Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of Image Matching by using the Proximity Criterion: Application to Omnidirectional and Perspective Images

Problem statement: In computer vision, matching is an important phase for several applications (object reconstruction, robot navigation .). The similarity measures used provided results which could be improved. Approach: This research proposed to improve image matching by using the proximity criterion. The similarity measures used mutual information and correlation coefficient. The matching was done between neighborhoods of points of interest extracted from the images. The second chance algorithm was also applied. We have worked in case which the sensor had a slight displacement between two images. The tests were performed on omnidirectional and perspective grayscale images. Results: The improvement by introducing the proximity criterion reached 15.9% for non-noised perspective images, 32.1% for noised perspective images, 47.69% for non-noised omnidirectional images and 58.5% for noised omnidirectional images. Conclusion/Recommendations: The introduction of the proximity criterion has significantly improved the performance of the matching. The method is recommended in mobile robotics, knowing that a good matching leads to a better location and better movement of the robot.
TSNLP: Test Suites for Natural Language Processing

The growing language technology industry needs measurement tools to allow researchers, engineers, managers, and customers to track development, evaluate and assure quality, and assess suitability for a variety of applications.The TSNLP (Test Suites for Natural Language Processing) project has investigated various aspects of the construction, maintenance and application of systematic test suites as diagnostic and evaluation tools for NLP applications. The paper summarizes the motivation and main results of TSNLP: besides the solid methodological foundation of the project, TSNLP has produced substantial (i.e. larger than any existing general test suites) multi-purpose and multi-user test suites for three European languages together with a set of specialized tools that facilitate the construction, extension, maintenance, retrieval, and customization of the test data.The publicly available results of TSNLP represent a valuable linguistic resource that has the potential of providing a wide-spread pre-standard diagnostic and evaluation tool for both developers and users of NLP applications.
Cyberbullying Detection on Social Network Services

Social networks such as Facebook or Twitter promote the communication between people but they also lead to some excessive uses on the Internet such as cyberbullying for malicious users. In addition, the accessibility of the social network also allows cyberbullying to occur at anytime and evoke more harm from other users’ dissemination. This study collects cyberbullying cases in Twitter and attempts to establish an auto-detection model of cyberbullying tweets base on the text, readability, sentiment score, and other user information to predict the tweets with harassment and ridicule cyberbullying tweets. The novelty of this study is using the readability analysis that has not been considered in past studies to reflect the author's education level, age, and social status. Three data mining techniques, k-nearest neighbors, support vector machine, and decision tree are used in this study to detect the cyberbullying tweets and select the best performance model for cyberbullying prediction.
A Scalable High Performant Cholesky Factorization for Multicore with GPU Accelerators LAPACK Working Note #223

We present a Cholesky factorization for multicore with GPU accelerators. The challenges in developing scalable high performance algorithms for these emerging systems stem from their heterogeneity, massive parallelism, and the huge gap between the GPUs’ compute power vs the CPU-GPU communication speed. We show an approach that is largely based on software infrastructures that have already been developed for homogeneous multicores and hybrid GPU-based computing. The algorithm features two levels of nested parallelism. A coarse-grained parallelism is provided by splitting the computation into tiles for concurrent execution between GPUs. A ﬁne-grained parallelism is further provided by splitting the work-load within a tile for high eﬃciency computing on GPUs but also, in certain cases, to beneﬁt from hybrid computations by using both GPUs and CPUs. Our resulting computational kernels are highly optimized. An eﬃcient task scheduling mechanism ensures a load balanced execution over the entire multicore with GPU accelerators system. Furthermore, the communication overhead is minimized by trading oﬀ the amount of memory allocated on GPUs. This results in a scalable hybrid Cholesky factorization of unprecedented performance. In particular, using NVIDIA’s Tesla S1070 (4 C1060 GPUs, each with 30 cores @1.44 GHz) connected to two dual-core AMD Opteron @1.8GHz processors, we reach up to 1 . 163 TFlop/s in single and up to 275 GFlop/s in double precision arithmetic
Profiles as Conversation: Networked Identity Performance on Friendster

Profiles have become a common mechanism for presenting one’s identity online. With the popularity of online social networking services such as Friendster.com, Profiles have been extended to include explicitly social information such as articulated "Friend" relationships and Testimonials. With such Profiles, users do not just depict themselves, but help shape the representation of others on the system. In this paper, we will discuss how the performance of social identity and relationships shifted the Profile from being a static representation of self to a communicative body in conversation with the other represented bodies. We draw on data gathered through ethnography and reaffirmed through data collection and visualization to analyze the communicative aspects of Profiles within the Friendster service. We focus on the role of Profiles in context creation and interpretation, negotiating unknown audiences, and initiating conversations. Additionally, we explore the shift from conversation to static representation, as active Profiles fossilize into recorded traces.
Image-Content-Adaptive Color Breakup Index for Field-Sequential-Color Displays Using Dominant Visual Saliency Method

