Miracle-dz/UniFAR-FTUs huggingface dataset:
IT'S NOT JUST THE PEDAGOGY: CHALLENGES IN SCALING MOBILE LEARNING APPLICATIONS INTO INSTITUTION-WIDE LEARNING TECHNOLOGIES

Whilst m-learning pedagogy has received considerable attention (e.g. Sharples et. al. 2007, Kukulska-Hulme, 2012), the process of adopting this potentially disruptive innovation within universities has been neglected. Based on a PhD thesis (Bird, 2014), this paper presents some of the findings from a longitudinal study which examined the adoption of mlearning projects in five UK higher education institutions in the period 2008 to 2011. Many m-learning projects have been started as pilots often as a result of external research funding. Using Actor-Network Theory to model embedding trajectories, this paper looks at how the outputs of these projects are encapsulated into wider institutional IT strategy to become institutionally embedded learning technologies.
Application of Binary System-Based Spatial Mining Algorithm in Mobile Intelligent Systems

A spatial association rule mining algorithm based on binary system is presented in this paper,which can extract spatial association rules from the database according to the set target objective,spatial object and spatial association so as to provide useful information for decision-making of the mobile user. Its efficiency of extracting is faster than the present mining algorithm,and the algorithm can efficiently improve the capability of mobile intelligent system.
Perancangan Network Monitoring Tools Menggunakan Autonomous Agent Java

Network management tasks are performed by the Network Administrator such as gathering available network resources information. The SNMP (Simple Network Management Protocol) Technology provides flexibility for network administrators in managing the overall network from a single location.Agent based Network Monitoring Tools JAVA Application consists of Master agent whose job is to perform as well as the management agent Request and database access. The Request agent in charge for monitoring servers that implement SNMP4j library with multi -agent systems. For interface, Network Monitoring Tools application using web media as an administrator interface that can be used from anywhere and at anytime.The results of this study showed that the application as a Network Monitoring Tools are able to work with the percent error in the range of 0-18 % . Besides these applications generate trend data readout server more stable and faster than the application Cacti do. This is supported by the ability of the Request Agent to respond the level of server workloads
Parallel transformation of K-SVD solar image denoising algorithm

The images obtained by observing the sun through a large telescope always suffered with noise due to the low SNR. K-SVD denoising algorithm can effectively remove Gauss white noise. Training dictionaries for sparse representations is a time consuming task, due to the large size of the data involved and to the complexity of the training algorithms. In this paper, an OpenMP parallel programming language is proposed to transform the serial algorithm to the parallel version. Data parallelism model is used to transform the algorithm. Not one atom but multiple atoms updated simultaneously is the biggest change. The denoising effect and acceleration performance are tested after completion of the parallel algorithm. Speedup of the program is 13.563 in condition of using 16 cores. This parallel version can fully utilize the multi-core CPU hardware resources, greatly reduce running time and easily to transplant in multi-core platform.
Early fusion and query modification in their dual late fusion form.

In this paper, we prove that specific widely used models in Content-based Image Retrieval for information fusion are interchangeable. In addition, we show that even advanced, non-standard fusion strategies can be represented in dual forms. These models are often classified as representing early or late fusion strategies. We also prove that the standard query modification method with specific similarity measurements can be represented in a late fusion form.
Wireless Communication Network Tools Kits with Visualization Techniques: Design Consideration for Proposed Architecture

Wireless Communication network is an inescapable reality of our days, being part of curricular proposed by almost all institute. Effective learning and teaching of wireless communication however, require student to visualize and illustrate in understanding the technical and how the communication network is operates. This paper recommends a conceptual framework to overcome the inability student in learning wireless communication network course which is complex and difficult especially for novice student. The aim of the course is to teach student not only programming and hardware design but also basic team-work and project management skills in the field of wireless networks through the active learning using the tools that we proposed. In this paper we proposed the architecture named Wireless Kit (Wkits) as one of the support tools that help novice student in enhancing their understanding of learning wireless communication. This kits focus in wireless communication such as Bluetooth, Wi-Fi and FRID technology.
Cyclostationarity-based spectrum sensing in cognitive radio

A spectrum sensing method exploiting cyclostationarity in frequency domain was proposed.This method can distinguish the types of authorized signal and unauthorized signal and gain different spectrum sharing strategy.The authorized signal and unauthorized signal were modeled to linear periodically time variant(LPTV) transformation,the method for distinguishing these two signals were given,and an approximate computation of covariance matrix for practice was derived. Simulation results show that this method can distinguish the authorized and unauthorized signals effectively at low SNR.
Beautiful beyond Useful? The Role of Web Aesthetics

