Aimee Jay (UK)
Having built her experience in web, animation and motion graphics, Aimee's skills in interactive design was always characterised by her interest in community, communication and social good. Co-founding Double Days in 2011, Aimee's work with Gather & Fold and Designers Who were built to challenge digital norms and notions of online publishing.
Aimee is currently working as a senior digital designer at D3R, and conducting research into distribution and communication tools within disaster-stricken communities.
Bonnie Abbott (AUS)
With a background in publication design, Bonnie's interest shifted to the designer-as-author model in 2011, writing with publications such as MADE Quarterly, Process Journal and Creative Review, and leading to the formation of Double Days and its experimental publishing projects.
Bonnie worked as role of editor at Australia's oldest design publication Desktop from 2013—2015, and has presented talks and workshops on self-publishing, adaptive design processes, and content-driven design. She wrote and taught RMIT's 'Design Inﬁdelity' studio in 2015, and currently works as Creative Lead at In Common.
Ongoing Inqueries
How can digital design help circulate improved building standards within low-literacy, earthquake affected communities?
Aimee is currently conducting research in Nepal, where an English builder is working to teach building standards for earthquake-resistant homes.
ONGOING
Resisting the styleguide: How can we empower clients to produce their own branded material?
Bonnie is working on a model that would see the designer work as a facilitator, providing the tools that enable clients to produce their own creative branding material. In an appropriate space where a brands maybe fluid, the inquiry proposes that continuous, authentic production of material can only come from an equipped stakeholder.
ONGOING
Past Projects
Designers Who: offsite publishing, onsite commentary Bonnie, Aimee, and The Brandling's Anne Miltenberg conceived and built Designers Who to share design articles and host the commentary that surrounds it. DW contains no imagery and no articles, instead treating reader comments as a form of design publishing. VIEW PROJECT
Design Futures Lab: Public lecture programme and promotion Bonnie curated the weekly public talks delivered through RMIT's DFL. Seeking to deconstruct podium-to-audience delivery, and challenge the tradition of 'thumbnail jpeg' promotion, speakers were invited to experiment with their lectures, and the lecture themes were used to inform the material and construction of the promotional posters.
Design As Activity: A Workshop in 4 Parts A day-long intensive workshop broke design practice into four pillars: writing, research, type and image making, and challenged students using unconventional processes to experiment within short timeframes. In conjunction with Paul Fuog (U-P.co), Veronica Grow (Old School New School) and Jack Musset (Motherbird).
#Platform Project with Mimi Zeigler and Neil Donnelly As part of the D-Crit Summer Intensive, design writer attendees, contributed 140-character critiques of their work and their environment. After two weeks, these mini-critiques were published through public display.
Gather and Fold: The Print Index
At the completion of the web-based publishing project, Gather and Fold was translated into a book. By interrogating its online origins, the publication's design and layout was informed by digital materials and approaches, affecting how the content was consumed, and leading to inclusions like printed "tumblr" scrolling. BUY A COPY SOLD OUT
Gather and Fold: The Web Index Over 15 months, 29 creatives local to Melbourne were interviewed about their process, practice and community. The interviews ran on a referral basis, meaning that each interviewee chose who was interviewed next, ensuring there was no author-led curation, and that the connections between disciplines, ages and experience levels were exposed as organically as possible. Now existing as an archive, the interviews act as a collective cross-section into the wider creative scene in Melbourne, as a snapshot in 2011-2013. VIEW ARCHIVE


--- layout: post excerpt: Meet my publication permalink: /Sentiment-Analysis-On-Twitter-Data-Theory-and-Practice published: true images: - url: /assets/Cover11th.png ---
SENTIMENT ANALYSIS ON TWITTER DATA: THEORY AND PRACTICE
RAHUL R KALLIL
RJ College of Arts, Science and Commerce
<EMAIL_ADDRESS>ABSTRACT
This paper focuses on analysis of Twitter data, which include sentiment analysis for the tweets posted in Twitter. Social networks are the main resources to gather information about people’s opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion.
There has been lot of work in the field of sentiment analysis of twitter data.
In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches,
Using various machine learning algorithms like Naive Bayes, we provide research on twitter data streams.
Keywords: Opinion Mining, Text Mining, Social Media, Sentiment Analysis, Twitter
INTRODUCTION
Social media platform as the data source are categorize into three general categories: blogs, micro-blogging sites, and review site [12-15]. Among these categories, Twitter, a microblogging site is highly popular with over 500 million users around the world.
It allows users to use a short text within a limit of 140 characters as their posts (also called tweets). Twitter sees a daily stream of more than 400 million tweets.
These tweets include from remaining socially connected to their friends, family members and co-workers, to updates of wider interest about current affairs, encompassing all kinds of information.
The topics of these tweets range widely from the simple, such as “what I’m doing right now,” to the thematic, such as “football games”.
Twitter also categorizes tweets and trends according to region and on its web interface it displays the most recent tweets as feeds.
As a social-networking service, Twitter features two social models called “following” and ‘Hashtags’. Following is a model wherein a user is allowed to choose any other users that she wants to follow. The one she follows is her friend, and she is the follower. A follower can follow any user without any permission or reciprocating by following her back. A follower receives all the updates of her friends [4].
Users can also add tags for the tweets. On Twitter the tag is called “hashtag”, and begins with sign “#”. Hash ‘#’, followed by the trend-name, is to identify and group tweets about a particular trending topic. The Hashtags generally are telegram style word(s) description without spaces and attempts to summarize the trend.
Different types of tweets and hashtags make Twitter data stream more readable.
Twitter also provides its data in the form of API for developers to create applications that require data gathered by tweets and trends.
There are three types of tweets: reply, retweet and normal tweet. Reply is a message to any user with the sign “@” followed by the target user's name at the beginning of the tweet. Retweet is a message which user shares from friends to her followers which begins with the sign “RT”. Normal tweet are the tweets except replies and retweets.
In this section, we provide background on Twitter relevant to this work, describing the syntax utilized by its users, and the way they interact with each other and spread information. Then, we get into detail of how Twitter presents and deals with trends (defined as trending topics by the microblogging system), which we utilize as the input to our trend classifier and understand the sentiment of the trending tweets.
Sentiment analysis (SA) tells user whether the information about the product is satisfactory or not before they buy it.
Sentiment Analysis
Sentiment analysis can be defined as a process that automates mining of attitudes, opinions, views and emotions from text, speech, tweets and database sources through Natural Language Processing (NLP). Sentiment analysis involves classifying opinions in text into categories like "positive" or "negative" or "neutral".
Pak and Paroubek(2010) proposed a model to classify the tweets as objective, positive and negative. They created a twitter corpus by collecting tweets using Twitter API. Using that corpus, they developed a sentiment classifier based on the multinomial Naive Bayes method that uses features like N-gram and POS-tags. The training set they used was less efficient since it contains only tweets having emoticons.
Parikh and Movassate(2009) implemented two models, a Naive Bayes bigram model and a Maximum Entropy model to classify tweets. They found that the Naive Bayes classifiers worked much better than the Maximum Entropy model.
Go and L.Huang (2009) proposed a solution for sentiment analysis for twitter data by using distant supervision, in which they build models using Naive Bayes, Maximum Entropy and Support Vector Machines (SVM). Their feature space consisted of unigrams, bigrams and POS. They concluded that SVM outperformed other models and that unigram were more effective as features.
Opinion mining (sentiment extraction) is employed on Twitter posts by means of following techniques
3.1 Lexical analysis
The input text is converted to tokens by the Tokenizer. Every new token encountered is then matched for the lexicon in the dictionary. If there is a positive match, the score is added to the total pool of score for the input text. For instance if “dramatic” is a positive match in the dictionary then the total score of the text is incremented. Other-wise the score is decremented or the word is tagged as negative.
3.2 Machine learning based analysis
Following are the phases required for sentiment analysis of twitter data:
3.2.1 Data Collecting – There are three possible ways to collect Twitter data for research as follows.
The link to download the dataset from Stanford development program:
http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip
This data consists of 4 million tweets categorized as positive and negative.
3.2 Pre-processing – The collected data is raw data. In order to apply classifier it is necessary to pre-process the raw data. In this stage, the acquired data is cleaned and made ready for feeding it into the classifier. Cleaning includes extraction of keywords and symbols. For instance – Emoticons are the smiley used in textual form to represent emotions e.g. “:-)”, “:)” etc and Removing retweets:
Many of the tweets were the same; they were what Twitter calls a `retweet'. Since a retweet does not take as much thought as an original tweet we decided to remove the retweets as to prevent a bias in the data. If several columns in our term document matrix were identical, we removed all but one of those columns.
The pre-processing task involves:
3.3 Training Data –
In the training data, a collection of tagged corpora is provided.. Training the classifier makes it easier for future predictions for unknown data. We can figure out
Words And Their Frequencies, Parts Of Speech Tags ,Opinion Words And Phrases , Position Of Term, Negation
Features:
Generally, unigrams (single word phrases), bi-grams (two consecutive phrases), tri-grams (three consecutive phrases) are selected as feature vectors. There are a variety of proposed features namely number of positive words, number of negative words,
A hand-tagged collection of data is prepared by most commonly used crowd-sourcing method. This data is the fuel for the classifier; it will be fed to the algorithm for learning purpose.
3.4 Classification – This is the heart of the whole technique. Depending upon the requirement of the application SVM or Naïve Bayes is deployed for analysis. The classifier (after completing the training) is ready to be deployed to the real time tweets/text for sentiment extraction purpose.
3.5 Results – Results are plotted based on the type of representation selected i.e. charts, graphs, etc. Performance tuning is done prior to the release of the algorithm
Accuracy is reported to vary from 63% to 80% depending upon the combination of various features selected.
In this paper, we used python to implement sentimental analysis. Some packages have utilized including tweepy and textblob. We can install the required libraries by following commands:
The second step is downloading the dictionary by running the following command:
python -m textblob.download_corpora.
The textblob is a python library for text processing and it uses NLTK for natural language processing [6]. Corpora is a large and structured set of texts which we need for analyzing tweets.
The twitter dataset is publicly made available by Stanford University. Analyses were done on this labelled datasets using various feature extraction technique. We used the framework where the pre-processor is applied to the raw sentences which make it more appropriate to understand. Further, the different machine learning techniques trains the dataset with feature vectors and then the semantic analysis offers a large set of synonyms and similarity which provides the polarity of the content.
Table 1: Negative and Positive sentiment score of Training data
Following are the details on most informative features after the classifier is executed on train data.
sad = True neg : pos = 37.6 : 1.0
worst = True neg :pos = 32.4 : 1.0
crying = True neg : pos = 24.7 : 1.0
fml = True neg : pos = 24.1 : 1.0
hurts = True neg : pos = 21.2 : 1.0
awful = True neg : pos = 21.1 : 1.0
ugh. = True neg :pos = 20.4 : 1.0
Effect of Stop words
When Naive Bayes was run, it gave an accuracy of 73.65 percent, which is considered as the baseline result.
When stop words were removed and Naive Bayes was run, it gave an accuracy of 74.56 percent. Following table shows the accuracy obtained at different sizes for the Naïve Bayes with stop words removed and using pre-processed data and based on unigram model.
sad = True neg : pos = 27.6 : 1.0
awful = True neg : pos = 20.3 : 1.0
ugh = True neg : pos = 19.3 : 1.0
poor = True neg : pos = 19.3 : 1.0
sucks = True neg : pos = 18.7 : 1.0
upset = True neg : pos = 18.0 : 1.0
argh = True neg : pos = 17.3 : 1.0
battery = True neg : pos = 16.6 : 1.0
This shows that stop words really affect the predictions. Thus removal of stop words makes a lot of difference to the accuracy.
Effect of Bigram:
Bigram uses a combination of two words as a feature. Bigram effectively captures some features in the data that unigram fails to capture. For example, words like ‟not sad‟, ‟not good‟ clearly say that the sentiment is negative. This effect can be clearly seen from the increase in accuracy from 74.56(Unigram) to 76.44 percent which is almost a 2% increase.
The most informative features for Naive Bayes with Bigrams as features:
('so', 'sad') = True neg :pos = 55.2 : 1.0
sad. = True neg :pos = 44.2 : 1.0
('i', 'lost') = True neg :pos = 24.7 : 1.0
('miss', 'him') = True neg :pos = 24.1 : 1.0
Effect of using Trigram.
Running Naïve Bayes using Trigrams, bigrams and unigrams together gave an accuracy of 75.41 percent which is less than the accuracy obtained when Bigrams were used as a feature.
Hence for further analysis, the trigrams are not considered as they do not have a notice able impact on the accuracy.
The most informative features for Naive Bayes with Trigrams as features.:
('so', 'sad') = True neg :pos = 59.1 : 1.0
('lost', 'my') = True neg :pos = 38.9 : 1.0
('i', 'miss', 'my') = True neg :pos = 36.9 : 1.0
('going', 'to', 'miss') = True neg :pos = 28.5 : 1.0
('happy', "mother's", 'day') = True pos :neg = 25.0 : 1.0
Table 2. Accuracy of Naive Bayes Algorithm
CONCLUSION
In a survey conducted by [6], comparison of all approaches has showed that best results have been observed from machine learning approaches, and least by lexical approaches. However, without any proper training of a classifier in machine learning approach results may deteriorate drastically.
We focused on Twitter as and have implemented the python program to implement sentimental analysis.
Research results show that machine learning methods, such as SVM and naive Bayes have the highest accuracy and can be regarded as the baseline learning methods, while lexicon-based methods are very effective in some cases, which require little effort in human-labelled document.
APPENDIX
Table 1: Negative and Positive sentiment score of Training data
Table 2. Accuracy of Naive Bayes Algorithm
REFERENCES
Top of Form
Bottom of Form
[1] H. Bagheri and M. J. Islam, “Twitter Sentiment Analysis”.
Top of Form
Bottom of Form
[2] M. Desai and M. A. Mehta, “A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks”, vol. 6, no. 2, pp. 73-81, Jan. 2016.
Top of Form
Bottom of Form
[3] G. Gautam and D. Yadav, “Sentiment analysis of twitter data using machine learning approaches and semantic analysis”, Jan. 2014.
Top of Form
Bottom of Form
[4] “Feature Extraction for Sentiment Classification on Twitter Data”, vol. 5, no. 2, pp. 2183-2189, Jan. 2016.
Top of Form
Bottom of Form
[5] M. Karanasou, A. Ampla, C. Doulkeridis, and M. Halkidi, “Scalable and Real-Time Sentiment Analysis of Twitter Data”, Jan. 2016.
Top of Form
Bottom of Form
[6] V. A. and S. Sonawane, “Sentiment Analysis of Twitter Data: A Survey of Techniques”, vol. 139, no. 11, pp. 5-15.


