Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks

This study presents the successful application of artificial neural networks (ANNs) for downscaling Meteosat Second Generation thermal infrared satellite imagery. The scope is to examine, propose, and develop an integrated methodology to improve the spatial resolution of Meteosat satellite images. The proposed approach may contribute to the development of a general methodology for monitoring and downscaling Earth’s surface characteristics and cloud systems, where there is a clear need for contiguous, accurate, and high-spatial resolution data sets (e.g. improvement of climate model input data sets, early warning systems about extreme weather phenomena, monitoring of parameters such as solar radiation fluxes, land-surface temperature, etc.). Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to validate the downscaled Meteosat images. In terms of the ANNs, a multilayer perceptron (MLP) is used and the results are shown to compare favourably against a linear regression approach.
A fast algorithm to cluster high dimensional basket data

Clustering is a data mining problem that has received significant attention by the database community. Data set size, dimensionality and sparsity have been identified as aspects that make clustering more difficult. The article introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero. This is the case with basket data transactions containing items, that can be represented as sparse binary vectors with very high dimensionality. An experimental section shows performance, advantages and limitations of the proposed approach.
Built-in current mode circuits for I/sub ddq/ monitoring

A novel circuit for I/sub ddq/ monitoring is presented. The circuit is based on current mode signaling and is intended for use in a built-in test mode. A test chip has been fabricated in 1.2 micron CMOS. Preliminary simulation result are very promising and demonstrate that the basic technique may be quite practical.
ARQ protocols for the Gaussian collision channel

In next generation wireless communication systems, packet-oriented data transmission will be implemented in addition to standard mobile telephony. Designing efficient schemes for packet transmission on top of an existing connection-oriented CDMA system will be a challenge for system designers. We take an information-theoretic view of some simple protocols for reliable packet communication based on "hybrid ARQ".
An Automatic Alarm Scheme of Video Security Monitoring System Based on Autocorrelation Function

This study presents an autocorrelation function-based program of dynamic image recognition and automatic alarm. Lighting effect analysis, binarization of the dynamic image and other methods were used to enhance the image; a dynamic image autocorrelation function was established to determine the threshold of automatic alarm system; and using Matlab software a simulation experiment was conducted on the autocorrelation function. The experiment showed that the correlation function value is inversely proportional to the amount of change of the video surveillance image; the autocorrelation function value can be used to determine when to trigger the automatic alarm system. This dynamic image recognition program, characterized by strong stability and little impact from the outside, can be used for automatic alarm of monitoring system.
Analyzing the Optimal Performance of Pest Image Segmentation using Non Linear Objective Assessments

In modern agricultural field, pest detection is a major role in plant cultivation. In order to increase the Production rate of agricultural field, the presence of whitefly pests which cause leaf discoloration is the major problem. This emphasizes the necessity of image segmentation, which divides an image into parts that have strong correlations with objects to reflect the actual information collected from the real world. Image processing is affected by illumination conditions, random noise and environmental disturbances due to atmospheric pressure or temperature fluctuation. The quality of pest images is directly affected by atmosphere medium, pressure and temperature. The fuzzy c means (FCM) have been proposed to identify accurate location of whitefly pests. The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to pest image analysis, it has important drawbacks (over segmentation, sensitivity to noise). In this paper, pest image segmentation using marker controlled watershed segmentation is presented. Objective of this paper is segmenting the pest image and comparing the results of fuzzy c means algorithm and marker controlled watershed transformation. The performance of an image segmentation algorithms are compared using nonlinear objective assessment or the quantitative measures like structural content, peak signal to noise ratio, normalized correlation coefficient, average difference and normalized absolute error. Out of the above methods the experimental results show that fuzzy c means algorithm performs better than watershed transformation algorithm in processing pest images. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijece.v6i6. 11564
A probabilistic model for distributed merging of mobile agents

We propose a probabilistic algorithm for mobile agents which roam around in the network following a random walk. We consider the following problem: when two or more agents meet at a node, they merge into a single agent. As the graph is connected, the agents meet in finite time. We are interested in the time it takes for all agents to merge. More precisely, we study a probabilistic model and we analyse the time complexity of a distributed algorithm for all the agents to merge into a single one.
Implementing the liquid curriculum: the impact of virtual world learning on higher education

