Miracle-dz/UniFAR-FTUs huggingface dataset:
How computer technology transforms writing performance: An integration of the process/genre approach and blogs in efl writing courses

This quasi-experimental research examined the effects of the process/genre approach (PGA) with the integration of blogs in EFL university-level students’ writing classrooms. This was a mixed-method study in which the participants’ essays and adopted questionnaires contributed to accumulating quantitative data while the interviews and observations provided qualitative data. The study took place over ten weeks at a university in central Taiwan in which paper-pencil instruction was applied in the control group, while blog instruction was employed for the experimental group. The students’ essays were computed by applying a paired-sample t-test, and the questionnaires were analysed by applying a Wilcoxon signed-rank test to determine whether there were any statistically significant differences in terms of the students’ writing performance and perceptions toward the PGA and blog writing after comparing the results of the pre-test and the post-test. The qualitative data was analysed to provide in-depth evidence to support the quantitative results. After the data analysis was completed, the paired-sample t-test demonstrated that there were significant differences in terms of the students’ writing performance in both groups, which demonstrates how the PGA developed the EFL university students’ writing performance. Because the students had more interactive opportunities to be exposed to the language inputs, this approach facilitated their English writing performance. The Wilcoxon signed-rank test presented that the students revealed positive attitudes toward writing blogs and also positive affections toward blog writing because they had higher levels of satisfaction and lower levels of apprehension when blogging. Moreover, there was a significant difference in terms of the factor of “learning with peers” in the control group since the PGA provided more interactive and communicative opportunities. The students also indicated greater understanding about their writing samples in the experimental group, since they were allowed to refer to the instructional contents on the blogs anytime and anywhere.
Performance Analysis and Improvement of Express Forwarding in Wireless Mesh Networks

Metropolitan Wi-Fi based wireless mesh networks are being deployed in large cities such as Philadelphia, Houston, Taipei and Hongkong, to provide convenient Internet access for government and public users. However, since IEEE 802.11 was originally designed for single-hop communications, there are significant problems when it is used in multi-hop networks, among them are inefficient use of the medium, unfairness for long-hop flows [1], and high delay that makes it difficult to ensure QoS for nodes that are far from the gateways. Express forwarding [2], which has been proposed to the IEEE 802.11s Task Group, is a possible strategy for solving these problems. The key idea of express forwarding is that the receiving node of a frame will not automatically backoff. Instead, it forwards the frame immediately toward the destination node. As shown in [3] and [2], express forwarding is a good method to provide QoS since it can shorten the endto-end delay especially for long-hop flows. However, existing work is limited to simulation studies, which prevents us from obtaining better insight on the benefits and disadvantage of express forwarding. More importantly, as shown in our work, express forwarding can be explored for improving the throughput and fairness. The purpose of this work is to give a higher-level analysis of the benefits of express forwarding schemes. As seen from the definition, the intention of “express forwarding” is to build an “expressway” in mesh networks, which should have much higher efficiency and capacity than a “local road” built on the traditional backoffbefore-forward mechanism. However, it still waits to be explored how effective an “expressway” can be built, especially when a single channel is used .
AFRICOM: hard or soft power initiative?

ABSTRACT The United States (US) Africa Command (AFRICOM) was launched in 2007, ostensibly to foster African security. Rather than focusing on traditional military operations, AFRICOM also embraces non-military activities such as humanitarian aid and African development. This begs the question as to what type of power (hard or soft) the US intends to wield through AFRICOM. Several US official statements have emphasised the soft power attributes of this military project. To this end, this article seeks to respond to two fundamental questions. First, is AFRICOM a soft power project? Second, how, if at all, has AFRICOM enhanced perceptions of the US in Africa? The article concludes that sceptical and negative perceptions of AFRICOM inhibit its soft-power objective of winning the hearts and minds of the African people.
VLAD-based loop closure detection for monocular SLAM

Loop closure detection is very important in visual simultaneous and localization and mapping system (SLAM), which can reduce the accumulated drift. The most successful methods are based on Bag-of-Words model and need a large visual word dictionary. Recently, the VLAD, the state-of-the art compact descriptor, has achieved a great success in large scale image retrieval. In this paper, a novel vlad-based loop closure detection approach is proposed. We compare our method with the state-of-the-art DBoW2 in some open datasets. Our method performs better in accuracy and memory cost.
Towards Probabilistic Plan Management

In temporally uncertain domains, taking uncertainty into account while planning leads to problems with scalability. One alternative to this is to plan deterministically and replan when execution deviates from schedule. In large, complex problems, however, replanning during execution can be prohibitively expensive. To address this, we have developed a general plan management framework called Probabilistic Plan Management (PPM). PPM probabilistically limits how far in the future it is necessary to consider tasks while repairing and replanning during execution. PPM also decides whether to replan based on the probability that a violated constraint will occur in execution, not on the presence of a conflict in the plan. These features decrease replanning during execution while ensuring that the quality of execution does not unduely suffer. In this paper, we describe our approach and discuss results in simulation that show large savings in the total time spent replanning during execution.
QPSK differential space time coding on different unitary matrices sets and initializations

