Author: yuhang Liu

03/04/2020 – Pull the Plug? Predicting If Computers or Humans Should Segment Image – Yuhang Liu

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Summary:

This paper examines a new image segmentation method. Image segmentation is a key step in any image analysis task. There have been many methods before, including low-efficiency manual methods and automated methods that can produce high-quality pictures, but these methods have certain disadvantages. The authors therefore propose a distribution framework that can predict how best to assign fixed labor to collect higher quality segmentation for a given image and automated method. Specifically, the author has implemented two systems, which can perform the following processing on images when doing image segmentation:

  1. Use computers instead of humans to create the rough segmentation needed to initialize the segmentation tool,
  2. Use computers to replace humans to create the final fine-grained segmentation. The final experiments also proved that relying on this hybrid, interactive segmentation system can achieve faster and more efficient segmentation.

Reflection:

Once, I did a related image recognition project. Our subject is a railway turnout monitoring system based on computer vision, which is to detect the turnout of the railroad track from the picture, and the most critical step is to separate the outline of the railroad track. At that time, we only using the method of computer separation, the main problem we encountered at the time was that when the scene became complicated, we would face to complex line segments, which would affect the detection results. As mentioned in this paper, using human-machine, the combined method can greatly improve the accuracy rate. I very much agree with it, and hope that one day I can try it myself. At the same time, what I most agree with is that the system can automatically assign work instead of all photos going through a same process. For a photo, only the machine can participate, or artificial processing is required. This variety of interactive methods, It is far more advantageous than a single method, which can greatly save workers’ time without affecting accuracy, and the most important point is that complex interaction methods can adapt to process more diverse pictures. Finally, I think similar operations can be applied to other aspects. This method of assigning tasks through the system can coordinate the working relationship between humans and machines, for example, in other fields, such as sound sentiment analysis and musical background separation. In these aspects, humans have the incomparable advantages of machines and can achieve good results, but it takes a long time and is very expensive. Therefore, if we can classify this kind of thinking, deal with the common working relationship between humans and machines, and give complex situations to people or pass the rough points of the machine first, then the separation cost will be greatly reduced, and the accuracy rate will not be affected, so I It is believed that this method has great application prospects, not only because of the many application directions of image separation, but we can also learn from this idea to complete more detailed analysis in more fields.

Question:

  1. Is this idea of cooperation between man and machine worth learning?
  2. Because the system defines the working range of people and machines, will the machine reduce the accuracy due to the results of human work?
  3. Does man-machine cooperation pose new problems, such as increasing costs?

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03/04/2020 – Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind – Yuhang Liu

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Summary:

The authors of this paper explored that visually impaired users are limited by the availability of suitable alternative text when accessing images in social media. The author believes that the beneficial of those new tools that can automatically generate captions are unknown to the blind. So through experiments, the authors studied how to use crowdsourcing to evaluate the value provided by existing automation methods, and how to provide a scalable and useful alternative text workflow for blind users. Using real-time crowdsourcing, the authors designed crowd-interaction experiments that can change the depth. These experiments can help explain the shortcomings of existing methods. The experiments show that the shortcomings of existing AI image captioning systems often prevent users from understanding the images they cannot see , And even some conversations can produce erroneous results, which greatly affect the user experience. The authors carried out a detailed analysis and designed a design that is scalable, requires crowdsourced workers to participate in improving the display content, and can effectively help users without real-time interaction.

