Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Uniform Bounds in the Poisson Approximation With Applications to Informational Distances I

We explore asymptotically optimal bounds for deviations of Bernoulli convolutions from the Poisson limit in terms of the Shannon relative entropy and the Pearson <inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula>-distance. The results are based on proper non-uniform estimates for densities. This part deals with the so-called non-degenerate case.
BioID: A Multimodal Biometric Identification System

Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Biometric features such as the face or a fingerprint can be stored on a microchip in a credit card, for example. A single feature, however, sometimes fails to be exact enough for identification. Another disadvantage of using only one feature is that the chosen feature is not always readable. Dialog Communication Systems (DCS AG) developed BioID, a multimodal identification system that uses three different features-face, voice, and lip movement-to identify people. With its three modalities, BioID achieves much greater accuracy than single-feature systems. Even if one modality is somehow disturbed-for example, if a noisy environment drowns out the voice-the ether two modalities still lead to an accurate identification. This article goes into detail about the system functions, explaining the data acquisition and preprocessing techniques for voice, facial, and lip imagery data. The authors also explain the classification principles used for optical features and the sensor fusion options (the combinations of the three results-face, voice, lip movement-to obtain varying levels of security).
Language Edit Distance & Maximum Likelihood Parsing of Stochastic Grammars: Faster Algorithms & Connection to Fundamental Graph Problems

Given a context free language $L(G)$ over alphabet $\Sigma$ and a string $s \in \Sigma^*$, the language edit distance (Lan-ED) problem seeks the minimum number of edits (insertions, deletions and substitutions) required to convert $s$ into a valid member of $L(G)$. The well-known dynamic programming algorithm solves this problem in $O(n^3)$ time (ignoring grammar size) where $n$ is the string length [Aho, Peterson 1972, Myers 1985]. Despite its vast number of applications, there is no algorithm known till date that computes or approximates Lan-ED in true sub-cubic time. In this paper we give the first such algorithm that computes Lan-ED almost optimally. For any arbitrary $\epsilon > 0$, our algorithm runs in $\tilde{O}(\frac{n^{\omega}}{poly(\epsilon)})$ time and returns an estimate within a multiplicative approximation factor of $(1+\epsilon)$, where $\omega$ is the exponent of ordinary matrix multiplication of $n$ dimensional square matrices. It also computes the edit script. Further, for all substrings of $s$, we can estimate their Lan-ED within $(1\pm \epsilon)$ factor in $\tilde{O}(\frac{n^{\omega}}{poly(\epsilon)})$ time with high probability. We also design the very first sub-cubic ($\tilde{O}(n^\omega)$) algorithm to handle arbitrary stochastic context free grammar (SCFG) parsing. SCFGs lie at the foundation of statistical natural language processing, they generalize hidden Markov models, and have found widespread applications. To complement our upper bound result, we show that exact computation of SCFG parsing, or Lan-ED with insertion as only edit operation in true sub-cubic time will imply a truly sub-cubic algorithm for all-pairs shortest paths, and hence to a large range of problems in graphs and matrices. Known lower bound results on parsing implies no improvement over our time bound of $O(n^\omega)$ is possible for any nontrivial multiplicative approximation.
BANZKP: A Secure Authentication Scheme Using Zero Knowledge Proof for WBANs

Wireless body area network (WBAN) has shown great potential in improving healthcare quality not only for patients but also for medical staff. However, security and privacy are still an important issue in WBANs especially in multi-hop architectures. In this paper, we propose and present the design and the evaluation of a secure lightweight and energy efficient authentication scheme BANZKP based on an efficient cryptographic protocol, Zero Knowledge Proof (ZKP) and a commitment scheme. ZKP is used to confirm the identify of the sensor nodes, with small computational requirement, which is favorable for body sensors given their limited resources, while the commitment scheme is used to deal with replay attacks and hence the injection attacks by committing a message and revealing the key later. BANZKP reduces the memory requirement by 56,13% compared to TinyZKP [16], the comparable alternative so far for Body Area Networks. Also, the simulation results demonstrate that our proposed scheme is 17 and 5 times more efficient in term of execution time, and uses 94.11% and 80% less energy compared to TinyZKP and W-ECDSA [25], respectively.
Detection of key organs in tomato based on deep migration learning in complex

