Miracle-dz/UniFAR-FTUs huggingface dataset:
GeoCharacters creating visual character representations with geo-tagged photographs

In this thesis I will present an algorithm for constructing coherent visual representations of story characters from existing photographs with geotags. The core idea in this algorithm is that by pair wise searching for two opposing characters, a protagonist and an antagonist, it is possible to achieved good results by looking at the coherency and contrast of the tags assigned to pictures. An implementation of the algorithm that uses WordNet to measure the semantic similarity between tags is discussed, results are shown and the responses to a small user test are presented that show respondents perceived the generated visual representations as more coherent and more logical than collages created using only tag-based image retrieval.
On Medical Image Segmentation Based on Wavelet Transform

The Image segmentation is the focus in the image processing technology all the time. Medical image segmentation is an important application in the field of image segmentation. Wavelet transform is proposed to segment medical image. Firstly the gray level histogram of the medical image was processed using multiscale wavelet transform. Then the gray threshold was gradually emerged by the performance from large scale factor to small scale factor. At last the difference of the effect between traditional method and wavelet transform method were compared. The experimental results showed that the last method surpasses the traditional one in image segmentation and the results has been demonstrated to have validity and practicability. Image segmentation can achieve the anatomical structures and other interesting information of a medical image automatically or semi-automatically, which is helpful on the medical diagnosis.
Using Apriori with WEKA for Frequent Pattern Mining

Knowledge exploration from the large set of data,generated as a result of the various data processing activities due to data mining only. Frequent Pattern Mining is a very important undertaking in data mining. Apriori approach applied to generate frequent item set generally espouse candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper demonstrates the use of WEKA tool for association rule mining using Apriori algorithm.
Hypothalamic Orexin Prevents Hepatic Insulin Resistance via Daily Bidirectional Regulation of Autonomic Nervous System in Mice

Circadian rhythm is crucial for preventing hepatic insulin resistance, although the mechanism remains uncovered. Here we report that the wake-active hypothalamic orexin system plays a key role in this regulation. Wild-type mice showed that a daily rhythm in blood glucose levels peaked at the awake period; however, the glucose rhythm disappeared in orexin knockout mice despite normal feeding rhythm. Central administration of orexin A during nighttime awake period acutely elevated blood glucose levels but subsequently lowered daytime glucose levels in normal and diabetic db/db mice. The glucose-elevating and -lowering effects of orexin A were suppressed by adrenergic antagonists and hepatic parasympathectomy, respectively. Moreover, the expression levels of hepatic gluconeogenic genes, including Pepck, were increased and decreased by orexin A at nanomolar and femtomolar doses, respectively. These results indicate that orexin can bidirectionally regulate hepatic gluconeogenesis via control of autonomic balance, leading to generation of the daily blood glucose oscillation. Furthermore, during aging, orexin deficiency enhanced endoplasmic reticulum (ER) stress in the liver and caused impairment of hepatic insulin signaling and abnormal gluconeogenic activity in pyruvate tolerance test. Collectively, the daily glucose rhythm under control of orexin appears to be important for maintaining ER homeostasis, thereby preventing insulin resistance in the liver.
Enhanced pain-induced activity of pain-processing regions in a case-control study of episodic migraine

Objective The objective of this study was to identify brain regions having aberrant pain-induced activation in migraineurs, thereby gaining insight into particular aspects of pain processing that are atypical in migraineurs. Methods Functional magnetic resonance imaging assessed whole brain responses to painful heat in 24 adult episodic migraineurs who were at least 48 hours pain free and 27 healthy controls. Regions differentially activated in migraineurs compared to controls were identified. Activation intensities in these regions were correlated with headache frequency, number of migraine years, and time to next migraine attack. Results Migraineurs had greater pain-induced activation of lentiform nucleus, fusiform gyrus, subthalamic nucleus, hippocampus, middle cingulate cortex, premotor cortex, somatosensory cortex, and dorsolateral prefrontal cortex, and less activation in precentral gyrus and superior temporal gyrus. There were significant correlations between activation strength and headache frequency for middle cingulate (r = 0.627, p = 0.001), right dorsolateral prefrontal cortex (r = 0.568, p = 0.004), left fusiform gyrus (r = 0.487, p = 0.016), left precentral gyrus (r = 0.415, p = 0.044), and left hippocampus (r = 0.404, p = 0.050) and with number of migraine years for left fusiform gyrus (r = 0.425, p = 0.038). There were no significant correlations between activation strength and time to next migraine attack. Conclusions The majority of regions with enhanced pain-induced activation in headache-free migraineurs participate in cognitive aspects of pain perception such as attending to pain and pain memory. Enhanced cognitive pain processing by migraineurs might reflect cerebral hypersensitivity related to high expectations and hypervigilance for pain.
Exposing commercial value in social networks : matching online communities and businesses

