Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Knowledge Sharing Process using CSCL based on PAL Approach to Enhance Knowledge Creation Behaviors of Graduate Students

The purposes of this research were to develop a knowledge sharing process using Computer-Supported Collaborative Learning (CSCL) based on Peer-Assisted Learning (PAL) approach to enhance knowledge creation behaviors of graduate students. The Research and Development (R&D) methods were divided into four phase: Phase 1 Study from analyzing and synthesizing related documents, investigate the experts’ opinion about a knowledge sharing process using CSCL based on PAL approach to enhance knowledge creation behaviors of graduate students. Phase 2 Develop a prototype of knowledge sharing process using CSCL based on PAL approach. Phase 3 Evaluate knowledge sharing process using CSCL based on PAL approach in higher education institutions. Phase 4 Revise and Propose a knowledge sharing process using CSCL based on PAL approach to enhance knowledge creation behaviors of graduate students. The subjects of the first phase were seven experts in the field of Educational Technology and Knowledge Management (KM). Individual Interview was used to investigate the experts’ opinion. The participations of the third phase were twenty-five graduate students. This phase was administered during the first semester of the academic year 2008 in the field of Curriculum Instruction and Educational Technology at the faculty of Education Chulalongkorn University, Thailand. The Knowledge Creation Behaviors of the subjects were assessed before and after the experiment. A dependent t-test was used to compare pretest and posttest results. The research findings indicated that: 1. The five components were: 1) People 2) Content 3) Computer-Support Collaborative Tools (CSCT) 4) Transition and Behavior Management 5) Evaluation. 2. The six step were: 1) Orientation and Group Socialization 2) Definition of Objective Knowledge 3) Peer Meeting and Knowledge Sharing 4) Acquisition of New Knowledge 5) Creation and Revision of the Knowledge 6) Evaluation and Integration of Thoughts. 3. There were significant differences between Knowledge Creation Behaviors pretest and posttest scores at the . 05 level. 4. The products developed by the learners were in a good level which was considered appropriate.
Using Hybrid Markov Model for Web Access Prediction

Mining user patterns of log file can provide significant and useful informative knowledge. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user’s next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. This algorithm has been used to cluster similar transition behaviors for efficient used to further improve the efficiency of prediction. From this comparison we propose a best overall method and empirically test the proposed model on real web logs.
Congestion control schemes for single and parallel tcp flows in high bandwidth-delay product networks

In this work, we focus on congestion control mechanisms in Transmission Control Protocol (TCP) for emerging very-high bandwidth-delay product networks and suggest several congestion control schemes for parallel and single-flow TCP. Recently, several high-speed TCP proposals have been suggested to overcome the limited throughput achievable by single-flow TCP by modifying its congestion control mechanisms. In the meantime, users overcome the throughput limitations in high bandwidth-delay product networks by using multiple parallel TCP flows, without modifying TCP itself. However, the evident lack of fairness between the high-speed TCP proposals (or parallel TCP) and existing standard TCP has increasingly become an issue. In many scenarios where flows require high throughput, such as grid computing or content distribution networks, often multiple connections go to the same or nearby destinations and tend to share long portions of paths (and bottlenecks). In such cases benefits can be gained by sharing congestion information. To take advantage of this additional information, we first propose a collaborative congestion control scheme for parallel TCP flows. Although the use of parallel TCP flows is an easy and effective way for reliable high-speed data transfer, parallel TCP flows are inherently unfair with respect to single TCP flows. In this thesis we propose, implement, and evaluate a natural extension for aggregated aggressiveness control in parallel TCP flows. To improve the effectiveness of single TCP flows over high bandwidth-delay product networks without causing fairness problems, we suggest a new TCP congestion control scheme that effectively and fairly utilizes high bandwidth-delay product networks by adaptively controlling the flow's aggressiveness according to network situations using a competition detection mechanism. We argue that competition detection is more appropriate than congestion detection or bandwidth estimation. We further extend the adaptive aggressiveness control mechanism and the competition detection mechanism from single flows to parallel flows. In this way we achieve adaptive aggregated aggressiveness control. Our evaluations show that the resulting implementation is effective and fair. As a result, we show that single or parallel TCP flows in end-hosts can achieve high performance over emerging high bandwidth-delay product networks without requiring special support from networks or modifications to receivers.
Schema Extraction on Semi-structured Data

