Miracle-dz/UniFAR-FTUs huggingface dataset:
3D DMB Player and Its Reliable 3D Services in T-DMB Systems

This chapter introduces a 3D DMB player which can provide realistic 3D services to consumers in terrestrial-digital multimedia broadcasting (T-DMB) systems. This chapter also provides a parameter approximation method which can create auto-stereoscopic images reliably in the 3D DMB player. Since the bit-budget for the transmission of additional data stream is strictly limited in current T-DMB systems, depth-image-based rendering (DIBR) techniques have been studied to provide 3D services in mobile devices. In order to create the auto-stereoscopic images reliably in the 3D DMB player, exact parameters such as convergence distance, scale factor, and far/near clipping plane should be given in contents. However, some contents contain unknown or inappropriate parameter values in a real environment. This makes it extremely difficult to create auto-stereoscopic images and provide consumers with reliable 3D services. Therefore, we explain how to approximate the rendering parameters by taking mobile display size into consideration. Experimental results show that the parameter approximation method can create auto-stereoscopic images reliably in the 3D DMB player.
Evolving Management Ideas

This chapter reviews the different conceptualizations of ‘the’ historical development of management ideas. It focuses on three approaches to the evolution of research on management ideas: (1) mainstream, (2) embedded, and (3) critical alternatives. The mainstream is the most dominant approach, both in research and in textbooks of management, while the embedded approach is increasingly popular among institutionally oriented researchers. A key suggestion is that it is also important to find and follow less researched avenues in the evolution of management ideas, such as the critical alternatives approach, drawing on forgotten and marginalized discourses. The authors explicate limitations and research opportunities emerging from the three approaches and seek to motivate researchers to look for new research directions in the study of management ideas.
Financial Time Series Prediction with Information Fusion(本文)

Our proposed prediction and learning method is a hybrid referred to as MKL-GA, which combines multiple kernel learning (MKL) for regression (MKR) and a genetic algorithm (GA) to construct the trading rules. In this study, we demonstrate that the evaluation criteria used to examine the effectiveness of a financial market price forecasting method should be the profit and profit-risk ratio, rather than errors in prediction. Thus, it is necessary to use a price prediction method and a trading rules learning method. We tested the proposed method on the foreign exchange market and stock market, and we tested MKR on crude oil market. The features used for prediction on FX market were extracted from the trading history of multiple markets and multiple time horizons, and the features used for prediction on stock market were from historical stock prices and volumes, as well as social network services (SNS). MKR is essential for utilizing the information contained in many of the features derived from different information sources and for various representations of the same information source. The GA is essential for generating trading rules, which are described using a mixture of discrete structures and continuous parameters. First, the MKR predicts the change in the FX market or stock market based on technical indicators such as the moving average convergence and divergence (MACD). Next, the GA generates a trading rule by combining the results of the MKR with several commonly used overbought/oversold technical indicators. For simulation, we show the application of MKL-GA on FX market and stock market, as well as application of MKL on two well-known crude oil markets. The experimental results show that the proposed method outperforms other benchmark methods in terms of the price prediction, returns and return-risk ratio.
Multi-language Programming Based on IDL and Visual C++

IDL is a complete computing environment for the interactive analysis and visualization of data, and Visual C++ is a comprehensive IDE. These two tools have their own advantages and disadvantages, so the integration of them can make programming much easier. Take the Windows operating system as an example, the mechanism of integrating IDL with Visual C++ is discussed in this paper. Moreover, some simple examples are presented and the advantages and disadvantages of these methods are compared.
Fragrance In Sight: Personalized Perfume Production Based on Style Recognition

Fragrance in sight" is an interactive art installation that correlates visual and olfactory sensations. Besides, the art installation presents human's synesthesia capabilities by artificial intelligence. Fragrance in sight" recognizes the clothing style of user by artificial intelligence, then matches the results to the composition and proportion of the perfume to generate a customized perfume. Ultimately, build an individual cognition at the olfactory level.
An Improved VLSI Algorithm for Modular Operation in Cryptography

Objectives: This paper presents a new proposed algorithm which performs an efficient modular multiplication method which is advantage because of its reduction in hardware and software. This proposed method implies a systematic approach which increases the parallelism level when compared to the previous versions. Methods/Analysis: Two conventional methods are effectively used to find the modular multiplication output. The previous work effectively combines the first conventional and next two algorithms which are invented to overcome the disadvantages of the first two algorithms. The proposed method effectively eliminates one conventional method. Findings: This process reduces the number of iterations hence, reducing the time consumption required to synthesis the entire algorithm. Thus, the above mentioned method efficiently condenses the hardware utilization for implementing the conventional and previous algorithm so far practiced before. Application/Improvements: This paper replaces the classical algorithm by other method which effectively reduces the number of iterations. This reduction in computation makes a drastic reduction in hardware and time delay to execute the algorithms. This paper shows a modification in the existing parallelism method which further shows a great improvement in reduction of hardware and time delay.
Anomaly detection based on the dynamic feature of network traffic: Anomaly detection based on the dynamic feature of network traffic

