Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

We introduce Kapre, Keras layers for audio and music signal preprocessing. Music research using deep neural networks requires a heavy and tedious preprocessing stage, for which audio processing parameters are often ignored in parameter optimisation. To solve this problem, Kapre implements time-frequency conversions, normalisation, and data augmentation as Keras layers. We report simple benchmark results, showing real-time on-GPU preprocessing adds a reasonable amount of computation.Comment: ICML 2017 machine learning for music discover

LEADERSHIP SKILLS IN CHILDREN WHO PREFER RISKY PLAY: A STUDY BASED ON REPUBLIC OF KOREAN CHILDREN

Purpose: This study was to investigate the difference in leadership skill depending on the preference of risky play in Korean children. Methodology: In order to archive that purpose, we have collected data from 381(215 male and 166 female) Korean elementary school students, upon distributing papers of questionnaire which is composed of Preference to Playing Forms Scale for Children and Scale to Research and Evaluate Youth Leadership Life Skills Development and performed statistical analysis using SPSS. Main Findings: The children who prefer risky play more showed significantly higher scores in all sub-factors of leadership skill such as communication, decision making, human relationship, solving problem, positive belief and consideration than the children who prefer risky play less. Implications: This can mean that the leadership skill of the children who prefer risky play more is superior to that of the children who prefer it less. Therefore it can be suggested that children’s participating in well-controlled risky play be an effective method to develop their leadership skill

Clock buffer polarity assignment under useful skew constraints

Clock trees, which deliver the clock signal to every clock sink in the whole system, switch actively at high frequency, which makes them one of the most dominant sources of noise. While many clock polarity assignment (PA) techniques were proposed to mitigate the clock noise, no attention has been paid to the PA under useful skew constraints. In this work, we show that the clock PA problem under useful skew constraints is intractable and propose a comprehensive and scalable clique search based algorithm to solve the problem effectively. In addition, we demonstrate the applicability of our solution by extending it for PA under delay variation environment. Through experiments with ISPD'10 benchmark circuits, we show that our proposed clock PA algorithm is able to reduce the peak noise by 10.9% further over that of the conventional global skew bound constrained PA. Finally, we compare our PA technique against decoupling capacitor embedding technique which is a commonly used method for noise reduction.OAIID:RECH_ACHV_DSTSH_NO:T201711155RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A076159CITE_RATE:.906DEPT_NM:전기·정보공학부EMAIL:[email protected]_YN:YN

Fuzzability Testing Framework for Incomplete Firmware Binary

Fuzzing is a practical approach for finding bugs in various software. So far, a number of fuzzers have been introduced based on new ideas towards enhancing the efficiency in terms of increasing code coverage or execution speed. The majority of such work predicates under the assumption that they have sound executable binary or source code to transform the target program as a whole. However, in legacy systems, source codes are often unavailable and even worse, some binaries do not provide a sound executable environment (e.g., partially recovered firmware). In this paper, we provide FT-Framework: fuzzability testing framework based on forced execution for binaries such as firmware chunks recovered in abnormal way so that they are hard to execute/analyze from intended booting phase. The essence of our work is to automatically classify functions inside a binary which we can apply coverage-guided fuzzing via forced execution. We evaluate FT-Framework using PX4 and ArduPilot firmwares which is based on 32-bit ARM architecture and demonstrate the efficacy of this approach and limitations