Miracle-dz/UniFAR-FTUs huggingface dataset:
Time sensitive blog retrieval using temporal properties of queries

Blogs are one of the main user-generated contents on the web and are growing in number rapidly. The characteristics of blogs require the development of specialized search methods which are tuned for the blogosphere. In this paper, we focus on blog retrieval, which aims at ranking blogs with respect to their recurrent relevance to a user’s topic. Although different blog retrieval algorithms have already been proposed, few of them have considered temporal properties of the input queries. Therefore, we propose an efficient approach to improving relevant blog retrieval using temporal property of queries. First, time sensitivity of each query is automatically computed for different time intervals based on an initially retrieved set of relevant posts. Then a temporal score is calculated for each blog and finally all blogs are ranked based on their temporal and content relevancy with regard to the input query. Experimental analysis and comparison of the proposed method are carried out using a standard dataset with 45 diverse queries. Our experimental results demonstrate that, using different measurement criteria, our proposed method outperforms other blog retrieval methods.
The Dynamic Effects of Fiscal Shocks in Latin American Countries

This paper analyses the dynamic effects of Fiscal Policy in countries from Latin America. We use a Structural VAR Model in order to analyze the effects of exogenous shocks of fiscal variables on the economic system. The identification procedure was proposed by Mountford and Uhlig (2009), which assumes some restrictions on the sign of the shock in some variables, whereas it is agnostic on the effect of fiscal variables in the system. We use quarterly data from three different countries in order to analyse those effects: Brazil, Chile and Mexico. Our findings suggests that, in general, an exogenous fiscal shock have a positive effect on GDP and private consumption, whereas the effects are in the opposite direction in interest rate and inflation. Our results corroborate the hypothesis of procyclical fiscal policy in Latin American Countries.
Eye detection and tracking in image with complex background

To detect and track eye images with complex background, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using mixture of Gaussian, this will cause the images background to be non effective in our next steps. We used the horizontal projection obtained from face region, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as bread. Finally, in proposed method points with the highest values ∗ of are selected as the eye candidate's. The eye region is well detected among these points. Color entropy in the eye region is used to eliminate the irrelevant candidates. In the next step, we perform eye tracking. In the proposed method, eye detection and tracking are applied on testing sets, gathered from different images of face data with complex backgrounds. Experiments indicate correct detection rate of 94.9%, which is indicative of the method's superiority and high robustness.
A study of the incorporation of the Irish in Britain with special reference to Catholic state education : involving a comparison of the attitudes of pupils and teachers in selected secondary schools in London and Liverpool

The central hypothesis of the thesis is ttat en important aim of Catholic state education has been to Incorporate the children of Irish working-class migrants. This hypothesis Is differently explored in part one and part two of the thesis. Part one is essentially a sociological account of the relationship between the English Catholic Church, the State and the migrant comminity in the 19th century. This eccount is preceded by an analysis of Irish migrants and the political, economic and cultural contexts of their settlement In Britain. Part one attempts to show the role which the structure and content of Catholic elementary education played in the process of denatlonalising the children of this migrant corrnunity. This was achieved by institutionalising a silence on the p01 itic& origins of the migrants and by forging a bond between the Church and Its Irish congregation based on Cetholicity and corm.inity. In this way the pclItic& voice of the Irish In Britain was stilled end the process of incorporation was facilitated Renewed migration from Ireland ensured that these incorporatist strategies continued to be employed end relevant in the 20th century. Part two consists of en exploratory empirical study comparing the attitudes of pupils and teachers from selected Catholic schools In LIVerpool and London, the former being the main site of 19th-century migration and the latter the main site of 20th-century migration. The aims of this investigation were two-fold: to explore whether the practices of Catholic schools continue to renderthe Irish antecedents of the majority of their pupils Invisible and thus perpetuate the incorporatist strategies of the 19th century; and to discover the degree to which class, religion and national Identity, mediated through generation and region, still significantly determine the Identity and experience of being Irish In Britain.
Automatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coders

This paper focuses on automatic segmentation of spontaneous data using continuous dimensional labels from multiple coders. It introduces efficient algorithms to the aim of (i) producing ground-truth by maximizing inter-coder agreement, (ii) eliciting the frames or samples that capture the transition to and from an emotional state, and (iii) automatic segmentation of spontaneous audio-visual data to be used by machine learning techniques that cannot handle unsegmented sequences. As a proof of concept, the algorithms introduced are tested using data annotated in arousal and valence space. However, they can be straightforwardly applied to data annotated in other continuous emotional spaces, such as power and expectation.
The Association between Social Context and Phase of Recovery among Drug Court Clients: A Gender Comparison