This paper explores the problem of user recommendations in group settings. Research in networked social behavior has found evidence that individuals tend to form connections to those having similar interests to their own. Known as homophily, this tendency has been shown to significantly inform the structure of social networks. Collaborative filtering (CF) techniques, however, typically do not take into account explicit social relationships among users in predicting user preferences, although the importance of peer influence in user consumption and marketing has long been recognized. We test for the existence of homophily on the Yelp! dataset and find ample evidence that social ties are indicative of similar preferences. Based on this insight, experimental results show we are able to improve on traditional CF methods by 1) using clustering algorithms to detect communities of users with similar tastes, and 2) incorporating information about social structure in CF techniques. We find that leveraging social structure information improves the performance of CF and reveals relevant and valuable matches between businesses and the members of the group. These results have important implications for targeted marketing.
The research of the brain default network using cascading algorithm

As to studies on the human brain, researchers merely have focused on the structure, function, etc. of the encephalic region, and the cascading failure of the encephalic region is yet to be explored. Here, we recruited 15 age-matched normal people as the experimental group. We got Pearson correlation coefficient of 90 brain regions by using the resting brain functional connectivity and structured connection matrix by using one sample T test. Then, according to the load capacity model algorithm, each brain area node was assigned a certain amount of load. We selected the important node of the default mode network (DMN) to attack, and found that the scale of big degree attack cascading failure was greater than the small degree attack. Protecting the important brain areas is of great significance to prevent the brain diseases.
PRESSOR SUBSTANCES IN ARTERIAL HYPERTENSION

Pherentasin, a material producing a prolonged pressor response in rats, has been procured from the arterial blood of hypertensive patients and purified. Pherentasin is of small molecular size, non-protein in nature, dialyzable, soluble in water and 90 per cent ethanol, and extractable into organic solvents from alkaline solution. It contains an amino group essential to activity, which is probably primary, and also contains an active carbonyl group. It has been found in concentrations up to approximately 20 γ per liter of blood.
HIT_LTRC at TREC 2010 Blog Track: Faceted Blog Distillation

This paper describes our participation in the faceted blog distillation task at Blog Track 2010. In our approach, indri toolkit is applied for basic topic relevance retrieval. Then the Maximum Entropy (ME) model is adopted to judge the relevance of each blog to specified facet. Feed faceted relevance is calculated by integrating the average relevance of all blogs within a feed and the average relevance of the most relevant N blogs. Two implementations are applied to calculate feed faceted relevance. Experimental results on Blogs08 dataset show the effectiveness of our approach.
Genetic Rearrangements of the Regions Adjacent to Genes Encoding Heat-Labile Enterotoxins (eltAB) of EnterotoxigenicEscherichia coli Strains

ABSTRACT One of the most common bacterially mediated diarrheal infections is caused by enterotoxigenic Escherichia coli (ETEC) strains. ETEC-derived plasmids are responsible for the distribution of the genes encoding the main toxins, namely, the heat-labile and heat-stable enterotoxins. The origins and transfer modes (intra- or interplasmid) of the toxin-encoding genes have not been characterized in detail. In this study, we investigated the DNA regions located near the heat-labile enterotoxin-encoding genes (eltAB) of several clinical isolates. It was found that the eltAB region is flanked by conserved 236- and 280-bp regions, followed by highly variable DNA sequences which consist mainly of partial insertion sequence (IS) elements. Furthermore, we demonstrated that rearrangements of the eltAB region of one particular isolate, which harbors an IS91R sequence next toeltAB, could be produced by a recA-independent but IS91 sequence-dependent mechanism. Possible mechanisms of dissemination of IS element-associated enterotoxin-encoding genes are discussed.
BUILDING DAMAGE DUE TO 2008 WENCHUAN EARTHQUAKE AND COOPERATIVE ACTIVITIES ON DAMAGE RESTORATION BY JAPANESE EXPERTS

A destructive earthquake occurred on May 12, 2008, and caused extensive damage to Sichuan Province in China. Immediately after the event, Japanese scientific/engineering societies that had experiences and know-how on damage restoration of structures in the past major earthquakes jointly formed a liaison council to technically support the restoration of the hardest hit areas through mutual cooperation and sharing information. Since then, the council has been actively cooperating with Chinese experts from academic and engineering fields through damage investigations and extensive discussions on technical and practical issues for damage restoration. This paper briefly overviews damage to buildings and their restoration activities made in cooperation with Southwest Jiaotong University in Chengdu city, Sichuan Province.
An Open Architecture for Monitoring and Measuring QoS Indicators in Wireless Community Networks

