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Saturday, February 6, 2016
WATCH ANONYMOUS ZONE
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Issue #13237
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Anonymous: Canto di uomini vecchi allegri e goditori
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I have a new follower on Twitter
Flomio
We help developers integrate proximity ID technologies. #NFC #mPOS #RFID #Payment #mobilepayment #contactless #app
Miami, FL
https://t.co/dPG7ygqrQD
Following: 221 - Followers: 708
February 06, 2016 at 06:39AM via Twitter http://twitter.com/flomio
Maru OS — Android ROM that Turns into Debian Linux When Connected to a PC
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variable undefined error while using anonymous function with user input - MATLAB Answers
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MIT Develops Hack-Proof RFID Chip — Here's How It Works
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Massive Stars in NGC 6357
Friday, February 5, 2016
E-mail in non-English language after anonymous purchase
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Orioles: RP Zach Britton agrees to 1-year, $6.75 million contract to avoid arbitration; 4-1 with 36 saves last season (ESPN)
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How to Unlock (and Play) Hidden Chess Game Inside Facebook Messenger
- Chat with your friends
- Send GIFs, stickers, and photos
- Make video calls
- Send people money in Messenger
- B for “Bishop”
- R for “Rook”
- Q for “Queen”
- K for “King”
- N for “Knight”
- P for “Pawn”
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Tracking Anonymous Web Users
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United Nations Rules in Favor of WikiLeaks Founder Julian Assange
But, Victory! The decision is in favor of Assange.
"Having concluded that there was a continuous deprivation of liberty, the Working Group also found that the detention was arbitrary because he was held in isolation during the first stage of detention and because of the lack of diligence by the Swedish Prosecutor in its investigations, which resulted in the lengthy detention of Mr. Assange," reads the UN report.
But, Will UK and Sweden Let Assange Leave the country?
- Sweden where he is facing sexual assault allegations, which he has always denied.
- The United States where he could face cyber espionage charges for publishing classified US military and diplomatic documents via his website Wikileaks.
Assange's Lawyer: Sweden and UK should Respect UN’s Decision
"If WGAD issues a positive determination, Mr. Assange expects the United Kingdom and Sweden to ensure that he can immediately exercise his right to personal liberty, in a manner which is consistent with his right to safety and protection from retaliatory acts associated with his groundbreaking work at WikiLeaks in exposing government violations and abuses," Taylor told Newsweek reporter.
- Washington Post journalist Jason Rezaian, who was released in Iran last month.
- Former pro-democracy President Mohamed Nasheed freed in the Maldives last year.
- Burmese stateswoman Aung San Suu Kyi from house arrest in 2010.
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ISS Daily Summary Report – 02/4/16
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Someone Hijacks Botnet Network & Replaces Malware with an Antivirus
What is Dridex Banking Trojan? How it Works?
Hacker replaces Trojan with Anti-virus
"The content behind the malware download [link] has been replaced, it is now providing [a legitimate], up-to-date Avira web installer instead of the usual Dridex loader," explained Avira malware expert Moritz Kroll, reported Reg.
What can be done to protect From Malware Attacks?
- Ensure you have an updated antivirus program running on your PC, which should be able to intercept the malicious attachments before they are opened.
- One of the best measures for securing your online environment is to deploy an Intrusion Detection System (IDS) at the network layer, which is especially useful to quickly detect malware and other threats in your network when integrated with a real-time threat intelligence and SIEM (Security Intelligence and Event Monitoring) solution, such as AlienVault Unified Security Management (USM).
- Be careful of opening email attachments sent from an unknown email address, particularly (in this case) Microsoft Word and Excel files.
- Disable Macros in MS Office, or at least set the Macros to request permission before they run.
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Food Addicts in Recovery Anonymous
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Dwarf Planet Ceres
Thursday, February 4, 2016
A Factorized Recurrent Neural Network based architecture for medium to large vocabulary Language Modelling. (arXiv:1602.01576v1 [cs.CL])
Statistical language models are central to many applications that use semantics. Recurrent Neural Networks (RNN) are known to produce state of the art results for language modelling, outperforming their traditional n-gram counterparts in many cases. To generate a probability distribution across a vocabulary, these models require a softmax output layer that linearly increases in size with the size of the vocabulary. Large vocabularies need a commensurately large softmax layer and training them on typical laptops/PCs requires significant time and machine resources. In this paper we present a new technique for implementing RNN based large vocabulary language models that substantially speeds up computation while optimally using the limited memory resources. Our technique, while building on the notion of factorizing the output layer by having multiple output layers, improves on the earlier work by substantially optimizing on the individual output layer size and also eliminating the need for a multistep prediction process.