School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, 5 China; sun2000jun@sina.com (J.S.); 769598959@qq.com (X.H.); 2685326848@qq.com (X.G.) 6 wxh419@ujs.edu. cn (X.W.); shenjifeng@ujs.edu. cn (J.S.); 810541544@qq.com (Y.S.) 7 *Correspondence: sun2000jun@sina.com; Tel. : +13775544650 8 9 Abstract:In the current natural environment, due to the complexity of the background and the high 10 similarity of the color between immature green tomato and plant, the occlusion of the key organs 11 (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization 12 of the detection model. Therefore, an improved tomato organ detection method based on 13 convolutional neural network has been proposed in this paper. Based on the original Faster R-CNN 14 algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature 15 extraction network, and K-means clustering method was used to adjust more appropriate anchor 16 size than manual setting to improve detection accuracy. A variety of data augmentation techniques 17 were used to train the network. The test results showed that compared with the traditional Faster R18 CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 19 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection 20 time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can 21 be transplanted to the embedded system, which lays a theoretical foundation for the development of 22 precise targeting pesticide application system and automatic picking device. 23 24
ParCFD 2014 Design and Optimization of OpenFOAM-based CFD Applications for Hybrid and Heterogeneous HPC Platforms

Hardware-aware design and optimization is crucial in exploiting emerging architectures for PDE-based computational fluid dynamics applications. In this work, we study optimizations aimed at acceleration of OpenFOAM-based applications on emerging hybrid heterogeneous platforms. OpenFOAM uses MPI to provide parallel multi-processor functionality, which scales well on homogeneous systems but does not fully utilize the potential per-node performance on hybrid heterogeneous platforms. In our study, we use two OpenFOAM applications, icoFoam and laplacianFoam, both based on Krylov iterative methods. We propose a number of optimizations of the dominant kernel of the Krylov solver, aimed at acceleration of the overall execution of the applications on modern GPU-accelerated heterogeneous platforms. Experimental results show that the proposed hybrid implementation significantly outperforms the state-of-the-art implementation. c © 2014 Published by Elsevier Ltd.
NCAM: Near-Data Processing for Nearest Neighbor Search

Deep down in many applications like natural language processing (NLP), vision, and robotics is a form of the k-nearest neighbor search algorithm (kNN). The kNN algorithm is primarily bottlenecked by data movement, limiting throughput and incurring latency in these applications. While there do exist well bounded kNN approximations that improve the performance of kNN, these algorithms trade-off accuracy and quickly degrade into linear search for high dimensionality. To address data movement, we designed the nearest neighbor content addressable memory (NCAM) which employs processing in-memory (PIM) to eliminate costly data transfers, and provides exact nearest neighbor search. NCAMs benefit from the modularity offered by 3D die-stacking technology which allows us to side-step the issues of direct integration with DRAM dies. The NCAM benefits from the higher density and speeds of emerging memory technologies and interfaces. We characterize a state-of-the-art software kNN implementation and expose the shortcomings of approximate kNN search. We present a full NCAM design and estimate its performance using post-placement and route estimates, and we show that its power characteristics are compatible with emerging memory substrates. We then evaluate energy efficiency and latency compared to modern multi-core CPUs and GPGPU platforms using parameters typical of mobile and server workloads. Our simulation results show that the NCAM can achieve up to 160x throughput per watt and three orders of magnitude latency improvement over a NVIDIA Titan X GPU for server workloads, and ~2413x throughput per watt and ~94.5x latency improvements over a multi-core Intel E5-2620 CPU. Finally, we show that the NCAM is not limited to just kNN and can be generalized to act as a content addressable memory (CAM) or ternary-CAM (TCAM).
Maximizing System Throughput by Cooperative Sensing in Cognitive Radio Networks

Cognitive radio networks (CRNs) allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of multiple PUs and SUs to study the problem of maximizing the total expected system throughput. First, we study the sensing decision problem for maximizing the system throughput subject to a constraint on the PU throughput, and we design a Bayesian decision rule-based algorithm. The problem is shown to be strongly NP-hard and solved via a greedy algorithm with time complexity O([(N5)/(log2[1/(1-ε)])]), where N is the total number of SUs. The algorithm achieves a throughput strictly greater than 1/2(1-ε) of the optimal solution and results in a small constraint violation that goes to zero with ε. We then investigate the more general problem with constraints on both PU throughput and the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We illustrate the efficacy of the performance of our algorithms and provide sensitivity analysis via a numerical investigation.
OTS/CafeOBJ2JML: An attempt to combine Design By Contract with Behavioral Specifications

