Miracle-dz/UniFAR-FTUs huggingface dataset:
Security and Privacy Issues in Private CloudStorage

With the expansion of business, associate degree enterprise would like to create its PSC (private storage cloud) approach associate degree infrastructure service in a very Partner/Public Cloud. In such PSCs, there are a unit some new security problems, First, a way to isolate the data keep within the PSC from internal and external attackers; Second, a way to create secure intra-cloud information migration inside an enterprise, Third, a way to secure intercloud information migration between the PSC and therefore the Partner/Public Cloud. during this paper, the proposed associate degree design of imposing security services on the layer of HDFS, together with information Isolation Service, Secure Intra-Cloud information Migration Service, and Secure Inter-Cloud Data Migration Service Finally, a prototype has been implemented based on HDFS by our security policies, and the time cost is given and evaluated.
Cooperative mobile agents in dynamic network environment

A multi-MA (mobile agents) cooperative system leads to flexible and efficient solutions for distributed systems, but a lot of research is not suitable for dynamic network environments. Based on the modeling of the multi-MA cooperative system and analysis of MA behavior, we present some new ideas and a theoretic model on fuzzy knowledge representation and exchange, in which some key problems such as fuzzy belief composition and contradiction coordination are solved. With the help of simulation experiments on dynamic network topology discovery, we prove the model's availability and efficiency.
ECoG Classification Research Based on Wavelet Variance and Probabilistic Neural Network

For a typical ECoG-based brain-computer interface system that the subjects task is to imagine movements of either the left small finger or the tongue, a feature extraction algorithm using wavelet variance was proposed. Firstly the wavelet transform was discussed, and the definition and significance of wavelet variance were bring out and taken as feature, 6 channels with most distinctive features were selected from 64 channels for analysis; consequently the EEG data were decomposed using db4 wavelet, the wavelet coefficients variances containing Mu rhythm and Beta rhythm were taken out as features based on ERD/ERS phenomenon, and the features were classified by probabilistic neural network under a optimal spread with an algorithm of cross validation. The result of off-line showed high average classification accuracies of 89.21% and 88% for training and test data were achieved, the wavelet variance has characteristics of more simple and effective and it is suitable for feature extraction in BCI research.
Monitoring early lung disease in cystic fibrosis: where are we now?

Educational aims To understand which techniques are available to monitor early lung disease in cystic fibrosis, recognise difficulties inherent to each measure, and describe the use of tests in clinical practice or interventional trials. Summary It is a particular challenge to detect and monitor lung disease in children younger than 6 years old. Various methods exist that can reveal abnormality in young children, but each technique presents difficulties on performing the test or interpreting results. Most children with cystic fibrosis are now diagnosed shortly after birth, but it is still unclear how to evaluate new therapies, or at what age to initiate them. This review summarises current options for monitoring early disease, limitations of individual techniques and how evidence to date influences their use in research and clinical practice. Key points Lung disease remains the major cause of morbidity and mortality in cystic fibrosis Despite clear improvements in outcomes such as nutrition, the benefit of early diagnosis by newborn screening on pulmonary health in cystic fibrosis has yet to be proven. Early lung disease is challenging to detect and monitor in young children with cystic fibrosis, but there is an urgent requirement to define useful outcome measures in this population, particularly as new treatments emerge. Many techniques are available, but difficulties with measurement or interpretation must be recognised when appraising past studies, considering their use in practice or as endpoints in clinical trials. There is no superior monitoring test at present, but emerging evidence from longitudinal studies in children with cystic fibrosis will help define which are most useful.
IMPLEMENTASI KURIKULUM BARU TAHUN 2013 MATA PELAJARAN PENDIDIKAN JASMANI (STUDI DESKRIPTIF KUALITATIF PADA SDN CILENGKRANG)

This study focused on the implementation of the curriculum in 2013 with respect to all aspects of planning, implementation, and evaluation activities in the physical education subject Elementary School Cilengkrang Sumedang. The study was intended to obtain an overview of the problem of how teachers develop curriculum 2013. The method used is descriptive method with qualitative naturalistic approach by describing, analyzing and discussing the data sourced directly from the principal, teachers and students during the learning process that comes with the syllabus documents and lesson plan and document the results. The results showed that the implementation of the curriculum in 2013 has not been in accordance with the demands of school teachers even seem difficult, therefore, to improve the quality of the learning process is recommended for teachers to understand more about the implementation of the curriculum in 2013 and the principals and related parties in order to complete the infrastructure for learning improving the quality of processes and maximum learning results. Keywords: implementation, curriculum 2013, physical education, elementary school.
SORT: A Similarity-Ownership Based Routing Scheme to Improve Data Read Performance for Deduplication Clusters