Anomaly detection is always one of the most important issues that network security researchers focus on. For the complexity of network structure and the diversity of the attack, detecting abnormal traffic in the network accurately becomes much harder. This paper proposes a method of anomaly detection based on the dynamic feature of network traffic, which is called DF-IPEAD. Firstly, this method constructs the dynamic feature of network traffic, and then builds a detection model of Elman neural network with the history data of dynamic feature. To optimize the Elman detection model, an improved Particle Swarm Optimization (PSO) algorithm is proposed in this paper. We implemented DF-IPEAD in this paper and performed experiments to compare it with other methods. Experimental results show that the proposed method can detect abnormal traffic accurately. current network. One class of anomaly detection method adopts statistical analysis. Daniela Brauckhoff (Roesch, 1999) applied Principal Component Analysis (PCA) in the detection of abnormal traffic and obtained good results. Due to high complexity, the approach has not been widely used. Another detection method with statistical analysis uses time series analysis models. It firstly selects an appropriate time series model to fit the network traffic, and then predicts the traffic of next moment with the model chosen. It detects abnormal traffic by comparing the predicted value with the normal traffic baseline (Brauckhoff, 2009; Bianco, 2001; Galeano, 2006). The ways described above are easy to utilize, but there is no universally accepted standard for the threshold or parameter setting. Other kinds of anomaly detection methods are on the basis of machine learning including Support Vector Machine (SVM) (Yaacob, 2010), Artificial Neural Network (ANN) (George, 2012), Naïve Bayes (Shah, 2012), K-means (Sharma, 2012), decision tree (Yasami, 2010), K-neighbor (Guan, 2006) and so on. These methods have high sensitivity, strong adaptability and are able to detect unknown abnormal traffic. Appropriate parameters are set by training models. Sometimes, the false alarm rate of these methods is a little high. The above methods are not so effective as to satisfy the demand of abnormal traffic detection. Anomaly detection approaches needs a stable traffic to train the detection model, while the current research shows that the network traffic is not stable with dynamic features, such as self-similarity, long-term correlation, recurrence, and so on (Willinger, 1998; Su, 2011). Yuan indicated that multi-scale and recurrence are the two important
Improvement on DV-Hop localization algorithm in wireless sensor networks

DV-Hop localization algorithm uses the hop count multiplied by the average distance per hop to estimate the distance between nodes. Under the condition of not changing the step of the original DV-Hop algorithm and not needing an additional hardware,the traditional DV-Hop algorithm was improved from two aspects to solve the problem of the large error in the localization. On the one hand,the hop count between the nodes based on the communication radius was corrected. On the other hand,with the help of the deviation between the actual distance and the estimated distance of the beacon nodes,the average hop distance per hop was corrected. In the same network environment,the positioning error of the proposed algorithm was effectively reduced by about 15% compared with the original DV-Hop algorithm,as well as reduced by 5%- 7%compared with another improved algorithm which also used the ideal estimated hop count value between the beacon nodes to correct the actual value between them. The experimental results show that the proposed algorithm can effectively reduce the distance estimation error between nodes and improve the positioning accuracy.
A data-locality aware mapping and scheduling framework for data-intensive computing

Science is increasingly becoming more and more data-driven. With technological advancements such as advanced sensing technologies that can rapidly capture data at high resolutions and Grid technologies that enable increasingly realistic simulation of complex numerical models, scientific applications have become very data-intensive and involve storing and accessing large amounts of data. The LHC experiment at CERN is an example of a high energy physics initiative where the amount of data stored is in petabytes. The end goal in collecting petabytes of simulation data is to gain a better understanding of the problem under study. This essentially involves collaborative analysis of data by scientists across the world which conforms to a distributed data-intensive computing paradigm where a set of compute, storage and network resources are used in a collective fashion to advance science. Effective scheduling and resource management for such data-intensive applications on distributed resources is critical in order to meet their performance requirements. Efficient scheduling in the aforementioned scenario encompasses two key interrelated problems. The first one is the data staging problem which involves the staging of data from the simulation/experimental sites to the computational sites where the data analysis needs to be performed. The second one is the job mapping problem which involves the mapping of data analysis jobs to compute resources in such a manner so as to maximize the locality of data usage. Traditional batch job schedulers are designed for compute-intensive jobs running at supercomputer centers. They take into account CPU related metrics (e.g., user estimated job run times) and system state (e.g., queue wait times) to make scheduling decisions, but they do not take into account data related metrics. Therefore, there is a need for designing scheduling mechanisms for data-analysis jobs that take into account not only the computation time of the jobs, but also the overheads of retrieving files requested by those jobs. In this dissertation, we address the problem of data staging and job mapping for data-intensive jobs in both homogeneous and heterogeneous environments. We achieve this by taking into account the effects of data staging, end-point contention and data locality. For the mapping problem, we propose algorithms for mapping data-intensive jobs in both an offline and online setting. We also study the interplay between job mapping and data replication and propose algorithms which perform job mapping and data replication in a coordinated manner. For the data staging problem, we propose efficient data staging mechanisms for data centers consisting of coupled collections of storage and compute clusters. Furthermore, we extend our data staging work to a heterogeneous distributed system like the Grid. To accomplish that, we employ multi-hop path splitting and multi-pathing optimizations to improve wide-area file transfer throughput.
Development of a 103-year high-resolution climate data set for the conterminous United States

