04/29/2020 – Subil Abraham – Fraser et al., “DiscoverySpace”

April 27, 2020April 27, 2020 Subil Abraham 2 Comments

Software tends to grow in complexity as time goes on, with more features being added as more needs arise. This can be problematic especially for consumer software intended to be used by a wide array of people, where they can’t figure out how they need to do the specific thing they need to do just once because of the vast number of options arrayed before them. DiscoverySpace is proffered as a solution to this problem specifically for Photoshop, by allowing users to browse crowd sourced pre-built actions to apply on their image. They create an additional panel and ask the user to enter the kind of image they have. DiscoverySpace does keyword matching in order to identify the most suitable collection of actions to suggest, returning a random sample mix of results to promote discoverability of new Photoshop features by the user. They conduct a couple of studies, first examining how novices interact with the vanilla software with experts guiding them and showing them what they can do. And another study where they compare the usage of Photoshop by novices with and without the presence of DiscoverySpace. They find that having DiscoverySpace as a feature greatly helps novices in performing their tasks and they don’t struggle as much, but also find that it is limiting because DiscoverySpace only applies effects on the whole image.

I feel like a really good expansion of this work can be to have an additional window pop up after an action has been done with various tweakable parameters specific to that action. This feels like it could really promote the Photoshop’s plugin ecosystem and make it more accessible for ordinary folks to contribute to plugins, not just companies that specialize in it. Another idea in this paper that I find interesting is the use of random sampling to find and suggest actions. I think this is genius because it promotes curiosity in the user about the different things that Photoshop can offer and slowly, over time, allow the user to become more familiar with all the features particularly if they are curious and look through the History panel to see what effects were applied when they clicked on a DiscoverySpace action. It can serve as a useful learning tool that will allow those who prefer to work with practical examples to learn how different things work and emergently get an idea of how Photoshop as a whole functions.

Would you find this a useful learning tool or do you only see it as something that is used to just immediately serve your purpose?
What other software would something like this be useful?
Is the random sampling they do to promote discoverability a good idea? Would their purpose be better served by optimizing suggestions solely for the specific task at hand?

04/29/20 – Jooyoung Whang – DiscoverySpace: Suggesting Actions in Complex Software

April 27, 2020 Jooyoung Whang Leave a comment

In this paper, the authors introduce an add-on prototype interface called DiscoverySpace that recommends actionable items to a user using photoshop. For this application, the authors focused on providing the followings:

– List possible actions at the start

– Use human language

– Show previews of before and after action

– Offer faceted browsing

– Provide relevant and possible new suggestions.

The authors conducted a between-users study to measure the performance of the software. One control group did not have access to DiscoverySpace and used plain Photoshop whereas the other group did. The authors report improved performance for users using DiscoverySpace.

In this interface, the authors require the users to provide information about an image at the start. They mentioned this could be improved in the future by automatic image analysis. I think this feature is desperately needed, at least in my case. When I decide to use a tool, I prefer the tool automatically configuring basic things for me. Also, surprisingly many users interact with interfaces in the wrong way even if it is a very simple one. I am certain some people will fail to configure DiscoverySpace. Object classification inside an image is pretty well-established today, so I think this feature will greatly improve the accessibility to users.

It was interesting to find that users still could not figure out some functionality 50% of the time while using DiscoverySpace. It is certainly better than 80% from the control group, but this percentage still looks too high. I think this says something about the complexity of the tool or the lack of information in the database that they used to provide the suggestions.

Overall, I felt that the study was done in a bit of a rush. In the result section, the study’s participants talk about the lack of functionalities such as dialing down the effect of a filter. I think the idea of the tool itself is pretty cool, but it could have benefitted from more time. I think there could have been a better result from refining the tool a bit more.

The followings are the questions that I had while reading the paper:

1. Do you think this tool has a benefit over simply using Internet search? The authors state that their tool suggests more efficient solutions. However, I think it’ll take a significantly shorter time to search on the Internet. What do you think? Would you use Discovery Space?

2. Did you also feel that the study was a bit rushed? What do you think could have changed given that the authors spent more time refining the tool? Would the participants have provided more positive feedback? What parts of the tool could be improved?

