Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Similarity Calculation of Chinese Word

This paper puts forward a two layers computing method to calculate semantic similarity of Chinese word. Firstly, using Latent Dirichlet Allocation (LDA) subject model to generate subject spatial domain. Then mapping word into topic space and forming topic distribution which is used to calculate semantic similarity of word(the first layer computing). Finally, using semantic dictionary "HowNet" to deeply excavate semantic similarity of word (the second layer computing). This method not only overcomes the problem that it’s not specific enough merely using LDA to calculate semantic similarity of word, but also solves the problems such as new words (haven’t been added in dictionary) and without considering specific context when calculating semantic similarity based on semantic dictionary "HowNet". By experimental comparison, this thesis proves feasibility,availability and advantages of the calculation method.
GPU video retargeting with parallelized SeamCrop

In this paper, we present a fast parallel algorithm for the retargeting of videos. It combines seam carving and cropping and is aimed for real-time adaptation of video streams. The basic idea is to first find an optimal cropping path over the whole sequence with the target size. Then, the borders are slightly extended to be reduced again by seam carving on a frame-by-frame basis. This allows the algorithm to get more important content into the cropping window as it is also able to remove pixels from within the window. In contrast to the previous SeamCrop algorithm, the presented technique is optimized for parallel processes and a CUDA GPU implementation. In comparison, the computation time of our GPU algorithm is 10.5 times faster (on a 960 x 540 video with a retarget factor of 25%) than the already efficient CPU implementation.
EEG Signal Classification using an Association Rule-Based Classifier

In this paper, the Electroencephalogram (EEG) of twenty schizophrenic patients and twenty age-matched healthy subjects are analyzed for classification purposes. Several features including AR model coefficients, band power and fractal dimension are extracted from EEG signals. This paper proposes a new classification method based on association rule mining. The system we propose consists of a preprocessing phase, a phase for mining the resulted transactional database, and a final phase to improve the resulted association rules. In this case, Fuzzy Accuracy-based Classifier System (F-XCS) is used to improve the resulted fuzzy associative rules for discriminating between healthy and schizophrenic subjects. The experimental results show that the method performs well reaching over 80% in accuracy.
The Impact of Information Infrastructure Capabilities on Knowledge Manipulation Skills: A Conceptual Framework

The globalisation of business, the growing use of information and communications technology (ICT) and, the shift from production-based to knowledge-based economy have given knowledge management (KM) a critical position across firms. However, there is limited empirical link between KM and information infrastructure capabilities [IICs] [1, 2]. A key objective of this paper, therefore, is to determine the impact of information infrastructure capabilities on knowledge manipulation skills. This study will also examine specific applications that enable KM activities in firms. We will collect data from a sample of about 600 firms in MSC using questionnaires. Multiple analytical tools will be used in this study to determine the significant levels of associations and interactions between the variables. This research would provide useful information that could help MSC companies determine types of information infrastructures for effective KM implementation. To academics, the empirical insights from this study would be useful for theory building in the subject area.
Kobe University Repository : Kernel タイトル Tit le Fuzzy Classifiers Based on Kernel Discriminant Analysis 著者

In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and calculate the component that maximally separates two classes in the feature space. Then, in the one-dimensional space obtained by KDA, we generate fuzzy rules with one-dimensional membership functions and tune the slopes and bias terms based on the same training algorithm as that of linear SVMs. Through the computer experiments for two-class problems, we show that the performance of the proposed classifier is comparable to that of SVMs, and we can easily and visually analyze its behavior using the degrees of membership functions.
Business Analytics in Action: The Case of Asset Pricing for a Rent-a-Car Company

The Internet enables businesses to acquire a great deal of reference information, including the price in the open markets, for estimating appropriate prices of the assets to sell off. In particular, what is the value of reference price information to a company in the market and how can the company make use of this information? Using a multiple regression model for price estimation applied to used cars for a rent-a-car company, we propose a framework to collect the reference price information, in order to estimate appropriate prices in the future. The result is a new information system that collects online reference price information and provides very accurate information in estimating future prices of used cars and rental prices.
Model To Prevent Websites From XSS Vulnerabilities

