Miracle-dz/UniFAR-FTUs huggingface dataset:
You Only Propagate Once : Accelerating Adversarial Training Using Maximal Principle

Deep learning achieves state-of-the-art results in many areas. However recent works have shown that deep networks can be vulnerable to adversarial perturbations which slightly changes the input but leads to incorrect prediction. Adversarial training is an effective way of improving the robustness to the adversarial examples, typically formulated as a robust optimization problem for network training. To solve it, previous works directly run gradient descent on the “adversarial loss”, i.e. replacing the input data with the corresponding adversaries. A major drawback of this approach is the computational overhead of adversary generation, which is much larger than network updating and leads to inconvenience in adversarial defense. To address this issue, we fully exploit structure of deep neural networks and propose a novel strategy to decouple the adversary update with the gradient back propagation. To achieve this goal, we follow the research line considering training deep neural network as an optimal control problem. We formulate the robust optimization as a differential game. This allows us to figure out the necessary conditions for optimality. In this way, we train the neural network via solving the Pontryagin’s Maximum Principle (PMP). The adversary is only coupled with the first layer weight in PMP. It inspires us to split the adversary computation from the back propagation gradient computation. As a result, our proposed YOPO (You Only Propagate Once) avoids forward and backward propagating the data too many times in one iteration, and restricts core descent directions computation to the first layer of the network, thus speeding up every iteration significantly. For adversarial example defense, our experiment shows that YOPO can achieve comparable defense accuracy using around 1/5 GPU time of the original projected gradient descent training.
Cartographie harmonisée des mangroves de l'Outre-mer français

A partir d'une typologie physionomique standardisee, cette these entend proposer une methode de cartographie des mangroves de l'Outre-mer francais. Plusieurs jeux de donnees satellites sont ainsi exploites en vue (i) de determiner l'extension des surfaces de mangroves, (ii) quantifier la fermeture de canopee, (iii) quantifier la hauteur de la canopee et (iv) identifier les principales especes de paletuvier. La determination des surfaces de mangroves se fait via l'exploitation des particularites spectrales des paletuviers dans le moyen-infrarouge visibles sur les images Landsat et Sentinel-2. La quantification de la fermeture de canopee est basee sur un algorithme doublement contraint (FCLSU) de demelengeage lineaire des pixels d'images tres haute resolution Pleiades. La quantification de la hauteur de canopee a ete effectuee quant a elle grâce a l'utilisation d'une analyse photogrammetrique d'un couple d'images panchromatiques stereoscopiques WorldView-3 (0.3 metres de resolution). Enfin, la discrimination des principales especes de mangroves a ete permise grâce a l'exploitation de releves spectraux effectues sur le terrain et calibration des proprietes spectrales des differentes especes visibles sur les images satellites.
Monadjemi Iris Localization using Circle and Fuzzy Circle Detection Method S

Iris localization is a very important approach in biometric identification systems. Identification pr ocess usually is implemented in three levels: iris localization, fea ture extraction, and pattern matching finally. Accuracy of iris localiza tion as the first step affects all other levels and this shows the importa nce of iris localization in an iris based biometric system. In this paper, we consider Daugman iris localization method as a stan dard method, propose a new method in this field and then analyze and compare the results of them on a standard set of iris images. T he proposed method is based on the detection of circular edge of iris, and improved by fuzzy circles and surface energy difference context s. Implementation of this method is so easy and compared to the other methods, have a rather high accuracy and speed. Test results show t at the accuracy of our proposed method is about Daugman method and com putation speed of it is 10 times faster. Keywords—Convolution, Edge detector filter, Fuzzy circle, Identification
A Domain and Type Enforcement UNIX Prototype

UNIX system security today often relies on correct operation of numerous privileged subsystems and careful attention by expert system administrators. In the context of global and possibly hostile networks, these traditional UNIX weaknesses raise a legitimate question about whether UNIX systems are appropriate platforms for processing and safeguarding important information tesources. Domain and Type Enforcement (DTE) is an access control technology for partitioning host operáting systems such as UNIX into access control domains. Such partitioning has promise both to enforce organizationalìecurity póücies that protect special classes of information and to generically strengthen operating systems against penetration attacks. This paper presents the primary DTE concepts, discusses their application to single hosts, IP networks, and NFS, and then describes the design and implementation of a DTE IINIX prototype system. Portions reprinted, with permission, from the 1995 IEEE Symposium on Security and Privacy, Mray 1995, pages 6Ç77 . O 1995 IEEE. @ 1996 The USENIX Association, Computing Systems, Vol. 9 . No. 1 . Winter 1996 47
Illumination Invariant Data Cost using Modified Census Transform