Past research has long set web usability as the ultimate goal in the evaluation of web design attributes. This study examines the roles of web aesthetics and web usability in online shoppers' central and peripheral information processing. The results indicate that: (1) web usability has a positive effect on the intention to purchase and a negative effect on the activation of search; (2) web aesthetics has positive effects both on the intention to purchase and on the activation of search, and its effect on the activation of search is much stronger than its effect on the intention to purchase; and (3) web usability has a stronger effect on the intention to purchase than web aesthetics does. The findings reinforce the determinant role of web usability in online shoppers' central information processing, and reveal that, although its role in central information processing is supportive, web aesthetics plays a substantial role in activating online shoppers' peripheral information processing. Managerial implications are provided for web planners and web designers.
A GIS Spatial Data Oriented Clustering Approach

A perfect clustering algorithm should have few parameter input by user and be insensitive to noise. It can discover the clusters with arbitrary shape and deal with high dimensional dataset, and also has good interpretability and expansibility. GIS data information can be generalized and synthesized through clustering analysis in Geographic Information System. This paper presents a clustering method based on distance threshold neighbor. The method adds objects to a cluster by distance reachability. It also can find arbitrary shape cluster and has good separation effect to noise. In our experiment, the proposed algorithm is applied to GIS data mining and the results show that the algorithm has the satisfactory performance for GIS clustering.
A Practical View of Large-Scale Classication: Feature Selection and Real-Time Classication

The increasing volume of data during last years together with the emergence of classification systems requiring real-time responses have given rise to a new school of thought in machine learning owing to the impossibility of applying many of the classification methods that, traditionally, had been successful. This impossibility can be due to hardware limitations, the data cannot be completely stored in memory as required by many of the existing algorithms, or requirements concerning the training and/or classification times that traditional classifiers are not able to fulfill. In any case, it is mandatory to adapt the current machine learning solutions to the new scenario which, inevitably, leads to the design of simple and easily scalable algorithms. In particular, this thesis proposes two complementary solutions that make it possible to face up those classification problems requiring real-time predictions. The first of these contributions is a new feature selection algorithm independent of the classifier and capable of reducing its computational cost in the classification and training phases. The proposed method can be categorized into the multivariate filters group and it yields similar classification rates that its state-of-the-art counterparts while reducing their computational complexity. In addition, the new algorithm is reformulated in a higher dimensional space induced by a kernel turning out to be equivalent to the wellknown Kernel Fisher Discriminant Analysis (KFDA). This equivalence is theoretically proven providing new insights into the KFDA. The second contribution of this work is focused on the design of an algorithm capable of classifying patterns in few milliseconds. Motivated by the difficulty of applying nonlinear Support Vector Machines (SVMs) to real-time classification domains, the new method attempts to approximate the nonlinear decision boundaries by means of piecewise linear functions while locally preserving the maximum margin criteria. The results presented in this thesis show how the proposed algorithm can bridge the gap between the simplicity but low accuracy of linear SVMs and the effectiveness but sophistication of nonlinear SVMs in real-time classification systems. In conclusion, the large amount of data makes it sometimes necessary to leave aside the precision of the most complex models in favor of approximate solutions fulfilling the requirements of the classification system.
Research on dynamic feed-forward neural network structure based on growing and pruning methods

Due to the unchangable on-line problem of hidden neurons in feed-forward neural networks,an adaptive growing and pruning algorithm(AGP) was presented in this paper.This algorithm can insert and prune hidden neurons during the training process to adjust the structure of the network and achieve self organization of neural network structure,which can improve the performance of the neural network.Additionally,this algorithm has been applied to the biochemical oxygen demand(BOD) soft measurement of the wastewater treatment process.Experimental results show that the proposed algorithm can forecast the effluent BOD with better generalization ability and higher accuracy than other self-organizing neural networks.
Vehicle sensor data real time visualizer