This paper presents findings from a large-scale study which explored the socio-political impact of teaching and learning in virtual worlds on UK higher education. Three key themes emerged with regard to constructing curricula for virtual world teaching and learning, namely designing courses, framing practice and locating specific student needs. The authors argue that the findings indicate that ‘liquid learning’ and ‘liquid curricula’ are central concepts and practices that can be used to democratise the way technology-enhanced learning can and should be integrated into higher education.
SketchUML: The Design of a Sketch-based Tool for UML Class Diagrams

In Software Engineering classes, students need to learn how to use Unified Modeling Language (UML) in the software design process. In this paper, we describe a sketch-based software tool called SketchUML that allows students to create UML class diagrams as naturally as they would on paper, but with the editing capability provided by software. We describe the design choices made in SketchUML for creating a flexible paper-like interface to facilitate students to engage in learning-by-doing design exercises. Results from a formative evaluation suggest that SketchUML is capable to help students easily create UML class diagrams in class.
The Research of Recommendation Algorithms in P2P Lending

With the rapid development of P2P lending in China In recent years, how to recommend bid projects to the bidder to meet its preferences will become a research hotspot, but there are few studies in this area. And since the P2P lending platform is not a dominant scoring mechanism, it is an urgent need to solve the problem of how to generate a reasonable score matrix. First of all, from the PPDai.com to climb over more than 320 thousand tenders of the last two years. Then, based on the deeply analysis of the data characteristics of the P2P lending areas, three types of different types of scoring matrix (Boolean, continuous and mixed) are generated. Then, three kinds of classical collaborative filtering recommendation algorithms are used in the experiment. The results show that the performance of the recommendation algorithm has important influence on the performance of the recommendation algorithm, and the performance of cooperative filtering algorithms is different in P2P lending compared with in some other areas. Finally, the experimental results are analyzed, and the relevant suggestions are given for the P2P lending areas.
An Analysis of Presence in an Asynchronous Online Undergraduate Mastery Course Using Structural Equation Modeling

This study examined students’ perceptions of teaching, social, and cognitive presence in an online, asynchronous mastery course as they related to interaction and student course satisfaction. The study design used structural equation modeling to examine the relationships. Data was collected from 166 students who were enrolled in an asynchronous online mastery course, which covered information technology literacy skills. The study was conducted over three years using a questionnaire built upon three previously validated instruments: The Community of Inquiry (CoI) Survey (Arbaugh, Cleveland-Innes, Diaz, Garrison, Ice, Richardson, & Swan, 2008), The Noel-Levitz Priorities Survey for Online Learners (Ruffalo Noel-Levitz, 2016) and the Distance Education Learning Environments Survey (Walker & Fraser, 2005). The results of the study included a confirmatory factor analysis and a structural equation model. Results showed that students’ perception of teaching presence had the strongest positive direct effect on student course satisfaction, while cognitive presence was not a significant predictor of course satisfaction. Cognitive presence was a significant positive predictor of interaction. Social presence had a positive effect on interaction, however interaction was not a significant predictor of course satisfaction. In addition, results showed that as students’ perception of social presence increased, their reported course satisfaction decreased. The results of this research enhance the understanding of how the interdependent relationships between teaching, social, and cognitive presence affect one another. This study adds to the literature on asynchronous online learning, mastery-type courses, can serve as a model for analyzing and improving online course design and implementation, and may be used for future research and development in similar contexts. INDEX WORDS: Community of Inquiry, Teaching presence, Social presence, Cognitive presence, Interaction, Course satisfaction, Mastery-type course, Asynchronous AN ANALYSIS OF PRESENCE IN AN ASYNCHRONOUS ONLINE UNDERGRADUATE MASTERY COURSE USING STRUCTURAL EQUATION MODELING by JOHNATHAN YERBY
Joint moment estimation from electromyography of patients with osteoarthritis

