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Saturday, May 23, 2015
Orioles Video: P Brian Matusz ejected in 12th for having foreign substance, team loses 1-0 in 13 innings to Marlins (ESPN)
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MLB: Orioles P Brian Matusz ejected in 12th inning vs. Marlins for having a foreign substance on right forearm (ESPN)
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Benfica replaces Porto on preseason tour
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Editorial: Anonymous platforms for cyberbullies hurt our kids
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Benfica celebrates title with sons of fan beaten by police
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Ancelotti: 'I continue at Madrid, or I take yearlong break'
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Monaco claims final Champions League birth
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Valencia grabs Spain's last CL spot; Almeria, Eibar go down
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Juventus beats Napoli 3-1 to celebrate league and cup double
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Orioles: LHP Ariel Miranda agrees to minor league contract - MLB.com report; 22-25 with 3.78 ERA from 2007-13 in Cuba (ESPN)
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Ben Stiller, Anonymous Donor
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Ben Stiller, Anonymous Donor
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Paderborn, Freiburg relegated from Bundesliga on final day
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Blatter set to polarize opinion for 4 more years at FIFA
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Urawa beats Kashima 2-1 to keep lead in J-League
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Orioles: C Matt Wieters (elbow) targeting June 4 as his return from 60-day DL - \"My arm will let me know when I'm ready\" (ESPN)
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Astoria — Advanced Tor Client Designed to Avoid NSA Attacks
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Google Brillo OS — New Android-based OS for Internet of Things
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Anonymous on Twitter: "NSA Planned To Hack Google App Store And Install Malware On All ...
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A Dark and Dusty Sky
Orioles Highlight: Miami native Manny Machado with 3 RBI, Caleb Joseph 3 H, including 2-run single, in win over Marlins (ESPN)
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Friday, May 22, 2015
[FD] Broken, Abandoned, and Forgotten Code, Part 5
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[FD] call for paper(information retrieval, privacy)
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[FD] Multiple Vulnerabilities in Openlitespeed <= 1.3.10 - CVE-b045-73d a.k.a. Analbleed.
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[FD] Hue 3.7.1 Local Privilege Escalation
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[FD] 0day Mailbird XSS
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Ebola-free Liberia cleared to host football games again
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[FD] Hacktivity 2015 CFP
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Orioles: IF Everth Cabrera (left foot contusion) reinstated from 15-day DL; IF Rey Navarro optioned to Triple-A (ESPN)
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Coach Ernesto Valverde to stay another season at Bilbao
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I have a new follower on Twitter
Everyday Carry
A peek into the pockets of people worldwide, showcasing the essentials they carry every day. Share your EDC at http://t.co/GMpq61Vi2y
Miami Beach, FL
http://t.co/k5XLIdh33b
Following: 42327 - Followers: 110168
May 22, 2015 at 03:37PM via Twitter http://twitter.com/everydaycarry
CAS hears Gibraltar's appeal to join FIFA
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Army removes WWII bomb near London's Wembley Stadium
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Greece escapes soccer suspension, FIFA happy with changes
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Scolari out of a job, without many supporters in Brazil
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Court releases 4 suspects in soccer fight death
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Bid to topple Blatter from FIFA rests with Jordanian prince
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Paul-Georges Ntep receives 1st call with France
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Osaka, Yokohama picked as 2015 Club World Cup host cities
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FIFA scandals while Sepp Blatter has been president
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Flawed Android Factory Reset Failed to Clear Private Data from Smartphones
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Adult Friend Finder... Hacked & 3.5 Million Users' Database Leaked Online
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NGC 6240: Merging Galaxies
Thursday, May 21, 2015
Towards Efficient Evolving Multi-Context Systems (Preliminary Report). (arXiv:1505.05502v1 [cs.AI])
Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. Recently, evolving Multi-Context Systems (eMCSs) have been introduced as an extension of mMCSs that add the ability to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. However, the general complexity of such an expressive formalism may simply be too high in cases where huge amounts of information have to be processed within a limited short amount of time, or even instantaneously. In this paper, we investigate under which conditions eMCSs may scale in such situations and we show that such polynomial eMCSs can be applied in a practical use case.
from cs.AI updates on arXiv.org http://ift.tt/1ejuwAm
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Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications. (arXiv:1505.05613v1 [cs.IR])
The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.