3. It seems that the participants’ survey ratings were used to measure creativity. What other metrics could have been used to measure creativity? I would especially like to hear about this since my project is about creative writing. Would it be possible to measure creativity without simple human input? Would the method be quantitative or qualitative?

04/29/2020 – Yuhang Liu – VisiBlends: A Flexible Workflow for Visual Blends

April 27, 2020May 4, 2020 yuhang Liu 1 Comment

Summary:

This article is devoted to researching a flexible system to complete visual blends. Visual blends is an advanced graphic design technology designed to attract people’s attention. It combines two objects together, which has applications in many fields, because it can express information well. However, visual blends do not simply combine or paste the two pictures together. It need to combine the characteristics of the two images, and it also need to be able to separate the two images. The previous working methods have some drawbacks, such as assigning tasks to a work fixedly person. Therefore, the author of this paper proposes a new workflow that can achieve flexible visual blends. The workflow of the system decomposes the flow of integrated computing technology and human microtasks, so that users can collectively generate visual fusion through brainstorming, synthesis and iterative steps. The workflow includes: brainstorming, Finding Images, Annotate Images for Shape and Coverage, Matching Algorithm, Automatic Blend Synthesis and Evaluation.

Reflection:

Before reading each article, I will anticipate the method of the article from the title of the article. Also before reading this article, I am thinking about how to implement a flexible workflow for visual blends. I think that when combining two images to form a new image and complete a new idea, increasing the flexibility of the workflow requires a wider selection of pictures and the order of work that can be changed, for example, to determine the images then to achieve the purpose, or determine a purpose, and then find two images. But I have never thought about starting from the worker’s perspective, through the participation of more workers, to build a flexible workflow.

And, I think the more important benefit of introducing brainstorming is inspiring workers. Compared to the method I mentioned before, and combined with real life, when completing a creative task, inspiration is the most difficult to find. So I think this is indeed a very good way. The benefits of setting up the first step of the system as brainstorming are:

Help workers better understand the purpose of the task, which is not limited to the purpose of the current task, and at the same time, the understanding of visual blends can also be improved.
Inspire inspiration. In the multi-person work style, people can choose the tasks they want to do, and can look at the results of others, and even collaborate with each other, which is very helpful for the construction of ideas.

So I think that this system has improved the flexibility of inspiring ideas and work. At the same time, because of the introduction of annotations, merge two images by computers and the evaluation system, I think the quality can be guaranteed in all aspects. But I think there are some limitations, for example, the generation of the machine may not control some details, but I think that a good design must pay great attention to details.

Question:

Do you think this system can realize a flexible workflow, or it is more inclined to a heuristic system.
How the system should control the details of the images automatically synthesized by the machine
What are the limitations of this system?

04/29/2020 – Yuhang Liu – Accelerating Innovation Through Analogy Mining

April 27, 2020May 4, 2020 yuhang Liu 1 Comment

Summary:

This article is similar to a previous paper “SOLVENT: A Mixed Initiative System for Finding Analogies between Research Papers” in that it focuses on how to search for articles in a vast corpus of papers. In the massive essay library, constantly get ideas. The research method of this paper is based on previous papers and previous papers with some changes. In previous papers, this system of discovering new ideas by analogy usually requires an understanding of the deep similarity between two entities, and then comparison or research, However, finding analogies is challenging for machines, as it is based on having an understanding of the deep relational similarity between two entities that may be very different in terms of surface attributes, and in previous studies there were methods based on similarity, such as TF-IDF, LSA, LDA, and GloVe, but the authors of this paper investigate a weaker structural representation, the goal is to come up with a representation that can be learned, while still being expressive enough to allow analogical mining. So the method proposed in this article requires crowdsourced workers to annotate the purpose and mechanism of a paper. Then through learning and systematic research, new ideas for similar purposes but through different mechanisms are obtained. And from the test, the authors found it has a better effect than other methods.