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. (arXiv:1602.01585v1 [cs.AI])
Building a successful recommender system depends on understanding both the dimensions of people's preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users' fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users' past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform state-of-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.
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Fuzzy Object-Oriented Dynamic Networks. II. (arXiv:1602.01628v1 [cs.AI])
This article generalizes object-oriented dynamic networks to the fuzzy case, which allows one to represent knowledge on objects and classes of objects that are fuzzy by nature and also to model their changes in time. Within the framework of the approach described, a mechanism is proposed that makes it possible to acquire new knowledge on the basis of basic knowledge and considerably differs from well-known methods used in existing models of knowledge representation. The approach is illustrated by an example of construction of a concrete fuzzy object-oriented dynamic network.
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A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras. (arXiv:1602.01635v1 [cs.CL])
Categorical compositional distributional semantics is a model of natural language; it combines the statistical vector space models of words with the compositional models of grammar. We formalise in this model the generalised quantifier theory of natural language, due to Barwise and Cooper. The underlying setting is a compact closed category with bialgebras. We start from a generative grammar formalisation and develop an abstract categorical compositional semantics for it, then instantiate the abstract setting to sets and relations and to finite dimensional vector spaces and linear maps. We prove the equivalence of the relational instantiation to the truth theoretic semantics of generalized quantifiers. The vector space instantiation formalises the statistical usages of words and enables us to, for the first time, reason about quantified phrases and sentences compositionally in distributional semantics.
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Formal Verification of Autonomous Vehicle Platooning. (arXiv:1602.01718v1 [cs.AI])
The coordination of multiple autonomous vehicles into convoys or platoons is expected on our highways in the near future. However, before such platoons can be deployed, the new autonomous behaviors of the vehicles in these platoons must be certified. An appropriate representation for vehicle platooning is as a multi-agent system in which each agent captures the "autonomous decisions" carried out by each vehicle. In order to ensure that these autonomous decision-making agents in vehicle platoons never violate safety requirements, we use formal verification. However, as the formal verification technique used to verify the agent code does not scale to the full system and as the global verification technique does not capture the essential verification of autonomous behavior, we use a combination of the two approaches. This mixed strategy allows us to verify safety requirements not only of a model of the system, but of the actual agent code used to program the autonomous vehicles.
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Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams. (arXiv:1403.6348v4 [cs.AI] UPDATED)
Despite growing interest in data stream mining the most successful incremental learners, such as VFDT, still use periodic recomputation to update attribute information gains and Gini indices. This note provides simple incremental formulas and algorithms for computing entropy and Gini index from time-changing data streams.
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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems. (arXiv:1506.08909v3 [cs.CL] UPDATED)
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. We also describe two neural learning architectures suitable for analyzing this dataset, and provide benchmark performance on the task of selecting the best next response.
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Breaking Symmetries in Graph Search with Canonizing Sets. (arXiv:1511.08205v2 [cs.AI] UPDATED)
There are many complex combinatorial problems which involve searching for an undirected graph satisfying given constraints. Such problems are often highly challenging because of the large number of isomorphic representations of their solutions. This paper introduces effective and compact, complete symmetry breaking constraints for small graph search. Enumerating with these symmetry breaks generates all and only non-isomorphic solutions. For small search problems, with up to $10$ vertices, we compute instance independent symmetry breaking constraints. For small search problems with a larger number of vertices we demonstrate the computation of instance dependent constraints which are complete. We illustrate the application of complete symmetry breaking constraints to extend two known sequences from the OEIS related to graph enumeration. We also demonstrate the application of a generalization of our approach to fully-interchangeable matrix search problems.
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VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback. (arXiv:1510.01784v1 [cs.IR] CROSS LISTED)
Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text. However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.
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Orioles: RP Brian Matusz agrees to 1-year deal, avoiding arbitration; 2.94 ERA over 49 innings last season (ESPN)
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[FD] Netgear RP614v3 : Authentication Bypass
� | # | IP-Adresse | Ger�tename | MAC-Adresse |
1 | *192.168.1.2* | LapTop | *00:15:a5:d5:f7:7c* | |
2 | *192.168.1.3* | Accesspoint | *21:6e:5c:23:86:a2* |
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[FD] osTicket multiple vulnerabilities
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Orioles: SP Odrisamer Despaigne acquired from Padres for minor-league SP J.C. Cosme -- multiple reports (ESPN)
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[FD] NDI5aster – Privilege Escalation through NDIS 5.x Filter Intermediate Drivers
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[FD] Apple iOS v9.1, 9.2 & 9.2.1 - Application Update Loop Pass Code Bypass
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Reps. Maloney, King and Senator Whitehouse introduce bills to stop anonymous money ...
