM failures, virtual nodes and transaction states are replicated to one or more LTMs. After an LTM server failure, the latest updates can then be recovered and affected transactions can continue execution while satisfying ACID properties. 52 CHAPTER 4. CLOUDTPS: TRANSACTIONAL CONSISTENCY .
SPICE temperature modeling for VLSI design using the level-4 model

Complete VLSI (very large scale integration) circuit simulation requires accurate temperature modeling. A modeling methodology using the sensitive parameter subset concept is proposed to provide accurate and efficient prediction of temperature effects for the SPICE level-4 model. A model parameter subset that has large effects on the transistor output characteristics is identified through a sensitivity analysis. Circuit simulation using this approach has shown good agreement with experimental data on device characteristics and the performance of several digital and analog circuits. <<ETX>>
Semantic smoothing for Twitter sentiment analysis

Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifier from tweets data often faces the data sparsity problem partly due to the large variety of short forms introduced to tweets because of the 140-character limit. In this work we propose using semantic smoothing to alleviate the data sparseness problem. Our approach extracts semantically hidden concepts from the training documents and then incorporates these concepts as additional features for classifier training. We tested our approach using two different methods. One is shallow semantic smoothing where words are replaced with their corresponding semantic concepts; another is to interpolate the original unigram language model in the Naive Bayes NB classifier with the generative model of words given semantic concepts. Preliminary results show that with shallow semantic smoothing the vocabulary size has been reduced by 20%. Moreover, the interpolation method improves upon shallow semantic smoothing by over 5% in sentiment classification and slightly outperforms NB trained on unigrams only without semantic smoothing.
Grammar Formalism for ECG Signal Interpretation and Classification

This paper shows that the grammar formalism can be pushed to be applied for the description and the classification of electrocardiograms signals (ECG). We will describe an ECG signal as a sequence of tokens based on specific vocabularies and a set of grammatical rules. QRS complexes, RR distances, PR and QT intervals will be calculated. This type of work is intended for medical diagnosis assistance of an ECG signal.
Looking at large data sets using binned data plots

This report addresses the monumental challenge of developing exploratory analysis methods for large data sets. The goals of the report are to increase awareness of large data sets problems and to contribute simple graphical methods that address some of the problems. The graphical methods focus on two- and three-dimensional data and common task such as finding outliers and tail structure, assessing central structure and comparing central structures. The methods handle large sample size problems through binning, incorporate information from statistical models and adapt image processing algorithms. Examples demonstrate the application of methods to a variety of publicly available large data sets. The most novel application addresses the too many plots to examine'' problem by using cognostics, computer guiding diagnostics, to prioritize plots. The particular application prioritizes views of computational fluid dynamics solution sets on the fly. That is, as each time step of a solution set is generated on a parallel processor the cognostics algorithms assess virtual plots based on the previous time step. Work in such areas is in its infancy and the examples suggest numerous challenges that remain. 35 refs., 15 figs.
An Improved Feature Selection Algorithm Utilizing the within Category Variance

Theχ2 statistics is a commonly used and effective method of feature selection for corpus. However, it suffers several deficiencies. First, it only counts the document frequency for each feature. Secondly, this method does not distinguish among features that have different frequency distributions within a category. To overcome these shortcomings, two indexes, naming, the within category frequency and the within category variance, are introduced. Experiments are carried out to compare the traditionalχ2 statistics, some existing improvement, and the improvedχ2 statistics proposed in this paper using either naive Bayesian or SVM on the corpus collected by Fudan University and Sogou. Experimental results reveal that the improvement of this paper is effective and robust with respect to various classifiers and corpus.
On-board encoding of the ENVISAT wave mode data

The synthetic aperture radar sensor on the ENVISAT remote sensing satellite will utilize a data reduction system called Flexible Block Adaptive Quantization (FBAQ). This technology will allow the 8 bits/sample SAR signal data to be reduced to 4, 3, or 2 bits/sample, as specified by the mission controller. For wave mode operation, in which small SAR "vignettes" are acquired over oceanic regions, the 8 to 2 bits/sample mode of FBAQ must be employed due to on-board storage limitations. The authors quantitatively evaluate the effect of the FBAQ data reduction on the power spectra of the vignettes, obtained using ESA wave mode processing methods. Using ERS-1 SAR signal data modified to reflect the properties of ENVISAT SAR data, the authors show that the use of the 2-bit FBAQ algorithm will yield power spectra results better than those currently available from ERS-1, with the degree of improvement dependent on the distribution shape of the SAR signal data and the radiometric variation within the vignette.
On the convergence of path vector routing protocols

