Miracle-dz/UniFAR-FTUs huggingface dataset:
L2 Learners' Discourse and SLA Theories in CMC: Collaborative Interaction in Internet Chat

Studies of L2 classroom interaction suggest that co-operative and comprehensible interactions facilitate L2 learning. The nature of the L2 classroom environment with its less imposing con-texts, however, has restricted the range of interaction that occurs among learners of the same proficiency level (Ellis, 1991). Computer-Mediated Communication (CMC) may provide potential benefits for L2 learning because it provides a broader range of interactions, among NS–NNS and NNS–NNS with different levels of proficiency. CMC is, however, a newly invented variety of interaction and has unique linguistic and interactional features (e.g., interactive text-based discourse and no turn taking competition) which are distinct from both written and oral interactions. This study explores the L2 learners' interactions in CMC qualitatively to clarify to what extent CMC is actually a useful device for L2 learning. This study examines how Japanese as a foreign language learners actually interact in Internet Chat (IC) and reports that L2 learners' strategies in CMC take advantage of distinct linguistic and interactional features of IC. The results indicate that CMC provides potential benefits for learning: facilitating comprehensible and contextualized interaction, learners' self-correction, and collaborative learning environment.
A Novel Phase Locked Loop for Grid-Connected Converters under Non-Ideal Grid Conditions

Grid synchronization is one of the key techniques for the grid-connected power converters used in distributed power generation systems. In order to achieve fast and accurate grid synchronization, a new phase locked loop (PLL) is proposed on the basis of the complex filter matrixes (CFM) orthogonal signal generator (OSG) crossing-decoupling method. By combining first-order complex filters with relation matrixes of positive and negative sequence voltage components, the OSG is designed to extract specific frequency orthogonal signals. Then, the OSG mathematical model is built in the frequency-domain and time-domain to analyze the spectral characteristics. Moreover, a crossing-decoupling method is suggested to decouple the fundamental voltage. From the eigenvalue analysis point of view, the stability and dynamic performance of the new PLL method is evaluated. Meanwhile, the digital implementation method is also provided. Finally, the effectiveness of the proposed method is verified by experiments under unbalanced and distorted grid voltage conditions.
The Effect of Incongruent Instruction/Execution Pairs on Working Memory

This study tests how information is encoded into working memory when the type of instruction is incongruent with a task. To determine where information is encoded in working memory interruptions will be used to disrupt performance. A spatial Lego® construction task and a verbal letter arrangement task will be compared. Operation Span will be measured against performance. The conclusions drawn from this study will impact the understanding of working memory which will help in discovering effective types of instruction and in dealing with interruptions.
Compact Circularly Polarized Antenna with a Capacitive Feed for GPS/GLONASS Applications

This letter presents a novel compact circularly polarized patch antenna for Global Positioning System/Global Navigation Satellite System (GPS/GLONASS) applications. The proposed antenna is composed of a simple square radiating patch fed by a capacitive dual‐feeder to increase the impedance bandwidth and a lumped element hybrid coupler to achieve the broadband characteristic of the axial ratio (AR). The realized antenna dimensions are 28 mm × 28 mm × 4 mm, which is the most compact size among the dual‐band GPS/GLONASS antennas reported to date. The measured results demonstrate that the proposed antenna has a gain of 2.5 dBi to 4.2 dBi and an AR of 0.41 dB to 1.51 dB over the GPS/GLONASS L1 band (1.575 GHz to 1.61 GHz).
Reality: a cousin twice removed [object technology]

Reasoning too much in terms of the real world can actually be detrimental to software quality. What matters is not how closely we model today's reality but how extensible and reusable our software is, so it can be adapted to a new or changed reality. The paper considers how reusability and extensibility are object technology's central goals. Object technology is not about modeling the real world. Object technology is about producing quality software, and the way to obtain this is to devise the right abstractions, whether or not they model what someone sees as the reality.
Dynamic hierarchical algorithm for accelerated microfossil identification

Marine microfossils provide a useful record of the Earth's resources and prehistory via biostratigraphy. To study Hydrocarbon reservoirs and prehistoric climate, geoscientists visually identify the species of microfossils found in core samples. Because microfossil identification is labour intensive, automation has been investigated since the 1980s. With the initial rule-based systems, users still had to examine each specimen under a microscope. While artificial neural network systems showed more promise for reducing expert labour, they also did not displace manual identification for a variety of reasons, which we aim to overcome. In our human-based computation approach, the most difficult step, namely taxon identification is outsourced via a frontend website to human volunteers. A backend algorithm, called dynamic hierarchical identification, uses unsupervised, supervised, and dynamic learning to accelerate microfossil identification. Unsupervised learning clusters specimens so that volunteers need not identify every specimen during supervised learning. Dynamic learning means interim computation outputs prioritize subsequent human inputs. Using a dataset of microfossils identified by an expert, we evaluated correct and incorrect genus and species rates versus simulated time, where each specimen identification defines a moment. The proposed algorithm accelerated microfossil identification effectively, especially compared to benchmark results obtained using a k-nearest neighbour method.
On the effect of data reduction on classification accuracy