Stereo matching in non-ideal illumination is a challenging area of research. It assumes identical corresponding color values and this assumption is not guaranteed for real-time environment. As a result most of the stereo algorithms fail to generate good disparity. This paper proposes a Modified Census Correlation (MCC) data cost for stereo matching. The proposed data cost will be derived from modified census transformed indexed image and it is robust to change in lighting direction, exposure and illumination color. The obtained total energy is optimized for disparity estimation using Graph-Cut. An exhaustive evaluation using Middlebury stereo image proves the robustness of the proposed technique for variety of illumination and exposure conditions.
A revisit of fixed and mobile broadband diffusion in OECD: A new classification

Broadband communications lie at the heart of any developing information and digital society. Employing the Gompertz model in a time-series study, we analyze the factors that influence the diffusion of fixed and mobile broadband across the OECD countries that have been categorized in five groups based on the stage of innovation. We find that although the diffusion time is similar for both technologies, the mobile broadband diffusion's inflection time is asymmetric over the symmetric fixed broadband. The adoption time is almost double compared to the fixed, revealing a strong complement effect, when mostly the developed countries continue to prefer and depend on fixed broadband technology.
A NEW METHOD OF CRYPTOGRAPHY USING LAPLACE TRANSFORM OF HYPERBOLIC FUNCTIONS

Network security is very important in the Internet and other form of electronic communications such as mobile communications, Pay-TV, e- commerce, sending private emails, transmitting financial information, security of ATM cards, computer passwords, etc, which touches on many aspects of our daily lives. In this paper we developed a new algorithm for cryptography, in which we used Laplace transform of hyperbolic functions for encrypting the plain text and corresponding inverse Laplace transform for decryption. Starting with basic theory of Laplace transforms in section 2, we obtained the main results in section 3. The generalization of the results are included in section 4. This paper is based on the work of [5], A. P. Hiwarekar and [6], G. Naga Lakshmi, B. Ravi Kumar and A. Chandra Sekhar.
Reservoir Computing Approaches to Recurrent Neural Network Training

Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both: current ways of generating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current “brand-names” of reservoir methods, and thus aims to help unifying the field and providing the reader with a detailed “map” of
Phytoplankton chlorophyll a concentration and community structure in two temporarily open/closed estuaries in the Eastern Cape, South Africa

i ACKNOWLEDGEMENTS. iii TABLE OF CONTENTS. iv LIST OF ACRONYMS AND ABBREVIATIONS USED. vii LIST OF TABLES. viii LIST OF FIGURES. ix LIST OF PLATES . xiii 1. General Introduction. 14 1.1 Background. 14 1.1.1 Phytoplankton Research in South Africa with reference to Temporarily Open/Closed Estuaries: Present Understanding. 16 1.1.2 Chlorophyll a Size-Fractionation . 18 1.1.3 Primary Productivity . 20 1.2 Aims. 21 1.3 Hypotheses . 21 2. Literature Review. 22 2.
Compiler for a Processor With a Reconfigurable Functional Unit

With the flexibility of the FPGA, reconfigurable systems are able to get significant speedups for some applications. Several architectures have been proposed to integrate a processor with an FPGA in the same chip. Based on the role played by the FPGA, we can divide these architectures into two categories: FPGA as a coprocessor or FPGA as a reconfigurable functional unit (RFU). This thesis describes a C compiler for Chimaera, which is a processor/RFU architecture. First, it talks about the construction of a compiler framework based on the GCC compiler. Second, it presents three compilation techniques to extract instruction sequences from the applications so that they can be mapped onto the RFU. Next, it discusses the building of the simulator for the Chimaera architecture. Finally, it presents some experimental results, which evaluate both the compiler techniques and the architecture. The average speedup of all the benchmarks is 2.6. This thesis makes three contributions. First, it describes two techniques, namely Control Localization and SWAR, that can effectively create more and larger instruction sequences for the RFU, Second, it shows an algorithm to extract the instruction sequences in a basic block. Finally, it shows that the instruction sequences suitable for the RFU occur in a wide range of applications and the Processor/RFU approach can indeed achieve significant speedups.
Fractionation and Speciation of Arsenic in Agricultural Soils in West Bengal, India

