PRE2017 4 Groep7: Difference between revisions

Revision as of 21:00, 2 May 2018

0LAUK0 - Group 7

Group Members

- Bas Voermans | 0967153

- Julian Smits | 0995642

- Tijn Centen | 1006867

- Bart van Schooten | 0999971

- Jodi Grooteman | 1006743

- Emre Aydogan | 0902742

Planning

The planning can be found here:

Planning PRE2017 4 Groep7

Problem Statement

A Personal assistant (PA) works closely with a person to provide administrative support, this support is usually delivered on a one-to-one basis. A PA helps a person to make the best use of their time because they limit the time spent on secretarial and administrative tasks. unfortunately having the luxury of a personal assistant is reserved for the rich and successful only, this is because of the one-to-one nature and the extensive knowledge usually required to perform PA tasks successfully. Less fortunate people have to take on these tasks themselves costing them time which could be spent on their core business, which can lead to stress and discomfort. The goal of this study is to map current novelties in this field, find new areas of improvement from them and (hopefully) making a prototype, incorporating these improvements, such that it is able to handle almost all the tasks of a PA. Also this prototype would improve itself by learning what each specific user expects from a PA.

Users

Who are the users?

Many people would benefit from being relieved of their secretarial and administrative tasks. The users that benefit most from a PA would be the users that have to devote a lot of their time to make sure that their administration and agenda are updated regularly. Users that fit this criteria are for example people that work in the management sector. These users often arrange and visit meetings which require quite a bit of planning. Another key group of users is the group of students. Knowing when a lecture begins or when that important deadline approaches or even when a student needs to be free from distractions are operations where a PA is useful. This study will direct its attention on the student user group.

Requirements of the users

The system should run on pre-owned devices
The system should communicate with existing university infrastructure
The system should manage the agenda of a user (e.g. notifications of upcoming deadlines, lectures and exams)
The system should filter important information out of incoming messages. (email, whatsapp and canvas notifications)
The system should, when desired by the user, correspond on behalf of the student.
The system should tune its intrusiveness based on the users feedback

Approach

To have a more insight in what we want to accomplish we start our project with making a State-of-the-art. By doing this we acquire the knowledge to do a good study on what we want to accomplish. When this is clear we will start making a prototype. And thus implement the results that we got from our research. When this prototype is working we will reflect on the goal of the project, hereby also comes improvement to the prototype.

State of the art research

Understanding adoption of intelligent personal assistants: A parasocial relationship perspective^[1]

The article is about intelligent personal assistants (IPA’s). IPA’s help for example with sending text messages, setting alarms, planning schedules, and ordering food. In the article is a review of existing literature on intelligent home assistants given. The writers say that they don’t know a study that analyzes factors affecting intentions to use IPA’s. They only know a few studies that have investigated user satisfaction with IPA’s. Furthermore is the parasocial relationship (PSR) theory presented. This theory says that a person responds to a character “similarly to how they feel, think and behave in real-life encounters” even though the character appears only on TV, according to the article. Lastly is there a lot about the study in the article. The hypotheses of this study are: H1. Task attraction perceived by a user of an IPA will have a positive influence on his or her PSR with the IPA. H2. Task attraction perceived by a user of an IPA will have a positive influence on his or her satisfaction with the IPA. H3. Social attraction perceived by a user of an IPA will have a positive influence on his or her PSR with the IPA. H4. Physical attraction perceived by a user of an IPA will have a positive influence on his or her PSR with the IPA. H5. Security/privacy risk perceived by a user of an IPA will have a negative influence on his or her PSR with the IPA. H6. A person’s PSR with an IPA will have a positive influence on his or her satisfaction with the IPA. H7. A person’s satisfaction with an IPA will have a positive influence on his or her continuance intention toward the IPA.

Personal assistant for your emails streamlines your life^[2]

This article is about GmailValet, which is a personal assistant for emails. Normally is a personal assistant for turning an overflowing inbox into a to-do list only a luxury of the corporate elite. But the developers of GmailValet wanted to make this also affordable for less then $2 a day.

Everyone's Assistant^[3]

This article is about “Everyone’s Assistant”, which is a California based service company for personal assistant services in Los Angeles and surrounding areas. The company makes personal assistant service affordable and accessible for everyone. The personal assistants cost $25 a hour and can be booked the same day or for future services.

Experience With a Learning Personal Assistant^[4]

This article is about the potential of machine learning when it comes to personal software assistants. So the automatic creating and maintaining of customized knowledge. A particular learning assistant is a calancer manager what is calles Calendar APprentice (CAP). This assistant learns by experience what the user scheduling preferences are.

