Miracle-dz/UniFAR-FTUs huggingface dataset:
2 Paper 2 : Private information retrieval techniques for enabling location privacy in location-based services [ 5 ]

The general area that the papers selected below are on is in privacy in location-based services using cryptographic techniques. Location-based services (LBS) are becoming increasingly common with handheld devices like GPS, PDAs, etc. where the purpose of the service is that a user wants to know some location-related information like the location of places of interest to it such as restaurants or the answer to queries concerning the user’s nearest neighbours e.g. exact nearest neighbour, group nearest neighbour or k-nearest neighbours. With the increasing proliferation of these services, privacy becomes a major concern for the users of these services. A user may ask location-related queries to a server or to its peers, but may not want to intentionally or otherwise reveal information about its own location since location-related information is at times tied to a user’s identity. Recently there have been cryptographic approaches proposed to address privacy issues in LBS. Below we select 3 such papers and summarize them:
CD80 and CD86 Differentially Regulate Mechanical Interactions of T-Cells with Antigen-Presenting Dendritic Cells and B-Cells

Functional T-cell responses are initiated by physical interactions between T-cells and antigen-presenting cells (APCs), including dendritic cells (DCs) and B-cells. T-cells are activated more effectively by DCs than by B-cells, but little is known about the key molecular mechanisms that underpin the particular potency of DC in triggering T-cell responses. To better understand the influence of physical intercellular interactions on APC efficacy in activating T-cells, we used single cell force spectroscopy to characterize and compare the mechanical forces of interactions between DC:T-cells and B:T-cells. Following antigen stimulation, intercellular interactions of DC:T-cell conjugates were stronger than B:T-cell interactions. DCs induced higher levels of T-cell calcium mobilization and production of IL-2 and IFNγ than were elicited by B-cells, thus suggesting that tight intercellular contacts are important in providing mechanically stable environment to initiate T-cell activation. Blocking antibodies targeting surface co-stimulatory molecules CD80 or CD86 weakened intercellular interactions and dampen T-cell activation, highlighting the amplificatory roles of CD80/86 in regulating APC:T-cell interactions and T-cell functional activation. The variable strength of mechanical forces between DC:T-cells and B:T-cell interactions were not solely dependent on differential APC expression of CD80/86, since DCs were superior to B-cells in promoting strong interactions with T-cells even when CD80 and CD86 were inhibited. These data provide mechanical insights into the effects of co-stimulatory molecules in regulating APC:T-cell interactions.
A HRRP Recognition Method Based on KFD

High resolution range profiles (HRRP) could accurately reflect the structure of target, so it is an important method for radar target recognition. Kernel Fisher discriminant (KFD), which is a machine learning method based on kernel function, is suitable for classification of high dimensional samples which couldn't be separated by linear classifier. In this paper, KFD were used for HRRP classification with KMOD kernel function. A multiple classifier was proposed, and better anti-noise performance was achieved with phase-subtraction alignment and a special rejecting method. The experimental results by three classes of measured HRRP data proved out the effectiveness of KFD
Sampling and reconstruction with adaptive meshes

An approach to visual sampling and reconstruction motivated by concepts from numerical grid generation is presented. Adaptive meshes that can nonuniformly sample and reconstruct intensity and range data are presented. These meshes are dynamic models which are assembled by interconnecting nodal masses with adjustable springs. Acting as mobile sampling sites, the nodes observe properties of the input data, such as intensities, depths, gradients, and curvatures. Based on these nodal observations, the springs automatically adjust their stiffnesses so as to distribute the available degrees of freedom of the reconstructed model in accordance with the local complexity of the input data. The adaptive mesh algorithm runs at interactive rates with continuous 3-D display on a graphics workstation It is applied to the adaptive sampling and reconstruction of images and surfaces. <<ETX>>
A Mixed Model Based on Watershed and Active Contour Algorithms for Brain Tumor Segmentation

Segmentation of anatomical regions is the fundamental problem in medical image analysis. The watershed algorithm is used for the segmentation of anatomical regions and it is computationally simple. The active contour algorithm is used to extract the tumor region in the segmented image, but it suffers from computational complexity and it is insensitive to noise. The proposed method combines watershed algorithm with active contour algorithm to reduce the computational complexity and to improve the insensitiveness to noise. The real brain MR image is first segmented using watershed segmentation and then edges produced will be the initial contour of active contour algorithm. The proposed method is superior in terms of capture range, concave tumor region extraction, segmentation accuracy and sensitivity to noise.
Reconstruction of compressively sampled texture images in the graph-based transform domain