Reflection:

First of all, I very much agree with the author’s approach. In a society where the role of social networks is increasingly important, we really should strive to make social media serve more people, especially for the disadvantaged groups in our lives. The blind daliy travel inconveniently, social media is their main way to understand the world, so designing such a system would be a very good idea if it can help them. Secondly, the author used the crowdsourcing method to study the existing methods. The method they designed is also very effective. As a cheap human resource, the crowdsourcing method can test a large number of systems in a short time, but I think this method There are also some limitations. It may be difficult for these crowdsourced workers to think about the problem from the perspective of the blind, which makes their ideas, although similar to the blind, not very accurate, so there are some gaps of the results with blind users. Finally, I have some doubts about the system proposed by the author. The authors finally proposed a workflow that combines different levels of automation and human participation. This shows that this interaction requires the participation of another person, so I think this interaction There are some disadvantages to this method. Not only will it cause a certain delay, but because it requires other human resources, it also requires some blind users to pay more. I think the ultimate direction of development should be free from human constraints, so I think we can compare the results of workers with the original results and let machine learning. That is to use the results of crowdsourcing workers for machine learning. I think it can reduce the cost of the system while increasing the efficiency of the system, and provide faster and better services for more blind users.

Question：

  1. Do you think there is a better way to implement these functions, such as studying the answers of workers, and achieving a completely automatic display system?
  2. Are there some disadvantages to using crowdsourcing platforms?
  3. Is it better to change text to speech for the visually impaired?

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02/26/2020 – Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment – Yuhang Liu

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This paper mainly explores the injustice of the results of machine learning. These injustices are usually reflected in gender and race, so in order to make the results of machine learning better serve people, the author of the paper conducted an empirical study with four types of programmatically generated explanations to understand how they impact people’s fairness judgments of ML systems. In the experiment, these four interpretations have different characteristics, and after the experiment, the author has the following findings:

  1. Some interpretations are inherently considered unfair, while others can increase people’s confidence in the fairness of the algorithm;
  2. Different interpretations can more effectively expose different fairness issues, such as the model-wide fairness issue and the fairness difference of specific cases.
  3. There are differences between people, different people have different positions, and the perspective of understanding things will affect people’s response to different interpretation styles.

In the end, the authors obtained that in order to make the results of machine learning generally fair, in different situations, different corrections are needed and differences between people must be taken into account.

Reflection：

In another class this semester, the teacher gave three reading materials on the results of machine learning and increased discrimination. In the discussion of those three articles, I remember that most students thought that the reason for discrimination should not be Is the inaccuracy of the algorithm or model, and I even think that machine learning is to objectively analyze things and display the results, and the main reason that people feel uncomfortable and even feel immoral in the face of the results is that people are not willing to face these results. It is often difficult for people to have a clear understanding of the whole picture of things, and when these unnoticed places are moved to the table, people will be shocked or even condemn others, but it is difficult to really think about the cause of things. But after reading this paper, I think my previous understanding was narrow: First, the results of the algorithm and the interpretation of the results must be wrong and discriminatory in some cases. So only if we resolve this discrimination can the results of machine learning be able to better serve people. At the same time, I also agree with the ideas and conclusions in the article. Different interpretation methods and different emphasis will indeed affect the fairness of interpretation. All the prerequisites to eliminate injustices are to understand the causes of these injustices. At the same time, I think the main solution to eliminate injustice is still on the researcher. Reason why I think computer is fascinating is it can always keep things rational and objective to deal with problems. People’s response to different results and the influence of different people on different model predictions are the key to eliminating this injustice. Of course, I think people will think that part of the cause of injustice is also the injustice of our own society. When people think that the results of machine learning carry discrimination based on race, sex, religion, etc., should we think about this discrimination itself, should we pay more attention to gender equality, ethnic equality and how to make the results look better.

Question:

  1. Do you think that this unfairness is more because the results of machine learning mislead people or it is existed in people’s society for a long time.
  2. The article proposes that in order to get more fair results, more people need to be considered, what changes should users make.
  3. How to combine the points of different machine learning explanations to create a fairer explanation.