This report seeks to determine if virtual or augmented reality is suitable for use in a sensor verification tool. The purpose of the tool is to verify the output of sensors reading surroundings of autonomous cars, as well as the corresponding object detection algorithms. This was done through research and by implementing such a tool with features that could not be evaluated through research. Verification is performed by comparing a reference with the output from the sensors. The resulting tool uses two modes, off-line for desktop verification and on-line for verification during test drives. The off-line mode shows a virtual reality in Oculus Rift where the reference is shown as a point cloud stream produced by a Lidar scanner. The on-line mode mimics augmented reality by having a stereoscopic camera feed in Oculus Rift which acts as the reference. The objects detected by the sensors are shown as color coded cuboids for both off-line and on-line. The tool itself was developed in the game engine Unity. An interesting area of discussion is the simulator sickness, induced by the virtual reality tool. There are a lot of factors affecting simulator sickness, e.g. frame rate, latency, field of view, and how and if the view is changed by other than bodily movements such as head rotations. As long as the simulator sickness is sufficiently suppressed, virtual reality could be beneficial for an off-line verification tool since the user gets a reliable overview of the vehicle’s surroundings faster than by using an ordinary monitor. Augmented reality for on-line verification has potential, but the mimicked augmented reality implemented in this report require improvements for real verification purposes.
ActivMathComp - Computers and Active Learning as support of a whole learning environment to Calculus / Mathematical Analysis

Research reports many success cases where computers and active learning enhance students understanding. ActivMathComp is a whole learning environment created to this research to teach Calculus/Mathematical Analysis 1 (MA1) to Civil Engineering students of the Polytechnic Institute of Lisbon, Portugal. Some principles guide the whole learning environment: Students are active and collaborate with colleagues during classes; Computers are embedded in all activities, as a communication, interaction, and computational tool; Students use a set of digital interactive learning documents created to the approach that include the slides, the daily diary, the exercises list, the theory book, etc; Students answer weekly to short quizzes with immediate feedback on a Learning Management System; The teacher/student relationship is grounded on trust, on mutual understanding and on students' involvement on their own learning; Students explore concepts from multiple perspectives (visual, verbal, numerical, and analytical); Students keep in touch with mathematical applications, particularly in Civil Engineering. ActivMathComp was applied to a class with the students that choose to subscribe it; they could not be randomly assigned because they must take their personal laptop to every class. This way, it was created a quasi-experiment to answer the research question: Does ActivMathComp approach generate higher grades and higher success rate than the traditional approach to teach MA1 course? A questionnaire was applied to all MA1 students to study if the students that participated in ActivMathComp were different from the others in terms of background, attitudes or behaviours. A second questionnaire and two focus groups were applied to ActivMathComp students in order to understand what evaluation they have made of the approach. Participating students got significantly higher grades and higher success rate than other students (some threats to validity remain). Participating students evaluated the learning environment as highly positive in nearly all aspects.
Research on Event-oriented Ontology Model

Event-oriented knowledge representation method is reasonable according to the philosophical viewpoints that the world is of material,material is of movement,movement is absolute and static is relative.Nowadays,the academic community has paid more and more attention to event that is the unit of knowledge.By studying of the Event-oriented Knowledge representation,the definition of event was given and the representation of event in a 6-tuple formalized structure was presented.Then,an event ontology model was proposed.Taking text understanding as an application domain,the representation,building and application of the event network of texts and sentences were briefly introduced.In comparison with the conventional method,event ontology model represents knowledge with a higher granularity,meanwhile,it'll be much more suitable for computers to simulate human brain.
A Comprehensive Assessment and Comparative Analysis of Simulations Tools for Cloud Computing

Offering "Computing as a utility" on pay per use plan, Cloud computing has emerged as a technology of ease and flexibility for thousands of users over last few years. In Cloud, computational resources are rented avoiding huge investment on the industry part. Due to this attractive offering, adoption and deployment of Cloud have become very popular not only among industries but also in the research community. But, due to lack of infrastructure and due to problems using the real hardware and software resources because of its high costs and other economical issues, it is not always possible for the research community to work on the actual Cloud for carrying out their experiments and testing & implementing proposals. For fulfilling such requirement, modeling and simulation technologies are required which make them available a look and feel of real Cloud offerings. In this paper, we make an exhaustive assessment and comparative analysis of around twenty simulation tools for Cloud environment.
The Effectiveness of Computer-Enhanced Shadowing and Tracking Pronunciation Exercises for Intermediate Level Foreign Language Learners

The Effectiveness of Computer-Enhanced Shadowing and Tracking Pronunciation Exercises for Intermediate Level Foreign Language Learners Véronique Marichal Willardson Center for Language Studies, BYU Master of Arts This study examines the effectiveness of on-line video-assisted pronunciation exercises in beginning level classes of foreign-language learning. A review of the current literature on pronunciation is presented, followed by a description of a study used to test the effectiveness of computer-based exercises in improving pronunciation. The participants were a group of high school students that were members of a fourth year French class. As part of their regular class time, the students participated in two types of exercises, in-class group work and computer-lab self-directed exercises, in which the students watched videos with subtitles while repeating what they heard. Satisfaction with the program was determined by collecting feedback from the students using qualitative and quantitative surveys. The students found the videos interesting and appreciated the learning autonomy provided by the selfdirected exercises. Improvement was assessed by comparing performance on preand posttests measuring both free response and reading pronunciation. Significant improvements were observed in both categories, but the improvements in reading pronunciation were most striking. The results demonstrate that computer-based exercises can be engaging and effective in teaching French pronunciation, and can be readily incorporated into the high school classroom. Keywords: communicative approach, pronunciation, computer-assisted language instruction, early foreign language learners
Computational Analysis of Human Adenovirus Evolution and Development of Bioinformatics Tools

