Miracle-dz/UniFAR-FTUs huggingface dataset:
Rating philosophy and dynamic properties of internal rating systems: a general framework and an application to backtesting

The paper draws a general framework for asset and default dynamics, separating the influence of the economic cycle into a component which is embedded in the rating system and an unobservable risk factor that determines the movements of defaults around the ex ante estimated PDs. The two components – the sensitivity of ratings to credit cycle and conditional asset correlation - can be quantified through a Maximum Likelihood approach, giving a measure of the cyclicality of the rating system, and allowing for a number of applications: among those the modified binomial test proposed here.
Boundary Spanning Practices in BPM: A Dynamic Capability Perspective

Collaborating with actors from outside of an organization, such as consultancies, customers, or suppliers, has proven to be a success factor for business process change. Research on business process change maturity views effective collaboration as a sign for mature organizations. It argues that establishing systematic cross-boundary collaboration and making use of business process change networks is a key challenge that, while not yet achieved, lies ahead of the majority of organizations. However, business process change practice seems to struggle with implementing these envisioned comprehensive collaborative policies. Despite the importance of the phenomenon, research has not yet been able to provide sufficient insight into potential barriers to establishing cross-boundary collaboration in business process change. Therefore, we conduct nine in-depth case studies and explore barriers to collaboration with firm-external actors. Drawing from the Resource-Based View, dynamic capability and boundary spanning theory, we identify a set of resource dependencies that hinder establishing collaborative business process change. Based on these findings, we lay out elements of a problem-oriented theory which contributes to coping with the challenges of and to harvesting the potential benefits of collaboration in business process change.
A Novel Stack based Dynamic Programming for Reducing Memory Complexity Applied on DNA Sequences

Dynamic programming partitions the problem into not completely independent sub problems and solves every sub problem just once and then saves its answer in a table in forward path. The required space for this table usually is proportional to the square of the input size that is contained a huge part of memory. In this paper we describe a new method for reducing the space complexity of dynamic programming. In this method, that information is saved in forward path, which they cannot reproduce at backward path. A stack is used for saving this data. By this way the path of constructing optimal solution can be reproduced by using saved information in stack. We can find some rules for selecting saved information. As an example we applied this method on Longest Common Subsequence (LCS) problem for global alignment of DNA sequences. As we examined in proposed algorithm, the size of stack in comparing to using space for LCS algorithm was reduced about 10 times and we could increase the input size in global alignment
Learning tag relevance by neighbor voting for social image retrieval

Social image retrieval is important for exploiting the increasing amounts of amateur-tagged multimedia such as Flickr images. Since amateur tagging is known to be uncontrolled, ambiguous, and personalized, a fundamental problem is how to reliably interpret the relevance of a tag with respect to the visual content it is describing. Intuitively, if different persons label similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Starting from this intuition, we propose a novel algorithm that scalably and reliably learns tag relevance by accumulating votes from visually similar neighbors. Further, treated as tag frequency, learned tag relevance is seamlessly embedded into current tag-based social image retrieval paradigms. Preliminary experiments on one million Flickr images demonstrate the potential of the proposed algorithm. Overall comparisons for both single-word queries and multiple-word queries show substantial improvement over the baseline by learning and using tag relevance. Specifically, compared with the baseline using the original tags, on average, retrieval using improved tags increases mean average precision by 24%, from 0.54 to 0.67. Moreover, simulated experiments indicate that performance can be improved further by scaling up the amount of images used in the proposed neighbor voting algorithm.
Vulnerabilities Caused by Single-Bit Errors in the SELinux Kernel Policy File

System security is often analyzed and studied assuming error-free operation of the computer. Recently, due to increasing transistor densities and processing speeds, there has been renewed interest in the research of single-bit transient errors and the effect they have on availability and performance. We believe that it is also important to analyze security enforcement, specifically kernel level enforcement, in the face of single-bit transient errors. Error-injection experiments are used to quantify the impact that single-bit errors have on the Linux kernel with SELinux enhancements. The SELinux enhancements provide Linux with mandatory access controls, fine-grained access control lists, and role-based-access controls. These controls are meant to isolate processes and limit the power of users, including the root user. Any failure to enforce the security policy could allow a major security breach. Error-injections are performed on the policy database as it is being loaded into the kernel. Tests are then run against a target file system to determine if the error created any security vulnerabilities. The results show that approximately 33% of the errors cause the policy to fail during loading. Of the remaining errors we found 8 which allowed one of our prohibited tests to succeed.
Passive collision force suppression mechanism for robot manipulator

