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Saturday, January 21, 2017
Anonymous proxy ip address
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Anonymous hacks nic.go.th for OpSingleGateway
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Anonymous secrets, from murder to medical marijuana, become art at new Glore exhibit
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"Anonymous" Hacking Collective Tweets Warning to Trump Administration
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anonymous-sums
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Lavabit — Encrypted Email Service Once Used by Snowden Is Back
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Five-Year Global Temperature Anomalies from 1880 to 2016
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Friday, January 20, 2017
[FD] Apple iOS 10.2 (Notify - iTunes) - Filter Bypass & Persistent Vulnerability
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How A Bug Hunter Forced Apple to Completely Remove A Newly Launched Feature
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NFL: Ravens leading tackler LB Zach Orr to retire at age 24 - NFL Network; suffered a stinger in his neck last month (ESPN)
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ISS Daily Summary Report – 1/19/2017
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ISS Daily Summary Report – 1/18/2017
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Port Anonymous Subscriptions to Drupal 8
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The Elephant's Trunk Nebula in Cepheus
Anonymous sad poems
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Thursday, January 19, 2017
Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation rules. (arXiv:1701.05221v1 [cs.CV])
A new, radical CNN design approach is presented in this paper, considering the reduction of the total computational load during inference. This is achieved by a new holistic intervention on both the CNN architecture and the training procedure, which targets to the parsimonious inference by learning to exploit or remove the redundant capacity of a CNN architecture. This is accomplished, by the introduction of a new structural element that can be inserted as an add-on to any contemporary CNN architecture, whilst preserving or even improving its recognition accuracy. Our approach formulates a systematic and data-driven method for developing CNNs that are trained to eventually change size and form in real-time during inference, targeting to the smaller possible computational footprint. Results are provided for the optimal implementation on a few modern, high-end mobile computing platforms indicating a significant speed-up of up to x3 times.
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Reasoning in Non-Probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples. (arXiv:1701.05226v1 [cs.AI])
This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: Logic Programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of Input/Output logic for dealing with uncertainty in dynamic normative contexts.
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Heterogeneous Information Network Embedding for Meta Path based Proximity. (arXiv:1701.05291v1 [cs.AI])
A network embedding is a representation of a large graph in a low-dimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity between vertices in the original graph. This way, typical search and mining methods can be applied in the embedded space with the help of off-the-shelf multidimensional indexing approaches. Existing network embedding techniques focus on homogeneous networks, where all vertices are considered to belong to a single class.
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Semantic Evolutionary Concept Distances for Effective Information Retrieval in Query Expansion. (arXiv:1701.05311v1 [cs.IR])
In this work several semantic approaches to concept-based query expansion and reranking schemes are studied and compared with different ontology-based expansion methods in web document search and retrieval. In particular, we focus on concept-based query expansion schemes, where, in order to effectively increase the precision of web document retrieval and to decrease the users browsing time, the main goal is to quickly provide users with the most suitable query expansion. Two key tasks for query expansion in web document retrieval are to find the expansion candidates, as the closest concepts in web document domain, and to rank the expanded queries properly. The approach we propose aims at improving the expansion phase for better web document retrieval and precision. The basic idea is to measure the distance between candidate concepts using the PMING distance, a collaborative semantic proximity measure, i.e. a measure which can be computed by using statistical results from web search engine. Experiments show that the proposed technique can provide users with more satisfying expansion results and improve the quality of web document retrieval.
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Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling. (arXiv:1701.05334v1 [cs.AI])
Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and SWRL rule-based decision-making to monitor transportation activities and to make a city- feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Prot\'eg\'e OWL and Java, respectively.
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T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects. (arXiv:1701.05498v1 [cs.CV])
We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from simple scenes with several isolated objects to very challenging ones with multiple instances of several objects and with a high amount of clutter and occlusion. The images were captured from a systematically sampled view sphere around the object/scene, and are annotated with accurate ground truth 6D poses of all modeled objects. Initial evaluation results indicate that the state of the art in 6D object pose estimation has ample room for improvement, especially in difficult cases with significant occlusion. The T-LESS dataset is available online at http://ift.tt/2iQaqzF.