With the continuous development of NoSQL databases, more and more developers choose to use semi-structured data for development and data management, which puts forward requirements for schema management of semi-structured data stored in NoSQL databases. Schema extraction plays an important role in understanding schemas, optimizing queries, and validating data consistency. Therefore, in this survey we investigate structural methods based on tree and graph and statistical methods based on distributed architecture and machine learning to extract schemas. The schemas obtained by the structural methods are more interpretable, and the statistical methods have better applicability and generalization ability. Moreover, we also investigate tools and systems for schemas extraction. Schema extraction tools are mainly used for spark or NoSQL databases, and are suitable for small datasets or simple application environments. The system mainly focuses on the extraction and management of schemas in large data sets and complex application scenarios. Furthermore, we also compare these techniques to facilitate data managers' choice.
Learning recognition of temporal sequences by coding temporal distance in neural networks

Presents a neural network approach for recognition of temporal sequences. A dynamic-weight neural network (DNN) capable of explicitly extracting the temporal order of input sequences is introduced. The architecture of DNN employs a fully connected structure in that each neuron is linked to other neurons by a pair of long-term excitatory and short-term inhibitory weights. A two-pass training rule is developed to encode the temporal distance between two arbitrary occurrences. The overwriting problem is solved in the DNN by using the minimum requirement of hardware resources. We formally prove that a trained DNN ensures correct recognition of input training sequences and rejection of incorrect inputs.
Log-based transactional memory

Transactional memory gives programmers the ability to declare the synchronization properties their programs need, without requiring that they develop a mechanism (e.g., a locking scheme) to enforce these properties. Ideally, an implementation of transactional memory should: (1) make the expected common case (short transactions that commit) fast; (2) defer rare and difficult-to-implement cases to software; (3) allow transactions of any size and duration; and, (4) allow programmers to nest transactions to arbitrary depths. Log-Based Transactional Memory (LogTM) is framework for transactional memory that combines software-based version management (with limited hardware support) and conservative hardware conflict detection to support arbitrary-size transactions with limited hardware. Version management requires maintaining multiple versions of data values: old values, the values at the start of the transaction, and new values, which are generated inside a transaction. Any memory location modified in a transaction will hold its new value if the transaction commits, and its old value if the transaction aborts. LogTM performs version management by eagerly updating memory in place during transactions and saving old values in a per-thread transaction log. No further action is needed to commit a transaction since new values are kept in place. To abort a transaction, LogTM restores saved values from the log. Fortunately, (for most workloads) aborts are rare. Because aborts are infrequent, LogTM can reduce hardware complexity by performing aborts in software without degrading performance. Conflict detection identifies overlaps between the write set of each transaction with the read set or write set of other concurrent transactions. Like other hardware transactional memory systems, LogTM detects conflicts on cached data by augmenting the cache coherence mechanism, e.g., by adding a read (R) and write (W) bit to each cache line. LogTM implementations extend this mechanism (e.g., with sticky states, which enable the directory to continue to send coherence messages to a processor for a blocks it has evicted from its cache) so that processors can conservatively detect conflicts even for blocks not present in the cache.
Attractiveness of Coffee and the Elements for Building a Attractive Coffee Shop

Consumers today are now enjoying more than simply drinking coffee or having snacks in coffee shops, but they also find that the cafe ambient is also quite important. The study aims to study the group “enjoying drinking coffee” with context research on attractive factors applying in-depth interview and Evolution Grid Method (EGM), and applies One-way ANOVA and Quantification Theory Type I to conduct attractive factors analysis on the preference difference and building of attractive coffee shops according to the different gender, occupation and different degrees of preference. The study reveals the following conclusion: 1) Female are more likely to “drink coffee while thinking” and also prefer “beautiful latte art patterns” and “coffee cups / plates with superior quality” than male. The study also discovers that members of society preference “coffee tasting bitter and savory” than students. In terms of the difference among the different degrees of preference, the respondents drinking 1-3 cups of coffee per week emphasize more on the “timing for coffee” and “environment and ambient for coffee” than respondents drinking 4-7 cups and 8 cups. The respondents drinking 8 cups or more per week emphasize to “patterns of latte art” than respondents drinking 1-3 cups. The respondents drinking 4-7 cups per week emphasize to the “coffee equipments” than respondents drinking 1-3 cups. 2) Adding milk or liquor to coffee for flavoring can create a perception of taste, elegance and grace, in order to search for the optimal proportion. To build a coffee shop that offers both warm and romantic perception, start with the design on the “spatial dimension” of coffee shops. On the other hand, the two elements on “cleanness of lavatory” and “artwork decoration” should be taken into consideration for building a cafe that offers warm, comfort, relaxation and revitalization.
Trends in low-power RAM circuit technologies