Arsenic a toxic trace metalloid is of great environmental concern due to its presence in soil, water, plant and human continuum. Its high toxicity and increased appearance in biosphere has triggered public and political concern. Widespread arsenic (As) contamination in West Bengal, India is of great concern as it affects millions of people due to its toxicity. Arsenic in ground water, soil present as dissolved oxyanions namely arsenate and arsenite.Groundwater, when used for irrigation, helps entry of arsenic into the food chain via a soil-plant-animal continuum. In this study the extent of geo accumulation is measured in order to assess the degree of arsenic contamination in soil. A sequential fractionation study of arsenic revealed that out of all the fractions, the Fe/Mn bound arsenic has emerged to be most predominant fraction in all the experimental soils, sharing 38.2 to 73.3% of total soil arsenic recovered from the soils under study while the least proportions were retrieved from water soluble fractions. The variation in fractions may be attributed to the mineralogical make-up of soils along with some physicochemical factors. Arsenic speciation study showed that As-V appeared to be the major species while As (III) was found at a minor concentration. The extraction efficiency of the selected reagent was observed to be substantially low in a number of soils as the majority of As is strongly bonded to Fe, Al and Mn particles of the soil. are not heavily contaminated with arsenic, but may c ontain meaningful concentrations of this Arsenic, soil, fractionation, Abstract
On the Classification of Imbalanced Datasets

The Classification of Imbalanced Data Sets have received considerable attention in recent research. In this paper, we present an overview of the problem of imbalanced data sets, explain the most commonly used techniques such as sampling and cost sensitive learning, present some evaluation metrics used on imbalanced data sets, quote some interesting points drawn from various popular and latest research papers related to imbalanced classification problem. This paper does not mention all the available research solutions, but try to give a clear picture of imbalanced data set classification problem and present a brief review of existing solutions on this problem. Here, we consider binary classification problem on imbalanced data sets.
Company-University Collaboration Types As A Determinant For Knowledge Transfer

This paper develops a framework for a novel measurement of outcomes of different types of company-university collaboration. We test whether the level of formalization and the type of interaction influences the outcomes, in particular knowledge transfer. We extend the existing research by applying novel statistical computational methods form the field of natural language processing to identify the knowledge transfer. We investigate how the level of formalization of collaboration affects the knowledge transfer between universities and companies. Preliminary results indicate that we are able to identify additional forms of knowledge transfer and give companies insights into their potential benefits from different types of relationships. We propose a new perspective that enables companies to shape and adapt their external knowledge search as effective as possible.
Effects of Agricultural Land Use on the Ecohydrology of Small- Medium Mediterranean River Basins: Insights from a Case Study in the South of Portugal

Southern Europe has been experiencing an accelerated intensification of agricultural systems in the last decades with consequent environmental effects. This study aimed to evaluate the effects of agricultural land use in two small-medium river basins in the South of Portugal, regarding: (i) water quality and stream habitat; (ii) fish fauna; and (iii) soil. Sampling included fish captures, water, and soil sample collection. Hydromorphological habitat features were also assessed. Land use was quantified at the basin and local scales. Results showed that the most negative effects were associated with intensive, heavily irrigated, fertilized, and pastured local systems, mostly represented at the basin scale by olive groves, irrigated crops, and pastures. Conversely, local agricultural intensity did not prove to be a threat to the integrity and quality of the soil, seeming to ensure the sustainability of the local uses and their systems. Negative effects were observed on water quality and instream habitat and degradation of riparian vegetation, resulting in fish assemblages’ impoverishment. This study contributes to a comprehensive approach to the effects of agricultural land use, highlighting the need to integrate the results of different natural resources to efficiently support policy and decision makers toward a sustainable agriculture, water management, and land use planning.
Multimodal Biometric System Based on Near-Infra-Red Dorsal Hand Geometry and Fingerprints for Single and Whole Hands

