Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-algorithmic IRIS Recognition

ABSTRACT Modern societies give higher relevance to personal recognition system that contribute to the increase of security and reliability, essentially due to terrorism and other extremism or illegal activities. The objective of this work is to present a multi-algorithmic biometric authentication system for physical access control based on iris pattern for high security access. The CASIA database of IRIS images provided by C hinese A cademy of S ciences I nstitute of A utomation is used and the system is implemented in MATLAB. large database due to high pattern variability among different The iris recognition is based on Daugman's approach and multiple classifiers using Hamming distance and Neural networks. In Daugman's approach, the iris features are extracted using 2D Gabor Wavelets. The proposed work provides match for iris pattern if situations. Though many security forces have launched hamming distance is below 0.15 whereas for the existing works it is 0.20. The Neural Classifier uses a feed forward network with three hidden layers legible fingerprint biometrics system. Thus iris has been taken and used after normalization and feature extraction phase. Features given to neural network are Energy, Entropy, Standard deviation, Covariance. The error rate has been reduced from e
Calcium oscillations in interstitial cells of the rabbit urethra

INTRODUCTION The rabbit urethra contains a sub-population of cells which are distinct both functionally and morphologically from the bulk smooth muscle cells (Sergeant et al., 2000). These " interstitial cells " are non-contractile but are spontaneously active showing regular depolarisations STDs (spontaneous transient depolarisations) under current clamp conditions. The currents or STICs (spontaneous transient inward currents), which underlie the STDs, are believed to result from activation of Ca 2+-dependent Cl-channels by Ca 2+ released from intracellular stores (Sergeant et al., 2001a; Sergeant et al., 2001b). In a recent study we demonstrated that interstitial cells fired large STICs and two types of STOCs (spontaneous transient outward currents), " fast " (>100 ms in duration) and " slow " (>1 s in duration). The latter were coupled to STICs, suggesting that they shared the same mechanism, while the former occurred independently at faster rates. All of these currents were abolished by cyclopiazonic acid, caffeine, or ryanodine, suggesting that they were activated by Ca 2+ release. When D-myo-inositol 1,4,5-trisphosphate (IP 3)-sensitive stores were blocked with 2-aminoethoxydiphenyl borate (2-APB), the STICs and slow STOCs were abolished, but the fast STOCs remained. In all of the above experiments it was inferred that spontaneous intracellular calcium oscillations were responsible for the observed changes in membrane potential or currents so we found it curious that 2-APB appeared to suppress the calcium release necessary for the STICs but not that required for the STOCs. Using purely electrophysiological techniques we were unable to resolve this apparent contradiction so the purpose of the present investigation was to measure changes in intracellular calcium to shed further light on the nature of spontaneous activity in urethral interstitial cells. SUMMARY Measurements were made (using fast confocal microscopy) of intracellular calcium levels in fluo-4 loaded interstitial cells isolated from the rabbit urethra. These cells exhibited regular calcium oscillations which were associated with spontaneous transient inward currents recorded under voltage clamp. Interference with IP 3 induced calcium release using 100 µM 2-APB, or the phospholipase C inhibitors NCDC or U73122 decreased the amplitude of spontaneous oscillations but did not abolish them. However oscillations were abolished when RyR were blocked with tetracaine or ryanodine. Oscillations ceased in the absence of external calcium and frequency was directly proportional to external calcium concentration. Frequency of calcium oscillation was reduced by SKF96365 but not by nifedipine. Lanthanum and cadmium completely blocked oscillations. These results suggest that calcium oscillations in …
Recursive autoencoders based unsupervised feature learning for hyperspectral image classification

—For hyperspectral image (HSI) classification, it is very important to learn effective features for the discrimination purpose. Meanwhile, the ability to combine spectral and spatial information together in a deep level is also important for feature learning. In this letter, we propose an unsupervised feature learning method for HSI classification, which is based on recursive autoencoders (RAE) network. RAE utilizes the spatial and spectral information and produces high-level features from the original data. It learns features from the neighborhood of the investigated pixel to represent the whole local homogeneous area of the image. In addition, to obtain more accurate representation of the investigated pixel, a weighting scheme is adopted based on the neighboring pixels, where the weights are determined by the spectral similarity between the neighboring pixels and the investigated pixel. The effectiveness of our method is evaluated by the experiments on two hyperspectral datasets and the results show that our proposed method has better performance.
Abbasi, Babak;Babaei, Toktam;Hosseinifard, Zahra; Smith-Miles, Kate;Dehghani, Maryam Predicting solutions of large-scale optimization problems via machine learning: a case study