This paper investigates a distinct set of complex unitary matrices for differential space time coding by using QPSK modulation. The numerical results show that the proper selection of the initial transmission matrix and the set of unitary matrices can efficiently improve the bit error rate performance, especially for the antennas correlated fading channel. The computer simulations are evaluated over slow and fast Rayleigh fading channels. Copyright © 2005 John Wiley & Sons, Ltd.
Discriminating Between Pitched Sources in Music Audio

Though humans find it relatively easy to identify and/or isolate different sources within polyphonic music, the emulation of this ability by a computer is a challenging task, and one that has direct relevance to music content description and information retrieval applications. For an automated system without any prior knowledge of a recording, a possible solution is to perform an initial segmentation of the recording into notes or regions with some time-frequency contiguity, and then collect into groups those units that are acoustically similar, and hence have a high likelihood of arising from a common source. This article addresses the second subtask, and provides two main contributions: (1) a derivation of a suboptimal subset out of a wide range of common audio features that maximizes the potential to discriminate between pitched sources in polyphonic music and (2) an estimation of the improvement in accuracy that can be achieved by using features other than pitch in the grouping process. In addition, the hypothesis was tested that more discriminatory features can be obtained through the application of source separation techniques prior to feature computation. Machine learning techniques have been applied to an annotated database of polyphonic recordings (containing 3181 labeled audio segments) spanning a wide range of musical genres. Average source-labeling accuracies of 68% and 76% were obtained with a 10-dimensional feature subset when the number of sources per recording was unknown and known a priori.
Dependence Uncertainty Bounds for the Expectile of a Portfolio

We study upper and lower bounds on the expectile risk measure of risky portfolios when the joint distribution of the risky components is not fully specified. First, we summarize methods for obtaining bounds when only the marginal distributions of the components are known, but not their interdependence (unconstrained bounds). In particular, we provide the best-possible upper bound and the best-possible lower bound (under some conditions), as well as numerical procedures to compute them. We also derive simple analytic bounds that appear adequate in various situations of interest. Second, we study bounds when some information on interdependence is available (constrained bounds). When the variance of the portfolio is known, a simple-to-compute upper bound is provided, and we illustrate that it may significantly improve the unconstrained upper bound. We also show that the unconstrained lower bound cannot be readily improved using variance information. Next, we derive improved bounds when the bivariate distributions of each of the risky components and a risk factor are known. When the factor induces a positive dependence among the components, it is typically possible to improve the unconstrained lower bound. Finally, the unconstrained dependence uncertainty spreads of expected shortfall, value-at-risk and the expectile are compared.
A CLASS OF ' L ITE ' ADVERBS IN FRENCH *

0. Introduction It is well known that Romance languages have certain weak or cliti c adverbs.1 In this paper, we examine the syntactic properties of a subclass of scalar (beaucoup, 'much') and manner (bien, 'well ') adverbs in French, which seems to make them good candidates for being weak forms in the sense of Cardinaletti & Starke (1994) (by contrast with -ment derived strong forms). We show that their abilit y to be modified or conjoined however casts doubts on such an analysis. After discussing the possible relevance of Cinque's (1999) structural hierarchy, we propose an analysis based on a lite vs non-lite distinction. We formalize our proposal with a feature WEIGHT, which is part of the syntactic description of lexical items as well as of phrases, and is taken into account by constraints on word order and extraction.
Implementing and Testing Expressive Description Logics: a Preliminary Report

The aim of the crack project is the research and the development of a knowledge representation architecture based on description logics. The crack system is di erent from other knowledge representation systems for the high expressivity of the language and the possibility of having sound and complete reasoning procedures. With respect to other systems available in the research community, crack is more expressive, it is expandable to new constructs, it treats the conceptual and individual levels in a homogeneous way, it is modular, and it is comparably fast. However, crack algorithms are not optimal in the worst cases, e.g. in some (arguably rare in practice) worst cases they may require exponential memory. The performance of the system has been tested against several di erent classes of random knowledge bases, characterized by an order parameter generating phase transitions in the satis ability probability space.
Ensembling Rule Based Classifiers for Detecting Network Intrusions