1. INTRODUCTION Currently, the only high-quality, high-resolution temperature and precipitation data sets for the continental United States suitable for use on climatological time scales are for mean values. None yet exist that represent sequential monthly values over an extended historical period. Such data sets would enable, for example: transient ecological, hydrological, and natural resource modeling for use in global change assessment; analysis of local and regional climate variations; analysis of frequency, duration, and spatial patterns of extreme climatological events; and investigation of relationships between climatological variability and large-scale forcing mechanisms (e.g., ENSO or QBO). A small number of gridded sequential data sets are under development, but they are at a coarse resolution or have a limited spatial extent. The only long-term sequential data sets currently available for the U.S. are for individual sites or are based on simple averages of any available stations within major climatic divisions. The site data are sparse and located primarily in valley bottoms, and the climatic division data suffer from inherent biases in sensor placement and availability over time. The current project, under NOAA/NASA funding, involves development of 103 years (1895-1997) of gridded monthly precipitation and maximum and minimum temperature at a 4-km resolution for the contiguous United States. PRISM, a knowledge-based interpolation system was used to produce the gridded climate datsets (Daly et al. 1994, 1997, 2002). The project involves development of serially complete monthly data for approximately 8,000 stations in the U.S. An important part of this work has been to develop and apply a semi-analytical, spatially-intelligent quality control system, based on PRISM, for monthly station observations.
Acupoint Positioning and Tracking based on Template Matching

As the movements of the body,it is necessary to get the coordinate acupoint and tracking acupoint when the massage robot is working.An acupoint positioned method based on template matching is proposed,which is combined with human back features and the characteristics of movement.First,making use of fast template matching algorithm to acquire the center coordinates of red artificial mark which put on human back.Second,the world coordinates of acupoint can be get through camera calibration method.Third,camera will track the artificial watchpoint on neck and calculate motion vector which would be added acupoint coordinate.And a cupoint visual tracking can be achieved.Experimental results show that the algorithm has more stability.Template matching is better than HOUGH algorithm in the aspect of error detection.
Emerging Tools and Applications of Virtual Reality in Education

Virtual reality is the next frontier of communication. As technology exponentially evolves, so do the ways in which humans interact and depend upon it. It only follows that to educate and stimulate the next generation of industry leaders, one must use the most innovative tools available. By coupling education with the most immersive technology available, teachers may inspire students in exciting new ways.
Techniques for Medical Image Segmentation: Review of the Most Popular Approaches

This chapter contains a survey of the most popular techniques for medical image segmentation that have been gaining attention of the researchers and medical practitioners since the early 1980s until present time. Those methods are presented in chronological order along with their most important features, examples of the results that they can bring and examples of application. They are also grouped into three generations, each of them representing a significant evolution in terms of algorithms’ novelty and obtainable results compared to the previous one. This survey helps to understand what have been the main ideas standing behind respective segmentation methods and how were they limited by the available technology. In the following part of this chapter several of promising, recent methods are evaluated and compared based on a selection of important features. Together with the survey from the first section this serves to show which are the directions currently taken by researchers and which of them have the potential to be successful. DOI: 10.4018/978-1-60566-280-0.ch001
Hepatotoxic metabolites in Polygoni Multiflori Radix— Comparative toxicology in mice

