|
l8w8jwt
|
Core DECODE function for l8w8jwt. Use this to decode and validate a JWT! More...
#include "algs.h"
#include "claim.h"
#include "version.h"
#include "retcodes.h"
#include <time.h>
#include <stddef.h>
#include <stdint.h>
Go to the source code of this file.
Classes | |
| struct | l8w8jwt_decoding_params |
Macros | |
| #define | L8W8JWT_MAX_KEY_SIZE 8192 |
Enumerations | |
| enum | l8w8jwt_validation_result { L8W8JWT_VALID = (unsigned)0, L8W8JWT_ISS_FAILURE = (unsigned)1 << (unsigned)0, L8W8JWT_SUB_FAILURE = (unsigned)1 << (unsigned)1, L8W8JWT_AUD_FAILURE = (unsigned)1 << (unsigned)2, L8W8JWT_JTI_FAILURE = (unsigned)1 << (unsigned)3, L8W8JWT_EXP_FAILURE = (unsigned)1 << (unsigned)4, L8W8JWT_NBF_FAILURE = (unsigned)1 << (unsigned)5, L8W8JWT_IAT_FAILURE = (unsigned)1 << (unsigned)6, L8W8JWT_SIGNATURE_VERIFICATION_FAILURE = (unsigned)1 << (unsigned)7, L8W8JWT_TYP_FAILURE = (unsigned)1 << (unsigned)8 } |
Functions | |
| L8W8JWT_API void | l8w8jwt_decoding_params_init (struct l8w8jwt_decoding_params *params) |
| L8W8JWT_API int | l8w8jwt_validate_decoding_params (struct l8w8jwt_decoding_params *params) |
| L8W8JWT_API int | l8w8jwt_decode (struct l8w8jwt_decoding_params *params, enum l8w8jwt_validation_result *out_validation_result, struct l8w8jwt_claim **out_claims, size_t *out_claims_length) |
Core DECODE function for l8w8jwt. Use this to decode and validate a JWT!
Enum containing the validation result flags.
| L8W8JWT_API int l8w8jwt_decode | ( | struct l8w8jwt_decoding_params * | params, |
| enum l8w8jwt_validation_result * | out_validation_result, | ||
| struct l8w8jwt_claim ** | out_claims, | ||
| size_t * | out_claims_length | ||
| ) |
Decode (and validate) a JWT using specific parameters.
The resulting l8w8jwt_validation_result written into the passed "out_validation_result" pointer contains validation failure flags (see the l8w8jwt_validation_result enum docs for more details).
This only happens if decoding also succeeded: if the token is malformed, nothing will be written into "out_validation_result".
If validation succeeds, the l8w8jwt_validation_result receives the value 0 (enum value L8W8JWT_VALID
).
The same applies to the "out_claims" argument: it is only allocated and written to if it (obviously) isn't NULL
and if the decoding was also successful!
| params | The parameters to use for decoding and validating the token. |
| out_validation_result | Where to write the validation result flags into (0 means success). In case of a decoding failure this is set to -1 (or ~L8W8JWT_VALID )! |
| out_claims | [OPTIONAL] Where the decoded claims (header + payload claims together) should be written into. This pointer will be dereferenced + allocated, so make sure to pass a fresh pointer! If you don't need the claims, set this to NULL (they will only be validated, e.g. signature, exp, etc...). Check the note down below for more infos! |
| out_claims_length | Where to write the decoded claims count into. This will receive the value of how many claims were written into "out_claims" (0 if you decided to set "out_claims" to NULL ). |
out_claims
parameter, REMEMBER to call l8w8jwt_free_claims() on it once you're done using them!| L8W8JWT_API void l8w8jwt_decoding_params_init | ( | struct l8w8jwt_decoding_params * | params | ) |
Initializes a l8w8jwt_decoding_params instance by setting its fields to default values.
| params | The l8w8jwt_decoding_params to initialize (set to default values). |
| L8W8JWT_API int l8w8jwt_validate_decoding_params | ( | struct l8w8jwt_decoding_params * | params | ) |
Validates a set of l8w8jwt_decoding_params.
| params | The l8w8jwt_decoding_params to validate. |


Musician, sound designer, lecturer, listener, and occasional moderator based in Toronto. Extremely tall, gray-haired male.
Available for hire and collaborations.
Drop me a line at clever.disdain @ gmail . com
Musician, sound designer, lecturer, listener, and occasional moderator based in Toronto. Extremely tall, gray-haired male.
Available for hire and collaborations.
Drop me a line at clever.disdain @ gmail . com


JSDraw/HELM Editor
(HELM Module is co-developed with
Pisotia
)
Copyright Scilligence, 2016 (
http://www.scilligence.com/
)


Introduction
This book is the official Apache Guacamole manual, written by the upstream developers of the Guacamole project. It is also the official general documentation, with an online version available at http://guacamole.apache.org/. It is a work in progress which will be continuously updated as Guacamole changes with each release.
We decided to maintain the documentation for Guacamole as a book, as there is an awful lot that can be done with the Guacamole web application, and even more that can be done with the API. This book is intended to explore the possibilities of Guacamole as an application, and to provide documentation necessary to install, maintain, and use Guacamole.
For the sake of users and administrators, we have provided a high-level overview of Guacamole’s architecture and technical design, as well as basic usage instructions and installation instructions for common platforms.
For the sake of developers, we have provided a protocol reference and tutorials for common tasks (implementing protocol support, integrating Guacamole into your own application, etc.) to give a good starting point beyond simply looking at the Guacamole codebase.
This particular edition of the Guacamole Manual covers Guacamole version 1.5.0. New releases which create new features or break compatibility will result in new editions of the user’s guide, as will any necessary corrections. As the official documentation for the project, this book will always be freely available in its entirety online.
What is Guacamole?
Guacamole is an HTML5 web application that provides access to desktop environments using remote desktop protocols (such as VNC or RDP). Guacamole is also the project that produces this web application, and provides an API that drives it. This API can be used to power other similar applications or services.
“Guacamole” is most commonly used to refer to the web application produced by the Guacamole project using their API. This web application is part of a stack that provides a protocol-agnostic remote desktop gateway. Written in JavaScript and using only HTML5 and other standards, the client part of Guacamole requires nothing more than a modern web browser or web-enabled device when accessing any of the desktops served.
Historically, Guacamole was an HTML5 VNC client, and before that, a JavaScript Telnet client called RealMint (“RealMint” is an anagram for “terminal”), but this is no longer the case. Guacamole’s architecture has grown to encompass remote desktop in general, and can be used as a gateway for any number of computers. Originally a proof-of-concept, Guacamole is now performant enough for daily use, and all Guacamole development is done over Guacamole.
As an API, Guacamole provides a common and efficient means of streaming text data over a JavaScript-based tunnel using either HTTP or WebSocket, and a client implementation which supports the Guacamole protocol and renders the remote display when combined with a Guacamole protocol stream from the tunnel.
It provides cross-browser mouse and keyboard events, an XML-driven on-screen keyboard, and synchronized nestable layers with hardware-accelerated compositing. Projects that wish to provide remote desktop support over HTML5 can leverage the years of research and development that went into Guacamole by incorporating the API into their application or service.
Why use Guacamole?
The principle reason to use Guacamole is constant, world-wide, unfettered access to your computers.
Guacamole allows access one or more desktops from anywhere remotely, without having to install a client, particularly when installing a client is not possible. By setting up a Guacamole server, you can provide access to any other computer on the network from virtually any other computer on the internet, anywhere in the world. Even mobile phones or tablets can be used, without having to install anything.
As a true web application whose communication is over HTTP or HTTPS only, Guacamole allows you to access your machines from anywhere without violating the policy of your workplace, and without requiring the installation of special clients. The presence of a proxy or corporate firewall does not prevent Guacamole use.
Access your computers from any device
As Guacamole requires only a reasonably-fast, standards-compliant browser, Guacamole will run on many devices, including mobile phones and tablets.
Guacamole is specifically designed to not care whether you have a mouse, keyboard, touchscreen, or any combination of those.
One of the major design philosophies behind Guacamole is that it should never assume you have a particular device (ie: a mobile phone) just because your browser has or is missing a specific feature (ie: touch events or a smallish screen). Guacamole’s codebase provides support for both mouse and touch events simultaneously, without choosing one over the other, while the interface is intended to be usable regardless of screen size.
Barring bugs, you should be able to use Guacamole on just about any modern device with a web browser.
Keep a computer in the “cloud”
Ignoring the buzzword, it’s often useful to have a computer that has no dedicated physical hardware, where its processing and storage power are handled transparently by redundant systems in some remote datacenter.
Computers hosted on virtualized hardware are more resilient to failures, and with so many companies now offering on-demand computing resources, Guacamole is a perfect way to access several machines that are only accessible over the internet.
In fact, all Guacamole development is done on computers like this. This is partly because we like the mobility, and partly because we want to ensure Guacamole is always performant enough for daily use.
Provide easy access to a group
Guacamole allows you to centralize access to a large group of machines, and specify on a per-user basis which machines are accessible. Rather than remember a list of machines and credentials, users need only log into a central server and click on one of the connections listed.
If you have multiple computers which you would like to access remotely, or you are part of a group where each person has a set of machines that they need remote access to, Guacamole is a good way to provide that access while also ensuring that access is available from anywhere.
Adding HTML5 remote access to your existing infrastructure
As Guacamole is an API, not just a web application, the core components and libraries provided by the Guacamole project can be used to add HTML5 remote access features to an existing application. You need not use the main Guacamole web application; you can write (or integrate with) your own rather easily.
If you host an on-demand computing service, adding HTML5-based remote access allows users of your service more broad access; users need nothing more than a web browser to see their computers’ screens.


org.flixel.system.input.Touch
- type
- class
- extends
- org.flixel.FlxPoint
Helper class, contains and track touch points in your game.
Automatically accounts for parallax scrolling, etc.
Constructor
- Parameters:
X
- stageX touch coordinate
Y
- stageX touch coordinate
PointID
- touchPointID of the touch
- Parameters:
Point
- Any FlxPoint
.
- Returns:
- A reference to itself.
- Parameters:
Point
- Any Point
.
- Returns:
- A reference to itself.
- Parameters:
Point
- Any FlxPoint
.
- Returns:
- A reference to the altered point parameter.
- Parameters:
Point
- Any Point
.
- Returns:
- A reference to the altered point parameter.
- Parameters:
Camera
- If unspecified, first/main global camera is used instead.
point
- An existing point object to store the results (if you don't want a new one created).
- Returns:
- The touch point's location in screen space.
- Parameters:
Camera
- If unspecified, first/main global camera is used instead.
point
- An existing point object to store the results (if you don't want a new one created).
- Returns:
- The touch point's location in world space.
- Returns:
- Whether the touch is active.
- Returns:
- Whether the touch was just began.
- Returns:
- Whether the touch was just ended.
- Parameters:
X
- The X-coordinate of the point in space.
Y
- The Y-coordinate of the point in space.
- Returns:
- Whether the touch is pressed.
- Parameters:
X
- stageX touch coordinate
Y
- stageY touch coordinate