Existing data routing schemes developed for deduplication clusters have never addressed the data read performance, although it has been a well-known problem that the reads require non-trivial random disk seeks significantly affecting the data read performance in deduplication systems. In this paper, we propose SORT, a Similarity-Ownership based Routing scheme that exploits both the data similarity and ownership to improve the data read performance for deduplication clusters. Our experimental results fed with real-world datasets show that SORT reduces about 10% of random disk seeks while at the cost of only 0.11% of deduplication efficiency, achieving an optimal trade off between the deduplication efficiency and data read performance compared to other existing routing schemes. This result indicates that the exploration of data ownership to data routing schemes has the potential benefit in optimizing the data read performance for deduplication clusters.
iPhone application development

Apple's mobile handheld devices have been a huge success; beginning with the launching of the iPhone in 2007 and that has been maintained until present days with the launching of the iPad in 2010. There are now more than 85,000 apps available for the more than 50 million iPhone and iPod touch customers worldwide and over 125,000 developers in Apple's iPhone Developer Program. That is why iPhone is a new and widely extended platform to develop object-oriented applications. iPhone platform involves several and amazing technologies that makes programming it a cool activity for experienced programmers willing to know the goodness of a mobile device, and a great option for students or novices to learn programming. Both of them will find the power of the iPhone libraries and development tools amazing to quickly start building powerful iPhone apps. We will be using Xcode under Mac OS X as our IDE, and Objective-C and iPhone API as our programming tools to create iPhone applications (from basic to medium level), which finally can be loaded into the iPhone device. We'll start with the classical "Hello World" and continue to develop applications using graphical user interfaces, handling multi-touch and motion detection, communication interfaces, and different media.
Formal Model for Data Dependency Analysis between Controls and Actions of a Graphical User Interface

1 Abstract—End-user development is an emerging computer science discipline that provides programming paradigms, techniques, and tools suitable for users not trained in software engineering. One of the techniques that allow ordinary computer users to develop their own applications without the need to learn a classic programming language is a GUI-level programming based on programming-by-demonstration. To build wizard-based tools that assist users in application development and to verify the correctness of user programs, a computer-supported method for GUI-level data dependency analysis is necessary. Therefore, formal model for GUI representation is needed. In this paper, we present a finite state machine for modeling the data dependencies between GUI controls and GUI actions. Furthermore, we present an algorithm for automatic construction of finite state machine for arbitrary GUI application. We show that proposed state aggregation scheme successfully manages state explosion in state machine construction algorithm, which makes the model applicable for applications with complex GUIs.
Trial and Critique of Alceste as a Tool for Analyzing Semi-Structured Interviews in Sociology

As the number of textual analysis software programs increase, sociology is faced with the problem of gauging their suitability. On one hand, they save time insofar as they can process a large volume of texts but, on the other hand, the way in which the data is analyzed and the default hypotheses implemented in these tools require consideration. In this paper, we focus on the Alceste program and, based on a particular set of problems, attempt to deconstruct the way in which it processes text in general and semi-structured interviews, in particular. Reinert constructs a “summary” (among many possible alternatives) of the corpus considered as a whole by taking into account mainly hypotheses on words that concentrate the meaning of a text according to their grammatical category, but also hypotheses on text partitioning into Context Units, while seeking to remain within the “proper” conditions for applying certain statistical tools. This summary and analysis, based on regularities, do not, at any point, incorporate the sociologist’s hypotheses or theoretical viewpoint.
Emotion Classification of Mandarin Speech Based on TEO Nonlinear Features

To study effective speech features which can represent different emotion styles in mandarin speech, nonlinear features based on Teager Energy Operator(TEO) are researched. Neutral state and 3 emotional states (i.e. happiness, anger and sadness) are classified from the mandarin speech database. MFCC extraction and HMM-based emotion recognition are used as baseline system to evaluate the emotional classification performance of TEO-based features. In comparison with MFCC, while text- dependent, improvements of classification capacity are obtained when using all 4 nonlinear features (i.e. NFD_Mel, AF_Mel, DAF_Mel, AM_SBCC). While text-independent, the performance of emotion classification are improved by using NFD_Mel, AF_Mel and DAF_Mel, but deteriorated by using AM_SBCC. The results of classification demonstrate that the nonlinear features based on TEO, when using NFD_Mel, AF_Mel and DAF_Mel, are better able to represent different emotion styles in speech than that of MFCC.
Arbitrary Finite-time Tracking Control for Magnetic Levitation Systems