Polygoni Multiflori Radix (PM) and Rhei radix et rhizoma (rhubarb) contain similar hepatocyte-toxic anthraquinones such as emodin (major free anthraquinone in PM), physcion and their glycosides. In clinical practice, PM hepatotoxicity has been widely reported, although rhubarb is not recognized as hepatotoxic. To clarify the substances basis (key components) of PM hepatotoxicity, based on the characteristic components’ similarity within PM, rhubarb and their concocted forms, a comparative sub-acute toxicity study was designed in mice. Nine groups of mice with 28 days of oral administration of these herbal extracts or 2,3,5,4′-tetrahydroxystilbene-2-O-β-D-glucoside (TSG, major and unique characteristic component in PM)-herb combinations were set as follows: Group-1, control; Group-2, PM ethanol-extract (PME); Group-3, PM praeparata ethanol-extract (PMPE); Group-4, Rhubarb ethanol-extract (RME); Group-5, Steamed rhubarb ethanol-extract (RMPE); Group-6, TSG; Group-7, PMPE-TSG combination; Group-8, RME-TSG combination; Group-9, RMPE-TSG combination. Each experimental group received an equivalent emodin dose of 29 mg/kg except for the TSG group, and an equivalent TSG dose of 1,345 mg/kg except for the PMPE, RME and RMPE groups. The results showed that PME, PMPE-TSG and RME-TSG induced liver lesions and biochemical abnormalities of liver function compared with the control. In contrast, PMPE, RME, RMPE, TSG and RMPE-TSG caused no liver lesions and fewer biochemical abnormalities. Considering the related components, only the co-administration of high doses of TSG and emodin-8-O-β-D-glucoside (EMG, major anthraquinone glycoside in PM) in these groups could cause liver lesions. According to tissue distribution and correlation analysis, EMG dose was positively correlated with the high hepatic emodin and TSG exposure, and the hepatic emodin and TSG exposure were positively correlated with the biochemical abnormalities of liver function. Cell viability test in vitro showed emodin was more hepatotoxic than TSG and EMG, and mainly emodin and TSG of the three had synergistic hepatotoxic effects. Therefore, creatively using rhubarb as a reference, this study revealed that PM hepatotoxicity in mice mainly came from the integrative contribution of TSG, EMG and emodin.
To Identify a Torque Controller System Approximating a Neural Network Based on Model Reference Technique

In this paper, a system of a torque controller assembly, using a split field winding dc motor, controls the sliding movement of an object, is proposed. This system is identified using the neural network (NN) based model reference technique. At first, a neural network, called plant identification neural network controller (PINNC) is configured by learning the behavior of the torque controller assembly through training the network. The 'process of learning' uses a method of training (trainlm) to train the network. Then the outputs of the proposed system and this PINNC are compared. The knowledge of this comparison is given as feedback to a second neural network controller, called NN-plant or neural network model reference controller (NNMRC). The NNMRC is configured by learning the behavior of a reference model system, provided to it. The 'process of learning' uses a method of training (trainbfgc) to train this network. The plant identification neural network controller (PINNC) drives the neural network model reference controller (NNMRC) and controls the output of the NNMRC. Ultimately, the neural network model reference controller (NNMRC) or NN-plant identifies the proposed system, the torque controller assembly reliably and successfully.
Indexation en locuteurs de documents sonores

Cette contribution montre a travers une synthese de l'etat de l'art que les systemes d'indexation en locuteurs dependent fortement des performances de l'etape de segmentation dont l'element determinant est la mesure de similarite adoptee. La definition de cette mesure depend notamment de l'espace de representation des donnees et de la methode de classification adoptee. Dans ce cadre, nous adoptons le formalisme des Methodes a Vecteurs de Support (SVM), comme outil de decision discriminant. Son avantage principal est de controler la complexite du modele ajuste aux donnees et de prendre en compte l'information parametree selon diverses configurations et tailles des vecteurs acoustiques. Nous exploitons les informations extraites de l'entrainement du classifieur (SVM) , pour definir une distance dans l'espace des caracteristiques. Son caractere geometrique facilite son calcul et son interpreratation. Cette mesure est mise en oeuvre dans le cadre d'algorithmes pour la detection de ruptures puis le regroupement de segments correspondant a l'intervention des differents locuteurs.
Systematic transcriptome analysis reveals molecular mechanisms and indications of bupleuri radix

Pharmacogenomic analysis based on drug transcriptomic signatures is widely used to identify mechanisms of action and pharmacological indications. Despite accumulating reports on the efficacy of medicinal herbs, related transcriptome-level analyses are lacking. The aim of the present study was to elucidate the underlying molecular mechanisms of action of Bupleuri Radix (BR), a widely used herbal medicine, through a systematic transcriptomic analysis. We analyzed the drug-responsive transcriptome profiling of A549 lung cancer cell line after treating them with multiple doses of BR water (W-BR) and ethanol (E-BR) extracts and their phytochemicals. In vitro validation experiments were performed using both A549 and the immortalized human keratinocyte line HaCaT. Pathway enrichment analysis revealed the anti-cancer effects of BR treatment via inhibition of cell proliferation and induction of apoptosis. Enhanced cell adhesion and migration were observed with the W-BR but not with the E-BR. Comparison with a disease signature database validated an indication of the W-BR for skin disorders. Moreover, W-BR treatment showed the wound-healing effect in skin and lung cells. The main active ingredients of BR showed only the anti-cancer effect of the E-BR and not the wound healing effect of the W-BR, suggesting the need for research on minor ingredients of BR.