The association between drug court clients’ pro-drug and pro-recovery social context at multiple ecological levels, and phase of recovery was assessed, and gender comparisons were evaluated. Drug courts provide alternatives to incarceration for substance abusing offenders, providing treatment within clients’ social environments. The findings indicated that social context is associated with recovery, and that this relation differs by gender. Specifically, increased favorable attitudes toward drug use among social referents were associated with men being in an earlier phase of recovery and women being in a later phase of recovery. Furthermore, perceived encouragement to use drugs was associated with being in earlier phases of recovery, while positive outcome beliefs related to recovery were associated with being in later phases of recovery. Therefore, drug courts may lead to positive long-term outcomes, as social context can be addressed during the recovery process. Additionally, these courts might benefit from incorporating gender-specific components into treatment. INDEX WORDS: Social context, Drug court, Gender, Substance abuse, Offender, Ecological theory THE ASSOCIATION BETWEEN SOCIAL CONTEXT AND PHASE OF RECOVERY AMONG DRUG COURT CLIENTS: A GENDER COMPARISON
CUIML: A language for generating multimodal human-computer interfaces

CUIML, the Cooperative User Interfaces Markup Language, was developed as part of the DWARF project. The goal of DWARF is the development of a framework for augmented reality applications running on wearable computers. For wearable systems, the HCI (human-computer interface) consists of numerous I/O devices such as head mounted displays, palm-size devices, and speech recognition systems. This should lead to a multimodal interaction with the user. To make the development of multimodal user interfaces easier, we propose a unified user interface description language. This language describes the user interface in an abstract way and allows to map it to different output and input components. To keep the different parts of the user interface in a consistent state, a controller object is needed. We used UIML, the User Interface Markup Language, as a starting point. CUIML offers an abstract description language for the views and the controller. By XSL transformations, these presentation free HCI descriptions are converted to markup languages that can be displayed on the various I/O devices. Furthermore the controller, whose internal structure is a deterministic finite automaton (DFA), is also configured by CUIML. CUIML is a promising approach to solving the problems that occur, when developing a multimodal HCI. On a higher level of abstraction CUIML is a description language for views and controllers of the MVC (Model/View/Controller) design pattern that can be transformed to the required structures at runtime. Because it is based on XML it can be extended easily to support many different wearable systems.
Attitude Control of Missile Via Fliess Expansion

Motivated by the attitude control of missiles, we consider the tracking problem of nonlinear systems via control with finite admissible values. First, the Fliess functional expansion is used to describe the outputs. Using piecewise admissible value control and the quadratic criteria, the optimal control is obtained. Then for certain model uncertainties, the model predictive control is considered. Online predicted uncertain parameters are used to update the system's dynamics. The method developed is used to the attitude control of missile. Simulation results show the effectiveness of the proposed method.
Effects of recombinant human granulocyte colony-stimulating factor on central and peripheral T lymphocyte reconstitution after sublethal irradiation in mice

Granulocyte colony-stimulating factor (G-CSF) is one of the most critical cytokines used for the treatment of acute radiation syndrome (ARS). In addition to the hematopoietic effects of G-CSF on the differentiation and proliferation of myeloid progenitor cells, G-CSF is also known to have immunomodulatory effects. The aim of the present study was to investigate whether G-CSF could accelerate central and peripheral T lymphocyte recovery after a sublethal dose of irradiation. Female BALB/c mice were subjected to 6 Gy of total body irradiation and then were treated with either 100 μg/kg G-CSF or an equal volume of PBS once daily for 14 days. Percentages of thymocyte subpopulations including CD4 − CD8 − , CD4 + CD8 + , CD4 + CD8− and CD4 − CD8+ T cells, peripheral CD3 + , CD4+ and CD8+ cells were analyzed by flow cytometry. Recent thymic emigrants (RTEs) were assessed by real-time polymerase chain reaction (PCR) using primers specific to the 257-bp T cell receptor rearrangement excision circles (sjTRECs). The proliferative capacity of splenic mononuclear cells upon exposure to ConA was measured by using the Cell Count Kit-8 (CCK-8). G-CSF treatment promoted thymocyte regeneration, accelerated the recovery of CD4 + CD8+ cells and increased the frequency of thymocyte sjTRECs. These effects were more prominent at early time points (Day 28) after irradiation. G-CSF also increased the rate of recovery of peripheral CD3 + , CD4+ and CD8+ cells and shortened the period of severe lymphopenia following irradiation. G-CSF also increased the splenic mononuclear cell mitotic responsiveness to ConA more than control-treated cells. Our results show that G-CSF accelerates T cell recovery through both thymic-dependent and thymic-independent pathways, which could be used to increase the rate of immune reconstitution after sublethal irradiation.
Multi-tiered Security Architecture for ARM via the Virtualization and Security Extensions