COMPUTATIONAL ANALYSIS OF HUMAN ADENOVIRUS EVOLUTION AND DEVELOPMENT OF BIOINFORMATICS TOOLS Elizabeth Liu, Ph.D. George Mason University, 2015 Dissertation Director: Dr. Donald Seto Human adenoviruses (HAdVs) may be highly contagious and may be human pathogens that can cause a wide range of illnesses including respiratory, gastrointestinal and ocular infections. Individuals with immune deficiency are especially prone to such infections, leading to fatalities. Even though adenoviruses continue to cause concerns due to notable mortality and morbidity in human populations, their existence may also provide a possible benefit for patients with a broader range of illnesses as well. In recent studies, for example, adenoviruses have been used in gene therapy and vaccine vector development. Recently, bioinformatics and genomics are both high-resolution approaches and resources available for studying the adenovirus for such purposes. One observation is that genome recombination is a driving force in the molecular evolution of human adenoviruses. This has implications for their use as vectors, particular across host species. Computational analysis of this event can provide a better understanding of the role recombination plays in the human adenovirus evolution and pathology, which may later provide for a rational design of vaccines and for gene delivery vector development. Custom developed bioinformatics tools will also help to facilitate the process of data mining and analysis, and its presentation. In the course of this project, the genomes of three respiratory and gastrointestinal pathogens, HAdV-B16, B21, D58 and D59, were analyzed using bioinformatics tools to understand their origins and evolution as pathogens, in particular, changes in their genomes. To facilitate this, several tools were developed to assist this genome analysis. The analysis of both HAdV-B16 and HAdVB21, archived 1950s prototypes, provided examples of “then novel and emergent” HAdVs arising as the result of genome recombination events with simian adenoviruses, across host species. Recently emergent HAdV-D58 and HAdV-D59 are novel pathogens that are characterized by genome sequencing and analysis. Their results have shown also that recombination plays an important role in their molecular evolution.
Receiver design for OFDM transmission with insufficient cyclic prefix.

Orthogonal frequency division multiplexing (OFDM) is desirable for broadband communication because of its high spectral efficiency and resistance to multipath fading. The latter is achieved by adding a cyclic prefix to the beginning of each OFDM symbol. The cyclic prefix length must be equal to or greater than the channel delay spread to avoid multipath interference. For long range transmission, this criterion leads to bandwidth inefficiency. If a shorter cyclic prefix is used, the interference caused corrupts both pilot and data subcarriers leading to degradation in channel estimation and data detection. This thesis focuses on developing effective receiver designs for multiple antenna orthogonal frequency division multiplexing systems when the cyclic prefix is insufficient. Closed form expressions for the short cyclic prefix induced interference are formulated and analyzed. Based on the analysis, we propose an iterative structure for channel estimation and data detection using a limited number of pilot sub-carriers. First, the number of channel paths is estimated from the channel least squares estimates at the pilot sub-carriers. We then formulate a maximum likelihood process to approximate the channel delay profile and subsequently the individual path coefficients. A search procedure is designed to reduce the estimator’s complexity. High performance trellis based equalization schemes are proposed. Two additional data detection methods based on the zero forcing and minimum mean square error criteria are introduced with lower complexity than the trellis equalizers at the cost of performance. Simulation results indicate that the proposed techniques outperform conventional receivers, especially when the trellis equalizer is utilized for data detection. The mean square error of the channel estimate converges to the Cramer-Rao bound after two iterations; and the achieved bit error rate can reach that of the sufficient cyclic prefix case even when the delay spread is significantly longer than the cyclic prefix.
The Methods of Increasing the Data Processing Precision of High-speed Moving Object

In using microwave interference to test the high-speed moving objects parameters, the noise is the key to influence the precision of data processing. On the analysis of the elementary characteristics of microwave interference signals, the methods based on wavelet transform and exact time-domain data processing are put forward to process the signals. Not only smooth testing curve is obtained but also the signal processing precision is effectively improved. In addition the methods can be used for frequency extract of other signals with high-noise and altering frequency and are of certain universality.