Automated Model Selection with AMSFin a production process of the automotive industry

Machine learning, statistics and knowledge engineering provide a broad variety of supervised learning algorithms for classification. In this paper we introduce the Automated Model Selection Framework (AMSF) which presents automatic and semi-automatic methods to select classifiers. To achieve this we split up the selection process into three distinct phases. Two of those select algorithms by static rules which are derived from a manually created knowledgebase. At this stage of AMSF the user can choose between different rankers in the third phase. Currently, we use instance based learning and a scoring scheme for ranking the classifiers. After evaluation of different rankers we will recommend the most successful to the user by default. Besides describing the architecture and design issues, we additionally point out the versatile ways AMSF is applied in a production process of the automotive industr

Bitcoin and the Uniform Commercial Code

Much of the discussion of bitcoin in the popular press has concentrated on its status as a currency. Putting aside a vocal minority of radical libertarians and anarchists, however, many bitcoin enthusiasts are concentrating on how its underlying technology – the blockchain – can be put to use for wide variety of uses. For example, economists at the Fed and other central banks have suggested that they should encourage the evolution of bitcoin’s blockchain protocol which might allow financial transactions to clear much efficiently than under our current systems. As such, it also holds out the possibility of becoming that holy grail of commerce – a payment system that would eliminate or minimize the roles of third party intermediaries. In addition, the NASDAQ and a number of issuers are experimenting with using the blockchain to record the issuing and trading of investments securities. In this Article, I examine the implications for bitcoin under the Uniform Commercial Code (the “U.C.C.”). Specifically, I consider three issues. In Part 1, I discuss the characterization of bitcoin – which I am using generically to refer to any virtual or cryptocurrency – under Article 9. The bad news is that it does not, and cannot be made to fit into, the U.C.C.’s definition of “money”. If held directly by the owner, bitcoin constitutes a “general intangible”. Unfortunately, general intangibles are non-negotiable. This could greatly impinge on bitcoin’s liquidity and, therefore, its utility as a payment system. In Part 2, I show how this may be mitigated by the rules of Article 8 governing investment securities. If the owner of bitcoin were to choose to hold it indirectly through a financial intermediary, then she and the intermediary could elect to have it treated as a “financial asset” which is super-negotiable. Unfortunately, this comes at the cost of eliminating one of the primary attractions of cryptocurrency, namely the ability to engage in financial transactions directly without a third-party intermediary. However, Article 8, may already provide a legal regime for another contemplated use for the blockchain – namely as a readily searchable means of recording the ownership and transfer of property generally. In Part 3, I explain how cryptosecurities fall squarely within Article 8\u27s definition of “uncertificated securities.” Ironically, therefore, the creation of bitcoin securities may finally breathe life to little used provisions that were invented almost 40 years ago in a failed attempt to solve a completely different problem

