Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of a slow x-ray pulsator, ax j1740.1-2847, in the galactic center region

We report the discovery of an X-ray pulsar AX J1740.1-2847 from the Galactic center region. This source was found as a faint hard X-ray object on 7--8 September 1998 with the ASCA Galactic center survey observation. Then, coherent pulsations of P=729 +/- 14 sec period were detected. The X-ray spectrum is described by a flat power-law of ~= 0.7 photon index. The large absorption column of log NH ~= 22.4 (cm^-2) indicates that AX J1740.1-2847 is a distant source, larger than 2.4 kpc, and possibly near at the Galactic center region. The luminosity in the 2--10 keV band is larger than 2.5 x 10^33 erg/s, or likely to be 3.2 x 10^34 erg/s at the Galactic center distance. Although the slow pulse period does not discriminate whether AX J1740.1-2847 is a white dwarf or neutron star binary, the flat power-law and moderate luminosity strongly favor a neutron star binary.
Multimedia traffic security architecture for the internet of things

An important challenge for supporting multimedia applications in the Internet of Things is the security heterogeneity of wired and wireless sensor and actuator networks. In this work, we design a new and efficient media-aware security framework for facilitating various multimedia applications in the Internet of Things. First, we present a novel multimedia traffic classification and analysis method for handling the heterogeneity of diverse applications. Then a media-aware traffic security architecture is proposed based on the given traffic classification to enable various multimedia services being available anywhere and anytime. Furthermore, we provide a design rule and strategy to achieve a good trade-off between a system¿s flexibility and efficiency. To the best of our knowledge, this study is the first to provide general media-aware security architecture by jointly considering the characteristics of multimedia traffic, security service, and the Internet of Things.
Access Control Management for Cloud

Managing data access control in an authorized and authenticated way is still one of the key challenge in cloud security. In a complex environment like cloud, data owner and Cloud Service Provider (CSP) need to monitor continuously who is accessing which data in order to prevent unauthorized access. Moreover, it should be pre-defined that who can perform which operation on particular data, which can reduce unauthorized access to a great extent. In this regard, user's access to any data, application and services reside in cloud should be controlled, managed dynamically and monitored continuously. Most of cases the traditional system is not efficient enough to cope up with dynamic cloud environment, due to high dynamicity, data virtualization and multi-tenancy, higher scalability and higher degree of integrity. Existing systems also merely provide efficient auditing and reporting functionality regarding access control management. In this research we have designed a data intensive dynamic access control model for cloud environment. Several authorization algorithms are devised in this paper. Our proposed access control model has portrayed the system framework and different module along with their functionalities. Multi Agent based System (MAS) is represented to define the accessibility and functionality of the proposed model. Moreover, an enhanced authorization scheme is driven in this work to improve the security of the proposed system. We have also represented security and efficiency analysis of proposed models which has shown that our proposed scheme is efficient and secured enough to deals with the access control management.
Efficient data transmission with random linear coding in multi-channel cognitive radio networks

Efficient data transmission in cognitive radio networks (CRNs) is critical for cognitive radio (CR) users to communicate with each other in an opportunistic manner. Even with successful access to required channels, the transmission could still suffer from failures due to channel fading. In this paper, we propose a random linear coded scheme for efficient data transmission in multi-channel CRNs under practical fading channel conditions. We develop theoretical analysis and derive general form solutions for the batch delay associated with the proposed scheme. We also use our theoretical model to analyze the performances of two multi-channel automatic repeat request (ARQ) based schemes. Simulation results validate the analysis and show that the coded scheme outperforms the ARQ based schemes in terms of batch transmission delay. Additionally, the coded scheme is less dependent on feedback channels than the other schemes.
Scaling Up Synchronization-Inspired Partitioning Clustering

Based on the extensive Kuramoto model, synchronization-inspired partitioning clustering algorithm was recently proposed and is attracting more and more attentions, due to the fact that it simulates the synchronization phenomena in clustering where each data object is regarded as a phase oscillator and the dynamic behavior of the objects is simulated over time. In order to circumvent the serious difficulty that its existing version can only be effectively carried out on considerably small/medium datasets, a novel scalable synchronization-inspired partitioning clustering algorithm termed LSSPC, based on the center-constrained minimal enclosing ball and the reduced set density estimator, is proposed for large dataset applications. LSSPC first condenses a large scale dataset into its reduced dataset by using a fast minimal-enclosing-ball based approximation for the reduced set density estimator, thus achieving an asymptotic time complexity that is linear in the size of dataset and a space complexity that is independent of this size. Then it carries out clustering adaptively on the obtained reduced dataset by using Sync with the Davies-Bouldin clustering criterion and a new order parameter which can help us observe the degree of local synchronization. Finally, it finishes clustering by using the proposed algorithm CRD on the remaining objects in the large dataset, which can capture the outliers and isolated clusters effectively. The effectiveness of the proposed clustering algorithm LSSPC for large datasets is theoretically analyzed and experimentally verified by running on artificial and real datasets.
Texture defect detection system with image deflection compensation