For faster mobile-friendly development, use these utility classes for showing and hiding content by device via media query. Also included are utility classes for toggling content when printed.
Try to use these on a limited basis and avoid creating entirely different versions of the same site. Instead, use them to complement each device’s presentation.
Contents
Available classes
- The
.hidden-*-up
classes hide the element when the viewport is at the given breakpoint or wider. For example,.hidden-md-up
hides an element on medium, large, and extra-large viewports. - The
.hidden-*-down
classes hide the element when the viewport is at the given breakpoint or smaller. For example,.hidden-md-down
hides an element on extra-small, small, and medium viewports. - There are no explicit “visible”/”show” responsive utility classes; you make an element visible by simply not hiding it at that breakpoint size.
- You can combine one
.hidden-*-up
class with one.hidden-*-down
class to show an element only on a given interval of screen sizes. For example,.hidden-sm-down.hidden-xl-up
shows the element only on medium and large viewports. Using multiple.hidden-*-up
classes or multiple.hidden-*-down
classes is redundant and pointless. - These classes don’t attempt to accommodate less common cases where an element’s visibility can’t be expressed as a single contiguous range of viewport breakpoint sizes; you will instead need to use custom CSS in such cases.
| Extra small devices Portrait phones (<544px) | Small devices Landscape phones (≥544px - <768px) | Medium devices Tablets (≥768px - <992px) | Large devices Desktops (≥992px - <1200px) | Extra large devices Desktops (≥1200px) | |
|---|---|---|---|---|---|
.hidden-xs-down |
Hidden | Visible | Visible | Visible | Visible |
.hidden-sm-down |
Hidden | Hidden | Visible | Visible | Visible |
.hidden-md-down |
Hidden | Hidden | Hidden | Visible | Visible |
.hidden-lg-down |
Hidden | Hidden | Hidden | Hidden | Visible |
.hidden-xl-down |
Hidden | Hidden | Hidden | Hidden | Hidden |
.hidden-xs-up |
Hidden | Hidden | Hidden | Hidden | Hidden |
.hidden-sm-up |
Visible | Hidden | Hidden | Hidden | Hidden |
.hidden-md-up |
Visible | Visible | Hidden | Hidden | Hidden |
.hidden-lg-up |
Visible | Visible | Visible | Hidden | Hidden |
.hidden-xl-up |
Visible | Visible | Visible | Visible | Hidden |
Print classes
Similar to the regular responsive classes, use these for toggling content for print.
| Class | Browser | |
|---|---|---|
.visible-print-block |
Hidden | Visible (as display: block ) |
.visible-print-inline |
Hidden | Visible (as display: inline ) |
.visible-print-inline-block |
Hidden | Visible (as display: inline-block ) |
.hidden-print |
Visible | Hidden |
Test cases
Resize your browser or load on different devices to test the responsive utility classes.