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02/26/2020 – Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning- Yuhang Liu

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This paper discusses people’s dependence on interpretive tools for machine learning. As mentioned in the article, machine learning (ML) models are commonly deployed in various fields from criminal justice to healthcare. Machine learning has gone beyond academia and has developed into an engineering discipline. To this end, interpretability tools have been designed to help data scientists and machine learning practitioners better understand how ML models work. This paper focuses on such software. According to the classification, this software can be divided into two categories, the Interpret ML implementation of GAMs ( glass box models) and the SHAP Python package (a post-hoc explanation technique for blackbox models). The author’s research The results show that users trust machine interpretative results too much and rely too much on the use of machine learning interpretive tools. Few of these users can accurately describe the visualization of the output of these tools. In the end, the authors came to the conclusion that the visualization of the output of the interpretability tool can sometimes help data scientists find problems with data sets or models. For both tools, however, the existence of visualizations and the fact that the tools are publicly available have led to situations of excessive trust and abuse. Therefore, after the final experiments, the authors concluded that experts in two aspects of human-computer interaction and machine learning need to work together. The two interact better together to achieve better results.

First of all, after reading this article, I think that not only the explanatory tools of machine learning will make people over-trusted, including machine learning itself will also make people over-trusted, which may be caused by many aspects such as data sets. This reminds me of the course project I wanted to do this semester. My original intention was because a single, standard data set written by a large number of experts for a long time would cause the trained model to produce too high an accuracy rate, so the data set generated by crowdsourcing was used. Can get better results.

Secondly, for this article, I very much agree with the final solution proposed by the author, which is to better integrate the two aspects of human-machine interaction and machine learning as future research directions. This is because these interpretive tools are a visual display of the results. The better design of human-computer interaction allows users to better extract the results of machine learning, better understand the results, and understand the problems in them. Instead of overly trusting the results of machine learning. The future development direction is definitely that fewer and fewer users understand machine learning, but there will be more people using machine learning, and machine learning will become more and more instrumental, so I think that the interaction aspect will be made more Good for users to understand their results. On the other hand, machine learning should be more diverse and able to adapt to more application scenarios. Only when both aspects are done better can the effects of these tools be achieved.

  1. Is machine learning more academic or tool-oriented in the future?
  2. If the user does not know the meaning of the results, how to understand the accuracy of the results more clearly without using interpretive software
  3. The article mentioned that in the future, the joint efforts of human-computer interaction and machine learning will be required, and what changes should be made in human-computer interaction.

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02/19/2020 – Updates in Human-AI Teams: Understanding and Addressing the Performance / Compatibility Trade off – Yuhang Liu

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This paper first proposes the complementarity between humans and artificial intelligence. In many cases, humans and artificial intelligence will form a team. When people make decisions after checking the inferences of AI, this cooperation model has applications in many fields, and achieved significant results. Usually, this kind of achievement requires certain prerequisites. First, people must have their own judgments on the conclusions of artificial intelligence. At the same time, the results of artificial intelligence must be accurate. The tacit cooperation between the two can improve efficiency. However, with the updating of artificial intelligence systems and the expansion of data, this cooperation will be broken. On the one hand, the accuracy of artificial intelligence will decline, and because of the expansion of boundaries, people’s understanding of artificial intelligence will be broken. So after the system update, the efficiency will be reduced instead. This paper mainly studies this situation. The article hopes to be compatible with the previous method after the update, so several methods are proposed to achieve this purpose, so as to achieve more compatible and accurate updates.

It is also suggested that this idea is obtained by analogy. In software engineering, if the updated system can support legacy software, it will be compatible after the update. I agree with this kind of analogy greatly, which is similar to bionics. We can continuously apply new ideas to the computer field through this kind of thought. The method mentioned in this paper is also very necessary. In the ordinary process of artificial intelligence or machine learning, we usually build data sets for each time, and lack the concept of inheritance, which is very inconvenient. After adopting compatible ideas, it will greatly save energy and be able to serve people more smoothly.