In this paper, an arbitrary finite-time tracking control (AFTC) method is developed for magnetic levitation systems with uncertain dynamics and external disturbances. By introducing a novel augmented sliding-mode manifold function, the proposed method can eliminate the singular problem in traditional terminal sliding-mode control, as well as the reaching-phase problem. Moreover, the tracking errors can reach the reference value with faster convergence and better tracking precision in arbitrarily determined finite time. In addition, a fuzzy-arbitrary finite-time tracking control (F-AFTC) scheme that combines a fuzzy technique with AFTC to enhance the robustness and sliding performance is also proposed. A fuzzy logic system is used to replace the discontinuous control term. Thus, the chattering phenomenon is resolved without degrading the tracking performance. The stability of the closed-loop system is guaranteed by the Lyapunov theory. Finally, the effectiveness of the proposed methods is illustrated by simulation and experimental study in a real magnetic levitation system.
A COMPREHENSIVE MONITORING SYSTEM FOR PHOTOVOLTAIC ARRAYS

With increased adoption of renewable energy, more effective array management strategies are needed. Many arrays are located in remote locations, where faults within the array often go unnoticed and unattended for large periods of time. Technicians sent to rectify the faults must spend a large amount of time determining the location of the fault manually. Automated monitoring systems are needed to obtain the information about the performance of the array and detect faults. Such systems must monitor the DC side of the array in addition to the AC side to identify non catastrophic faults. In this paper, a prototype comprehensive monitoring system is presented for management of utilityscale PV installations. The system continuously monitors and logs the performance and condition of the array and provides a variety of data visualization and reporting functionality to the facility operator.
Doctoral students’ identity positioning in networked learning environments

Abstract In this study, the authors explored identity positioning as perceived by doctoral learners in online, networked-learning environments. The study examined two distance doctoral programs at a Canadian university. It was a qualitative study based on methodologies involving open coding and discourse analysis. The social positioning cycle, based on social positioning theory, was used as a theoretical lens guiding the analysis and organization of the data. The results indicate that distance doctoral students experience identity positioning across multiple aspects of their lives including, but not limited to, their social, intimate, professional, and academic contexts. The participants’ descriptions of their experiences highlight their ways of managing their relationships—through degrees of sharing, withdrawing, prioritizing, rationalizing, and changing practices. The results of this research suggest a need for additional studies of the distance doctoral experience and how institutions can better support learners and increase completion rates.
Energy-Efficient Multi-Path Routing with Short Latency and Low Overhead for Wireless Sensor Networks

In wireless sensor networks (WSNs), multi-path routing can find multiple paths from source node to sink node for achieving high reliability and high energy-efficiency. However, the existing multi-path routing protocols in the literature construct multiple paths with long latency and high overhead. In this paper, a novel multi-path routing protocol for WSNs is proposed, which can discover multiple paths with short latency and low overhead. Additionally, its energy efficiency is higher than that of the existing ones. Performance analyses and simulation results show that our proposed protocol has much better performance than the existing ones in terms of latency, overhead and network lifetime.
TINA: Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation

With the explosive growth of web data, effective and efficient technologies are in urgent needs for retrieving semantically relevant contents of heterogeneous modalities. Previous studies construct global transformations to project the heterogeneous data into a measurable subspace. However, global projections cannot appropriately adapt to diverse contents, and the naturally existing multi-level semantic relation in web data is ignored. We study the problem of semantic coherent retrieval, where documents from different modalities should be ranked by the semantic relevance to the queries. Accordingly, we propose TINA, a correlation learning method by Adaptive Hierarchical Semantic Aggregation. First, by joint modeling of content and ontology similarities, we build a semantic hierarchy to measure multi-level semantic relevance. Second, with a set of local linear projections aggregated by gating functions, we optimize the structure risk objective function that involves semantic coherence measurement, local projection consistency and the complexity penalty of local projections. Therefore, semantic coherence and a better bias-variance trade-off can be achieved by TINA. Extensive experiments on widely used NUS-WIDE and ICML-Challenge datasets demonstrate that TINA outperforms state-of-the-art, and achieves better adaptation to the multi-level semantic relation and content divergence.
Global search strategies for simulation optimisation

Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners, as several simulation packages include add-on optimization tools. We are concentrating on an automated optimization approach that is based on adapting model parameters in order to handle uncertainty that arises from stochastic elements of the process under study. We particularly investigate the use of global search methods in this context, as these methods allow the optimization strategy to escape from sub-optimal (i.e., local) solutions and, in that sense, they improve the efficiency of the simulation optimization process. The paper compares several global search methods and demonstrates the successful application of the Particle Swarm Optimizer to simulation modeling optimization and design of a steelworks plant, a representative example of the stochastic and unpredictable behavior of a complex discrete event simulation model.
A decision support system for academic advisors