# Got Feedback?
Hey Apaches! Now that this year's yearbook is
done and published, we need YOUR input for next
year's book! Please fill out the quick survey
below to help improve next year's coverage so
that we can make sure that what you want is in
the book.
We ask you to provide your name so that if you
decide that you want to be interviewed for next
year, we can have that on record. Your comments
regarding this year's 2019-2020 yearbook will be
kept confidential and will not be publicized.
[Link to form](https://goo.gl/forms/AOrQSxPRZfispF3X2)


--- layout: page title: Presentations #description: "Maxime Guyot - Software Defined Engineer" header-img: "img/about-bg.jpg" ---


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There are no workers currently online. In order to run builds you need at least one worker node connected.


---
title: Reliable and Trustworthy Artificial Intelligence
ref: rtai2022
description: Graduate course involving the analysis, robustness and visualization of neural networks.
semester: Fall 2022
number: 263-2400-00L
lecturer: Prof. Dr. Martin Vechev
ta: Maximilian Baader, Mislav Balunović, Benjamin Bichsel, Dimitar I. Dimitrov, Marc Fischer, Jingxuan He, Nikola Jovanović, Nikola Konstantinov, Mark Niklas Müller, Momchil Peychev, Samuel Steffen
assistants:
head-ta: Benjamin Bichsel
head-ta-details: use Moodle for questions unless they contain sensitive information
edoz: http://www.vvz.ethz.ch/Vorlesungsverzeichnis/lerneinheit.view?lerneinheitId=163199&semkez=2022W&ansicht=LEHRVERANSTALTUNGEN&lang=de
session-time-place:
lecture-time-place: Wed 14-16, HG G3 (physical recordings, virtual recordings)
exercise-time-place: Mon 12-14 (Zoom) or Wed 12-14 (CAB G 51)
credits: 6
image: assets/images/reliableai_logo.png
---
Overview
Creating reliable, secure, robust, and fair machine learning models is a core challenge in artificial intelligence and one of fundamental importance. The goal of the course is to teach both the mathematical foundations of this new and emerging area as well as to introduce students to the latest and most exciting research in the space. To facilitate deeper understanding, the course includes a group project where students build a system based on the learned material.
The course is split into 3 parts:
Robustness in Deep Learning
- Adversarial attacks and defenses on deep learning models.
- Automated certification of deep learning models (covering the major
trends: convex relaxations and branch-and-bound methods as well as
randomized smoothing).
- Certified training of deep neural networks to satisfy given properties (combining symbolic and continuous methods).
Privacy of Machine Learning
- Threat models (e.g., stealing data, poisoning, membership inference,
etc.).
- Attacking federated machine learning (across modalities such as vision,
natural language and tabular).
- Differential privacy for defending machine learning.
- Enforcing regulations with guarantees (e.g., via provable data minimization).
Fairness of Machine Learning
- Introduction to fairness (motivation, definitions).
- Enforcing individual fairness with guarantees (e.g., for both vision or tabular data).
- Enforcing group fairness with guarantees.
Lectures
Use your NETHZ account to access the files.
| Date |
Content |
Slides |
Exercises |
Solutions |
| Sep 21 |
Course Introduction, Adversarial Attacks and Defenses |
|
|
|
| Sep 28 |
Adversarial Attacks and Defenses (Cont), General Verification, Box Relaxations |
|
|
|
| Oct 05 |
MILP and DeepPoly for Certification |
|
|
|
| Oct 12 |
Differentiable refinement of DeepPoly |
|
|
|
| Oct 19 |
Certified Training |
|
|
|
| Oct 26 |
Project Introduction |
|
(Project Q&A)
|
|
| Nov 2 |
Randomized Smoothing for Robustness |
|
|
|
| Nov 9 |
Introduction to Privacy, Federated Learning and Attacks |
|
|
|
| Nov 16 |
Differential Privacy |
|
|
|
| Nov 23 |
AI Regulations and Synthetic Data |
|
(No Exercise)
|
|
| Nov 30 |
Introduction to Fairness, Incorporating Logic into Deep Learning |
|
|
|
| Dec 7 |
Individual Fairness |
|
|
|
| Dec 14 |
Group Fairness |
|
|
|
| Dec 21 |
Wrap up |
|
(No exercise) |
(No exercise) |
Recordings
All lecture recordings from this year will be available on the ETH
video portal or on Polybox.
While we do not provide recordings from 2021, all lecture recordings from
2020 are in a
Youtube
playlist (2020). Note that this 2022 version of the course contains
several new topics not found in the 2020 and 2021 versions.
Course project
The project description is uploaded
here.
Code, networks and example test cases are available
here.
Other project details will be added here.
Previous Exams
Previous exams (formerly, this course was named "Reliable and Interpretable
Artificial Intelligence") are available in the exam collection of the student association
(VIS).
Course Organization
Lectures
- The lecture will take place physically in room HG G3, but will be recorded.
-
For additional questions, we have prepared a Moodle
forum.
Exercises
- The exercise sessions will take place via Zoom on Monday and
physically on Wednesday.
- This mode was decided based on a student poll available here.
You will be able to change your vote throughout the semester, but we may not
strictly follow the majority opinion.
- Every week, we will publish an exercise sheet and its solutions on this
page, by Thursday evening.
- The exercise session will consist of a discussion of selected exercises
(typically not all exercises). On demand, the teaching assistant can also
discuss questions on specific exercises brought up by students.
- Some exercise sessions will also discuss prerequisites for the course.
The material covered in these sessions will be available online. This will
definitively be the case in the first exercise on Sep 26/28. For other
exercise sessions, we will announce by mail if they discuss
prerequisites.
- Attending the exercise sessions is optional. We will not cover additional material in the exercise sessions, except for prerequisites (see above). Therefore, we will also not record the exercise sessions.
- We strongly recommend to solve the exercises before next week's exercise
session, and before looking at the solutions. The style of the exam will be
similar to the exercises, so first-hand experience solving exercises is
critical.
- For additional questions, we have prepared a Moodle
forum.
- In case there is not enough material to cover the full exercise session,
we will stop it early.
- There is no need to attend both exercise sessions, as their contents
will be equivalent.
Communication
All communication (like special announcements) will be sent out by e-mail.
Literature
For students who would like to brush up on the basics of machine learning used
in this course, we recommend


