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Saturday, October 8, 2016
Join the Anonymous Million Mask March
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La bella Colomba (Anonymous)
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Atheist ex-nurse forced into Alcoholics Anonymous substance abuse program
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Yahoo Email Spying Scandal — Here's Everything that has Happened So Far
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The Hydrogen Clouds of M33
Friday, October 7, 2016
Ocean City, MD's surf is at least 5.18ft high
Ocean City, MD Summary
At 4:00 AM, surf min of 4.88ft. At 10:00 AM, surf min of 5.18ft. At 4:00 PM, surf min of 5.32ft. At 10:00 PM, surf min of 5.23ft.
Surf maximum: 6.19ft (1.89m)
Surf minimum: 5.18ft (1.58m)
Tide height: 0.64ft (0.19m)
Wind direction: NE
Wind speed: 10.61 KTS
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Fix anonymous caching incorrect increment of RewriteRule Skip
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Blank page after anonymous form submission
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Full-Text PDF
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2017 Atlantic South Regional Convention
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Ravens: OT Ronnie Stanley (foot) misses third straight day of practice, says he will be a game-time decision Sunday (ESPN)
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An Enhanced Lightweight Anonymous Authentication Scheme for a Scalable Localization ...
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Create an anonymous callback function as the second argument to the service method call.
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ISS Daily Summary Report – 10/06/2016
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London Police Arrest Romanian ATM Hacker Who Stole Millions
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Ocean City, MD's surf is at least 5.01ft high
Ocean City, MD Summary
At 4:00 AM, surf min of 3.37ft. At 10:00 AM, surf min of 3.91ft. At 4:00 PM, surf min of 4.27ft. At 10:00 PM, surf min of 5.01ft.
Surf maximum: 5.61ft (1.71m)
Surf minimum: 5.01ft (1.53m)
Tide height: 0.91ft (0.28m)
Wind direction: ENE
Wind speed: 15.2 KTS
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Verizon wants $1 Billion Discount on Yahoo Acquisition Deal after Recent Scandals
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[FD] NEW VMSA-2016-0015 - VMware Horizon View updates address directory traversal vulnerability
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GPM Monitors Hurricane Matthew Nearing Florida
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GPM Captures Hurricane Matthew Over Haiti
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Thursday, October 6, 2016
Anonymous users are not restricted when roles are set
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HaCkEd By Anonymous Ghost Gaza
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Human Decision-Making under Limited Time. (arXiv:1610.01698v1 [stat.ML])
Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predictions made by a formalization of bounded rationality based on ideas from statistical mechanics and information-theory. We systematically tested human subjects in their ability to solve combinatorial puzzles under different time limitations. We found that our bounded-rational model accounts well for the data. The decomposition of the fitted model parameter into the subjects' expected utility function and resource parameter provide interesting insight into the subjects' information capacity limits. Our results confirm that humans gradually fall back on their learned prior choice patterns when confronted with increasing resource limitations.
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Parallel Large-Scale Attribute Reduction on Cloud Systems. (arXiv:1610.01807v1 [cs.DC])
The rapid growth of emerging information technologies and application patterns in modern society, e.g., Internet, Internet of Things, Cloud Computing and Tri-network Convergence, has caused the advent of the era of big data. Big data contains huge values, however, mining knowledge from big data is a tremendously challenging task because of data uncertainty and inconsistency. Attribute reduction (also known as feature selection) can not only be used as an effective preprocessing step, but also exploits the data redundancy to reduce the uncertainty. However, existing solutions are designed 1) either for a single machine that means the entire data must fit in the main memory and the parallelism is limited; 2) or for the Hadoop platform which means that the data have to be loaded into the distributed memory frequently and therefore become inefficient. In this paper, we overcome these shortcomings for maximum efficiency possible, and propose a unified framework for Parallel Large-scale Attribute Reduction, termed PLAR, for big data analysis. PLAR consists of three components: 1) Granular Computing (GrC)-based initialization: it converts a decision table (i.e., original data representation) into a granularity representation which reduces the amount of space and hence can be easily cached in the distributed memory: 2) model-parallelism: it simultaneously evaluates all feature candidates and makes attribute reduction highly parallelizable; 3) data-parallelism: it computes the significance of an attribute in parallel using a MapReduce-style manner. We implement PLAR with four representative heuristic feature selection algorithms on Spark, and evaluate them on various huge datasets, including UCI and astronomical datasets, finding our method's advantages beyond existing solutions.