Image textural analysis technology has been widely used in the design of automated defect detection systems. Because the presence of defects may change the textural features of an image, a reference image without defects can be compared with the test image to detect whether there are any defects. However, besides defects, the deflection of the input test image could also change its textural features. When there is any angular difference between the reference and test images, their textural features would also be different, even if there is no defect in the test image. As a result, misjudgment of the defect detection system may occur. Most of the previous studies have focused on the development of textural analysis technology which could decrease the effect of test image deflection. This study aimed to estimate the deflection angle of test images through polar Fourier transform and phase correlation analysis, and rotate the reference image by the same angle to compensate for the deflection of the test image. After the angles of the reference and test images were brought into line, the textural analysis based on the gray level co-occurrence matrix was applied to analyze and compare the textural features of the two images. The results of actual texture defect detection demonstrated that the angular differences between the reference and test images could be estimated correctly, with an estimation error of only 0° to 0.5°. By compensating for the deflection of the test image, the accuracy of the texture defect detection could be effectively enhanced.
CREDIT RISK MANAGEMENT IN MICROFINANCE : THE CONCEPTUAL FRAMEWORK

Microfinance gained tremendous attention and became a buzz-word in policy-makers as well academic researchers mainly when Muhammad Yunus, the founder of Microcredit, received the Nobel Prize for Peace in 2006. This field attracted a considerable interest in the financial world and Micofinance institution sustainability problem was one of the main topics that has been hotly debated. In this paper, we traced developments in the credit risk management in microfinance institutions. We also discussed the interaction of judgemental and statistical forecasting methods as screening mechanism in the process of selecting individual microfinance borrowers.
Induction motor identification using dynamic two-time scales neural networks with sliding mode learning

This paper presents a novel identification method of induction motor via Dynamic Neural Networks with two-time scales using sliding mode learning. Due to the fast adaptation and superb learning capability, Dynamic Neural Networks with two-time scales using sliding mode learning are used to identify the induction motor including the aspects of fast and slow phenomenon. The sliding mode technique and singularly perturbed theories are used to develop the on-line update laws for dynamic neural networks weights. The global convergence of the identification error to zero is analyzed by means of the Lyapunov function. Simulation results are presented confirming the validity of the above approach.
Sparse Hidden Markov Models for Exemplar-based Speech Recognition Using Posterior Features

Statistical speech recognition has been cast as a natural realization of the compressive sensing problem in this work. The compressed acoustic observations are sub-word posterior probabilities obtained from a deep neural network. Dictionary learning and sparse recovery are exploited for inference of the high-dimensional sparse word posterior probabilities. This formulation amounts to realization of a \textit{sparse} hidden Markov model where each state is characterized by a dictionary learned from training exemplars and the emission probabilities are obtained from sparse representations of test exemplars. This new dictionary-based speech processing paradigm alleviates the need for a huge collection of exemplars as required in the conventional exemplar-based methods. We study the performance of the proposed approach for continuous speech recognition using Phonebook and Numbers'95 database.
Violent web images classification based on MPEG7 color descriptors

In this article, we present a contribution to the violent Web images classification. This subject is deeply important as it has a potential use for many applications such as violent Web sites filtering. We propose to combine the techniques of image analysis and data-mining to relate low level characteristics extracted from the image's colors to a higher characteristic of violence which could be contained in the image. We present a comparative study of different data mining techniques to classify violent Web images. Also, we discuss how the combination learning based methods can improve accuracy rate. Our results show that our approach can detect violent content effectively.
Compact single hidden layer feedforward network for mycobacterium tuberculosis detection

