Miracle-dz/UniFAR-FTUs huggingface dataset:
A Key Distribution Scheme for WSN Based on Deployment Knowledge and Dual-Key Pools

By using deployment Knowledge and cellular model, a novel key distribution scheme based on deployment and dual-key pools is proposed for WSN. Each sub-key pool in the network contains two types of key pools: one doubled by the offline server is shared by three adjacent cells before the deployment and the other one distributed to each cell is non-overlapping. The server distributes four random numbers for each cell and ensures each adjacent cell sharing one random number. In order to improve the network security, nodes among the neighboring cells adopt a binary hash function and a corresponding random number to generate pair-wise keys. Compared with Du scheme and q-composite scheme, the simulation results show that our proposed scheme not only has efficiently improved the probability of pair-wise key establishment and invulnerability, but also has certain advantage on the nodes’ storage space.
A GPU-Based Approach to Non-Photorealistic Rendering in the Graphic Style of Mike Mignola

The subject of Non-Photorealistic Rendering (NPR) is one which tends towards a certain, small set of targeted appearances. Work in NPR - in particular in real-time graphics - is generally aimed at a classic cel-animated, "cartoon" visual style - that is, solid black outlines with clamped ramp shading. While this look is often arresting and interesting, it is a narrow approach to NPR. Even within the bounds of a "cartoony" style, significant variations can be found. Of particular interest is the art of illustrator Mike Mignola, popularized in his Hellboy comic book series. We present a method for rendering a pre-built model in the style of Mignola’s illustration. GPU acceleration is used to funnel large numbers of geometry-based edges, without a huge performance drop. Vertex Buffer Objects (VBOs) are used to create strokes along the outside edge of geometry, and along the edges of shadows.
Genetic Variability , Correlation and Path Study in Radish ( Raphanus Sativus L . ) under near Temperate Conditions of Garhwal Hills Nagar

Twenty one genotypes of radish were evaluated in Rabi seasons of 2014-15 for different horticultural traits. The phenotypic and genotypic coefficients of variation were found moderate for root weight without leafs, leaf weight, root weight with leafs, root diameter and root length. High heritability coupled with moderate genetic gain observed for root length, root weight with leafs, root weight without leafs, leaf weight and root diameter. The phenotypic and genotypic correlation coefficients among different characters showed that marketable root yield per hectare had positive and significant association with root weight without leaf, root weight with leaf, root length, number of leaves and length of whole plant. The path coefficient analysis revealed that maximum positive direct effect towards root yield per plant was contributed by root weight without leaf, length of whole plant, root weight with leaf, number of leaves, thereby indicating the importance of these traits for yield improvement in radish through direct or indirect selection.
Clustering economic and financial time series : Exploring the existence of stable correlation conditions

Clustering plays an important role in extracting information from the noise in economic and financial time series; it is one way to perform a coarse-graining of the empirical data sets to extract stable dependence information from the surrounding noise. This discussion paper explores clustering as an econometric tool for studying dependencies between variables. It places clustering in the context of the classical statistical tools for dependence analysis (i.e., correlation, regression, cointegration and structural change); discusses similarity, the key concept in time-series clustering; and reviews the principal approaches to quantifying similarity with the distance function. It argues that concepts of similarity, such as dynamic time-warping, used in a wide range of application domains (e.g., speech recognition, medicine, biomathematics, seismology) might be useful in the context of finance and economics.
Faster Algorithms for Algebraic Path Properties in Recursive State Machines with Constant Treewidth

Interprocedural analysis is at the heart of numerous applications in programming languages, such as alias analysis, constant propagation, etc. Recursive state machines (RSMs) are standard models for interprocedural analysis. We consider a general framework with RSMs where the transitions are labeled from a semiring, and path properties are algebraic with semiring operations. RSMs with algebraic path properties can model interprocedural dataflow analysis problems, the shortest path problem, the most probable path problem, etc. The traditional algorithms for interprocedural analysis focus on path properties where the starting point is fixed as the entry point of a specific method. In this work, we consider possible multiple queries as required in many applications such as in alias analysis. The study of multiple queries allows us to bring in a very important algorithmic distinction between the resource usage of the one-time preprocessing vs for each individual query. The second aspect that we consider is that the control flow graphs for most programs have constant treewidth. Our main contributions are simple and implementable algorithms that support multiple queries for algebraic path properties for RSMs that have constant treewidth. Our theoretical results show that our algorithms have small additional one-time preprocessing, but can answer subsequent queries significantly faster as compared to the current best-known solutions for several important problems, such as interprocedural reachability and shortest path. We provide a prototype implementation for interprocedural reachability and intraprocedural shortest path that gives a significant speed-up on several benchmarks.
Decode-and-Forward Relay Beamforming for Secrecy with Finite-Alphabet Input