Reflection:

First of all, on the basis of reading another paper on a similar subject, I want to make a comparison between the two articles. In another article, People need to analyze an article from four aspects, Background, Purpose, Mechanism, Findings. Then the machine generates an analogy by comparing and learning these four structural aspects. In this article, a paper is divided into two parts, the purpose and the mechanism, which is also called as a relatively weak structured representation. Separating an idea into purpose and mechanisms enables core analogical innovation processes such as repurposing. So the final experiment in this article is also based on the same purpose, different mechanism. So, there are only two dimensions to represent a paper which is more abstract and broad, and directly learn them in a supervised method. The benefits of doing this is, it is possible to automatically extract these representations from product descriptions for potential wide applicability. Identifying key components and functions can also improve the search function of the system and better understand the needs of users.

Secondly, I think the most important thing about a system that finds analogies between papers or products is its feasibility, which is reason why I think the method in this paper is better. In terms of the feasibility of the system, letting a crowdsourced worker or machine discover the purpose and mechanism of an article described in a paper is far simpler than analyzing the structure (background, purpose, mechanism, finding) of an article, and there will be less errors in the simple tasks, so a better data sets will be available for learning. At the same time, in terms of the feasibility of the ideas that are formed, the system introduces graduate students to comprehensively judge the feasibility of ideas. From my aspect, this is particularly important, if an idea is unrealizable or cannot withstand the test , then no matter how novel, it is useless. So in my opinion this is the advantage of the system.

But at the same time, I have some doubts about the results of the system, because the system seems to be more inclined to find the different mechanism for the same purpose. For different purposes, the idea of the same mechanism seems difficult to obtain. And I think that the most difficult part of innovation is to apply an idea to a new field, and the examples of bionics that we think of most are: fish float and submarine, bat and radio. In my opinion, those inventions that have a significant impact on people’s lives are usually the application of mechanisms in other fields to achieve new goals. So for an unresearched field or unrealized purpose, I think these can be used as the direction of future research of the method.

Question:

What are the limitations do you think of the method proposed in this article?
What methods do you think can be used to evaluate the usefulness of analogy ideas?
What do you think is more important for the idea of finding an analogy, surface similarity, structural similarity, or some other factors?

04/29/2020 – Sushmethaa Muhundan – Accelerating Innovation Through Analogy Mining

April 27, 2020 Sushmethaa Muhundan 1 Comment

This paper aims to explore a mixed-initiative approach to find analogies in the context of product ideas to help people innovate. This system focuses on a corpus of product innovation from Quirky.com and aims to explore the feasibility of learning simple, structural representations of the problem schemas to find out analogies. The purpose and mechanism of each product idea are extracted and a vector representation is constructed. This is used to find analogies in order to inspire people to generate creative ideas. Three experiments were conducted as part of this study. The first experiment involved AMT crowd workers annotating the product ideas to segregate the purpose and mechanism. These annotations were used to construct the vectors in order to compare and compute the analogies. As part of the second experiment, 8000 Quirky products were chosen and crowd workers were asked to use the search interface to find out analogies for 200 seed documents. Finally, a within-study experiment was conducted wherein the participants were asked to redesign an existing product. For the given product idea, the participants received 12 product inspirations retrieved using the system developed, 12 using TF-IDF, and 12 were retrieved randomly. The system developed aimed to retrieve near-purpose, far-mechanism analogies to help users come up with innovative ideas. The results showed that the ideas generated by using the system’s results were more creative than the other two conditions.

Analogies often prove to be a source of inspiration and/or competitive analysis. In the case of inspiration, cross-domain analogies are specifically extremely helpful and can be difficult to find. Also, given the rate at which information generation is growing, it has become all the more difficult to explore and find analogies. Such systems would definitely help save time by predicting potential analogies from a large dataset. I feel that the system would definitely help come up with creative, alternate solutions when compared to traditional information retrieval systems.

With respect to the ideation evaluation experiment, the results showed that the randomly generated analogies actually were more successful in helping users come up with creative ideas when compared to the TF-IDF condition. This shows that the traditional information retrieval technologies would not work well as is in the setting of analogy predictions and needs to be tweaked in order to help serve the purpose of inspiring users. I feel that there is potential to expand the core concepts of the proposed system and use it in different applications. For instance, finding similar content could be augmented into a recommendation system where the system could recommend content similar to the user’s browsing history.