Lost Taiwan hiker says ghosts saved him
Man who went missing after hiking in Pingtung mountains and was rescued after 10 days says spirits guided him to safety
by Dave MakichukA Taiwanese hiker who went missing for 10 days and was found by indigenous villagers on Wednesday morning claimed that he had been guided by a ghost couple he met in the mountains, Taiwan News reported.
The family of 60-year-old Soong Rui-xiong (宋瑞雄), an employee at Formosa Heavy Industries (台朔重工), lost contact with him on Nov. 10 after he left home to hike in the mountains of Pingtung.
Soong was spotted Wednesday by a local villager and immediately escorted to the Chunri Precinct of the Pingtung County Police Department.
According to UDN, Soong used stream water and natural sugar from plants as his energy source since he had only prepared food sufficient for one day.
With tears welling up in his eyes, Soong expressed gratitude for the rescue crew’s nonstop efforts.
Soong also told reporters that a ghost couple, who he believed to be the ancestral spirits of indigenous Taiwanese, accompanied him for two hours and that the trio chatted about everyday life as they walked alongside one another.
Soong claimed that after some time, he turned around and could not see the couple but instead found the path leading to the foot of the mountain, reported Liberty Times.
The Pingtung County Fire Department said the rescue mission lasted for 10 days with help from 111 police officers and firefighters, 284 volunteers, and two helicopters.
Rescue workers consisted of local hunters and volunteers from International Search and Rescue and Formosa Heavy Industries.


Fornax.Net Namespace
Classes
| Class | Description | |
|---|---|---|
| ConfigFactory |
Configuration Factory for managing and building Fonax settings.
|
|
| Configuration |
Holds the configuration settings for Crawler,Repository, and Query.
|
|
| FormatExt |
Format extensions.
|
Enumerations
| Enumeration | Description | |
|---|---|---|
| CacheType | ||
| CachingMode |
Caching Mode determines what caching technique should be used.
|
|
| FetchAttribute |
Defines What fornax does when an exception is thrown on retrieval of a file.
|
|
| FileFormat |
File Formats Supported by Fornax.Net .
|
|
| FornaxFormat |
File Format Categories supported by Fornax.Net
|
|
| TraversalMode |
Traversal Mode specifies the mode at which fornax network crawler.
crawls the web.
|


FDA Food Label Claims Specifications¶
Attention
This section applies to USDA prime data, not to USDA select data. For explanation of USDA prime and USDA select data, see Deposition Workflow
Ontomatica Coverage¶
Ontomatica label classes integrate food label claims as specified by FDA, including coverage of all specifications below.
FDA Specifications for Food Label Claims¶
| Claim class | Label claim | Claim determination |
|---|---|---|
| Sodium content | Salt free | Less than 5 milligrams sodium per serving. |
| Sodium free | Less than 5 milligrams sodium per serving. | |
| No Salt added | No salt added during processing; does not necessarily mean sodium free. | |
| Very Low Sodium | 35 milligrams or less sodium per serving. | |
| Low Sodium | 140 milligrams or less per serving. | |
| Light in Sodium | 50% less sodium (as compared with a standard serving size of the traditional food), restricted to foods with more than 40 calories per serving or more than 3 grams of fat per serving. | |
| Less Sodium or Reduced Sodium | At least 25% less sodium (as compared with a standard serving size of the traditional food). | |
| Fat content | Fat Free | Less than 0.5 g of fat or saturated fat per serving. |
| Saturated fat free | Less than 0.5 g of saturated fat and less than 0.5 g of trans fatty fat. | |
| Low fat | 3 g or less of total fat. | |
| Low saturated fat | 1 g or less of saturated fat. | |
| Reduced fat or less fat | At least 25 percent less fat than the regular version. | |
| Cholesterol content | Cholesterol free | Less than 2 mg per serving. |
| Low cholesterol | 20 mg or less. | |
| Reduced cholesterol | At least 25 percent less cholesterol than the regular version or less cholesterol. | |
| Sugar content | Sugar free | Less than 0.5 grams of sugar per serving. |
| Reduced sugar | At least 25 percent less sugar than the regular version. | |
| Fiber content | High fiber | 5 g or more of fiber per serving. |
| Good source of fiber | 2.5 g or 4.9 g of fiber per serving | |
| Calorie content | Calorie free | Less than 5 calories per serving. |
| Low calorie | 40 calories or less per serving. |


Guabao is a programming environment encouraging the methodology that a program and its correctness proof should be developing hand in hand, and that how a program should be proved could sometimes give hints how the program can be written.
Guabao uses a variation of Edsger W. Dijkstra’s Guarded Command Language — see the language overview. However, Guabao is also an environment built into an editor. Shown below is a screen shot:
As the program is being written in the left pane, Guabao instantly parses the program, infers, and displays in the right pane verification conditions yet to be proved to show that the program meets the postcondition.
Code yet-to-be-written can be left as holes (specifications with pre and post consditions) to be filled in later, for example the hole between line 8 and 10. Attempts to prove verification conditions may give clues how the holes can be filled or, in case of failure, indicate that we need a stronger precondition or loop invariant.
See the tutorial for more explanations.
Guabao currently does not prove anything for the users. It is possible, in the future, to employ an external theorem prover to do the job.
The Name. GUA in Guabao comes from GUArded command language. Guabao (刈包) is a street food popular in places including Taiwan, where Guabao the software was designed.
The frontend of Guabao is an extension of Visual Code Studio. You can install it by searching for the extension “Guabao” in the editor, or through the Extensions Marketplace. A simple one-click installation downloads the frontend as well as pre-compiled backend (implemented in Haskell).
More information can be found in the tutorial, the Language Overview, and the Examples page.
Guabao is implemented and maintained by Shin-Cheng Mu, Ting-Yan Lai, and Thing-Han Lim in Institute of Information Science, Academia Sinica, Taiwan.
The backend of Guabao is implemented using Haskell, while the frontend is implemented using Reason and compiled to Javascript to run as a VS Code extension. Source code for the frontend and backend are stored on GitHub.
Guabao is still under development — everything may change or break tomorrow! We welcome bug reports, feature requests, and other suggestions. Feel free to submit them as issues through the GitHub repositories above, or contact the authors.


