Miracle-dz/UniFAR-FTUs huggingface dataset:
User-centred design for Chinese-oriented spoken english learning system

Oral production is an important part in English learning. Lack of a language environment with efficient instruction and feedback is a big issue for non-native speakers’ English spoken skill improvement. A computer-assisted language learning system can provide many potential benefits to language learners. It allows adequate instructions and instant feedback to the student, thus it facilitates self-study and encourages interactive use of the language. In this paper, we analyse Chinese college students' speaking requirements first, and then based on these requirements and usability criteria, a detailed description of an oral skills development system is provided. In this system, supported by Automatic Speech Recognition technology, special interactive activities and feedback forms were designed and a prototype was set up and tested to verify the effectiveness of this proposal.
A Novel keyless and key based Encryption Algorithm to handle Cyber Security in Microgrid Application

Smart grid technology, a reasonable move in modern power system, assures coveted resiliency, consistency, and the utmost efficiency of the entire grid system utilizing the cyber resources via extensive communication between the subordinate bodies. But, like other cyber communication process, the smart grid technology is now in the security concern because of the modern eavesdropping techniques. To keep the system safe and secure from the probable vulnerability, several encryption algorithms have already been developed to ascertain secure data transmission. But, the processing time of these traditional algorithms is comparatively higher and this drawback certainly endangers the system security. In this paper, the authors have introduced a short run time based secure encryption algorithm that can literally fulfil the security requirements in microgrid systems. In particular, this proposed algorithm is designed in two manners: Key-based and Key-less approaches with the elaborate delineation so that one can easily compare the security parameters, process runtime, and consistency with the current encryption algorithms. Finally, to vindicate the legitimacy of the proposed algorithm, all the results and issues have been verified in the simulation platform such as Matlab/ Simulink.
Has the Implementation of Learning Management System Facilitated Faculty Pedagogy Change?

Various research in literature on pedagogical change focused on the effect of professional development programs on pedagogical change. These researches focused on faculty resistance to change and teaching practice by faculty in the classrooms. Some universities in Ghana, have deployed Learning Management Systems (LMS) to aid in the pedagogy change. These universities have deployed various ways to get faculty to use the system but these have had little effect. The current research will focus on whether faculty is comfortable with technology provided to enable them change their pedagogy. The research will also look at their current pedagogical practices. The research will focus on higher educational institutions in Ghana using the University of Professional Studies as a case study. The research seeks to answer the question, “has the implementation of learning management system facilitated faculty pedagogy change? ” The study used a quantitative method, specifically a survey was done with a sample of 98 faculty members from the University of Professional Studies, Accra. The study found that majority agreed that the Moodle features is averagely helpful to them, and also that the features of the systems has helped them in their teaching.
Effect of Rayleigh fading in a multihop mobile packet radio network with capture

We study the effect of Rayleigh fading in a multihop mobile packet radio network (PRN) with capture. Four transmission strategies based on two previously proposed routing schemes, most forward with fixed range (MFR) and nearest with forward progress (NFP), and two new routing schemes, minimal angular deviation (MAD) and angular deviation to transmission range ratio (ARR), are studied. Results show that the transmission strategy based on the ARR routing scheme is generally superior to those based on the other three routing schemes. It is found that all four transmission strategies perform worse in the presence of Rayleigh fading. This is in contrast to single-hop systems in which Rayleigh fading generally does not result in poorer performance. >
High Performance Web Servers: A Study In Concurrent Programming Models

