Non sequitur
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An exploration of Python's random module for the curious programmer, this talk will give a little background in statistics and pseudorandom number generation, explain the properties of python's choice of pseudorandom generator and explore through visualizations the different distributions provided by the module.
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기계학습을 이용하여 정적 분석기의 안전성을 선별적으로 조절하는 방법
발표자: 허기홍 (발표 당시 서울대 박사과정)
발표일: 2017.6.
서울대학교 컴퓨터공학부 프로그래밍 연구실에서 프로그램 정적분석(static program analysis)을 연구하고 있다. 특히, 정적 분석기를 더욱 빠르고 정확하게 만들기 위한 다양한 기법을 연구, 개발했다.
최근에는 다양한 데이터를 학습하여 정적 분석기를 더욱 유연하게 만드는데 많은 관심을 두고 있다.
2017년 8월 서울대학교에서 박사학위를 받을 예정이다.
개요:
이 발표에서는 기계학습을 사용하여 정적 분석기의 안전성(soundness)을 조절하는 방법을 이야기한다.
실용적인 정적분석기는, 무결점 검증기를 제외하면, 대부분 필연적으로 불안전 (unsound) 하다. 즉, 실제 실행의 모든 경우를 항상 포섭하지는 않는다는 뜻이다. 정확도나 성능 등을 이유로 여러 부분에서 실제 의미의 일부만을 포섭하는 경우가 많다. 예를 들면, 순환문이 한 바퀴만 도는 경우만 고려한다거나, 내용을 모르는 라이브러리 호출은 아무일을 안한다고 가정해버리는 식이다. 그런데 이런 불안전한 기법을 무턱대고 사용할 시에는 분석기 사용자가 알고 싶어하는 정보 (예를 들면, 버그)를 지나치게 많이 놓치는 문제가 있다. 이를 해결하기 위해 이 연구에서는 안전성을 포기해도 버그를 놓치지 않는 지점, 그러면서도 동시에 정확도는 높일수 있는 지점을 잘 찾는 방법을 제시한다.
핵심은 이런 지점을 기계 학습을 이용해서 찾아내는 것이다. 버그가 있는 프로그램의 여러 지점을 안전성을 포기하면서 분석을 해보고, 그 분석 결과를 학습시키는 방식이다. 그렇게 학습된 분류기 (classifier) 는 새로운 프로그램을 분석할 때 안전성을 포기해도 될 만한 부분을 정확하게 짚어주어 분석기의 정확도를 높이는데 도움을 준다.
우리는 이 기술을 C 프로그램을 대상으로 하는 두 가지 정적 분석기 (버퍼오버런 오류 검출기, 포맷스트링 오류 검출기)에 적용하여 그 성능을 실험하였다. 그 결과, 맹목적으로 안전성을 포기하는 기존 분석기에 비해서 획기적으로 오류 검출능력을 높일 수 있었다. 이 연구는 올해 ICSE (International Conference on Software Engineering) 학회를 통해 소개되었다.
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기계학습을 이용하여 정적 분석기의 안전성을 선별적으로 조절하는 방법
발표자: 허기홍 (발표 당시 서울대 박사과정)
발표일: 2017.6.
서울대학교 컴퓨터공학부 프로그래밍 연구실에서 프로그램 정적분석(static program analysis)을 연구하고 있다. 특히, 정적 분석기를 더욱 빠르고 정확하게 만들기 위한 다양한 기법을 연구, 개발했다.
최근에는 다양한 데이터를 학습하여 정적 분석기를 더욱 유연하게 만드는데 많은 관심을 두고 있다.
2017년 8월 서울대학교에서 박사학위를 받을 예정이다.
개요:
이 발표에서는 기계학습을 사용하여 정적 분석기의 안전성(soundness)을 조절하는 방법을 이야기한다.
실용적인 정적분석기는, 무결점 검증기를 제외하면, 대부분 필연적으로 불안전 (unsound) 하다. 즉, 실제 실행의 모든 경우를 항상 포섭하지는 않는다는 뜻이다. 정확도나 성능 등을 이유로 여러 부분에서 실제 의미의 일부만을 포섭하는 경우가 많다. 예를 들면, 순환문이 한 바퀴만 도는 경우만 고려한다거나, 내용을 모르는 라이브러리 호출은 아무일을 안한다고 가정해버리는 식이다. 그런데 이런 불안전한 기법을 무턱대고 사용할 시에는 분석기 사용자가 알고 싶어하는 정보 (예를 들면, 버그)를 지나치게 많이 놓치는 문제가 있다. 이를 해결하기 위해 이 연구에서는 안전성을 포기해도 버그를 놓치지 않는 지점, 그러면서도 동시에 정확도는 높일수 있는 지점을 잘 찾는 방법을 제시한다.