Data reduction is an important pre-processing step to both supervised and unsupervised machine learning problems. In this paper, we investigate, in a first part, the two existing strategies for data reduction which are feature selection (FS) and dimensionality reduction (DR). In a second part, we study the impact of different data reduction methods on supervised machine learning in terms of classification accuracy and computational costs. In fact, we compare, in the one hand, the generated subsets of attributes by filter and wrapper algorithms as well as new variables constructed by two variants of a DR method. In the other hand, we compare the classification achieved on initial data set, reduced data sets and also on successively reduced size of the considered data sets.
“SEARCH for Health”: Developing a Credited Module in Health Information Skills

The importance of Information Literacy (IL) has been widely recognized within the health professions. The emergence of evidence-based practice and corresponding problem-based and evidence-based teaching techniques to develop the appropriate skills and approach in health professionals has resulted in a need for effective information skills training in the health sciences academic environment. However, librarians have often struggled to establish themselves within academic structures in ways that formally acknowledge the importance of such skills and allow sufficient time and resources to plan effectively. In response to a proposal from its librarian, the School of Community Health and Social Care within the faculty of Health and Human Sciences invited Thames Valley University Library Services to develop a compulsory 10 credit information skills module for two degree courses. The librarian and colleagues had the challenge of conveying the context of Health Information Literacy, the factual information required, and the searching and appraising skills necessary in a cogent, coherent, and interesting module. Practical considerations of staff time management and other resourcing issues had to be analyzed and assessed. An evaluation of the librarian's existing and potential roles in the promotion of Information Literacy is on-going.
Multilevel Analysis Methods

This special issue of SMR is about the analysis of data collected at different levels of observation, such as groups and individuals within these groups, and about the methodological problems that are present when natural experimentation and observations nested within existing social groups are the object of study. The methodological problems are summarized in the term multilevel problems. A multilevel problem is a problem that inquires into the relationships between a set of variables that are measured at a number of different levels of a hierarchy. This article discusses some traditional approaches to the analysis of multilevel data and their statistical shortcomings. The random coefficient linear model is presented, which resolves many of these problems, and the currently available software is discussed. Next, some more general developments in multilevel modeling are discussed. The authors end with an overview of this special issue.
Low power architecture for floating point MAC fusion

With the proliferation of floating point computing applications, the demand for high performance, low power floating point hardware has increased. A new architecture for low power floating point multiply-accumulate fusion is presented. The proposed architecture minimises power consumption through transition activity scaling and data path simplifications. The switching activity function of the proposed MAF is represented as a four-state FSM. During any given operation cycle, only a limited set of functional subunits are active, during which time, the logic assertion status of the circuit nodes of the unused functional units are maintained at their previous states. Critical path delay and latency are reduced by incorporating data path simplifications and speculative rounding. The scheme offers a worst case power reduction of more than 49%.
Pipelined serial/parallel multiplier with contraflowing data streams

An improved architecture of the Muller pipeline serialparallel multiplier is presented. The proposed solution is based on the integration of two Muller cells into one. This modification has resulted in a reduction of the number of latches by 25% and an increase in the effectiveness of the architecture by eliminating interspersed zeros in the input data streams. An area×time criteria is used to compare the modified architecture with the Muller multiplier.
Wide-area video exploitation (WAVE) joint data management (JDM) for layered sensing

Emerging technologies of high performance computing facilitate increased data collection for wide area sensing; however, joint data management concepts of operations (CONOPs) are important to fully realize system-level performance. Joint data management (JDM) includes the hardware (e.g. sensors/targets), software (e.g. processing/algorithms), entities (e.g. service-based collections), and operations (scenario-based environments) of data exchange that enable persistent surveillance in the context of a layered sensing or data-to-decision (D2D) information fusion enterprise. Key attributes of an information fusion enterprise system require pragmatic assessment of data and information management, distributed communications, knowledge representation as well as a sensor mix, algorithm choice, life-cycle data management, and human-systems interaction. In this paper, we explore the various issues surrounding Wide-Area Video Exploitation (WAVE) in a layered-sensing environment to include improvements in Joint Data Management such as (1) data collection, construction, and transformation, (2) feature generation, extraction and selection, and (3) information evaluation, presentation, and dissemination. Throughout the paper, we seek to describe the current technology, research directions, and metrics that instantiate a realizable JDM product. We develop the methods for joint data management for structured and unstructured WAVE data in the context of decision making. Discerning accurate track and identification target information from WAVE JDM provides a moving intelligence (MOVINT) capability.
Slam the Boards: Librarians’ Outreach into Social Q&A Sites