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On the relation between accuracy and fairness in binary classification. (arXiv:1505.05723v1 [cs.LG])
Our study revisits the problem of accuracy-fairness tradeoff in binary classification. We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions. We provide methodological recommendations for sound comparison of non-discriminatory classifiers, and present a brief theoretical and empirical analysis of tradeoffs between accuracy and non-discrimination.
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Variational Inference with Normalizing Flows. (arXiv:1505.05770v1 [stat.ML])
The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.
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How do you revise your belief set with %$;@*?. (arXiv:1504.05381v2 [cs.AI] UPDATED)
In the classic AGM belief revision theory, beliefs are static and do not change their own shape. For instance, if p is accepted by a rational agent, it will remain p to the agent. But such rarely happens to us. Often, when we accept some information p, what is actually accepted is not the whole p, but only a portion of it; not necessarily because we select the portion but because p must be perceived. Only the perceived p is accepted; and the perception is subject to what we already believe (know). What may, however, happen to the rest of p that initially escaped our attention? In this work we argue that the invisible part is also accepted to the agent, if only unconsciously. Hence some parts of p are accepted as visible, while some other parts as latent, beliefs. The division is not static. As the set of beliefs changes, what were hidden may become visible. We present a perception-based belief theory that incorporates latent beliefs.
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Add a link to node/add/session for anonymous users to log in
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Orioles Highlight: Steve Pearce hits his 1st grand slam, J.J. Hardy singles in tiebreaking run in 5-4 win over Mariners (ESPN)
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Anonymous on Twitter: "Here is the link to download part of 1TB data obtained from #Expo2015 ...
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Lokomotiv Moscow wins Russian Cup
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AA Ghent wins first league title, beating Standard Liege
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Anonymous users cannot see public comments
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NFL: Ray Rice has completed his pretrial intervention program, will have domestic violence charges dismissed (ESPN)
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I have a new follower on Twitter
Michael McLaughlin
Cloud evangelist at Capgemini, parental engineer, Crossfit exerciser, cookie enthusiast. Bacon.
ATL
Following: 4359 - Followers: 4684
May 21, 2015 at 11:11AM via Twitter http://twitter.com/mbmclaughlin
Hide view field if visitor is anonymous
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Spy Agencies Hijack Google Play Store to Install Spyware on Smartphones
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I have a new follower on Twitter
sarafina kunkk
Call me when you need a shoulder who is fun and relaxing:)
Following: 351 - Followers: 89
May 21, 2015 at 09:16AM via Twitter http://twitter.com/SarafinaKunk
ISS Daily Summary Report – 05/20/15
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Understanding the Shared Security Model in Amazon Web Services
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Monaco, Marseille and Saint-Etienne fight for 3rd place
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Relegation battle comes down to last day of Spanish league
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Austin completes remarkable rise to England squad
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FIFA Presidential Election Candidates
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Previous FIFA Presidential Elections
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Sepp Blatter set for another win in FIFA election
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Xavi to leave Barcelona after 17 trophy-filled seasons
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3 men to be tried after violence during Cyprus Cup final
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Google to Introduce New Photo-Sharing Platform to Kill Instagram
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Rome derby is a contest for Champions League entry
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Bundesliga relegation battle set for exciting finale
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Hull and Newcastle fighting for survival on final day
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Bayer Leverkusen extends coach Roger Schmidt's contract
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Platini defends Financial Fair Play ahead of easing rules
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FIFA candidate Van Praag withdraws to support Prince Ali
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Rights group: Qatar making little progress on labor reforms
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Sean Anonymous and Dimitry Killstorm - Big Bang feat. Lizzo and POS
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Sydney signs Senegal player Tavares for 2 more years
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Wednesday, May 20, 2015
I have a new follower on Twitter
Branch Metrics
Helping apps grow their user base with our SDKs for seamless referrals, sharing and deep linking across install.