In this letter, we compute the secrecy rate of decode-and-forward (DF) relay beamforming with finite input alphabet of size M. Source and relays operate under a total power constraint. First, we observe that the secrecy rate with finite-alphabet input can go to zero as the total power increases, when we use the source power and the relay weights obtained assuming Gaussian input. This is because the capacity of an eavesdropper can approach the finite-alphabet capacity of \frac{1}{2}\log_2 M with increasing total power, due to the inability to completely null in the direction of the eavesdropper. We then propose a transmit power control scheme where the optimum source power and relay weights are obtained by carrying out transmit power (source power plus relay power) control on DF with Gaussian input using semi-definite programming, and then obtaining the corresponding source power and relay weights which maximize the secrecy rate for DF with finite-alphabet input. The proposed power control scheme is shown to achieve increasing secrecy rates with increasing total power with a saturation behavior at high total powers.
0 Variational Methods , Multisymplectic Geometry and Continuum Mechanics

This paper presents a variational and multisymplectic formulation of both compressible and incompressible models of continuum mechanics on general Riemannian manifolds. A general formalism is developed for non-relativistic first-order multisymplectic field theories with constraints, such as the incompressibility constraint. The results obtained in this paper set the stage for multisymplectic reduction and for the further development of Veselov-type multisymplectic discretizations and numerical algorithms. The latter will be the subject of a companion paper. Research partially supported by the California Institute of Technology and NSF contract KDI/ATM-9873133. Research partially supported by NSF-KDI/ATM-9873133 and the Alfred P. Sloan Research Fellowship.
Globalisation of Chronic Illness Research

This chapter builds on my previous work (Wellard 1998) where I have explored discursive constructions of chronic illness and argued that discourses of science, individualism and normalisation underpinned our ways of working with people experiencing chronic illnesses and the research questions that are posed. The aims of this chapter are (a) to develop a contemporary understanding of chronic illness research globally and (b) to identify barriers and enablers to advancing knowledge about prevention and management of chronic illnesses. A review of contemporary literature identified a number of significant shifts that are relevant to the aims of this chapter, most notably the recognition of chronic illnesses as an urgent problem affecting global health. Discourses of science remain evident, but an increasing emphasis on economic and social consequences of chronic illnesses is emerging. There are a number of challenges in attempting to gain a global view of work in the field of chronic illness. First, the literature surrounding chronic illness is vast and the volume of material is overwhelming. Second, the analysis presented in this chapter is limited by my reliance on the English language. Although there is considerable work related to chronic illness published in many other languages, it was not accessible to me. Third, there are limitations in the databases available for bibliometric analysis by researchers. For example, Hofman et al. (2006) identified that MEDLINE ‘does not equally represent all countries, journals or topics’ (p. 418), resulting in a poor or inaccurate representation of research in middleand low-income countries. The strategy adopted for developing a contemporary view of the globalisation of chronic illness work was to develop an integrative literature review with the goal of developing a critical analytical view of trends in the field. GOOGLE scholar (http://scholar.google.com/) and MEDLINE database (using PubMed: http://www.ncbi.nlm.nih.gov/sites/entrez/) were searched, identifying the range of literature published between 1995 and 2007. Additionally, a search of the World Health Organization (WHO) web pages (http://www.who.int/en/)
Multi-Criteria Decision-Making Model for the Material Flow of Resonant Wood Production

This paper proposes a multi-criteria decision-making model, for the selection and evaluation of the most valuable wooden input—resonant wood. Application of a given model can improve the process of input valuation as well as impact and improve particular economic indicators for the resonant wood manufacturer. We have tried to describe and evaluate the supply chain of resonant wood manufacturing and production of musical instruments. Particular value-added and non-value-added activities have been chosen according to the logical sequence of technology. Then, concrete criteria were specified and their significance weightings. Another important part of our paper is the description of resonant wood, specifications, and demands on log and wood species. There are some important physical and mechanical properties which should be taken into account and evaluated during the production of musical instruments. By the application of this model, a particular enterprise can reach an enhanced tool for the continuous evaluation of the product flowing through the supply chain. Visibility of particular operations and their logical sequence, presented by Petri nets, can lead to easier detection of possible defects in these operations and their origin. So, the main purpose of the paper lies in the suggestion of an objective and quantified managerial tool for the decision making.
Effect of background noise on the SNR estimation of biometric parameters in forensic speaker recognition

In the field of automatic or semi-automatic speaker recognition, background noise is one of the main causes of alteration of the acoustic indexes used in the biometric identification/verification phase. The paper analyses the varying effect of background noise on the first three formants, i.e. the biometric indexes typically used in forensic speaker identification. The analysis is performed by calculating the signal to noise ratio (SNR) in the sub-bands relating to the first three formants with varying vowels and types of noise. The results obtained indicate that background noise has a different impact according to the vowel and single formant involved.
Document Level Novelty Detection: Textual Entailment Lends a Helping Hand