Slam the Boards (STB) is a grassroots activity in which librarians visit social Q&A sites on the tenth of each month and answer as many questions as possible. Through this activity, librarians hope to represent librarianship at a new venue; encounter users beyond the library and provide well-sourced, dependable answers to people's questions; and ultimately increase the public's awareness of libraries. Semistructured interviews were conducted with participating librarians, examining their motivation, experience, and reflection. Findings of the study can help develop an in-depth understanding of STB and yield insights on how to efficiently and effectively promote library resources and services beyond the library.
Long-term trend of indoor volatile organic compounds (VOC)

Volatile organic compounds (VOCs) are suspected to be associated with health effects. In a first step, the health relevance could be judged using the sum concentration of a set of specific VOCs. This sum consists of a typical pattern of VOCs which may contribute to health disorders in a different way – for example, substances with an irritative, inflammatory or allergy triggering effect. That’s why it is impossible to assess the health impact by means of the VOCsum. Furthermore, several groups of substances (or their percentage) can decrease or increase over time with or without influencing the VOCsum. From this point of view, it is necessary to know these typical patterns and to know whether these patterns have changed over the years. Within the frame of some epidemiological studies we investigated the indoor VOCs exposure burden during a 15 year period. 2679 measurements were performed and 4225 study participants were involved. The study was focused on homes with families. To avoid possible regional-specific confounder effects, this assessment was conducted for an urban centre in Germany with a sufficiently long and continuous time series (1994–2008). VOCs were determined using passive sampling over 4 weeks. The passive samplers were extracted and analysed with gas chromatography-mass spectrometry. All measurement periods were equally distributed over seasons and years. The time series of some groups of VOCs show a clear trend. A substantial reduction in the total sum of VOCs (about 60%) and aromatics (about 80%) could be demonstrated as well as a decreasing trend of alkanes. No such changes could be observed for cycloalkanes, chlorinated hydrocarbons and terpenes. The
Cultural algorithms, an alternative heuristic to solve the job shop scheduling problem

In this work, an approach for solving the job shop scheduling problem using a cultural algorithm is proposed. Cultural algorithms are evolutionary computation methods that extract domain knowledge during the evolutionary process. Additional to this extracted knowledge, the proposed approach also uses domain knowledge given a priori (based on specific domain knowledge available for the job shop scheduling problem). The proposed approach is compared with respect to a Greedy Randomized Adaptive Search Procedure (GRASP), a Parallel GRASP, a Genetic Algorithm, a Hybrid Genetic Algorithm, and a deterministic method called shifting bottleneck. The cultural algorithm proposed in this article is able to produce competitive results with respect to the two approaches previously indicated at a significantly lower computational cost than at least one of them and without using any sort of parallel processing.
Automatic Speech Recognition Applications: A Study of Methods for Defining Command Vocabularies

Phoneme-based automatic speech recognition (ASR) technology enables designers to easily create custom command words or phrases that users can employ to request service operations. In this paper, I report results from two experiments concerning important dimensions of these ASR command vocabularies, including command naturalness/appropriateness and command recallability. Ease of recall is a critical dimension for assessing ASR commands used in multi-step applications since service subscribers may be engaged in several different cognitive activities that divide attention. Yet techniques for measuring command recallability can be difficult to implement owing to the time required for data collection and analysis. Results of these studies indicate the the dimension of command “naturalness” and memorability are closely related: under appropriate conditions, the simple procedures associated with measuring command naturalness or appropriateness can predict retrievability of command expressions.
Combining Speech Enhancement and Cepstral Mean Normalization for LPC Cepstral Coefficients

A mismatch between the training and testing in noisy circumstance often causes a drastic decrease in the performance of speech recognition system. The robust feature coefficients might suppress this sensitivity of mismatch during the recognition stage. In this paper, we investigate the noise robustness of LPC Cepstral Coefficients (LPCC) by using speech enhancement with feature post-processing. At front-end, speech enhancement in the wavelet domain is used to remove noise components from noisy signals. This enhanced processing adopts the combination of discrete wavelet transform (DWT), wavelet packet decomposition (WPD), multi-thresholds processing etc to obtain the estimated speech. The feature post-processing employs cepstral mean normalization (CMN) to compensate the signal distortion and residual noise of enhanced signals in the cepstral domain. The performance of digit speech recognition systems is evaluated under noisy environments based on NOISEX-92 database. The experimental results show that the presented method exhibits performance improvements in the adverse noise environment compared with the previous features.
Influences of Transparency and Feedback on Customer Intention to Reuse Online Recommender Systems