Palo Alto, CA
https://t.co/D41jB3V14w
Following: 1182 - Followers: 1297
May 20, 2015 at 10:44PM via Twitter http://twitter.com/branchmetrics
Fan violence interrupts 2nd-division match in Rio
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Orioles Video: Adam Jones goes 3-for-4 to raise batting average to .343 in 4-2 loss to Mariners (ESPN)
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Rights group: Qatar making little progress on labor reforms
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Image Reconstruction from Bag-of-Visual-Words. (arXiv:1505.05190v1 [cs.CV])
The objective of this work is to reconstruct an original image from Bag-of-Visual-Words (BoVW). Image reconstruction from features can be a means of identifying the characteristics of features. Additionally, it enables us to generate novel images via features. Although BoVW is the de facto standard feature for image recognition and retrieval, successful image reconstruction from BoVW has not been reported yet. What complicates this task is that BoVW lacks the spatial information for including visual words. As described in this paper, to estimate an original arrangement, we propose an evaluation function that incorporates the naturalness of local adjacency and the global position, with a method to obtain related parameters using an external image database. To evaluate the performance of our method, we reconstruct images of objects of 101 kinds. Additionally, we apply our method to analyze object classifiers and to generate novel images via BoVW.
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A New Oscillating-Error Technique for Classifiers. (arXiv:1505.05312v1 [cs.AI])
This paper describes a new method for reducing the error in a classifier. It uses a weight adjustment update, but includes the very simple rule of either adding or subtracting the adjustment, based on whether the data point is currently larger or smaller than the desired value, and on a point-by-point basis. This gives added flexibility to the convergence procedure, where through a series of transpositions, values far away can continue towards the desired value, whereas values that are originally much closer can oscillate from one side to the other. Tests show that the method can successfully classify some known datasets. It can also work in a batch mode, with reduced training times and can be used as part of a neural network, or classifiers in general. There are also some updates on an earlier wave shape paper.
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Reactive Reasoning with the Event Calculus. (arXiv:1505.05364v1 [cs.AI])
Systems for symbolic event recognition accept as input a stream of time-stamped events from sensors and other computational devices, and seek to identify high-level composite events, collections of events that satisfy some pattern. RTEC is an Event Calculus dialect with novel implementation and 'windowing' techniques that allow for efficient event recognition, scalable to large data streams. RTEC can deal with applications where event data arrive with a (variable) delay from, and are revised by, the underlying sources. RTEC can update already recognised events and recognise new events when data arrive with a delay or following data revision. Our evaluation shows that RTEC can support real-time event recognition and is capable of meeting the performance requirements identified in a recent survey of event processing use cases.
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Towards Ideal Semantics for Analyzing Stream Reasoning. (arXiv:1505.05365v1 [cs.AI])
The rise of smart applications has drawn interest to logical reasoning over data streams. Recently, different query languages and stream processing/reasoning engines were proposed in different communities. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches are given only informally. Towards clear specifications and means for analytic study, a formal framework is needed to define their semantics in precise terms. To this end, we present a first step towards an ideal semantics that allows for exact descriptions and comparisons of stream reasoning systems.
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Multi-Context Systems for Reactive Reasoning in Dynamic Environments. (arXiv:1505.05366v1 [cs.AI])
We show in this paper how managed multi-context systems (mMCSs) can be turned into a reactive formalism suitable for continuous reasoning in dynamic environments. We extend mMCSs with (abstract) sensors and define the notion of a run of the extended systems. We then show how typical problems arising in online reasoning can be addressed: handling potentially inconsistent sensor input, modeling intelligent forms of forgetting, selective integration of knowledge, and controlling the reasoning effort spent by contexts, like setting contexts to an idle mode. We also investigate the complexity of some important related decision problems and discuss different design choices which are given to the knowledge engineer.
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Asynchronous Multi-Context Systems. (arXiv:1505.05367v1 [cs.AI])
In this work, we present asynchronous multi-context systems (aMCSs), which provide a framework for loosely coupling different knowledge representation formalisms that allows for online reasoning in a dynamic environment. Systems of this kind may interact with the outside world via input and output streams and may therefore react to a continuous flow of external information. In contrast to recent proposals, contexts in an aMCS communicate with each other in an asynchronous way which fits the needs of many application domains and is beneficial for scalability. The federal semantics of aMCSs renders our framework an integration approach rather than a knowledge representation formalism itself. We illustrate the introduced concepts by means of an example scenario dealing with rescue services. In addition, we compare aMCSs to reactive multi-context systems and describe how to simulate the latter with our novel approach.