As the ARM architecture has become the favored platform for the fastest growing computing segment, the mobile market, establishing a sound security architecture on the platform is paramount. The frightening increase in malware for the Android and iOS platforms in addition to the adoption of ARM architectures outside of the mobile market only bolster this need. In this paper, we investigate the ARM architecture as well as its security and virtualization extensions available only on the newest generation of ARM processors. Considering these extensions, we present a concept for a multi-tiered security architecture for mobile computing devices. Our concept combines a custom TrustZone component and leverages the advanced features of the Xen hypervisor to present an all encompassing framework for all aspects of security including both load and runtime verification of critical components, strong isolation between components, and virtual machine introspection for anomaly detection.
Exhaled and nasal nitric oxide measurements : recommendations

*Dept of Thoracic Medicine, National Heart and Lung Institute, Dovehouse Street, London, UK. **Dept of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden. Correspondence. P.J. Barnes, Dept of Thoracic Medicine, National Heart and Lung Institute, Dovehouse Street, London SW3 6LY, UK. Received: April 3 1997; accepted for publication April 25 1997 This document was prepared following a Satellite Meeting of the European Respiratory Society Annual Scientific Meeting, held in Stockholm on 9 September, 1996, and attended by members of the ERS Taskforce on Measurement of Mediators in Exhaled Air (Chairman: Professor P.J. Barnes). Participants of the ERS Special Task Force Meeting "Measurement of Nitric Oxide in Exhaled Air" 9 September 1996, Stockholm: K. Alving (Stockholm, Sweden), P.J. Barnes (London, UK), J. Barbera (Barcelona, Spain), E. Baraldi (Padova, Italy), T. Dinh-Xuan (Paris, France), C.G. Frostell (Danderyd, Sweden); H.W. F.M. de Gouw (Leiden, The Netherlands), L. Gustafsson (Stockholm, Sweden), G. Hedenstierna (Uppsala, Sweden), T.W. Higenbottam (Sheffield, UK), M. Hogman (Uppsala, Sweden), Q. Jobsis (Rotterdam, The Netherlands), J. De Jongste (Rotterdam, The Netherlands), H.A.M. Kerstjens (Groningen, The Netherlands), S.A. Kharitonov (London, UK), J. Kips (Ghent, Belgium), J.M. Lundberg (Stockholm, Sweden), J. Lundberg (Stockholm, Sweden), T.W. van der Mark (The Netherlands), N. Neuparth (Lisbon, Portugal), P.J. Sterk (Leiden, The Netherlands), P. Silkoff (Toronto, Canada), A. Sovijarvi (Helsinki, Finland), E. Weitzberg (Stockholm, Sweden).
Structural Factorization of Plants to Compute Their Functional and Architectural Growth

Numerical simulation of plant growth has been facing a bottleneck due to the cumbersome computation implied by the complex plant topological structure. In this article, the authors present a new mathematical model for plant growth, GreenLab, overcoming these difficulties. GreenLab is based on a powerful factorization of the plant structure. Fast simulation algorithms are derived for deterministic and stochastic trees. The computation time no longer depends on the number of organs and grows at most quadratically with the age of the plant. This factorization finds applications to build trees very efficiently, in the context of geometric models, and to compute biomass production and distribution, in the context of functional structural models.
WAVES: Automatic Synthesis of Client-Side Validation Code for Web Applications

The current practice of Web application development treats the client and server components of the application as two separate pieces of software. Each component is written independently, usually in distinct programming languages and development platforms - a process known to be prone to errors when the client and server share application logic. When the client and server are out of sync, an âimpedance mismatchâ occurs, often leading to software vulnerabilities as demonstrated by recent work on parameter tampering. This paper outlines the groundwork for a new software development approach, WAVES, where developers author the server-side application logic and rely on tools to automatically synthesize the corresponding client-side application logic. WAVES employs program analysis techniques to extract a logical specification from the server, from which it synthesizes client code. WAVES also synthesizes interactive client interfaces that include asynchronous callbacks (AJAX) whose performance and coverage rival that of manually written clients while ensuring no new security vulnerabilities are introduced. The effectiveness of WAVES is demonstrated and evaluated on three real-world web applications.
Applying cognitive walkthroughs to more complex user interfaces: experiences, issues, and recommendations

The Cognitive Walkthrough methodology was developed in an effort to bring cognitive theory closer to practice; to enhance the design and evaluation of use interfaces in industrial settings. For the first time, small teams of professional developers have used this method to critique three complex software systems. In this paper we report evidence about how the methodology worked for these evaluations. We focus on five core issues: (1) task selection, coverage, and evaluation, (2) the process of doing a Cognitive Walkthrough, (3) requisite knowlege for the evaluators, (4) group walkthroughs, and (5) the interpretation of results. Our findings show that many variables can affect the success of the technique; we believe that if the Cognitive Walkthrough is ultimately to be successful in industrial settings, the method must be refined and augmented in a variety of ways.
VARIATION IN THE VITAMIN D RECEPTOR GENE IS ASSOCIATED WITH MULTIPLE SCLEROSIS IN AN AUSTRALIAN POPULATION