JQL
Username:
Password:
JQL (in example: "project = ASM AND Sprint = 7076 AND type != Sub-task"):
Download
XML
Input:
Convert


In March 2020, Bitly deprecated the v3 of their API, and switched to v4. Unfortunately, even with the changes to this package to make it compatible, there are several unavoidable breaking changes. These are summarized below:
expand()
and shorten()
lookup
method and corresponding endpoint have been deprecatedWith these changes all previous versions of this library are now full deprecated and there is only 1 version starting with v7.0.0
Here is a simple example of how you might have to update your use of node-bitly to account for the change:
// Both versions
const BitlyClient = require('bitly').BitlyClient;
const bitly = new BitlyClient('<accessToken>');
// v6.1.0
async function example(url) {
const response = await bitly.shorten(url);
console.log(`Your shortened bitlink is ${response.url}`);
}
// v7.x.x
async function example(url) {
const response = await bitly.shorten(url);
console.log(`Your shortened bitlink is ${response.link}`);
}
This module provides calls to the Bitly API for Nodejs.
For more information on the API request and responses visit the Bitly API docs
node-bitly
is programmed with TypeScript
but is compiled to JavaScript and supports node >= 10.0.0
. When you import the client you get full type information. There maybe be some gaps in the information but this will be filled in, in future releases.
To install via NPM type the following: npm install bitly
You can also install via git by cloning: git clone https://github.com/tanepiper/node-bitly.git /path/to/bitly
This library uses the API provided by bitly and requires an OAuth token to use. To get your access token, visit OAuth Apps (under Generic Access Token)
See http://dev.bitly.com for format of returned objects from the API
To see the available libary APIs, you can view the API Documentation offline, or you can view the index here (the generated documentation does not work on Github).
import { BitlyClient } from 'bitly';
const bitly = new BitlyClient('<accessToken>', {});
async function init() {
let result;
try {
result = await bitly.shorten('https://github.com/tanepiper/node-bitly');
} catch (e) {
throw e;
}
return result;
}
init();
When the library throws an error, it should be the error object response from Bitly, but if something has gone wrong with your internet or intermediate requests, it is possible that a generic AxiosError might get returned. You can use an exported Type Guard to narrow the type:
import {BitlyClient, isBitlyErrResponse} from 'bitly';
const bitly = new BitlyClient(process.env.BITLY_API_KEY);
let data: BitlyLink;
try {
data = await bitly.shorten('http://bit.ly/38XaXKy');
} catch (error) {
if (isBitlyErrResponse(error)) {
// Inferred type by TS is `BitlyErrorResponse`
console.log(`Bitly error: ${error.description}`);
} else if (error.isAxiosError) {
// Infererred type is `any`, but you can cast to AxiosError safely
const axiosError = error as unknown as AxiosError;
console.log(`AxiosError:`, axiosError.toJSON());
}
}
const { BitlyClient } = require('bitly');
const bitly = new BitlyClient('<accessToken>', {});
let result;
try {
result = await bitly.shorten(uri);
} catch(e) {
throw e;
}
return result;
If you are not using node 8
then you can still use the library with Promise
values:
const BitlyClient = require('bitly').BitlyClient;
const bitly = new BitlyClient('<accessToken>');
bitly
.shorten('https://github.com/tanepiper/node-bitly')
.then(function(result) {
console.log(result);
})
.catch(function(error) {
console.error(error);
});
You can also do raw requests to any Bitly endpoint. With this you need to pass the access token to the method
const BitlyClient = require('bitly').BitlyClient;
const bitly = new BitlyClient('<accessToken>');
try {
return await bitly.bitlyRequest('link/referrers_by_domain', {
link: 'https://github.com/tanepiper/node-bitly',
unit: 'hour',
timezone: 'Europe/Amsterdam'
});
} catch(e) {
throw e;
}
To run tests type npm test
.
The tests use replay
, which caches the responses from Bitly under the /fixtures
directory, until you edit a test's requests payload. This means you can run the test suite without having a Bitly API key, until you need to edit or add a new test.
Once you need to run tests that can't use a cached response and actually hit Bitly's API, you will need to pass your API key to the tests by having an environment variable BITLY_API_KEY
set to the value of your key.
Generated using TypeDoc


The Impact columns are the result of a first attempt to make all the different kind of activities comparable.
The sum of the +1s, Reshares and comments that we can extract from the activity if shared with Google +, or if manually updated for people who use Twitter more, the values should of favorites, reshares, replies.
GDEs can set addictional impacts for the single activities
For example if I create an article with source code related to it, I have 2 different activity types:
This 2 columns where the first introduced, but we quickly found out huge spikes in the Metadata Impact (RAW) on videos and articles reshared by the Google Developers Page.
To make numbers smaller and comparable we came up with a simple formula to calculate the Total Impact of an activity.
For each activity we make the following:
The Total Impact full formula can be expressed as follows:
Total Impact = 1 + log10(Social Impact RAW) + log10(Metadata Impact RAW)
Activities from the following sources are updated regularly by the server


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I am mostly attracted by photographs that leaves me in a state of being pleasantly lost, of reverie or wonder.


Editor Font Size
webHydLa is the IDE for HydLa, the language to describe hybrid systems.
webHydLa uses Mathematica as an external constraint solver.
Simulation Output will be written here.


Lehmann names Finch as Northern Superchargers skipper
London - Australia's One Day International captain Aaron Finch is to be skipper of The Northern Superchargers for the inaugural edition of The Hundred, the franchise announced on Tuesday.
The 32-year-old batsman - who is also skipper of the T20 Australian side - had been Superchargers coach Darren Lehmann's first pick in the draft.
They know each other well from Lehmann's stint as head coach of Australia - the pinnacle winning the 2015 World Cup.
Finch - who has over 100 ODI's and 50 T20 international appearances to his credit - has played for seven IPL sides as well as the Melbourne Renegades in the Big Bash.
He also has valuable English county experience through stints at both Surrey and Yorkshire.
He will captain a side which includes England superstar all-rounder Ben Stokes.
"I've spent many years working alongside Darren and I'm really excited to be heading back to Headingley (Yorkshire's home ground) as captain of the Northern Superchargers," he said in a statement.
"I think we have an excellent side with a good mixture of quality English cricketers and some match-winning overseas stars and I'm very confident of our chances when The Hundred kicks off next summer."
Lehmann, who had a successful stint playing for Yorkshire and was an integral part of the side that ended a 33-year drought in landing the county championship in 2001, said he was licking his lips with anticipation at the potential of the team.
"I think we've picked a great team and I'm really excited to see what Aaron and I can do with the group," said Lehmann.
"He is a world class cricketer and a superb leader.
"We want to start The Hundred with a bang and get even more people watching this innovative new short-form of cricket."


Jimny Drag Races Wrangler And Bowler Bulldog With An Off-Road Twist
The tiny Jimny loves to push its luck.
by Nico YlananThe Suzuki Jimny has been a huge success for the Japanese brand, so much so that its retro styling and attractive pricing, combined with its impressive off-road performance have created a package that's so irresistible, that car reviewers can't get enough of it. It's so good in fact that it has the confidence to take on the likes of a Jeep Wrangler Rubicon and a Bowler Bulldog is a straight up set of performance tests. Fair fight? Not at all. Fun to watch? Yes, indeed. The guys over at Carwow have another interesting match-up in their latest video, and it's really all fun and games.
Gallery: Jimny Drag Races Wrangler And Bowler Bulldog With An Off-Road Twist
Quite obviously, the Suzuki Jimny has the disadvantage on paper. Coming in with a rather conservative 1.5-liter gasoline engine that makes 101 horsepower and 96 pound-feet of torque, it really doesn't hold a candle to its rivals in this drag race. It does have a manual and fuel economy on its side, however, and it's also lighter. The Jeep Wrangler here is a Rubicon, more beefed-up for off-roading, and comes with a 2.0-liter turbocharged gasoline engine that makes 272 horsepower and 295 pound-feet of torque. Impressive numbers, but it's really child's play compared to the Bowler Bulldog. The obvious winner on paper is the Bulldog, coming packed with an engine lifted from a Range Rover SVR; a 5.0-liter supercharged V8 with 550 horsepower and a crazy 516 pound-feet of torque.
The Jimny Relishes Being The Underdog:
Pretty clear cut, right? Not so. A handicap is in order, and that's why the Bowler Bulldog gets relegated to the grass next to the drag strip. And on a damp day for this race, and equipped with all-terrain tires, the wheelspin and lack of grip will give the Bowler Bulldog quite the challenge. At least that's what they're all hoping. Without spoiling too much, watch the video and see who wins. At least no one cheated off the line, right? Oh, wait.
Source: Carwow via YouTube


--- layout: post title: The starting line... date: 2013-08-12 14:01:48.000000000 -05:00 type: post published: true status: publish categories: - Life tags: - goals - writing meta: _edit_last: '51126004' geo_public: '0' _publicize_pending: '1' author: Nathan Ahlstrom ---
Beginning today, I am going to write about my discoveries and howto's related to web development and the intersection of God's word and life.
My goal is to write something here everyday...


---
layout: splash
author_profile: false
header:
overlay_image: /images/blog/background.jpg
title: The thoughts and adventures of Henry Yu
excerpt: "
"
---
{% include base_path %}


Mukesh Ambani's Reliance Industries becomes world's 6th largest oil company
The Mukesh Ambani-led conglomerate's market capitalisation stood at approximately $133 billion at Tuesday's closing and further increased to $138 billion as its shares kept climbing on Wednesday.
HIGHLIGHTS
- Mukesh Ambani-led RIL becomes world's sixth-largest oil company
- The RIL group's market capitalisation now stands at $138 billion
- With the fresh development, Ambani's net worth has now touched $56 billion
Mukesh Ambani-led Reliance Industries Limited (RIL) has become the world's sixth-largest oil company after overtaking BP Plc.
The Mukesh Ambani-led conglomerate's market capitalisation stood at approximately $133 billion at Tuesday's closing and further increased to $138 billion as its shares kept climbing on Wednesday.
According to Bloomberg, Reliance's shares have witnessed three-fold growth compared to the average pace of India's benchmark index this year. RIL shares started climbing after Mukesh Ambani announced plans about cutting the company's net debt to zero in the next 18 months.
With the fresh development, Ambani's net worth has now touched $56 billion, which makes him Asia's richest person above Alibaba Group's Jack Ma, showed Bloomberg Billionaire Index (BBI) data.
While RIL's market capitalisation briefly surpassed BP for the first time at the end of October, it has now regained the lead against the British company.
Next up, RIL is looking to narrow down its gap with PetroChina Company, which is Asia's biggest oil firm in terms of market capitalisation. It is also behind Saudi Aramco and Exxon Mobil Corporation, which has a market value of $290 billion.
RIL has increased its market cap by 35 per cent this year compared to BP Plc's 1.2 per cent gain. At a time when oil companies around the world are struggling to grow due to increased volatility, RIL's credentials have been lauded by market watchers and experts.
Though Reliance earns two-thirds of its revenue from the energy business, Ambani's conglomerate has also made significant investments in telecom and digital services. Ambani has also expanded the company's retail business to take on the likes of Amazon and Walmart Inc.