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On Minimal Change in Evolving Multi-Context Systems (Preliminary Report). (arXiv:1505.05368v1 [cs.AI])
Managed Multi-Context Systems (mMCSs) provide a general framework for integrating knowledge represented in heterogeneous KR formalisms. However, mMCSs are essentially static as they were not designed to run in a dynamic scenario. Some recent approaches, among them evolving Multi-Context Systems (eMCSs), extend mMCSs by allowing not only the ability to integrate knowledge represented in heterogeneous KR formalisms, but at the same time to both react to, and reason in the presence of commonly temporary dynamic observations, and evolve by incorporating new knowledge. The notion of minimal change is a central notion in dynamic scenarios, specially in those that admit several possible alternative evolutions. Since eMCSs combine heterogeneous KR formalisms, each of which may require different notions of minimal change, the study of minimal change in eMCSs is an interesting and highly non-trivial problem. In this paper, we study the notion of minimal change in eMCSs, and discuss some alternative minimal change criteria.
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Towards a Simulation-Based Programming Paradigm for AI applications. (arXiv:1505.05373v1 [cs.AI])
We present initial ideas for a programming paradigm based on simulation that is targeted towards applications of artificial intelligence (AI). The approach aims at integrating techniques from different areas of AI and is based on the idea that simulated entities may freely exchange data and behavioural patterns. We define basic notions of a simulation-based programming paradigm and show how it can be used for implementing AI applications.
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Towards Large-scale Inconsistency Measurement. (arXiv:1505.05375v1 [cs.AI])
We investigate the problem of inconsistency measurement on large knowledge bases by considering stream-based inconsistency measurement, i.e., we investigate inconsistency measures that cannot consider a knowledge base as a whole but process it within a stream. For that, we present, first, a novel inconsistency measure that is apt to be applied to the streaming case and, second, stream-based approximations for the new and some existing inconsistency measures. We conduct an extensive empirical analysis on the behavior of these inconsistency measures on large knowledge bases, in terms of runtime, accuracy, and scalability. We conclude that for two of these measures, the approximation of the new inconsistency measure and an approximation of the contension inconsistency measure, large-scale inconsistency measurement is feasible.
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Fuzzy Least Squares Twin Support Vector Machines. (arXiv:1505.05451v1 [cs.AI])
Least Squares Twin Support Vector Machine (LSTSVM) is an extremely efficient and fast version of SVM algorithm for binary classification. LSTSVM combines the idea of Least Squares SVM and Twin SVM in which two non-parallel hyperplanes are found by solving two systems of linear equations. Although, the algorithm is very fast and efficient in many classification tasks, it is unable to cope with two features of real-world problems. First, in many real-world classification problems, it is almost impossible to assign data points to a single class. Second, data points in real-world problems may have different importance. In this study, we propose a novel version of LSTSVM based on fuzzy concepts to deal with these two characteristics of real-world data. The algorithm is called Fuzzy LSTSVM (FLSTSVM) which provides more flexibility than binary classification of LSTSVM. Two models are proposed for the algorithm. In the first model, a fuzzy membership value is assigned to each data point and the hyperplanes are optimized based on these fuzzy samples. In the second model we construct fuzzy hyperplanes to classify data. Finally, we apply our proposed FLSTSVM to an artificial as well as three real-world datasets. Results demonstrate that FLSTSVM obtains better performance than SVM and LSTSVM.
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Memory Networks. (arXiv:1410.3916v10 [cs.AI] UPDATED)
We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs.
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Knowledge, Justification, and Reason-Based Belief. (arXiv:1412.1862v3 [cs.LO] UPDATED)
Is knowledge definable as justified true belief ("JTB")? We argue that one can legitimately answer positively or negatively, depending on how the notion of justification is understood. To facilitate our argument, we introduce a simple propositional logic of reason-based belief. We show that this logic is sufficiently flexible to accommodate various useful features, including quantification over reasons. We use our framework to contrast two notions of JTB: one internalist, the other externalist. We argue that Gettier cases essentially challenge the internalist notion but not the externalist one. In particular, we may equate knowledge and JTB if the latter is grounded in what we call "adequate" reasons.
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[FD] CVE for Apple's ECDHE-ECDSA SecureTransport bug?