Advances in imaging technology and artificial intelligence have greatly enhanced the research and development of computer-aided tuberculosis (TB) diagnosis system. The system aims to assist medical technologist and improve the accuracy of clinical diagnosis. A typical architecture of a computer-aided TB diagnosis system consists of image processing, feature extraction and classification. Finding an effective classifier for the system has been regarded as a critical topic, in order to improve the detection performance and avoid making false decision. In this study, the recent method called compact single hidden layer feedforward neural network (C-SLFN) trained by an improved Extreme Learning Machine (ELM) is evaluated for detecting the TB bacilli. Six affine moment invariants are extracted from segmented tissue slide images and fed into the C-SLFN. The network is trained and classified the input patterns into three classes: ‘TB’, ‘overlapped TB’ and ‘non-TB’. Further, the study compares the network performance with a SLFN trained using the standard ELM algorithm. The results obtained from this study suggested that the standard ELM still outperformed the C-SLFN in term of classification accuracy. The standard ELM, however requires a large number of hidden nodes compares to the C-SLFN.
Effective spelling correction in web queries and run-time DB construction

Studies on correcting errors in web search queries has some history over the past few years. There were novel ways in solving issues that occur when applying spelling correction techniques to query logs. This paper takes one more step forwards creating an effective and practical system out of what was learned so far, while also taking an intuitive approach to calculate a query's validity with the help of additional query information. This system consists of three phases that separates calculation from run-time database structure, which results in a faster program that can be used in real-time.
Integrating Genetic Algorithms and Text Learning for Financial Prediction

This paper takes two approaches to prediction of nancial markets using text data downloaded from web bulletin boards. The rst uses maximum entropy text classi cation to predict based on the whole body of text; the second uses a genetic algorithm to learn simple rules based solely on numerical data of trading volume, number of messages posted per day and total number of words posted per day. While both approaches produce positive excess returns in some cases, it is found that integrating the two predictors together produces far superior results. Furthermore, aggregating multiple GA trials to build single predictors increases performance even more.
Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing

Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers’ requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization (PSO) algorithm. In more detail, we develop a novel multi-objective PSO (MOPSO) algorithm, based on a new ranking strategy. The main insight of this algorithm is that the tasks are scheduled to the virtual machines to minimize waiting time and maximize system throughput. The algorithm leads to a reduction in execution time of 20%, a reduction the waiting time of 30%, and shows improvements of up to 40% in throughput compared to the current state of the art.
Dual Activation of TRIF and MyD88 Adaptor Proteins by Angiotensin II Evokes Opposing Effects on Pressure, Cardiac Hypertrophy, and Inflammatory Gene Expression

Hypertension is recognized as an immune disorder whereby immune cells play a defining role in the genesis and progression of the disease. The innate immune system and its component toll-like receptors are key determinants of the immunologic outcome through their proinflammatory response. Toll-like receptor–activated signaling pathways use several adaptor proteins of which adaptor proteins myeloid differentiation protein 88 (MyD88) and toll-interleukin receptor domain–containing adaptor protein–inducing interferon-&bgr; (TRIF) define 2 major inflammatory pathways. In this study, we compared the contributions of MyD88 and TRIF adaptor proteins to angiotensin II (Ang II)–induced hypertension and cardiac hypertrophy in mice. Deletion of MyD88 did not prevent cardiac hypertrophy and the pressor response to Ang II tended to increase. Moreover, the increase in inflammatory gene expression (Tnfa, Nox4, and Agtr1a) was significantly greater in the heart and kidney of MyD88-deficient mice when compared with wild-type mice. Thus, pathways involving MyD88 may actually restrain the inflammatory responses. However, in mice with nonfunctional TRIF (Trifmut mice), Ang II–induced hypertension and cardiac hypertrophy were abrogated, and proinflammatory gene expression in heart and kidneys was unchanged or decreased. Our results indicate that Ang II induces activation of a proinflammatory innate immune response, causing hypertension and cardiac hypertrophy. These effects require functional adaptor protein TRIF-mediated pathways. However, the common MyD88-dependent signaling pathway, which is also activated simultaneously by Ang II, paradoxically exerts a negative regulatory influence on these responses.
Genetic diversity within and genetic differentiation between blooms of a microalgal species

The field of genetic diversity in protists, particularly phytoplankton, is under expansion. However, little is known regarding variation in genetic diversity within populations over time. The aim of our study was to investigate intrapopulation genetic diversity and genetic differentiation in the freshwater bloom-forming microalga Gonyostomum semen (Raphidophyceae). The study covered a 2-year period including all phases of the bloom. Amplified fragment length polymorphism (AFLP) was used to determine the genetic structure and diversity of the population. Our results showed a significant differentiation between samples collected during the two blooms from consecutive years. Also, an increase of gene diversity and a loss of differentiation among sampling dates were observed over time within a single bloom. The latter observations may reflect the continuous germination of cysts from the sediment. The life cycle characteristics of G. semen, particularly reproduction and recruitment, most likely explain a high proportion of the observed variation. This study highlights the importance of the life cycle for the intraspecific genetic diversity of microbial species, which alternates between sexual and asexual reproduction.
IDMR Beamforming Under Direction-Independent Sterring Vector Mismatch