Publications
2023
- Guanqun Cao, Jiaqi Jiang, Danushka Bollegala, and Shan Luo
Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders,
Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October, 2023.˜˜
- Samantha Durdy, Michael W. Gaultois, Vladimir Gusev, Danushka Bollegala and Matthew J. Rosseinsky
Metrics for quantifying isotropy in high dimensional unsupervised clustering tasks in a materials context
, arXiv (per-print), May, 2023.
URL
- Xiohang Tang, Yi Zhou and Danushka Bollegala:
Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation,
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023), Toronto, Canada, July, 2023.
URL
- Haochen Luo, Yi Zhou and Danushka Bollegala:
Together We Make Sense--Learning Meta-Sense Embeddings,
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Findings of ACL 2023), Toronto, Canada, July, 2023.
URL
- Taichi Aida and Danushka Bollegala:
Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings,
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Findings of ACL 2023), Toronto, Canada, July, 2023.
URL
- Saeth Wannasuphoprasit, Yi Zhou and Danushka Bollegala:
Solving Cosine Similarity Underestimation between High Frequency Words by l2 Norm Discounting ,
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Findings of ACL 2023 Short Papers), Toronto, Canada, July, 2023.
URL
- Yoichi Ishibashi, Danushka Bollegala, Katsuhito Sudoh and Satoshi Nakamura:
Evaluating the Robustness of Discrete Prompts,
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), Croatia, May, 2023.
URL
- Masahiro Kaneko, Danushka Bollegala and Naoaki Okazaki:
Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples,
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), Croatia, May, 2023.
URL
- Guanqun Cao, Jiaqi Jiang, Ningtao Mao, Danushka Bollegala, Min Li, and Shan Luo:
Vis2Hap: Vision-based Haptic Rendering by Cross-modal Generation,
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2023), UK, May, 2023.
URL
2022
- Lauren E Walker, Aseel S Abuzour, Danushka Bollegala, Andrew Clegg, Mark Gabbay, Alan Griffiths, Cecil Kullu, Gary Leeming, Frances S Mair, Simon Maskell, Samuel Relton, Roy A Ruddle, Eduard Shantsila, Matthew Sperrin, Tjeerd Van Staa, Alan Woodall and Iain Buchan:
The DynAIRx Project Protocol: Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity,
URL
- Asahi Ushio and Danushka Bollegala:
Zero-shot cross-lingual counterfactual detection via automatic extraction and prediction of clue phrases,
Proc. of the EMNLP 2022 Workshop on Multilingual Repersentation Learning, Abu Dhabi, December, 2022.
URL
- Yi Zhou and Danushka Bollegala:
On the Curious case of l2 norm of Sense Embeddings,
Proc. of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi, December, 2022.
URL
- Masahiro Kaneko, Danushka Bollegala, and Naoaki Okazaki:
Gender Bias in Meta-Embedding, Proc. of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi, December, 2022.
URL
- Katie Atkinson and Danushka Bollegala:
AI for Patent Essentiality Review
Social Science Research Network (SSRN), November, 2022.
URL
- Masahiro Kaneko, Danushka Bollegala and Naoaki Okazaki:
Debiasing isn't enough! -- On the Effectiveness of Debiasing MLMs and their Social Biases in Downstream Tasks,
Proc. of the 29th International Conference on Computational Linguistics (COLING 2022), Gyeongju, Republic of Korea, October, 2022.
URL
- Tadashi Tsubota, Danushka Bollegala, Yang Zhao, Yingzi Jin and Tomotake Kozu:
Improvement of intervention information detection for automated clinical literature screening during systematic review,
Journal of Biomedical Informatics, August, 2022.
URL
- Samantha Durdy, Michael Gaultois, Vladimir Gusev, Danushka Bollegala and Matthew J. Rosseinsky:
Random projections and Kernelised Leave One Cluster Out Cross-Validation: Universal baselines and evaluation tools for supervised machine learning for materials properties, Digital Discovery, 2022.
URL
- Danushka Bollegala:
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings,
Proc. of 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI), Vienna, Austria, July, 2022.
URL
- Danushka Bollegala and James O'Neill:
A Survey on Word Meta-Embedding Learning,
Proc. of 31st International Joint Conference on Artificial Intelligence (IJCAI-ECAI), Vienna, Austria, July, 2022.
URL
- Masahiro Kaneko, Aizhan Imankulova, Danushka Bollegala and Naoaki Okazaki:
Gender Bias in Masked Language Models for Multiple Languages ,
Proc. of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Seattle, USA, 2022.
URL
- Huda Hakami, Mona Hakami, Angrosh Mandya and Danushka Bollegala:
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion,
Proc. of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Seattle, USA, 2022.
URL
- Danushka Bollegala, Tomoya Machide and Ken-ichi Kawarabayashi:
Query Obfuscation by Semantic Decomposition,
Proc. of the 13th Language Resources and Evaluation Conference (LREC), Marseille, France, 2022.
URL
- Keigo Takahashi and Danushka Bollegala:
Unsupervised Attention-based Sentence-Level Meta-Embeddings from Contextualised Language Models,
Proc. of the 13th Language Resources and Evaluation Conference (LREC), Marseille, France, 2022.
URL
- Micheal Abaho, Danushka Bollegala, Paula Williamson and Susanna Dodd:
Position-based Prompting for Health Outcome Generation,
Proc. of The 21st BioNLP workshop associated with the ACL SIGBIOMED special interest group, Dublin, Ireland, 2022.
URL
- Yi Zhou, Masahiro Kaneko and Danushka Bollegala:
Sense Embeddings are also Biased -- Evaluating Social Biases in Static and Contextualised Sense Embeddings,
Proc. of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 2022.
URL
- Masahiro Kaneko and Danushka Bollegala:
Unmasking the Mask -- Evaluating Social Biases in Masked Language Models,
Proc. of the 36th AAAI Conference on Artificial Intelligence, Vancouver, Canada, 2022.
URL
2021
- Micheal Abaho, Danushka Bollegala, Paula Williamson and Susanna Dodd:
Assessment of contextualised representations in detecting outcome phrases in clinical trials,
European Journal for Biomedical Informatics, Volume 17, Issue 9, September, 2021.
URL
- Mohammed Alsuhaibani and Danushka Bollegala:
Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications,
Knowledge-Based Intelligent Systems in E-Health and Medical Communication Services, December, 2021.
URL
- Yi Zhou and Danushka Bollegala:
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy,
Proc. of the 35th Pacific Asia Conference on Language, Information and Computation (PACLIC), November, 2021.
URL
- Masaru Isonuma, Danushka Bollegala, Junichiro Mori and Ichiro Sakata:
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance, Transactions of the Association for Computational Linguistics (TACL) (and presented at EMNLP), Volume 9, pp. 945-961, August, 2021.
URL
- Michael Abaho, Danushka Bollegala, Paula Williamson and Susanna Dodd:
Detect and Classify -- Joint Span Detection and Classification for Health Outcomes,
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 8709-8721, 2021.
URL
- James O'Neill, Polina Rozenshtein, Ryuichi Kiryo, Motoko Kubota and Danushka Bollegala:
I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews,
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7092-7108, 2021.
URL
- Mikhail Fain, Niall Twomey and Danushka Bollegala:
Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora, Proc. of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Online, July, 2021.
URL
- Iqra Muhammad, Frans Coenon and Danushka Bollegala:
Document Ranking for Curated Document Databases using BERT and Knowledge Graph Embeddings: Introducing GRAB-Rank,
Proc. of the 23rd International Conference on Big Data Analytics and Knowledge Discovery (DAWAK), September, 2021.
URL
- James O'Neill and Danushka Bollegala:
Semantically-Conditioned Negative Samples for Efficient Contrastive Learning,
arXiv preprint, 2021.
URL
- Danushka Bollegala, Huda Hakami, Yuichi Yoshida and Ken-ichi Kawarabayashi:
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding,
Proc. of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), April, 2021.
URL
- Masahiro Kaneko and Danushka Bollegala:
Debiasing Pre-trained Contextualised Embeddings,
Proc. of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), April, 2021.
URL
- Masahiro Kaneko and Danushka Bollegala:
Dictionary-based Debiasing of Pre-trained Word Embeddings,
Proc. of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), April, 2021.
URL
- Atsumu Harada, Danushka Bollegala and Naiwala P. Chandrasiri:
Discrimination of Human-Written and Human and Machine-written Sentences using Text Consistency,
Proc. of IEEE International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), February, 2021.
URL
2020
- Angrosh Mandya, Danushka Bollegala and Frans Coenon:
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction
Proc. of the 28th International Conference on Computational Linguistics (COLING), December, 2020.
URL
- Masahiro Kaneko and Danushka Bollegala:
Autoencoding Improves Pre-trained Word Embeddings
28th International Conference on Computational Linguistics (COLING), December, 2020.
URL
- Katie Atkinson, Trevor Bench-Capon and Danushka Bollegala:
Explanation in AI and Law: Past, Present and Future
Artificial Intelligence, Volume 289, December, 2020.
DOI
- Yi Zhou and Danushka Bollegala:
Predicting the quality of translations without an oracle
In Communications in Computer and Information Science, pages 3–23. Springer International Publishing, 2020.
URL
- Yash Khemchandani, Steve O'Hagan, Soumitra Samanta, Neil Swainston, Timothy J Roberts, Danushka Bollegala and Douglas Kell:
DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach
Journal of Cheminformatics, 2020.
DOI
- Guanqun Cao, Yi Zhou, Danushka Bollegala, Shan Luo:
Spatio-temporal Attention Model for Tactile Texture Recognition Spatio-temporal Attention Model for Tactile Texture Recognition
Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020.
URL
- Asir Saeed, Khai Mai, Pham Minh, Nguyen Tuan Duc, Danushka Bollegala:
Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents
arXiv pre-print, May, 2020.
URL
- Angrosh Mandya, Danushka Bollegala and Frans Coenon:
Contextualised Graph Attention for Improved Relation Extraction
arXiv pre-print, April, 2020.
URL
- Xia Cui and Danushka Bollegala:
Multi-source Attention for Unsupervised Domain Adaptation
1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP), December, 2020.
URL
- Masaru Isonuma, Junichiro Mori, Danushka Bollegala and Ichiro Sakata:
Tree-Structured Neural Topic Model
Proc. of the 20th Annual Conference of the Association for Computational Linguistics (ACL), July, 2020.
URL
- Danushka Bollegala, Ryuichi Kiryo, Kosuke Tsujino and Haruki Yukawa:
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction
Proc. of the 12th International Conference on Language Resources and Evaluation (LREC), May, 2020.
URL
- Angrosh Mandya, James O' Neill, Danushka Bollegala and Frans Coenen:
Do not let the history haunt you: Mitigating Compounding Errors in Conversational Question Answering
Proc. of the 12th International Conference on Language Resources and Evaluation (LREC), May, 2020.
URL
- James O'Neill and Danushka Bollegala:
Meta-Embedding as Auxiliary Task Regularization
Proc. of the 24th European Conference on Artificial Intelligence (ECAI), June, 2020.
URL
2019
- Alessandro Torrisi, Robert Bevan, Danushka Bollegala, Katie Atkinson and Frans Coenon:
Combining Textual and Visual Information for Typed and Handwritten Text Separation in Legal Documents
Proc. of the 32nd International Conference on Legal Knowledge and Information Systems (JURIX), December, 2019.
URL
- Mikhail Fain, Andrey Ponikar, Ryan Fox and Danushka Bollegala:
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA
arXiv, 2019.
URL
- James O'Neill and Danushka Bollegala:
Learning to Evaluate Neural Language Models
Proc. of the 16th International Conference of the Pacific Association for Computational Linguistics (PACLING), October, 2019.
URL
- Wenye Chen, Huda Hakami and Danushka Bollegala:
Learning to Compose Relational Embeddings in Knowledge Graphs
Proc. of the 16th International Conference of the Pacific Association for Computational Linguistics (PACLING), October, 2019.
URL
- Huda Hakami and Danushka Bollegala:
Context-guided Self-Supervised Relation Embeddings
Proc. of the 16th International Conference of the Pacific Association for Computational Linguistics (PACLING), October, 2019.
URL
- Pavithra Rajendran, Danushka Bollegala and Simon Parsons:
A Pilot Study on Argument Simplification in Stance-based Opinions
Proc. of the 16th International Conference of the Pacific Association for Computational Linguistics (PACLING), October, 2019.
URL
- Angrosh Mandya, Danushka Bollegala and Frans Coenon:
Evaluating Co-reference Chains based Conversation History in Conversational Question Answering
Proc. of the 16th International Conference of the Pacific Association for Computational Linguistics (PACLING), October, 2019.
URL
- Xia Cui and Danushka Bollegala:
Self-Adaptation for Unsupervised Domain Adaptation
Proc. of the Recent Advances in Natural Language Processing (RANLP), September, 2019.
URL
- Masahiro Kaneko and Danushka Bollegala:
Gender-preserving Debiasing for Pre-trained Word Embeddings
Proc. of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), July, 2019.
URL
- Yi Zhou and Danushka Bollegala:
Unsupervised Evaluation of Human Translation Quality
Proc. of the 11th International Conference on Knowledge Discovery and Information Retrieval (KDIR), pp. 55-64, September, 2019.
URL
- Micheal Abaho, Danushka Bollegala, Paula Williamson and Susanna Dodd:
Correcting Crowdsourced annotations to Improve Detection of Outcome types in Evidence Based Medicine
Proc. of the 4th International Workshop on Knowledge Discovery in Healthcare Data (KDH) at the 28th International Joint Conference on Artificial Intelligence, August, 2019.
URL
- James O'Neill, Danushka Bollegala, Peter Noble and Alan Radford:
Tick Parasitism Classification from Noisy Medical Records
Proc. of the 4th International Workshop on Knowledge Discovery in Healthcare Data (KDH) at the 28th International Joint Conference on Artificial Intelligence, August, 2019.
URL
- Robert Bevan, Alessandro Torrisi, Danushka Bollegala, Frans Coenen and Katie Atkinson:
Extracting Supporting Evidence from Medical Negligence Claim Texts
Proc. of the 4th International Workshop on Knowledge Discovery in Healthcare Data (KDH) at the 28th International Joint Conference on Artificial Intelligence, August, 2019.
URL
- Alessandro Torrisi, Robert Bevan, Danushka Bollegala, Katie Atkinson and Frans Coenon:
Automated Bundle Pagination Using Machine Learning
Proc. of the 17th International Conference on Artificial Intelligence and Law (ICAIL), June, 2019.
URL
- Huda Hakami and Danushka Bollegala:
Learning Relation Representations from Word Representations
Proc. of the Automatic Knowledge Base Construction Conference (AKBC), May, 2019.
URL
- Mohammed Alsuhaibani, Takanori Maehara and Danushka Bollegala:
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy
Proc. of the Automatic Knowledge Base Construction Conference (AKBC), May, 2019.
URL
- Angrosh Mandya, Danushka Bollegala, Frans Coenen and Katie Atkinson:
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
Proc. of the Automatic Knowledge Base Construction Conference (AKBC), May, 2019.
URL
- Shan Luo, Jet-Tsyn Lee and Danushka Bollegala:
"Touching to See" and "Seeing to Feel": Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception
Proc. of the IEEE International Conference on Robotics and Automation (ICRA), May, 2019.
URL
- Thai-Hoang Pham, Khai Mai, Nguyen Minh Trung, Nguyen Tuan Duc, Danushka Bollegala, Ryohei Sasano and Satoshi Sekine:
Multi-Task Learning with Contextualized Word Representations for Extended Named Entity Recognition
arXiv pre-print, 2019.
URL
- Abdullah Alsheri, Frans Coenon and Danushka Bollegala:
Behavioural Biometric Continuous User Authentication Using Multivariate Keystroke Streams in the Spectral Domain
Knowledge Discovery, Knowledge Engineering and Knowledge Management
URL
2018
- Robert Bevan, Alessandro Torrisi, Danushka Bollegala, Katie Atkinson and Frans Coenen:
Efﬁcient and Effective Case Reject-Accept Filtering: A Study Using Machine Learning
Proc. of the 31st International Conference on Legal Knoweledge and Information Systems (JURIX),
pp. 171-175, 2018.
URL
- Huda Hakami, Kohei Hayashi and Danushka Bollegala:
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection Proc. of the 27th International Conference on Computational Linguistics (COLING), pp. 2493-2504, 2018.
URL
- Cong Bao and Danushka Bollegala:
Learning Word Meta-Embeddings by Autoencoding Proc. of the 27th International Conference on Computational Linguistics (COLING), pp. 1650-1661, 2018.
URL
- Khai Mai, ThaiHoang Pham, Minh Trung Nguyen, Nguyen Tuan Duc, Danushka Bollegala, Ryohei Sasano and Satoshi Sekine:
Which Model Performs Best in This Situation? An Empirical Study on FineGrained Named Entity Recognition Proc. of the 27th International Conference on Computational Linguistics (COLING), pp. 711-722, 2018.
URL
- Danushka Bollegala, Kohei Hayashi and Ken-ichi Kawarabayashi:
Think Globally, Embed Locally -- Locally Linear Meta-Embedding of Words, Proc. of IJCAI-EACI, pp. 3970-3976, 2018.
URL
- Xia Cui, Sadamori Kojaku, Naoki Masuda and Danushka Bollegala:
Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition, Proc. of the Seventh Joint Conference on Lexical and Computational Semantics (*SEM), pp. 255-264, 2018.
URL
- Krasen Samardzhiev, Andrew Gargett and Danushka Bollegala:
Learning Neural Word Salience Scores,
Proc. of the Seventh Joint Conference on Lexical and Computational Semantics (*SEM), pp. 33-42, 2018.
URL
- Danushka Bollegala, Vincent Atanasov, Takanori Maehara and Ken-ichi Kawarabayashi:
ClassiNet - Predicting Missing Features for Short-Text Classification,
ACM Transactions on Knowledge Discovery from Data (TKDD), pp. 1-29, 2018.
PDF
DOI
- Danushka Bollegala, Richard Slone, Simon Maskell, Joanna Hajne and Munir Pirmohamed:
Learning Causality Patterns for Detecting Adverse Drug Reactions from Social Media,
Journal of Medical Internet Research Public Health Surveillance, doi:10.2196/publichealth.8214, pp. 1-20, 2018.
PDF
DOI
- Joshua Coates and Danushka Bollegala:
Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings
Proc. of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 194-198, 2018.
URL
- Pavithra Rajendran, Danushka Bollegala and Simon Parsons:
Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset.
Proc. of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 28-34, 2018.
URL
- Xia Cui, Noor Al-Bazzaz, Danushka Bollegala and Frans Coenen:
A Comparative Study of Pivot Selection Strategies for Unsupervised Cross-Domain Sentiment Classification,
The Knowledge Engineering Review, Vol. 33, 2018.
PDF
URL
- Mohammed Alsuhaibani, Danushka Bollegala, Takanori Maehara and Ken-ichi Kawarabayashi:
Jointly Learning Word Embeddings Using a Corpus and a Knowledge Base,
Plos One, Vol. 13, No. 3, pp. 1-26, 2018.
URL
- Abdullah Alsheri, Frans Coenen, and Danushka Bollegala:
Iterative Time Keystroke Continuous Authentication: A Time Series Based Approach,
KI - Künstliche Intelligenz, Vol. 32, No. 1, pp. 1-13, 2018.
PDF
URL
- Mohammed Alsuhaibani and Danushka Bollegala:
Joint Learning of Sense and Word Embeddings, Proc. of the 11-th edition of the Language Resources and Evaluation Conference, 7-12, pp. 1-7, Miyazaki, Japan, May, 2018.
URL
- Pavithra Rajendran, Danushka Bollegala and Simon Parsons:
Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions, to appear in the Proc. of the 11-th edition of the Language Resources and Evaluation Conference, pp. 1-7, 7-12, Miyazaki, Japan, May, 2018.
URL
- Angrosh Mandya, Danushka Bollegala, Frans Coenen and Katie Atkinson:
A Dataset for Inter-Sentence Relation Extraction using Distant Supervision, to appear in the Proc. of the 11-th edition of the Language Resources and Evaluation Conference, pp. 1-7, 7-12, Miyazaki, Japan, May, 2018.
URL
- Danushka Bollegala, Yuichi Yoshida, and Ken-ichi Kawarabayashi:
Using k-way Co-occurrences for Learning Word Embeddings, Proc. of the 32-nd AAAI Conference on Artificial Intelligence, pp. 5037-5044, New Orleans, Louisiana, USA, 2018.
URL
2017
- Abdullah Alsheri, Frans Coenen, and Danushka Bollegala:
Spectral Analysis of Keystroke Streams: Towards Effective Real-Time Continuous User Authentication,
4th International Conference on Information Systems Security and Privacy (ICISSP), pp. 62-73, Madeira, Portugal. (2017.11).
URL
- Danushka Bollegala, Kohei Hayashi, and Ken-ichi Kawarabayashi:
Learning Linear Transformations between Counting-based and Prediction-based Word Embeddings,
PLoS ONE, Vol. 12, no. 9, pp. 1-21, 2017.
URL
- Huda Hakami and Danushka Bollegala:
Compositional approaches for Representing Relations Between Words: A Comparative Study,
Knowledge-Based Systems, 136C, pp. 172-182, 2017.
URL
- Abdullah Alsheri, Frans Coenen, and Danushka Bollegala:
Accurate Continuous and Non-intrusive User Authentication with Multivariate Keystroke Streaming,
9th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR), pp. 61-70, Madeira, Portugal. (2017.11)
URL
- Abdullah Alsheri, Frans Coenen, and Danushka Bollegala:
Spectral Analysis of Keystroke Streams: Towards Effective Real-Time and Continuous User Authentication,
International Workshop on Spatial and Spatio-temporal Data Mining at the IEEE International Conference on Data Mining (ICDM), New Orleans, USA. (2017.11)
URL
- Xia Cui, Frans Coenen, and Danushka Bollegala:
Effect of Data Imbalance on Unsupervised Domain Adaptation of Part-of-Speech Tagging and Pivot Selection Strategies, Wokshop on Learning With Imbalanced Domains: Theory and Applications (LIDTA) at the
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), PRML 74:103-115. (2017.09)
URL
- Xia Cui, Frans Coenen, and Danushka Bollegala:
TSP: Learning Task-Specific Pivots for Unsupervised Domain Adaptation,
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 754-771. (2017.09)
URL
- Tomoyuki Kajiwara, Danushka Bollegala, Yuichi Yoshida, and Ken-ichi Kawarabayashi:
An Iterative Approach for the Global Estimation of Sentence Similarity,
PLoS ONE, Vol. 12, no 9, pp. 1-15, 2017.
URL
- Danushka Bollegala:
Dynamic Feature Scaling for Online Learning of Binary Classifiers,
Knowledge-Based Systems, Vol. 129, pp. 97-195, 2017.
DOI
URL
- Huda Hakami, Angrosh Mandya, and Danushka Bollegala:
Discovering Representative Space for Relational Similarity Measurement,
15th International Conference of the Pacific Association for Computational Linguistics (PACLING), pp. 76-87, Yangon, Myanmar. (2017.08)
URL
- Pavithra Rajendran, Danushka Bollegala, and Simon Parsons:
Identifying Argument based Relation Properties in Opinions,
15th International Conference of the Pacific Association for Computational Linguistics (PACLING), pp. 1-12, Yangon, Myanmar. (2017.08)
URL
- Angrosh Mandya, Danushka Bollegala, Frans Coenen, and Katie Atkinson:
Frame-based Semantic Patterns for Relation Extraction,
15th International Conference of the Pacific Association for Computational Linguistics (PACLING), pp. 51-62, Yangon, Myanmar. (2017.08)
URL
- Matias Garcia-Constantino, Katie Atkinson, Danushka Bollegala, Karl Chapman, Frans Coenen, Clare Roberts, and Katy Robson:
CLIEL: Context-Based Information Extraction from Commercial Law Documents,
16th International Conference on Artificial Intelligence and Law (ICAIL), pp. 79-87, London, UK. (2017.06)
PDF
DOI
- Angrosh Mandya, Danushka Bollegala, Frans Coenen, and Katie Atkinson:
Classifier-based Pattern Selection Approach for Relation Instance Extraction,
International Conference on Computational Linguistics and Intelligent Text Processing (CICLing), pp. 1-16, Budapest, Hungary. (2017.04)
PDF
2016
- Abdullah Alsheri, Frans Coenen, and Danushka Bollegala:
Towards Keystroke Continuous Authentication Using Time Series Analytics,
Research and Development in Intelligent Systems XXXIII, (SGAI 2016),
pp. 325-339 Springer. (2016.12)
First author received the Best Student Paper Award
PDF
- Pavithra Rajendran, Danushka Bollegala, and Simon Parsons:
Assessing weight of opinion by aggregating coalitions of arguments, 6th International Conference on Computational Models of Argument (COMMA), pp. 431-438, Postdam, Germany. (2016.8)
PDF
- Pavithra Rajendran, Danushka Bollegala, and Simon Parsons:
Contextual Stance Classification of Opinions: A Step Towards Enthymeme Reconstruction in Online Reviews,
3rd Workshop on Argument Mining at Annual Conference of the Association for Computational Linguistics,
pp. 31-39, Berlin, Germany. (2016.8)
URL
- Abdullah Alsheri, Frans Coenen, and Danushka Bollegala:
Keyboard Usage Authentication using Time Series Analysis,
18th International Conference on Big Data Analytics and Knowledge Discovery (DaWak 2016),
pp. 239-252, Porto, Portugal. (2016.9)
PDF
- Danushka Bollegala, Mohammed Alsuhaibani, Takanori Maehara, and Ken-ichi Kawarabayashi:
Joint Word Representation Learning using a Corpus and a Semantic Lexicon,
30th AAAI Conference on Artificial Intelligence (AAAI), pp. 2690-2696, Arizona, USA. (2016.2)
URL
2015
- Huda Hakami, and Danushka Bollegala:
A Classification Approach for Detecting Cross-lingual Biomedical Term Translations,
Journal of Natural Language Engineering (JNLE), Vol. 1, Issue 1469-8110, pp. 1-21, 2015.
PDF
URL
- Danushka Bollegala, Takanori Maehara, and Ken-ichi Kawarabayashi:
Unsupervised Cross-Domain Word Representation Learning,
53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015), and the 7th International
Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (IJCNLP), pp. 730-740, Beijing, China, July, 2015
URL
- Danushka Bollegala, Takanori Maehara, and Ken-ichi Kawarabayashi:
Embedding Semantic Relations into Word Representations,
International Joint Conferences on Artificial Intelligence (IJCAI 2015), pp. 1222-1228, Buenos Aires, Argentina, July, 2015.
URL
- Danushka Bollegala, Tingting Mu, and John Y. Goulermas:
Cross-domain Sentiment Classification using Sentiment Sensitive Embeddings ,
IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 28, Issue 2, pp. 398-410, 2015.
PDF
URL
- Pascal Kuyten, Danushka Bollegala, Bernd Hollerit, Helmut Prendinger, and Kiyoharu Aizawa:
A Discourse Search Engine based on Rhetorical Structure Theory,
37th European Conference on Information Retrieval (ECIR 2015), pp. 80-91, Vienna, Austria, 2015.03.
PDF
URL
- Richard Sloane, Orod Osanlou, David Lewis, Danushka Bollegala, Simon Maskell, and Munir Pirmohamed:
Social Media and Pharmacovigilance: A Review of the Opportunities and Challenges,
British Journal of Clinical Pharmacology, Vol. 80, Issue 4, pp. 910-920, 2015.
PDF
URL
- Danushka Bollegala, Takanori Maehara, Yuichi Yoshida, and Ken-ichi Kawarabayashi:
Learning Word Representations from Relational Graphs,
29th AAAI Conference on Artificial Intelligence (AAAI 2015), pp. 2146-2152, Texas, USA, 2015.1.
URL
- Danushka Bollegala, Georgios Kontonatsios, and Sophia Ananiadou:
A Cross-lingual Similarity Measure for Detecting Biomedical Term Translations,
PLoS ONE, pp. 1-28, Vol. 10, no. 6, 2015.
DOI
- Nozomi Nori, Danushka Bollegala, and Mitsuru Ishizuka:
Interest Prediction via User's Actions on Social Media,
Transactions of the Japanese Society for Artificial Intelligence, Vol. 30, No. 4, pp. 168-176, 2015.
PDF
URL
- Hiroyuki Sato, Yoshihiko Hasegawa, Danushka Bollegala, and Hitoshi Iba:
Improved sampling using loopy belief propagation for probabilistic model building
genetic programming, Swarm and Evolutionary Computation, Vol. 23, pp. 1-8, August, 2015.
DOI
PDF
- Nozomi Nori, Danushka Bollegala, and Hisashi Kashima:
Simultaneous Higher-order Relation Prediction via Collective Indicence Matrix Embedding,
Transactions of the Japanese Society for Artificial Intelligence, Vol. 30, No. 2, pp. 459-465, 2015.
PDF
DOI
2014
- Danushka Bollegala, David Weir, and John Carroll:
Learning to Predict Distributions of Words across Domains,
Annual Conference of the Association for Computational Linguistics (ACL 2014), pp. 613-623,
Baltimore, Maryland, USA. (2014.6)
URL
- Nozomi Nori, Danushka Bollegala, and Hisashi Kashima:
A Dimension Reduction Approach to Multinomial Relation Prediction
, Transactions of the Japanese Society for Artificial Intelligence, Vol. 29, No. 1, pp. 168-176, 2014.
Received the Award for the Best Journal Paper of the Year
PDF
2013
- Danushka Bollegala, Mitsuru Kusumoto, Yuichi Yoshida, and Ken-ichi Kawarabayashi:
Mining for Analogous Tuples from an Entity-Relation Graph,
23rd International Joint Conference on Artificial Intelligence
(IJCAI 2013), pp. 2064-2070, Beijing, China. (2013.8)
PDF
DOI
- Muhammad Asif Hossain Khan, Danushka Bollegala, Guangwen Liu, and Kaoru Sezaki:
Multi-Tweet Summarization of Real-Time Events,
ASE/IEEE International Conference on Social Computing, pp. 128-133, Washington D.C., USA, 2013.9.
DOI
PDF
- Hiroyuki Sato, Danushka Bollegala, Yoshihiko Hasegawa, and Hitoshi Iba:
Learning Non-linear Ranking Functions for Web Search using Probabilistic Model Building GP,
IEEE Congress on Evolutionary Computation (CEC 2013), pp. 3371-3378, Cancun, Mexico, 2013.6.
PDF
URL
- Muhammad Asif Hossain Khan, Danushka Bollegala, Guangwen Li, and Kaoru Sezaki:
Delineating Real-Time Events by Identifying Relevant Tweets with Popular Discussion Points,
ASE Human Journal, Vol. 2, No. 3, pp. 136-150, 2013.
DOI
PDF
- Danushka Bollegala: Deep Learning for Natural Language Processing,
Journal of the Japanese Society for Artificial Intelligence, Vol. 29, No. 2, pp. 195-203, 2013.
DOI
PDF
- Danushka Bollegala and Ekaterina Shutova:
Metaphor Interpretation using Paraphrases Extracted from the Web, PLoS
ONE, Vol. 8, No. 9, e74304, 2013.
DOI
- Ken-ichi Yokote, Danushka Bollegala, and Mitsuru Ishizuka:
Jointly Learning Similarity Transformations for Textual Entailment
Transactions of of the Japanese Society for Artificial Intelligence, Vol. 8, No. 2, pp. 220-229, 2013.
DOI
PDF
- Danushka Bollegala, Tomokazu Goto, Nguyen Tuan Duc, and Mitsuru Ishizuka:
Improving Relational Similarity Measurement using Symmetries in Proportional Word Analogies, Information Processing and Management Journal,
Vol. 49, Issue 1, pp. 355-369, Elsevier, January, 2013.
DOI
PDF
- Danushka Bollegala, David Weir, and John Carroll:
Cross-Domain Sentiment Classification using a Sentiment Sensitive Thesaurus,
IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 25, No. 8, pp. 1719-1731, August, 2013.