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[FD] CVE ID assignment - eZPublish vulnerability
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Allardyce ponders future at West Ham
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[FD] hardwear.io - Hardware Security Conference Call for Papers
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Yik Yak - the rise of anonymous geo-social connectivity
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Sunderland secures Premier League status with Arsenal draw
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Cantona sues Cosmos, alleging breach of contract
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Brazilian club Fluminense changes coach after 8 matches
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América: Lista preliminar de Perú
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América: Lista preliminar de Paraguay
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América: Lista preliminar de Ecuador
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Greaves 'improving' after severe stroke, says agent
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FIFA sponsors Coke, Visa express concern over Qatar labor
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FIFA making progress in Israeli-Palestinian soccer spat
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Sponsor Visa expresses 'grave concern' over Qatar labor
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New LogJam SSL Vulnerability Puts Internet Users At Risk
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Nimes to remain in 2nd division in match-fixing case
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ISS Daily Summary Report – 05/19/15
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Romanian mayor rents soccer field for daughter's wedding
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Sepp Blatter opponents considering joining forces
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Kane picked by England U21s despite burnout fears
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[FD] Eisbär SCADA (All Versions - iOS, Android & W8) - Persistent UI Vulnerability
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[FD] HiDisk 2.4 iOS - (currentFolderPath) Persistent Vulnerability
PoC: #2
NetUSB Driver Flaw Exposes Millions of Routers to Hacking
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中村康秀 (@nkmr0) favorited one of your Tweets!
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A-League's Newcastle placed into administration by owner
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I have a new follower on Twitter
Amanda Collins
Hardcore creator. Professional zombieaholic. Coffee lover. Bacon ninja. Organizer. Friendly food trailblazer.
Long Beach
Following: 3157 - Followers: 2986
May 20, 2015 at 01:04AM via Twitter http://twitter.com/maltdeceive
Tuesday, May 19, 2015
I have a new follower on Twitter
Cleotilde Garcia
Every good painter paints what he is.
Barclay town, MD, USA
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May 19, 2015 at 11:53PM via Twitter http://twitter.com/cleotildegarc50
Re: [FD] 0-day Denial of Service in IPsec-Tools
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Orioles Highlight: Jimmy Paredes homers and drives in 4 runs as Baltimore rallies for 9-4 win over Mariners (ESPN)
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What is Learning? A primary discussion about information and Representation. (arXiv:1505.04813v1 [cs.AI])
Nowadays, represented by Deep Learning techniques, the field of machine learning is experiencing unprecedented prosperity and its influence is demonstrated in academia, industry and civil society. "Intelligent" has become a label which could not be neglected for most applications; celebrities and scientists also warned that the development of full artificial intelligence may spell the end of the human race. It seems that the answer to building a computer system that could automatically improve with experience is right on the next corner. While for AI and machine learning researchers, it is a consensus that we are not anywhere near the core technique which could bring the Terminator, Number 5 or R2D2 into real life, and there is not even a formal definition about what is intelligence, or one of its basic properties: Learning. Therefore, even though researchers know these concerns are not necessary currently, there is no generalized explanation about why these concerns are not necessary, and what properties people should take into account that would make these concerns to be necessary. In this paper, starts from analysing the relation between information and its representation, a necessary condition for a model to be a learning model is proposed. This condition and related future works could be used to verify whether a system is able to learn or not, and enrich our understanding of learning: one important property of Intelligence.
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Towards Data-Driven Autonomics in Data Centers. (arXiv:1505.04935v1 [cs.DC])
Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on predictive holistic models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in one of their 12K-node clusters with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set capturing machine status over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line on live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.
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Recursion in RDF Data Shape Languages. (arXiv:1505.04972v1 [cs.DB])
An RDF data shape is a description of the expected contents of an RDF document (aka graph) or dataset. A major part of this description is the set of constraints that the document or dataset is required to satisfy. W3C recently (2014) chartered the RDF Data Shapes Working Group to define SHACL, a standard RDF data shape language. We refer to the ability to name and reference shape language elements as recursion. This article provides a precise definition of the meaning of recursion as used in Resource Shape 2.0. The definition of recursion presented in this article is largely independent of language-specific details. We speculate that it also applies to ShEx and to all three of the current proposals for SHACL. In particular, recursion is not permitted in the SHACL-SPARQL proposal, but we conjecture that recursion could be added by using the definition proposed here as a top-level control structure.
