Why Philosophers Should Care About Computational Complexity

One might think that, once we know something is computable, how efficiently it can be computed is a practical question with little further philosophical importance. In this essay, I offer a detailed case that one would be wrong. In particular, I argue that computational complexity theory---the field that studies the resources (such as time, space, and randomness) needed to solve computational problems---leads to new perspectives on the nature of mathematical knowledge, the strong AI debate, computationalism, the problem of logical omniscience, Hume's problem of induction, Goodman's grue riddle, the foundations of quantum mechanics, economic rationality, closed timelike curves, and several other topics of philosophical interest. I end by discussing aspects of complexity theory itself that could benefit from philosophical analysis.Comment: 58 pages, to appear in "Computability: G\"odel, Turing, Church, and beyond," MIT Press, 2012. Some minor clarifications and corrections; new references adde

Hadron models and related New Energy issues

The present book covers a wide-range of issues from alternative hadron models to their likely implications in New Energy research, including alternative interpretation of lowenergy reaction (coldfusion) phenomena. The authors explored some new approaches to describe novel phenomena in particle physics. M Pitkanen introduces his nuclear string hypothesis derived from his Topological Geometrodynamics theory, while E. Goldfain discusses a number of nonlinear dynamics methods, including bifurcation, pattern formation (complex GinzburgLandau equation) to describe elementary particle masses. Fu Yuhua discusses a plausible method for prediction of phenomena related to New Energy development. F. Smarandache discusses his unmatter hypothesis, and A. Yefremov et al. discuss Yang-Mills field from Quaternion Space Geometry. Diego Rapoport discusses theoretical link between Torsion fields and Hadronic Mechanic. A.H. Phillips discusses semiconductor nanodevices, while V. and A. Boju discuss Digital Discrete and Combinatorial methods and their likely implications in New Energy research. Pavel Pintr et al. describe planetary orbit distance from modified Schrödinger equation, and M. Pereira discusses his new Hypergeometrical description of Standard Model of elementary particles. The present volume will be suitable for researchers interested in New Energy issues, in particular their link with alternative hadron models and interpretation

Proceedings of the 11th Workshop on Nonmonotonic Reasoning

These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general

Symmetry in Graph Theory

This book contains the successful invited submissions to a Special Issue of Symmetry on the subject of ""Graph Theory"". Although symmetry has always played an important role in Graph Theory, in recent years, this role has increased significantly in several branches of this field, including but not limited to Gromov hyperbolic graphs, the metric dimension of graphs, domination theory, and topological indices. This Special Issue includes contributions addressing new results on these topics, both from a theoretical and an applied point of view

Program Similarity Analysis for Malware Classification and its Pitfalls

Malware classification, specifically the task of grouping malware samples into families according to their behaviour, is vital in order to understand the threat they pose and how to protect against them. Recognizing whether one program shares behaviors with another is a task that requires semantic reasoning, meaning that it needs to consider what a program actually does. This is a famously uncomputable problem, due to Rice\u2019s theorem. As there is no one-size-fits-all solution, determining program similarity in the context of malware classification requires different tools and methods depending on what is available to the malware defender. When the malware source code is readily available (or at least, easy to retrieve), most approaches employ semantic \u201cabstractions\u201d, which are computable approximations of the semantics of the program. We consider this the first scenario for this thesis: malware classification using semantic abstractions extracted from the source code in an open system. Structural features, such as the control flow graphs of programs, can be used to classify malware reasonably well. To demonstrate this, we build a tool for malware analysis, R.E.H.A. which targets the Android system and leverages its openness to extract a structural feature from the source code of malware samples. This tool is first successfully evaluated against a state of the art malware dataset and then on a newly collected dataset. We show that R.E.H.A. is able to classify the new samples into their respective families, often outperforming commercial antivirus software. However, abstractions have limitations by virtue of being approximations. We show that by increasing the granularity of the abstractions used to produce more fine-grained features, we can improve the accuracy of the results as in our second tool, StranDroid, which generates fewer false positives on the same datasets. The source code of malware samples is not often available or easily retrievable. For this reason, we introduce a second scenario in which the classification must be carried out with only the compiled binaries of malware samples on hand. Program similarity in this context cannot be done using semantic abstractions as before, since it is difficult to create meaningful abstractions from zeros and ones. Instead, by treating the compiled programs as raw data, we transform them into images and build upon common image classification algorithms using machine learning. This led us to develop novel deep learning models, a convolutional neural network and a long short-term memory, to classify the samples into their respective families. To overcome the usual obstacle of deep learning of lacking sufficiently large and balanced datasets, we utilize obfuscations as a data augmentation tool to generate semantically equivalent variants of existing samples and expand the dataset as needed. Finally, to lower the computational cost of the training process, we use transfer learning and show that a model trained on one dataset can be used to successfully classify samples in different malware datasets. The third scenario explored in this thesis assumes that even the binary itself cannot be accessed for analysis, but it can be executed, and the execution traces can then be used to extract semantic properties. However, dynamic analysis lacks the formal tools and frameworks that exist in static analysis to allow proving the effectiveness of obfuscations. For this reason, the focus shifts to building a novel formal framework that is able to assess the potency of obfuscations against dynamic analysis. We validate the new framework by using it to encode known analyses and obfuscations, and show how these obfuscations actually hinder the dynamic analysis process

Logic and Automata

Mathematical logic and automata theory are two scientific disciplines with a fundamentally close relationship. The authors of Logic and Automata take the occasion of the sixtieth birthday of Wolfgang Thomas to present a tour d'horizon of automata theory and logic. The twenty papers in this volume cover many different facets of logic and automata theory, emphasizing the connections to other disciplines such as games, algorithms, and semigroup theory, as well as discussing current challenges in the field

forall x (UBC Edition)

This is an open-access introductory logic textbook, prepared by Jonathan Ichikawa, based on P.D. Magnus's forallx. This (v2.0, July 2020) is intended as a stable, ready-for-teaching edition

The application of social cost benefit analysis to nuclear power in the United Kingdom

Imperial Users onl