What are your thoughts about the system proposed? Do you think this system is scalable?
The study uses only purpose and mechanism to compare and predict analogies. Do you think these parameters are sufficient? Are there any other features that can be extracted to improve the system?
The paper mentions the need for extensions to generalize the solution to apply the system to other domains. Apart from the suggestions in the paper, what are some potential extensions?

04/29/2020 – Sushmethaa Muhundan – VisiBlends: A FlexibleWorkflow for Visual Blends

April 27, 2020 Sushmethaa Muhundan 1 Comment

This work presents VisiBlends, a system for creating visual blends by providing a flexible workflow that follows an iterative design process. The paper explores the feasibility of decomposing the creative task of designing a visual blend into microtasks that can be completed collaboratively. The paper also aims to explore if this system is capable of enabling novices to create visual blends easily. The workflow proposed decomposes the task process of creating a visual blend into computation techniques and human tasks. The main tasks involved in the workflow are brainstorming, finding images for each concept, annotating images for shape and coverage, synthesis, evaluation, and iteration. Three studies were conducted as part of this paper. The first was to test the feasibility of decentralized collaboration and involved 7 individuals taking part in only one of the above-mentioned tasks. The results demonstrated that although they were working only on parts of the workflow, together they were able to successfully create visual blends. The second study was to test group collaborations and involved groups of 2-3 people working together to create visual blends. The third study involved testing the ability of VisiBlends in helping novice users create visual blends. Overall, the results showed that due to the flexible, iterative nature of the system, the complex task of creating visual blends was successfully decomposed into microtasks.

I feel that human cognition and AI capabilities have been leveraged innovatively to decompose a complex, creative problem into distributable microtasks. VisiBlends breaks down the task into microtasks that involve both human cognition as well as artificial intelligence. This paper addresses the fundamental elements involved in the creative task of visual blend designing and introduces the concept of the Single Shape Mapping design pattern.

I feel that VisiBlends is a great tool that allows novice users to create exciting visual content to promote their idea/product. It provides a platform that abstracts the complexities of creating a visual blend away. The user is tasked with finding the relevant images and annotating them for shapes and coverage. The rest of the magic happens on its own wherein VisiBlends runs a matching algorithm and synthesizes various potential blends from the user’s images. The iterative design flow allows users to modify the suggestions as required. I really liked the resue functionality of VisiBlends as well. This would definitely help with the brainstorming phase and would save a lot of time and effort.

It was interesting to note that each participant was trained on the entire workflow although they were made to contribute only to one step. This helped them understand the big picture and they were able to function more efficiently. Can this finding be applied to other systems to improve their performance?
Can similar techniques of decomposition be applied to other creative domains?
One challenge mentioned in the paper was regarding the difficulty faced during the search for relevant images where users were not able to find appropriate images. What are some techniques that could be employed to overcome this?

04/29/2020 – Ziyao Wang – VisiBlends: A Flexible Workflow for Visual Blends

April 26, 2020 Ziyao Wang 2 Comments

The authors designed the VisiBlends system, which is used to do visual blends. This is a quite challenging task before because the generated object should contain the two input objects while these objects should be still distinguishable. However, the system leveraged both human strength and AI strength to achieve this goal. Humans need to find relative images, annotate images for shape and coverage, and evaluate the outputs generated by Automatic Blend Synthesis. Machines will apply matching algorithm according to human’s classification and blend the objects. Though the evaluation section mentioned that the workflow often fails, the system is flexible, and users can iterate and adapt easily. With the system, even novices can collaboratively complete the difficult visual blending tasks easily.

Reflections:

The system is interesting and useful since a user who knows nothing about visual blending can obtain a good blended design from a group of people who knows little about design, program, or visual blending. This can be a good example of human-AI interaction. Neither the inexperienced workers nor the AI itself cannot design such a kind of challenging task. However, with the system, they can collaborate to complete the tasks.