This paper presents a robot manipulator with collision force suppression mechanism that can passively suppress collision force. The collision suppression mechanism consists of a release air pad, a transmission rack, a clutch gear and a compression spring. An air cushion bag is attached to the exterior of the robot manipulator. If a robot manipulator collides with an object or human when a task is performed, the collision force suppression mechanism disjoints the specific joint corresponding to the direction of the collision force to reduce the collision force. The robot manipulator will then return to the former task when the colliding object is eliminated. Through collision experiments, the effectiveness of the collision force suppression mechanism is verified.
Integrating Local Search into Genetic AlgorithmsColin ReevesSchool of Mathematical and Information

Genetic algorithms (GAs) have proved to be a versatile and eeective approach for solving combinatorial optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. These often involve incorporating other methods such as simulated annealing or local optimization as a post-processor to the basic GA strategy of selection and reproduction. In this paper, rather than keeping the local optimization separate from recombination, we intend to explore the possibilities of integrating it more directly into the standard genetic algorithm. The graph partitioning problem and the no-wait owshop sequencing problem will be used to illustrate the approach.
Efficient content based image retrieval based on Semantic Object Detection

This paper proposes an efficient content-based image retrieval scheme based on Semantic Object Detection (SOD). The feature extraction process uses quantized discrete cosine transform (DCT) blocks where the retrieval of the query image is performed using a histogram-based similarity measure. SOD aims to reduce the size of the database from which the retrieval of similar images is conducted. The use of SOD has been shown to improve the retrieval performance and outperform related work in the literature.
Real-time rough refraction

We present an algorithm to render objects of transparent materials with rough surfaces in real-time, under distant illumination. Rough surfaces cause wide scattering as light enters and exits objects, which significantly complexifies the rendering of such materials. We present two contributions to approximate the successive scattering events at interfaces, due to rough refraction: First, an approximation of the bidirectional scattering function (BSDF = BRDF + BTDF), using spherical Gaussians, suitable for real-time estimation of environment lighting using pre-convolution; second, a combination of cone tracing and macro-geometry filtering to efficiently integrate the scattered rays at the exiting interface of the object. We demonstrate the quality of our approximation by comparison against stochastic raytracing.
Distributed Fiber Optical Sensing of Oxygen with Optical Time Domain Reflectometry

In many biological and environmental applications spatially resolved sensing of molecular oxygen is desirable. A powerful tool for distributed measurements is optical time domain reflectometry (OTDR) which is often used in the field of telecommunications. We combine this technique with a novel optical oxygen sensor dye, triangular-[4] phenylene (TP), immobilized in a polymer matrix. The TP luminescence decay time is 86 ns. The short decay time of the sensor dye is suitable to achieve a spatial resolution of some meters. In this paper we present the development and characterization of a reflectometer in the UV range of the electromagnetic spectrum as well as optical oxygen sensing with different fiber arrangements.
The first planet detected in the WTS: an inflated hot Jupiter in a 3.35 d orbit around a late F star

We report the discovery of WTS-1b, the first extrasolar planet found by the WFCAM Transit Survey, which began observations at the 3.8-m United Kingdom Infrared Telescope. Light curves comprising almost 1200 epochs with a photometric precision of better than 1 per cent to J � 16 were constructed for � 60000 stars and searched for periodic transit signals. For one of the most promising transiting candidates, highresolution spectra taken at the Hobby-Eberly Telescope allowed us to estimate the spectroscopic parameters of the host star, a late-F main sequence dwarf (V=16.13) with possibly slightly subsolar metallicity, and to measure its radial velocity variations. The combined analysis of the light curves and spectroscopic data resulted in an orbital period of the substellar companion of 3.35 days, a planetary mass of 4.01±0.35MJ, and a planetary radius of 1.49 +0.16 0.18 RJ. WTS-1b has one of the largest radius anomalies among the known hot Jupiters in the mass range 3-5MJ.
Using bidirectional associative memories for joint spectral envelope modeling in voice conversion

The spectral envelope is the most natural representation of speech signal. But in voice conversion, it is difficult to directly model the raw spectral envelope space, which is high dimensional and strongly cross-dimensional correlated, with conventional Gaussian distributions. Bidirectional associative memory (BAM) is a two-layer feedback neural network that can better model the cross-dimensional correlations in high dimensional vectors. In this paper, we propose to reformulate BAMs as Gaussian distributions in order to model the spectral envelope space. The parameters of BAMs are estimated using the contrastive divergence algorithm. The evaluations on likelihood show that BAMs have better modeling ability than Gaussians with diagonal covariance. And the subjective tests on voice conversion indicate that the performance of the proposed method is significantly improved comparing with the conventional GMM based method.
Development of a Scalable System for Stealthy P 2 P Botnet Detection