SwiftFile: An Intelligent Assistant for Organizing E-Mail^[5]

This article is about SwiftFile, which is an intelligent assistant for organizing e-mail. It helps by classifying email by predicting the three folders that are most likely to be correct. It also provides shortcut buttons which makes selecting between folders faster.

An intelligent personal assistant robot: BoBi secretary^[6]

This article is about an intelligent robot with the name BoBi secretary. Closed it is a box with the size of a smart phone, but it can be transformed to a movable robot. The robot can entertain but can also do all the work a secretary does. The three main functions are: intelligent meeting recording, multilingual interpretation and reading papers.

RADAR: A Personal Assistant that Learns to Reduce Email Overload^[7]

This article discusses artificial learning agents that manage an email system. The problem described in the article is that overload of email causes stress and discomfort. A big question remains that it is not sure whether or not the user will accept an agent managing their email system. Nevertheless the agent improved really fast and improved the productivity of the user.

Intelligent Personal Assistant — Implementation^[8]

This article does research to the best and most promising current Agents used by the major companies such as apple and microsoft. The conclusion of this paper states that cortana is currently the best working agent in assisting the user.

Intelligent Personal Assistant^[9]

This article is about the current by speech driven agents that perform tasks for the user. In the paper this communication would become bi-directional and therefore will the agent respond back to the user. It will also store user preferences to have a better learning capacity

Voice mail system with personal assistant provisioning^[10]

A patent that describes a PA that can be used to keep track of address books and to make predictions on what the user wants to do. The patent also suggests text-to-speech so that the user can listen to, rather than read the response. The PA should also remember previous commands and respond accordingly on related follow-up commands.

USER MODEL OF A PERSONAL ASSISTANT IN COLLABORATIVE DESIGN ENVIRONMENTS^[11]

The article is about creating models of the users of PA’s and the different domains associated to the user and the PA. The article suggests four different user models, user interest model, user behavior model, inference component and collaboration component. According to the article the user should have the right to change the user model, since ‘the user model can be more accurate with the aid of the user.’ Two approaches are through periodically promoted dialogs or by giving the user the final word.

A Personal Email Assistant^[12]

The paper is about Personal Email Assistants (PEA) that have the ability of processing emails with the help of machine-learning. The assistant can be used in multiple different email systems. Some key features of the PEA described in the paper are: smart vacation responder, junk mail filter and prioritization. The team members of the paper found the PEA good enough to be used in daily life.

Rapid development of virtual personal assistant applications^[13]

This patent is about creating a platform for development of a virtual personal assistant (VPA). The patent works by having three ‘layers’, first the user interface that interacts with the user. Next is the VPA engine that analyses the user intent and also generates outputs. The last layer is the domain layer that contains domain specific components like grammar or language.

A Softbot-Based Interface to the Internet^[14]

The article describes an early version of a PA that is able to interact with files, search databases and interact with other programs. The interface for the Softbot is build on four ideas: Goal oriented, Charitable, Balanced and Integrated. Furthermore, different modules could be created to communicate with the softbot in different ways, like speech or writing.

Socially-Aware Animated Intelligent Personal Assistant Agent^[15]

The article describes a Socially-Aware Robot Assistant (SARA) that is able to analyse the user in other ways than normal input, for example the visual, vocal and verbal behaviours. By analysing these behaviours SARA is able to have its own visual, vocal and verbal behaviours. The goal of SARA is to create a personalized PA that, in case of the article, can make recommendations to the visitors of an event.

JarPi: A low-cost raspberry pi based personal assistant for small-scale fishermen^[16]

This article describes how fisherman can also have a form of a personal assistant, that keeps track of the weather and current position on the sea. Normally such systems are really expensive and not available for small-scale fisherman, but using cheap technology such as the raspberry pi a great alternative can be created.

Solution to abbreviated words in text messaging for personal assistant application^[17]

This article describes how a personal assistant that reads incoming text messages such as SMS-messages can handle abbreviations, which are commonly used in text based messaging. The study was performed with abbreviations common in the Indonesian language, based on a survey.

A voice-controlled personal assistant robot^[18]

This article described the design and testing of a voice controlled physical personal assistant robot. commands can be given via a smartphone to the robot, which can perform various tasks.