Mirror
BIM creates a mirror of the feed for each student blog or resource. A mirror is a local up-to-date copy of the feed.
While this option is selected the feeds registered for this course will be mirrored. De-select and the mirror process will cease.
Typically the mirror process is only left on while the course is being offered.
The mirror process is run once when the student first registers their feed. It is then run every hour on all student feeds to see if any changes have occured.


PokerStrategy.com Privacy Policy
PokerStrategy.com Limited (“we”) commit to protecting and respecting your privacy. This policy (together with our terms of use and any other documents referred therein) sets out the basis upon which any personal data we collect from you, or that you provide to us, will be processed by us. Please read the following carefully to understand our views and practices regarding your personal data and how we will treat it.
What information we collect from you and how we collect it
We may collect the following data about you:
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Disclosure of your information to Third Parties
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With respect to the information relating to you that ends up in our possession, and recognising that it is your choice to provide us with your personally identifiable information, we commit to giving you the ability to do all of the following:
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Data Export
OpenDataDiscovery.org is constatly monitoring the status of hundreds of data portals. Every week we record the status of each data portal including
As all information is collected from the open data community, it also belongs to the public. To get a snapshoot of the history, simply select a date and you could download the historical data in JSON format.
Complete historical data?
The export of entire dataset is still under development. Please contact me via email for more information and further discussion.
Data Example
{{ format.example }}


GeneSetDb-class.Rd
Please refer to the multiGSEA vignette (vignette("multiGSEA")
),
(and the "The GeneSetDb Class" section, in particular) for a more deatiled
description of the sematnics of this central data object.
The GeneSetDb class serves the same purpose as the
GSEABase::GeneSetCollection()
class does: it acts as a centralized
object to hold collections of Gene Sets. The reason for its existence is
because there are things that I wanted to know about my gene set
collections that weren't easily inferred from what is essentially a
"list of GeneSets" that is the GeneSetCollection
class.
Gene Sets are internally represented by a data.table
in "a tidy"
format, where we minimally require non NA
values for the following
three character
columns:
collection
name
feature_id
The (collection
, name
) compound key is the primary key of a gene set.
There will be as many entries with the same (collection
, name
) as there
are genes/features in that set.
The GeneSetDb
tracks metadata about genesets at the collection
level. This means that we assume that all of the feature_id
's used
within a collection use the same type of feature identifier (such as
a GSEABase::EntrezIdentifier()
, were defined in the same organism,
etc.
Please refer to the "GeneSetDb" section of the vignette for more
details regarding the construction and querying of a GeneSetDb
object.
GeneSetDb(x, featureIdMap = NULL, collectionName = NULL)
| x | A |
|---|---|
| featureIdMap | A data.frame with 2 character columns. The first
column is the ids of the genes (features) used to identify the genes in
|
| collectionName | If |
table
The "gene set table": a data.table with geneset information,
one row per gene set. Columns include collection, name, N, and n. The end
user can add more columns to this data.table as desired. The actual
feature IDs are computed on the fly by doing a db[J(group, id)]
db
A data.table
to hold all of the original geneset id
information that was used to construct this GeneSetDb
.
featureIdMap
Maps the ids used in the geneset lists to the ids (rows) over the expression data the GSEA is run on
collectionMetadata
A data.table
to keep metadata about each
individual geneset collection, ie. the user might want to keep track of
where the geneset definitions come from. Perhaps a function that parses
the collection,name combination to generate an URL that points the user
to more information about that geneset, etc.
The GeneSetDb()
constructor is sufficiently flexible enough to create
a GeneSetDb
object from a variety of formats that are commonly used
in the bioconductor echosystem, such as:
GSEABase::GeneSetCollection()
: If you already have a GeneSetCollection
on your hands, you can simply pass it to the GeneSetDb()
constructor.
list of ids: This format is commonly used to define gene sets in the
edgeR/limma universe for testing with camera, roast, romer, etc. The names
of the list items are the gene set names, and their values are a character
vector of gene identifiers. When it's a single list of lists, you must
provide a value for collectionName
. You can embed multiple
collections of gene sets by having a three-deep list-of-lists-of-ids.
The top level list define the different collections, the second level
are the genesets, and the third level are the feature identifiers for
each gene set. See the examples for clarification.
a data.frame
-like object: To keep track of your own custom gene sets, you
have probably realized the importance of maintaing your own sanity, and
likely have gene sets organized in a table like object that has something
like the collection
, name
, and feature_id
required for a GeneSetDb
.
Simply rename the appropriate columns to the ones prescribed here, and pass
that into the constructor. Any other additional columns (symbol, direction,
etc.) will be copied into the GeneSetDb
.
You might wonder what gene sets are defined in a GeneSetDb
: see
the geneSets()
function.
Curious about what features are defined in your GeneSetDb
? See
the featureIds()
function.
Want the details of a particular gene set? Try the geneSet()
function.
This will return a data.frame
of the gene set definition. Calling
geneSet()
on a MultiGSEAResult()
will return the same data.frame
along
with the differential expression statistics for the individual members of the
geneSet across the contrast that was tested in the multiGSEA()
call that
created the MultiGSEAResult()
.
You can subset a GeneSetDb to include a subset of genesets defined in it.
To do this, you need to provide an indexing vector that is as long as
length(gdb)
, ie. the number of gene sets defined in GeneSetDb. You
can construct such a vector by performing your boolean logic over the
geneSets(gdb)
table.
Look at the Examples section to see how this works, where we take the MSIgDB c7 collection (aka. "ImmuneSigDB") and only keep gene sets that were defined in experiments from mouse.
## exampleGeneSetDF provides gene set definitions in "long form". We show ## how this can easily turned into a GeneSetDb from this form, or convert ## it to other forms (list of features, or list of list of features) to ## do the same. gs.df <- exampleGeneSetDF() gdb.df <- GeneSetDb(gs.df) ## list of ids gs.df$key <- encode_gskey(gs.df) gs.list <- split(gs.df$feature_id, gs.df$key) gdb.list <- GeneSetDb(gs.list, collectionName='custom-sigs') ## A list of lists, where the top level list splits the collections. ## The name of the collection in the GeneSetDb is taken from this top level ## hierarchy gs.lol <- as.list(gdb.df, nested=TRUE) ## examine this list-of lists gdb.lol <- GeneSetDb(gs.lol) ## note that collection is set propperly ## GeneSetDb Interrogation gsets <- geneSets(gdb.df) nkcells <- geneSet(gdb.df, 'cellularity', 'NK cells') fids <- featureIds(gdb.df) # GeneSetDb Manipulation .................................................... # Subset ImmuneSigDB down to only gene sets defined from mouse # NOTE: This doesn't work with updated MSigDB collections if (FALSE) { idb <- getMSigGeneSetDb('c7', 'mouse') igs <- geneSets(idb) table(igs$organism) ## Homo sapiens Mus musculus ## 1888 2984 idb.mm <- idb[igs$organism == 'Mus musculus'] length(idb.mm) ## 2984 }


