Miracle-dz/UniFAR-FTUs huggingface dataset:
Personal Mobility in the ICEBERG IntegratedCommunication Network

Communication technology is currently seeing rapid growth, characterized by new access networks, end-devices and an ever growing user-community. However, there is a lack of integration between the diierent services: a user may have a cell-phone, a pager, a regular phone, a laptop, an e-mail account, and so on. But she has little control over managing communication services in a personalized, uniied, and clean fashion across these devices. The Iceberg Project ((10, 23]) proposes an Internet-Core based approach for integration of telephony and data services across heterogeneous access networks. Personal mobility and personalized communication management as deened by Iceberg are much richer than the corresponding concepts in traditional telephony. In this report, we present the design of the architectural components that provide personal mobility in the Iceberg integrated communication network. We present our model for a clean, extensible integration of end-devices and networks. We also discuss our mechanism for providing a high degree of personalization of communication. We have a prototype implementation of the integration components in a testbed consisting of four diierent communication services: GSM cell-phones, Voice-over-IP end-points, Voice-mail, and E-mail.
Extrema Propagation: Fast Distributed Estimation of Sums and Network Sizes

Aggregation of data values plays an important role on distributed computations, in particular, over peer-to-peer and sensor networks, as it can provide a summary of some global system property and direct the actions of self-adaptive distributed algorithms. Examples include using estimates of the network size to dimension distributed hash tables or estimates of the average system load to direct load balancing. Distributed aggregation using nonidempotent functions, like sums, is not trivial as it is not easy to prevent a given value from being accounted for multiple times; this is especially the case if no centralized algorithms or global identifiers can be used. This paper introduces Extrema Propagation, a probabilistic technique for distributed estimation of the sum of positive real numbers. The technique relies on the exchange of duplicate insensitive messages and can be applied in flood and/or epidemic settings, where multipath routing occurs; it is tolerant of message loss; it is fast, as the number of message exchange steps can be made just slightly above the theoretical minimum; and it is fully distributed, with no single point of failure and the result produced at every node.
GENETIC PROGRAMMING, INDEXED MEMORY, THE HALTING PROBLEM, AND OTHER CURIOSITIES

The genetic programming (GP) paradigm was designed to evolve functions that are progressively better approximations to some target function. The introduction of memory into GP has opened the Pandora's box which is algorithms. It has been shown that the combination of GP and Indexed Memory can be used to evolve any target algorithm. What has not been shown is the practicality of doing so. This paper addresses some of the fundamental issues in the process of evolving algorithms and proposes a variety of partial solutions, in general and for GP in particular.
Efficient transcoding for spatially misaligned compositions for HEVC

The visualization of (ultra) high-resolution compositions created from multiple input bitstreams requires several decoders at the receiving device. Therefore, not all devices can properly display such compositions. To address this problem, the input streams are decoded, merged into a single video, and re-encoded by a transcoder in the network. However, this approach requires a computationally complex re-encoding step. To reduce this complexity, information from the input bit-streams can be reused during transcoding. In HEVC, simply reusing the original encoding information is not compression efficient if the inserted content is not aligned with the grid of coded blocks. In this paper, we applied feature selection based on a decision tree, which was used in a fast HEVC transcoding model for misaligned content. The performance varies depending on the shift and average transform size in the original sequence, resulting in complexity reductions of up to 76%.
Real-time adaptive point splatting for noisy point clouds

Regular point splatting methods have a lot of problems when used on noisy data from stereo algorithms. Just a few unfiltered outliers, depth discontinuities, and holes can destroy the whole rendered image. We present a newmulti-pass splatting method on GPU hardware called Adaptive Point Splatting (APS) to render noisy point clouds. By taking advantage of image processing algorithms on the GPU, APS dynamically fills holes and reduces depth discontinuities without loss of image sharpness. Since APS does not require any preprocessing on the CPU and does all its work on the GPU, it works in real-time with linear complexity in the number of points in the scene. We show experimental results on Teleimmersion stereo data produced by approximately
Hierarchical clustering and outlier detection for effective image data organization

This paper proposes an approach of hierarchical image data organization for effective image retrieval. Our approach is basically based on the Cross-Association (CA) that was originally devised for uncovering hidden communities in data without requiring any parameters. We first modify the CA to be appropriate for the clustering context, and propose a hierarchical clustering algorithm based on the modified version of CA. Then, we propose a novel algorithm for outlier detection that is well matched to the CA framework. We perform extensive experiments to show the effectiveness of our clustering algorithm and also our outlier detection algorithm. We also demonstrate the results obtained by applying our algorithms to real-world data.
Interest Point Detection in Videos Using Long Point Trajectories