An intrusion is defined as a violation of the security policy of the system, and hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Recently, data mining techniques have gained importance in providing the valuable information which in turn can help to enhance the decision on identifying the intrusions (attacks). In this paper; we evaluate the performance of various rule based classifiers like: JRip, RIDOR, NNge and Decision Table using ensemble approach in order to build an efficient network intrusion detection system. We use KDDCup’99, intrusion detection benchmark dataset (which is a part of DARPA evaluation program) for our experimentation. It can be observed from the results that the proposed approach is accurate in detecting network intrusions, provides low false positive rate, simple, reliable and faster in building an efficient network intrusion system.
Automatic tagging based on linked data: Unsupervised methods for the extraction of hidden information

We have created a web agent for collecting Call for Papers (CFP) announcements. Our web agent obtains CFP announcements from websites or from mailbox. The most important information is extracted and published on our own special website in a user and machine readable way. One of the most important problems is event classification, categorization and clustering. In this paper we describe unsupervised methods for automatic tagging based on information extraction from Linked data. These methods are usable in situations where we have to tag unknown data and we have no corpus for learning methods. Tagged data can have the form of short messages from RSS, short blog posts or emails. The automatic tags can be used for classifying the conferences. Users can use our web service to search for interesting events and sort them by their own preferences. We obtain tags with their relationship parameters and we can use them for automatic clustering of collected events.
Design of power-optimal buffers tunable to process variability

In many digital designs, multi-stage tapered buffers are needed to drive large capacitive loads. These buffers contribute a significant percentage of overall power. In this paper, we propose two novel tunable buffer designs that enable power reduction in the presence of process variation. A strategy to derive the optimal buffer size and tuning rule in post-silicon phase is developed. By comparing several tunable buffer circuit topologies, we also demonstrate the tradeoffs in tunable buffer topology selection as a function of switching activity, timing requirements, and the magnitude of process variation. Using a combination of HSPICE simulations and our optimization algorithm, we show that up to 30% average power reduction can be achieved with the proposed buffer structures.
A Corpus-Based Study of Compounding in English

L'A. presente les resultats d'une etude menee sur les modeles de formation lexicale en anglais a partir d'un vaste corpus de journaux britaniques. L'A. s'interesse ici uniquement aux composes lexicaux et montre que le fait de traiter des donnees reelles peut poser des problemes tant pour le theoricien lexicologue que pour la description grammaticale. L'A. montre egalement que certaines de ces structures, qui sont productives depuis le debut des annees 90 et qui ne sont pourtant pas decrites dans la majorite des manuels, demontent les principes fondamentaux sur lesquels s'appuient certains travaux theoriques
Learning Organization Characteristics Contributed to its Readiness-to-Change: A Study of the Thai Mobile Phone Service Industry

This paper aims to verify the relationship between Learning Organizations (LO) characteristics and an organization's readiness-to-change. LOs, based on a review of the literature, seem to have the competitive advantage of high readiness-to-change in today is economic business environment. The mobile service providers in Thailand are selected for this study. The results have shown a substantial relationship between readiness-to-change and the LO characteristics of cultural values, leadership commitment and empowerment, communication, knowledge transfer, employee characteristics, and performance upgrading. This study confirms that LO characteristics are correlated to an organization's readiness-to-change, suggesting that it is essential for organizations to develop into LOs in order to survive and/or prosper in a competitive and ever changing in business environment.
Fast Hard and Soft Shadow Generation on Complex Models using Selective Ray Tracing

We present fast algorithms to generate high-quality hard and soft shadows in complex models. Our method combines the efficiency of rasterization-based shadow mapping approaches with the accuracy of a ray tracer based on conservative image space bounds. The algorithm can handle moving light sources as well as dynamic scenes. In practice, our approach is able to generate shadows on CAD and scanned models composed of millions of triangles at close to interactive rates on current high-end GPUs.
DEBS Grand Challenge: Top-K Queries in RDF Graph-based Stream Processing with Actors

In this paper, we describe our novel system named as RGraSPA an RDF Graph-based Stream Processing with Actors, which adheres to the realm of RDF graph and knowledge reasoning, and uses an actor model for distribution of continuous queries. Furthermore, we present our approach to solve DEBS Grand Challenge by employing our system. RGraSPA uses RDF graph-based event model to encapsulate a set of triples and process them in continuous manner. We also present our synchronised structure traversal algorithm that uses Range tree to store results in a sorted view, where each node of the tree maintains a balanced Multimap Binary Search Tree (BST). The range of each node is adaptive and updated according to the incoming values and deﬁned size of the Multimap BST for each node. Inorder to solve the DEBS challenge, we provide a formal method to calculate cell IDs from the longitude and latitude in a streaming fashion and use two Range trees for 10 most frequent routes and proﬁtable areas. Our experimental results show that the query execution time can be optimised by carefully adjusting the cardinality values of Range tree. Our solution processes 1 year worth of RD-Fised data (372 GB) (approx 3.4 billion triples) for Taxis in 1.8 hours.
Mutations in the Fusion Protein Cleavage Site of Avian Paramyxovirus Serotype 4 Confer Increased Replication and Syncytium Formation In Vitro but Not Increased Replication and Pathogenicity in Chickens and Ducks

