Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cross-Layer Based Handover for TCP Applications

In a mobile communication scenario handover functions must be supported in order to keep connection alive while switching between different points of access. Moreover, if the infrastructure is composed of different networks or technologies, intersegment handover is necessary to roam from a component to another. The handover function is implemented either for necessity or to improve performance. Necessity means that the terminal must perform a handover because its current network connection is worsening; in the other case switching to another network can help to achieve better performance even if the current network connection is fine. Furthermore, the handover can be soft or hard, according to the possible overlap in the connection of the two segments involved. This paper illustrates the behavior of TCP during intersegment handovers in an architecture including two segments: satellite and WLAN. In particular, the goal is to carry out a thorough analysis of a cross-layer based mechanism applicable during handovers to optimize the TCP performance. Such a mechanism is validated with an ad-hoc software simulator written in C++ and the most meaningful results are shown.
List of Criteria for a Secure Computer Architecture

The security of a digital system depends directly onthe security of the hardware platform the system is based on.The analysis of currently available computer architectures hasshown that such systems offer a lot of security gaps. This is dueto the fact that in the past hardware has only been optimizedfor speed - never for security. In this paper we propose a set ofhardware features to support system security.
A case-study of scoring schemes for the PvS-index

Recently we have proposed a new indexing method for high-dimensional data, the PvS-index. It provides fast query processing in constant time and is well suited for doing similarity search in Image Retrieval Systems using local descriptors. It is based on projecting data points onto random lines and uses this information to segment them into appropriately sized buckets, which can be read in just one I/O operation. After this preprocessing step the search queries just three buckets per query descriptor and uses a recent rank aggregation method, OMEDRANK, in order to provide good approximate results for the nearest neighbour problem.We have recently shown that PvS-indexing works well for large collections of real image data. In that work, however, we used a simple scoring scheme and collected few nearest neighbours for each query descriptor. In this study we examine how much the actual number of nearest neighbours, gathered for each local descriptor, influences the final query result, when searching a PvS-index. Based on the results we propose two new alternative scoring schemes, which improve the retrieval quality and stabilise the results, making the search less affected by the actual number of nearest neighbours accumulated.
Combining Real-Time fMRI Neurofeedback Training of the DLPFC with N-Back Practice Results in Neuroplastic Effects Confined to the Neurofeedback Target Region

In traditional fMRI, individuals respond to exogenous stimuli and are naïve to the effects of the stimuli on their neural activity patterns. Changes arising in the fMRI signal are analyzed post-hoc to elucidate the spatial and temporal activation of brain regions associated with the tasks performed. The advent of real-time fMRI has enabled a new method to systematically alter brain activity across space and time using neurofeedback training (NFT), providing a new tool to study internally-driven processes such as neuroplasticity. In this work, we combined n-back practice with fMRI-NFT of the left dorsolateral prefrontal cortex (DLPFC) to better understand the relationship between open- and closed-loop neuromodulation. FMRI data were acquired during both traditional n-back and NFT across five imaging sessions. Region-of-interest (ROI) and voxel-wise 2 × 2 within subjects ANOVAs were carried out to determine the effects of, and interaction between, training session and neuromodulation type. A main effect of training session was identified for only a single, highly focused cluster that shared spatial properties with the fMRI-NFT target region (left DLPFC). This finding indicates that combined open- and closed-loop neuroplastic enhancement techniques result in focal changes that are confined to the target area of NFT, and do not affect up- or down-stream network components that are normally engaged during working memory. Additionally, we identified a main effect of neuromodulation type for 15 clusters with significantly different activation between open- and closed-loop neuromodulation during training, 12 of which demonstrated higher activity during the open-loop neuromodulation. Our results, taken together with previous reports, indicate that fMRI-NFT combined with n-back practice leads to a highly focal volume exhibiting neuroplasticity without additional network effects.
Two Dimensional Principle Component Analysis of Gabor features for face representation and recognition

In this paper, a new technique called two dimensional Gabor principle component analysis (2DGPCA) is derived and implemented for image representation and recognition. The 2DGPCA method addresses the problems of feature extraction, feature selection and classification. In this approach, the Gabor wavelets are used to extract facial features. The principle component analysis (PCA) is then applied directly on the Gabor transformed image matrices in order to remove redundant information and form an efficient representation more suitable for face recognition. During classification, the Euclidean classifier is explored for simplicity and robustness in the presence of facial variations. The justification behind combining the 2D Gabor wavelets and the PCA is that Gabor transformed face images contain spatial locality, scale and orientation. These images are robust to variations due to pose, expression and scale, thus, making them most suitable for face representation and recognition. The proposed 2DGPCA method was tested on face recognition using the ORL database, where the images vary in expression, pose, and scale. In particular, the 2DGPCA method achieves 97.5% face recognition accuracy when using feature matrices of size 40times5times1 incorporating 5 different scales and 8 orientations compare to 93.5% and 94.5% with feature matrices of size 56times8 and 56times6 for the 2DPCA and 2DFLD method.
Wage Implications of Trade Liberalisation: Evidence for Effective Policy Formation