Interest points in videos are spatio-temporal points within a video that can be used to build complex video processing systems such as action recognition and video retrieval systems. In this paper, we propose a method to localize spatial interest points in a video in the temporal domain, and discuss the results obtained with our experiments in comparison to the popular traditional handcrafted interest point detectors under various transformations. Our scheme is primarily based on the extraction of dense trajectories of objects in videos obtained through optical flow computations. The Ramer|Douglas|Peucker(RDP) algorithm is used on the dense trajectories in order to extract key points that are localised in the temporal dimension.
The Skill Complementarity of Broadband Internet

Does adoption of broadband internet in firms enhance labor productivity and increase wages? And is this technological change skill biased or factor neutral? We exploit rich Norwegian data with firm-level information on value added, factor inputs and broadband adoption to answer these questions. We estimate production functions where firms can change their technology by adopting broadband internet. A public program with limited funding rolled out broadband access points, and provides plausibly exogenous variation of broadband adoption in firms. This enables us to address endogeneity of broadband adoption and examine how it shifts the production technology and changes the productivity and labor outcomes of different types of workers. We find that broadband adoption favors skilled labor by increasing its relative productivity. The increase in productivity of skilled labor is especially large for college graduates in fields such as science, technology, engineering and business. By comparison, broadband internet is a substitute for workers without high school diploma, lowering their marginal productivity. Consistent with the estimated changes in labor productivity, wage regressions show the expansion of broadband internet improves (worsens) the labor outcomes of skilled (unskilled) workers. We explore several possible explanations for the skill bias of broadband internet. We find suggestive evidence that broadband internet complements skilled workers in executing nonroutine abstract tasks, and substitutes for unskilled workers in performing routine tasks. When we use our production function estimates to construct measures of firm level productivity, we find that broadband internet accounts for a few percent of the standard deviation in total factor productivity across firms. Taken together, our findings have important implications for the ongoing policy debate over government investment in broadband infrastructure to encourage productivity and wage growth.
From Plato's Dualism to user Interface Development

Today, many devices are available, and they use different languages. From a user point of view, the system is its user interface, and this idea is used in this paper to provide a pattern-based design solution where platform independent and dependent levels are connected in a model-based user interface development and reflective environment. Platform independent and dependent levels have similarities with Plato’s dual view
Efficient algorithms for ‘universally’ constrained matrix and tensor factorization

We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in unsupervised learning. The new framework is a hybrid between alternating optimization (AO) and the alternating direction method of multipliers (ADMM): each matrix factor is updated in turn, using ADMM. This combination can naturally accommodate a great variety of constraints on the factor matrices, hence the term `universal'. Computation caching and warm start strategies are used to ensure that each update is evaluated efficiently, while the outer AO framework guarantees that the algorithm converges monotonically. Simulations on synthetic data show significantly improved performance relative to state-of-the-art algorithms.
Oil Spill Candidate Detection from SAR Imagery Using a Thresholding-Guided Stochastic Fully-Connected Conditional Random Field Model

The detection of marine oil spill candidate from synthetic aperture radar (SAR) images is largely hampered by SAR speckle noise and the complex marine environment. In this paper, we develop a thresholding-guided stochastic fullyconnected conditional random field (TGSFCRF) model for inferring the binary label from SAR imagery. First, an intensity thresholding approach is used to estimate the initial labels of oil spill candidates and the background. Second, a Gaussian mixture model (GMM) is trained using all the pixels based on the initial labels. Last, based on the GMM model, a graph-cut optimization approach is used for inferring the final labels. By using a threholding-guided approach, TGSFCRF can exploit the statistical characteristics of the two classes for better label inference. Moreover, by using a stochastic clique approach, TGSFCRF efficiently addresses the global-scale spatial correlation effect, and thereby can better resist the influence of SAR speckle noise and background heterogeneity. Experimental results on RADARSAT-1 ScanSAR imagery demonstrate that TGSFCRF can accurately delineate oil spill candidates without committing too much false alarms.
Association of Common Variants in LOX with Keratoconus: A Meta-Analysis