The output SINR achieved via indirect dominant mode rejection (IDMR) beamforming is substantially higher than the achieved with other beamforming algorithms. IDMR is based on a parametric estimate of the covariance matrix which is obtained using an estimate of the directions of the dominant sources and assuming that the array manifold is available. In most applications the array manifold is not known precisely and performance of IDMR can be deteriorated. The focus of this paper is to enable the IDMR beamformer to operate in a scenario of direction-independent steering vector mismatch. A modified version of an algorithm introduced by Friedlander is employed to estimate the direction-independent mismatch. Thereafter IDMR is implemented. Simulation analysis reveals that this technique enables IDMR to operate in a scenario of direction-independent manifold mismatch
A Survey on Content Adaptation Systems towards Energy Consumption Awareness

The availability of heterogeneous devices has rapidly changed the way people access the World Wide Web that includes rich content applications such as video streaming, 3D games, video conferencing, and mobile TV. However, most of these devices' (i.e., mobile phone, PDA, smartphone, and tablet) capabilities differ in terms of built-in software and library (what they can display), display size (how the content appears), and battery supply (how long the content can be displayed). In order for the digital contents to fit the target device, content adaptation is required. There have been many projects focused on energy-aware-based content adaptation that have been designed with different goals and approaches. This paper reviews some of the representative content adaptation solutions that have been proposed during the last few years, in relation to energy consumption focusing on wireless multimedia streaming in mobile devices. Also, this paper categorizes the research work according to different classifications of multimedia content adaptation requirements. In addition, we discuss some energy-related challenges content adaptation systems.
The Rendezvous architecture and language for constructing multiuser applications

When people have meetings or discussions, frequently they use conversational props: physical models, drawings, or other concrete representations of information used to enhance the exchange of information. If the participants are geographically separated, it is difficult to make effective use of props since each physical prop can only exist in one place. Computer applications that allow two or more users to simultaneously view and manipulate the same data can be used to augment human-to-human telecommunication. We have built the Rendezvous system is similar to many UIMSs or user interface toolkits in that it is intended to simplify the construction of graphical direct-manipulation interfaces. It goes beyond these systems by adding functionality to support the construction of multiuser applications. Based on experience with several large applications built with the Rendezvous system, we believe that it is useful for building conversational props and other computer-supported cooperative work (CSCW) applications. We present a list of required features of conversational props, some example applications built with the Rendezvous system, and a description of the Rendezvous system.
Measuring User Influence Based on Multiple Metrics on YouTube

User influence in online social networks has been measured by different metrics and algorithms, and these methods roughly fall into two main genres: attribute-based approaches and graph-based approaches. However, most attribute-based approaches only consider single metric, such as total view counts or retweet counts. And graph-based approaches cannot apply to the platforms where it is difficult to obtain graphs of some metrics. In this paper, we propose a triangular fuzzy number-based method to measure user influence, which covers multiple metrics and is graph free. By taking You Tube as an example, based on triangular fuzzy number, we synthesize view, comment and like counts to measure user influence. We first compute the triangular fuzzy number of each metric to represent user influence, and then use the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to synthesize multiple triangular fuzzy numbers and rank users. The experiments based on You Tube data show that, when only considering view counts, our results are in good agreement with other popular measures such as h-index. However, beyond view counts, our method provides a comprehensive measure which can cover multiple metrics.
The Determinants of Workers' Union Status: Evidence from the National Longitudinal Surveys

Using data from the 1980 wave of the young men NLS sample, this paper provides estimates of the queue model developed by Farber to explain the process of union status determination. The use of NLS data largely avoids the censoring problem encountered by Farber in estimating his model and permits direct estimation of crucial probabilities associated with the model. Despite important differences between econometric methodologies and the data sets analyzed, the NLS results support Farber's conclusion that queueing for vacancies in existing union jobs is an important labor market phenomenon. In particular, supply constraints on the availability of union jobs are found to dominate worker preferences in explaining differences in extent of unionization by occupation and region but not race.