Determine if IDAPro and WinDbg are still effective in today's environment.
For this project we wanted to make sure that these tools are still up to date in our ever-changing environment. Yes, both tools are still great choice for any cybersecurity engineer.
WinDbg is the debugger of choice for Windows. It is best known for its kernel level debugging but is also very powerful for non-kernel level debugging.
IDAPro – Is considered by many to be the best one of the best tools to reverse engineer malware on the market. This is because the it is very stable, has no major flaws, and can is capable of reverse engineering an astonishing amount of different malware ("Aslani").
Research & document how malware evades detection.
Stalling delays – This technique is when a piece of malware will remain idle in order to defeat timer-based scans ("Blaine").
Action required delays - Much like stalling delays this tactic will have the piece of malware be idle. Except this tactic will only execute barring a certain function performed by the user ("Blaine"). Almost all sandboxes are not able to detect this malware until it is too late.
Intelligent suspension of malware - This sophisticated evasion technique is used for when the malware has detected a sandbox on the system ("Blaine"). The malware will suspend all malicious actives until the host machine has been fully compromised. Only then will the malware start injecting and modifying code, decrypting files, and move laterally across a network ("Blaine").
Fragmentation - This technique is when a piece of malware will split into fragments in order to avoid detection from a sandbox. The sandbox will scan each of the separate fragments and not detect them as malicious. The malware will only run once the fragments are reassembled after the sandbox ("Blaine").
Research & document how malware evades detection
Rootkits – The rootkit is an application(or multiples) that will use tactics to hide its malicious code on the lower layers of the operating system ("Blaine").
Social Engineering - Hackers can use social engineering tactic to trick a user into willingly downloading malware on their system. This tactic plays to the needs, wants, and weaknesses of potential victims.
Phishing Attacks – A form of social engineering in the form of an email. Tricks the user with a convincing email into downloading a malicious file.
Certain malware will only execute after a system reboot has been performed. This is almost impossible for a sandbox to be able to detect.
Research & document how malware evades detection
One way that malware evades detection is by looking for features that are present in a real system, but not a sandbox. These features can include CPU Core Counts, the digital system signature, and the availability of certain programs.
Certain types of malware can change or encrypt its code.
Fast Fluxing - Changing DNS and IP addresses to bypass blacklists.
Malware is growing ever cleverer and quickly learning work arounds for sandboxing environments.
How to better protect and mitigate against the risks of malware
Extend/Change sleep duration: By extending the analysis time, you can potentially catch malware exploiting timing- based techniques.
Simulate Human Interaction: Most sandboxes do not simulate human interaction by default, however with the increasing prevalence of malware evasion tactics involving user interaction, adding user-like actions to the analysis process is a necessity.
Add real hardware/environmental artifacts: By including hardware information in your Sandbox, you could thwart Malware that attempts to detect Operating System characteristics to determine if the system is real or virtual.
How to better protect and mitigate against the risks of malware
Perform static code analysis: Analyzing the code of the suspicious file could reveal common malware evasion tactics or encrypted pieces of code that simply subjecting it to the Sandbox wouldn’t detect.
Fingerprint Analysis: “Fingerprinting” technology allows you to analyze a file for common indicators of malicious code, as well as documenting the evasion tactics of the current piece of malware for future reference.
Behavior-based analysis: Sandboxes utilizing BBA tactics interact with the Malware and emulate host computer processes in order to detect Malware Evasion techniques, once discovered, the malicious code is countered.
Kernel Analysis: As mentioned above, some forms of Malware are designed to inject malicious code into the lower levels of the OS. By implementing Kernel Analysis, you can prevent malware from getting into the Kernel
Implement Machine Learning: Analysis based on Machine Learning Algorithms can detect Malware Evasion techniques before their execution.
Using a code emulator: Code emulators directly handles each instruction executed within the analysis system and is thus able to tamper with the execution in any way the system wants to. This is done in a way that is completely transparent and invisible to the malware program under analysis.
Tampering with the outcome of string comparisons: When tampering with the outcome of string comparisons(e.g., when used to compare the username of the system) and forcing executions down a path that reveals a program’s true intent. It can also detect when a program is executing instructions that allow fingerprinting the hardware configuration and manipulate the effect of this code in a way to trigger additional behavior, helping it to correctly classify malware.
Companies’ main goal(s) when it comes to protection from malware infection should be to:
- Protect sensitive information and critical business workflows.
- Maintain the trust of customers, partners, and shareholders.
- Prevent huge monetary losses due to damage or ransom payments.
Cybersecurity policies are essential to any business because data breaches and cyber-attacks can become costly for a business to recover from. Also, employees are viewed as the weakest link when it comes to a company’s security. This is due to employees sharing passwords, clicking on malicious URLs and attachments, using unapproved cloud applications, and neglecting to encrypt sensitive files. Having certain security policies in place will allow employees to be more aware, decrease their vulnerability to security attacks, as well as maintain the secrecy and protection of the company’s data.
Creation of Password Policies: Good, difficult-to-guess passwords are essential to computer security. A strong password has at least eight characters long which include letters, numbers, special characters and capitalization; and are changed infrequently. Create a company policy that includes these specifications as well as a strict timeframe for when passwords are changed. Whether that is every 3-6 months, twice a year, etc.
Creation of Equipment Use Policy: Set boundaries as far as what your staff members are permitted to do on company-owned equipment. To what extent can they use things like laptops and phones for personal purposes? Can they install software of their choosing? Will there be mandatory scans, backups, or encryption? Establish clear rules to ensure that employees understand the privileges they do and do not have when it comes to company equipment.
Creation of Employee Separation Policy: Is your business doing anything to ensure that access to your network is effectively revoked immediately upon an employee’s departure? When an employee leaves—whether they’re your CEO or your receptionist—this termination policy must be enforced so that disgruntled former employees or any other unauthorized personnel cannot introduce malware to your system or access confidential data.


Coding Quiz Challenge
Try to answer the following code-related questions within the time limit. Keep in mind that incorrect answers WILL penalize your score/time by 15 seconds! Correct answers WILL add an extra 5 seconds to your remaining time and your final score! GLHF!


| Hostname | unknown |
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| Server Zeit | |
| Version | version |
| Starttime | |
| Uptime | |
| Classpath | |
| Number of threads | |
| Number of processors: | |
| Average load: | |
| Speicher: |
|
| OS arch | |
| OS name | |
| OS version |
| Property | Wert |
|---|---|
| Name | Value |
| Variable | Value |
|---|---|
| Name | Value |
Caches will be displayed after first use.
| Name | Type | Entries | requests | Hits | rate | misses | rate | loadPenalty | Clear |
|---|---|---|---|---|---|---|---|---|---|
| Health Check | Health | Description |
|---|---|---|
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| Name | Description |


Components, Blocking, Futures, and things you should know...¶
You should have a pretty solid understanding of concurrent patterns before writing applications in Megapode, but it isn’t strictly necessary. The framework aims to hold your hand somewhat.
You can skip this section if you want and come back to it at a later time, but a lot of what happens from here, depends on the following subject matter.
Invoking Megapode Components¶
All core components (except Pages, which are never called directly), make use of Dynamic proxies to wire up the messages. These take three forms.
The Widgets proxy, only ever returns a IWidget interface which only has a build method. This takes a argument list which is passed into the initialise method of the widget and the coordinated methods are then called in order, as described in the previous section.
The Renderers proxy always returns IRenderer, which allows you to call render in two ways.
The other components, DataAccessor, Task, Calculation all allow you to define an interface, and a class, and will route the message accordingly, and return the response (except task, which always returns void / null).
Each of these is addressed in their sections in this guide.
Futures¶
Megapode uses Akka’s (Scala’s) future model. This [http://doc.akka.io/docs/akka/2.0/java/futures.html] is an excellent primer on the topic.
In brief though, a Future is a wrapper for a value that will be fulfilled at some stage to come. A Promise is returns a future, and allows a developer to fulfil that future value at some point.
Most of Megapode consists of passing messages that will be handled later by some other process. For this, the future is a tailor made mechanism. This enables us to define arbitrary methods, use an interface to define a contract that you can use in your client code, and implement a concrete implementation with the same signature but very different internals. Dynamic proxies then handle the task of routing and fulfilment for you. This is demonstrated in the Calculation section.
Once again, you could just ignore this, and wire the messages yourself, should you choose to.
Future completion, and flattening of Futures¶
OK, so you have a future. This isn’t much use. At some stage, you actually need the result...
You can wait for completion by blocking, using:
Object result = Await.result(future, timeout.duration());
You can use a callback:
future.onComplete(new OnComplete<Object>() {
@Override
public void onComplete(Throwable arg0, Object response) throws Throwable {
//handle result here
}
});
Obviously, the non-blocking callback is preferred. But what if you want to use the result to call another Future. Look at the following
future.onComplete(new OnComplete<Object>() {
@Override
public void onComplete(Throwable arg0, Object response) throws Throwable {
Future<Object> new_future = someMethod(response);
new_future.onComplete(new OnComplete<Object>() {
@Override
public void onComplete(Throwable arg0, Object response) throws Throwable {
... and potentially more nesting
}
});
}
});
This could lead to a tangle of nested completions handlers.
Megapode provides a When(){ finishes() }.itWill() pattens to flatten futures.
When takes a list of arguments, some of them futures and itWill returns a future. When all of the futures have completed, it will call finishes, passing in all the arguments in the original order, with the completed results in place of any futures. It then maps the new future to the itWill future returned earlier, and will complete accordingly.
Therefore in the above example
// context is the current execution context
Future future ....
Future<Object> new_future = (new When(context, future){
@Override
Future<?> finishes(Throwable exception, Object... params){
return someMethod(params[0]);
}
}).itWill();
Blocking and Components¶
In general, blocking is to be avoided, but in Megapode two of the Component types block, as they need to retain state during their execution. These are the Widget and the Page components. By default (it’s Megapode, change it if you want...) these components provide a default flow. The object is:
- constructed
- initialised
- collect is called
- prepare is called
- render is called
and the response is send via a message back to the caller.
There is a rule here, a developer should attempt to make sure that, as far as is logical, as much of the computation is done in parallel as is possible. To do this, most tasks are done via calling Futures. But, at some time, you will want the result back, and that this returns reliably.
To facilitate this, Megapode provides a set method. To use it however, the field you use must have a setter and a getter. This is the most common bug that we have found.
The set method takes a field name, and a future, and sets the value of that field with the result of that future before the next coordinated method is called. As an example
@Page(path="/")
public class ArticlePage extends PageBase{
...
User author;
//setter and getter
@Override
public void collect() {
Future<User> futureAuthor = ...
set("author", futureAuthor);
//at this point author == null
}
@Override
public void prepare() {
//at this point author will have been populated or an exception thrown.
}
...
}
OK, so that takes care of setting, but what if we want to run a number of tasks, perhaps nested? We can create a delaying promise...
Delaying Promise¶
if, for some reason, you need to ensure that the next method doesn’t start until you have completed some work, you can call
Promise<Object> delay = delayUntilComplete()
this will cause the next method to wait until the delay is complete, which you can indicate by calling delay.successful(...) or delay.failure(...). Normall this is done in a callback, but be careful, you will have to complete it or the component will not ever complete.