The relationship between trade and wages has been subject to intense scrutiny in the academic literature with no clear consensus emerging. This paper adds to this body of research by moving beyond the single country analysis level to a panel including developed and developing countries and data through the mid 2000.s. First we examine the relationship between wages and trade using the approach of Feenstra and Hanson to calculate mandated wage changes for our dataset. We find that imports have a significant and positive impact on wages while the sign on tariffs is negative and significant. We also look at the relationship of wage differentials at the occupation level between partner countries. We find that the difference in occupation wage is smaller for large trade partners. Finally, we discuss the potential role of NTMs in influencing the wage and trade relationship.
Embodied cognition and passive processing: What hand-tracking tells us about syntactic processing in L1 and L2 speakers of English

In the current study, hand motions captured by a mousetracking system were used to index listener’s cognitive processes while making commitments to different choice alternatives during the processing of English passive and active structures. Fifty-seven second language (L2) speakers and 19 first language (L1) speakers of English carried out an aural forced-choice picture identification task comprised of 75 items. The findings indicate that although L1 participants have quicker response times for both active and passive structures than L2 participants, both L1 and L2 participants demonstrate similar difficulties in processing passive constructions.
Reduction of T-Helper Cell Responses to Recall Antigen Mediated by Codelivery with Peptidoglycan via the Intestinal Nanomineral–Antigen Pathway

Naturally occurring intestinal nanomineral particles constituently form in the mammalian gut and trap luminal protein and microbial components. These cargo loaded nanominerals are actively scavenged by M cells of intestinal immune follicles, such as Peyer’s patches and are passed to antigen-presenting cells. Using peripheral blood mononuclear cell populations as an in vitro model of nanomineral uptake and antigen presentation, we show that monocytes avidly phagocytose nanomineral particles bearing antigen and peptidoglycan (PGN), and that the presence of PGN within particles downregulates their cell surface MHC class II and upregulates programmed death receptor ligand 1. Nanomineral delivery of antigen suppresses antigen-specific CD4+ T cell responses, an effect that is enhanced in the presence of PGN. Blocking the interleukin-10 receptor restores CD4+ T cell responses to antigen codelivered with PGN in nanomineral form. Using human intestinal specimens, we have shown that the in vivo nanomineral pathway operates in an interleukin-10 rich environment. Consequently, the delivery of a dual antigen–PGN cargo by endogenous nanomineral in vivo is likely to be important in the establishment of intestinal tolerance, while their synthetic mimetics present a potential delivery system for therapeutic applications targeting the modulation of Peyer’s patch T cell responses.
Region sampling and estimation of geosocial data with dynamic range calibration

Location based social networks (LBSNs) are becoming increasingly popular with the fast deployment of broadband mobile networks and the growing prevalence of versatile mobile devices. This success has attracted great interest in studying and measuring the characteristics of LBSNs, such as Facebook Places, Yelp, and Google+ Local. However, it is often prohibitive, and sometimes too costly, to obtain a detailed and complete snapshot of a LBSN due to its usually massive scale. In this work, taking Foursquare as an example, we focus on sampling and estimating restricted geographic regions in LBSNs, such as a city or a country. By exploiting the application programming interfaces (APIs) provided by Foursquare for geographic search, we first introduce how to obtain the “ground truth”, namely, a complete set of all venues (i.e., places) in a specified region. Then, we propose random region sampling algorithms that allow us to draw representative samples of venues, and design unbiased estimators of regional characteristics of venues. We validate the efficiency of our sampling algorithms on Foursquare using complete datasets obtained from 12 regions, such as Switzerland, New York City and Los Angeles. Our results are applicable to perform sampling and estimation in all GeoDatabases, such as Facebook Places, Yelp, and Google+ Local, which have similar venue search APIs as Foursquare. These location service providers can also benefit from our results to enable efficient online statistic estimation.
Hardware-Software Codesign and Coverification Methodology for Dynamically Reconfigurable System-on-Chips