This paper addresses the problem of texture images recovery from compressively sampled measurements. Texture images hardly present a sparse, or even compressible, representation in transformed domains (e.g. wavelet) and are therefore difficult to deal with in the Compressive Sampling (CS) framework. Herein, we resort to the recently defined Graph-based transform (GBT), formerly introduced for depth map coding, as a sparsifying transform for classes of textures sharing the similar spatial patterns. Since GBT proves to be a good candidate for compact representation of some classes of texture, we leverage it for CS texture recovery. To this aim, we resort to a modified version of a state-of-the-art recovery algorithm to reconstruct the texture representation in the GBT domain. Numerical simulation results show that this approach outperforms state-of-the-art CS recovery algorithms on texture images.
Real-time height map fusion using differentiable rendering

We present a robust real-time method which performs dense reconstruction of high quality height maps from monocular video. By representing the height map as a triangular mesh, and using efficient differentiable rendering approach, our method enables rigorous incremental probabilistic fusion of standard locally estimated depth and colour into an immediately usable dense model. We present results for the application of free space and obstacle mapping by a low-cost robot, showing that detailed maps suitable for autonomous navigation can be obtained using only a single forward-looking camera.
Data-hiding with host state at the encoder and partial side information at the decoder

In this paper, we extend a traditional robust datahiding set-up with host state at the encoder to a case when a partial side information about host statistics is also available at the decoder. We demonstrate that the knowledge of host statistics at the decoder can relax the critical requirements of random binning-based methods concerning attack channel statistics at the encoder. We also analyze performance improvement of some known data-hiding methods showing that they are particular cases of the generalized set-up. Finally, we consider some related open research issues and future possible extensions.
A Neural Framework for Web Ranking Using Combination of Content and Context Features

Containing enormous amounts of various types of data, web has become the main source for finding the desired information. Meanwhile retrieving the desired information in such a vast heterogeneous environment is much difficult. This situation has led to a drastic increase in the popularity of internet search engines. Undoubtedly, designing both efficient and effective ranking strategies as the basic core of web information retrieval systems are unavoidable. Unfortunately most of the proposed ranking algorithms do not work very well over general datasets because of their fixed configurations. Many of these algorithms also suffer from their computational costs. Regarding these shortcoming, in this paper, a new ranking framework named "NNRank" is proposed which uses the primitive features of web documents from the categories of content and context using an artificial neural network. The neural networks selected in our approach is a radial basis function or a principle component analysis neural network which due to their high convergence rate, have the capability to exhibit a high performance with a limited number of features. Experimental results based on TREC 2004 gathered in Microsoft LETOR dataset, indicate a noticeable enhancement comparing to the well-known ranking algorithms such as TF-IDF, PageRank and HITS. The results are also comparable with those of BM25.
Sampling Techniques for Streaming Cross Document Coreference Resolution

We present the first truly streaming cross document coreference resolution (CDC) system. Processing infinite streams of mentions forces us to use a constant amount of memory and so we maintain a representative, fixed sized sample at all times. For the sample to be representative it should represent a large number of entities whilst taking into account both temporal recency and distant references. We introduce new sampling techniques that take into account a notion of streaming discourse (current mentions depend on previous mentions). Using the proposed sampling techniques we are able to get a CEAFe score within 5% of a non-streaming system while using only 30% of the memory.
Dynamic Determinism Checking for Structured Parallelism

Determinism is a powerful property, making it much easier to design, implement, and debug parallel programs when it can be guaranteed. There has been much work on guaranteeing determinism of programs, both through dynamic and static approaches. The dynamic approaches tend to have high overheads, while the static approaches can be difficult for non-experts to understand and can require high programmer effort in the form of typing annotations and code restructuring to fit the static type system. In this work, we present a compiler and runtime system that ensures determinism of application code, assuming that libraries satisfy their determinism specifications. In our system, the library writer includes determinism specifications in the form of annotations on the methods in the library. When these libraries are used by application programmers, our compiler will insert dynamic checks where necessary to ensure determinism at runtime. We demonstrate that our inserted dynamic checks lead to an average (geometric mean) slowdown of only 1.26× on 16 cores for our benchmark, thereby establishing the practicality of our approach.
Comparison and clinical suitability of eight prediction models for cardiac surgery-related acute kidney injury.