2018 AUVSI SUAS Competition Day 3
Hello UAARG nation!
We woke up early today as it was the first official day of flying at the competition. We arrived at the military base around 7 to sign in and get our personal escort to where we will be set up for the next few days. A quick breakfast and it was on to the preliminary safety checks before flight. We were ordered 38th in the competition so we were not going to fly today but wanted to get all our initial set up done to ensure everything was going according to plan.
A few hiccups came along with the fail-safe, but they got worked out and we were all set for when we get called to fly. The rest of our time at the base today was spent doing more setup and getting prepared to fly including testing the camera and making Ryan walk far from the booth for the right distance, before going back to the college shortly after lunch. When we arrived back at the dorm, it was the beginning of working out the flight plan for the flight and planning some of the specifics. A quick nap for a tired Jackie and many lemon cookies for Dat were key in re-energizing and planning our trek to the ocean for seafood dinner. A lovely traffic jam led to a seaside adventure, filled with softshell crab straight from the ocean – almost all the crab the restaurant had.
After dinner, in search of a lighthouse, we discovered that the light house was the name of a restaurant… but that didn’t halt our fun and we discovered a marina filled with boats- some of which Weilon hit while skipping rocks.
Our next stop was the end of the peninsula the college and base were located on for a romantic sunset watch and walk on the beach – just one of the many great perks of being part of the UAARG nation. Being driven out by mosquitos, we made our way back to the college and continued work on the flight plan and other tasks, and then off to bed, except for the fearless Janay who stayed up all night working. Tomorrow will be the second day of flying, and hopefully our turn to test our project.
Matt


Book Store Theme is a Magento Responsive Theme built on Bootstrap 3.0. It is compatible with the latest Magento version 1.3. This theme is responsive, so it adapt with the screen. Try to open this page with your smartphone.


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* VAT-inclusive
ACTIVE : T3 / 90 DAYS
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[caption id="attachment_6298" align="alignleft" width="240"] common_schema booth in Percona Live 2013 (someone left their soup on our table)[/caption]
[caption id="" align="alignright" width="256"] Allen Kinnard[/caption]
Yay! I got a booth!
I confess it was mostly deserted. My hero Allen Kinnard, whom I've never met before, was kind enough to volunteer to occupy the booth.
So between him and me, both of course also looking to visit other booths and talk to people, the common_schema booth was only moderately attended.
Well, this was the DotOrg Pavilion: free booths for free & open source projects (e.g. OpenStack, PhpMyAdmin, etc.).
We both did our best to explain common_schema to the visitors.
The booth was actually titled "common_schema & openark-kit". However I don't recall that anyone asked me about openark-kit. Most were just interested in what common_schema was. I get that openark-kit is well known by now to many.
We did not have so many visitors, which played well with our occasional absence. But this was a last moment arrangement. In future events I may try to get things on top and have an army of volunteers to help me out (and I hope that by next time common_schema is widely used).
Now for the criminal department: practically all other DotOrg booths came prepared, with printed material, giveaway stuff and the like. We were like way beyond "the minimalist". We were "the poor and the pitiful". So I asked Kortney if she could arrange me some decorations (pulling some strings - yeah!). We were thinking a bowl of candies or something. Eventually she brought a few dozen candies (no bowl) and laid them nicely on the table; very nice touch! Good mixture of colors, nice wave design.
And guess what? Visitors actually thought they may take one of our candies! Oh, I schooled them.
But on Wednesday, when we arrived to the booth, a horrid sight stroke us: someone stole (and probably ate) 80% of our candies!
Well, I hope you've got bellyache, and next time you should know there will be surveillance, candy-thief!


Enjoy surfing in Arugam Bay, world famous surfing place and enjoy the rest of the day...
Stay night at Arugam Bay
-


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{% extends "global/Base.html" %} {% load staticfiles otree_tags %} {% block title %} Part 1 {% endblock %} {% block scripts %} {% endblock %} {% block content %}
You are ready to begin part 1.
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Cannot connect to Bazaar server.
Please check you connection to the Internet. Consult Nginx error log for details.
OK, return to my Red Pitaya.


--- layout: post title: The Big Damn Lie tags: - Music status: publish type: post published: true meta: _edit_last: "1" ---
Thursday, January 22nd, 2009
Established in 1907, The Great American Music Hall is San Francisco's oldest nightclub. It is located on O'Farrell Street in the Tenderloin district. Getting to the venue is a simple matter - just take any Market Street bus to Van Ness and then any Van Ness bus to O'Farrell Street. It is a short walk further and just down the street from the also famous Mitchell Brothers O'Farrell Theatre. Almost immediately upon stepping inside the club you begin to notice the ornate woodwork of the columns and balconies along the edge of the main floor.
Reverend Peyton's Big Damn Band was the first of the two co-headlining acts of the evening to take the stage. The big damn band consists of a trio hailing from Indiana - the reverend on guitar and harmonica, his wife on the washboard and brother on drums. The act is full of energy and engages the audience between songs.
The reverend serves up a rowdy mix of blues evocative of 1930's depression era for the modern day. The songs cover a variety of subjects such as "Your Cousin's On Cops", "Wal-Mart Killed The Country Store", "DT's Or The Devil", and "Mama's Fried Potatoes". Audience participation is encouraged with instruction given during the breaks to provide the appropriate training of the crowd provided backing vocals or clapping percussion for the upcoming song.
After a fiery finish to the big damn band's act, it's time for a short break while the stage is set for the next act. This also a great time to make the circle and hit the restroom, merch tables, bar for a fresh drink, and look around a bit more. For most shows, the stairs are open and additional seating is available upstairs in the balconies. However, with only a small crowd the upstairs area remained closed. Also for most shows at this venue, dinner tickets can be purchased as an alternative to the basic show tickets. For an additional fee you can get a seat at one of the tables surrounding the main floor and get a dinner choice served from the limited nightly menu.
The house lights dim and Vermillion Lies open their act. An environmentally conscious act, these sisters from Oakland travel in a bio-diesel powered tour van. Adorned with flair fashioned from bills and encouraging crowd donations on account of their birthdays they delivered a range of clever folk ballads, silly circus marches, and sultry southern tunes. Instrumentally, the sisters rely on a host of makeshift instruments recovered from the local dump. The instruments range from an old barbecue grill, typewriter, toy piano, kazoo depending on the particular song. Between songs, a trunk is opened to produce the instrument for the upcoming song. This becomes a game between the sisters and the audience. The crowd is prompted to ask the question "What's In The Box?" each time before the box is opened to reveal the next new item.
The evening is wrapped up with an encore including a crowd pleasing sing along about global warming and a stirring ballad conceived in New Orleans.


It has been estimated that 30% to 80% of all resumes/applications
contain false information. References either do not have or are
reluctant to share employment information for fear of slander/libel
suit. Drug testing is easy to defeat by temporary abstinence,
polygraphs are already illegal for the private sector and psychological
tests are banned in several states with federal legislation pending.
Record checks are limited to those information needs for which records
exist - often not the case with many critical information needs.
Yet, in this age of negligent hiring/retention litigation, fiscal
restraint and demands for greater productivity and ethical behavior,
the need for accurate hiring information - at a reasonable cost - has
never been greater.
Learn
how to recognize the signs of verbal, vocal, and
visual behavior occurring during selection interviews as they
relate to the widespread problems of exaggeration, fabrication,
minimization, omission, and deceptive denials.
Develop
interviewing techniques that encourage accurate
information particularly when the applicant perceives that candor
could lead to disqualification.
Refine
the ability to discuss critical but sensitive topics
(dismissals, integrity, substance abuse, etc.) in ways that do not
offend or alienate candidates.
Reduce
selection time and costs while improving the quality
of information necessary to make accurate hiring decisions.


Easter is a a time where families gather together to have fun and celebrate. There are a lot of fun things to do, like easter egg hunts, eating a lot of food, and getting to see all of your family from all over. In easter egg hunts, you get to collect a lot of colorful eggs, filled with treats like candy or money. The night before Easter, the Easter Bunny comes to visit children, and they get baskets full of chocolate and other gifts. On Easter, there is usually a lot of food to eat, like turkey, ham, potatoes, green beans, and more. Getting to see your family is always so fun. When you haven't seen them in a long time it is nice to catch up with them, especially if they live far away to hear about their city.


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I am experienced in exploring solutions for AR applications since 2015, currently working on a multisensory AR approach for cultural heritage. I'm also a lecturer at Polytechnic Institute of Leiria for 6 years, and a researcher at CIIC (ESTG-IPLeiria), and at INESC TEC, Porto.
In short, my main skills are related to 3D production (Blender, Maya, 3D Studio Max), AR app development (Unity, AR SDks such as Vuforia or AR Core), Graphics (Photoshop, Illustrator), Video Editing (After Effects, Premiere, DaVinci Resolve), Web development (HTML5, Hugo), and Animation 2D (Animator, Spriter 2D.)
I use LinkedIn, ResearchGate and OrcID for professional purposes, Instagram and Facebook mainly for personal reasons:
I started my teaching experience in an elementary school during two years, teaching ITC disciplines, and one more year in a professional school covering Web and ICT subjects. I also had one year experience as invited lecturer at Polytechnic Institute of Leiria during this period of time.
I worked for two years in a research scholarship, at Polytechnic Institute of Leiria, creating 3D contents mainly for museums, such as MCCB (awards: Best Portuguese Museum in 2012, Kenneth Hudson Award in 2013, and Prémio Acesso Cultural in 2014) and m|i|mo. After this period, I was invited to be a lecturer at this polytechnic.
For family reasons, I made a big change in my life and embraced a new challenge by becoming a merchant and supplier of construction materials in my family business. After 3 years and a half driving trucks, operating with cranes and dealing with very different people, the company was solid again, it was time to come back to IT: I accepted a new invitation to be a lecturer again and I applied for a master's degree.
I am currently an invited lecturer at Polytechnic Institute of Leiria, a total of 6 years of experience by now, still counting. As a professor in the IT department, I have been teaching disciplines related to computer graphics and to multimedia contents, mainly to Computer Engineering and to Games and Multimedia degrees.
I started my research while doing my master's and, as a PhD student, I become a researcher at CIIC (ESTG-IPLeiria) in April 2018, and more recently, since January 2019, a researcher at INESC TEC (Porto) as well.