Management Information Systems in Knowledge Economy^[19]

AI Personal Assistants: How will they change our lives�^[20]

How artificial intelligence will redefine management^[21]

How can AI transform public administration?^[22]

Extra Bronnen - Spam Filters/Machine Learning

Intellert: a novel approach for content-priority based message filtering^[23]

This article described how filtering text based on its content and keywords leads to great reduction in the amount of notification that has to be send, by only sending those messages that are marked urgent or important. The results look promising.

Content-based SMS spam filtering based on the Scaled Conjugate Gradient backpropagation algorithm^[24]

Classification of english phrases and SMS text messages using Bayes and Support Vector Machine classifiers^[25]

Generative and Discriminative Text Classification with Recurrent Neural Networks^[26]

This article analyses the difference between discriminative and generative Recurrent Neural Networks (RNN) for text classification. The authors find that the generative model is more effective most of the time, while it does have a higher error rate. The generative model is especially effective for zero-shot learning, which is about applying knowledge from different tasks to tasks that the model did not see before. The discriminative model is more effective on larger datasets. The datasets that are tested range from two to fourteen classifications.

SMS spam filtering and thread identification using bi-level text classification and clustering techniques^[27]

The problem that this article is addressing is the large amount of sms messages that are sent and that identifying spam or threads in these messages is difficult. First the spam is classified, which could be done with one of four popular text classifiers, NB, SVM, LDA and NMF. These are all binary classification algorithms that either work with hyper planes, matrices or probabilities to split up the classes. Next, the clustering is applied to construct the sms threads, which is done by either the K-means algorithm or NMF. The results of the article are that the choice of the algorithms is very important. The algorithms used in the experiment are SVM classification and NMF clustering which give good results.

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks^[28]

This article is about creating a spam filter with the help of a Recurrent Neural Network. The spam filter is intended for both SMS and email. The network is tested on four spam datasets, Enron, SpamAssassin, SMS and Social Networking. The experiment starts by pre-processing the datasets such that there are only lower cases, no special characters and no stop words, since these contain no semantic information. The results of the experiment are compared with the following spam filters, Minimum description length, Factorial design analysis using SVM and NB, Incremental Learning, Random Forest, Voting and CNN. The results of the experiment are that the model is better on three of the four datasets by a small amount and the accuracy is around 98% for the three and 92% for the last one.

A Comparative Study on Feature Selection in Text Categorization^[29]

This article researches five different techniques to categorize text.

A Learning Personal Agent for Text Filtering and Notification^[30]

This article is about an agent that is used for managing notifications. This agent acts as a personal assistant. This agent learns the model of the user preferences in order to notify a user when relevant information becomes available.

Combining Collaborative Filtering with Personal Agents for Better Recommendations^[31]

This article is about information filtering agents that identify which item a user finds worthwhile. This paper shows that Collaborative filtering can be used to combine personal Information filtering agents to produce better recommendations.

Spam filtering using integrated distribution-based balancing approach and regularized deep neural networks. ^[32]

This article is about anti-spam filters by using machine-learning and calculation of word weights. This categorizes spam and non-spam messages. This categorizing is more and more difficult because spammers use more legitimate words.

Robust personalizable spam filtering via local and global discrimination modeling^[33]

There are two options of filtering: a single global filter for all users or a personalized filter for each user. In this article a personalized filter is presented and the challenges of it. They also present a strategy to personalize a global filter.

Mail server probability spam filter^[34]

This article is about a spam filter that uses a white list, black list, probability filter and keyword filter. The probability filter uses a general mail corpus and a general spam corpus to calculate the probability that the email is a spam.

The Art and Science of how spam filters work^[35]

This article explains the principle of blacklists which analysis the header of a message to determine whether something is spam. Also messages that contain statistically dangerous files, such as .exe files, are often automatically blocked by content filters. The article end with a piece about Machine Learning in spam filters. Algorithms used in these filters try to find similar characteristics found in spam.

The Effects of Different Bayesian Poison Methods on the Quality of the Bayesian Spam Filter ‘SpamBayes’^[36]

This article discusses how spammers try to elude spam filters. The principle works as follows: add a few words that are more likely to appear in non-spam messages in order to trick spam filters in believing the message is legitimate. This article illustrates that even spam evolves, and as a result filters have to evolve with them.

A review of machine learning approaches to Spam filtering^[37]

This paper presents a review of currently existing approaches to spam filtering and how the researchers believe we could improve certain methods.