I like the experiment in which workers are divided into two groups. One group is exposed to a set of previous blended objects while the other one does not. I was surprised about the result that the group of workers who have experience with what the previous outputs look like performs worse than the other group. I think this is because their minds are limited by the successful outputs so they cannot have novel ideas of blending after brainstorming. The system works like it uses an exhaustive mechanism to get human ideas. If the human’s ideas are limited by some previous success, the result will lose some blending with novel ideas. If I am a worker who has some experience with image blending, I would consider whether I can blend the images or not instead of just thinking about whether the output would be interesting or not. However, the blending tasks are carried out by automatic mechanisms, so those guys who list all kinds of possibilities would probably obtain better ideas.

I think the system can be better used by experts instead of workers. If the workers have no experience with this system, it is hard for them to find proper images that can be used in the blending task later. They may spend a huge amount of time uploading images having no objects with proper shapes. Instead, experts can have an image of how the output would be looked at and provide only the proper images. Or a database of images should be provided to the users so that they can only make selections from the images which are good for blending.

Questions:

How can the system’s success rate be improved?

Does it worth to hire some professionals to involve in some of the stages of the system?

Can we design a system in which the user can upload pictures, annotate the images on himself, and the system generates the output according to his annotations? What is the advantage of hiring workers to do these tasks?

4/29/2020 – Nan LI – DiscoverySpace: Suggesting Actions in Complex Software

April 26, 2020 NAN LI 3 Comments

Summary

This paper introduced an interface, DiscoverySpace, which provides task-level action recommendations. The main objective of this interface is to help novices gain confidence when using sophisticated software. To achieve this goal, the author designed the DiscoverySpace prototype as a Photoshop extension panel for Adobe Photoshop. This prototype allows users to explore the software functionality by providing refinement or radical recommendations based on the current task classification or the natural language. The author conducted an experiment that compares the participants in the DiscoverySpace condition used Photoshop with DS panel and the participants in the control condition using Photoshop without it. The experiment results indicate that those beginner participants tend to gain confidence with the help of the DiscoverySpace condition while losing confidence in the Control condition. However, there is no significant difference between participants who already have Photoshop expertise. Nevertheless, most of the participants indicated that this interface is most useful for quick exploration of complicated software.

Reflection

Nowadays, when talking about editing the image, I believe the majority of people use a “one-click” retouching application on their phone, such as meitu, Ulike, VSCO, etc. These tools can provide a variety of retouching effects, provide users with powerful picture editing capabilities, and have no particular requirement for users. I think this is what the user expected in this paper regarding recommending an “item-based” collaborative filtering algorithm. From my perspective, what the user is trying to achieve in this prototype is to encapsulate the complicated process and present users the most straightforward effect based on the image user selected.

I agree with the assumption the author put up with at the beginning, “complex software offers power for experts, yet overwhelms new users”—my experience of using Photoshop at first, just like what the author described. Thus, I think the author’s work would improve the user experience and confidence significantly. I wish I could have such extension penal when I was first using Photoshop; I might continue to use it now.

Besides, I think the target users of using Photoshop are people who have higher requirements of image editing instead of people who just want to publish their selfies to Instagram. Thus, I would expect the system aims to help the novice get familiar with the system faster and facilitate exploration.

However, I do not think that automatic image analysis would help a lot. One benefit of letting the user select the feature of the image is to provide corresponding advice regarding the user-selected features. For example, the user would perceive an image with Sunset, seaside, back view of themselves as landscape pictures, while automatic image analysis would probably recognize it as a portrait. Therefore, letting the user enter the feature of their figure would facilitate the system to identify the portion that the user wants to emphasize.

Questions

Even though the author just takes Photoshop as an example, I am still curious to do people still use PS frequently except for people who have professional needs nowadays, as the emergence of multiple “one-click” retouching software. Do you agree with the assumption made at the beginning of the paper?
Do you think it is helpful that encapsulates a sequence of operations for users to apply in one click, meanwhile, demonstrate those operations to users?
When would you prefer to use DiscoverSpace instead of “one-click” retouching Apps?
What kind of software(or what specific software) do you most like to implement the idea similar to DiscoverySpace? Here the idea refers to that system provides users aggregated action suggestions based on users’ current tasks.