Multiple Sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS) resulting in accumulating neurological disability. The disorder is more prevalent at higher latitudes. To investigate VDR gene variation using three intragenic restriction fragment length polymorphisms (Apa I, Taq I and Fok I) in an Australian MS case-control population. One hundred and four Australian MS patients were studied with patients classified clinically as Relapsing Remitting MS (RR-MS), Secondary Progressive MS (SP-MS) or Primary Progressive MS (PP-MS). Also, 104 age-, sex-, and ethnicity-matched controls were investigated as a comparative group. Our results show a significant difference of genotype distribution frequency between the case and control groups for the functional exon 9 VDR marker Taq I (pGen = 0.016) and interestingly, a stronger difference for the allelic frequency (pAll = 0.0072). The Apa I alleles were also found to be associated with MS (pAll = 0.04) but genotype frequencies were not significantly different from controls (pGen = 0.1). The Taq and Apa variants are in very strong and significant linkage disequilibrium (D′ = 0.96, P < 0.0001). The genotypic associations are strongest for the progressive forms of MS (SP–MS and PP–MS). Our results support a role for the VDR gene increasing the risk of developing multiple sclerosis, particularly the progressive clinical subtypes of MS.
Visualizing Outliers

Outliers have more than two centuries’ history in the field of statistics. Recently, they have become a focal topic because of their relevance to terrorism, network intrusions, financial fraud, and other areas where rare events are critical to understanding a process. This paper presents a new algorithm, called hdoutliers, for detecting multidimensional outliers. It is unique for a) dealing with a mixture of categorical and continuous variables, b) dealing with the curse of dimensionality (many columns of data), c) dealing with many rows of data, d) dealing with outliers that mask other outliers, and e) dealing consistently with unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, hdoutliers is based on a distributional model that allows outliers to be tagged with a probability.
From Subcategories to Visual Composites: A Multi-level Framework for Object Detection

The appearance of an object changes profoundly with pose, camera view and interactions of the object with other objects in the scene. This makes it challenging to learn detectors based on an object-level label (e.g., "car"). We postulate that having a richer set of labelings (at different levels of granularity) for an object, including finer-grained subcategories, consistent in appearance and view, and higher order composites - contextual groupings of objects consistent in their spatial layout and appearance, can significantly alleviate these problems. However, obtaining such a rich set of annotations, including annotation of an exponentially growing set of object groupings, is simply not feasible. We propose a weakly-supervised framework for object detection where we discover subcategories and the composites automatically with only traditional object-level category labels as input. To this end, we first propose an exemplar-SVM-based clustering approach, with latent SVM refinement, that discovers a variable length set of discriminative subcategories for each object class. We then develop a structured model for object detection that captures interactions among object subcategories and automatically discovers semantically meaningful and discriminatively relevant visual composites. We show that this model produces state-of-the-art performance on UIUC phrase object detection benchmark.
Optical Flow-Based Gait Modeling Algorithm for Pedestrian Navigation Using Smartphone Sensors

An optical flow-based pedestrian gait modeling method integrating with attitude acquisition is proposed. The proposed method accomplishes online training of the gait model with displacement and frequency information whenever steps are detected. The displacement information inferred from optical flow is assigned adaptive weight to suppress outliers that arise from the pedestrian's feet and legs in the images. Moreover, a self-pruning linear regression mechanism is presented in gait modeling process to attenuate the adverse effects of abnormal samples. The experimental results demonstrate that the proposed method can achieve better performance compared with the existing methods in terms of accuracy and efficiency. Furthermore, complex scenario experiments where the textures of the ground changed, were also conducted and the results verified the adaptability of our proposed method.
AN EFFECTIVE EVALUATION STUDY OF OBJECTIVE MEASURES USING SPECTRAL SUBTRACTIVE ENHANCED SIGNAL

Unwanted noises have a negative influence over communication because it disturbs the conversation and make the communication impossible. Speech enhancement algorithms are used for improving the quality and intelligibility or to reduce listener fatigues. Assessment of speech quality can be done by using either subjective listening test or objective quality measure. Evaluation of several objective measures with the speech processed by enhancement algorithms has been performed but these having limitations to assess original speech signal. This paper represents the study of speech quality measures and compute the values used for regression analyses of the objective measures evaluation study using spectral subtraction algorithm based enhanced speech signal.