Practical constrained optimization models are often large, and solving them in a reasonable time is a challenge in many applications. Further, many industries have limited access to professional commercial optimization solvers or computational power for use in their day-to-day operational decisions. In this paper, we propose a novel approach to deal with the issue of solving large operational stochastic optimization problems (SOPs) by using machine learning models. We assume that decision makers have access to facilities to optimally solve their large-scale optimization model for some initial and limited period and for some test instances. This might be through a collaborative project with research institutes or through short-term use of high-performance computing facilities. We propose that longer term support can be provided by utilizing the solutions (i.e., the optimal value of the actionable decision variables) of the stochastic optimization model from this initial period to train a machine learning model to learn optimal operational decisions in the future. In this study, the proposed approach is employed to make decisions on transshipment of blood units in a network of hospitals. We compare the decisions learned by several machine learning models with the optimal results obtained if the hospitals had access to commercial optimization solvers and computational power, and with the hospital network’s current empirical heuristic policy. The results show that using a trained neural network model reduces the average daily cost by about 29% compared with current policy, while the exact optimal policy reduces the average daily cost by 37%. Although optimization models cannot be fully replaced by machine learning, our proposed approach while not guaranteed to be optimal can improve operational decisions when optimization models are computationally expensive and infeasible for daily operational decisions in organizations such as not-forprofit and small and medium-sized enterprises.
Efficient Mean Shift Clustering Using Exponential Integral Kernels

detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on background-differenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of non-uniform kernels for clustering, which dramatically increases robustness without giving up the efficiency of the integral data structures. Experimental results demonstrating the power of this approach are presented.
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix

In many applications, the data consist of (or may be naturally formulated as) an $m \times n$ matrix $A$. It is often of interest to find a low-rank approximation to $A$, i.e., an approximation $D$ to the matrix $A$ of rank not greater than a specified rank $k$, where $k$ is much smaller than $m$ and $n$. Methods such as the singular value decomposition (SVD) may be used to find an approximation to $A$ which is the best in a well-defined sense. These methods require memory and time which are superlinear in $m$ and $n$; for many applications in which the data sets are very large this is prohibitive. Two simple and intuitive algorithms are presented which, when given an $m \times n$ matrix $A$, compute a description of a low-rank approximation $D^{*}$ to $A$, and which are qualitatively faster than the SVD. Both algorithms have provable bounds for the error matrix $A-D^{*}$. For any matrix $X$, let $\|{X}\|_F$ and $\|{X}\|_2$ denote its Frobenius norm and its spectral norm, respectively. In the first algorithm, $c$ columns of $A$ are randomly chosen. If the $m \times c$ matrix $C$ consists of those $c$ columns of $A$ (after appropriate rescaling), then it is shown that from $C^TC$ approximations to the top singular values and corresponding singular vectors may be computed. From the computed singular vectors a description $D^{*}$ of the matrix $A$ may be computed such that $\mathrm{rank}(D^{*}) \le k$ and such that $$ \left\|A-D^{*}\right\|_{\xi}^{2} \le \min_{D:\mathrm{rank}(D)\le k} \left\|A-D\right\|_{\xi}^{2} + poly(k,1/c) \left\|{A}\right\|^2_F $$ holds with high probability for both $\xi = 2,F$. This algorithm may be implemented without storing the matrix $A$ in random access memory (RAM), provided it can make two passes over the matrix stored in external memory and use $O(cm+c^2)$ additional RAM. The second algorithm is similar except that it further approximates the matrix $C$ by randomly sampling $r$ rows of $C$ to form a $r \times c$ matrix $W$. Thus, it has additional error, but it can be implemented in three passes over the matrix using only constant additional RAM. To achieve an additional error (beyond the best rank $k$ approximation) that is at most $\epsilon\|{A}\|^2_F$, both algorithms take time which is polynomial in $k$, $1/\epsilon$, and $\log(1/\delta)$, where $\delta>0$ is a failure probability; the first takes time linear in $\mbox{max}(m,n)$ and the second takes time independent of $m$ and $n$. Our bounds improve previously published results with respect to the rank parameter $k$ for both the Frobenius and spectral norms. In addition, the proofs for the error bounds use a novel method that makes important use of matrix perturbation theory. The probability distribution over columns of $A$ and the rescaling are crucial features of the algorithms which must be chosen judiciously.
Compositional Fidelity and Taphonomy of freshwater Mollusks from Three Pampean shallow Lakes of Argentina