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A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text. (arXiv:1610.01891v1 [cs.CL])
One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text is special and has unique characteristics. In addition, the medical text mining poses more challenges, e.g., more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug. The mining is even more challenging due to the lack of labeled dataset sources and external knowledge, as well as multiple token representations for a single drug name that is more common in the real application setting. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, i.e., MLP (Multi-Layer Perceptrons). The second technique involves two deep network classifiers, i.e., DBN (Deep Belief Networks), and SAE (Stacked Denoising Encoders). The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, i.e., LSTM (Long Short Term Memory). In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645.
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Adaptive Online Sequential ELM for Concept Drift Tackling. (arXiv:1610.01922v1 [cs.AI])
A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect underfitting condition.
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Metaheuristic Algorithms for Convolution Neural Network. (arXiv:1610.01925v1 [cs.CV])
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
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The backtracking survey propagation algorithm for solving random K-SAT problems. (arXiv:1508.05117v4 [cs.CC] UPDATED)
Discrete combinatorial optimization has a central role in many scientific disciplines, however, for hard problems we lack linear time algorithms that would allow us to solve very large instances. Moreover, it is still unclear what are the key features that make a discrete combinatorial optimization problem hard to solve. Here we study random K-satisfiability problems with $K=3,4$, which are known to be very hard close to the SAT-UNSAT threshold, where problems stop having solutions. We show that the backtracking survey propagation algorithm, in a time practically linear in the problem size, is able to find solutions very close to the threshold, in a region unreachable by any other algorithm. All solutions found have no frozen variables, thus supporting the conjecture that only unfrozen solutions can be found in linear time, and that a problem becomes impossible to solve in linear time when all solutions contain frozen variables.
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A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents. (arXiv:1604.03655v10 [cs.DS] UPDATED)
We consider the well-studied cake cutting problem in which the goal is to find an envy-free allocation based on queries from $n$ agents. The problem has received attention in computer science, mathematics, and economics. It has been a major open problem whether there exists a discrete and bounded envy-free protocol. We resolve the problem by proposing a discrete and bounded envy-free protocol for any number of agents. The maximum number of queries required by the protocol is $n^{n^{n^{n^{n^n}}}}$. We additionally show that even if we do not run our protocol to completion, it can find in at most $n^{n+1}$ queries a partial allocation of the cake that achieves proportionality (each agent gets at least $1/n$ of the value of the whole cake) and envy-freeness. Finally we show that an envy-free partial allocation can be computed in $n^{n+1}$ queries such that each agent gets a connected piece that gives the agent at least $1/(3n)$ of the value of the whole cake.
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Extending Unification in $\mathcal{EL}$ to Disunification: The Case of Dismatching and Local Disunification. (arXiv:1609.05621v2 [cs.LO] UPDATED)
Unification in Description Logics has been introduced as a means to detect redundancies in ontologies. We try to extend the known decidability results for unification in the Description Logic $\mathcal{EL}$ to disunification since negative constraints can be used to avoid unwanted unifiers. While decidability of the solvability of general $\mathcal{EL}$-disunification problems remains an open problem, we obtain NP-completeness results for two interesting special cases: dismatching problems, where one side of each negative constraint must be ground, and local solvability of disunification problems, where we consider only solutions that are constructed from terms occurring in the input problem. More precisely, we first show that dismatching can be reduced to local disunification, and then provide two complementary NP-algorithms for finding local solutions of disunification problems.