Background Several case-control studies have been performed to examine the association of genetic variants in lysyl oxidase (LOX) with keratoconus. However, the results remained inconclusive and great heterogeneity might exist across populations. Method A comprehensive literature search for studies that published up to June 25, 2015 was performed. Summary odds ratios (OR) and 95% confidence intervals (CI) of each single nucleotide polymorphism (SNP) were estimated with fixed effects model when I 2<50% in the test for heterogeneity or random effects model when I 2>50%. Publication bias was evaluated using funnel plots and Egger’s test. Results A total of four studies including 1,467 keratoconus cases and 4,490 controls were involved in this meta-analysis. SNPs rs2956540 and rs10519694 showed significant association with keratoconus, with ORs of 0.71 (95% CI: 0.63–0.80, P = 1.43E-08) and 0.77 (95% CI: 0.61–0.97, P = 0.026), respectively. In contrast, our study lacked sufficient evidences to support the association of rs1800449/rs2288393 with keratoconus across populations. Conclusion This meta-analysis suggested that two LOX variants, rs2956540 and rs10519694, may affect individual susceptibility to keratoconus, while distinct heterogeneity existed within this locus. Larger-scale and multi-ethnic genetic studies on keratoconus are required to further validate the results.
Dietary Determinants of Changes in Waist Circumference Adjusted for Body Mass Index – a Proxy Measure of Visceral Adiposity

Background Given the recognized health effects of visceral fat, the understanding of how diet can modulate changes in the phenotype “waist circumference for a given body mass index (WCBMI)”, a proxy measure of visceral adiposity, is deemed necessary. Hence, the objective of the present study was to assess the association between dietary factors and prospective changes in visceral adiposity as measured by changes in the phenotype WCBMI. Methods and Findings We analyzed data from 48,631 men and women from 5 countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Anthropometric measurements were obtained at baseline and after a median follow-up time of 5.5 years. WCBMI was defined as the residuals of waist circumference regressed on body mass index, and annual change in WCBMI (ΔWCBMI, cm/y) was defined as the difference between residuals at follow-up and baseline, divided by follow-up time. The association between energy, energy density (ED), macronutrients, alcohol, glycemic index (GI), glycemic load (GL), fibre and ΔWCBMI was modelled using centre-specific adjusted linear regression, and random-effects meta-analyses to obtain pooled estimates. Men and women with higher ED and GI diets showed significant increases in their WCBMI, compared to those with lower ED and GI [1 kcal/g greater ED predicted a ΔWCBMI of 0.09 cm (95% CI 0.05 to 0.13) in men and 0.15 cm (95% CI 0.09 to 0.21) in women; 10 units greater GI predicted a ΔWCBMI of 0.07 cm (95% CI 0.03 to 0.12) in men and 0.06 cm (95% CI 0.03 to 0.10) in women]. Among women, lower fibre intake, higher GL, and higher alcohol consumption also predicted a higher ΔWCBMI. Conclusions Results of this study suggest that a diet with low GI and ED may prevent visceral adiposity, defined as the prospective changes in WCBMI. Additional effects may be obtained among women of low alcohol, low GL, and high fibre intake.
A network intrusion detection approach combined with genetic algorithm and back propagation neural network

IDS can offer protection from external users and internal attackers, where traffic doesn't go past the firewall at all. The research on IDS attempted to use neural networks for intrusion detection has been carried on and will continue. Such systems were trained on normal or attack behavior information and then detect intrusions or attacks. In this paper, we have described five kinds of Neural Network technologies that are used in IDS. An IDS combining with GA and BP is put forward, and functions of each module are detailed. Finally, a discussion of the future NN technologies, which promise to enhance the detection ability of IDS is provided.
Towards automated wide area visual surveillance: tracking objects between spatially-separated, uncalibrated views

This paper presents a solution to the problem of tracking intermittent targets that can overcome long-term occlusions, as well as movement between camera views. Unlike other approaches, our system does not require topological knowledge of the site or labelled training patterns during the learning period. The approach uses the statistical consistency of data obtained automatically over an extended period of time rather than explicit geometric calibration to automatically learn the salient reappearance periods for objects. This allows us to predict where objects may reappear, and within how long. We demonstrate how these salient reappearance periods can be used with a model of physical appearance to track objects between spatially separate regions in single and separated views.
Learning Hierarchical Semantic Description Via Mixed-Norm Regularization for Image Understanding

This paper proposes a new perspective-Vicept representation to solve the problem of visual polysemia and concept polymorphism in the large-scale semantic image understanding. Vicept characterizes the membership probability distribution between visual appearances and semantic concepts, and forms a hierarchical representation of image semantic from local to global. In the implementation, incorporating group sparse coding, visual appearance is encoded as a weighted sum of dictionary elements, which could obtain more accurate image representation with sparsity at the image level. To obtain discriminative Vicept descriptions with structural sparsity, mixed-norm regularization is adopted in the optimization problem for learning the concept membership distribution of visual appearance. Furthermore, we introduce a novel image distance measurement based on the hierarchical Vicept description, where different levels of Vicept distance are fused together by multi-level separability analysis. Finally, the wide applications of Vicept description are validated in our experiments, including large-scale semantic image search, image annotation, and semantic image re-ranking.
Combining Time Reversal and Fast Marching Method in Wireless Indoor Positioning