Peer-to-peer (P2P) botnets are the modern and most resilient bot structure which might be more difficult to take down and stealthier to notice their malicious activity, in view that of which these are adopted by means of the various recent botmasters. In this paper, we recommend a novel botnet detection process which is ready to identify resilient P2P botnets. Our method at the beginning identifies the p2p communications present within the network. It then derives p2p traffic and further distinguishes between the botnet generated traffic and legitimate traffic. The parallelized computation makes scalability a default function of our process. Excessive detection accuracy and prodigious scalability are the extra features of our proposed process. Keywords—Botnet, network security, P2P, intrusion detection.
"Over-Learning" Phenomenon of Wavelet Neural Networks in Remote Sensing Image Classifications with Different Entropy Error Functions

Artificial neural networks are widely applied for prediction, function simulation, and data classification. Among these applications, the wavelet neural network is widely used in image classification problems due to its advantages of high approximation capabilities, fault-tolerant capabilities, learning capacity, its ability to effectively overcome local minimization issues, and so on. The error function of a network is critical to determine the convergence, stability, and classification accuracy of a neural network. The selection of the error function directly determines the network’s performance. Different error functions will correspond with different minimum error values in training samples. With the decrease of network errors, the accuracy of the image classification is increased. However, if the image classification accuracy is difficult to improve upon, or is even decreased with the decreasing of the errors, then this indicates that the network has an “over-learning” phenomenon, which is closely related to the selection of the function errors. With regards to remote sensing data, it has not yet been reported whether there have been studies conducted regarding the “over-learning” phenomenon, as well as the relationship between the “over-learning” phenomenon and error functions. This study takes SAR, hyper-spectral, high-resolution, and multi-spectral images as data sources, in order to comprehensively and systematically analyze the possibility of an “over-learning” phenomenon in the remote sensing images from the aspects of image characteristics and neural network. Then, this study discusses the impact of three typical entropy error functions (NB, CE, and SH) on the “over-learning” phenomenon of a network. The experimental results show that the “over-learning” phenomenon may be caused only when there is a strong separability between the ground features, a low image complexity, a small image size, and a large number of hidden nodes. The SH entropy error function in that case will show a good “over-learning” resistance ability. However, for remote sensing image classification, the “over-learning” phenomenon will not be easily caused in most cases, due to the complexity of the image itself, and the diversity of the ground features. In that case, the NB and CE entropy error network mainly show a good stability. Therefore, a blind selection of a SH entropy error function with a high “over-learning” resistance ability from the wavelet neural network classification of the remote sensing image will only decrease the classification accuracy of the remote sensing image. It is therefore recommended to use an NB or CE entropy error function with a stable learning effect.
Word-Length Oriented Multiobjective Optimization of Area and Power Consumption in DSP Algorithm Implementation

The word-length of functional units (FU) has a great impact on design costs. This paper addresses the problem of choosing different word-lengths for each FU while considering circuit area and power consumption. A high-level synthesis tool is used to minimize the circuit area and power consumption by selecting an optimal word-length for each FU in the system. Our results demonstrate that by customizing word lengths to non-standard sizes, savings can be made in the overall area and power without losing accuracy
Development of Hemispheric Specialization for Lexical Pitch–Accent in Japanese Infants

Infants' speech perception abilities change through the first year of life, from broad sensitivity to a wide range of speech contrasts to becoming more finely attuned to their native language. What remains unclear, however, is how this perceptual change relates to brain responses to native language contrasts in terms of the functional specialization of the left and right hemispheres. Here, to elucidate the developmental changes in functional lateralization accompanying this perceptual change, we conducted two experiments on Japanese infants using Japanese lexical pitch–accent, which changes word meanings with the pitch pattern within words. In the first behavioral experiment, using visual habituation, we confirmed that infants at both 4 and 10 months have sensitivities to the lexical pitch–accent pattern change embedded in disyllabic words. In the second experiment, near-infrared spectroscopy was used to measure cortical hemodynamic responses in the left and right hemispheres to the same lexical pitch–accent pattern changes and their pure tone counterparts. We found that brain responses to the pitch change within words differed between 4- and 10-month-old infants in terms of functional lateralization: Left hemisphere dominance for the perception of the pitch change embedded in words was seen only in the 10-month-olds. These results suggest that the perceptual change in Japanese lexical pitch–accent may be related to a shift in functional lateralization from bilateral to left hemisphere dominance.
Plant Running Management Based on Wireless Sensor Network