고객 취향에 가장 적합한 제품을 선택하는 것은 전자상거래에서 중요한 문제이다. 전자상거래 그러나 온라인 추천시스템으로서 알려진 소셜 필터링은 전자상거래에서 기술적 접근이 활발히 연구되어왔다. 온라인 추천시스템은 사용자의 개인적 취향과 관련하여 적절한 제품을 필터링하여 제공함으로서 사용자의 의사결정 품질을 향상시키는 것에 목적을 두고 있으며 그 결과 사용자의 제품 탐색과 선택에 대한 지원이 가능하다. 그러나 대다수 추천시스템의 선행연구들은 추천 알고리즘의 정확성을 향상시키는 것에 집중해 왔으며 사용자 기반의 인터페이스나 사용자 관점의 사용방식에 대한 연구는 매우 적은 실정이다. 추천시스템의 추천 상황에 대한 시스템 투명성과 사용자의 추천에 대한 피드백을 통한 추천방식 개선을 통하여 본 연구는 사용자 관점의 추천시스템 활용에 대한 시스템 투명성과 피드백의 영향력을 파악하고자 하였다. 실험을 통한 연구 결과에 따라, 시스템 투명성과 사용자 피드백 모두 추천시스템에 대한 사용자의 인지된 신뢰, 프로세스 가치, 인지된 즐거움에 영향을 주는 것으로 나타났다. 특히, 인지된 신뢰, 프로세스 가치, 즐거움은 사용자가 추천시스템을 지속적으로 사용하기 위한 의도를 향상시키는 것으로 나타났다.
Using PDM on Multiport Memory Allocation in Data Path

A data path consists of memory elements (i.e. registers), data operators (i.e. ALUs) and interconnection units (i.e. buses) to control the data transfers in the digital system. Many approaches to memory synthesis have been proposed in the literature. However, only single port memory is considered for register allocation and no efficient synthesis approach for multiport memory synthesis. In this paper, an efficient method, Partitioned Dependence Matrix (PDM), is presented for memory synthesis which deals not only with single port memory synthesis but also multiport memory synthesis according to the design constraints. With suitable modifications, the proposed technique can also be applied to multiport memory synthesis in which the maximum number of read ports is different from the maximum number of write ports. Therefore, the entire design space is explored and has the capability to handle early architectural design exploration so that the quality of designs produced by an automatic synthesis tool is more adequate for production use in comparison to manual design. Illustrations of applying this method to different synthesis examples are presented. Results and improvements over previous techniques are demonstrated. A key element in our approach is the successful adoption of techniques originally developed for problems in test generation to the field of memory synthesis.
XWFP: an x-ray wavefront propagation software package for the IDL computer language

A suite of computer routines is presented that uses scalar wavefront theory to calculate the propagation of arbitrarily-shaped X-ray wavefronts through a series of objects and drift spaces that represent an experimental setup. The routines are coded in IDL, the Interactive Data Language, and can be included into or extended with any other IDL code. Objects and apertures in the X-ray path are modeled in the thin-object approximation, where they are represented by a two-dimensional complex transmission function. Fresnel propagation through drift spaces is carried out in Fourier space in the paraxial approximation. A variety of predefined, ready-for-use objects is included in the package. Among these are optical elements such as gratings, lenses, slits, or pinholes, as well as reference sample objects such as spheres, cylinders, test grids, etc. The materials and geometrical parameters of these objects can be freely chosen. Optical materials constants are looked up automatically in the DABAX database. The modular structure of the code makes it reasonably easy for users to add support for objects of any degree of complexity, or even other propagation schemes.
Multi-Level Based Hybridized Intrusion Detection and Prevention System

Global spread of Information Technologyincreases security challenges; over intrusion detection system against sophisticated attacks. This has become of paramount importance since single mode intrusion detection systems have a lot of difficulties in protecting Network from sophisticated attackers. However, this paper propose a Multi-Level Based Hybridized Intrusion Detection System whereby the incoming packets will be examinedfor attacks at two layers; both at Network level as Network Intrusion Detection (NID) and further double check those that escape through the initial test at the second level using Bayes' theorem at Host level as Host based Intrusion Detection (HID). This will help to prevent against intrusion, minimize false alarms and maximize the numbers of detected bad events. It handle masses of information (often in real-time) so as to report the abnormal use of networks and computer systems in a real time before it get out of hand.
Beyond Butlers: Intelligent Agents as Mentors

This article discusses pedagogical issues for intelligent agents to successfully serve as mentors for educational purposes. Broader issues about the nature or persona necessary for an intelligent agent as mentor are discussed, incorporating usability and human-computer interaction issues such as the anthropomorphic qualities of the agent and the social relationship between learner and agent. Overall, to be effective for learning, it is argued that there are three main requirements for agents as mentors: 1) regulated intelligence; 2) the existence of a persona; and 3) pedagogical control.