Word Count: 602

04/29/2020 – Mohannad Al Ameedi – DiscoverySpace Suggesting Actions in Complex Software

April 26, 2020April 27, 2020 mohada4 Leave a comment

Summary

In this paper, the authors aim to help beginner users of complex software to execute complex tasks in a simple way to help them build confidence and not lose interest in the software. The approach used as an extension to photoshop and collects instructions available in the community to build macros that can execute multiple steps to achieve an action or a goal. Users might use information available online, but they might get lost with the overloaded information available or choose a solution that is not efficient.

The approach offers suggestions to the users in the context of the current action to help them with executing the next desired action.

The authors asked the users to participate in two surveys to measure their confidence level of using photoshop application. The participants level of experience in photo editing software varied from beginner to expert and first survey showed that beginner users might lose confidence when there is no suggestion feature that can help on executing tasks. The second survey was about DiscoverySpace system which showed that beginner users gained confidence when they use a tool that can help them with executing complex task to achieve their goal.

Reflection

I found the approach used by the authors to be very interesting. Providing suggestions to new users to execute different tasks to achieve a goal can help beginner to be successful in their job or on the project that they are working on.

Using information and instruction available online to build the list of tasks that are required to achieve a certain action is a nice implementation and can be used in lots of domains.

The idea of using recommendations based on the context of the available action is more effective than the help options available on the application that might give too much information and links that make it difficult to find the solution especially for beginner users.

I think this approach can also be used for new hires or new students to offer templates for a certain actions. Often new hires and new students need to execute multiple steps in order to achieve a goal and failing on these tasks might cause multiple issues that prevent them from having a smooth onboarding experience. I also think that this approach can be used in software development to help new programmers to use template to design new systems or to solve coplex problems. Stack overflow has solutions to thousands of programming issues and can help on building macros or templates to solve a specific issue.

Questions

The approach used by the authors can help beginners on execute a set of tasks to accomplish a goal in photoshop, can we use a similar approach in a different application or in different domain?
Can you use a similar approach in your project?
Do you think that this approach can also help experts on using these systems especially when a new major feature is released?

04/29/2020 – Mohannad Al Ameedi – Accelerating Innovation Through Analogy Mining

April 25, 2020April 25, 2020 mohada4 1 Comment

Summary

In this paper, the authors aim to improve the search and discovery for ideas using analogies in massive and unstructured datasets. Their approach combines both crowd workers and recurrent neural network to learn from a week structural representation of vectors. The authors used a patent dataset to search for product description and used crowd workers to extracted purpose and mechanics to help with finding ideas across different domains. They have used Amazon Mechanical Turk to hire workers to perform a dual annotation on each product description by labeling the parts of text that is related to the purpose of the product and another labeling related to the mechanism or the way the product work and used . The authors then used bidirectional recurrent neural network and information retrieval techniques to find a deep and more accurate similarity between the searched idea and available innovation and research about it. The authors approach has a high precision and recall and can improve the retrieval accuracy by 25%.

Reflection

I think the approach used by the authors is very interesting. Extracting the purpose and mechanism from a production description is like looking at the data from two different angles. Calculating the similarity base on two vectors is a nice implementation and can help on finding a close relationship between two subjects in different domains that share a common attribute.

I also like the idea of using deep learning instead of TF-IDF to calculate the similarity between to products’ description as it can improve the quality of the search.

I personally use google scholars to search for a similar ideas but didn’t use the websites mentioned in the paper and that is something that I have learned while reading the paper.

This approach can be used as a verification tool when reviewing a copy right application. The idea might be the same as other idea but in different domain and the application that was built by the authors can help on finding this out.

This approach is like mapping vocabulary to concept space to improve information retrieval by performing latent space indexing rather than just performing similarity on keywords. Different words might have the same meaning and one word might have different meanings. Searching based on the keywords might retrieval incorrect results, while searching based on the concept might lead to a much accurate result.

Questions

The authors asked the crowd workers to extract two pieces of information, the purpose and mechanism, from the product description. Can we use this approach to solve a different problem?
Do you agree with the authors that the recurrent neural network is better than traditional TF-IDF in calculating the similarity for the two vectors? Why or why not?
Can you use a similar approach in your project to ask the crowd workers to annotate your data from two different perspectives or looking at your data from two different angles?
The authors mentioned more than two websites that store information about patents, have you used these websites?