DOI
PDF
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
Minimally Supervised Novel Relation Extraction using
Latent Relational Mapping, IEEE Transactions on
Knowledge and Data Engineering (TKDE), Vol. 25, Issue 2, pp. 419-432, February, 2013.
DOI
PDF
2012
- Francisco Tacoa, Danushka Bollegala, and Mitsuru Ishizuka:
A Context Expansion Method for Supervised Word Sense Disambiguation,
Sixth IEEE International Conference on Semantic Computing (ICSC 2012), poster paper, pp. 339-341, Palremo,
Italy. (2012.9)
DOI
PDF
- Nozomi Nori, Danushka Bollegala, Hisashi Kashima:
Multinomial Relation Prediction in Social Data: A Dimension Reduction Approach
Proceedings of the 26th National Conference on Artificial Intelligence (AAAI 2012),
pp. 115-121, Toronto, Canada. (2012.7)
DOI
PDF
- Ken-ichi Yokote, Danushka Bollegala, Mitsuru Ishizuka:
Similarity is not Entailment - Jointly Learning
Similarity Transformations for Textual Entailment Proceedings of the 26th
National Conference on Artificial Intelligence (AAAI 2012),
pp. 1720-1726, Toronto, Canada. (2012.7)
PDF
- Hiroyuki Sato, Yoshihiko Hasegawa, Danushka Bollegala and Hitoshi Iba:
Probabilistic Model Building GP with Belief Propagation,
IEEE Congress on Evolutionary Computation (CEC 2012), pp. 1-8, Brisbane, Australia. (2012.6)
PDF
- Danushka Bollegala, Naoaki Okazaki, and Mitsuru Ishizuka:
A Prefernce Learning Approach to Sentence Ordering for Multi-document Summarization, Journal of Information Sciences, Vol. 217, pp. 78-95,
Elsevier, December, 2012.
DOI
PDF
- Danushka Bollegala, Naoki Tani, and Mitsuru Ishizuka:
Improving the Accuracy of Attribute Extraction using the Relatedness between Attribute Values
Transactions of the Japanese Society for Artificial Intelligence, Vol. 27, No. 2, pp. 245-252, 2012.
DOI
PDF
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
Measuring the degree of Synonymy between Words using Relational Similarity between Word Pairs as a Proxy,
Institute of Electronics, Information and Communication Engineers (IEICE) Transactions on Information Systems,
Vol. E95-D, No. 8, pp. 2116 - 2123, 2012.
DOI
PDF
- Nguen Tuan Duc, Danushka Bollegala, and Mitsuru
Ishizuka: Cross-Language Latent Relational
Search between Japanese and English Languages Using a
Web Corpus, ACM Transactions on Asian
Language Information Processing (TALIP), Vol. 11, Issue 3,
pp. 11:1 - 33, September, 2012.
DOI
PDF
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
Automatic Annotation of Ambiguous Personal Names on the Web,
Computational Intelligence, Vol. 28, Issue 3, pp. 398 - 425,
August, 2012.
DOI
PDF
2011
- Nugyen Duc, Danushka Bollegala, and Mitsuru Ishizuka:
Cross-Language Latent Relational Search: Mapping Knowledge across Languages,
Proceedings of the 25th National Conference on Artificial Intelligence (AAAI 2011),
pp. 1237-1242, San Francisco, USA, 2011.8.
URL
- Naoki Tani, Danushka Bollegala, Naiwala Chandrasiri, Shuhei Iitsuka, Keisuke Okamoto, Kazunari Nawa, Yutaka Matsuo:
Collaborative Search in a Real-World Context, Proceedings of the 20th ACM Conference on
Information and Knowledge Management (CIKM 2011) (poster paper), pp. 2137-2140, Glasgow, UK, 2011.10.
PDF
DOI
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
Relation Adaptation: Learning to Extract Novel
Relations with Minimum Supervision, Proceedings of the 22nd
International Joint Conference on Artificial Intelligence (IJCAI 2011), pp. 2205-2210, Barcelona, Spain, 2011.7.
PDF
- Nozomi Nori, Danushka Bollegala, and Mitsuru Ishizuka:
Interest Prediction on Multinomial, Time-Evolving Social Graphs,
Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011),
pp. 2507-2512, Barcelona, Spain, 2011.7.
PDF
- Nozomi Nori, Danushka Bollegala, and Mitsuru Ishizuka:
Exploiting User Interest on Social Media for Aggregating Diverse Data and Predicting Interest,
Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM 2011), pp. 241-248, Barcelona,
Spain, 2011.7.
URL
- Danushka Bollegala, Nasimul Noman, and Hitoshi Iba:
RankDE: Learning a Ranking Function for Information Retrieval using Differential Evolution,
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 1771-1778, Dublin, Ireland, 2011.7.
PDF
URL
- Nasimul Noman, Danushka Bollegala, and Hitoshi Iba:
Differential Evolution with Self Adaptive Local Search,
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 1099-1106, Dublin, Ireland, 2011.7.
PDF
URL
- Nasimul Noman, Danushka Bollegala, and Hitoshi Iba:
An Adaptive Differential Evolution Algorithm,
Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC 2011), pp. 2229-2236, New Orleans, USA, 2011.6.
PDF
URL
- Danushka Bollegala, David Weir, and John Carroll:
Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification,
Proceedings of 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL/HLT 2011), pp. 132-141, Portland, Oregon, USA, 2011.6.
URL
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
From Actors, Politicians, to CEOs: Domain Adaptation of Relational Extractors using a Latent Relational Mapping,
Proceedings of the 20th International World Wide Web Conference (WWW 2011) poster session, pp. 13-14, Hyderabad, India, 2011.3.
PDF
URL
- Hugo Hernault, Danushka Bollegala, and Mitsuru Ishizuka:
Semi-Supervised Discourse Relation Classification with Structural Learning,
Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2011),
Lecture Notes in Computer Science, Volume 6608/2011, pp. 340-352, Tokyo, Japan, 2011.02.
PDF
URL
- Haibo Li, Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
Using Graph based Method to Improve Bootstrapping Relation Extraction,
Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2011),
Lecture Notes in Computer Science, Volume 6609/2011, pp. 127-138, Tokyo, Japan, 2011.02.
PDF
URL
- Danushka Bollegala:
A Supervised Ranking Approach for Detecting Relationally Similar Word Pairs,
Proceedings of the IEEE International Conference on Information and Automation for Sustainability (ICAfs 2011), pp. 323-328,
Colombo, Sri Lanka, 2011.
PDF
URL
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
A Web Search Engine-based Approach to Measure
Semantic Similarity between Words, IEEE
Transactions on Knowledge and Data Engineering (TKDE),
Vol. 23, no. 7, pp. 977-990, July, 2011.
DOI
PDF
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
Automatic Discovery of Personal Name Aliases
from the Web, IEEE Transactions on Knowledge
and Data Engineering (TKDE), Vol. 23, no. 6,
pp. 831-844, July, 2011.
DOI
PDF
- Danushka Bollegala, Yutaka Matsuo, and Mitsuru Ishizuka:
A Supervised Classification approach for Measuring
Similarity between Word Pairs, Transactions of the Institute of Electronics, Information and Communication Engineers (IEICE), Vol.E94-D, no. 11, pp. 2227-2233, November, 2011.
PDF
URL
- Nguyen Tuan Duc, Danushka Bollegala, and Mitsuru Ishizuka:
Exploiting Relational Similarity between Entity Pairs
for Latent Relational Search,
Transactions of the Information Processing Society of Japan,
Vol. 52, no. 4, pp. 1790-1802, 2011.
PDF
URL
- Nguyen Tuan Duc, Danushka Bollegala, and Mitsuru Ishizuka:
Relation Representation and Indexing Method for Fast and High Precision Latent
Relational Search Engine
Special issue of the Transactions of the Japanese Society for Artificial Intelligence, Vol. 26, no. 2, pp. 307-312, 2011.
PDF
URL
- Wataru Sunayama, Yasufumi Takama, Danushka
Bollegala, Yoko Nishihara, Hidekazu Tokunaga, Mineo
Kushima, Mitsunori Matsushita：
Total Environment for Text Data Mining, Transactions of the Japanese Society for Artificial
Intelligence，Vol.26, No.4, pp. 483--493, 2011.
PDF
URL
- Tomokazu Goto, Nguyen Tuan Duc, Danushka
Bollegala, and Mitsuru Ishizuka: Improving Relational Search Performance using Relational Symmetries and Predictors,
Transactions of the Japanese Society for Artificial Intelligence,
Vol. 26, no. 6, pp. 649-656, 2011.
URL
PDF
2010
- Danushka Bollegala, Naoaki Okazaki and Mitsuru Ishizuka:
A bottom up Approach to Sentence Ordering for Multi-document Summarization
, Information Processing and Management, Vol. 46,
Issue 1, pp. 89-109, Elsevier, 2010.
URL
DOI
- Keigo Watanabe, Danushka Bollegala, Yutaka Matsuo,
and Mitsuru Ishizuka: Automatic Extraction of Related Terms using Web Search Engines,
Journal of the Japan Society for Fuzzy Theory and Intelligent Informatics, Vol. 23, no. 4, pp. 483-493, 2010.
URL
- Hugo Hernault, Danushka Bollegala, and Mitsuru Ishizuka:
A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations using Feature Vector Extension,
Proceedings of the Empirical Methods in Natural Language Processing (EMNLP 2010), pp. 399-409, Massachusetts, USA. (2010.10)
URL
- Hugo Hernault, Danushka Bollegala, and Mitsuru Ishizuka:
Towards Semi-Supervised Classification of Discourse Relations using Feature Correlations,
Proceesings of the 11th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL 2010) (short paper), pp.55-58, Tokyo,
Japan. (2010.09)
URL
- Nugyen Duc, Danushka Bollegala, and Mitsuru Ishizuka:
Using Relational Similarity between Word Pairs for Latent Relational Search on the Web,
Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2010) (short paper),
pp. 196-199, Toronto, Canada. (2010.08)
PDF
DOI
- Tomokazu Goto, Nugyen Duc, Danushka Bollegala, and Mitsuru Ishizuka:
Exploiting Symmetry in Relational Similarity for Ranking Relational Search Results,
Proceedings of the 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2010) (short paper), pp. 595-600, Daegu, South Korea. (2010.08)
PDF
DOI
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Relational Duality: Unsupervised Extraction of Semantic Relations between Entities on the Web,
Proceedings of the 19th International World Wide Web Conference (WWW 2010), pp. 151-160, North Carolina, USA. (2010.04)
PDF
DOI
- Hugo Hernault, Danushka Bollegala and Mitsuru Ishizuka:
A Sequential Model for Discourse Segmentation, Proceedings of the 11th International Conference on
Intelligent Text Processing and Computational Linguistics (CICLing 2010), Lecture Notes in Computer Science, vol. 6008/2010, pp. 315-326, Romania. (2010.03)
PDF
DOI
2009
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
A Relational Model of Semantic Similarity between Words using Automatically Extracted Lexical Pattern Clusters from the Web, Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2009), pp. 803-812, Singapore (2009.08).
URL
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Measuring the Similarity between Implicit Semantic Relations from the Web, Proceedings of the 18th International World Wide Web Conference (WWW 2009), pp. 651-660, Madrid, Spain (2009.04)
PDF
DOI
- Keigo Watanabe, Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
A Two-Step Approach to Extracting Atrtibutes for People on the Web,
Proceedings (online) of the Second Web People Search Task (WePS2)
Workshop at 18th International World Wide Web Conference (WWW 2009), Madrid, Spain (2009.04)
PDF
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Measuring the Similarity between Implicit Semantic Relations using Web Search Engines,
Proceedings of the Second ACM International Conference on Web Search and Data Mining (WSDM 2009),
pp. 104-113, Barcelona, Spain (2009.02)
PDF
DOI
2008
- Danushka Bollegala, Taiki Honma, Yutaka Matsuo and Mitsuru Ishizuka:
Automatically Extracting Personal Name Aliases from the Web, Proceedings of the 6th International Conference on Natural Language Processing (GoTAL 2008), Advances in Natural Language Processing Springer LNCS 5221, pp. 77-88, Gothenburg, Sweeden (2008.08).
PDF
DOI
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
WWW sits the SAT: Measuring Relational Similarity from the Web, Proceedings of the 18th European Conference on Artificial Intelligence (ECAI 2008), pp. 333-337, Patras, Greece (2008.07)
PDF
DOI
- Danushka Bollegala, Taiki Honma, Yutaka Matsuo and Mitsuru Ishizuka:
Mining for Personal Name Aliases on the Web, Proceedings of 17th International World Wide Web Conference (WWW 2008), Vol.II, pp. 1107-1108, Beijing, China (2008.04)
PDF
DOI
- Danushka Bollegala, Taiki Honma, Yutaka Matsuo and Mitsuru Ishizuka:
Identification of Personal Name Aliases on the Web, Online Proceedings of WWW 2008 Workshop on Social Web Search and Mining (SWSM 2008), 8 pages, Beijing, China (2008.04)
PDF
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
A Co-occurrence Graph-based Approach for Personal Name Alias Extraction from Anchor Texts, Proceedings of 3rd International Joint Conferences on Natural Language Processing
(IJCNLP 2008), Vol.II, pp. 865-870, Hyderabad, India (2008.1)
URL
2007
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Measuring Semantic Similarity between Words Using Web Search Engines,
Proceedings of the 16th International World Wide Web Conference (WWW 2007), pp. 757-766, Banff, Canada (2007.5)
PDF
DOI
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
An Integrated Approach to Measuring Semantic Similarity between Words Using Information Available on the Web ,
Proceedings of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2007), pp. 340-347, Rochester, New York (2007.4)
URL
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Disambiguating Personal Names on the Web using Automatically Extracted Key Phrases ,
Proceedings of International Joint Conferences on Artificial Intelligence
(IJCAI) 2007 Workshop on Text-Mining and Link-Analysis (TextLink 2007), 10 pages, Hyderabad, India (2007.1)
PDF
2006
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Disambiguating Personal Names on the Web using Automatically Extracted Key Phrases,
Proceedings of the European Conference of Artificial Intelligence (ECAI2006), pp. 553-557, Italy (2006.8)
PDF
DOI
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Extracting Key Phrases to Disambiguate Personal Name Queries in Web Search,
Proceedings of the Workshop "How can Computational Linguistics improve Information Retreival?", at the joint 21st International Conference on
Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL 2006), pp.17-24 , Sydney, Australia, (2006.07)
PDF
DOI
- Danushka Bollegala, Naoaki Okazaki and Mitsuru Ishizuka:
A bottom-up approach to Sentence Ordering for Multi-document Summarization,
Proceedings of the joint 21st International Conference on Computational Linguistics and the
44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL 2006),
pp. 385-392, Sydney, Australia. (2006.07)
DOI
- Yutaka Matsuo, Masahiro Hamasaki, Hideaki Takeda, Junichiro Mori, Danushka
Bollegala, Hiroyuki Nakamura, Takuichi Nishimura, Koiti Hashida and Mitsuru Ishizuka:
Spinning Multiple Social Networks for Semantic Web,
Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), pp 1381-1387, Boston, MA, USA,(2006.07)
PDF
DOI
- Danushka Bollegala, Yutaka Matsuo and Mitsuru Ishizuka:
Extracting Key Phrases to Disambiguate Personal Names on the Web,
Proceedings of the 7th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2006), pp. 223-234, Mexico City, Mexico, (2006.02)
PDF
2005
- Danushka Bollegala, Naoaki Okazaki and Mitsuru Ishizuka:
A Machine Learning Approach to Sentence Ordering for Multi-document Summarization and its Evaluation,
Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP 2005), pp. 624-635, Jeju, South Korea, (2005.10)
DOI