Biomechanical gait analysis may be used to determine treatment options, evaluate the success of rehabilitation programs or post-surgery recuperation, and provide insight for surgical planning, including functional outcomes for patients. However, gait analysis requires expensive equipment – a limiting factor for many clinical settings. One alternative that has been examined is the utilization of an artificial neural network (ANN) to model nonlinear relationships of gait. Researchers have shown initial success in ANN predictions of pathological conditions in gait as well as modeling other parameters. The purpose of this study was to evaluate the performance of a previously developed three layer feed-forward ANN model at estimating ankle, knee and hip joint moments for subjects with osteoarthrits (OA) from surface electromyography (EMG) signals. The broader purpose was to further validate the use of the ANN model as an alternative, less expensive method to traditional gait analysis. Eighteen subjects (13 female, 5 male) with physician diagnosed OA participated in this study. Each subject completed a full gait analysis session. Data from surface EMGs located on seven muscles of their symptomatic lower limb was recorded as well as kinematic and ground reaction force data. After postprocessing, this data was entered into the ANN model and the model’s ability to estimate lower extremity joint moments was evaluated. The ANN was able to accurately map the joint moments of subjects with OA. For the ankle, the mean correlation coefficients between the experimental joint moment and the estimated joint moment were between 0.97 and > 0.99. For the knee, the values ranged between 0.89-0.97, and for the hip, the values were between 0.91 and 0.97. Additionally, the ANN’s ability to accurately map the lower extremity joint moments was demonstrated through the evaluation of casespecific joint moment time-series curves. Results have been presented in this study to show that the ANN model is adaptive to a subject group with more diversity than the previously tested group of young, healthy subjects. These findings further support the supposition that the ANN model may provide an alternative to traditional gait analysis methods.
An Improvement Method of Duplicate Webpage Detection

As Internet is very easy to implement the diffusing and sharing of resources, duplication of pages on the Internet is very large. The search engine as an index tool of Internet resources is facing a serious repeat testing, its crawler will encounter a large number of links of duplicate content. If these links are all added to the download queue, it will cause a serious drop in performance and this would seriously affect the user experience. In this paper, we adopt an improved duplicate detection method------using BloomFilter combining with fuzzy Hamming distance. This will not only meet the detection of duplicate content, but also it will meet the needs of users.
Modeling Uncertain Context Information via Event Probabilities

To be able to support context-awareness in an Ambient Intelligent (AmI) environment, one needs a way to model context. Previous research shows that a good way to model context is using Description Logics (DL). Since context data is often coming from sensors and therefore exhibits uncertain character, it is essential to take this uncertainty into account when exploiting and reasoning about context data. In this paper, we provide an event-based probabilistic method to model context uncertainty. Each context can be viewed as a probabilistic event, which can be either a basic or complex event. We show how to deal with correlations of events (i.e., inclusion, identicalness, disjunction, and independence) which are inherent in context data and investigate their in uences on the context uncertainty.
Analysis of Hardware Impairments on the Energy Harvesting Hybrid Relay Networks

In real communication systems, hardware impairments are often considered. When the system is suffering hardware impairments, the system performance will be worse. For this reason, in this paper, the impact of hardware impairments on the system performance for the energy harvesting hybrid relay networks is investigated. Especially, the closed-form expression of the outage performance and the optimal analysis of the instantaneous throughput for the system are derived, from the analysis we know that the impairments level leads great loss on the system performance. In addition, numerical results are derived to verify the correctness of our analysis.
TDM-FDM Transmultiplexer: Digital Polyphase and FFT

The cascading of a discrete Fourier transform processor and a digital polyphase network is shown to reduce the computation rate in frequency multiplexing-demultiplexing systems to a value close to minimum. Implementation advantages of that technique are pointed out. The highest efficiency is achieved when the number of channels is close to a power of 2, which is demonstrated by the '60-channel frequency-division-multiplexing (FDM)-2 X 30-channel time- division-multiplexing (TDM) transmultiplexer; a rough estimate of the computation rate is carried out in a practical case and appears to be quite within reach of the present technological capabilities. Significant cost advantage over equivalent analog equipment is expected. A digital version of the 12-channel FDM system using the same technique is also considered.
Nonlinear robust controller design for thyristor controlled series compensation

The problem of transient stability for a single machine infinite bus system with thyristor controlled series compensation (TCSC) is addressed in this paper. The system does not need to be linearized, and the damping coefficient is measured inaccurately. A nonlinear robust controller and a parameter updating law are obtained simultaneously based on modified adaptive backstepping sliding mode control and Lyapunov methods. The closed-loop error system is guaranteed to be asymptotically stable. The simulation results show that rapid speed response and strong robustness can be obtained by the proposed method than the conventional adaptive backstepping and adaptive backstepping sliding mode control methods. The proposed method can be also be applied to other nonlinear systems with lower-triangular structure.
Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks

Neuromorphic computing refers to brain inspired algorithms and architectures. This paradigm of computing can solve complex problems which were not possible with traditional computing methods. This is because such implementations learn to identify the required features and classify them based on its training, akin to how brains function. This task involves performing computation on large quantities of data. With this inspiration, a comprehensive multi-pronged approach is employed to study and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance computing (HPC) framework and optimizations are proposed for heterogeneous clusters to modularize complex neuromorphic applications in a distributed manner. To achieve best possible throughput and load balancing for such modularized architectures a set of algorithms are proposed to suggest the optimal mapping of different modules as an asynchronous pipeline to the available cluster resources while considering the complex data dependencies between stages. On the other hand, SNNs are more biologically plausible and can achieve ultra-low power implementation due to its sparse spike based communication, which is possible with emerging non-Von Neumann computing platforms. As a significant progress in this direction, spiking neuron models capable of distributed online learning are proposed. A high performance SNN simulator (SpNSim) is developed for simulation of large scale mixed neuron model networks. An accompanying digital hardware neuron RTL is also proposed for efficient real time implementation of SNNs capable of online learning. Finally, a methodology for mapping probabilistic graphical model to off-the-shelf neurosynaptic processor (IBM TrueNorth) as a stochastic SNN is presented with ultra-low power consumption. EFFICIENT IMPLEMENTATION OF STOCHASTIC INFERENCE ON HETEROGENEOUS CLUSTERS AND SPIKING NEURAL NETWORKS by Khadeer Ahmed B.E. , Visvesvaraya Technological University, 2006 M.S., Syracuse University, 2014 DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering. Syracuse University August 2017 Copyright © Khadeer Ahmed 2017 All Rights Reserved
Book Reviews: Parallel Text Processing: Alignment and Use of Translation Corpora

Only for you today! Discover your favourite parallel text processing alignment and use of translation corpora book right here by downloading and getting the soft file of the book. This is not your time to traditionally go to the book stores to buy a book. Here, varieties of book collections are available to download. One of them is this parallel text processing alignment and use of translation corpora as your preferred book. Getting this book b on-line in this site can be realized now by visiting the link page to download. It will be easy. Why should be here?
On the security of NoSQL cloud database services

Processing a vast volume of data generated by web, mobile and Internet-enabled devices, necessitates a scalable and flexible data management system. Database-as-a-Service (DBaaS) is a new cloud computing paradigm, promising a cost-effective and scalable, fully-managed database functionality meeting the requirements of online data processing. Although DBaaS offers many benefits it also introduces new threats and vulnerabilities. While many traditional data processing threats remain, DBaaS introduces new challenges such as confidentiality violation and information leakage in the presence of privileged malicious insiders and adds new dimension to the data security. We address the problem of building a secure DBaaS for a public cloud infrastructure where, the Cloud Service Provider (CSP) is not completely trusted by the data owner. We present a high level description of several architectures combining modern cryptographic primitives for achieving this goal. A novel searchable security scheme is proposed to leverage secure query processing in presence of a malicious cloud insider without disclosing sensitive information. A holistic database security scheme comprised of data confidentiality and information leakage prevention is proposed in this dissertation. The main contributions of our work are: (i) A searchable security scheme for non-relational databases of the cloud DBaaS; (ii) Leakage minimization in the untrusted cloud. The analysis of experiments that employ a set of established cryptographic techniques to protect databases and minimize information leakage, proves that the performance of the proposed solution is bounded by communication cost rather than by the cryptographic computational effort.
Using SPC and template monitoring method for fault detection and prediction in discrete event manufacturing systems

The behavior of manufacturing systems with discrete I/O signals can be characterized by the timing and sequencing of changes (events) in these I/O. In this paper, we present a method to monitor these signals to alarm when faulty sequencing or timing behavior occurs, and also to warn when the timing starts to deviate from its normal behavior. This is accomplished by using a combination of the time template monitoring technique and statistical process control (SPC).
Detailing Sentiment Analysis to Consider Entity Aspects: An Approach for Portuguese Short Texts

Sentiment analysis is useful for identifying trends, or for discovering user preferences, which can later be applied to campaign targeting or recommendations. In this paper, we describe an approach to classify the sentiment polarity regarding aspects, and how this technique was used in a previous system, for short texts in Portuguese, giving it greater sensitivity to detail. Aspect extraction is done by locating candidates for aspect as expressions having a relationship with the entity and possibly some polarized term, through rules based on POS tags. For each aspect, the sentiment polarity is determined by a Maximum Entropy classifier, whose features depend on the entity mention, on the aspect and its support text, including negation detection, bigrams, POS tags, and sentiment lexicon-based polarity clues. For aspect sentiment, our classifier evaluation indicated a precision of 68% for the positive class and 73% for the negative class, with the dataset used in our research.