As social websites get more and more users across internet, Cross Site Scripting is becoming one of the major problems, which results in serious consequences, such as theft of some personal trusted data and information. This Paper describes the possibilities of securing web applications on client side as well as on server side. The attacks are the worst because they are easy to make but very tough to be traced. Cross-Site Scripting is one of the major attacks of many Web based Applications. Web browsers support the execution of Malicious JavaScript and Attacker access the system feature maliciously to violate the security such as confidentiality. Networking sites (i.e. social network) provide the attacker with flexibility to put there malicious code into the web applications. Detecting these malicious script codes is very tough for client side; the detection can be done by using detection tools both at client end and server end as well. Our approach is to minimize theft space for such unlawful contents by using QualysGuard (WAS) tool, so to minimize the vulnerabilities to cause any harm to web applications. This paper will help us to overcome from this problem and elaborates on the possibilities to reduce this problem of Cross-Site Scripting attack to some extent. Keywords— XSS Cross Site Scripting,DOM Document Object Model, HTML Hypertext Markup Language, HTTP Hypertext Transfer Protocol, IDS Intrusion Detection System, URL Uniform Resource Locator, WAS Web Application Scanning
Racial and Ethnic Disparities in the Police Handling of Juvenile Arrests

The overrepresentation of racial and ethnic minority youth in early stages of juvenile justice processing remains a long-standing concern. The current study uses data from the National Incident-Based Reporting System (NIBRS) to examine the effects of race and ethnicity on postarrest handling of juvenile cases by the police. Multilevel analyses controlling for extralegal and contextual factors found that disproportionate minority contact (DMC) was not observed for more severe charges, but Hispanic and minority youth facing less severe charges were more likely to be referred to authorities. Because even small disparities in postarrest handling may have a cumulative effect, the findings highlight the continuing need to better understand police officer behaviors and agency processes that result in DMC.
Towards Unified Management of Networked Services in Wired and Wireless Enterprise Networks

Organizations world-wide are adopting wireless networks at an impressive rate, and a new industry has sprung up to provide tools to manage these networks. Unfortunately, these tools do not integrate cleanly with traditional wired network management tools, leading to unsolved problems and frustration among the IT staff. We explore the problem of unifying wireless and wired network management and show that simple merging of tools and strategies, and/or their trivial extension from one domain to another does not work. Building on previous research on network service dependency extraction, fault diagnosis, and wireless network management, we introduce MnM, an endto-end network management system that unifies wired and wireless network management. MnM treats physical location of end devices as a core component of its management strategy. It also dynamically adapts to the frequent topology changes brought about by end-node mobility. We have a prototype deployment in a large organization that shows that MnM’s root-cause analysis engine easily out-performs systems that do not take user mobility into account in terms of correctly localizing faults and blame attribution.
A quantitative metric of visual-words separability for a more discriminative visual vocabulary in an unsupervised manner

The task of visual vocabulary construction plays an important role in the bag-of-words based pattern analysis and robotic applications. A discriminative vocabulary generation in unsupervised case is an open issue for reducing perceptual aliasing in image matching based applications. In this paper, we present a scheme to evaluate the discriminative power of each visual word quantitatively in terms of Mahalanobis separability, and a discriminative visual vocabulary is obtained through adaptively updating the poor discriminative visual words in an unsupervised manner. The effectiveness of our metric is demonstrated in the experiment of loop-closure detection under strong perceptual aliasing condition in both indoor and outdoor image sequences.
A Network based, Delay-tolerant, Integrated Navigation System for a differential drive UGV using Harmonic Potential Field

Network based integrated navigation systems are a multidisciplinary effort whose aim is to produce a network structure and components that are capable of integrating sensors, actuators, communication, and control algorithms in a manner to suit real-time navigation and/or obstacle avoidance. There are many challenges to be overcome in order to put such a distributed and heterogeneous system together. This paper deals with some of these issues, i.e. the adverse effect of processing delays on the system and efficient integration of different modules. This paper describes a delay-resistant sensory-motor module for navigating a differential drive unmanned ground vehicle (UGV) in an indoor cluttered environment. The module consists of an early vision edge detection stage, a harmonic potential field (HPF) planner; a network based quadratic curve fitting controller and gain schedule middleware, (GSM). Though the different techniques and algorithms used to implement the navigation system have been previously well-studied algorithms, the novel contribution in this paper is the way all these different modules are integrated together for the first time to create an efficient structure for a network based integrated navigation system making it easy to implement and realize for many different applications. The structure of this module and its components are described. Thorough experimental results along with performance assessment comparing to the previous implementation are also provided to illustrate the effectiveness of the new integrated navigation structure
Plan Recognition as Planning