Book calendar from Microsoft Graph
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RackHD Benchmark Report
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Summary Report
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Compared Data:
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Summary Report
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Mongo Document Statistics Table


Visualization
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"I’m so excited for this journey and what it will bring! Miss Planet is not just an organization about beauty but it’s about taking care of our planet and to advocate for it, after all it is our home and we should be fighting for it. From our Oceans to our land, it is extremely important to take good care of it because our decisions are the ones that will affect future generations to come. It’s all in our hands! Join me in this amazing journey!" -Janice Jimenez


{% extends "layout.html" %} {% block body %}
To get started, login or register below. (It's free.)
For guidance, see the User Guide
NB: This is version 2 of the CSV conversion tool. Users and models have not been migrated from the old version, so please sign up for a new account. Please contact<EMAIL_ADDRESS>if you need support with transferring models.


|
lwIP
2.1.3
Lightweight IP stack
|
#include "lwip/opt.h"
#include "lwip/dhcp6.h"
#include "lwip/prot/dhcp6.h"
#include "lwip/def.h"
#include "lwip/udp.h"
#include "lwip/dns.h"
#include <string.h>
#include "path/to/my/lwip_hooks.h"
Enumerations | |
| enum | dhcp6_option_idx |
Functions | |
| void | dhcp6_set_struct (struct netif *netif, struct dhcp6 *dhcp6) |
| void | dhcp6_cleanup (struct netif *netif) |
| err_t | dhcp6_enable_stateful (struct netif *netif) |
| err_t | dhcp6_enable_stateless (struct netif *netif) |
| void | dhcp6_disable (struct netif *netif) |
| void | dhcp6_nd6_ra_trigger (struct netif *netif, u8_t managed_addr_config, u8_t other_config) |
| void | dhcp6_tmr (void) |
Variables | |
| struct dhcp6_option_info | dhcp6_rx_options [DHCP6_OPTION_IDX_MAX] |
| enum dhcp6_option_idx |
Option handling: options are parsed in dhcp6_parse_reply and saved in an array where other functions can load them from. This might be moved into the struct dhcp6 (not necessarily since lwIP is single-threaded and the array is only used while in recv callback).
| void dhcp6_nd6_ra_trigger | ( | struct netif * | netif, |
| u8_t | managed_addr_config, | ||
| u8_t | other_config | ||
| ) |
This function is called from nd6 module when an RA messsage is received It triggers DHCPv6 requests (if enabled).
| void dhcp6_tmr | ( | void | ) |
DHCPv6 timeout handling (this function must be called every 500ms, see DHCP6_TIMER_MSECS).
A DHCPv6 server is expected to respond within a short period of time. This timer checks whether an outstanding DHCPv6 request is timed out.
| struct dhcp6_option_info dhcp6_rx_options[DHCP6_OPTION_IDX_MAX] |
Holds the decoded option info, only valid while in dhcp6_recv.


New York Excelsior
Upcoming Matches
Stage 4
| Friday, August 9 |
|
win prob. |
map +/- |
|
Excelsior |
3 |
39% |
-0.5 |
|
Spark |
2 |
61% |
+0.5 |
Stage 3 Title Matches
Stage 3
| Saturday, June 29 |
|
win prob. |
map +/- |
|
Excelsior |
3 |
94% |
+2.5 |
|
Fuel |
2 |
6% |
-2.5 |
Stage 2 Title Matches
Stage 2
Stage 1 Title Matches
Stage 1


-
As a beginning, I would like to introduce myself to you.
My name is Tamara Hussien, I am 23 years old, I am holding a bachelor degree in Computer Information Systems
from Al-Albayt University. I have got it in June of 2020,
after I have graduated I joined a course with the United Nations about "How to start your own project"
it extended from June to August. After it has finished, I started a new course with LTUC about Software
Development.
-
In an experienced side, I have strong volunteer experience in community services,
I spend nearly 8 years in volunteering, in different fields, such as cleaning the public areas,
restoration the old places, working with children, encouraging the tourism, work for poor people,
organize different events, and so on.
Away from volunteering, I spent 3 months training in IT fields and data entry, I was a business
analyst and tester for web sites.
-
On a personal side, I an active person, love to wake up early and enjoy my morning Nescafe, I love to make breakfast,
also I love to arrange the food tables. Also, I'm a social and adaptive person, wherever you put me I can deal
with the place and people. I love desserts and love to make it, recently I have released my own business,
I make desserts with a ball shape and put it in gifts, it appears too much cute.
if you are interested you can view my business pages from here:
Facebook - Balls World
Instagram - Ballsworld_
Top 10 facts about me:
-
I have a high voice.
-
I don't love Molokhia, and people get surprised when I say I don't like it and don't eat it!
-
I love the Panda too much.
-
I love Dr. Ibrahim Al-Faqi. and love to read his books and watch his vedios.
-
I don't like coffee, except the eid coffee.
-
I become sad for short time, then everything is Ok. I mean I get satisfied once the person
who made me sad talk to me in a lovely words.
-
I hate insects too much and I really get afraid when I see one of it.
-
Many times I think to deactivate all of my social media accounts, but then I explore
that it is very useful and I can't live without it!
-
I am in love with details, and collect memories.
-
I have addiction through notebooks and pens with beatiful colors and shapes, and
I continuously buy these things even if I don't need it.


Follow these steps to place a new bid on an Alpha:
- Log in to the Algorithm Lab.
- In the left navigation bar, click Strategies > Strategies.
- On the Strategies page, click the Alpha that you want to place a bid on.
- On the Alpha page, click License Alpha.
- Enter an allocation and maximum bid.
Click Show Detailed View to see other bids on the order book.
- If you want to license the Alpha for more than one month, click Show Detailed View and then select a longer period.
- Click License Now or Place Bid.
Your bid will be automatically accepted if the Alpha has capacity available. Otherwise, your bid will stay on the order book until the next auction.


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Issue #4213
This test-case is intended for Issue #4213
- Script panel's location button is not updated.
Jan Odvarko<EMAIL_ADDRESS>- Open Firebug and enable, select the Script panel. Reload this page.
- Select issue4213.html in the location bar.
- Create breakpoint at line 22
- Click on the
Execute Test
button below.
- Step into
myFunc1
- Check the location bar, issue4213-1.js file should be selected -> BUG.
If you step over within the issue4213-1.js file, the location button is updated.
But it's too late.