Trends in low-power RAM circuit technologies are reviewed. The following provide major contribution to power reduction: lowering operating voltage by lowering the external supply voltage, half-V/sub DD/ data-line precharging and on-chip voltage down converting; reducing charging capacitance through partial activation of multi-divided array, and CMOS NAND decoder; reducing DC current through partial activation of multi-divided word line and pulsed operations of periphery using address transition detection. These contributions have made possible a DRAM active power reduction of as much as 2 to 3 orders of magnitude over the last decade. Moreover, MOS transistor subthreshold current reduction circuits such as source-gate backbiasing scheme, which are essential in an ultra-low voltage era, might reduce an active current of a 1-V 16Gb DRAM from 1.2 A down to 22 mA.
Automated Topic Spotting Provides Added Efficiency in a Chat Based Tutoring Environment

Dr Math is a mobile, online tutoring project which allows primary and secondary school pupils to contact tutors in mathem atics using text based chat systems on their cell phones. The tutors use tradi tional Internet based workstations. Dr Math is extremely popular with school pupils and Dr Math tutors are often chatting to twenty pupils concurrently. It is impo rtant that the tutors are as efficient as possible. This paper describes the implementati on of an automated topic spotter in a mobile chat-based tutoring environment. The tu tors, although well versed in mathematics, were not all home language English speakers. They often struggled with the cryptic abbreviations which the pupils use d in their queries. In addition, the number of pupils asking questions far exceeded the number of tutors available. It became important to ensure that the tutors were as efficient as possible. The automated topic spotter would process the cryptic q uestions, determine the general mathematical topic, and automatically provide suppo rting material to the tutors. The learning gained on this project could be easily rei mplemented in other domains besides education including health and public servi ces.
Optimal delayed-coding of video sequences subject to a buffer-size constraint

A video encoder has the task of producing lowest-distortion coded video subject to some constraints on delay, rate, and buffer conditions. We present a general optimization approach to this problem in a framework of delayed coding and we motivate a certain formulation of the optimization objective. Two forms of distortion measures are considered, namely, the maximum distortion and the total distortion, each defined over a segment of the video to be coded. These distortion measures are chosen for their mathematical tractability and practical importance. A solution (computational algorithm) for each case is described. Subject to some conditions, the solutions may be suboptimal. Simulation results show an improved performance with this approach compared to a simple typical approach which varies the quantization scale linearly with the encoder buffer level.
THE TEACHING OF WRITING A DESCRIPTIVE TEXT USING CLUSTERING TECHNIQUE FOR BUNG HATTA UNIVERSITY STUDENTS

ABSTRACT The purpose of writing this paper is to discuss the implementation of clustering technique in teaching descriptive text. The low students’ ability in writing descriptive text is caused by the students are unable to develop their idea in writing descriptive text. The descriptive text is a kind of factual text which consists of generic structure and language features. The descriptive text is used to describe a person, place or thing that describe the detail of the person, place or thing in term of shape, taste, smell, sound. Thus, the reader can imagine the object which is described in the text. There are three phases in teaching descriptive text in using clustering technique; they are pre- teacing, whilst- teaching, and post- teaching activity. Pre- teaching activity is the lecturer’s activity before teaching the instructional material. Whilst- teaching activity is the main activity in teaching process, the lecturer teaches the instructional material by elaborating the students’ opinion about the topic of instructional material which their opinion is collected through clustering technique, the students are involved in teaching and learning process and asked to write descriptive text based on the topic given. Moreover, post- teaching activity is the final phase which the lecturer directs the students to conclude the instructional material which has been taught. The writer hopes this paper will give significance for lecturer in teaching descriptive text by using clustering technique so it can enhance the students’ ability in writing descriptive text. Key Words: Teaching Writing, Descriptive Text, Clustering Technique
Music Retrieval Based on the Relation between Color Association and Lyrics

Various methods for music retrieval have been proposed. Recently, many researchers are tackling developing methods based on the relationship between music and feelings. In our previous psychological study, we found that there was a significant correlation between colors evoked from songs and colors evoked only from lyrics, and showed that the music retrieval system using lyrics could be developed. In this paper, we focus on the relationship among music, lyrics and colors, and propose a music retrieval method using colors as queries and analyzing lyrics. This method estimates colors evoked from songs by analyzing lyrics of the songs. On the first step of our method, words associated with colors are extracted from lyrics. We assumed two types of methods to extract words associated with colors. In the one of two methods, the words are extracted based on the result of a psychological experiment. In the other method, in addition to the words extracted based on the result of the psychological experiment, the words from corpora for the Latent Semantic Analysis are extracted. On the second step, colors evoked from the extracted words are compounded, and the compounded colors are regarded as those evoked from the song. On the last step, colors as queries are compared with colors estimated from lyrics, and the list of songs is presented based on similarities. We evaluated the two methods described above and found that the method based on the psychological experiment and corpora performed better than the method only based on the psychological experiment. As a result, we showed that the method using colors as queries and analyzing lyrics is effective for music retrieval.
A Study of Spontaneous Learning of an Augmented Reality after Class

