Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.Comment: 35 pages, 3 figures, based on KES 2008 keynote and ALT 2007 / DS 2007 joint invited lectur

Designing a CPU model: from a pseudo-formal document to fast code

For validating low level embedded software, engineers use simulators that take the real binary as input. Like the real hardware, these full-system simulators are organized as a set of components. The main component is the CPU simulator (ISS), because it is the usual bottleneck for the simulation speed, and its development is a long and repetitive task. Previous work showed that an ISS can be generated from an Architecture Description Language (ADL). In the work reported in this paper, we generate a CPU simulator directly from the pseudo-formal descriptions of the reference manual. For each instruction, we extract the information describing its behavior, its binary encoding, and its assembly syntax. Next, after automatically applying many optimizations on the extracted information, we generate a SystemC/TLM ISS. We also generate tests for the decoder and a formal specification in Coq. Experiments show that the generated ISS is as fast and stable as our previous hand-written ISS.Comment: 3rd Workshop on: Rapid Simulation and Performance Evaluation: Methods and Tools (2011

Towards a verified compiler prototype for the synchronous language SIGNAL

International audienceSIGNAL belongs to the synchronous languages family which are widely used in the design of safety-critical real-time systems such as avionics, space systems, and nuclear power plants. This paper reports a compiler prototype for SIGNAL. Compared with the existing SIGNAL compiler, we propose a new intermediate representation (named S-CGA, a variant of clocked guarded actions), to integrate more synchronous programs into our compiler prototype in the future. The front-end of the compiler, i.e., the translation from SIGNAL to S-CGA, is presented. As well, the proof of semantics preservation is mechanized in the theorem prover Coq. Moreover, we present the back-end of the compiler, including sequential code generation and multithreaded code generation with time-predictable properties. With the rising importance of multi-core processors in safety-critical embedded systems or cyber-physical systems (CPS), there is a growing need for model-driven generation of multithreaded code and thus mapping on multi-core. We propose a time-predictable multi-core architecture model in architecture analysis and design language (AADL), and map the multi-threaded code to this model

Unsupervised Time Series Extraction from Controller Area Network Payloads

This paper introduces a method for unsupervised tokenization of Controller Area Network (CAN) data payloads using bit level transition analysis and a greedy grouping strategy. The primary goal of this proposal is to extract individual time series which have been concatenated together before transmission onto a vehicle's CAN bus. This process is necessary because the documentation for how to properly extract data from a network may not always be available; passenger vehicle CAN configurations are protected as trade secrets. At least one major manufacturer has also been found to deliberately misconfigure their documented extraction methods. Thus, this proposal serves as a critical enabler for robust third-party security auditing and intrusion detection systems which do not rely on manufacturers sharing confidential information.Comment: 2018 IEEE 88th Vehicular Technology Conference (VTC2018-Fall

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.Comment: 8 page