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Galaxy Wars: M81 versus M82
Wednesday, February 3, 2016
[FD] ArpON (ARP handler inspection) 3.0-ng release
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[FD] CALL FOR PAPERS - FAQin Congress - Madrid
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[FD] [CERT 777024 / CVE-2016-1524/5]: RCE and file download in Netgear NMS300
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[FD] Dell SecureWorks iOS Application - MITM SSL Certificate Vulnerability
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[FD] MailPoet Newsletter 2.6.19 - Security Advisory - Reflected XSS
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[FD] Time-based SQL Injection in Admin panel UliCMS <= v9.8.1
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[FD] Opendocman 1.3.4: CSRF
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[FD] Atutor 2.2: XSS
[FD] Equibase.com HTML Injection/Possible Reflected XSS
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[FD] VMWare Zimbra Mailer | DKIM longterm Mail Replay vulnerability
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[FD] Symphony CMS 2.6.3 – Multiple SQL Injection Vulnerabilities
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Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences. (arXiv:1602.01208v1 [cs.AI])
In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We address the problem of learning novel words by a robot that has no prior knowledge of these words except for a primitive acoustic model. Further, we propose a method that allows a robot to effectively use the learned words and their meanings for self-localization tasks. The proposed method is nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates the generative model for self-localization and the unsupervised word segmentation in uttered sentences via latent variables related to the spatial concept. We implemented the proposed method SpCoA on SIGVerse, which is a simulation environment, and TurtleBot2, which is a mobile robot in a real environment. Further, we conducted experiments for evaluating the performance of SpCoA. The experimental results showed that SpCoA enabled the robot to acquire the names of places from speech sentences. They also revealed that the robot could effectively utilize the acquired spatial concepts and reduce the uncertainty in self-localization.
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Finding the different patterns in buildings data using bag of words representation with clustering. (arXiv:1602.01398v1 [cs.AI])
The understanding of the buildings operation has become a challenging task due to the large amount of data recorded in energy efficient buildings. Still, today the experts use visual tools for analyzing the data. In order to make the task realistic, a method has been proposed in this paper to automatically detect the different patterns in buildings. The K Means clustering is used to automatically identify the ON (operational) cycles of the chiller. In the next step the ON cycles are transformed to symbolic representation by using Symbolic Aggregate Approximation (SAX) method. Then the SAX symbols are converted to bag of words representation for hierarchical clustering. Moreover, the proposed technique is applied to real life data of adsorption chiller. Additionally, the results from the proposed method and dynamic time warping (DTW) approach are also discussed and compared.
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Incremental Truncated LSTD. (arXiv:1511.08495v2 [cs.LG] UPDATED)
Balancing between computational efficiency and sample efficiency is an important goal in reinforcement learning. Temporal difference (TD) learning algorithms stochastically update the value function, with a linear time complexity in the number of features, whereas least-squares temporal difference (LSTD) algorithms are sample efficient but can be quadratic in the number of features. In this work, we develop an efficient incremental low-rank LSTD({\lambda}) algorithm that progresses towards the goal of better balancing computation and sample efficiency. The algorithm reduces the computation and storage complexity to the number of features times the chosen rank parameter while summarizing past samples efficiently to nearly obtain the sample complexity of LSTD. We derive a simulation bound on the solution given by truncated low-rank approximation, illustrating a bias- variance trade-off dependent on the choice of rank. We demonstrate that the algorithm effectively balances computational complexity and sample efficiency for policy evaluation in a benchmark task and a high-dimensional energy allocation domain.
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ISS Daily Summary Report – 02/3/16
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Mismatched anonymous define
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Latest Windows 10 May Have a Linux Subsystem Hidden Inside
Why Linux Subsystem?
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Better fields not recorded when 'Cache pages for anonymous users'
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[FD] Compal ConnectBox Wireless - Passphrase Settings Filter Bypass Vulnerability
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[FD] Getdpd Bug Bounty #1 - (asm0option0) Persistent Web Vulnerability
- ``>
[FD] SimpleView CRM - Client Side Open Redirect Vulnerability
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