Implementasi dan Analisis Ripple-down Rule pada Diagnosa Penyakit Jantung

Abstraksi Penyakit jantung merupakan salah satu penyakit yang berbahaya yang dapat menyebabkan kematian. Penyebab dari penyakit jantung bisa bermacam-macam tergantung dari jenisnya. Seperti gagal jantung, yang merupakan salah satu masalah kesehatan yang pada jaman sekarang sedang marak, dapat disebabkan oleh konsumsi alkohol, penumpukan lemak pada dinding pembuluh darah, atau akibat konsumsi obat-obatan tertentu. Berbagai macam jenis penyakit jantung ini dapat menimbulkan gejala-gejala tertentu yang dapat berbeda-beda tiap jenisnya. Berdasarkan berbagai macam gejala yang ditimbulkan dari penyakit-penyakit ini dapat ditentukan penyakitnya oleh dokter. Namun biasanya orang yang mengidap penyakit jantung memeriksakan kesehatannya ke dokter ketika penyakit yang dideritanya sudah cukup parah. Untuk itu tujuan pembuatan tugas akhir ini adalah dengan membuat sebuah sistem pakar yang dapat menentukan jenis penyakit jantung berdasarkan dari gejala-gejala yang diketahui. Sehingga orang yang merasakan gejala-gejala akan mengidap suatu penyakit, dapat menggunakan sistem pakar ini untuk mengetahui penyakit lebih dini dan dapat melakukan langkah penyembuhan dengan segera. Sistem pakar ini merupakan sistem yang dibangun menggunakan metode Ripple-down Rule (RDR), dimana sistem tidak hanya dapat menentukan jenis penyakit berdasarkan gejala-gejala yang dimasukkan namun juga dapat memperbarui atau memperbaiki kemampuan diagnosanya. Dalam tugas akhir ini, sistem pakar dibangun menggunakan Bahasa pemrograman Java dengan Extensible Mark-up Language (XML) sebagai basis data untuk menampung basis pengetahuan yang disimpan dari sistem pakar ini. Hasil diagnosa sistem pakar yang menggunakan metode RDR ini sangat akurat, namun masih tergantung kepada pelatihan sistem. Sehingga untuk memaksimalkan fungsi dari sistem pakar ini akan diperlukan pelatihan sistem yang cukup dengan data yang juga baik. Kata kunci: penyakit jantung, ripple-down rule,sistem pakar, diagnosa, XML, pemrograman Java

Characterization and Design of High-Level VHDL I/Q Frequency Downconverter via Special Sampling Scheme

This study explores the characterization and implementation of a Special Sampling Scheme (SSS) for In-Phase and Quad-Phase (I/Q) down conversion utilizing top-level, portable design strategies. The SSS is an under-developed signal sampling methodology that can be used with military and industry receiver systems, specifically, United States Air Force (USAF) video receiver systems. The SSS processes a digital input signal-stream sampled at a specified sampling frequency, and down converts it into In-Phase (I) and Quad-Phase (Q) output signal-streams. Using the theory and application of the SSS, there are three main objectives that will be accomplished: characterization of the effects of input, output, and filter coefficient parameters on the I/Q imbalances using the SSS; development and verification of abstract, top-level VHDL code of the I/Q SSS for hardware implementation; and finally, development, verification, and analysis of variation between synthesizable pipelined and sequential VHDL implementations of the SSS for Field Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuits (ASIC)

The use of proof plans in tactic synthesis

We undertake a programme of tactic synthesis. We first formalize the notion of a tactic as a rewrite rule, then give a correctness criterion for this by means of a reflection mechanism in the constructive type theory OYSTER. We further formalize the notion of a tactic specification, given as a synthesis goal and a decidability goal. We use a proof planner. CIAM. to guide the search for inductive proofs of these, and are able to successfully synthesize several tactics in this fashion. This involves two extensions to existing methods: context-sensitive rewriting and higher-order wave rules. Further, we show that from a proof of the decidability goal one may compile to a Prolog program a pseudo- tactic which may be run to efficiently simulate the input/output behaviour of the synthetic tacti

Advancement of Data-Driven Short-Term Flood Predictions on an Urbanized Watershed Using Preprocessing Techniques

Supervised classification can be applied for short-term predictions of hydrological events in cases where the label of the event rather than its magnitude is crucial, as in the case of early flood warning systems. To be effective, these warning systems must be able to forecast floods accurately and to provide estimates early enough. Following the approach of transforming hydrological sensor data into a phase space using time-delay embedding, an attempt was made to improve the performance of the models and to increase the lead-time of reliable predictions. For this, the available set of attributes supplied by stream and rain gauges was extended by derivatives. In addition, imbalanced data techniques were applied at the data preprocessing step. The computational experiments were conducted on various data sets, lead-times, and years with different hydrological characteristics. The results show that especially derivatives of water level data improve model performance, increasingly when added for only one or two hours before the prediction time. In addition to that, the imbalanced data techniques allowed for overall improved prediction of floods at the cost of slight increase of misclassification of low-flow events