With the advent of commodity large-scale multi-core computers, the performance of software running on these computers has become a challenge to researchers and enterprise developers. While academic research and industrial products have moved in the direction of writing scalable and highly available services using distributed computing, single machine performance remains an active domain, one which is far from saturated. This thesis selects an archetypal software example and workload in this domain, and describes software characteristics affecting performance. The example is highly-parallel webservers processing a static workload. Particularly, this work examines concurrent programming models in the context of high-performance web-servers across different architectures — threaded (Apache, Go and μKnot), event-driven (Nginx, μServer) and staged (WatPipe) — compared with two static workloads in two different domains. The two workloads are a Zipf distribution of file sizes representing a user session pulling an assortment of many small and a few large files, and a 50KB file representing chunked streaming of a large audio or video file. Significant effort is made to fairly compare eight web-servers by carefully tuning each via their adjustment parameters. Tuning plays a significant role in workload-specific performance. The two domains are no disk I/O (in-memory file set) and medium disk I/O. The domains are created by lowering the amount of RAM available to the web-server from 4GB to 2GB, forcing files to be evicted from the file-system cache. Both domains are also restricted to 4 CPUs. The primary goal of this thesis is to examine fundamental performance differences between threaded and event-driven concurrency models, with particular emphasis on user-level threading models. However, a secondary goal of the work is to examine high-performance software under restricted hardware environments. Over-provisioned hardware environments can mask architectural and implementation shortcomings in software – the hypothesis in this work is that restricting resources stresses the application, bringing out important performance characteristics and properties. Experimental results for the given workload show that memory pressure is one of the most significant factors for the degradation of web-server performance, because it forces both the onset and amount of disk I/O. With an ever increasing need to support more content at faster rates, a web-server relies heavily on in-memory caching of files and related content. In fact, personal and small business web-servers are even run on minimal hardware, like the Raspberry Pi, with only 1GB of RAM and a small SD card for the file system. Therefore, understanding behaviour and performance in restricted contexts should be a normal aspect of testing a web server (and other software systems).
Cloud Computing Databases: Latest Trends and Architectural Concepts

— The Economic factors are leading to the rise of infrastructures provides software and computing facilities as a service, known as cloud services or cloud computing. Cloud services can provide efficiencies for application providers, both by limiting up-front capital expenses, and by reducing the cost of ownership over time. Such services are made available in a data center, using shared commodity hardware for computation and storage. There is a varied set of cloud services available today, including application services (salesforce.com), storage services (Amazon S3), compute services (Google App Engine, Amazon EC2) and data services (Amazon SimpleDB, Microsoft SQL Server Data Services, Google’s Data store). These services represent a variety of reformations of data management architectures, and more are on the horizon.
Interviews With College Students: Evaluating Computer Programming Environments For Introductory Courses

Different methods, strategies, or tools have been proposed for teaching Object Oriented Programming (OOP). However, it is still difficult to introduce OOP to novice learners. The problem may be not only adopting a method or language, but also use of an appropriate integrated development environment (IDE). Therefore, the focus should be on the needs of learners when selecting an IDE and the evaluations for instructional purposes may allow making objective decisions for an introductory course design. There are different methods or frameworks for evaluating IDEs and the majority focuses on the experts’ needs. Unfortunately, studies done on instructional appropriateness of IDEs are insufficient. In this study, an evaluation framework is initially proposed, then the candidate IDEs are evaluated, and finally, the perceptions of college students are explored by conducting semi-structured interviews. The data are analyzed by the Verbal Analysis technique, and the results are discussed in view of the evaluation criteria. The results imply that the learners view one of the criteria relatively more supportive for learning.
Video resolution enhancement using deep neural networks and intensity based registrations

© 2018 ICIC International. Thanks to the recent rapid improvements made to the maximum possible resolution of display devices, higher qualities of experience have been made possible, which necessitates either producing and transmitting considerably higher volumes of data or super-resolving lower-resolution contents at the display side, where the former might not be practically feasible. Therefore, aiming at the latter, this paper proposes a novel super-resolution technique, which takes advantage of convolutional neural networks. Each image is registered into a window consisting of two frames, the second one standing for the reference image, using various intensity-based techniques, which have been tested and compared throughout the paper. According to the experimental results, the proposed method leads to substantial enhancements in the quality of the super-resolved images, compared with the state-of-the-art techniques introduced within the existing literature. On the Akiyo video sequence, on average, the result possesses 5.38dB higher PSNR values than those of the Vandewalle registration technique, with structure adaptive normalised convolution being utilized as the reconstruction approach.
Winding Function Modeling of Synchronous Reluctance Machines