Wireless community networks (WCNs) are wide-area wireless networks whose nodes are owned and managed by volunteers. These networks are rarely supported by any infrastructure for measuring network performance indicators. In this paper, we present the design, the implementation and the experimental evaluation of KIXEM, an open agent-based architecture, whose design meets the special requirements and characteristics of WCNs. The proposed architecture is designed to passively monitor multiple wireless routing nodes within a WCN, in order to extract flow based network measurements. KIXEM is evaluated on a simulated WCN, representing part of the Athens wireless metropolitan network.
Local Binary Patterns as Texture Descriptors for User Attitude Recognition

Texture plays an important role in numerous computer vision applications. Many methods for describing and analyzing of textured surfaces have been proposed. Variations in the appearance of texture caused by changing illumination and imaging conditions, for example, set high requirements on different analysis methods. In addition, real-world applications tend to produce a great deal of complex texture data to be processed that should be handled effectively in order to be exploited. A local binary pattern (LBP) operator offers an efficient way of analyzing textures. It has a simple theory and combines properties of structural and statistical texture analysis methods. LBP is invariant against monotonic gray-scale variations and has also extensions to rotation invariant texture analysis. Analysis of real-world texture data is typically very laborious and time consuming. Often there is no ground truth or other prior knowledge of the data available, and important properties of the textures must be learned from the images. This is a very challenging task in texture analysis.
Design of a Wearable Sensing System for Human Motion Monitoring in Physical Rehabilitation

Human motion monitoring and analysis can be an essential part of a wide spectrum of applications, including physical rehabilitation among other potential areas of interest. Creating non-invasive systems for monitoring patients while performing rehabilitation exercises, to provide them with an objective feedback, is one of the current challenges. In this paper we present a wearable multi-sensor system for human motion monitoring, which has been developed for use in rehabilitation. It is composed of a number of small modules that embed high-precision accelerometers and wireless communications to transmit the information related to the body motion to an acquisition device. The results of a set of experiments we made to assess its performance in real-world setups demonstrate its usefulness in human motion acquisition and tracking, as required, for example, in activity recognition, physical/athletic performance evaluation and rehabilitation.
Parallel Computing of CG Using an Open Source Windows Grid

Recent times have seen a strong increase in the size, scope and number of information processing tasks computers are commonly used to perform. Grids are one form of distributed computing that, generally, aim for HTC (high throughput computing). Grids can also be constructed to perform HPC (high performance computing) at a relatively low cost when compared to clusters. There currently exists open source middleware for building grids. But when such grids consist of Windows machines it is difficult to use members of the grid efficiently. This research concerns the construction of a cheap grid of Windows machines. In this paper, we describe a Windows grid that uses multiple core processors which are becoming increasingly popular.
Application of nitroarene dioxygenases in the design of novel strains that degrade chloronitrobenzenes

Widespread application of chloronitrobenzenes as feedstocks for the production of industrial chemicals and pharmaceuticals has resulted in extensive environmental contamination with these toxic compounds, where they pose significant risks to the health of humans and wildlife. While biotreatment in general is an attractive solution for remediation, its effectiveness is limited with chloronitrobenzenes due to the small number of strains that can effectively mineralize these compounds and their ability to degrade only select isomers. To address this need, we created engineered strains with a novel degradation pathway that reduces the total number of steps required to convert chloronitrobenzenes into compounds of central metabolism. We examined the ability of 2‐nitrotoluene 2,3‐dioxygenase from Acidovorax sp. strain JS42, nitrobenzene 1,2‐dioxygenase (NBDO) from Comamonas sp. strain JS765, as well as active‐site mutants of NBDO to generate chlorocatechols from chloronitrobenzenes, and identified the most efficient enzymes. Introduction of the wild‐type NBDO and the F293Q variant into Ralstonia sp. strain JS705, a strain carrying the modified ortho pathway for chlorocatechol metabolism, resulted in bacterial strains that were able to sustainably grow on all three chloronitrobenzene isomers without addition of co‐substrates or co‐inducers. These first‐generation engineered strains demonstrate the utility of nitroarene dioxygenases in expanding the metabolic capabilities of bacteria and provide new options for improved biotreatment of chloronitrobenzene‐contaminated sites.
Integration of Heterogeneous Web Services for Event-Based Social Networks