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MLB: Man who threw beer at Orioles OF Hyun Soo Kim in AL wild-card game charged with mischief, due in court in November (ESPN)
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I have a new follower on Twitter
Ali Campbell
Husband / Dad / Youth & Children's Ministry Consultant / Leadership / Mission / Discipleship / @ali_theresource / Read / Write / Draw / Politics || Views Mine
Mid Sussex
https://t.co/Qz9YP0FDxK
Following: 2550 - Followers: 3546
October 06, 2016 at 06:43PM via Twitter http://twitter.com/AliCampbell_68
Orioles: Pitching coach Dave Wallace, 69, retiring after 3 years with Baltimore and 36 in coaching; worked for 7 teams (ESPN)
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[FD] [KIS-2016-12] Magento <= 1.9.2.2 (RSS Feed) Information Disclosure Vulnerability
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Parent/child form for anonymous users
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Mac Malware Can Secretly Spy On Your Webcam and Mic – Get This Free Tool to Stay Safe
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What The Anonymous Visitor Is Telling You: How to Engage the 95% that Don't Convert
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ISS Daily Summary Report – 10/05/2016
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[FD] RealEstate CMS 3.00.50 - Cross Site Scripting Vulnerability
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How to Start Secret Conversations on Facebook Messenger
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A Crumbling Layered Butte on Mars
GPM Captures Hurricane Matthew Before Haiti Landfall
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Wednesday, October 5, 2016
MLB Image: Toronto police tweet picture of fan suspected of throwing beer can at Orioles' Hyun Soo Kim on Tuesday (ESPN)
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Orioles: Ex-Baltimore P Andrew Miller defends Buck Showalter, says he's one of the best at "running a game" - Crasnick (ESPN)
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Seer: Empowering Software Defined Networking with Data Analytics. (arXiv:1610.01221v1 [cs.NI])
Network complexity is increasing, making network control and orchestration a challenging task. The proliferation of network information and tools for data analytics can provide an important insight into resource provisioning and optimisation. The network knowledge incorporated in software defined networking can facilitate the knowledge driven control, leveraging the network programmability. We present Seer: a flexible, highly configurable data analytics platform for network intelligence based on software defined networking and big data principles. Seer combines a computational engine with a distributed messaging system to provide a scalable, fault tolerant and real-time platform for knowledge extraction. Our first prototype uses Apache Spark for streaming analytics and open network operating system (ONOS) controller to program a network in real-time. The first application we developed aims to predict the mobility pattern of mobile devices inside a smart city environment.
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Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy. (arXiv:1610.01238v1 [cs.RO])
We present a weakly-supervised approach to segmenting proposed drivable paths in images with the goal of autonomous driving in complex urban environments. Using recorded routes from a data collection vehicle, our proposed method generates vast quantities of labelled images containing proposed paths and obstacles without requiring manual annotation, which we then use to train a deep semantic segmentation network. With the trained network we can segment proposed paths and obstacles at run-time using a vehicle equipped with only a monocular camera without relying on explicit modelling of road or lane markings. We evaluate our method on the large-scale KITTI and Oxford RobotCar datasets and demonstrate reliable path proposal and obstacle segmentation in a wide variety of environments under a range of lighting, weather and traffic conditions. We illustrate how the method can generalise to multiple path proposals at intersections and outline plans to incorporate the system into a framework for autonomous urban driving.
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EPOpt: Learning Robust Neural Network Policies Using Model Ensembles. (arXiv:1610.01283v1 [cs.LG])
Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks -- especially when the policies are represented using rich function approximators like deep neural networks. Model-based methods where the real-world target domain is approximated using a simulated source domain provide an avenue to tackle the above challenges by augmenting real data with simulated data. However, discrepancies between the simulated source domain and the target domain pose a challenge for simulated training. We introduce the EPOpt algorithm, which uses an ensemble of simulated source domains and a form of adversarial training to learn policies that are robust and generalize to a broad range of possible target domains, including to unmodeled effects. Further, the probability distribution over source domains in the ensemble can be adapted using data from target domain and approximate Bayesian methods, to progressively make it a better approximation. Thus, learning on a model ensemble, along with source domain adaptation, provides the benefit of both robustness and learning/adaptation.
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Soft-margin learning for multiple feature-kernel combinations with Domain Adaptation, for recognition in surveillance face datasets. (arXiv:1610.01374v1 [cs.CV])
Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets.
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The Predictive Context Tree: Predicting Contexts and Interactions. (arXiv:1610.01381v1 [cs.AI])
With a large proportion of people carrying location-aware smartphones, we have an unprecedented platform from which to understand individuals and predict their future actions. This work builds upon the Context Tree data structure that summarises the historical contexts of individuals from augmented geospatial trajectories, and constructs a predictive model for their likely future contexts. The Predictive Context Tree (PCT) is constructed as a hierarchical classifier, capable of predicting both the future locations that a user will visit and the contexts that a user will be immersed within. The PCT is evaluated over real-world geospatial trajectories, and compared against existing location extraction and prediction techniques, as well as a proposed hybrid approach that uses identified land usage elements in combination with machine learning to predict future interactions. Our results demonstrate that higher predictive accuracies can be achieved using this hybrid approach over traditional extracted location datasets, and the PCT itself matches the performance of the hybrid approach at predicting future interactions, while adding utility in the form of context predictions. Such a prediction system is capable of understanding not only where a user will visit, but also their context, in terms of what they are likely to be doing.
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Towards semi-episodic learning for robot damage recovery. (arXiv:1610.01407v1 [cs.RO])
The recently introduced Intelligent Trial and Error algorithm (IT\&E) enables robots to creatively adapt to damage in a matter of minutes by combining an off-line evolutionary algorithm and an on-line learning algorithm based on Bayesian Optimization. We extend the IT\&E algorithm to allow for robots to learn to compensate for damages while executing their task(s). This leads to a semi-episodic learning scheme that increases the robot's lifetime autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.
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Visual Question Answering: Datasets, Algorithms, and Future Challenges. (arXiv:1610.01465v1 [cs.CV])
Visual Question Answering (VQA) is a recent problem in computer vision and natural language processing that has garnered a large amount of interest from the deep learning, computer vision, and natural language processing communities. In VQA, an algorithm needs to answer text-based questions about images. Since the release of the first VQA dataset in 2014, several additional datasets have been released and many algorithms have been proposed. In this review, we critically examine the current state of VQA in terms of problem formulation, existing datasets, evaluation metrics, and algorithms. In particular, we discuss the limitations of current datasets with regard to their ability to properly train and assess VQA algorithms. We then exhaustively review existing algorithms for VQA. Finally, we discuss possible future directions for VQA and image understanding research.
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$\ell_1$ Regularized Gradient Temporal-Difference Learning. (arXiv:1610.01476v1 [cs.AI])
In this paper, we study the Temporal Difference (TD) learning with linear value function approximation. It is well known that most TD learning algorithms are unstable with linear function approximation and off-policy learning. Recent development of Gradient TD (GTD) algorithms has addressed this problem successfully. However, the success of GTD algorithms requires a set of well chosen features, which are not always available. When the number of features is huge, the GTD algorithms might face the problem of overfitting and being computationally expensive. To cope with this difficulty, regularization techniques, in particular $\ell_1$ regularization, have attracted significant attentions in developing TD learning algorithms. The present work combines the GTD algorithms with $\ell_1$ regularization. We propose a family of $\ell_1$ regularized GTD algorithms, which employ the well known soft thresholding operator. We investigate convergence properties of the proposed algorithms, and depict their performance with several numerical experiments.
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Lifted Message Passing for the Generalized Belief Propagation. (arXiv:1610.01525v1 [cs.AI])
We introduce the lifted Generalized Belief Propagation (GBP) message passing algorithm, for the computation of sum-product queries in Probabilistic Relational Models (e.g. Markov logic network). The algorithm forms a compact region graph and establishes a modified version of message passing, which mimics the GBP behavior in a corresponding ground model. The compact graph is obtained by exploiting a graphical representation of clusters, which reduces cluster symmetry detection to isomorphism tests on small local graphs. The framework is thus capable of handling complex models, while remaining domain-size independent.
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A Novel Representation of Neural Networks. (arXiv:1610.01549v1 [stat.ML])
Deep Neural Networks (DNNs) have become very popular for prediction in many areas. Their strength is in representation with a high number of parameters that are commonly learned via gradient descent or similar optimization methods. However, the representation is non-standardized, and the gradient calculation methods are often performed using component-based approaches that break parameters down into scalar units, instead of considering the parameters as whole entities. In this work, these problems are addressed. Standard notation is used to represent DNNs in a compact framework. Gradients of DNN loss functions are calculated directly over the inner product space on which the parameters are defined. This framework is general and is applied to two common network types: the Multilayer Perceptron and the Deep Autoencoder.
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A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. (arXiv:1610.01578v1 [cs.CV])
Identity verification based on authenticity assessment of a handwritten signature is an important issue in biometrics. There are many effective methods for signature verification taking into account dynamics of a signing process. Methods based on partitioning take a very important place among them. In this paper we propose a new approach to signature partitioning. Its most important feature is the possibility of selecting and processing of hybrid partitions in order to increase a precision of the test signature analysis. Partitions are formed by a combination of vertical and horizontal sections of the signature. Vertical sections correspond to the initial, middle, and final time moments of the signing process. In turn, horizontal sections correspond to the signature areas associated with high and low pen velocity and high and low pen pressure on the surface of a graphics tablet. Our previous research on vertical and horizontal sections of the dynamic signature (created independently) led us to develop the algorithm presented in this paper. Selection of sections, among others, allows us to define the stability of the signing process in the partitions, promoting signature areas of greater stability (and vice versa). In the test of the proposed method two databases were used: public MCYT-100 and paid BioSecure.