The current electric vehicle market, as well as many other industrial applications, is heavily focused on the application of permanent magnet (PM) machines and induction machines (IM). However the limitations of these two types of electric machines reveal the necessity for further research in the field of electrical machines to substitute the existing types with less expensive, and more fault tolerant machines. The synchronous reluctance machine (SynRM) is a singly excited machine with great potential to replace permanent magnet machines and induction machines in traction, as well as many other industrial applications. The rotor structure is simple and only made of steel laminations, which makes the manufacturing procedure less expensive compared to PM machines and IMs. Moreover the absence of rotor windings and a rotor cage eliminates the rotor copper loss, thus enhancing the efficiency compared to IMs. However, the use of the SynRM has not yet reached the same maturity level of its counterparts and still faces problems like low power density, poor power factor, and limited torque-speed envelope, representing a fertile terrain for research. The aim of this research is to develop an analytical approach based on the winding function method for modeling, analysis, and design of SynRMs and PM assisted SynRMs. The proposed analytical approach is based on the winding configuration of the machine, and the magnetic characteristics of the rotor topology. It provides insight of the machine’s characteristics, and can be used to apply design modifications on existing SynRMs to improve the performance of future designs. The analytical model is later modified for designing a PM assisted SynRM using AlNiCo magnets, with the aim to improve the power factor and torque density of the regular SynRM. The self-excitation phenomenon in synchronous reluctance generators (SynRGs) is also investigated and the requirements for the assurance of self-excitation are identified. Therefore in traction applications, and electric vehicles with vehicle to grid (V2G) capability, the possibility of utilizing the onboard SynRM as a power generation unit for emergency situations can be considered. The stand-alone SynRG can also be used as a fault tolerant power generation unit to supply electric power in remote areas.
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings

It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions. In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives. We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).
An Aspect Level Sentiment Analysis Based on Embedding Tuning & Attention Neural Network

A novel framework based on neural network is proposed to predict sentiment polarity of aspects in a sentence. The proposed model finely tunes pre-trained word embedding in order to get more accurate embedding for aspect-based sentiment analysis task. Aspect attention based on its location and the whole sentence is calculated to generate importance of context words. The merits of our model are tested on three datasets: reviews of restaurants, laptops from SemEval2014, and review collected from twitter for testing robust of our model on language form. It is shown by experiments that the proposed model outperforms the state-of-the-art methods on these datasets.
Big Data in cloud data centers

Big Data refers to a collection of massive volume of data that cannot be processed by conventional data processing tools and technologies. In recent years, the data production sources are enlarged noticeably, such as high-end streaming devices, wireless sensor networks, satellite, wearable Internet of Things devices. These data generation sources generate massive amount of data in continuous manner. Nowadays, Big Data analytics plays a significant role in various environments it includes business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring. A data center is a facility composed of networked computers and storage that businesses or other organizations use to process, analyze, store and distribute huge volume of data. In recent years, cloud data centers have been used to store and process the Big Data. This chapter reviews various architectures to store and process the Big Data in cloud data centers. In addition, this chapter also describesthe challenges and applications of Big Data analytics in cloud data centers.
A Feature Selection process Optimization in multi-class Miner for Stream Data Classification

Multi-class miner resolves the problem of feature evaluation, data drift and concept evaluation of stream data classification. The process of stream data classification in multi-class miner based on ensemble technique of clustering and classification on feature evaluation technique. The process of feature evaluation technique faced a problem of correct point selection of cluster centre for the process of data grouping. For the proper selection of features point we used optimization technique for feature selection process. The feature selection process based on advance genetic algorithm (AGA). The advance genetic algorithm poses a process of feature point for neighbour class detection for finding a correct point in classification. Our proposed algorithm tested on some well know data set provided by UCI machine learning repository. Our empirical evaluation result shows that better result in comparison of multi-class miner for stream data classification.
Identification and classification of biological micro-organisms by holographic learning

The identification and classification of biological samples is high-demanded in biomedical imaging for diagnostic purposes. Among all imaging modalities, digital holography has gained credits as a powerful solutions, thanks to its ability to perform full-field and label –free quantitative phase imaging. On the other hand, machine learning is nowadays the most used approach for classification purposes. The robustness and the accuracy of the classification depend of the features used for the training step. Therefore, the identification of micro-organism becomes strictly related to the features that can be extracted from their images. In other word, the more the image contains information, the higher the possibility of extracting highly distinctive descriptors to differentiate biological phenotypes. Digital holography can be considered one of the richest in terms of information content due to the fact that a single digital hologram encode both amplitude and phase information about the imaged cells. This opens the way to improve the features extraction, thus making more accurate the classification step. In this paper we analyze a test case by using a holographic image dataset for classification, by extracting unique features that can be solely obtained by holographic images.
Region of interest-based medical image compression with application to MRI brain image