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A new look at reweighted message passing. (arXiv:1309.5655v3 [cs.AI] UPDATED)
We propose a new family of message passing techniques for MAP estimation in graphical models which we call {\em Sequential Reweighted Message Passing} (SRMP). Special cases include well-known techniques such as {\em Min-Sum Diffusion} (MSD) and a faster {\em Sequential Tree-Reweighted Message Passing} (TRW-S). Importantly, our derivation is simpler than the original derivation of TRW-S, and does not involve a decomposition into trees. This allows easy generalizations. We present such a generalization for the case of higher-order graphical models, and test it on several real-world problems with promising results.
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An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax. (arXiv:1611.01886v2 [cs.LG] UPDATED)
A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic optimum can be obtained by a hierarchical infomax method. Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases. As confirmed by numerical experiments, our method is robust and highly efficient for extracting salient features from image datasets. Compared with the main existing methods, our algorithm has a distinct advantage in both the training speed and the robustness of unsupervised representation learning.
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Liquid Democracy: An Analysis in Binary Aggregation and Diffusion. (arXiv:1612.08048v2 [cs.MA] UPDATED)
The paper proposes an analysis of liquid democracy (or, delegable proxy voting) from the perspective of binary aggregation and of binary diffusion models. We show how liquid democracy on binary issues can be embedded into the framework of binary aggregation with abstentions, enabling the transfer of known results about the latter---such as impossibility theorems---to the former. This embedding also sheds light on the relation between delegation cycles in liquid democracy and the probability of collective abstentions, as well as the issue of individual rationality in a delegable proxy voting setting. We then show how liquid democracy on binary issues can be modeled and analyzed also as a specific process of dynamics of binary opinions on networks. These processes---called Boolean DeGroot processes---are a special case of the DeGroot stochastic model of opinion diffusion. We establish the convergence conditions of such processes and show they provide some novel insights on how the effects of delegation cycles and individual rationality could be mitigated within liquid democracy.
The study is a first attempt to provide theoretical foundations to the delgable proxy features of the liquid democracy voting system. Our analysis suggests recommendations on how the system may be modified to make it more resilient with respect to the handling of delegation cycles and of inconsistent majorities.
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MLB: Orioles, Mark Trumbo agree on 3-year, $37M contract - Jim Bowden; MLB-leading 47 HR last season (ESPN)
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MLB Buzz: Orioles, Mark Trumbo closing in on 3-year deal expected to be for less than $40M - multiple reports (ESPN)
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Free ssl proxy server anonymous web blocked sites
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[anonymous_subscriptions] Anonymous Subscriptions
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Anonymous Short Trader Attacks Cellceutix (CTIX) Again
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Ravens: Michigan S Jabrill Peppers goes No. 16 in Mel Kiper's Mock Draft 1.0; doesn't fill major need - Jamison Hensley (ESPN)
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[FD] Tap 'n' Sniff
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I have a new follower on Twitter
David Borean
Husband, dance dad and alpha to a schnoodle. Delivering true #Customer360 and next gen #MDM in #DataLake https://t.co/hZm9rPLRZf
Aurora, Canada
https://t.co/8UdLbpy3Kk
Following: 956 - Followers: 1045
January 19, 2017 at 11:39AM via Twitter http://twitter.com/DaveBorean
Alcoholics Anonymous-Open Meeting
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[FD] [RCESEC-2016-012] Mattermost <= 3.5.1 "/error" Unauthenticated Reflected Cross-Site Scripting / Content Injection
Unknown Error
Something went wrong with the provided link, open it with a right click instead!
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[FD] Persistent XSS in Ghost 0.11.3
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[FD] CALL FOR PAPERS - br3aking c0de
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[FD] [ERPSCAN-16-037] SAP NetWeaver AS JAVA P4 - INFORMATION DISCLOSURE
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[FD] [ERPSCAN-16-036] SAP ASE ODATA SERVER - DENIAL OF SERVICE
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[FD] APPLE-SA-2017-01-18-2 Logic Pro X 10.3
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[FD] APPLE-SA-2017-01-18-1 GarageBand 10.1.5
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ANC is behind Western Cape fires, a man alleges in anonymous recording
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THN Deal — Become A Certified Ethical Hacker With This Online Training Course
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Anonymous warns Trump
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You Can Crash Anyone's iPhone Or iPad With A Simple Emoji Text Message
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Newly Discovered Mac Malware with Ancient Code Spying on Biotech Firms
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8th Street, Ocean City, MD's surf is at least 5.63ft high
8th Street, Ocean City, MD Summary
At 2:00 AM, surf min of 5.63ft. At 8:00 AM, surf min of 5.05ft. At 2:00 PM, surf min of 4.63ft. At 8:00 PM, surf min of 4.17ft.
