19,997 machine learning datasets
19,997 dataset results
We aim to improve the bAbI benchmark as a means of developing intelligent dialogue agents. To this end, we propose concatenated-bAbI (catbAbI): an infinite sequence of bAbI stories. catbAbI is generated from the bAbI dataset and during training, a random sample/story from any task is drawn without replacement and concatenated to the ongoing story. The preprocessig for catbAbI addresses several issues: it removes the supporting facts, leaves the questions embedded in the story, inserts the correct answer after the question mark, and tokenises the full sample into a single sequence of words. As such, catbAbI is designed to be trained in an autoregressive way and analogous to closed-book question answering.
This data set includes all raw data (e.g., collected certificates) of the WWW 2021 paper "Security of Alerting Authorities in the WWW: Measuring Namespaces, DNSSEC, and Web PKI".
This includes all data from the ACM IMC 2018 paper "The Rise of Certificate Transparency and Its Implications on the Internet Ecosystem".
The sRGB2XYZ dataset contains ~1,200 pairs of camera-rendered sRGB and the corresponding scene-referred CIE XYZ images (971 training, 50 validation, and 244 testing images).
Landscape Dataset consists of landscape images collected from Flickr.
A portrait dataset of images collected from Flickr.
This dataset includes Direct Borohydride Fuel Cell (DBFC) impedance and polarization test in anode with Pd/C, Pt/C and Pd decorated Ni–Co/rGO catalysts. In fact, different concentration of Sodium Borohydride (SBH), applied voltages and various anode catalysts loading with explanation of experimental details of electrochemical analysis are considered in data. Voltage, power density and resistance of DBFC change as a function of weight percent of SBH (%), applied voltage and amount of anode catalyst loading that are evaluated by polarization and impedance curves with using appropriate equivalent circuit of fuel cell. Can be stated that interpretation of electrochemical behavior changes by the data of related cell is inevitable, which can be useful in simulation, power source investigation and depth analysis in DB fuel cell researches.
The ground truth betweenness-centralities for the real-world graphs are provided by AlGhamdi et al. (2017), which are computed by the parallel implementation of Brandes algorithm on a 96000-core supercomputer. The ground truth scores for the synthetic networks are provided by Fan et al. (2019) and are computed using the graph-tool (Peixoto, 2014) library.
This dataset contains samples to generate Python code for security exploits. In order to make the dataset representative of real exploits, it includes code snippets drawn from exploits from public databases. Differing from general-purpose Python code found in previous datasets, the Python code of real exploits entails low-level operations on byte data for obfuscation purposes (i.e., to encode shellcodes). Therefore, real exploits make extensive use of Python instructions for converting data between different encoders, for performing low-level arithmetic and logical operations, and for bit-level slicing, which cannot be found in the previous general-purpose Python datasets. In total, we built a dataset that consists of 1,114 original samples of exploit-tailored Python snippets and their corresponding intent in the English language. These samples include complex and nested instructions, as typical of Python programming. In order to perform more realistic training and for a fair evaluat
This is an assembly dataset built on top of Shellcode_IA32, a dataset for automatically generating assembly from natural language descriptions that consists of 3,200 assembly instructions, commented in the English language, which were collected from shellcodes for IA-32 and written for the Netwide Assembler (NASM) for Linux. In order to make the data more representative of the code that we aim to generate (i.e., complete exploits, inclusive of decoders to be delivered in the shellcode), we enriched the dataset with further samples of assembly code, drawn from the exploits that we collected from public databases. Different from the previous dataset, the new one includes assembly code from real decoders used in actual exploits. The final dataset contains 3,715 unique pairs of assembly code snippets/English intents. To better support developers in the automatic generation of the assembly programs, we looked beyond a one-to-one mapping between natural language intents and their correspond
This dataset contains 1125 X-ray images of the studied individuals’ chests, including 125 images labeled as COVID-19, 500 images labeled as pneumonia, and 500 images labeled as no findings.
BioLeaflets is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website. Package leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately, under the guidance of their healthcare professional. Each document contains six sections: 1) What is the product and what is it used for 2) What you need to know before you take the product 3) product usage instructions 4) possible side effects, 5) product storage conditions 6) other information.
PMPC (Persona Match on Persona-Chat) is a dataset for Speaker Persona Detection (SPD) which aims to detect speaker personas based on the plain conversational text.
FunKPoint is a dataset for finding correspondences in visual data that has ground truth correspondences for 10 tasks and 20 object categories.
The MFH dataset is a multi-viewpoint fine-grained hand hygiene dataset. It contains 73,1147 samples in total, which are collected by 6 camera views in 6 different locations. All samples are split into 7 classes in total. MFH dataset is distinguished from existing datasets in three aspects: the large intra-class difference, the subtle inter-class difference, and the data mismatch in distribution between the training phase and the inference phase. This dataset thus provides a more realistic benchmark.
WhyAct is a dataset for identifying human action reasons in online videos, consisting of 1,077 visual actions manually annotated with their reasons.
TREx-2p is a dataset to probe whether a pretrained LM possesses “indirect” 2-hop knowledge. It is a 2-hop variant of the T-REx dataset. It has been built by manually examining the 2-hop link existing in the knowledge graph of TREx-1p, and select eight 2- hop relation types that make sense to humans
A dataset of A 3D Computed Tomography (CT) image dataset, ImageTBAD, for segmentation of Type-B Aortic Dissection is published. ImageTBAD contains 100 3D Computed Tomography (CT) images, which is of decent size compared with existing medical imaging datasets.
ComSum is a data set of 7 million commit messages for text summarization. When documenting commits, software code changes, both a message and its summary are posted. These messages are gathered and filtered to curate developers' work summarization data set.
Indiscapes2, a new large-scale diverse dataset of Indic manuscripts with semantic layout annotations. Indiscapes2 contains documents from four different historical collections and is 150% larger than its predecessor, Indiscapes.