In this paper we present a way of detecting novelty of a document with respect to the relevant source documents with the aid of methods used in detecting Textual En-tailment (TE). The proposed TE system is based on supervised machine learning approach that makes use of different similarity metrics. The TE system is further interpreted to detect the novelty of an incoming document with respect to some source document(s) already seen by the system. We design a corpus to support this foundation of novelty at the document level and coin it as the Document Level Nov-elty Detection (DLND) corpus. We employ standard supervised classiﬁcation algorithms such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) and investigate their performance on DLND. Evaluation results show the accuracies of 78.78%, 77.27% and 74.24% for SVM, MLP and RF, respectively on DLND. To establish the ef-ﬁcacy of our methods we evaluate our model on the benchmark datasets released in the shared task of Recognizing Textual Entailment - 6 (RTE-6) and Recognizing Textual Entailment - 7 (RTE-7) . Experiments show the accuracies of 94.91% and 96.72% on RTE-6 and RTE-7 dataset, respectively.
Mass Transfer Uptake Mortality Mortality EP HP Uptake Sinking GrowthGrowth XP HD ED XD Decay Decay Action DOCs

Organic particles sinking from the sunlit surface are oases of food for heterotrophic bacteria living in the deep ocean. Particle-attached bacteria need to solubilize particles, so they produce exoenzymes that cleave bonds to make molecules small enough to be transported through bacterial cell walls. Releasing exoenzymes, which have an energetic cost, to the external environment is risky because there is no guarantee that products of exoenzyme activity, called hydrolysate, will diffuse to the particleattached bacterium that produced the exoenzymes. Strategies used by particle-attached bacteria to counteract diffusive losses of exoenzymes and hydrolysate are investigated in a water column model. We find that production of exoenzymes by particle-attached bacteria is only energetically worthwhile at high bacterial abundances. Quorum sensing provides the means to determine local abundances, and thus the model results support lab and field studies which found that particle-attached bacteria have the ability to use quorum sensing. Additional model results are that particle-attached bacterial production is sensitive to diffusion of hydrolysate from the particle and is enhanced by as much as 15 times when diffusion of exoenzymes and hydrolysate from particles is reduced by barriers of biofilms and particle-attached bacteria. Bacterial colonization rates and activities on particles in both the euphotic and mesopelagic zones impact remineralization length scales. Shoaling or deepening of the remineralization depth has been shown to exert significant influence on the residence time and concentration of carbon in the atmosphere and ocean. By linking variability in remineralization depths to mechanisms governing bacterial colonization of particles and group coordination of exoenzyme production using a model, we quantitatively connect microscale bacteria-particle interactions to the carbon cycle and provide new insights for future observations.
Straintronics-Based Random Access Memory as Universal Data Storage Devices

Nanomagnetic and spin-based memories are distinguished for their high data endurance in comparison with their charge-based peers. However, they have drawbacks, such as high write energy and poor scalability due to high write current. In this paper, we apply the straintronics principle that seeks the combination of piezoelectricity and inverse magnetostriction (Villari effect), to design a proof-of-principle 2 Kb nonvolatile magnetic memory in 65 nm CMOS technology. Our simulation results show read-access and write-cycle energies as low as 49 and 143 fJ/b, respectively. At a nominal supply level of 1 V, reading can be performed as fast as 562 MHz. Write error rates <;10-7 and 10-15 can be obtained at 10 and 5 MHz, respectively. In addition to nonvolatility, ultralow energy per operation, and high performance, our STRs memory has a high storage density with a cell size as small as 0.2 μm2.
Neural Morphological Disambiguation Using Surface and Contextual Morphological Awareness

Morphological disambiguation, particularly for morphologically rich languages, is a crucial step in many NLP tasks. Morphological analyzers provide multiple analyses of a word, only one of which is true in context. We present a language-agnostic deep neural sys-tem for morphological disambiguation, with experiments on Hindi. We achieve accuracies of around 95.22% without the use of any language-specific features or heuristics, which outperforms the existing state of the art. One contribution through this work is building the first morphological disambiguation system for Hindi. We also show that using phonological features can improve performance. On using phonological features and pre-trained word vectors, we report an accuracy of 97.02% for Hindi.
Image Enhancement Techniques for Fingerprint Identification

The aim of this paper is to propose a new method in fingerprint enhancement with application of wavelet transform which is more efficient than existing methods. At present the methods that are in use are the ones involving the use of Gabor filtering and Fourier filtering. But the accuracy of these techniques is far from satisfactory. A new technique is being proposed that incorporates wavelet transform and Gabor filtering. The performance of this technique is being discussed in the paper.
Ubiquitously expressed secretory carrier membrane proteins (SCAMPs) 1-4 mark different pathways and exhibit limited constitutive trafficking to and from the cell surface