Surf maximum: 6.41ft (1.95m)
Surf minimum: 5.63ft (1.72m)
Tide height: 1.39ft (0.42m)
Wind direction: NW
Wind speed: 15.41 KTS
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El Paso County Sheriff Elder denies anonymous claims of departmental wrongdoing
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Five-Year Global Temperature Anomalies from 1880 to 2016
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Wednesday, January 18, 2017
I have a new follower on Twitter
Stephane Rossignol
Ingénieur d'affaires chez @salesforce basé à #Lyon. Je tweete sur le #crm et l' #innovation. Ex-@google @dell @oracle
Lyon
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January 18, 2017 at 09:39PM via Twitter http://twitter.com/srossignol
Anonymous to Trump: You Will 'Regret' the Next 4 Years
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Unknowable Manipulators: Social Network Curator Algorithms. (arXiv:1701.04895v1 [cs.AI])
For a social networking service to acquire and retain users, it must find ways to keep them engaged. By accurately gauging their preferences, it is able to serve them with the subset of available content that maximises revenue for the site. Without the constraints of an appropriate regulatory framework, we argue that a sufficiently sophisticated curator algorithm tasked with performing this process may choose to explore curation strategies that are detrimental to users. In particular, we suggest that such an algorithm is capable of learning to manipulate its users, for several qualitative reasons: 1. Access to vast quantities of user data combined with ongoing breakthroughs in the field of machine learning are leading to powerful but uninterpretable strategies for decision making at scale. 2. The availability of an effective feedback mechanism for assessing the short and long term user responses to curation strategies. 3. Techniques from reinforcement learning have allowed machines to learn automated and highly successful strategies at an abstract level, often resulting in non-intuitive yet nonetheless highly appropriate action selection. In this work, we consider the form that these strategies for user manipulation might take and scrutinise the role that regulation should play in the design of such systems.
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Converting Cascade-Correlation Neural Nets into Probabilistic Generative Models. (arXiv:1701.05004v1 [q-bio.NC])
Humans are not only adept in recognizing what class an input instance belongs to (i.e., classification task), but perhaps more remarkably, they can imagine (i.e., generate) plausible instances of a desired class with ease, when prompted. Inspired by this, we propose a framework which allows transforming Cascade-Correlation Neural Networks (CCNNs) into probabilistic generative models, thereby enabling CCNNs to generate samples from a category of interest. CCNNs are a well-known class of deterministic, discriminative NNs, which autonomously construct their topology, and have been successful in giving accounts for a variety of psychological phenomena. Our proposed framework is based on a Markov Chain Monte Carlo (MCMC) method, called the Metropolis-adjusted Langevin algorithm, which capitalizes on the gradient information of the target distribution to direct its explorations towards regions of high probability, thereby achieving good mixing properties. Through extensive simulations, we demonstrate the efficacy of our proposed framework.
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Ontology based system to guide internship assignment process. (arXiv:1701.05059v1 [cs.AI])
Internship assignment is a complicated process for universities since it is necessary to take into account a multiplicity of variables to establish a compromise between companies' requirements and student competencies acquired during the university training. These variables build up a complex relations map that requires the formulation of an exhaustive and rigorous conceptual scheme. In this research a domain ontological model is presented as support to the student's decision making for opportunities of University studies level of the University Lumiere Lyon 2 (ULL) education system. The ontology is designed and created using methodological approach offering the possibility of improving the progressive creation, capture and knowledge articulation. In this paper, we draw a balance taking the demands of the companies across the capabilities of the students. This will be done through the establishment of an ontological model of an educational learners' profile and the internship postings which are written in a free text and using uncontrolled vocabulary. Furthermore, we outline the process of semantic matching which improves the quality of query results.
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Deep Learning Features at Scale for Visual Place Recognition. (arXiv:1701.05105v1 [cs.CV])
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.