Proposing an unprecedented color breakup (CBU) evaluation index based on dominant visual saliency of natural images for field-sequential-color displays, this paper achieves a linear correlation coefficient of 0.82 between subjective and objective CBU visibilities. Consequently, this study is promising for being a criterion to assess CBU in FSC-related devices—low power and virtual/augmented reality displays.
Scalable Computing: Practice and Experience

The X1.V1 platform is a service oriented architecture, deployed on a set of Virtual Machines, that integrates services for managing fundamental activities in the ehealth scenario, such as Patient Identification, Clinical Document Repository, Prescription, and many others. The efficient provisioning of such services to citizens and to health professionals requires the adoption of increasingly powerful computing systems, in order to ensure that the growing number of service requests are served in acceptable time, even in case of computational peaks. Porting the X1.V1 platform on the Cloud could address the problem of the computational requirement mutability, since Cloud elasticity allows the reallocation of resources to Virtual Machines when necessary. This paper proposes a framework for managing the execution of the X1.V1 platform on the Cloud. This framework enables an easy, quick, and secure management of the Cloud resources allocation and reallocation to X1.V1 Virtual Machines, in order to enhance the platform performances, optimize resource utilization and, consequently, reduce the whole services cost. The design of the framework is focused on security aspects as well, because unauthorized accesses could lead to serious inefficiencies of the ehealth services. Finally, besides describing the architecture design and implementation of the X1.V1 Cloud manager framework, this paper also presents a set of experimental results which confirm the validity of the proposed approach to solve the X1.V1 platform performance issues in distinct reference scenarios.
Random-Walk Term Weighting for Improved Text Classification

Quite recently, a few hundreds of semantic concepts are detected automatically with varied performance and subsequently, a new video retrieval paradigm of query-by-concept emerges. In this paper, we consider the problem of exploiting the potential of learned semantics concepts, together with the combination of traditional methods, for automatic and interactive retrieval. We argue that it is important, in both automatic and interactive retrieval scenarios, to find a few relevant concepts to search with, given a multimedia query. For automatic retrieval, we show that both text and image inputs are useful for solving this query-concept-mapping (QUCOM) problem. For interactive retrieval, searching with relevant concepts plus conventional feedback methods is quite effective and is robust to initial search results. Experimental evidence on the search task of TRECVID 2006 shows that by solving QUCOM with a large lexicon of 311 semantic concept detectors, the automatic retrieval performance increases 20% and the interactive retrieval performance has the potential to outperform the state-of-the-art systems.
Hamburger Beitrage zur Angewandten Mathematik An Active Set-type Newton Method for Constrained Nonlinear Systems

We consider the problem of nding a solution of a nonlinear system of equations subject to some box constraints. To this end, we introduce a new active set-type Newton method with global and local fast convergence properties. The method generates feasible iterates and has to solve only one linear system of equations at each iteration. Due to our active set strategy, this linear system is of reduced dimension.
E-Utaut : An Extended Model in Addressing Student Course Registration

Student course registration is a fundamental process within most educational institutions enabling students to interactively ascertain courses to be registered for seasonally. The adoption of software based registration system has eliminated the fundamentals issues associated with previous based registration systems. The restrictiveness of UTAUT was a fundamental purpose for extending it in addressing course registration using two additional construct of UTUAT: ease of use and system availability. Eighteen (18) anonymous system users cutting across six universities with experience ranging between 1 – 12 years of system usage and aging ranging between 18 – 30 years were employed for this research. All system users were employed on a voluntary basis using the construct of UTAUT: performance expectancy, effort expectancy, and social influence, ease of use, system availability and facilitating condition. The performance of the Extended UTUAT (E-UTUAT) was portrayed using six graphs depicting UTAUT constructs with results ranging for 83%, 39%, 56%, 72%, 72% and 67% for Yes respectively.
Intangible Assets : How the Interaction of Computers and Organizational Structure Affects Stock Market Valuations