Abstract. Three shallow lakes from the southeastern pampas, which are different in their physicochemical characteristics, were studied with the aim of comparing live/dead fidelity and preservation of mollusk shells. The studied lakes were Las Mostazas, Los Carpinchos and Nahuel Rucá, which proved different in terms of their specific richness, abundance, compositional fidelity and taphonomic alteration. The lake that presented the higher values of conductivity (3.34±0.72), hardness (286±83 mgl-1) and carbonates saturation index (SIcalcite;= 1.95±0.21) was the one for which lower specific richness in living assemblages was recorded and differences in richness and diversity between the living and the dead assemblages became apparent. It was also the lake where the number of species was higher in death assemblages (R= 5) than in living communities (R= 2). Such characteristics suggest a higher residence time in the taphonomically active zone. On the other hand, the lakes featuring lower values of the same characteristics (conductivity= 2.2±1.2; 0.8±0.5; hardness= 157±55; 250±127 mgl-1; saturation index of calcite= 0.35±0.15; 0.43+0.36, for LC and NR respectively) presented a higher species richness, lower numbers of species in death assemblages (R= 8) than in living communities (R= 10) and taphonomic attributes that suggested recently dead and deposited individuals. All these results indicate that water conditions in these lakes result in the decrease of the preservation of carbonate remains in comparison with the previously mentioned lake. Even though the lakes are located close to each other and are of similar shape and depth, such differences in water characteristics may be affecting shell preservation.
Microstructural analysis of aluminium alloy 2096 as a function of heat treatment.

A detailed microstructural investigation into possible metallurgical culprits of stress corrosion cracking (SCC) of Al-Li-Cu alloy AA2096 was carried out as a function of isothermal aging time at 160°C. Two tempers were analysed for this study: under-aged at 4 hours (UA4) and under-aged at 12 hours (UA12). Transmission electron microscopy (TEM) was used to document T1 (Al2CuLi) precipitation near to and on low and high angle misorientation grain boundaries. The low-angle grain boundaries of the UA4 temper exhibited a fine, near continuous decoration of T1 precipitates and a narrow, but distinct T1 precipitate free zone (PFZ) adjacent to the boundary. The high angle boundaries of the UA4 temper either showed no grain boundary T1 precipitation, or contained a single, large T1 precipitate that grew along the boundary causing it to pucker. The low-angle boundaries of the UA12 temper exhibited no such PFZ, with no greater T1 precipitation than observed in the grain matrix. An energy dispersive X-ray (EDX) study was conducted to investigate the possible occurrence of copper solute depletion across grain boundaries. In the UA4 temper, the low-angle grain boundaries exhibited a distinct copped depleted zone that corresponded with the PFZ observed.
Acquiring Contour Following Behaviour in Robotics through Q-Learning and Image-based States

In this work a visual and reactive contour following behaviour is learned by reinforcement. With artificial vision the environment is perceived in 3D, and it is possible to avoid obstacles that are invisible to other sensors that are more common in mobile robotics. Reinforcement learning reduces the need for intervention in behaviour design, and simplifies its adjustment to the environment, the robot and the task. In order to facilitate its generalisation to other behaviours and to reduce the role of the designer, we propose a regular image-based codification of states. Even though this is much more difficult, our implementation converges and is robust. Results are presented with a Pioneer 2 AT on a Gazebo 3D simulator. Keywords— Image-based State Codification, Mobile Robotics, Reinforcement Learning, Visual Behaviour.
Secure Pairwise Key Sharing using Geometric Group Key Sharing Method (Full Paper)

In recent years, the concept of Internet of Things (IoT) network used to enable everyday objects and electronic devices to communicate with each other has been extensively discussed. There are three main types of communication that can be assumed in an IoT network: unicast, group, and broadcast communication. Apart from that, Hamasaki et al. considered geometric characteristics and proposed a method of geometric group key sharing. Thereafter, a key sharing method suitable for sharing a pairwise key by implementing the method proposed by Hamasaki et al. had been proposed by Nishigami et al. However, testing this method, we found that when a node and its fellow nodes are attacked together, the keys of the rest of the nodes will be leaked. Therefore, in this paper, using the feature introduced in geometric group key sharing, we propose a method that enables a pairwise key to be securely shared. In addition, we extend our method of pairwise key sharing to be applicable for group key sharing to achieve a way to share efficiently pairwise, group, and global keys used in broadcast communication. Finally, we evaluate the efficiency of our proposed method. Keywords—Group Key Sharing, Pairwise Key Sharing, Global Key Sharing, Symmetric Key Encryption, IoT network
ACL 2006 Task-Focused Summarization and Question Answering

A key task in an extraction system for query-oriented multi-document summarisation, necessary for computing relevance and redundancy, is modelling text semantics. In the Embra system, we use a representation derived from the singular value decomposition of a term co-occurrence matrix. We present methods to show the reliability of performance improvements. We find that Embra performs better with dimensionality reduction.
Hadoop Cluster Slave Node DataNode Child Task TaskTracker Master Nodes Hadoop Framework JobTracker