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On the Performance of Network Parallel Training in Artificial Neural Networks. (arXiv:1701.05130v1 [cs.AI])
Artificial Neural Networks (ANNs) have received increasing attention in recent years with applications that span a wide range of disciplines including vital domains such as medicine, network security and autonomous transportation. However, neural network architectures are becoming increasingly complex and with an increasing need to obtain real-time results from such models, it has become pivotal to use parallelization as a mechanism for speeding up network training and deployment. In this work we propose an implementation of Network Parallel Training through Cannon's Algorithm for matrix multiplication. We show that increasing the number of processes speeds up training until the point where process communication costs become prohibitive; this point varies by network complexity. We also show through empirical efficiency calculations that the speedup obtained is superlinear.
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ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution. (arXiv:1602.02334v3 [cs.DB] UPDATED)
Entity resolution (ER), an important and common data cleaning problem, is about detecting data duplicate representations for the same external entities, and merging them into single representations. Relatively recently, declarative rules called "matching dependencies" (MDs) have been proposed for specifying similarity conditions under which attribute values in database records are merged. In this work we show the process and the benefits of integrating four components of ER: (a) Building a classifier for duplicate/non-duplicate record pairs built using machine learning (ML) techniques; (b) Use of MDs for supporting the blocking phase of ML; (c) Record merging on the basis of the classifier results; and (d) The use of the declarative language "LogiQL" -an extended form of Datalog supported by the "LogicBlox" platform- for all activities related to data processing, and the specification and enforcement of MDs.
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Properties of ABA+ for Non-Monotonic Reasoning: Errata. (arXiv:1603.08714v2 [cs.AI] UPDATED)
This technical report provides errata of [K. Cyras, F. Toni, Properties of ABA+ for Non-Monotonic Reasoning, in: 16th International Workshop on Non-Monotonic Reasoning (NMR), Cape Town, South Africa, 2016 pp. 25-34.] Propositions 19, 20, 22, Corollary 23 and (partially) Proposition 24 from Section 6 (Non-Monotonic Reasoning Properties) in that paper are withdrawn as unproven, and thus assumed to be false, while additional results are provided. The rest of the paper in question stands unchanged.
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A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams. (arXiv:1607.08485v2 [cs.AI] UPDATED)
Influence diagrams provide a compact graphical representation of decision problems. Several algorithms for the quick computation of their associated expected utilities are available in the literature. However, often they rely on a full quantification of both probabilistic uncertainties and utility values. For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation. Within this approach expected utilities correspond to families of polynomials. After characterizing their polynomial structure, we develop an efficient symbolic algorithm for the propagation of expected utilities through the diagram and provide an implementation of this algorithm using a computer algebra system. We then characterize many of the standard manipulations of influence diagrams as transformations of polynomials. We also generalize the decision analytic framework of these diagrams by defining asymmetries as operations over the expected utility polynomials.
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Orioles: Mike Mussina (51.8%) falls short of election to Baseball Hall of Fame (ESPN)
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Smile! Hackers Can Remotely Access Your Samsung SmartCam Security Cameras
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It’s time. The Deep Learning for Computer Vision with Python Kickstarter is LIVE.
The Deep Learning for Computer Vision with Python Kickstarter is officially LIVE!
To back the Kickstarter campaign, just use the link below:
Remember, there are only a handful of early bird spots at the reduced prices — you’ll definitely want to act now if you want to claim your spot!
Thank you so much for being supportive of myself and the PyImageSearch blog.
I hope to see you on the other side.
The post It’s time. The Deep Learning for Computer Vision with Python Kickstarter is LIVE. appeared first on PyImageSearch.
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ISS Daily Summary Report – 1/17/2017
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[FD] Announce Keypatch v2.1, a better assembler for IDA Pro!
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I have a new follower on Twitter
Salesforce
Connect to your customers in a whole new way. Stay up-to-date on news, announcements, and innovation. On duty Mon - Fri 9am - 5pm PT.
San Francisco, CA
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Following: 20002 - Followers: 321567
January 18, 2017 at 04:04AM via Twitter http://twitter.com/salesforce
8th Street, Ocean City, MD's surf is at least 5.42ft high
8th Street, Ocean City, MD Summary
At 2:00 AM, surf min of 5.42ft. At 8:00 AM, surf min of 4.78ft. At 2:00 PM, surf min of 3.39ft. At 8:00 PM, surf min of 2.27ft.