The information is an effective approach to raise the level of plant management in modern enterprises. Aimed at the problem that most of the enterprises face wire-monitoring, so starting with the work in plant running management and adopting the technology of WSN, and analyzes deeply the based on wireless sensor network in plant running management. By collecting plants states signal in WSN, get monitoring data of plant in time, and that clear up and analyzes deeply it, and build the failure diagnostician database of plants. It’s provided availability data for preventing failure and for maintain work.
A Novel ( t , n ) Threshold Convertible Authenticated Encryption Scheme

Authenticated encryption schemes allow the signer to generate an authenticated ciphertext such that only the designated recipient has the ability to recover the message and verify its corresponding signature. It can be seen that authenticated encryption schemes are applicable to lots of E-Commerce applications like credit card transactions, since these schemes simultaneously fulfill the security requirements of integrity, authenticity, confidentiality and non-repudiation. For more extended applications, an alternative designed for multi-user setting is necessary. In this paper, we propose a novel group-oriented authenticated encryption scheme called the (t, n) threshold convertible authenticated encryption (TCAE) scheme. The (t, n)-TCAE scheme can allow any t or more of n signers cooperatively to produce a valid authenticated ciphertext on behalf of the original group while less than or equal to t − 1 can not. Further, when the case of a later dispute over repudiation occurs, the designated recipient has the ability to convert the signature into an ordinary one for convincing anyone of the signers’ dishonesty.
Multi-Standard Quadratic Optimization Problems

A Standard Quadratic Optimization Problem (StQP) consists of maximizing a (possibly indefinite) quadratic form over the standard simplex. Likewise, in a multi-StQP we have to maximize a (possibly indefinite) quadratic form over the cartesian product of several standard simplices (of possibly different dimensions). Two converging monotone interior point methods are established. Further, we prove an exact cone programming reformulation for establishing rigid yet affordable bounds and finding improving directions.
The Roles and Evolutionary Patterns of Intronless Genes in Deuterostomes

Genes without introns are a characteristic feature of prokaryotes, but there are still a number of intronless genes in eukaryotes. To study these eukaryotic genes that have prokaryotic architecture could help to understand the evolutionary patterns of related genes and genomes. Our analyses revealed a number of intronless genes that reside in 6 deuterostomes (sea urchin, sea squirt, zebrafish, chicken, platypus, and human). We also determined the conservation for each intronless gene in archaea, bacteria, fungi, plants, metazoans, and other eukaryotes. Proportions of intronless genes that are inherited from the common ancestor of archaea, bacteria, and eukaryotes in these species were consistent with their phylogenetic positions, with more proportions of ancient intronless genes residing in more primitive species. In these species, intronless genes belong to different cellular roles and gene ontology (GO) categories, and some of these functions are very basic. Part of intronless genes is derived from other intronless genes or multiexon genes in each species. In conclusion, we showed that a varying number and proportion of intronless genes reside in these 6 deuterostomes, and some of them function importantly. These genes are good candidates for subsequent functional and evolutionary analyses specifically.
Association Analysis of ULK1 with Crohn's Disease in a New Zealand Population

The gene ULK1 is an excellent candidate for Crohn's disease (CD) due to its role in autophagy. A recent study provided evidence for the involvement of ULK1 in the pathogenesis of CD (Henckaerts et al., 2011). We attempted to validate this association, using a candidate gene SNP study of ULK1 in CD. We identified tagging SNPs and genotyped these SNPs using the Sequenom platform in a Caucasian New Zealand dataset consisting of 406 CD patients and 638 controls. In this sample, we were able to demonstrate an association between CD and several different ULK1 SNPs and haplotypes. Phenotypic analysis showed an association with age of diagnosis 17–40 years and inflammatory behaviour. The findings of this study provide evidence to suggest that genetic variation in ULK1 may play a role in interindividual differences in CD susceptibility and clinical outcome.
ENABLING COLLABORATION IN THE SEMANTIC GRID : SURVEY OF WEB SERVICE COMPOSITION APPROACHES

Web Service is loosely coupled highly accessible distributed computing technology that can expose applications beyond the firewall. Composition of Web Services has received much attention from the business and the research community. Composition techniques are classified as static, dynamic and semi-automatic composition, each addressing different application areas and requirements. In this contribution we analyze workflow-based and semantic-based composition approaches, primarily focusing on the facilitation to the service participants and the scalability required in a Grid-based environment.