Big data is the tar sands of the data world: vast reserves of raw gritty data whose valuable information content can only be extracted at great cost. MapReduce is a popular parallel programming paradigm well suited to the programmatic extraction and analysis of information from these unstructured Big Data reserves. The Apache Hadoop implementation of MapReduce has become an important player in this market due to its ability to exploit large networks of inexpensive servers. The increasing importance of unstructured data has led to the interest in MapReduce and its Apache Hadoop implementation, which has led to the interest of data processing vendors in supporting this programming style. Oracle RDBMS has had support for the MapReduce paradigm for many years through the mechanism of user defined pipelined table functions and aggregation objects. However, such support has not been Hadoop source compatible. Native Hadoop programs needed to be rewritten before becoming usable in this framework. The ability to run Hadoop programs inside the Oracle database provides a versatile solution to database users, allowing them use programming skills they may already possess and to exploit the growing Hadoop eco-system. In this paper, we describe a prototype of Oracle InDatabase Hadoop that supports the running of native Hadoop applications written in Java. This implementation executes Hadoop applications using the efficient parallel capabilities of the Oracle database and a subset of the Apache Hadoop infrastructure. This system’s target audience includes both SQL and Hadoop users. We discuss the architecture and design, and in particular, demonstrate how MapReduce functionalities are seamlessly integrated within SQL queries. We also share our experience in building such a system within Oracle database and follow-on topics that we think are promising areas for exploration. ∗This work was done when Xueyuan Su was at Oracle Corporation. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SIGMOD’12, May 20–24, 2012, Scottsdale, Arizona, USA. Copyright 2012 ACM 978-1-4503-1247-9/12/05 . $10.00.
Characteristics of peri-urbanization of a secondary city: a challenge in recent urban development

Urbanization process creates a tremendous spatial phenomenon since the last century. Especially for the country of the South, the phenomenon is still relevant to the situation today and the processes will still going until the foreseeable future. The metropolitan-based of urbanization process involves the development of peri-urban areas, which could be defined as transitional zones between city and rural areas characterized by integrated mixed-structures of agricultural and non-agricultural activities. This article reveals the characteristics of periurbanization process of an emerging secondary city in Java, which uses Surakarta, the second largest city in Central Java Province based on the population size, as the case. During the last ten years, there have been significant changes in peri-urban areas regarding urban population, land use, and urban activities that strengthening the contribution of the urban component into peri-urban system.
Communications of the Association for Information Systems Communications of the Association for Information Systems

: Information systems (IS) researchers persistently examine how information and communications technology (ICT) changes attitudes and behaviors but rarely leverage the persuasion literature when doing so possibly due to this literature’s well-documented complexity. Accordingly, in this study, we help researchers understand and apply persuasion theory in IS research. To do so, we develop a common frame of reference to help IS researchers to conceptualize persuasion and conceptually differentiate it from related concepts. In doing so, we also comprehensively summarize existing research and theory and provide suggestions to guide future IS research into persuasion and behavior change.
Introduction to Combinatorial Testing

Combinatorial testing of software analyzes interactions among variables using a very small number of tests. This advanced approach has demonstrated success in providing strong, low-cost testing in real-world situations. Introduction to Combinatorial Testing presents a complete self-contained tutorial on advanced combinatorial testing methods for real-world software. The book introduces key concepts and procedures of combinatorial testing, explains how to use software tools for generating combinatorial tests, and shows how this approach can be integrated with existing practice. Detailed explanations and examples clarify how and why to use various techniques. Sections on cost and practical considerations describe tradeoffs and limitations that may impact resources or funding. While the authors introduce some of the theory and mathematics of combinatorial methods, readers can use the methods without in-depth knowledge of the underlying mathematics. Accessible to undergraduate students and researchers in computer science and engineering, this book illustrates the practical application of combinatorial methods in software testing. Giving pointers to freely available tools and offering resources on a supplementary website, the book encourages readers to apply these methods in their own testing projects.
Ratcheting Behaviour of Nano-Scale Copper by Classical Molecular Dynamics Simulations

The aim of this investigation is to study the tensile and fatigue behavior of nanoscale copper at various temperatures through simulations based on molecular dynamics. The tensile stress–strain curve at various temperatures was obtained for nanoscale copper. Then, ultimate tensile strength (UTS) values of the nanoscale copper at different temperatures were determined. It was found that UTS decreases with increase in temperature. It was also observed that Young’s modulus of nanoscale copper decreased with increase in temperature. Variation of tensile properties with simulation box size has also been analyzed. With increase in dimensions of the simulation box, UTS was found to decrease appreciably. The nature of accumulation of ratcheting strain varies with several parameters. Effect of few of these parameters like temperature, stress ratio, maximum stress on ratcheting behavior has been studied in this investigation. The ratcheting behavior of nanoscale copper with varying temperatures has been obtained. The accumulation of ratcheting strain was observed to increase with increase in temperature. With increase in stress ratio (R), it was observed that there is an increase in accumulation of ratcheting strain.