This paper investigates the proposition that the widespread use of information technology has increased investment in intangible organizational assets. Using firm-level data, we find that each dollar of installed computer capital in a firm is associated with at least five dollars of market value, after controlling for other assets. We interpret this value as revealing the existence of a large stock of intangible assets that are complementary with computer investment. Using data on organizational practices at each firm, we identify a specific cluster of practices that appear to represent at least some portion of these intangible assets. Not only are these practices correlated with computer investments, but firms that combine higher computer investments with these organizational characteristics have disproportionate increases in their market valuations. We conclude that investors believe that the contribution of computers is increased when they are combined with certain intangible assets, specifically including the cluster of organizational changes that we have identified. In developed economies, production not only requires the traditional factors such as capital and labor, but skills, organizational structures, know-how, information, and other factors that are collectively referred to as " intangible assets. " Detailed investigation of these types of assets has found that they are often large and have substantial productivity benefits. For example, Jorgenson and Fraumeni (1995) found that the stock of " human capital " in the U.S. economy dwarfs the stock of physical capital and has grown substantially over time; B. (1996) have found that the R&D assets bring benefits in the form of positive marginal product and market valuation. Results from analyses of " Tobin's q " have shown that the stock market valuation of firms has increasingly diverged from their measured book One possible explanation for the recent increase in Tobin's q increasing importance of intangible capital is the growing use of information technology and the associated early applications of computers were primarily directed at factor substitution (particularly of low-skill clerical workers), modern uses of computers and flexible manufacturing technologies have both enabled and necessitated substantial organizational redesign Bresnahan, Brynjolfsson and Hitt, 1999). Collectively, these papers argue for a Intangible Assets Page 2 complementarity between computer investment and organizational investment, and specifically a relationship between information technology use and increased demand for skilled workers, greater decentralization of decision rights, and team-oriented production. Moreover, case studies suggest that these complementary investments are large. For example, less than 20% of the …
Fuzzy Rule Classifier for Generalized K-Label Set Ensemble

In multi-label classification, set of labels are associated with each example. An algorithm called Random k-labelsets (RAkEL) is an algorithm for multi-label classification that follows problem transformation approach. RAkEL algorithm uses Label powerset (LP) classifier and it assumes equal weightage for each label set. To overcome this drawback, a new approach is reported in the literature that is GLE. GLE performs the basis expansion method to train LP classifier on random k labelsets. To decrease the global error between the estimated and ground truth, the expansion coefficients are learned. GLE uses SVM classifier which uses crisp vales as the base classifier. Fuzzy rule classifier (FURIA) as reported in literature gives the better results compared with other rule based classifiers, for problem transformation methods such as Binary Relevance, Classifier Chain, and LP. It would be interesting to observe the performance of GLE with FURIA. This work aims at implementation of GLE with FURIA algorithm and compares its performance using SVM as a base classifier. Experimental results shows that GLE using fuzzy rule classifier FURIA provides better performance in terms of hamming loss, ranking loss, subset 0/1 loss, one error, average precision. Keyword:multi label classification, LP, RAkEL, GLE, fuzzy rule classifier
Recent citations

Computational scientists often develop large models and codes intended to be used by larger user communities or for repetitive tasks such as parametric studies. Lowering the barrier of entry for access to these codes is often a technical and sociological challenge. Portals help bridge the gap because they are well known interfaces enabling access to a large variety of resources, services, applications, and tools for private, public, and commercial entities, while hiding the complexities of the underlying software systems to the user. This paper presents an overview of the current state-of-the-art in grid portals, based on a component approach that utilizes portlet frameworks and the most recent Grid standards, the Web Services Resource Framework and a summary of current DOE portal efforts.
Mobile Cloud Computing

Cloud computing is used to offer programmes to mobile devices via mobile cloud computing. These mobile apps can be delivered remotely utilizing development tools that provide speed and flexibility. Cloud services allow for speedy development and revision of mobile cloud applications. It combines mobile app development and cloud-based technologies to make cloud and mobile app distribution easier. In distant data centers, data is stored and apps are run. Despite its increasing popularity, fully achieving the potential of mobile computing is difficult due to inherent constraints such as resource scarcity, frequent disconnections, and mobility. By executing mobile apps on resource providers outside of the mobile device, mobile cloud computing can address these issues. In this paper, we provide an in-depth analysis of mobile cloud computing research, with an emphasis on mobile cloud computing's unique problems. We propose a taxonomy based on the major issues in this field and the many approaches taken to overcome them. We finish the paper with a critical assessment of difficulties that have yet to be fully addressed, as well as recommendations for future research.
INFRAESTRUTURA VERDE-AZUL NA BACIA DO ALTO MANDAQUI: CONECTANDO FRAGMENTOS VERDES ATRAVÉS DO CAMINHO DAS ÁGUAS

This article aims to point out the means to establish a blue-green infrastructure in the neighborhoods cut by the Mandaqui Stream, from the path of its tributaries. The study conforms through the disclosure of some of hidden streams of Mandaqui River Basin, suggesting a possible way to rescue and link them to existing and potential green areas, having Caetano Alvares Avenue, where canalized Mandaqui Stream passes through, as the main axis of a blue-green infrastructure.
Estudio del potencial neural de las células progenitoras del ligamento periodontal

