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dimensionalityreduction.jl

Methods for dimensionality reduction

dingdang-robot

叮当是一款可以工作在 Raspberry Pi 上的中文语音对话机器人/智能音箱项目。

disc_align

linguistic alignment and discourse

discogan_tf

Tensorflow Implementation of DiscoGAN

discoursesenser

Sense Disambiguation of Connectives for PDTB-style Discourse Parsing

discriminative-patches

Unsupervised Discovery of Mid-Level Discriminative Patches

distributional-soft-actor-critic

distro

Torch installation in a self-contained folder

django

The Web framework for perfectionists with deadlines.

dkpro-similarity

Word and text similarity measures

dl-models-for-qa

Keras DL models to answer 8th grade science multiple choice questions (Kaggle AllenAI competition).

dl4g

Example code for the Siggraph Asia Tutorial CreativeAI

dlf

dlib

A toolkit for making real world machine learning and data analysis applications in C++

dltk

Deep Learning Toolkit for Medical Image Analysis

dmtn

dne

A set of neuroevolution experiments with/towards deep networks

dnn-for-speech-enhancement

DNN-for-speech-enhancement

dnsspoofanddetect

develop 1) an on-path DNS packet injector, and 2) a passive DNS poisoning attack detector. Part 1: The DNS packet injector you are going to develop, named 'dnsinject', will capture the traffic from a network interface in promiscuous mode, and attempt to inject forged responses to selected DNS A requests with the goal to poison the resolver's cache. Your program should conform to the following specification: dnsinject [-i interface] [-h hostnames] expression -i Listen on network device <interface> (e.g., eth0). If not specified, dnsinject should select a default interface to listen on. The same interface should be used for packet injection. -h Read a list of IP address and hostname pairs specifying the hostnames to be hijacked. If '-h' is not specified, dnsinject should forge replies for all observed requests with the local machine's IP address as an answer. <expression> is a BPF filter that specifies a subset of the traffic to be monitored. This option is useful for targeting a single or a set of particular victims. The <hostnames> file should contain one IP and hostname pair per line, separated by whitespace, in the following format: 10.6.6.6 foo.example.com 10.6.6.6 bar.example.com 192.168.66.6 www.cs.stonybrook.edu Pay attention to the time needed for generating the spoofed response! Your code should be fast enough so that the injected reply reaches the victim sooner than the server's actual response. The spoofed packet and content should also be valid according to the initial DNS request, and the forged response should be accepted and processed normally by the victim. Part 2: The DNS poisoning attack detector you are going to develop, named 'dnsdetect', will capture the traffic from a network interface in promiscuous mode and detect DNS poisoning attack attempts, such as those generated by dnsinject. Detection will be based on identifying duplicate responses towards the same destination that contain different answers for the same A request, i.e., the observation of the attacker's spoofed response followed by the server's actual response. You should make every effort to avoid false positives, e.g., due to legitimate consecutive responses with different IP addresses for the same hostname due to round robin DNS load balancing. Your program should conform to the following specification: dnsdetect [-i interface] [-r tracefile] expression -i Listen on network device <interface> (e.g., eth0). If not specified, the program should select a default interface to listen on. -r Read packets from <tracefile> (tcpdump format). Useful for detecting DNS poisoning attacks in existing network traces. <expression> is a BPF filter that specifies a subset of the traffic to be monitored. Once an attack is detected, dnsdetect should print to stdout a detailed alert containing a printout of both the spoofed and legitimate responses. You can format the output in any way you like. Output must contain the detected DNS transaction ID, attacked domain name, and the original and malicious IP addresses - for example: 20160406-15:08:49.205618 DNS poisoning attempt TXID 0x5cce Request www.example.com Answer1 [List of IP addresses] Answer2 [List of IP addresses]

doa

DOA

doc-han-att

Hierarchical Attention Networks for Chines Sentiment Classification

doc2text

Detect text blocks and OCR poorly scanned PDFs in bulk. Python module available via pip.

doc2vec

Tutorial and review of word2vec / doc2vec

doc2vec-1

doc2vec_pymongo

Machine learning prediction of movies genres using Gensim's Doc2Vec and PyMongo - (Python, MongoDB)

docker

Docker - the open-source application container engine

docker-1

This repository contains Dockerfiles for building Docker images of popular malware analysis tools, which are distributed through the REMnux repository on Docker Hub.

docker-tensorflow-serving-slim

A docker image that serves google's slim models, e.g., inception-v4, inception-resnet-v2, via tensorflow serving.

docrec

Keyword extraction and document recommendation in conversations

domain-adaptation

Code for the [ACL 2014 paper]