Student advising is a vital activity in higher education institutes, yet it is a time consuming effort in academic life. Through experimental research, this article examines whether multiple sources of knowledge are helping academic advisors providing more effective decision to their students. Drawing on multiple knowledge sources, including the students' historical records, university's regulations, students' preferences, and career trends, the article explains why these knowledge sources are essential and discusses ways of constructing it. The article then presents the results of a study of recent developed prototype at University of Bahrain, compares data from the years 2009 through 2011, and discusses implications of the data. It concludes that the proposed model have made significant progress toward more effective decision making for students, but it also identifies a need for further improvement, which can be accomplished through advanced techniques on data mining and artificial intelligence.
A Run-Length Coding Based Dynamic Trust Model in P2P Network

The critical challenges for peer-to-peer (P2P) systems must be the task to manage risks in interacting with unknown peers. Reputation-based trust management can decrease this risk by evaluating trustworthiness of a certain peer from its historic behaviors. However, many existing trust models do not provide adequate reaction to quick changes in peers¿ behavior, showing their ineffectiveness coping with dynamic malicious peers. In this paper, we propose a run-length-coding based trust model - RunTrust - computing trust degree based on behaviors history in P2P networks. In particular, our proposed model is capable to detect and penalize both the sudden changes in peers¿ behaviors and their potential oscillatory malicious behavior. Simulation shows our model¿s unique features and advantages over the existing models.
The Improved Transductive Support Vector Machine and Its Application to Environment Monitoring in Industrial Seaculture

The study on Transductive Support Vector Machine(TSVM) has made little progress since Vapnik put forth the concept in the late 1990s, as algorithm for TSVM optimization model can not be easily found. Here we try to transform the problem of TSVM optimization into an unconstrained one before constructing the smooth unconstrained optimization that has a kernel, and on the basis of which to devise a TSVM whose optimization problem is easier to solve to breakthrough the bottleneck in order to deepen the research into TSVM and apply TSVM to environment monitoring of industrial sea culture.
Enhanced Traverse of Web Pages

Correct navigational behavior of a Web application is essential to its reliability. An effective means to improve our confidence in the correct behavior of a Web application is to test it by exploring the possible navigation among the Web pages at client side: The tester carries out the testing by consecutively clicking the hyperlinks along with some possible search parameters and checking whether the returned Web pages are as expected. Traditional conformance testing techniques based on finite state machines can be adopted in this setting to automatically generate suitable test sequences to traverse among client pages. This paper presents our initial result in improving T-method for test sequence generation to considerably reduce its length by making use of the characteristics provided by the Web browsers. Our experiments show a 29% - 68% saving on the test sequence lengths compared to the direct application of T-method.
Knowledge Transfer and Opinion Detection in the TREC 2006 Blog Track

The paper describes the opinion detection system developed in Carnegie Mellon University for TREC 2006 Blog track. The system performed a two-stage process: passage retrieval and opinion detection. Due to lack of training data for the TREC Blog corpus, online opinion reviews provided in other domains, such as movie review and product review, were used as the training data. Knowledge transfer was performed to make the cross-domain learning possible. Logistic regression ranked the sentence-level opinions vs. objective statements. The evaluation shows that the algorithm is effective in the task.
Interaction and visualization supporting Web browsing patterns

Provides advanced visualization and interaction paradigms to support Web browsing through the intensive use of bookmarks, navigation history and best-of lists. Our approach is motivated by past studies of Web browsing patterns that proved the usefulness of such tools even though standard browsers provide very few interaction and visualization features for them. In this paper, we first motivate our work and present our main visualization and interaction paradigms. We then present the architecture we used to integrate interactive visualization with normal Web navigation. We then review lessons learned. Further, we report the results of a preliminary comparative user study conducted to evaluate the effect of the use of our visualization and interaction techniques on user performances.
Network Traffic Classification Using K-means Clustering

Network traffic classification and application identification provide important benefits for IP network engineering, management and control and other key domains. Current popular methods, such as port-based and payload-based, have shown some disadvantages, and the machine learning based method is a potential one. The traffic is classified according to the payload-independent statistical characters. This paper introduces the different levels in network traffic-analysis and the relevant knowledge in machine learning domain, analysis the problems of port-based and payload-based methods in traffic classification. Considering the priority of the machine learning-based method, we experiment with unsupervised K-means to evaluate the efficiency and performance. We adopt feature selection to find an optimal feature set and log transformation to improve the accuracy. The experimental results on different datasets convey that the method can obtain up to 80% overall accuracy, and, after a log transformation, the accuracy is improved to 90% or more.