Surf maximum: 6.45ft (1.96m)
Surf minimum: 5.42ft (1.65m)
Tide height: 1.93ft (0.59m)
Wind direction: SSW
Wind speed: 8.4 KTS
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Anonymous Users filtering
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Tuesday, January 17, 2017
El Paso commissioners express confidence in Sheriff Elder after anonymous charges
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From Community Detection to Community Profiling. (arXiv:1701.04528v1 [cs.SI])
Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.
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Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling. (arXiv:1701.04569v1 [cs.AI])
Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.
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Une mesure d'expertise pour le crowdsourcing. (arXiv:1701.04645v1 [cs.AI])
Crowdsourcing, a major economic issue, is the fact that the firm outsources internal task to the crowd. It is a form of digital subcontracting for the general public. The evaluation of the participants work quality is a major issue in crowdsourcing. Indeed, contributions must be controlled to ensure the effectiveness and relevance of the campaign. We are particularly interested in small, fast and not automatable tasks. Several methods have been proposed to solve this problem, but they are applicable when the "golden truth" is not always known. This work has the particularity to propose a method for calculating the degree of expertise in the presence of gold data in crowdsourcing. This method is based on the belief function theory and proposes a structuring of data using graphs. The proposed approach will be assessed and applied to the data.
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Intrinsically Motivated Acquisition of Modular Slow Features for Humanoids in Continuous and Non-Stationary Environments. (arXiv:1701.04663v1 [cs.AI])
A compact information-rich representation of the environment, also called a feature abstraction, can simplify a robot's task of mapping its raw sensory inputs to useful action sequences. However, in environments that are non-stationary and only partially observable, a single abstraction is probably not sufficient to encode most variations. Therefore, learning multiple sets of spatially or temporally local, modular abstractions of the inputs would be beneficial. How can a robot learn these local abstractions without a teacher? More specifically, how can it decide from where and when to start learning a new abstraction? A recently proposed algorithm called Curious Dr. MISFA addresses this problem. The algorithm is based on two underlying learning principles called artificial curiosity and slowness. The former is used to make the robot self-motivated to explore by rewarding itself whenever it makes progress learning an abstraction; the later is used to update the abstraction by extracting slowly varying components from raw sensory inputs. Curious Dr. MISFA's application is, however, limited to discrete domains constrained by a pre-defined state space and has design limitations that make it unstable in certain situations. This paper presents a significant improvement that is applicable to continuous environments, is computationally less expensive, simpler to use with fewer hyper parameters, and stable in certain non-stationary environments. We demonstrate the efficacy and stability of our method in a vision-based robot simulator.
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Can't enable Page Caching for anonymous users
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I have a new follower on Twitter
Oliver Christie
Artificial Intelligence. #ai #bigdata #machinelearning
New York
https://t.co/IXd1Sr29G5
Following: 162733 - Followers: 149932
January 17, 2017 at 04:59PM via Twitter http://twitter.com/OliverChristie
Anonymous users filters in Hooks.php broken
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Anonymous Threaten To Destroy Donald Trump
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ISS Daily Summary Report – 1/16/2017
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I have a new follower on Twitter
360 Healthcare Staff
360 Healthcare Staffing is a premier provider of interim staffing and executive search for leadership & nursing.
Tampa, FL
http://t.co/sLX7RYufSQ
Following: 670 - Followers: 894
January 17, 2017 at 09:49AM via Twitter http://twitter.com/360HCSJobs
ISS Daily Summary Report – 1/13/2017
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Anonymous calls on supporters to help take down Donald Trump
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8th Street, Ocean City, MD's surf is at least 5.02ft high
8th Street, Ocean City, MD Summary
At 2:00 AM, surf min of 4.13ft. At 8:00 AM, surf min of 5.02ft. At 2:00 PM, surf min of 5.63ft. At 8:00 PM, surf min of 5.43ft.
Surf maximum: 5.95ft (1.81m)
Surf minimum: 5.02ft (1.53m)
Tide height: 1.44ft (0.44m)
Wind direction: ESE
Wind speed: 26.36 KTS
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I have a new follower on Twitter
Aimeos
high performance e-commerce components for #Laravel, #SlimPHP, #Symfony, #TYPO3 and #neoscms
worldwide
https://t.co/UGVfqQfgzd
Following: 718 - Followers: 1144
January 17, 2017 at 07:24AM via Twitter http://twitter.com/aimeos
Simple Hack Lets Hackers Listen to Your Facebook Voice Messages Sent Over Chat
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