PALABRAS CLAVE Celulas madre adultas, celulas madre neurales, ligamento periodontal, cresta neural, diferenciacion neural, recambio celular, marcaje genetico, cerebro adulto. RESUMEN Varios estudios previos sugieren que las celulas madre derivadas de la cresta neural pueden todavia existir en tejidos derivados de la misma, incluido el ligamento periodontal humano (hPDL). En este trabajo se estudia el potencial de diferenciacion neural de las celulas derivadas del hPDL. In vitro, celulas derivadas del hPDL, bajo condiciones de proliferacion, expresan marcadores de celulas madre mesenquimales, neurales y de la cresta neural. Tras su cultivo en medio inductor neural pueden proliferar formando neuroesferas, cambiando su morfologia al cultivarlas con sustrato de adhesion y expresando marcadores de neuronas, astrocitos y oligodendrocitos. In vivo, los injertos celulares sobrevivieron al trasplante, se integraron, migraron y expresaron DCX, NF-M, GABA, GFAP y NG2 en el cerebro de raton adulto. Las propiedades de diferenciacion de las celulas derivadas hPDL en el cerebro de raton adulto indica que son una fuente potencial de celulas madre neurales para su uso en medicina regenerativa. ABSTRACT Several previous studies have suggested that stem cells derived from the neural crest (NC) may still reside in NC-derivatives including the human periodontal ligament (hPDL). In this study we have examined the expression of NC-markers and the neural differentiation potential of hPDL-derived cells. In vitro we found that hPDL-derived cells expressed stem cell markers and a subset of NC cell markers. hPDL-derived cells were able to differentiate into neural-like cells based on cellular morphology and the expression of neural markers. In vivo, grafts of hPDL-derived cells survive transplantation, integrate, migrate and generate surviving human DCX+, NF-M+, GABA+, GFAP+ and NG2+ cells in the adult mouse brain. Some of the grafted hPDL-derived cells could be localized in stem cell niches. The differentiation properties of hPDL-derived cells in the adult brain indicate that hPDL-derived cells are a potential source of neural stem cells for use in regenerative medicine.
FENNEC - Functional Exploration of Natural Networks and Ecological Communities

Background Assessment of species composition in ecological communities and networks is an important aspect of biodiversity research. Yet, for many ecological questions the ecological properties (traits) of organisms in a community are more informative than their scientific names. Furthermore, other properties like threat status, invasiveness, or human usage are relevant for many studies, but they can not be directly evaluated from taxonomic names alone. Despite the fact that various public databases collect such trait information, it is still a tedious manual task to enrich existing community tables with those traits, especially for large data sets. For example, nowadays, meta-barcoding or automatic image processing approaches are designed for high-throughput analyses, yielding thousands of taxa for hundreds of samples in very short time frames. Results Here we present the Fennec, a web-based workbench that eases this process by mapping publicly available trait data to the user’s community tables in an automated process. We applied our novel approach to a case study in pollination ecology to demonstrate the usefulness of the Fennec. The range of topics covered by the case study includes specialization, invasiveness, vulnerability, and agricultural relevance. Significance The Fennec is a free web-based tool that simplifies the inclusion of known species traits in ecological community analyses. We encourage scientists to participate in trait data submission to existing trait databases and to use the Fennec for their analysis. A public instance containing various traits related to pollination ecology is available at http://fennec.molecular. eco.
Pimpinan Redaksi

Indonesian people should turn to mobile payments, which will have an impact on economic efficiency because physical money is inefficient in use today. This study aims to identify the structural effects of Diffusion of Technology (DOI), Extended Unified Theory of Acceptance 2 (UTAUT2), and Perceieved Technology Security (PTS) models on behaviour intention to adopt (BIA) to adopt e-wallet-based mobile payment applications at Go-Pay and OVO using Structural Equation Modelling (SEM) Method. The second objective is to make structural identification of the BIA influence on behaviour intention to recommend (BIR) for the use of e-wallet-based mobile payment applications at Go-Pay and OVO. The results achieved in this study is that the latent compatibility variable significantly influences the behavior intention to adopt (BIA) with a coefficient value of 0.330, whereas innovativeness and perceived technology security have a positive but not significant effect on BIA variables. The variable intention to recommend (BIR) is significantly and positively influenced by the latent BIA variable and has an effect of 0.880.
Parallelizing Quickref

Quickref is a global documentation project for Common Lisp software. It builds a website containing reference manuals for Quicklisp libraries. Each library is first compiled, loaded, and introspected. From the collected information, a Texinfo file is generated, which is then processed into Html. Because of the large number of libraries in Quicklisp, doing this sequentially may require several hours of processing. We report on our experiments parallelizing Quickref. Experimental data on the morphology of Quicklisp libraries has been collected. Based on this data, we are able to propose a number of parallelization schemes that reduce the total processing time by a factor of 3.8 to 4.5, depending on the exact situation.