Most of current wireless indoor positioning methods could not accurately obtain channel model, the mapping between spatial position and received signal features. The main factor for a precise channel model in an indoor environment is multipath effect. Time reversed (TR) wireless indoor positioning method has been validated to effectively reduce signals fading or time delay affected by multipath effect. However, these advantages are depended on a prior known channel model, without this condition, the accuracy of TR method will be seriously deteriorated. To solve the shortcoming of a general TR method in an unknown channel model application, we present a combining Time Reversal and Fast Marching Method (TR-FMM) positioning method. This method locates a target with two stages. In the stage one, the precise channel model of an indoor environment is estimated by FMM and simultaneous algebraic reconstruction technique (SART). In this stage, Time of Flight (TOF) information generated by some fixed spatial position anchors are used to fulfill the indoor channel model estimation, then the needed channel impulse response (CIR) for TR method will be obtained based on the estimated channel model. In the stage two, with the obtained CIR, any new joint mobile target will be accurately located by a general TR wireless indoor positioning method. Some numerical simulations have been presented to validate the proposed method. Simulative results depict the positioning deviation is less than 3cm for a newly joined mobile target with 1cm scale in a moderate complex indoor configure, and the accuracy of the positioning is improved 30 times comparing to a general TR method. The positioning time in the stage 2 is less than 3 minutes in a PC with 1.6 GHz dual CPUs and 2G Bytes memory. Obviously, the proposed method has great advantage in high accuracy and low complexity for wireless indoor positioning system.
From logic to dialectics in legal argument

Tlis paper investigates the relation between declarative and procedural accounts of adversar-ial legal argument. A three-leveled model is proposed , where a formal argumentation framework is built around a logical system and itself embedded in a dialectical protocol for dispute, in such a way that, each time a party adds or retracts information, the argumentation framework reassesses the resulting state of the dispute. The proposed link between the first, logical level and an argumentation framework obviates the need for nonrnonotonic logics at the first level, while the proposed link between declarative and procedural models of argumentation enables us to regard induction and analogy not as forms of inference but as heuristics for introducing premises.
Optimal Grid Exploitation Algorithms for Data Mining

Although many data mining tasks have been parallelized and can thus be executed on dedicated clusters, few solutions currently exist to solve data mining problems on a grid or a non-specialized network of workstations. The current tendency is to focus on the use of grids and/or desktop grids in order to exploit any available workstations with no considerations of their physical positions. If a grid specific algorithm has some common characteristics with a dedicated-cluster algorithm, many constraints are inherent to the use of the grid. In particular, resource volatility and communications cost reduce the parallelism effectiveness. The DisDaMin project (distributed data mining) revisits the data mining tasks and proposes new exploitable algorithms for grids. The DisDaMin mechanisms first implement a specific fragmentation of the data using clustering methods, and then realize asynchronous collaborative techniques according to the specifics of execution on grids. The use of this fragmentation method makes it possible to carry out optimal local processing on each node, with a minimum of communications. Using this, we introduce the distributed algorithm DICCoop, an adaptation of DIC by Brin et al. (1997). Simulations were performed to prove the efficiency of the proposed mechanisms and are hosted on the French national grid GRID5000 (part of the European CoreGrid). We analyse the impact of the numerous parameters on optimization of parallel efficiency
Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography

Lower canopy temperature (CT), resulting from increased stomatal conductance, has been associated with increased yield in wheat. Historically, CT has been measured with hand-held infrared thermometers. Using the hand-held CT method on large field trials is problematic, mostly because measurements are confounded by temporal weather changes during the time required to measure all plots. The hand-held CT method is laborious and yet the resulting heritability low, thereby reducing confidence in selection in large scale breeding endeavors. We have developed a reliable and scalable crop phenotyping method for assessing CT in large field experiments. The method involves airborne thermography from a manned helicopter using a radiometrically-calibrated thermal camera. Thermal image data is acquired from large experiments in the order of seconds, thereby enabling simultaneous measurement of CT on potentially 1000s of plots. Effects of temporal weather variation when phenotyping large experiments using hand-held infrared thermometers are therefore reduced. The method is designed for cost-effective and large-scale use by the non-technical user and includes custom-developed software for data processing to obtain CT data on a single-plot basis for analysis. Broad-sense heritability was routinely >0.50, and as high as 0.79, for airborne thermography CT measured near anthesis on a wheat experiment comprising 768 plots of size 2 × 6 m. Image analysis based on the frequency distribution of temperature pixels to remove the possible influence of background soil did not improve broad-sense heritability. Total image acquisition and processing time was ca. 25 min and required only one person (excluding the helicopter pilot). The results indicate the potential to phenotype CT on large populations in genetics studies or for selection within a plant breeding program.