핵심은 이런 지점을 기계 학습을 이용해서 찾아내는 것이다. 버그가 있는 프로그램의 여러 지점을 안전성을 포기하면서 분석을 해보고, 그 분석 결과를 학습시키는 방식이다. 그렇게 학습된 분류기 (classifier) 는 새로운 프로그램을 분석할 때 안전성을 포기해도 될 만한 부분을 정확하게 짚어주어 분석기의 정확도를 높이는데 도움을 준다.
우리는 이 기술을 C 프로그램을 대상으로 하는 두 가지 정적 분석기 (버퍼오버런 오류 검출기, 포맷스트링 오류 검출기)에 적용하여 그 성능을 실험하였다. 그 결과, 맹목적으로 안전성을 포기하는 기존 분석기에 비해서 획기적으로 오류 검출능력을 높일 수 있었다. 이 연구는 올해 ICSE (International Conference on Software Engineering) 학회를 통해 소개되었다.
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This document discusses several Python peculiarities and quirks through examples of code snippets. Some key points covered include:
- Importing * can override expected values
- Default arguments are evaluated once at compile time
- Built-in functions like str behave differently on integers vs other objects
- Special modules and future imports can enable unusual syntax as easter eggs
- Implicit variables like _ can cause unexpected behavior
- Indexing issues can arise from iterating over lists in certain ways
The examples serve to illustrate subtle behaviors in Python and emphasize writing clear and intentional code over clever tricks that may confuse others or cause bugs. Readability, commenting, and avoiding imports of all symbols are suggested.
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Abstract: As the use of Python coupled to Numpy/SciPy for numerical computation increases, many tools to optimize performance have emerged. Indeed, this duo has relatively poor performance when compared to scientific codes written in legacy languages like C or Fortran. Cython, Numba, numexpr and parakeet belongs to this new compiler ecosystem. And so does Pythran, a Python to C++11 translator for scientific Python.
Pythran uses a static compilation approach a la Cython, but with full backward compatibility with Python. It does not only turns Python code into C++ code, it also performs Python/Numpy specific optimizations, generates calls to a parallel, vectorized runtime and makes it possible to write OpenMP annotation in the original Python code. It supports a large range of Numpy functions and can combine them in efficient ways: it can optimize highlevel modern Python/Numpy codes and not only Fortran with a Python flavor ones.
This talk presents the existing compilation approach and optimization opportunities for numerical Python, their strengths and weaknesses, then focus on the specificities of the Pythran compiler.
Statistical inference for (Python) Data Analysis. An introduction.
A brief showcase on how Python can help perform statistical inference. Few examples with a short library overview.
Доклад Антона Поварова "Go in Badoo" с Golang Meetup
This document summarizes notes from a Go meetup at Badoo in April 2015. It discusses Badoo's use of Go in their backend systems, including replacing 25 C/C++ daemons with Go services. It provides examples of memory profiling Go code to reduce garbage collection pauses. It also discusses using protocol buffers with Go and strategies for reducing allocations when marshaling data.
Random Number Generator Using Seven Segment Display In Labview
Random number generator [RNG] is use to generate random numbers between any given limit, RNG's are two kinds 1.True random number and 2.pseudom numbers. True random numbers are not predictable by any mathematical formula because they are mainly depends on the atmospheric noise, coming to the pseudo numbers are mainly used in most of computers, this randomness can be predictable by using mathematical formula and it is fine many purposes, but it may not be random in the way you expect if you are used to dice rolls and lottery drawings. In this mini project we are doing RNG [pseudo numbers] by using NI labview software and generating random numbers by pressing push button and coming output we are displaying on seven segment display. In the labview it easy to generate a random number by using different block‟s and main advantage of the labview is there is no need of any programming languages like[c,c++,java,matlab].Main function of this project is used for gaming and priority number generation and etc. A sequence of uniform random numbers, which is generated within the computer in a deterministic manner, is often referred to as a pseudo-random number sequence.[1]
A CTF Hackers Toolbox
Capture the Flag (CTF) are information security challenges. They are fun, but they also provide a opportunity to practise for real-world security challenges.