BACKGROUND Cardiac surgery-related acute kidney injury (CS-AKI) results in increased morbidity and mortality. Different models have been developed to identify patients at risk of CS-AKI. While models that predict dialysis and CS-AKI defined by the RIFLE criteria are available, their predictive power and clinical applicability have not been compared head to head. METHODS Of 1388 consecutive adult cardiac surgery patients operated with cardiopulmonary bypass, risk scores of eight prediction models were calculated. Four models were only applicable to a subgroup of patients. The area under the receiver operating curve (AUROC) was calculated for all levels of CS-AKI and for need for dialysis (AKI-D) for each risk model and compared for the models applicable to the largest subgroup (n = 1243). RESULTS The incidence of AKI-D was 1.9% and for CS-AKI 9.3%. The models of Rahmanian, Palomba and Aronson could not be used for preoperative risk assessment as postoperative data are necessary. The three best AUROCs for AKI-D were of the model of Thakar: 0.93 [95% confidence interval (CI) 0.91-0.94], Fortescue: 0.88 (95% CI 0.87-0.90) and Wijeysundera: 0.87 (95% CI 0.85-0.89). The three best AUROCs for CS-AKI-risk were 0.75 (95% CI 0.73-0.78), 0.74 (95% CI 0.71-0.76) and 0.70 (95% CI 0.73-0.78), for Thakar, Mehta and both Fortescue and Wijeysundera, respectively. The model of Thakar performed significantly better compared with the models of Mehta, Rahmanian, Fortescue and Wijeysundera (all P-values <0.01) at different levels of severity of CS-AKI. CONCLUSIONS The Thakar model offers the best discriminative value to predict CS-AKI and is applicable in a preoperative setting and for all patients undergoing cardiac surgery.
Experiments in Data Management for Wireless Sensor Networks

In this paper we present our experimental results in data management for wireless sensor networks when an adapted Fuzzy ART model of neural networks is applied. Our system provides high dimension reduction and transfer savings, sending only classified identification number instead of whole sample. The system implementation is based on MicaZ sensor motes and adapted Fuzzy ART model. It was applied in real-time environments and presents an efficient tool for monitoring and visualization of sensor data. Testing, debugging, data robustness and other implementation issues are also discussed.
Automatic selection of resting-state networks with functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥92%) and the occipital network, which includes the medial visual cortical areas (≥94%), and consistent for the attention network (≥80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.
Using the ROSAT Catalogues to find counterparts for the second IBIS/ISGRI Survey Sources

The second IBIS/ISGRI survey has produced a catalogue containing 209 hard X-ray sources visible down to a flux limit of around 1 milliCrab. The point source location accuracy of typically 1-3 arcminutes has allowed the counterparts for most of these sources to be found at other wavelengths. In order to help identify the remaining objects, we have used the cross-correlation recently found between the ISGRI catalogue and the ROSAT All Sky Survey Bright Source Catalogue. In this way, for ISGRI sources which have a counterpart in soft X-rays, we can use the much smaller ROSAT error box to search for identifications. For this second survey, we find 114 associations with the number expected by chance to be ~2. Of these sources, 8 are in the list of unidentified objects and, using the smaller ROSAT error boxes, we can find tentative counterparts for five of them. We have performed the same analysis for the ROSAT Faint Source Catalogue, finding a further nine associations with ISGRI unidentified sources from a total of 29 correlations, and, notwithstanding the poorer location accuracy of these sources and higher chance coincidence possibility, we have managed to find a counterpart for another source. Finally, we have used the ROSAT HRI catalogue to search the ISGRI error boxes and find 5 more X-ray objects, of which two are neither in the bright or faint source catalogues, and for which we have managed to find optical/near infrared associations. This makes a total of 19 objects with X-ray counterparts for which we have found possible identifications for nine, most of which are extragalactic.
SELF-DIRECTED NEGATIVE AFFECT: THE DISTINCT ROLES OF INGROUP IDENTIFICATION AND OUTGROUP DEROGATION

Prior research has shown that negative affect directed at the self follows from intergroup evaluation. Building on social identity theory, we hypothesized that ingroup identification impacts on felt self-directed negative affect after making intergroup differentiating evaluations. Additionally, prior work has demonstrated that people downgrade the outgroup to the extent that they identify with the ingroup. Accordingly, building on previous work on the so-called positive-negative asymmetry in intergroup discrimination, we expected that outgroup derogation but not ingroup favoritism would mediate the hypothesized affective impact of ingroup identification. Results indicated support for both predictions. These findings add to previous work by tracing the sources of self-directed negative affect in striving of group members for positive social identity through the process of establishing intergroup differentiation.
Observation of the 3He(n, tp) Reaction by Detection of Far-Ultraviolet Radiation

