Marco
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February 20, 2016 at 10:56PM via Twitter http://twitter.com/marco651211
"He has since been released and is the subject of a report to the procurator fiscal," a Police Spokesman told a Scottish journal.
"I estimate by the end of the day well over 100,000 new endpoints will be infected with Locky, making this a genuine major cybersecurity incident — 3 days in, approximately a quarter of Million PCs will be infected," Kevin said in a blog post.
Traditional inconsistency-tolerent query answering in ontology-based data access relies on selecting maximal components of an ABox/database which are consistent with the ontology. However, some rules in ontologies might be unreliable if they are extracted from ontology learning or written by unskillful knowledge engineers. In this paper we present a framework of handling inconsistent existential rules under stable model semantics, which is defined by a notion called rule repairs to select maximal components of the existential rules. Surprisingly, for R-acyclic existential rules with R-stratified or guarded existential rules with stratified negations, both the data complexity and combined complexity of query answering under the rule {repair semantics} remain the same as that under the conventional query answering semantics. This leads us to propose several approaches to handle the rule {repair semantics} by calling answer set programming solvers. An experimental evaluation shows that these approaches have good scalability of query answering under rule repairs on realistic cases.
In this paper we explore the application of some notable Boolean methods, namely the Disjunctive Normal Form representation of logic table expansions, and apply them to a real-valued logic model which utilizes quantities on the range [0,1] to produce a probabilistic programming of a game character's logic in mathematical form.
Conceptual spaces are geometric representations of conceptual knowledge, in which entities correspond to points, natural properties correspond to convex regions, and the dimensions of the space correspond to salient features. While conceptual spaces enable elegant models of various cognitive phenomena, the lack of automated methods for constructing such representations have so far limited their application in artificial intelligence. To address this issue, we propose a method which learns a vector-space embedding of entities from Wikipedia and constrains this embedding such that entities of the same semantic type are located in some lower-dimensional subspace. We experimentally demonstrate the usefulness of these subspaces as (approximate) conceptual space representations by showing, among others, that important features can be modelled as directions and that natural properties tend to correspond to convex regions.
We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art and introduces new ones based on cardinality and majority principles. It relies on two key notions: modifiers and inference strategies. An inconsistency-tolerant semantics is seen as a composite modifier plus an inference strategy. We compare the obtained semantics from a productivity point of view.
We develop a general duality between neural networks and compositional kernels, striving towards a better understanding of deep learning. We show that initial representations generated by common random initializations are sufficiently rich to express all functions in the dual kernel space. Hence, though the training objective is hard to optimize in the worst case, the initial weights form a good starting point for optimization. Our dual view also reveals a pragmatic and aesthetic perspective of neural networks and underscores their expressive power.
The literature on judgment aggregation is moving from studying impossibility results regarding aggregation rules towards studying specific judgment aggregation rules. Here we give a structured list of most rules that have been proposed and studied recently in the literature, together with various properties of such rules. We fi?rst focus on the majority-preservation property, which generalizes Condorcet-consistency, and identify which of the rules satisfy it. We study the inclusion relationships that hold between the rules. Finally, we consider two forms of unanimity, monotonicity, homogeneity, and reinforcement, and we identify which of the rules satisfy these properties.
"Unfortunately, there's no easy workaround for the issue since the keypad happily sends unencrypted PINs out to anyone listening," Zonenberg explains.
"Normally, the vendor would fix the vulnerability in a new firmware version by adding cryptography to the protocol," Zonenberg adds. But, "this isn't an option for the affected SimpliSafe products because the microcontrollers in currently shipped hardware are one-time programmable."
"The quickest and most efficient way to restore our systems and administrative functions was to pay the ransom and obtain the decryption key," the hospital CEO Allen Stefanek said in a letter.
In a series of tweets late Wednesday, Pichai sided with Apple while saying "forcing companies to enable hacking could compromise users' privacy" and "requiring companies to enable hacking of customer devices & data. Could be a troubling precedent."
"The @FBI is creating a world where citizens rely on #Apple to defend their rights, rather than the other way around," Snowden tweeted on Wednesday. Snowden called on Google to stand with Apple, saying, "This is the most important tech case in a decade."
We describe a large-scale functional brain model that includes detailed, conductance-based, compartmental models of individual neurons. We call the model BioSpaun, to indicate the increased biological plausibility of these neurons, and because it is a direct extension of the Spaun model \cite{Eliasmith2012b}. We demonstrate that including these detailed compartmental models does not adversely affect performance across a variety of tasks, including digit recognition, serial working memory, and counting. We then explore the effects of applying TTX, a sodium channel blocking drug, to the model. We characterize the behavioral changes that result from this molecular level intervention. We believe this is the first demonstration of a large-scale brain model that clearly links low-level molecular interventions and high-level behavior.
In practice, training language models for individual authors is often expensive because of limited data resources. In such cases, Neural Network Language Models (NNLMs), generally outperform the traditional non-parametric N-gram models. Here we investigate the performance of a feed-forward NNLM on an authorship attribution problem, with moderate author set size and relatively limited data. We also consider how the text topics impact performance. Compared with a well-constructed N-gram baseline method with Kneser-Ney smoothing, the proposed method achieves nearly 2:5% reduction in perplexity and increases author classification accuracy by 3:43% on average, given as few as 5 test sentences. The performance is very competitive with the state of the art in terms of accuracy and demand on test data. The source code, preprocessed datasets, a detailed description of the methodology and results are available at http://ift.tt/1mICOp3.