Design by Constract (DBC) has influenced the development of formal specification languages that allow the mix of specification and implementation code, like Eiffel, the Java Modeling Language (JML) and Spec#. Meanwhile algebraic specification languages have been developing independently and offer full support for specification and verification of design for large and complex systems in a mathematical rigorous way. However there is no guarantee that the final implementation will comply to the specification. In this paper we proposed the use of the latter for the specification and verification of the systems design and then by presenting a translation between the two, the use of the former to ensure that the implementation respects the specification and thus enjoy the verified properties.
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Network

Overparameterization in deep learning typically refers to settings where a trained Neural Network (NN) has representational capacity to fit the training data in many ways, some of which generalize well, while others do not. In the case of Recurrent Neural Networks (RNNs), there exists an additional layer of overparameterization, in the sense that a model may exhibit many solutions that generalize well for sequence lengths seen in training, some of which extrapolate to longer sequences, while others do not. Numerous works studied the tendency of Gradient Descent (GD) to fit overparameterized NNs with solutions that generalize well. On the other hand, its tendency to fit overparameterized RNNs with solutions that extrapolate has been discovered only lately, and is far less understood. In this paper, we analyze the extrapolation properties of GD when applied to overparameterized linear RNNs. In contrast to recent arguments suggesting an implicit bias towards short-term memory, we provide theoretical evidence for learning low dimensional state spaces, which can also model long-term memory. Our result relies on a dynamical characterization which shows that GD (with small step size and nearzero initialization) strives to maintain a certain form of balancedness, as well as on tools developed in the context of the moment problem from statistics (recovery of a probability distribution from its moments). Experiments corroborate our theory, demonstrating extrapolation via learning low dimensional state spaces with both linear and non-linear RNNs.
AMIE : An annotation model for information research

The objective of most users for consulting any information database, information warehouse or the internet is to resolve one problem or the other. Available online or offline annotation tools were not conceived with the objective of assisting users in their bid to resolve a decisional problem. Apart from the objective and usage of annotation tools, how these tools are conceived and classified has implication on their usage. Several criteria have been used to categorize annotation concepts. Typically annotation are conceived based on how it affect the organization of document been considered for annotation or the organization of the resulting annotation. Our approach is annotation that will assist in information research for decision making. Annotation model for information exchange (AMIE) was conceived with the objective of information sharing and reuse. Categories and subject descriptors Information systems design and development, cooperative information systems, Information retrieval, filtering, and extraction
Metamorphic Virus Variants Classification Using Opcode Frequency Histogram

In order to prevent detection and evade signature-based scanning methods, which are normally exploited by antivirus softwares, metamorphic viruses use several various obfuscation approaches. They transform their code in new instances as look entirely or partly different and contain dissimilar sequences of string, but their behavior and function remain unchanged. This obfuscation process allows them to stay away from the string based signature detection. In this research, we use a statistical technique to compare the similarity between two files infected by two morphed versions of a given metamorphic virus. Our proposed solution based on static analysis and it uses the histogram of machine instructions frequency in various offspring of obfuscated viruses. We use Euclidean histogram distance metric to compare a pair of portable executable (PE) files. The aim of this study is to show that for some particular obfuscation methods, the presented solution can be exploited to detect morphed varieties of a file. Hence, it can be utilized by non-string based signature scanning to identify whether a file is a version of a metamorphic virus or not.
RHEEMix in the Data Jungle – A Cross-Platform ery Optimizer –

In pursuit of ecient and scalable data analytics, the insight that “one size does not t all” has given rise to a plethora of specialized data processing platforms and today’s complex data analytics are moving beyond the limits of a single platform. To cope with these new requirements, we present a cross-platform optimizer that allocates the subtasks of data analytic tasks to the most suitable platforms. Our main contributions are: (i) a mechanism based on graph transformations to explore alternative execution strategies; (ii) a novel graph-based approach to eciently plan data movement among subtasks and platforms; and (iii) an ecient plan enumeration algorithm, based on a novel enumeration algebra. We extensively evaluate our optimizer under diverse real tasks. e results show that our optimizer is capable of selecting the most efcient platform combination for a given task, freeing data analysts from the need to choose and orchestrate platforms. In particular, our optimizer allows certain tasks to run more than one order of magnitude faster than on state-of-the-art platforms, such as Spark.
Outage Minimization via Power Adaptation and Allocation in Truncated Hybrid ARQ