This work is motivated by previously reported experimental results on the delayed convergence of the border gateway protocol (BGP), which is the standard for inter-domain routing in the Internet. BGP is a path vector protocol. We investigate the convergence properties of path vector protocols and, in particular, their alleged effectiveness in dealing with the count-to-infinity problem that plagues conventional distance vector protocols. We assume synchronous operation of the protocols, and we study the cases when a destination comes up, the network topology changes while preserving connectedness, and a destination goes down. It is known that path vector protocols do not count to infinity. We show that they may still count to the length of the longest possible path in the network, when a destination goes down. On the other hand, when a destination comes up or the topology of the network changes, convergence time depends on the diameter of the network. The length of the longest path in a network may differ substantially from its diameter. In order to further understand the convergence properties of path vector protocols, we simulated their behavior over random graphs. We verified that path vector protocols converge much more quickly when a destination comes up than when it goes down.
The Design of Digital Communication System Based on FPGA

The paper investigates the Multi-FPGA systems structure, including interconnect and configuration structure of the systems. After analyzing several common FPGA systems interconnection structure, we present its features. The paper analyses the feasibility and technical advantages for FPGA systems to use in digital array radar receiver design, and discusses their specific implementation, and designs a set of IF receiver with twenty reception elements.
The direct power spectral estimation of unevenly sampled cardiac event series

The application of the Lomb-Scargle periodogram to heart rate variability (HRV), which makes spectral analysis on unevenly sampled data allowable, is considered. To this end, the Lomb-Scargle periodogram was applied to various cardiac event series from IPFM simulation and real ECG signals. To assess the performance of the Lomb-Scargle periodogram, the resultant power spectrum was compared with that obtained by classical periodogram analysis on the evenly-spaced time series derived from a cardiac event series. In conclusion, it has been shown that the Lomb-Scargle periodogram is very effective in the power spectral analysis of HRV, especially in the presence of arrhythmia and/or dropouts of cardiac events.
Models of Interactive Videodisc Development

Over the last decade, Brigham Young University has developed over a dozen interactive videodisc programs for personalized foreign language instruction. From the early prototypal projects to the current HyperCard and ToolBook packages, each of these programs has explored a different emphasis in interactivity. This article is a broad description of these different models, their variations and their implementation.
Digital implementation of a mixed objectives MIMO controller for a compact disc player using a multiprocessor system

Describes the implementation of an LMI-based mixed objectives MIMO controller on a digital multiprocessor system for the control of a CD player mechanism. In order to cope with the constraints imposed by real-time applications, several engineering choices have to be made. Firstly, model reduction has to be performed on the controller obtained from the LMI solver. Secondly, an appropriate choice of the sampling rate to discretize the continuous time controller has to be made. Both these steps, controller order reduction and discretization, contribute to a performance degradation. This is particularly critical in a mixed objectives design since, ideally, one would like to keep this degradation within acceptable bounds uniformly over all considered channels and performance indices. Thirdly, we have to choose a computation scheme for the controller that exploits as much as possible the parallelism of the processors. Our implementation allows relatively high controller orders and sampling rates with a computational delay that does not depend on the specific controller order. Finally, we present experimental results showing that the mixed objectives controller exhibits significant performance improvements if compared to a single-objective /spl Hscr//sub /spl infin// design.
Recent Large Graph Visualization Tools : A Review

Large graph visualization tools are important instruments for researchers to understand large graph data sets. Currently there are many tools available for download and use under free license,others in research papers or journals, each with its own functionalities and capabilities. This review focuses on giving an introduction to those large graph visualization tools and emphasizes their advantages over other tools. Criteria for selection of the tools being reviewed are it was recently published (2009 or later), or a new version was released during the last two years. The tools being reviewed in this paper are igraph, Gephi, Cytoscape, Tulip, WiGis, CGV, VisANT, Pajek, In Situ Framework, Honeycomb and two visualization toolkits which are JavaScript InfoVis Toolkit and GraphGL. The last part of the review presents our suggestion on building large graph visualization platform based on advantages of tools and toolkits that are being reviewed.
An unsupervised learning and fuzzy logic approach for software category identification and capacity planning

A hybrid unsupervised neural network and fuzzy logic approach is presented to achieve the primary goal of software categorization and feature interpretation. This method permits new software applications to be evaluated quickly for capacity planning and project management purposes. Fuzzy logic techniques were successfully applied to interpret the internal structure of the trained network, leading to an understanding of which application attributes most clearly distinguish the resulting categories. The resulting fuzzy membership functions can be used as inputs to subsequent analysis. These techniques can derive useful categories based on broad, external attributes of the software. This makes the technique useful to users of off-the-shelf software or to developers in the early stages of program specification. Experiments explicitly demonstrated the advantages of this method. <<ETX>>