In this talk we present the concept of CTF. We focus on some tools used by our team, which can also be used to solve real-world problems.
Exact Real Arithmetic for Tcl
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Simple APIs and innovative documentation
Reasons for the succes of Scientific Python
Emmanuelle Gouillart, CNRS/Saint Gobain SVI
@ PyDataParis2016
Fatima Aliasgher Portfolio
The document contains the algorithms, flowcharts, and code for 15 programming practical exercises involving calculations of volumes, areas, temperature conversions, exponents, GCD, interest, averages, accelerations, and determining even and prime numbers within ranges. For each practical, it provides the step-by-step algorithm, corresponding flowchart, and C code to solve the given programming problem. It also includes the output for each code example.
Tarikul islam -205031.docx
The document describes an experiment to use ternary operators to determine if an integer is positive, negative, or equal to zero, and to find the greatest of three integers. The experiment objective is to learn about relational and logical operators and how ternary operators can solve real-life problems. The experiment involves writing code using ternary operators and necessary conditions to compare integers and output the results. The programs were successfully executed and verified.
40120140502003
This document summarizes an article from the International Journal of Electronics and Communication Engineering & Technology about generating cryptographically secure pseudo-random numbers using an FPGA. It discusses different methods of random number generation including pseudo-random number generators (PRNGs) and true random number generators (TRNGs). It also describes the Blum Blum Shub generator for generating cryptographically secure pseudo-random numbers and its implementation on a Virtex-5 FPGA using VHDL. Simulation results are shown validating the random number generation using CORDIC and ChipScope Pro.
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEM
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
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留信网认证的作用:
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3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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3:国家专业人才认证中心颁发入库证书
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Non sequitur
- 1. Non Sequitur An exploration of Python's random module Jair Trejo, EuroPython 2014
- 2. Jair Trejo Director of Operations at Vinco Orbis
- 9. Simulation
- 12. 4.77 Counter({ '9': 14, '2': 12, '8': 12, '3': 11, '4': 10, '6': 9, '1': 8, '0': 8, '5': 8, '7': 8})
- 23. 7638, 3390, 4921, 2162, 6742, 4545, 6570, 1649, 7192, 7248, 5335, 4622, 3628, 1623, 6341, 2082, 3347, 2024, 965, 3122, 7468, 7710, 4441, 7224, 1861, 4633, 4646, 5853, 2576, 6357, 4114, 9249, 5440, 5936, 2360, 5696, 4444, 7491, 1150, 3225, 4006, 480, 3040, 2416, 8370, 569, 2376, 6453, 6412, 1137, 2927, 5673, 1829, 3452, 9163, 9605, 2560, 5536, 6472, 8867, 6236, 8876, 7833, 3558, 6593, 4676, 8649, 8052, 8347, 6724, 2121, 4986, 8601, 9772, 4919, 1965, 8612, 1665, 7722, 6292, 5892, 7156, 2083, 3388, 4785, 8962, 3174, 742, 5056, 5631, 7081, 1405, 9740, 8676, 2729, 4474, 166, 2755, 5900, 8100
- 26. 7, 23, 41, 59, 89
- 36. Python’s random module source code
- 39. import random
- 42. r.randrange(start, stop) start + r.randrange(stop - start)
- 48. sorted(a_list, key=lambda e: r.random())
- 58. Conclusions • The definition of randomness is more a philosophical than a mathematical problem. • But we can use mathematical definitions that are useful for our purposes. • If we need sequences that are deterministic, but behave as if random, we can use pseudo-random number generation. • If we need numbers that are completely unpredictable, we need sources of entropy like input devices, noise measurements or other external sources. • For most of our random number needs, python provides more than adequate capabilities.
- 59. References
- 60. 10 PRINT CHR$(205.5+RND(1)); : GOTO 10 By Nick Montfort, Patsy Baudoin, John Bell, Ian Bogost, Jeremy Douglass, Mark C. Marino, Michael Mateas, Casey Reas, Mark Sample and Noah Vawter
- 61. The art of computer programming, Volume 2 Seminumerical Algorithms
- 62. On randomness in cryptography! http://blog.cloudflare.com/why-randomness-matters On random number generators testing! http://www.fourmilab.ch/hotbits/statistical_testing/stattest.html On the possible NSA backdoor into RSA’s random number generator! http://arstechnica.com/security/2014/01/how-the-nsa-may-have-put-a-backdoor rsas-cryptography-a-technical-primer/
- 63. Thank you!