In this work, we analyze hybrid ARQ (HARQ) transmission over the independent block fading channel. We consider two scenarios with respect to the message sent by the receiver via the feedback channel: i) “conventional”, one-bit feedback used to inform the transmitter about the decoding success/failure (ACK/NACK), and ii) the multi-bit feedback message, where on top of ACK/NACK, the transmitter is provided with additional information about the state of the receiver. To minimize the outage probability under long-term average and peak power constraints, we cast the problems into the dynamic programming (DP) framework and solve them for Nakagami-m fading channels. An approximate, closed-form solution for the high signal-to-noise ratio (SNR) regime is proposed using geometric programming (GP). The obtained results quantify the advantage of the multi-bit feedback over the conventional one-bit approach, and show that the power optimization can provide significant gains over conventional power-constant HARQ transmissions even in the presence of peak-power constraints.
Symmetry of Complex Networks

Our paper analyzes some new lines to advance on quickly evolving concepts, the so-called Entropy, or its Symmetry/Asymmetry degrees, on graphs in general. It will be very necessary to analyze the mutual relationship between some fuzzy measures, with their very interesting applications, as may be the case of Graph Entropy and Graph Symmetry; in particular, working on Complex Networks and Systems.
Cached Suucient Statistics for Eecient Machine Learning with Large Datasets 1. Caching Suucient Statistics

This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its leaves. We show how the ADtree can be used to accelerate Bayes net structure nding algorithms, rule learning algorithms, and feature selection algorithms, and we provide a number of empirical results comparing ADtree methods against traditional direct counting approaches. We also discuss the possible uses of ADtrees in other machine learning methods, and discuss the merits of ADtrees in comparison with alternative representations such as kd-trees, R-trees and Frequent Sets. 1. Caching Su cient Statistics Computational e ciency is an important concern for machine learning algorithms, especially when applied to large datasets (Fayyad, Mannila, & Piatetsky-Shapiro, 1997; Fayyad & Uthurusamy, 1996) or in real-time scenarios. In earlier work we showed how kd-trees with multiresolution cached regression matrix statistics can enable very fast locally weighted and instance based regression (Moore, Schneider, & Deng, 1997). In this paper, we attempt to accelerate predictions for symbolic attributes using a kind of kd-tree that splits on all dimensions at all nodes. Many machine learning algorithms operating on datasets of symbolic attributes need to do frequent counting. This work is also applicable to Online Analytical Processing (OLAP) applications in data mining, where operations on large datasets such as multidimensional database access, DataCube operations (Harinarayan, Rajaraman, & Ullman, 1996), and association rule learning (Agrawal, Mannila, Srikant, Toivonen, & Verkamo, 1996) could be accelerated by fast counting. Let us begin by establishing some notation. We are given a data set with R records and M attributes. The attributes are called a1; a2; : : :aM . The value of attribute ai in the c 1998 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.
Cached Suucient Statistics for Eecient Machine Learning with Large Datasets

This paper introduces new algorithms and data structures for quick counting for machine learning datasets. We focus on the counting task of constructing contingency tables, but our approach is also applicable to counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptions, the costs of these operations can be shown to be independent of the number of records in the dataset and loglinear in the number of non-zero entries in the contingency table. We provide a very sparse data structure, the ADtree, to minimize memory use. We provide analytical worst-case bounds for this structure for several models of data distribution. We empirically demonstrate that tractably-sized data structures can be produced for large real-world datasets by (a) using a sparse tree structure that never allocates memory for counts of zero, (b) never allocating memory for counts that can be deduced from other counts, and (c) not bothering to expand the tree fully near its leaves. We show how the ADtree can be used to accelerate Bayes net structure nding algorithms , rule learning algorithms, and feature selection algorithms, and we provide a number of empirical results comparing ADtree methods against traditional direct counting approaches. We also discuss the possible uses of ADtrees in other machine learning methods , and discuss the merits of ADtrees in comparison with alternative representations such as kd-trees, R-trees and Frequent Sets.
Multiple Nonlinear Time Series with Extended Granger Causality

Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations. In this work we consider nonlinear extensions of Granger’s idea and refer to the result as Extended Granger Causality. A simple approach implementing the Extended Granger Causality is presented and applied to multiple chaotic time series and other types of nonlinear signals. In addition, for situations with three or more time series we propose a conditional Extended Granger Causality measure that enables us to determine whether the causal relation between two signals is direct or mediated by another process.