In this work we aim to narrow the gap between plan recognition and planning by exploiting the power and generality of recent planning algorithms for recognizing the set G* of goals G that explain a sequence of observations given a domain theory. After providing a crisp definition of this set, we show by means of a suitable problem transformation that a goal G belongs to G* if there is an action sequence π that is an optimal plan for both the goal G and the goal G extended with extra goals representing the observations. Exploiting this result, we show how the set G* can be computed exactly and approximately by minor modifications of existing optimal and suboptimal planning algorithms, and existing polynomial heuristics. Experiments over several domains show that the suboptimal planning algorithms and the polynomial heuristics provide good approximations of the optimal goal set G* while scaling up as well as state-of-the-art planning algorithms and heuristics.
ProPRED: A probabilistic model for the prediction of residual defects

In this paper, we propose ProPRED, a probabilistic model for predicting residual defects based on Bayesian Networks (BN) in the software development lifecycle. With the chain rule for BN, ProPRED can be used to take the evidence of the influential factors to the activities (Analyze and Design, Development, Maintain, and Review and Test) that bring about the defects introduction and removal to reason and predict the probable residual defects. We refine and classify the influential factors to the four basic activities, and construct the ProPRED. Giving a case study, we conclude that the ProPRED improve its performance in reasoning under uncertainty and convenience in decision-making and quality control.
Path recognition for outdoor navigation

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community. In order to execute a safe navigation on outdoors it is necessary to identify parts of the terrain that can be traversed by the robot and parts that should be avoided. This paper describes an analyses of an image-based terrain identification based on different visual information features. Experimental tests using an outdoor robot and a video camera have been conducted in real scenarios to evaluate the proposed methods.
Structural and functional organization of RNA regulons in the post-transcriptional regulatory network of yeast

Post-transcriptional control of mRNA transcript processing by RNA binding proteins (RBPs) is an important step in the regulation of gene expression and protein production. The post-transcriptional regulatory network is similar in complexity to the transcriptional regulatory network and is thought to be organized in RNA regulons, coherent sets of functionally related mRNAs combinatorially regulated by common RBPs. We integrated genome-wide transcriptional and translational expression data in yeast with large-scale regulatory networks of transcription factor and RBP binding interactions to analyze the functional organization of post-transcriptional regulation and RNA regulons at a system level. We found that post-transcriptional feedback loops and mixed bifan motifs are overrepresented in the integrated regulatory network and control the coordinated translation of RNA regulons, manifested as clusters of functionally related mRNAs which are strongly coexpressed in the translatome data. These translatome clusters are more functionally coherent than transcriptome clusters and are expressed with higher mRNA and protein levels and less noise. Our results show how the post-transcriptional network is intertwined with the transcriptional network to regulate gene expression in a coordinated way and that the integration of heterogeneous genome-wide datasets allows to relate structure to function in regulatory networks at a system level.
Out-of-core assessment of clustering tendency for large data sets

Determining the number of clusters present in a data set automatically is a very important problem. Conventional clustering techniques assume a certain number of clusters, and then try to find out the possible cluster structure associated to the above number. For very large and complex data sets it is not easy to guess this number of clusters. There exists validity based clustering techniques, which measure a certain cluster validity measure of a certain clustering result by varying the number of clusters. After doing this for a broad range of possible number of clusters, this method selects the number for which the validity measure is optimum. This method is, however, awkward and may not always be applicable for very large data sets. Recently an interesting visual technique for determining clustering tendency has been developed. This new technique is called VAT in abbreviation. The original VAT and its different versions are found to determine the number of clusters, before actually applying any clustering algorithm, very satisfactorily. In this paper, we have proposed an out-of-core VAT algorithm (o-VAT) for very large data sets.
Secret Image Sharing Based CTS with Cheating Detection

Cheque Truncation System(CTS) is an automatic cheque clearance system implemented by RBI.CTS uses cheque image, instead of the physical cheque itself, for cheque clearance thus reducing the turn around time drastically. This approach holds back the physical movement of cheque from presenting bank to the drawee bank. In CTS, digital image of the cheque is protected using standard public key and symmetric key encryptions like RSA, triple DES etc. This involves a lot of computation overhead and key management. The security also depends on the hard mathematical problem and is only computationally secure.Information theoretically secure, secret image sharing techniques can be used in the CTS for the secure and efficient processing of cheque image . In this paper, we propose two simple and efficient secret image sharing schemes and a Cheque Truncation System based on these algorithms . In the proposed scheme,the presenting bank is acting as the dealer and the participants are the customer, and the drawee bank.The dealer should generate the shares of cheque and distributes it to customer and drawee bank.The validity of the shares are important during the reconstruction process. The proposed scheme also suggests a method for cheating detection which identify any invalid shares submitted by the customers, using the hashing technique. The experimental results shows that the proposed scheme is efficient and secure compared with the existing scheme.
Stereoscopic images quality assessment based on deep learning