Dynamically reconfigurable system-on-chips can change its hardware as well as software functionalities during run-time. Such system designs need more than what traditional design and verification techniques can provide. For example, partitioning is complicated by spatial placements in FPGA while OS needs more transparent handling between hardware and software tasks. We propose a hardware-software codesign and coverification methodology for dynamically reconfigurable SoC. We also applied the proposed methodology to a self-designed SoC supporting two systems: MMDS (Modular Mobile Dispatch System) and PSMS (Wireless Physiological Signal Monitoring System), which illustrate the feasibility of our approach.
In Vivo Caprine Model for Osteomyelitis and Evaluation of Biofilm-Resistant Intramedullary Nails

Bone infection remains a formidable challenge to the medical field. The goal of the current study is to evaluate antibacterial coatings in vitro and to develop a large animal model to assess coated bone implants. A novel coating consisting of titanium oxide and siloxane polymer doped with silver was created by metal-organic methods. The coating was tested in vitro using rapid screening techniques to determine compositions which inhibited Staphylococcus aureus growth, while not affecting osteoblast viability. The coating was then applied to intramedullary nails and evaluated in vivo in a caprine model. In this pilot study, a fracture was created in the tibia of the goat, and Staphylococcus aureus was inoculated directly into the bone canal. The fractures were fixed by either coated (treated) or non-coated intramedullary nails (control) for 5 weeks. Clinical observations as well as microbiology, mechanical, radiology, and histology testing were used to compare the animals. The treated goat was able to walk using all four limbs after 5 weeks, while the control was unwilling to bear weight on the fixed leg. These results suggest the antimicrobial potential of the hybrid coating and the feasibility of the goat model for antimicrobial coated intramedullary implant evaluation.
Mathematical representation of joint time-chroma distributions

Originally coined by the sensory psychologist Roger Shepard in the 1960s, chroma transforms frequency into octave equivalence classes. By extending the concept of chroma to chroma strength and how it varies over time, we have demonstrated the utility of chroma in simplifying the processing and representation of signals dominated by harmonically-related narrowband components. These investigations have utilized an ad hoc procedure for calculating the chromagram from a given time-frequency distribution. The present paper is intended to put this ad hoc procedure on more sound mathematical ground.
Psychosocial correlates of physical activity in healthy children

BACKGROUND Understanding the determinants of physical activity in children is critical for the treatment and prevention of childhood obesity. Social-cognitive theory has been used to understand behavioral patterns in children. OBJECTIVES To explore the relationship between health beliefs, self-efficacy, social support, and sedentary activities and physical activity levels in children and to examine the relationship between physical activity and children's self-esteem. METHODS Ninety-two children aged 10 to 16 years completed the study. Physical activity was monitored for 1 week with a motion detector (Actitrac; IM Systems, Baltimore, Md). Moderate-level activity and high-level activity were defined based on the results of treadmill testing. Health beliefs, self-efficacy, social influences, and time spent in sedentary behaviors were determined through questionnaires. Self-esteem was measured using the Piers-Harris Children's Self-Concept Scale. Chronic anxiety was measured with the Revised Children's Manifest Anxiety Scale. RESULTS There was a significant decline in physical activity levels between ages 10 and 16 years, particularly in girls. Preteen girls spent approximately 35% more time in low- and high-level activity than did teenage girls (P<.001). Overall, children spent 75.5% of the day inactive, with a mean +/- SD of 5.2 +/- 1.8 hours watching television, sitting at the computer, and doing homework. In contrast, only 1.4% of the day (12.6 +/- 12.2 minutes) was spent in vigorous activity. Time spent in sedentary behaviors was inversely correlated with the amount of moderate-level activity (P<.001) but not high-level activity. In contrast, time spent in high-level activity correlated with self-efficacy scores (P<.001) and social influences scores (P<.005). High-level physical activity was also associated with improved self-esteem (P<.05). Higher health beliefs scores were not correlated with physical activity levels. CONCLUSIONS Children and adolescents are largely sedentary. Correlates of high- and low-level physical activity are different. Time spent on sedentary activities is inversely correlated with moderate-level activity, while self-efficacy and social influences are positively correlated with more intense physical activity. In addition, increased high-level physical activity is an important component in the development of self-esteem in children.
The Size of the Shadow Economy for 25 Transition Countries over 1999/00 to 2006/07: What do we know? *)

CONTENTS 1 Introduction 2 2 The MIMIC/Model Approach 2 3 Econometric Results using the MIMIC-Method 5 4 The Size of the Shadow Economies for 25 Transition Countries for 1999/00 to 2006/07 8 5 Summary and Conclusions 10 6 References 12
Vehicle to vehicle communications applied to road intersection safety, field results