To evaluate the role of the F protein cleavage site in the replication and pathogenicity of avian paramyxoviruses (APMVs), we constructed a reverse genetics system for recovery of infectious recombinant APMV-4 from cloned cDNA. The recovered recombinant APMV-4 resembled the biological virus in growth characteristics in vitro and in pathogenicity in vivo. The F cleavage site sequence of APMV-4 (DIQPR↓F) contains a single basic amino acid, at the -1 position. Six mutant APMV-4 viruses were recovered in which the F protein cleavage site was mutated to contain increased numbers of basic amino acids or to mimic the naturally occurring cleavage sites of several paramyxoviruses, including neurovirulent and avirulent strains of NDV. The presence of a glutamine residue at the -3 position was found to be important for mutant virus recovery. In addition, cleavage sites containing the furin protease motif conferred increased replication and syncytium formation in vitro. However, analysis of viral pathogenicity in 9-day-old embryonated chicken eggs, 1-day-old and 2-week-old chickens, and 3-week-old ducks showed that none the F protein cleavage site mutations altered the replication, tropism, and pathogenicity of APMV-4, and no significant differences were observed among the parental and mutant APMV-4 viruses in vivo. Although parental and mutant viruses replicated somewhat better in ducks than in chickens, they all were highly restricted and avirulent in both species. These results suggested that the cleavage site sequence of the F protein is not a limiting determinant of APMV-4 pathogenicity in chickens and ducks.
A Method of Bandwidth Allocation Mechanism in ForCES Transport Mapping Layer

This paper proposes a bandwidth allocation mechanism called rate and queue controlled dynamic probabilistic priority (RQ-DPP) based on ForCES transport mapping layer (ForCES TML). We apply this method to counter denial-of-service (DoS) attacks for ForCES router from redirect messages. The algorithm adjusts bandwidths between different messages based on message's kind, per-flow estimated arrival rate and buffer occupancy. By comparing with DRR, the simulation results show the effectiveness of the new bandwidth allocation mechanism.
Robust correlation based auto-target tracking for armoured fighting vehicles

Real time target tracking is a serious and complex application. Target tracking is one of the essential steps for surveillance. Auto-target tracker (ATT) is one of improvement in armoured fighting vehicles. The objective of an auto-target tracking system is to maintain a stable image sensor-to-target line of sight in the presence of relative target motion and base motion disturbance to the sensor platform due to harsh terrain conditions in the war field. ATT aids the Gunner to track the target automatically using real time video acquired from the camera to Main Sight System. ATT performs Normalized Correlation algorithm on real time video and generates error signal owing to displacement of target with respect to tracking window. This error signal replaces the Gunner's command signal. The proposed technique ensures robust tracking of the ground target based on correlation with the reference image captured in clutter background in the war field.
Comparison of the Effects of Endotracheal Tube and Laryngeal Mask Airway on Immediate Postoperative Complications in Elective Operations

Objectives: To compare the laryngeal and gastro-intestinal complications of using ETT and LMA after elective orthopedic operations. Materials & Methods: Eighty patients who were candidate for elective orthopedic operation and were in class 1 and 2 of ASA, participated in this study and. Patients randomized into two groups. Laryngeal Mask Airway(LMA) was used in one group and Endo Tracheal Tube(ETT)in the other one. Postoperative complications including nausea, vomiting, coughing and sore throat were assessed in all patients during the first 24 hours. Results: Cases of nausea, vomiting and sore throat in LMA group were less than ETT group. However, this difference wasn’t statistically significant. But cough incidence in ETT group was significantly further than the LMA group. Conclusion: There was no significant difference between the LMA and ETT regarding mentioned complications in the first 24 hours after the surgery. Of course more research is recommended in this area, considering the limitations and contrasting evidence.
Learning Multiple Sequence-Based Kernels for Video Concept Detection

Kernel based methods are widely applied to concept and event detection in video. Recently, kernels working on sequences of feature vectors of a video segment have been proposed for this problem, rather than treating feature vectors of individual frames independently. It has been shown that these sequence-based kernels (based e.g., on the dynamic time warping or edit distance paradigms) outperform methods working on single frames for concepts with inherently dynamic features. Existing work on sequence-based kernels either uses a single type of feature or a fixed combination of the feature vectors of each frame. However, different features (e.g., visual and audio features) may be sampled at different (possibly even irregular) rates, and the optimal alignment between the sequences of features may be different. Multiple kernel learning (MKL) has been applied to similarly structured problems, and we propose MKL for combining different sequence-based kernels on different features for video concept detection. We demonstrate the advantage of the proposed method with experiments on the TRECVID 2011 Semantic Indexing data set.