#assign findEachWhile= "true">
Execute the query using callbacks to a visitor to process the resulting
beans one at a time, and allow breaking out of loop (by returning false
)
Note that findEachWhile (and findEach and findIterate) use a per graph
persistence context scope and adjusts jdbc
fetch buffer size for large
queries. As such it is better to use findList for small queries.
This method is functionally equivalent to findIterate() but instead of using an
iterator uses the Predicate (SAM (Single Abstract Method)
) interface which is better suited to use with Java 8+ closures.


|
|
Monthly Severe Weather Summary
March 2015
[RETURN TO ANNUAL SUMMARY]
* Data is preliminary and subject to revision
Daily Tabulation - March 2015
Day |
Total |
Torn |
Hail |
Wind |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
7 |
0 |
2 |
5 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
3 |
0 |
1 |
2 |
|
3 |
0 |
3 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
2 |
0 |
2 |
0 |
|
2 |
0 |
1 |
1 |
|
46 |
0 |
46 |
0 |
|
179 |
9 |
127 |
43 |
|
11 |
0 |
6 |
5 |
|
2 |
0 |
0 |
2 |
|
9 |
0 |
0 |
9 |
|
3 |
0 |
3 |
0 |
|
2 |
0 |
1 |
1 |
|
180 |
4 |
140 |
36 |
TOTAL |
449 |
13 |
332 |
104 |
|
State Tabulation - March 2015
State |
Total |
Torn |
Hail |
Wind |
|
50 |
0 |
50 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
73 |
7 |
54 |
12 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
9 |
0 |
3 |
6 |
|
21 |
0 |
9 |
12 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
3 |
0 |
3 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
13 |
0 |
13 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
28 |
0 |
24 |
4 |
|
65 |
0 |
57 |
8 |
|
0 |
0 |
0 |
0 |
|
1 |
0 |
0 |
1 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
1 |
0 |
1 |
0 |
|
0 |
0 |
0 |
0 |
|
8 |
0 |
2 |
6 |
|
8 |
0 |
0 |
8 |
|
0 |
0 |
0 |
0 |
|
151 |
6 |
101 |
43 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
4 |
0 |
4 |
0 |
|
13 |
0 |
11 |
2 |
|
2 |
0 |
0 |
2 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
|
0 |
0 |
0 |
0 |
TOTAL |
450 |
13 |
332 |
104 |
|
|
If you have comments regarding
this information, contact John
Hart of the Storm Prediction Center.
|


This kimono shop was opened with the desire to share one of the most beautiful forms of traditional clothing--the kimono--with the world.
Over twenty years' time acting as a homestay family for travelers from all over the world, I received numerous requests for kimono fittings, shared outings where kimono are traditionally worn, as well as several orders for kimono themselves. With that experience in mind, I began volunteering as a cultural envoy, and eventually decided to open a store of my own.
I invite you, travelers to Japan, to bring your memories of Japan home—to display authentic kimono in your home, to share with children, perhaps even wear to parties or as a costume in your home country.
A wide range of accessories—from handbags to tablecloth
Coin purses with strap, pen cases, tapestries and more
Kids items are currently on sale at our Nihonbashi Location.
Click here for directions
We carry a wide line of kimono for children—a perfect gift for children, friends, or display.
Professional fittings for formal and informal dress
Even if you would just like to try kimono on without buying, we can fit you to the perfect for your figure and occasion.
The head teacher at our shop has had years' of experience fitting kimono for travelers; she can find the perfect pattern and fit for you tastes in a relaxing, casual atmosphere.
Our professional dressers can arrange the obi for your dress into nearly any style of graceful knot.
The feeling of owning your own kimono for only a few thousand yen
Romina came to visit Japan recently, from Germany.
While browsing together at the fleemarket, she mentioned that she would have liked to buy a kimono, but none were available. I happen to collect certain antiques, so I took her to my warehouse to show her some of mine; when she saw it her eyes went wide with excitement. She bought five kimono before heading home to Germany.
I hear a lot of my homestay guests say things like, "I'd really like to try on a haori jacket," "I love kimono, but would really like to buy old kimono fabric to make western clothes. But, no matter where I go I have a hard time finding good, reasonablely priced, used kimono," and so on. They would urge me to open an online shop, or even start a mail order company. "In Germany, even old used kimono sell for tens of thousands of yen. If it were possible to get them for cheaper, I would be so happy." So, today we are working on starting a mail order company in Germany.
Actual kimono in person are often more vivid and reveal much more fineness in their pattern than is captured in photographs. Please stop by our shop to have a closer look; it would be our pleasure to share them with you.
KIMONO SHOP - SAKURA KIMONO
1-4-13 Nihonbashi-cho; Chuou-ku, Tokyo 103-0023
Japan Office : 2-177-1 -cho; Saitama City Kita-ku, Saitama Prefecture
TEL ( Domestic ) : 048 - 664 - 5006
TEL ( Internationl ) : +81 48 - 664 - 5006
American Office : 444 Kingsly Drive Unit 235; Los Angeles, California 90020
Contact Us<EMAIL_ADDRESS>Copyright KIMONO SHOP - SAKURA KIMONO - All Rights Reserved.


Data management has historically started at the point of ingest where users manually placed data into a system. While this process was sufficient for a while, the volume and velocity of automatically created data coming from sequencers, satellites, and microscopes have overwhelmed existing systems. In order to meet these new requirements, the point of ingest must be moved closer to the point of data creation.
iRODS now supports packaged capabilities which implement the necessary automation to scale ingest horizontally. This shifts the application of data management policy directly to the edge.
Once your data resides in the iRODS namespace, additional capabilities such as storage tiering, data integrity, and auditing may be applied. This includes tiering data to scratch storage for analysis, archiving, and collaboration. The combination of these capabilities plus additional policy allow for the implementation of the data to compute pattern which can tailored to meet your specific use cases.


Test case for console API: profile, profileEnd
Steps to reproduce
- Open Firebug
- Enable the Console panel
- Verify that
console.profile();
prints a warning message
about the Script panel being required
- Enable the Script panel and switch to the Console panel
- Click on the Execute Test button above
Expected result
- The Console panel should display a result table with profile data


<!DOCTYPE html>
<html>
<style>
* {
everything:awesome;
}
</style>
<body>
ERROR 404!
FILE NOT FOUND!
<!--The file you are looking for,
is not where you think it is.-->
</body>
</html>


Galaxy S11e To Ship With A Considerably Larger Battery
According to newly-surfaced information, the Samsung Galaxy S11e will have a larger battery than its predecessor. To be more specific, it will have a 20%+ larger higher battery capacity compared to the Galaxy S10e.
This info comes from SafetyKorea, as a new Samsung-branded battery has been certified. This battery comes with a model number EB-BG980ABY, and is meant to be included in the Galaxy phone with a model number 'G980'.
The Samsung Galaxy S10e was marked as the 'SM-G970', so, by sheer logic, the Galaxy S11e will be marked with an 'SM-G980' model number.
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A photo of the image has been provided, though exact mAh was not listed. What can be seen is that this battery is rated at 14.36Wh. Now, if we take into consideration that the battery operates at 3.85V, you'll realize that this battery comes with a capacity of 3,730mAh.
Samsung Galaxy S11e expected to include 3,800-3,900mAh battery
If 3,730mAh is its rated capacity, its typical capacity will be 3,800 or 3,900mAh, most probably. That is quite an improvement over what the Galaxy S10e delivered. The Galaxy S10e includes a 3,100mAh battery, and battery life is often mentioned as this phone's major downside.
If Samsung ends up keeping a 5.8-inch fullHD+ display on the Galaxy S11e, which could easily happen, that device may end up providing considerably better battery life.
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A 3,800-3,900mAh battery on a 5.8-inch fullHD+ OLED display would be more than enough to please users. Presuming that Samsung will do some optimizations on the software side as well, of course.
Don't get us wrong, the Galaxy S10e battery life isn't horrible by any means, but it's not great either. If that device ended up shipping with a 3,800-3,900mAh unit instead, that would be a completely different story.
The device will probably launch in February or March
The Samsung Galaxy S11e is not expected to arrive until February or March. That is when Samsung is expected to present its Galaxy S11 series of devices.
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The Galaxy S11e is expected to arrive alongside the Galaxy S11 and S11+. That has not been confirmed just yet, though, so Samsung may opt to change up its flagship phones and their branding.
It is nice to see that the company is planning to make some improvements in the battery capacity department. We do hope that the company will do the same for the Galaxy S11 and S11+ as well.
All of those devices will probably be made out of metal and glass. They will all include OLED displays, and ship with Android 10 out of the box. We're also expecting Samsung to implement a periscope-style camera in the Galaxy S11 series.
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