systems have several tradeoffs like noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. In order for the biometric system to be more secure and to provide high performance accuracy, more than one form of biometrics are required. Hence, the need arise for multimodal biometrics using combinations of different biometric modalities. This paper introduces a multimodal biometric system (MMBS) based on fusion of whole dorsal hand geometry and fingerprints that acquires right and left (Rt/Lt) near-infra-red (NIR) dorsal hand geometry (HG) shape and (Rt/Lt) index and ring fingerprints (FP). Database of 100 volunteers were acquired using the designed prototype. The acquired images were found to have good quality for all features and patterns extraction to all modalities. HG features based on the hand shape anatomical landmarks were extracted. Robust and fast algorithms for FP minutia points feature extraction and matching were used. Feature vectors that belong to similar biometric traits were fused using feature fusion methodologies. Scores obtained from different biometric trait matchers were fused using the Min-Max transformation-based score fusion technique. Final normalized scores were merged using the sum of scores method to obtain a single decision about the personal identity based on multiple independent sources. High individuality of the fused traits and user acceptability of the designed system along with its experimental high performance biometric measures showed that this MMBS can be considered for med-high security levels biometric identification purposes.
Extracting safe and precise control flow from binaries

As a starting point for static program analysis, a control flow graph (CFG) is needed. If only the binary executable is available, this CFG has to be reconstructed from sequences of instructions. The usual way to do this is a top-down approach: the executable's information about routines is used to split the sequence into routines, and then each instruction is analysed for branch targets in order to compute basic block boundaries. When analysing safety-critical real-time systems, safe and precise results are needed. The CFG that the analyses traverse has to satisfy the same safety and precision requirements, because the analyses inherit all deficiencies. In this paper, a bottom-up approach for CFG approximation is presented. It starts at a set of entry points and clusters the sequence of instructions into larger units like blocks and routines. In this way, the algorithm is able to account for uncertainties early enough to generate a safe CFG.
Locating Microseism Sources Using Spurious Arrivals in Intercontinental Noise Correlations

The accuracy of Green's functions retrieved from seismic noise correlations in the microseism frequency band is limited by the uneven distribution of microseism sources at the surface of the Earth. As a result, correlation functions are often biased as compared to the expected Green's functions, and they can include spurious arrivals. These spurious arrivals are seismic arrivals that are visible on the correlation and do not belong to the theoretical impulse response. In this article, we propose to use Rayleigh wave spurious arrivals detected on correlation functions computed between European and United States seismic stations to locate microseism sources in the Atlantic Ocean. We perform a slant stack on a time distance gather of correlations obtained from an array of stations that comprises a regional deployment and a distant station. The arrival times and the apparent slowness of the spurious arrivals lead to the location of their source, which is obtained through a grid search procedure. We discuss improvements in the location through this methodology as compared to classical back projection of microseism energy. This method is interesting because it only requires an array and a distant station on each side of an ocean, conditions that can be met relatively easily.
Sparse coding in the primate cortex

A central goal in the study of cortical function is to understand how states of the environment are represented by firing patterns of cortical neurons. Electrophysiological recordings from single cells have revealed a remarkably close relationship among stimuli, neural activity, and perceptual states. The nature of this relationship and interpretations of experimental results are fiercely debated. Is sensory information represented by the activity of single, individually meaningful cells, or is it only the global activity pattern across a whole cell population that corresponds to interpretable states? There are now strong theoretical reasons and experimental evidence suggesting that the brain adopts a compromise between these extremes which is often referred to as sparse coding.
Credit card fraud detection using Hidden Markov Model

Since past few years there is tremendous advancement in electronic commerce technology, and the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper we present the necessary theory to detect fraud in credit card transaction processing using a Hidden Markov Model (HMM). An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected by using an enhancement to it(Hybrid model). In further sections we compare different methods for fraud detection and prove that why HMM is more preferred method than other methods.
Efficient Feature Selection by using Global Redundancy Minimization and Constraint Score

: A central problem in automatic learning is the identification of a representative set of characteristics from which to construct a classification model for a particular task. This thesis deals with the problem of the selection of characteristics for automatic learning by a correlation - based approach. The central assumption is that good sets of characteristics contain characteristics that are highly correlated with the class but not correlated with each other. A formula for evaluating characteristics, based on ideas derived from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. Other experiments compared the CFS to a wrapper - a well-known approach to feature selection that uses the target learning algorithm to evaluate sets of features. In many cases CFS has given results comparable to the envelope, and in general, surpassed the envelope on small sets of data. CFS runs much faster than the wrapper, enabling it to extend to larger sets of data.
UniverSC: a flexible cross-platform single-cell data processing pipeline

Single-cell RNA-sequencing analysis to quantify RNA molecules in individual cells has become popular owing to the large amount of information one can obtain from each experiment. We have developed UniverSC (https://github.com/minoda-lab/universc), a universal single-cell processing tool that supports any UMI-based platform. Our command-line tool enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms.