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Saturday, November 19, 2016
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Shawn Elledge
CEO - Integrated Marketing Association is dedicated to the continued education and support of BtoB and BtoC marketers Next Event Tampa Oct 12-13th
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November 19, 2016 at 06:38AM via Twitter http://twitter.com/iMarketingAssn
Dangerous Rootkit found Pre-Installed on nearly 3 Million Android Phones
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[FD] Stored Cross-Site Scripting in WP Canvas - Shortcodes WordPress Plugin
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[FD] Persistent Cross-Site Scripting in Instagram Feed plugin via CSRF
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[FD] Cross-Site Scripting in Huge IT Portfolio Gallery WordPress Plugin
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[FD] Cross-Site Scripting in Check Email WordPress Plugin
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Philadelphia Perigee Full Moon
Friday, November 18, 2016
Actors Anonymous (2016)
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[FD] Tetris heap spraying: spraying the heap on a budget
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[FD] CVE-2016-3247 Microsoft Edge CTextExtractor::GetBlockText OOB read details
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Rumor Central: Orioles interested in free-agent relief pitchers Kevin Jepsen and Anthony Bass - Baltimore Sun (ESPN)
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Ravens: Joe Flacco says Ray Lewis' comments that the QB lacked passion for football were "a little surprising" (ESPN)
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[FD] SQL injection and unserialization vulnerability in Relevanssi Premium could allow admins to execute arbitrary code (in some circumstances) (WordPress plugin)
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[FD] Unserialization vulnerability in Relevanssi Premium could allow admins to execute arbitrary code (in some circumstances) (WordPress plugin)
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[FD] Unserialisation in Post Indexer could allow man-in-the-middle to execute arbitrary code (in some circumstances) (WordPress plugin)
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[FD] SQL Injection in Post Indexer allows super admins to read the contents of the database (WordPress plugin)
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[FD] /tmp race condition in Teradata Studio Express v15.12.00.00 studioexpressinstall
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[FD] Teradata Virtual Machine Community Edition v15.10 Insecure creation of files in /tmp
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[FD] [ERPSCAN-16-032] SAP Telnet Console – Directory traversal vulnerability
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[FD] [ERPSCAN-16-031] SAP NetWeaver AS ABAP – directory traversal using READ DATASET
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[FD] FUDforum 3.0.6: LFI
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[FD] FUDforum 3.0.6: Multiple Persistent XSS & Login CSRF
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[FD] Lepton 2.2.2: Code Execution
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[FD] Lepton 2.2.2: CSRF, Open Redirect, Insecure Bruteforce Protection & Password Handling
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[FD] Lepton 2.2.2: SQL Injection
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[FD] MoinMoin 1.9.8: XSS
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[FD] MyLittleForum 2.3.6.1: CSRF
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[FD] Mezzanine 4.2.0: XSS
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[FD] SPIP 3.1: XSS & Host Header Injection
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[FD] MyLittleForum 2.3.6.1: XSS & RPO
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[FD] Microsoft Internet Explorer 11 iertutil LCIEGetTypedComponentFromThread use-after-free details
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[FD] CVE-2015-2482 MSIE 8 jscript RegExpBase::FBadHeader use-after-free details
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[FD] CVE request - Samsumg Mobile Phone SVE-2016-6343: Unauthorized API access via system service call
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[FD] Reason Core Security v1.2.0.1 - Unqoted Path Privilege Escalation Vulnerability
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[FD] EditMe CMS - CSRF Privilege Escalate Web Vulnerability
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3 Mobile UK Hacked – 6 Million Customers' Private Data at risk
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[FD] Habari CMS v0.9.2 - (Backend Comments) XSS Vulnerability
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iPhone Secretly Sends Your Call History to Apple Even If iCloud Backups are Turned Off
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TotalTrax, Inc.
TotalTrax, Inc. is the leading provider of real time vehicle, driver, impact and inventory tracking technologies.
Newport, De
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November 18, 2016 at 01:30AM via Twitter http://twitter.com/TotalTrax
Soyuz vs Supermoon
Thursday, November 17, 2016
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Richard Perry
I show people who want to make a difference 🌎 how to walk their talk with purpose & power. And I give awesome high fives! Grab Your FREE eBook📚 + Training🔽🔽
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Richard W Newton
Speaker, Founder & MD at @freshlearninghq. Adventurous Business Traveler | Conferenciante, Fundador y DG de https://t.co/OJulUeaZR5 | Tweets: EN/ES
tbc
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Charles
Consultant to 400+ Businesses In The Past 15yrs | Founder/CEO | Big Ideas | Macro Solutions | VC | Think Beyond Your Own Lifetime | Yes, I'm Wearing Black Again
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November 17, 2016 at 09:35PM via Twitter http://twitter.com/WishExist
Explicable Robot Planning as Minimizing Distance from Expected Behavior. (arXiv:1611.05497v1 [cs.AI])
In order for robots to be integrated effectively into human work-flows, it is not enough to address the question of autonomy but also how their actions or plans are being perceived by their human counterparts. When robots generate task plans without such considerations, they may often demonstrate what we refer to as inexplicable behavior from the point of view of humans who may be observing it. This problem arises due to the human observer's partial or inaccurate understanding of the robot's deliberative process and/or the model (i.e. capabilities of the robot) that informs it. This may have serious implications on the human-robot work-space, from increased cognitive load and reduced trust in the robot from the human, to more serious concerns of safety in human-robot interactions. In this paper, we propose to address this issue by learning a distance function that can accurately model the notion of explicability, and develop an anytime search algorithm that can use this measure in its search process to come up with progressively explicable plans. As the first step, robot plans are evaluated by human subjects based on how explicable they perceive the plan to be, and a scoring function called explicability distance based on the different plan distance measures is learned. We then use this explicability distance as a heuristic to guide our search in order to generate explicable robot plans, by minimizing the plan distances between the robot's plan and the human's expected plans. We conduct our experiments in a toy autonomous car domain, and provide empirical evaluations that demonstrate the usefulness of the approach in making the planning process of an autonomous agent conform to human expectations.
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Zero-Shot Visual Question Answering. (arXiv:1611.05546v1 [cs.CV])
Part of the appeal of Visual Question Answering (VQA) is its promise to answer new questions about previously unseen images. Most current methods demand training questions that illustrate every possible concept, and will therefore never achieve this capability, since the volume of required training data would be prohibitive. Answering general questions about images requires methods capable of Zero-Shot VQA, that is, methods able to answer questions beyond the scope of the training questions. We propose a new evaluation protocol for VQA methods which measures their ability to perform Zero-Shot VQA, and in doing so highlights significant practical deficiencies of current approaches, some of which are masked by the biases in current datasets. We propose and evaluate several strategies for achieving Zero-Shot VQA, including methods based on pretrained word embeddings, object classifiers with semantic embeddings, and test-time retrieval of example images. Our extensive experiments are intended to serve as baselines for Zero-Shot VQA, and they also achieve state-of-the-art performance in the standard VQA evaluation setting.
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Stream Packing for Asynchronous Multi-Context Systems using ASP. (arXiv:1611.05640v1 [cs.LO])
When a processing unit relies on data from external streams, we may face the problem that the stream data needs to be rearranged in a way that allows the unit to perform its task(s). On arrival of new data, we must decide whether there is sufficient information available to start processing or whether to wait for more data. Furthermore, we need to ensure that the data meets the input specification of the processing step. In the case of multiple input streams it is also necessary to coordinate which data from which incoming stream should form the input of the next process instantiation. In this work, we propose a declarative approach as an interface between multiple streams and a processing unit. The idea is to specify via answer-set programming how to arrange incoming data in packages that are suitable as input for subsequent processing. Our approach is intended for use in asynchronous multi-context systems (aMCSs), a recently proposed framework for loose coupling of knowledge representation formalisms that allows for online reasoning in a dynamic environment. Contexts in aMCSs process data streams from external sources and other contexts.
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Learning to detect and localize many objects from few examples. (arXiv:1611.05664v1 [cs.CV])
The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.
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Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition. (arXiv:1611.05675v1 [cs.LG])
Feature subspace selection is an important part in speech emotion recognition. Most of the studies are devoted to finding a feature subspace for representing all emotions. However, some studies have indicated that the features associated with different emotions are not exactly the same. Hence, traditional methods may fail to distinguish some of the emotions with just one global feature subspace. In this work, we propose a new divide and conquer idea to solve the problem. First, the feature subspaces are constructed for all the combinations of every two different emotions (emotion-pair). Bi-classifiers are then trained on these feature subspaces respectively. The final emotion recognition result is derived by the voting and competition method. Experimental results demonstrate that the proposed method can get better results than the traditional multi-classification method.
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Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs. (arXiv:1611.05735v1 [cs.AI])
Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones' flying routes. Furthermore, this is being done today on an individual level (one vehicle per operators), with some exceptions of a handful of new systems, that are comprised of a small number of self-organizing swarms, manually guided by a human operator.
Drones-based monitoring is of great importance in variety of civilian domains, such as road safety, homeland security, and even environmental control. In its military aspect, efficiently detecting evading targets by a fleet of unmanned drones has an ever increasing impact on the ability of modern armies to engage in warfare. The latter is true both traditional symmetric conflicts among armies as well as asymmetric ones. Be it a speeding driver, a polluting trailer or a covert convoy, the basic challenge remains the same -- how can its detection probability be maximized using as little number of drones as possible.
In this work we propose a novel approach for the optimization of large scale swarms of reconnaissance drones -- capable of producing on-demand optimal coverage strategies for any given search scenario. Given an estimation cost of the threat's potential damages, as well as types of monitoring drones available and their comparative performance, our proposed method generates an analytically provable strategy, stating the optimal number and types of drones to be deployed, in order to cost-efficiently monitor a pre-defined region for targets maneuvering using a given roads networks.
We demonstrate our model using a unique dataset of the Israeli transportation network, on which different deployment schemes for drones deployment are evaluated.
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Fast Non-Parametric Tests of Relative Dependency and Similarity. (arXiv:1611.05740v1 [cs.AI])
We introduce two novel non-parametric statistical hypothesis tests. The first test, called the relative test of dependency, enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbert-Schmidt Independence Criterion (HSIC). The second test, called the relative test of similarity, is use to determine which of the two samples from arbitrary distributions is significantly closer to a reference sample of interest and the relative measure of similarity is based on the Maximum Mean Discrepancy (MMD). To construct these tests, we have used as our test statistics the difference of HSIC statistics and of MMD statistics, respectively. The resulting tests are consistent and unbiased, and have favorable convergence properties. The effectiveness of the relative dependency test is demonstrated on several real-world problems: we identify languages groups from a multilingual parallel corpus, and we show that tumor location is more dependent on gene expression than chromosome imbalance. We also demonstrate the performance of the relative test of similarity over a broad selection of model comparisons problems in deep generative models.
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Learning to reinforcement learn. (arXiv:1611.05763v1 [cs.LG])
In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applications is their demand for massive amounts of training data. A critical present objective is thus to develop deep RL methods that can adapt rapidly to new tasks. In the present work we introduce a novel approach to this challenge, which we refer to as deep meta-reinforcement learning. Previous work has shown that recurrent networks can support meta-learning in a fully supervised context. We extend this approach to the RL setting. What emerges is a system that is trained using one RL algorithm, but whose recurrent dynamics implement a second, quite separate RL procedure. This second, learned RL algorithm can differ from the original one in arbitrary ways. Importantly, because it is learned, it is configured to exploit structure in the training domain. We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL. We consider prospects for extending and scaling up the approach, and also point out some potentially important implications for neuroscience.
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Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance. (arXiv:1611.05817v1 [stat.ML])
At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior.
In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.
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Designing and Training Feedforward Neural Networks: A Smooth Optimisation Perspective. (arXiv:1611.05827v1 [cs.LG])
Despite the recent great success of deep neural networks in various applications, designing and training a deep neural network is still among the greatest challenges in the field. In this work, we present a smooth optimisation perspective on designing and training multilayer Feedforward Neural Networks (FNNs) in the supervised learning setting. By characterising the critical point conditions of an FNN based optimisation problem, we identify the conditions to eliminate local optima of the corresponding cost function. Moreover, by studying the Hessian structure of the cost function at the global minima, we develop an approximate Newton FNN algorithm, which is capable of alleviating the vanishing gradient problem. Finally, our results are numerically verified on two classic benchmarks, i.e., the XOR problem and the four region classification problem.
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Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks. (arXiv:1602.02685v2 [cs.LG] UPDATED)
In clinical data sets we often find static information (e.g. patient gender, blood type, etc.) combined with sequences of data that are recorded during multiple hospital visits (e.g. medications prescribed, tests performed, etc.). Recurrent Neural Networks (RNNs) have proven to be very successful for modelling sequences of data in many areas of Machine Learning. In this work we present an approach based on RNNs, specifically designed for the clinical domain, that combines static and dynamic information in order to predict future events. We work with a database collected in the Charit\'{e} Hospital in Berlin that contains complete information concerning patients that underwent a kidney transplantation. After the transplantation three main endpoints can occur: rejection of the kidney, loss of the kidney and death of the patient. Our goal is to predict, based on information recorded in the Electronic Health Record of each patient, whether any of those endpoints will occur within the next six or twelve months after each visit to the clinic. We compared different types of RNNs that we developed for this work, with a model based on a Feedforward Neural Network and a Logistic Regression model. We found that the RNN that we developed based on Gated Recurrent Units provides the best performance for this task. We also used the same models for a second task, i.e., next event prediction, and found that here the model based on a Feedforward Neural Network outperformed the other models. Our hypothesis is that long-term dependencies are not as relevant in this task.
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Finite LTL Synthesis is EXPTIME-complete. (arXiv:1609.04371v2 [cs.LO] UPDATED)
LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applications. In this research note we consider De Giacomo and Vardi's variant of the synthesis problem for LTL formulas interpreted over finite rather than infinite traces. Rather surprisingly, given the existing claims on complexity, we establish that LTL synthesis is EXPTIME-complete for the finite interpretation, and not 2EXPTIME-complete as previously reported. Our result coincides nicely with the planning perspective where non-deterministic planning with full observability is EXPTIME-complete and partial observability increases the complexity to 2EXPTIME-complete; a recent related result for LTL synthesis shows that in the finite case with partial observability, the problem is 2EXPTIME-complete.
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Universal adversarial perturbations. (arXiv:1610.08401v2 [cs.CV] UPDATED)
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.
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Edward: A library for probabilistic modeling, inference, and criticism. (arXiv:1610.09787v2 [stat.CO] UPDATED)
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make inferences about the model given data, and criticize the model's fit to the data. Edward supports a broad class of probabilistic models, efficient algorithms for inference, and many techniques for model criticism. The library builds on top of TensorFlow to support distributed training and hardware such as GPUs. Edward enables the development of complex probabilistic models and their algorithms at a massive scale.
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A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs. (arXiv:1611.05104v1 [cs.CL])
LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular. We propose and analyze a series of architectural modifications for LSTM networks resulting in improved performance for text classification datasets. We observe compounding improvements on traditional LSTMs using Monte Carlo test-time model averaging, deep vector averaging (DVA), and residual connections, along with four other suggested modifications. Our analysis provides a simple, reliable, and high quality baseline model.
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Sonya Clark Receives Anonymous Was a Woman Prize
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Ravens Video: Terrell Suggs reveals meaning behind Hacksaw Smithers, the alias he used on Dak Prescott conference call (ESPN)
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Disable rate content for anonymous users
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User mail token PHP notices for anonymous
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ISS Daily Summary Report – 11/16/2016
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Military police probe anonymous threat to Dutch airport
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New Hack: How to Bypass iPhone Passcode to Access Photos and Messages
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The Heart and Soul Nebulas
Wednesday, November 16, 2016
A São Sebastião (Anonymous)
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I have a new follower on Twitter
Exiger
Global regulatory and financial crime, risk and compliance company. Our experts offer practical advice and cutting edge, technology-enabled solutions.
Americas, EMEA, APAC
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November 16, 2016 at 09:55PM via Twitter http://twitter.com/ExigerLLC
Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery. (arXiv:1611.05136v1 [cs.AI])
Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.
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The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study. (arXiv:1611.05170v1 [cs.AI])
Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.
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