With the popularity of stereoscopic 3D (S3D) images and videos, many advanced objective quality assessment methods have been proposed to evaluate viewers' Quality of Experience (QoE). Among them, most algorithms take advantages of the disparity maps to extract useful features. On the other hand, deep learning has been one of the hottest research topics during these years, but limited efforts focused on the field in objective quality evaluation of S3D images. In this paper, we propose a S3D image quality assessment (S3D IQA) method based on deep learning. In this method, the Convolutional Restricted Boltzmann Machines (CRBM) combined with Factored Third-Order RBM (FTO-RBM) is considered as learning model to extract feature maps from pre-processed left and right images automatically. Then an improved traversal algorithm based on two pooling strategies is put forward to optimize extracted feature maps, which improves the final quality assessment performance significantly. Experimental results show that our S3D IQA method achieves good performance on 3D databases tested.
The Nature of Services

Though both the results and the outcomes of services are in many cases tangible and visible to us, and the same holds for their ‘ingredients’, services themselves are rather of an intangible and immaterial nature. There are many interesting definitions that try to organise knowledge in the area of services with aspects of the service delivery process. At a great extent we are used in operational definitions that relate a service with Here the author elaborates on the intangible nature of services – and how it materializes to tangible commodities and results. The aim of this chapter is to increase the awareness of the reader regarding the complexities related to even simple forms of services that we regard as trivial. Five propositions are presented upon which the proposed service development framework and the underlying processes are built; all five of them are also related with corresponding problem areas in the real world and the markets. Two sections are devoted respectively to the nowadays more increasingly and intensively faced step changes in the conceptualisation of services and the e-services iceberg.
Replication in a Superficial Epithelial Cell Niche Explains the Lack of Pathogenicity of Primate Foamy Virus Infections

ABSTRACT Foamy viruses (FVs) are ancient retroviruses that are ubiquitous in nonhuman primates (NHPs). While FVs share many features with pathogenic retroviruses, such as human immunodeficiency virus, FV infections of their primate hosts have no apparent pathological consequences. Paradoxically, FV infections of many cell types in vitro are rapidly cytopathic. Previous work has shown that low levels of proviral DNA are found in most tissues of naturally infected rhesus macaques, but these proviruses are primarily latent. In contrast, viral RNA, indicative of viral replication, is restricted to tissues of the oral mucosa, where it is abundant. Here, we perform in situ hybridization on tissues from rhesus macaques naturally infected with simian FV (SFV). We show that superficial differentiated epithelial cells of the oral mucosa, many of which appear to be shedding from the tissue, are the major cell type in which SFV replicates. Thus, the innocuous nature of SFV infection can be explained by replication that is limited to differentiated superficial cells that are short-lived and shed into saliva. This finding can also explain the highly efficient transmission of FVs among NHPs.
The SonicFinder: An Interface that Uses Auditory Icons (Abstract Only)

The appropriate use of non-speech sounds has the potential toadd a great deal to the functionality of computer interfaces. Soundis a largely unexploited medium of output, even though it plays anintegral role in our everyday encounters with the world, one thatis complementary to vision. I argue that sound should be used incomputers as it is in the world, where it conveys information aboutthe nature of sound-producing events. Such a strategy leads toauditory icons, which are everyday sounds meant to conveyinformation about computer events by analogy with everyday events.Auditory icons are an intuitively accessible way to use sound toprovide multidimensional, organized information to users. These ideas are instantiated in the SonicFinder, which is anauditory interface I developed at Apple Computer. In thisinterface, information is conveyed using auditory icons as well asstandard graphical feedback. I discuss how events are mapped toauditory icons in the SonicFinder and illustrate how sound is usedby describing a typical interaction with this interface.Two majorgains are associated with using sound in this interface: anincrease in direct engagement with the model world of the computerand an added flexibility for users in getting information aboutthat world. These advantages seem to be due to the iconic nature ofthe mappings used between sound and the information it is toconvey. I discuss sound effects and source metaphors as methods ofextending auditory icons beyond the limitations implied by literalmappings and speculate on future directions for suchinterfaces.