We have detected Lyman alpha radiation, 121.6 nm light produced from the n = 2 to n = 1 transition in atomic hydrogen, as a product of the 3He(n, tp) nuclear reaction occurring in a cell of 3He gas. The predominant source of this radiation appears to be decay of the 2p state of tritium produced by charge transfer and excitation collisions with the background 3He gas. Under the experimental conditions reported here we find yields of tens of Lyman alpha photons for every neutron reaction. These results suggest a method of cold neutron detection that is complementary to existing technologies that use proportional counters. In particular, this approach may provide single neutron sensitivity with wide dynamic range capability, and a class of neutron detectors that are compact and operate at relatively low voltages.
Network management and monitoring for cloud systems

Monitoring represents an important factor in improving the quality of the services provided in cloud computing, given the fact that it allows scaling resource utilization in an adaptive manner. It is widely used for detecting critical events and abnormalities of distributed systems and also it helps identifying the faults within the system, discovering application patterns for the users. As cloud systems increase their architecture, the degree of workload also grows in datacenters, causing node failures and performance issues. This paper aims to provide a solution for the monitoring of cloud computing systems and services, allowing users and also providers to optimize the usage of the computational resources according to the constantly changing business requirements inside an organization. The main contribution of the paper consists of the integration of the monitoring system, which is based on Nagios and NConf with a test cloud architecture. Finally, the paper discusses the main findings for a reference implementation using the OpenStack cloud platform.
On the Determinants of Growth Volatility: a Nonparametric Approach

We propose a model where the growth rate volatility of a country is explained by structural change and the size of the economy. We test these predictions by means of nonparametric techniques. Growth volatility appears to (i) decrease with total GDP, (ii) increase with the share of the agricultural sector on GDP. Trade openness can also play a role in conjunction with total GDP. In accordance with our model, the explanatory power of per capita GDP, a relevant variable in other empirical works, vanishes when we control for these variables.
Impact of Contextual Information for Hypertext Documents Retrieval

Because the notion of context is multi-disciplinary [17], it encompasses lots of issues in Information Retrieval. In this paper, we define the context as the information surrounding one document that is conveyed via the hypertext links. We propose different measures depending on the information chosen to enrich a current document, in order to assess the impact of the contextual information on hypertext documents. Experiments were made over the TREC-9 collections and significant improvement of the precision shows the importance of taking account of the contextual information.
A model-based constraint on CO ¡ sub ¿ 2

We derive a constraint on the strength of CO 2 fertilisation of the terrestrial biosphere through a “top-down” approach, calibrating Earth system model parameters constrained by the post-industrial increase of atmospheric CO 2 concentration. We derive a probabilistic prediction for the globally averaged strength of CO 2 fertilisation in nature, for the period 1850 to 2000 AD, implicitly net of other limiting factors such as nutrient availability. The approach yields an estimate that is independent of CO 2 enrichment experiments. To achieve this, an essential requirement was the incorporation of a land use change (LUC) scheme into the GENIE Earth system model. Using output from a 671-member ensemble of transient GENIE simulations, we build an emulator of the change in atmospheric CO 2 concentration change since the preindustrial period. We use this emulator to sample the 28-dimensional input parameter space. A Bayesian calibration of the emulator output suggests that the increase in gross primary productivity (GPP) in response to a doubling of CO2 from preindustrial values is very likely (90 % confidence) to exceed 20 %, with a most likely value of 40– 60 %. It is important to note that we do not represent all of the possible contributing mechanisms to the terrestrial sink. The missing processes are subsumed into our calibration of CO2 fertilisation, which therefore represents the combined effect of CO2 fertilisation and additional missing processes. If the missing processes are a net sink then our estimate represents an upper bound. We derive calibrated estimates of carbon fluxes that are consistent with existing estimates. The present-day land–atmosphere flux (1990–2000) is estimated at −0.7 GTC yr−1 (likely, 66 % confidence, in the range 0.4 to −1.7 GTC yr−1). The present-day ocean–atmosphere flux (1990–2000) is estimated to be −2.3 GTC yr−1 (likely in the range−1.8 to−2.7 GTC yr−1). We estimate cumulative net land emissions over the post-industrial period (land use change emissions net of the CO 2 fertilisation and climate sinks) to be 66 GTC, likely to lie in the range 0 to 128 GTC.