The spontaneous learning method has already become a predominant tendency in today's learning. Therefore, we would like to further investigate what motivates students to take advantage of their free time to do their spontaneous learning. Thus, spontaneous environment of an augmented reality based on cognitive load theory is arranged in a classroom. Students could learn by using some cards to interact with a computer after class. We had several meaningful findings from our observation and questionnaires. In this experiment, we established effectively spontaneous learning environment of an augmented reality based on cognitive load theory, and it would be a useful reference for the future study of information-incorporated teaching and e-learning.
k-means Based Document Clustering with Automatic "k" Selection and Cluster Refinement

In recent years use of web has been increased manifold. Efficiency is as important as accuracy. Automatic document clustering is an important part of many important fields such as data mining, information retrieval etc. Most of the document clustering techniques are based on k-means and it's variants. K-means is a fast algorithm but there are some shortcomings with this technique. K in k-means stands for no of clusters which a user has to provide but most of the times users don't have any clue about k. In our implementation of document clustering technique we used SVD (Singular Vector Decomposition) to find out no of clusters (value of k) required. Then k-means algorithm is used to create clusters and in last phase of algorithm the clusters are refined by feature voting. Refinement phase enable us to make our algorithm much faster than k-means algorithm.
The Contextual Analysis of Chinese Sentences with Punctuation Marks

Les phrases chinoises sont composees de plusieurs segments. Un segment peut etre une phrase, un syntagme nominal, verbal, adjectival, adverbial, ou prepositionnel. La liberte dans l'utilisation des categories pro, le grand nombre de segments, les differents types de segments et le probleme d'associativite rend l'analyse syntaxique difficile. L'A. propose une analyse prenant le segment comme unite et utilise les marques de ponctuation, les categories de segment et quelques regles heuristiques pour lier le segment a des unites de sens. Ce type d'analyse peut avoir de reelles applications notamment en traduction automatique
New, faster, image-based scatter correction for 3D PET

The authors report on a new numerical implementation of the single-scatter simulation scatter correction algorithm for 3D PET. Its primary advantage over the original implementation is that it is a much faster calculation, currently requiring less than 30 sec execution time per bed position for an adult thorax, thus making clinical whole-body scatter correction more practical. The new code runs on a single processor workstation CPU instead of a vector processor array, making it highly portable. It is modular and independent of any particular reconstruction code. The computed scatter contribution is now intrinsically scaled relative to the emission image and no longer requires normalization to the scatter tails in the sinogram when all activity is contained within the field of view, making it more robust against noise. The new algorithm has been verified against the original code on both phantom and human thorax studies. Initial results indicate that scatter correction may be accurately performed following, instead of prior to, either 3D reprojection or Fourier rebinning. Some evidence is presented that the single-scatter operator, when applied to an uncorrected emission image provides reasonable compensation for multiple scatter.
Heart diseases diagnosis using heart sounds

Heart sound is one of the oldest means for assessing the function of heart valves. It helps, together with echocardiograms and electrocardiographs, to give a clear and proper diagnosis of several diseases. Artificial neural networks are used to classify several valve-related heart disorders. A library of heart sound files, recorded via the traditional stethoscope, are used to extract relevant features using several signal processing tools, e.g., discrete wavelet transform (DWT), fast Fourier transform (FFT) and linear predictive coding (LPC). The achieved recognition rates were around 95.7%.
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems

The 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011) was held in Chicago, IL, USA, on the 27th of October 2011, under the frame of the 5th ACM Conference on Recommender Systems (RecSys 2011). HetRec workshop represented a meeting point for researchers and practitioners interested in addressing the challenges posed by information heterogeneity in recommender systems and studying information fusion in this context. In this second edition, we made available on-line datasets with heterogeneous information from several social systems. These datasets could be used by participants to experiment and evaluate their recommendation approaches, and can be enriched with additional data, which may be published at the workshop website for future use. These datasets are kindly hosted by GroupLens research group at University of Minnesota. A total of ten papers were presented at the workshop. There were six long papers, and four short papers. Fourteen submissions were received, and each of them was reviewed by two members of the Program Committee.