Event-based online social networks are Internet-based services that enable users to participate in real world experiences together. Event-based social networks can be created by a community of end-users based on their own interests in specific types of event and sources of event information. We propose a method to create such event-based social networks through integration of existing online information sources of events using a Semantic Web framework. In order to match people with common interests in such activities to self-organize into a social network, we integrate information from heterogeneous information sources related to event schedules, ticket purchases, and group attendance from multiple online sources. The Semantic Web framework is used to represent these heterogeneous datasets and unstructured online data is converted into ontologies. Links between event information in different sources are discovered using both the syntactic similarity and semantic similarity between ontology classes. We use an approach based on Latent Dirichlet Allocation (LDA) over the space of topics related to each event and user profiles for event recommendation. This enables the event-based social network to recommend friends based on shared interest in an event - online friendship is established after mutual attendance of the same event. We demonstrate this approach with EasyGo, a web-based mashup application which integrates information of events such as concerts, sports, theatres, as well as tickets and group purchase from multiple online sources.
A Comparative Anatomy of Credit Risk Models

Within the past two years, important advances have been made in modeling credit risk at the portfolio level. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Less progress has been made, however, with comparative analyses. Direct comparison often is not straightforward, because the diAerent models may be presented within rather diAerent mathematical frameworks. This paper oAers a comparative anatomy of two especially influential benchmarks for credit risk models, the RiskMetrics Group’s CreditMetrics and Credit Suisse Financial Product’s CreditRisk a . We show that, despite diAerences on the surface, the underlying mathematical structures are similar. The structural parallels provide intuition for the relationship between the two models and allow us to describe quite precisely where the models diAer in functional form, distributional assumptions, and reliance on approximation formulae. We then design simulation exercises which evaluate the eAect of each of these diAerences individually. ” 2000 Elsevier Science B.V. All rights reserved.
T‐wise combinatorial interaction test suites construction based on coverage inheritance

Combinatorial interaction testing (CIT) is a testing technique that requires covering all t‐sized tuples of values out of n parameter attributes or properties modelled after the input parameters or the configuration domain of a system under test. CIT test suites have shown to be very effective in software testing already at pairwise (t = 2) level, and the effectiveness of CIT grows with the tuple width t. Unfortunately, the number of tuples to be tested also does grow. In order to reduce the testing effort, researchers addressed the issue of computing minimal‐sized CIT test suites with effective and scalable algorithms. However, still very few generally applicable t‐wise covering construction algorithms (and tools) do exist in literature. This paper presents an original greedy algorithm to compute t‐wise covering mixed covering arrays with constant space complexity, irrespective of the number of involved parameters and strength of interaction. The proposed algorithm has been implemented in a prototype tool, featuring also support for user constraints over the inputs. Assessment of the tool performance on a set of large, real‐world test systems is reported, with results encouraging its adoption in industrial production environments. Copyright © 2011 John Wiley & Sons, Ltd.
Location Prediction via Social Contents and Behaviors: Location-Aware Behavioral LDA

Massive user generated contents in social media platforms like Twitter asks for analyzing conversations on topics of interest. Unlike traditional text-dominant settings, such platforms are rich in social interactions that are accompanied with textual content, e.g., reply and mention. Furthermore, the proliferation of mobile devices equipped with geo-positioning components creates a growing volume of social content with geographical locations. However, social content analysis considering location has not been well studied. It is still vague whether a linkage between location and topics from social interactions exists. In this paper, we propose a generative model, called Location-aware Behavioral LDA (La-LDA), that is not only addressing what are topics of interest in social content, but also linking topics with 1) user interactions, and 2) locations. We analyzed Twitter and our experimental studies show that our model can find location-aware topics relevant to user behaviors that are specified by the social media platform and also can be applied for location prediction.
Petri Net Models of Pull Control Systems for Assembly Manufacturing Systems

Pull control systems are a good way to control material flow in manufacturing systems. In this paper we are interested in four control mechanisms: the Base Stock Control System (BSCS), the Kanban Control System (KCS), the Generalized Kanban Control System (GKCS) and the Extended Kanban Control System (EKCS). The BSCS and the KCS are well known simple pull control mechanisms. The better performing GKCS and EKCS were recently defined as combinations of both the BSCS and the KCS. Most of the work on the modeling and analysis of these pull control systems is ill ustrated on systems having stages in series. The purpose of this paper is to provide a common modeling approach based on Petri nets for these four control systems in the case of assembly manufacturing systems.