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Modeling State-Conditional Observation Distribution using Weighted Stereo Samples for Factorial Speech Processing Models. (arXiv:1503.02578v2 [cs.LG] UPDATED)
This paper investigates the effectiveness of factorial speech processing models in noise-robust automatic speech recognition tasks. For this purpose, the paper proposes an idealistic approach for modeling state-conditional observation distribution of factorial models based on weighted stereo samples. This approach is an extension to previous single pass retraining for ideal model compensation which is extended here to support multiple audio sources. Non-stationary noises can be considered as one of these audio sources with multiple states. Experiments of this paper over the set A of the Aurora 2 dataset show that recognition performance can be improved by this consideration. The improvement is significant in low signal to noise energy conditions, up to 4% absolute word recognition accuracy. In addition to the power of the proposed method in accurate representation of state-conditional observation distribution, it has an important advantage over previous methods by providing the opportunity to independently select feature spaces for both source and corrupted features. This opens a new window for seeking better feature spaces appropriate for noisy speech, independent from clean speech features.
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Characteristics of Visual Categorization of Long-Concatenated and Object-Directed Human Actions by a Multiple Spatio-Temporal Scales Recurrent Neural Network Model. (arXiv:1602.01921v2 [cs.CV] UPDATED)
The current paper proposes a novel dynamic neural network model for categorization of complex human action visual patterns. The Multiple Spatio-Temporal Scales Recurrent Neural Network (MSTRNN) adds recurrent connectivity to a prior model, the Multiple Spatio-Temporal Scales Neural Network (MSTNN). By developing adequate recurrent contextual dynamics, the MSTRNN can learn to extract latent spatio-temporal structures from input image sequences more effectively than the MSTNN. Two experiments with the MSTRNN are detailed. The first experiment involves categorizing a set of human movement patterns consisting of sequences of action primitives. The MSTRNN is able to extract long-ranged correlations in video images better than the MSTNN. Time series analysis on neural activation values obtained from the recurrent structure shows that the MSTRNN accumulates extracted spatio-temporal features which discriminate action sequences. The second experiment requires that the model categorize a set of object-directed actions, and demonstrates that the MSTRNN can learn to extract structural relationships between actions and action-directed-objects (ADOs). Analysis of characteristics employed in categorizing both object-directed actions and pantomime actions indicates that the model network develops categorical memories by organizing relational structures between each action and appropriate ADO. Such relational structure may be necessary for categorizing human actions with an adequate ability to generalize.
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Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity. (arXiv:1607.02061v2 [cs.CL] UPDATED)
Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better. Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus.
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A Geometric Framework for Convolutional Neural Networks. (arXiv:1608.04374v2 [stat.ML] UPDATED)
In this paper, a geometric framework for neural networks is proposed. This framework uses the inner product space structure underlying the parameter set to perform gradient descent not in a component-based form, but in a coordinate-free manner. Convolutional neural networks are described in this framework in a compact form, with the gradients of standard --- and higher-order --- loss functions calculated for each layer of the network. This approach can be applied to other network structures and provides a basis on which to create new networks.
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MLB: Blue Jays apologize to Orioles for "embarrassing incident" when fan threw beer at Hyun Soo Kim in wild-card game (ESPN)
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[FD] KL-001-2016-007 : Cisco Firepower Threat Management Console Remote Command Execution Leading to Root Access
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[FD] KL-001-2016-006 : Cisco Firepower Threat Management Console Local File Inclusion
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[FD] KL-001-2016-005 : Cisco Firepower Threat Management Console Hard-coded MySQL Credentials
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[FD] KL-001-2016-004 : Cisco Firepower Threat Management Console Authenticated Denial of Service
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XMLGold Introduces Anonymous Prepaid Credit Cards
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BREAKING! Another NSA Contractor Arrested For Stealing 'Secret' Documents
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TalkTalk Telecom Ordered to Pay Record £400,000 Fine Over 2015 Data Breach
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NFL: Family of Ravens fan who suffered serious brain injury in assault Sunday "encouraged" by progress - Baltimore Sun (ESPN)
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MLB: Toronto police are investigating a fan's toss of a beer can at Orioles' Hyn Soo Kim in Tuesday's AL wild-card game (ESPN)
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Create an anonymous callback function as the second argument to the service method call.
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ISS Daily Summary Report – 10/04/2016
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via IFTTT
[FD] Flash Operator Panel 2.31.03 - CSV Persistent Vulnerability
[FD] Cyberoam iview UTM v0.1.2.7 - (Ajax) XSS Web Vulnerability
Source: Gmail -> IFTTT-> Blogger
[FD] Clean Master v1.0 - Unquoted Path Privilege Escalation
Source: Gmail -> IFTTT-> Blogger