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@import com.gu.googleauth.UserIdentity @import com.gu.janus.model.JanusData @(user: UserIdentity, janusData: JanusData)(implicit assetsFinder: AssetsFinder) @main("Home", Some(user), janusData) {
You are logged in as @user.email. Not you? Logout here.
You do not have any permissions. If this doesn't seem right, you can check your permissions in @janusData.permissionsRepo match { case None => { Janus' configuration. } case Some(repoUrl) => { Janus' configuration. } }


{% extends "layout.html" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} Which of the following access needs did they have? Hearing Select all that apply Deaf (uses British Sign Language as first or preferred language) Hearing impairment/hearing loss Other (please specify) Physical & Motor Select all that apply Physical disability Wheelchair User/mobility difficulties Other (please specify) Thinking & Feeling Select all that apply ADHD Autistic Spectrum/Aspergers Dyslexia Learning difficulties Mental Health condition Other (please specify) Vision Select all that apply Blind Partially sighted (vision impairment not corrected by glasses) Other Select all that apply Hidden disability such as epilepsy or diabetes please specify Disability or condition not listed above please specify {% endblock %}


This assumes SublimeText 3, though may also work with older versions.
Copy the files in the docs/sublime
folder to your
Packages/User
folder (on Windows this is
\Users\YourUserName\AppData\Roaming\Sublime Text 3\Packages\User
).
Open up lobster.sublime-build
and make sure the path
to your lobster executable is correct. Also note that it refers to a
custom version of exec.py
called
lobster_exec.py
. The only difference is that the default
script surpresses windows from popping up, which disables any graphical
output from Lobster.
Load some lobster code. pressing ctrl+b
should run
it.
By default, SublimeText comes set up insert tabs into your code,
which is against the Lobster style guide,
and can result in errors if you modify the existing examples. Add
"translate_tabs_to_spaces": true
to your user settings.
The build script parses Lobster error messages, so just pressing F4 will get you to the line of the error.
The lobster.tmLanguage
file you copied should provide
automatic syntax highlighting for any .lobster files.


{{page.title}}
{{page.abstract}}
{{ content }}
Edit in Prose
This article has been written and published using Prose.
Please help improving it by contributing to the text.


The problem
Api Tokens are like user passwords, except they are always managed by Jenkins (even if you're using an external authentication scheme), and can only be used for "API" actions (e.g. using curl
).
The storage scheme of these tokens is a little weird - the stored value is hashed and then compared to the user-submitted input (usually the process goes the other way around):
// https://github.com/jenkinsci/jenkins/blob/master/core/src/main/java/jenkins/security/ApiTokenProperty.java#L111
public boolean matchesPassword(String password) {
return getApiTokenInsecure().equals(password);
}
// https://github.com/jenkinsci/jenkins/blob/master/core/src/main/java/jenkins/security/ApiTokenProperty.java#L99
@Nonnull
@Restricted(NoExternalUse.class)
/*package*/ String getApiTokenInsecure() {
String p = apiToken.getPlainText();
if (p.equals(Util.getDigestOf(Jenkins.getInstance().getSecretKey()+":"+user.getId()))) {
// if the current token is the initial value created by pre SECURITY-49 Jenkins, we can't use that.
// force using the newer value
apiToken = Secret.fromString(p=API_KEY_SEED.mac(user.getId()));
}
return Util.getDigestOf(p);
}
The implication of this is that in order to modify the API token, it's not enough to know the token, but rather the "seed" used to calculate it. One could say that the UI/API doesn't offer a way to set the token directly anyway (only to generate a random one), but I still don't see the logic behind this.
Because of the storage scheme, the normal "ensure password is XYZ" action one would expect is split into 2 actions:
- Calculate the token from the current seed and compare it to our required token, and if there's a mismatch, get angry (provided the required token)
- Compare the seed to our required seed, and modify it if it mismatches (provided the required seed)
I don't want my clients to depend on the hashing method done by Jenkins, so I have to present them with the token.
I also want to be able to reset the token if it diverges (rather than throw a tantrum), so I have to keep the seed.
So sadly I have to keep both.
The solution
I created this Groovy script to do both tasks (modify the seed if needed, and ensure it matches the token):
def username = '???'
def token = '???'
def seed = '???' // Leave null if unkown
import hudson.model.User;
import jenkins.security.ApiTokenProperty;
import hudson.util.Secret;
// Get the actual token
u = User.get(username)
tokprop = u.getProperty(ApiTokenProperty.class)
actual_token = tok.getApiTokenInsecure()
// Get pissed if not equal
if (token != actual_token)
if (seed) { // Try to modify seed
tokprop.apiToken = Secret.fromString(seed)
// Check that seed will yield required token
if (token == tokprop.getApiTokenInsecure())
u.save()
else
throw new Exception(sprintf('Token mismatch, seed wont cause token to become required token\\ncurrent key:%s\\nresulting key:%s',[actual_token, tokprop.getApiTokenInsecure()]))
}
else
// Can't set a better token, just complain
throw new Exception(sprintf('Mismatching tokens. Actual token:%s', [actual_token]))
I actually implemented this as a LWRP in Chef using the jenkins_script
resource found in the Jenkins cookbook. If the implementation is interesting, leave me a comment and I might add it.
Update 20.02.16: Here is my current implementation
Chef implementation
resources/jenkins_key:
actions :set
default_action :set
attribute :user, kind_of: String, name_attribute: true
attribute :key, kind_of: String, required: true # We can't set this, only verify it matches
# This is because the actual key is being realtime-hashed from some string
attribute :raw_key, kind_of: String, default: nil # Used to allow setting the new key
providers/jenkins_key:
use_inline_resources
action :set do
jenkins_script 'ensure_key' do
command <<-EOH
def username = '#{new_resource.user}'
def wanted_key = '#{new_resource.key}'
def raw_key = '#{new_resource.raw_key.to_s}'
import hudson.model.User;
import jenkins.security.ApiTokenProperty;
import hudson.util.Secret;
// Get the actual key
u = User.get(username)
tok = u.getProperty(ApiTokenProperty.class)
actual_key = tok.getApiTokenInsecure()
// Get pissed if not equal
if (wanted_key != actual_key)
if (raw_key) {
old_key = tok.getApiTokenInsecure()
tok.apiToken = Secret.fromString(raw_key)
// Check that raw key will yield required key
if (wanted_key == tok.getApiTokenInsecure())
u.save()
else
throw new Exception(sprintf('Key mismatch, raw_key wont cause wanted_key to become actualy key\\ncurrent key:%s\\nresulting key:%s',[actual_key, tok.getApiTokenInsecure()]))
}
else
// Can't set a better key, just complain
throw new Exception(sprintf('Mismatching keys. Actual key:%s', [actual_key]))
EOH
end
end


Jamaican students honoured at CXC regional awards
Four Jamaican students have been honoured by the Caribbean Examinations Council (CXC) in its Presentation of Awards for Outstanding Performances in the May/ June 2019 Examinations.
The ceremony was held last Thursday at the Radisson Grenada Beach Resort in Grenada.
Rhea-Simone Lawrence from the St Andrew High School for Girls received the ACCA Award as the most outstanding student across the region in Principles of Accounts at the Caribbean Secondary Education Certificate (CSEC) level.
Carl Webster from Glenmuir High School was the most outstanding candidate in CSEC Caribbean History.
He received the Menezes-Rodney Award, while Josh-Oniel Hemans from Guys Hill High School received the CARDI award for being the most outstanding candidate in CSEC Agricultural Science.
Kameika Robinson from the Queen’s High School was the most outstanding student in the Humanities for her performance in the Caribbean Advanced Proficiency Examination (CAPE).
She received the Hodder Award.
The four Jamaican students are among 20 students from the region who were honoured at the ceremony for outstanding performance in the May/ June 2019 exams.
Some 120 Jamaican students were also acknowledged via the CXC’s Merit List for their stellar performance in the 2019 exams.


Dynamic Thermodynamics
#### A study in static
Thermodynamics is the study of heat. What is heat? That’s like temperature, right? Hot and cold and so on. And thermodynamics has something to do with entropy, whatever that is (disorder something something). You might also have heard something about energy loss becoming heat somehow. All this is actually pretty easy if you look at it right. Let’s take a closer look, literally.
Main explanations
- What is heat/energy?

- Inverse popularity?
- What is pressure?
- Relation to volume (piston?)
- Smattering sound
- Negative pressure (e.g. a jar)
- Gravity -> air pressure
- What is entropy?
- Color mixing
- Warm/cold mixing
- Egg breaking?
- Arrow of time
- Messy initially -> run forwards and backwards
- Divided (ordered) initially -> run forwards and backwards
- Brownian motion
- Draw trajectory of one particle
- Friction
- Bigger ball collides into sea of small balls
- Annealing
- Local energy minima
- Need heat to randomly find lowest energy state
Small ideas
-
Top of each page: “This page assumes you are familiar with the following concepts (click on them to read more)”
-
We see that Energy is conserved, which should be true for elastic collisions. [Is momentum conserved? Why/why not?]
[If something is in brackets, it is meant as an exercise]
Math changes when radio button changes
- Two cold things coming together become warmer, because of the bond energy released.
- Warming something up helps it find lower energy state.
- Hole in the middle of structure does not get filled with T = 0, but with more energy, the particles wiggle loose.
- Exercises
- Try to lower the temperature by accelerating individual particles with mouse.
- Easy with big, few particles, hard with small, many
- Does expend energy, increase enthropy just to know which way to accelerate them!
- Which system has the higher enthropy?
- Make this system have higher/lower enthropy than that one
- Cannot lower, try as you might
- Heat conduction: Move heat from here to there
- as fast as possible?
- Friction: brake this particle
- Try to lower the temperature by accelerating individual particles with mouse.