Road intersections represent one of the most complex configurations encountered when traversing road networks. A high percentage of accidents occur at these locations. The introduction of wireless communications technologies onboard passenger vehicles is enabling the sharing of information and through it enhancing the situational awareness of vehicle drivers. In this paper the implementation of safety applications for cooperative vehicle systems is presented applied to road intersection safety. The system relies on three fundamental technologies: communications, localization and the modelling of the environment surrounding the subject vehicle. The paper centres in a case study, the priority crossing of an Emergency Service Vehicle at an intersection. An analysis of the issues identified during the implementation and testing is included. The implementation represents an instance of the architecture developed for cooperative vehicles applications as part of the European project, SAFESPOT.
Trading Freshness for Performance in Distributed Systems

Abstract : Many data management systems are faced with a constant, high-throughput stream of updates. In some cases, these updates are generated externally: a data warehouse system must ingest a stream of external events and update its state. In other cases, they are generated by the application itself: large-scale machine learning frameworks maintain a global shared state, which is used to store the parameters of a statistical model. These parameters are constantly read and updated by the application. In many cases, there is a trade-off between the freshness of the data returned by read operations and the efficiency of updating and querying the data. For instance, batching many updates together will significantly improve the update throughput for most systems. However, batching introduces a delay between when an update is submitted and when it is available to queries. In this dissertation, I examine this trade-off in detail. I argue that systems should be designed so that the trade-off can be made by the application, not the data management system. Furthermore, this trade-off should be made at query time, on a per-query basis, not as a global configuration. To demonstrate this, I describe two novel systems. LazyBase is a data warehouse system originally designed for to store meta-data extracted from enterprise computer files, for the purposes of enterprise information management. It batches updates and processes them through a pipeline of transformations before applying them to the database, allowing it to achieve very high update throughput. The novel pipeline query mechanism in LazyBase allows applications to select their desired freshness at query time, potentially reading data that is still in the update pipeline and has not yet been applied to the final database. LazyTables is a distributed machine learning parameter server - a shared storage system for sparse vectors and matrices that make up the bulk of the data in many machine learning applications.
Reduction of access control decisions

Access control has been proposed as "the" solution to prevent unauthorized accesses to sensitive system resources. Historically, access control models use a two-valued decision set to indicate whether an access should be granted or denied. Many access control models have extended the two-valued decision set to indicate, for instance, whether a policy is applicable to an access query or an error occurred during policy evaluation. Decision sets are often coupled with operators for combining decisions from multiple applicable policies. Although a larger decision set is more expressive, it may be necessary to reduce it to a smaller set in order to simplify the complexity of decision making or enable comparison between access control models. Moreover, some access control mechanisms like XACML~v3 uses more than one decision set. The projection from one decision set to the other may result in a loss of accuracy, which can affect the final access decision. In this paper, we present a formal framework for the analysis and comparison of decision sets centered on the notion of decision reduction. In particular, we introduce the notion of safe reduction, which ensures that a reduction can be performed at any level of policy composition without changing the final decision. We demonstrate the framework by analyzing XACML v3 against the notion of safe reduction. From this analysis, we draw guidelines for the selection of the minimal decision set with respect to a given set of combining operators.
Assessing the scalability of component-based frameworks: the CADENUS case study

This paper proposes an approach to scalability analysis of component-based systems. A theoretical model of the orchestrated behavior of a system's components is developed and potential bottlenecks are identified. The model is derived by performing an analysis of the average number of messages that each involved entity has to deal with, i.e. receive, elaborate and possibly forward. By appropriately setting the various model parameters, it is possible to evaluate a system's behavior in a number of different scenarios. The model itself is based upon a queuing network paradigm, whereby each component is associated with a "service centre" characterized by specific values of both the message arrival rate and the service time: based on such values, the utilization coefficient of the service centers is computed and the potential bottlenecks are identified. The queuing network model is also exploited to evaluate the performance of the overall system under various configurations. The proposed approach is introduced and developed by taking the CADENUS system as a running example. CADENUS is a component-based framework designed and developed within a recent IST project, whose main goal resides in the provisioning of Premium IP services by means of an effective application of the so-called mediation paradigm.
Survey Expectations

This paper focusses on survey expectations and discusses their uses for testing and modeling of expectations. Alternative models of expectations formation are reviewed and the importance of allowing for heterogeneity of expectations is emphasized. A weak form of the rational expectations hypothesis which focusses on average expectations rather than individual expectations is advanced. Other models of expectations formation, such as the adaptive expectations hypothesis, are briefly discussed. Testable implications of rational and extrapolative models of expectations are reviewed and the importance of the loss function for the interpretation of the test results is discussed. The paper then provides an account of the various surveys of expectations, reviews alternative methods of quantifying the qualitative surveys, and discusses the use of aggregate and individual survey responses in the analysis of expectations and for forecasting.