Secretory carrier membrane proteins (SCAMPs) 1-4 are ubiquitously expressed and are major components of the eukaryotic cell surface recycling system. We investigated whether different SCAMPs function along distinct pathways and whether they behave like itinerant cargoes or less mobile trafficking machinery. In NRK cells, we show by immunofluorescence microscopy that different SCAMPs are concentrated mostly adjacent to one another in the trans-Golgi network and endosomal recycling compartment. By immunoelectron microscopy, they were shown to be close neighbors on individual transferrin-containing endosomal elements and on the plasma membrane. Within the internal endosomal network, SCAMPs are located distal to rab5-containing endosomes, and the individual isoforms appear to mark pathways that diverge from the constitutive recycling route and that may be distinguished by different adaptors, especially AP-1 and AP-3. Based on comparisons of SCAMP localization with endocytosed transferrin as well as live imaging of GFP-SCAMP1, we show that SCAMPs are concentrated within the motile population of early and recycling endosomes; however, they are not detected in newly formed transferrin-containing endocytic vesicles or in vesicles recycling transferrin to the surface. Also, they are not detected in constitutive secretory carriers marked by VSV-G. Their minimal recycling to the surface is reflected by their inability to relocate to the plasma membrane upon inhibition of endocytosis. Thus SCAMPs exhibit limited exchange between the cell surface and internal recycling systems, but within each of these sites, they form a mosaic with individual isoforms marking distinct pathways and potentially functioning as trafficking machinery at sites of vesicle formation and fusion. A corollary of these findings is that early endosomes exist as a distinct SCAMP-containing compartment and are not formed de novo by fusion of endocytic vesicles.
On the InAccuracy and Influence of Android Pattern Strength Meters Work in Progress

A common method for helping users select stronger authentication secrets, e. g. , passwords, is to deploy a visual strength meter that provides feedback to the user while performing password selection. Recent work considered the accuracy of strength meters for passwords, but there is limited work on understanding the accuracy of strength meters for other knowledge-based authentication types, particularly Android’s graphical pattern unlock, despite there being multiple strength meters proposed for patterns in the literature. In this work, we present a preliminary analysis of the accuracy of strength meters for Android patterns, applying the same set of techniques from previous work. Using datasets of patterns collected in several user studies as a baseline, we compare strength meter estimations using weighted Spearman correlation. Overall, we find that strength estimations based on visual properties of the patterns (such as length, intersections, overlapping nodes, and similar) provide strength estimations that have low correlation with the real guessability of Android patterns. Motivated by these findings, we describe a set of research questions and experiments that are in progress that question whether the accuracy of a meter should even be the driving factor for nudging users to more secure choices.
Example‐Driven Procedural Urban Roads

Synthesizing and exploring large‐scale realistic urban road networks is beneficial to 3D content creation, traffic animation and urban planning. In this paper, we present an interactive tool that allows untrained users to design roads with complex realistic details and styles. Roads are generated by growing a geometric graph. During a sketching phase, the user specifies the target area and the examples. During a growing phase, two types of growth are effectively applied to generate roads in the target area; example‐based growth uses patches extracted from the source example to generate roads that preserve some interesting structures in the example road networks; procedural‐based growth uses the statistical information of the source example while effectively adapting the roads to the underlying terrain and the already generated roads. User‐specified warping, blending and interpolation operations are used at will to produce new road network designs that are inspired by the examples. Finally, our method computes city blocks, individual parcels and plausible building and tree geometries. We have used our approach to create road networks covering up to 200 km2 and containing over 3500 km of roads.
Bit-Level Transformation and Optimization for Hardware Synthesis of Algorithmic Descriptions

As the complexity of integrated circuit systems increases, automated hardware design from higherlevel abstraction is becoming more and more important. However, for many high-level programming languages, such as C/C++, the description of bitwise access and computation is not as direct as hardware description languages, and hardware synthesis of algorithmic descriptions may generate sub-optimal implementtations for bitwise computation-intensive applications. In this paper we introduce a bit-level transformation and optimization approach to assisting hardware synthesis of algorithmic descriptions. We introduce a bit-flow graph to capture bit-value information. Analysis and optimizing transformations can be performed on this representation, and the optimized results are transformed back to the standard data-flow graphs extended with a few instructions representing bitwise access. This allows high-level synthesis tools to automatically generate circuits with higher quality. Experiments show that our algorithm can reduce slice usage by 29.8% on average for a set of real-life benchmarks on Xilinx FPGAs. In the meantime, the clock period is reduced by 13.6% on average, with an 11.4% latency reduction.