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A New Fundamental Evidence of Non-Classical Structure in the Combination of Natural Concepts. (arXiv:1505.04981v1 [cs.AI])
We recently performed cognitive experiments on conjunctions and negations of two concepts with the aim of investigating the combination problem of concepts. Our experiments confirmed the deviations (conceptual vagueness, underextension, overextension, etc.) from the rules of classical (fuzzy) logic and probability theory observed by several scholars in concept theory, while our data were successfully modeled in a quantum-theoretic framework developed by ourselves. In this paper, we isolate a new, very stable and systematic pattern of violation of classicality that occurs in concept combinations. In addition, the strength and regularity of this non-classical effect leads us to believe that it occurs at a more fundamental level than the deviations observed up to now. It is our opinion that we have identified a deep non-classical mechanism determining not only how concepts are combined but, rather, how they are formed. We show that this effect can be faithfully modeled in a two-sector Fock space structure, and that it can be exactly explained by assuming that human thought is the supersposition of two processes, a 'logical reasoning', guided by 'logic', and a 'conceptual reasoning' guided by 'emergence', and that the latter generally prevails over the former. All these findings provide a new fundamental support to our quantum-theoretic approach to human cognition.
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An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. (arXiv:1505.05004v1 [stat.ML])
We present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from incremental and divide-and-conquer constraint-based methods to learn the parents and children of a target variable. We conduct an experimental comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning, on several benchmarks with various data sizes. Our extensive experiments show that H2PC outperforms MMHC both in terms of goodness of fit to new data and in terms of the quality of the network structure itself, which is closer to the true dependence structure of the data. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.
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Modular Action Language ALM. (arXiv:1505.05022v1 [cs.LO])
The paper introduces a new modular action language, ALM, and illustrates the methodology of its use. It is based on the approach of Gelfond and Lifschitz (1993; 1998) in which a high-level action language is used as a front end for a logic programming system description. The resulting logic programming representation is used to perform various computational tasks. The methodology based on existing action languages works well for small and even medium size systems, but is not meant to deal with larger systems that require structuring of knowledge. ALM is meant to remedy this problem. Structuring of knowledge in ALM is supported by the concepts of module (a formal description of a specific piece of knowledge packaged as a unit), module hierarchy, and library, and by the division of a system description of ALM into two parts: theory and structure. A theory consists of one or more modules with a common theme, possibly organized into a module hierarchy based on a dependency relation. It contains declarations of sorts, attributes, and properties of the domain together with axioms describing them. Structures are used to describe the domain's objects. These features, together with the means for defining classes of a domain as special cases of previously defined ones, facilitate the stepwise development, testing, and readability of a knowledge base, as well as the creation of knowledge representation libraries. To appear in Theory and Practice of Logic Programming (TPLP).
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Necessary and Sufficient Conditions for Surrogate Functions of Pareto Frontiers and Their Synthesis Using Gaussian Processes. (arXiv:1505.05063v1 [cs.AI])
This paper introduces the necessary and sufficient conditions that surrogate functions must satisfy to properly define frontiers of non-dominated solutions in multi-objective optimization problems. Given that this is the first time that those conditions are elicited, there is no reason to believe that the surrogates already proposed in the literature meet them. As a consequence, dominated solutions can be suggested by already proposed surrogates as valid candidates to represent the Pareto frontier. Conceptually speaking, the new conditions we are introducing work directly on the objective space, thus being agnostic on the evaluation methods. Therefore, real objectives or user-designed objectives' surrogates are allowed, opening the possibility of linking independent objective surrogates. To illustrate the practical consequences of adopting the proposed conditions, an oversimplified model for the surrogate is shown to be capable of suggesting a valid frontier of non-dominated solutions, though not the expect one from the data provided. On the other hand, when applying Gaussian processes as surrogates endowed with monotonicity soft constraints and with an adjustable degree of flexibility, the necessary and sufficient conditions proposed here are finely managed by the multivariate distribution, guiding to high-quality surrogates capable of suitably synthesizing an approximation to the Pareto frontier in challenging instances of multi-objective optimization.
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A Simplified and Improved Free-Variable Framework for Hilbert's epsilon as an Operator of Indefinite Committed Choice. (arXiv:1104.2444v7 [cs.AI] UPDATED)
Free variables occur frequently in mathematics and computer science with ad hoc and altering semantics. We present the most recent version of our free-variable framework for two-valued logics with properly improved functionality, but only two kinds of free variables left (instead of three): implicitly universally and implicitly existentially quantified ones, now simply called "free atoms" and "free variables", respectively. The quantificational expressiveness and the problem-solving facilities of our framework exceed standard first-order and even higher-order modal logics, and directly support Fermat's descente infinie. With the improved version of our framework, we can now model also Henkin quantification, neither using quantifiers (binders) nor raising (Skolemization). We propose a new semantics for Hilbert's epsilon as a choice operator with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the epsilon supports reductive proof search optimally.
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