References

[1] ttps://www.emeraldinsight.com/doi/full/10.1108/IMDS-05-2017-0214

[2] ttps://www.sciencedirect.com/science/article/pii/S0262407913600925

[3] ttps://search.proquest.com/docview/1704945627/50DE051B4E904379PQ/4?accountid=27128

[4] ttps://www.ri.cmu.edu/pub_files/pub1/mitchell_tom_1994_2/mitchell_tom_1994_2.pdf

[5] ttp://www.aaai.org/Papers/Symposia/Spring/2000/SS-00-01/SS00-01-023.pdf

[6] ttps://ieeexplore.ieee.org/document/8273196/

[7] ttp://www.aaai.org/Papers/Workshops/2008/WS-08-04/WS08-04-004.pdf

[8] ttps://link.springer.com/chapter/10.1007/978-1-85233-842-8_10

[9] ttp://ijifr.com/pdfsave/10-05-2017475IJIFR-V4-E8-060.pdf

[10] ttps://patents.google.com/patent/US6792082B1/en

[11] ttp://papers.cumincad.org/data/works/att/d5b5.content.06921.pdf

[12] ttps://s3.amazonaws.com/academia.edu.documents/39232090/0c9605226714eeedad000000.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1524910399&Signature=BjYwD6z0FRQMoazG%2BSJHjHc8nZQ%3D&response-content-disposition=inline%3B%20filename%3DA_personal_email_assistant.pdf

[13] ttps://patents.google.com/patent/US20140337814A1/en

[14] ttps://homes.cs.washington.edu/~weld/papers/cacm.pdf

[15] ttp://www.aclweb.org/anthology/W16-3628

[16] ttps://ieeexplore.ieee.org/document/8279618/

[17] ttps://ieeexplore.ieee.org/document/8251876/

[18] ttps://ieeexplore.ieee.org/document/7150798/

[19] ttps://books.google.nl/books?id=sRqlLLOboagC&lpg=PA246&dq=artificial%20intelligence%20personal%20assistant%20administrative%20tasks&hl=nl&pg=PR2#v=onepage&q=artificial%20intelligence%20personal%20assistant%20administrative%20tasks&f=false

[20] ttps://www.fungglobalretailtech.com/research/ai-personal-assistants-will-change-lives/

[21] ttps://hbr.org/2016/11/how-artificial-intelligence-will-redefine-management

[22] ttp://www.icdk.us/aai/public_administration

[23] ttps://ieeexplore.ieee.org/document/7940206/

[24] ttps://ieeexplore.ieee.org/document/7382023/

[25] ttps://ieeexplore.ieee.org/document/5090166/

[26] ttps://arxiv.org/abs/1703.01898

[27] ttp://journals.sagepub.com/doi/pdf/10.1177/0165551515616310

[28] ttps://link.springer.com/content/pdf/10.1007/s10489-018-1161-y.pdf

[29] ttp://www.surdeanu.info/mihai/teaching/ista555-spring15/readings/yang97comparative.pdf

[30] ttp://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=B2FB1F51D66DC0D90FA785BB9D087172?doi=10.1.1.44.562&rep=rep1&type=pdf

[31] ttp://www.aaai.org/Papers/AAAI/1999/AAAI99-063.pdf

[32] ttps://search.proquest.com/docview/2017545987/B4CAA5405B794CA8PQ/1?accountid=27128

[33] ttps://search.proquest.com/docview/1270351132/B4CAA5405B794CA8PQ/3?accountid=27128

[34] ttps://patents.google.com/patent/US7320020B2/en

[35] ttps://securityintelligence.com/the-art-and-science-of-how-spam-filters-work/

[36] ttps://www.cs.ru.nl/bachelorscripties/2009/Martijn_Sprengers___0513288___The_Effects_of_Different_Bayesian_Poison_Methods_on_the_Quality_of_the_Bayesian_Spam_Filter_SpamBayes.pdf

[37] ttps://www.sciencedirect.com/science/article/pii/S095741740900181X

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PRE2017 4 Groep7: Difference between revisions

Revision as of 21:00, 2 May 2018

Contents

0LAUK0 - Group 7

Planning

Problem Statement

Users

Who are the users?

Requirements of the users

Approach

State of the art research

Extra Bronnen - Spam Filters/Machine Learning

References

Navigation menu

PRE2017 4 Groep7: Difference between revisions

Revision as of 21:00, 2 May 2018

0LAUK0 - Group 7

Planning

Problem Statement

Users

Who are the users?

Requirements of the users

Approach

State of the art research

Extra Bronnen - Spam Filters/Machine Learning

References

Navigation menu

Search