VoIP represents the future of digital voice communications and many carriers are preparing for the VoIP revolution. However, a number of outstanding issues need to be settled. The most important are security, compression, packet size optimization, quality of service and performance in heterogeneous networks. We have addressed all of these issues [1,2,3,4] and here we summarise our key findings in each of these areas.
52. Updating Real-Time Flood Forecasting Using a Fuzzy Rule-Based Model

Flood forecasting is an important non-structural means of flood mitigation. An updating technique is a tool to update the forecasts of mathematical flood forecasting model based on data observed in real time, and is an important element in a flood forecasting model. An error prediction model based on a fuzzy rule-based method was proposed as the updating technique in this work to improve one-to four-hour-ahead flood forecasts by a model that is composed of the grey rainfall model, the grey rainfall-runoff model and the modified Muskingum flow routing model. The coefficient of efficiency with respect to a benchmark is applied to test the applicability of the proposed fuzzy rule-based method. The analysis reveals hat the fuzzy rule-based method can improve flood forecasts one to four hours ahead. The proposed updating technique can mitigate the problem of the phase lag in forecast hydrographs, and especially in forecast hydrographs with longer lead times.
Improving the Robustness of Question Answering Systems to Question Paraphrasing

Despite the advancement of question answering (QA) systems and rapid improvements on held-out test sets, their generalizability is a topic of concern. We explore the robustness of QA models to question paraphrasing by creating two test sets consisting of paraphrased SQuAD questions. Paraphrased questions from the first test set are very similar to the original questions designed to test QA models’ over-sensitivity, while questions from the second test set are paraphrased using context words near an incorrect answer candidate in an attempt to confuse QA models. We show that both paraphrased test sets lead to significant decrease in performance on multiple state-of-the-art QA models. Using a neural paraphrasing model trained to generate multiple paraphrased questions for a given source question and a set of paraphrase suggestions, we propose a data augmentation approach that requires no human intervention to re-train the models for improved robustness to question paraphrasing.
A Japanese Word Segmentation Proposal

Current Japanese word segmentation methods, that use a morpheme-based approach, may produce different segmentations for the same strings. This occurs when these strings appear in different sentences. The cause is the influence of different contexts around these strings affecting the probabilistic models used in segmentation algorithms. This paper presents an alternative to the current morpheme-based scheme for Japanese word segmentation. The proposed scheme focuses on segmenting inflections as single words instead of separating the auxiliary verbs and other morphemes from the stems. Some morphological segmentation rules are presented for each type of word and these rules are implemented in a program which is properly described. The program is used to generate a segmentation of a sentence corpus, whose consistency is calculated and compared with the current morpheme-based segmentation of the same corpus. The experiments show that this method produces a much more consistent segmentation than the morpheme-based one.
Pseudo-Three-Stage Miller Op-Amp With Enhanced Small-Signal and Large-Signal Performance

A simple technique to implement highly power efficient class AB–AB Miller op-amps is presented in this paper. It uses a composite input stage with resistive local common mode feedback that provides class AB operation to the input stage and essentially enhances the op-amp’s effective transconductance gain, the dc open-loop gain, the gain-bandwidth product, and slew rate with just moderate increase in power dissipation. The experimental results of op-amps in strong inversion and subthreshold fabricated in a 130-nm standard CMOS technology validate the proposed approach. The op-amp has <inline-formula> <tex-math notation="LaTeX">$9~\text {V} \cdot \text {pF}/\mu \text {s}\cdot \mu \text{W}$ </tex-math></inline-formula> large-signal figure of merit (FOM) and <inline-formula> <tex-math notation="LaTeX">$17~\text {MHz}\cdot \text {pF}/\mu \text{W}$ </tex-math></inline-formula> small-signal FOM with 1.2-V supply voltage. In subthreshold, the op-amp has <inline-formula> <tex-math notation="LaTeX">$10~\text {V}\cdot \text {pF}/\mu \text {s}\cdot \mu \text{W}$ </tex-math></inline-formula> large-signal FOM and <inline-formula> <tex-math notation="LaTeX">$92~\text {MHz}\cdot \text {pF}/\mu \text{W}$ </tex-math></inline-formula> small-signal FOM with 0.5-V supply voltage.