|
areaDetector
3-12-1
EPICS areaDetector framework
|
This is areaDetector plug-in support for firewire cameras that comply with the IIDC DCAM protocol. More...
#include <stddef.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <unistd.h>
#include <epicsString.h>
#include <epicsTime.h>
#include <epicsThread.h>
#include <epicsEvent.h>
#include <epicsMutex.h>
#include <epicsEndian.h>
#include <ADDriver.h>
#include <dc1394/dc1394.h>
Classes | |
| class | FirewireDCAM |
| Main driver class inherited from areaDetectors ADDriver class. More... | |
| struct | camList_t |
| class | BusManager |
| A singleton class that keep a list of the (configured) cameras on the bus. More... | |
| struct | camNode_t |
| struct | pix_lookup |
Macros | |
| #define | REG_CAMERA_TRIGGER_MODE 0x830U |
| #define | ERR(errCode) if (errCode != 0) fprintf(stderr, "ERROR [%s:%d]: dc1394 code: %d\n", __FILE__, __LINE__, errCode) |
| Print an errorcode to stderr. More... | |
| #define | PERR(pasynUser, errCode) this->err(pasynUser, errCode, __LINE__) |
| Convenience macro to be used inside the firewireDCAM class. More... | |
| #define | FDC_DC1394_NUM_BUFFERS 15 |
| Number of image buffers the dc1394 library will use internally. More... | |
| #define | FIRST_FDC_PARAM FDC_feat_val |
| Feature value (int32 read/write) addr: 0-17. More... | |
| #define | LAST_FDC_PARAM FDC_bandwidth |
| Read back the current bandwidth (int32, read) More... | |
| #define | FDC_feat_valString "FDC_FEAT_VAL" |
| Specific asyn commands for this support module. More... | |
| #define | FDC_feat_val_maxString "FDC_FEAT_VAL_MAX" |
| #define | FDC_feat_val_minString "FDC_FEAT_VAL_MIN" |
| #define | FDC_feat_val_absString "FDC_FEAT_VAL_ABS" |
| #define | FDC_feat_val_abs_maxString "FDC_FEAT_VAL_ABS_MAX" |
| #define | FDC_feat_val_abs_minString "FDC_FEAT_VAL_ABS_MIN" |
| #define | FDC_feat_modeString "FDC_FEAT_MODE" |
| #define | FDC_feat_availableString "FDC_FEAT_AVAILABLE" |
| #define | FDC_feat_absoluteString "FDC_FEAT_ABSOLUTE" |
| #define | FDC_framerateString "FDC_FRAMERATE" |
| #define | FDC_videomodeString "FDC_VIDEOMODE" |
| #define | FDC_bandwidthString "FDC_BANDWIDTH" |
| #define | NUM_FDC_PARAMS (&LAST_FDC_PARAM - &FIRST_FDC_PARAM + 1) |
| Number of asyn parameters (asyn commands) this driver supports. More... | |
| #define | FDC_DC1394_FEATOFFSET DC1394_FEATURE_MIN |
| Feature mapping from DC1394 library enums to a local driver enum The local driver identifies a feature based on the address of the asyn request. More... | |
Typedefs | |
| typedef struct camList_t | camList_t |
| typedef struct camNode_t | camNode_t |
Functions | |
| void | reset_bus () |
| int | FDC_InitBus (void) |
| Initialise the firewire bus. More... | |
| int | FDC_Config (const char *portName, const char *camid, int speed, int maxBuffers, size_t maxMemory, int disableScalable) |
| Configuration function to configure one camera. More... | |
| void | FDC_ResetBus () |
This is areaDetector plug-in support for firewire cameras that comply with the IIDC DCAM protocol.
This implements the FirewireDCAM class which inherits from the areaDetector ADDriver class.
The driver uses the Linux libraries dc1394 and raw1394.
Author: Ulrik Kofoed Pedersen Diamond Light Source Ltd, UK. Created: November 2008
| #define ERR | ( | errCode | ) | if (errCode != 0) fprintf(stderr, "ERROR [%s:%d]: dc1394 code: %d\n", __FILE__, __LINE__, errCode) |
Print an errorcode to stderr.
Convenience macro to be used when an asynUser is not yet available.
| #define FDC_bandwidthString "FDC_BANDWIDTH" |
| #define FDC_DC1394_FEATOFFSET DC1394_FEATURE_MIN |
Feature mapping from DC1394 library enums to a local driver enum The local driver identifies a feature based on the address of the asyn request.
The address range is [0..DC1394_FEATURE_NUM-1] and the dc1394 feature enum starts at an offset of DC1394_FEATURE_MIN...
| #define FDC_DC1394_NUM_BUFFERS 15 |
Number of image buffers the dc1394 library will use internally.
| #define FDC_feat_absoluteString "FDC_FEAT_ABSOLUTE" |
| #define FDC_feat_availableString "FDC_FEAT_AVAILABLE" |
| #define FDC_feat_modeString "FDC_FEAT_MODE" |
| #define FDC_feat_val_abs_maxString "FDC_FEAT_VAL_ABS_MAX" |
| #define FDC_feat_val_abs_minString "FDC_FEAT_VAL_ABS_MIN" |
| #define FDC_feat_val_absString "FDC_FEAT_VAL_ABS" |
| #define FDC_feat_val_maxString "FDC_FEAT_VAL_MAX" |
| #define FDC_feat_val_minString "FDC_FEAT_VAL_MIN" |
| #define FDC_feat_valString "FDC_FEAT_VAL" |
Specific asyn commands for this support module.
These will be used and managed by the parameter library (part of areaDetector).
| #define FDC_framerateString "FDC_FRAMERATE" |
| #define FDC_videomodeString "FDC_VIDEOMODE" |
| #define FIRST_FDC_PARAM FDC_feat_val |
Feature value (int32 read/write) addr: 0-17.
| #define LAST_FDC_PARAM FDC_bandwidth |
Read back the current bandwidth (int32, read)
| #define NUM_FDC_PARAMS (&LAST_FDC_PARAM - &FIRST_FDC_PARAM + 1) |
Number of asyn parameters (asyn commands) this driver supports.
| #define PERR | ( | pasynUser, | |
| errCode | |||
| ) | this->err(pasynUser, errCode, __LINE__) |
Convenience macro to be used inside the firewireDCAM class.
| #define REG_CAMERA_TRIGGER_MODE 0x830U |
| int FDC_Config | ( | const char * | portName, |
| const char * | camid, | ||
| int | speed, | ||
| int | maxBuffers, | ||
| size_t | maxMemory, | ||
| int | disableScalable | ||
| ) |
Configuration function to configure one camera.
This function need to be called once for each camera to be used by the IOC. A call to this function instanciates one object from the FirewireDCAM class.
| portName | Asyn port name to assign to the camera. |
| camid | The camera ID or serial number in a hexadecimal string. Lower case and upper case letters can be used. This is used to identify a specific camera on the bus. For instance: "0x00b09d01007139d0". |
| speed | The bus speed to be used. This indicates whether to use the bus in 1394A or 1394B mode. Valid values are: 400 or 800. If an invalid value is entered the function will always default to 400 (legacy mode). |
| maxBuffers | Maxiumum number of NDArray objects (image buffers) this driver is allowed to allocate. This driver requires 2 buffers, and each queue element in a plugin can require one buffer which will all need to be added up in this parameter. |
| maxMemory | Maximum memory (in bytes) that this driver is allowed to allocate. So if max. size = 1024x768 (8bpp) and maxBuffers is, say 14. maxMemory = 1024x768x14 = 11010048 bytes (~11MB) |
| disableScalable | Disable scalable modes if this is 1 |
| int FDC_InitBus | ( | void | ) |
Initialise the firewire bus.
This function need to be called only once to initialise the firewire bus before FDC_Config() can be called. The bus will be first be reset, then scanned for cameras and the number of cameras and their hexadecimal ID will be printed to stdout.
| void FDC_ResetBus | ( | ) |
| void reset_bus | ( | ) |


CHAPTER 801D
RIGHTS OF VICTIMS AND WITNESSES IN
CRIMINAL PROCEEDINGS
Section
801D-1 Legislative intent
801D-2 Definitions
801D-3 Eligibility of victims
801D-4 Basic bill of rights for victims and witnesses
801D-5 Responsibility for rights and services
801D-6 Intergovernmental cooperation
801D-7 Televised testimony


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Animal
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The lurking suspicion that something could be simplified is the world’s richest source of rewarding challenges.
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Preparation time
1-2 hours
Cooking time
30 mins to 1 hour
Serves
Serves 6
Dietary
For the pastry, place the flour, butter, egg, sugar and milk into a food processor and pulse briefly to bring the ingredients together as a dough. Remove and place into a bowl and mix well with your hands, then cover and place into the fridge to chill for 20 minutes.
Dust a clean flat surface with a mixture of flour and icing sugar and roll out the pastry. Place the pastry into a 20cm/8in tart case and return to the fridge to chill for two hours.
Preheat the oven to 200C/400F/Gas 6.
Place the tart case into the oven, covered with a sheet of baking paper weighed down with baking beans and bake blind for 25 minutes, or until golden, but not browned. Remove the baking paper and beans, brush with the egg wash and return to the oven for five more minutes, then remove and leave to cool.
For the filling, place the pumpkin and butternut squash onto a baking sheet, sprinkle with the sugar and place into the oven to roast for 40 minutes, then remove and allow to cool slightly.
Place the pumpkin and butternut squash into a food processor and blend until smooth.
Reduce the oven temperature to 180C/355F/Gas 4.
Meanwhile, place the cream, honey and spices into a saucepan over a medium heat and bring to simmering point.
Place the beaten eggs into a clean bowl and pour over the cream mixture, whisking constantly.
Add the pumpkin mixture to the pan, stir well to combine, then pour into the tart case.
Transfer the tart to the oven and bake for 35 minutes, or until the filling has set. Remove and leave to cool.
For the custard, whisk the eggs, sugar and vanilla together in a clean bowl.
Place the milk into a clean saucepan and heat up to near boiling point, then remove from the heat and pour over the egg mix, whisking constantly.
Place the custard mixture back into the milk pan and simmer over a low heat, stirring regularly, until the custard thickens enough to coat the back of a spoon.
To serve, cut a generous slice of the butternut squash and pumpkin pie, place onto a plate and serve with the homemade custard drizzled over the top.


You've got Lillian's link
Lily enjoys playing chess, cartoons, video games, board/tabletop games, shogi, programming, and making art. Lily is a professional software engineer in Minneapolis, Minnesota, but is actually from outer space.
Lily enjoys playing chess, cartoons, video games, board/tabletop games, shogi, programming, and making art. Lily is a professional software engineer in Minneapolis, Minnesota, but is actually from outer space.


Portfolio Github || Connect on Linkedin || View Resume <---Work Day Schedular I used this project to hone my jQuery skills Github Link || Live Deployment History of Coding Quiz---> A way to learn my two favorite subjects, history and coding! Github Link || Live Deployment <---Drink to the Weather An at home bartender app built for my first group project Github Link || Live Deployment Eat Da Burgers---> A simple way to track how you eat burgers but with a complex set of code Github Link || Live Deployment


Plugin Information |
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View Darcs on the plugin site for more information. |
This plugin integrates Darcs version control system to Jenkins. The plugin requires the Darcs binary (darcs) to be installed on the target machine.
With this plugin, you can designate a Darcs repository as the "upstream" repository. Every build will then run "darcs pull" to bring the tip of this upstream repository. In a similar manner, polling will check if the upstream repository contains any new changes, and use that as the triggering condition of the new build.
Changelog
0.2.0
- Main SCM functionality implemented.
0.2.1
- Sax parser handles file moves in changeset.
0.2.2
- Bugfixes.
0.3.0
- Implementation of SCM browsers.
- Integration of Darcsweb.
- Improved inter changeset links.
0.3.1
- Implemeted file diff link for Darcsweb.
0.3.2
- Factored out darcs command as abstracted object from SCM class.
- Fixed changelog parser stripped of whitespaces from patch names and comments.
- Fixed Findbug and PMD issues.
- Implemented revision state and polling.
- Fixed add/remove dir tag recognition.
- Fixed comment/name parsing.
0.3.3
- Betetr revision state logging.
- ChangeSets in the ChangeSetList are sorted by darcs hash for digest calculation.
- Fixed issue 4: Check polling when patches are removed
0.3.4
- Implemented XML sanitizing for mixed character encoding in Darcs XML output (Issue 1).
0.3.5
- URI input form validation for Darcsweb and Darcsden code browsers.
- Stripping of 'Ignore-this: ...' from patch comments.
0.3.6
- Add config option for local path.
- Set core dependnecy to LTS 1.480.
0.3.7
- Bug fixing (NPE and init checkout problms with new local path feature).
- Refactoring and code cleaning.
- Increase test coverage
- Increase Javadoc.
0.3.8
- Fix issues with deserializing global plugin config from older versions throws exception(#11).
- Known issue: Old global plugin config is not loaded.
- Use job directory as working directory for darcs.
0.3.9
- Refactoring of Darcs command abstraction.
- Fix NPE on calculating first revision state (issue #13).
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- Fix NPE: Because of serialization issues with the main the changelog parse may be null on build time (issue #13).
0.3.11
- Fix problem that old configuration files won't be loaded correctly from versions prior to 0.3.6.


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