This article introduces CAJA, a platform for measuring the impact of AI performance and updates on team performance. At the same time, a practical retraining goal is introduced in the article to improve update compatibility. The main idea is to improve update compatibility by punishing new errors. But it can also be seen from the text that trust is the core of team work. Admittedly, trust is the essence of a team, but only as the basis of work, I think that more simulations and improvements are needed to improve humanity. The combination of problem-solving factors and the key of machine learning, we know that after learning new things, people will not have a negative impact on previous skills, but we will have more perspectives and methods to think about a problem, so I think that humans and machines should be mixed, that is, a team as a whole, so that the results can be more compatible, and the human machine interaction can be more successful.

question：

  1. What are the implications of compatible AI updates?
  2. How to better treat people and machines as a whole?
  3. Whether compatible AI will affect the final training results?

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02/19/2020 – The Work of Sustaining Order in Wikipedia: The Banning of a Vandal – Yuhang Liu

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The authors of this paper examine the social role of software tools in Wikipedia, with a particular focus on automatic editing programs and assisted editing tools. The author showed by example that the content of Wikipedia has been modified by people, which may have a bad impact on society. This kind of repair can be done by administrators and some assisted software, and with the development of science and technology, this software also plays more and more roles in the recover. And using trace ethnography, the authors show how these unofficial technologies have fundamentally changed the nature of editing and management in Wikipedia. Specifically, “destructive fighting” can be analyzed as distributed cognition, emphasizing the role of non-human participants in decentralized activities that promote collective intelligence. Overall, this situation shows that software programs are not only used to enforce policies and standards. These tools can take coordinated but decentralized action, and can play a more widespread and effective role in subsequent applications.

I think this paper has given a large number of examples, including the impact of changes on society, and some analogy to perfectly explain the meaning of these network terms, which are very effective illustrations of Wikipedia and the impact of these applications on people’s lives. Among them I think the author made two main points.

  1. Robots or software, such assisted editing tools, play an increasingly important role in life and work. For example, the article mentioned two editing tools, Huggle, Twinkle, and the author introduced the use of the two software in detail. After reading the introduction, this completely subverted my concept of assisted editing tools. These unofficial tools can greatly help administrators to complete the maintenance work. This also led to the new concept of “trace ethnography”. In my opinion, trace ethnography is a way of generating rich accounts of interaction by combining a fine grained analysis of the various “trace” that are automatically recorded by the project ‟s software alongside an ethnographically derived understanding of the tools, techniques, practices, and procedures that generate such traces. it can integrate the small traces left by people on the Internet. I think it can play a vital role in controlling and monitoring people’s behavior on the Internet. In order to maintain the network environment, I even think we can use this feature more widely.
  2. The author describes the destructive behavior reform as distributed cognition through analogy of navigation. The user and the machine complete the judgment and then integrate in the network so that the intentional destructiveness can be seen. I think this kind of thinking will even greatly change the way people work in the future. The work introduced in navigation does not even require a lot of professional knowledge, it only needs to be able to read maps and use a magnifying glass. And in future work, people who work do not even need to have sufficient professional knowledge, they only need to be able to understand the information and have the right judgment. This will definitely change the way people work.

Question:

  1. In the future, will it be possible to complete inspection and maintenance by robots and computers(without people)?
  2. Is it possible to apply the ideas of trace Ethnography in other fields, such as monitoring cybercrime?
  3. Assisted editing tools reduce administrators’ requirements for related expertise. Will this change benefit these people in the long run? Does the easier job completion mean easier replacement by machines?
  4. The article mentioned that we need to consider the social impact of such assisted editing tools. What kind of social impact do you think the various software in your current life have?

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02/05/2020- Yuhang Liu -Power to the People: The Role of Humans in Interactive Machine Learning

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This paper proposes a new machine learning model-interactive machine learning. The ability to build this learning model is largely driven by advances in machine learning. However, more and more researchers are aware of the importance of studying the users of these systems. In this paper, the author promotes this method and demonstrates how it can lead to a better user experience and a more effective learning system. After exploring many examples, the authors also reached the following conclusions:

  1. This machine learning mode is different from the traditional machine learning mode. Because the user participates, the interaction cycle is faster than the traditional machine learning cycle, which increases the possibility of interaction between the user and the machine.
  2. Researching users is the key to advancing research in this area. Knowing the user can better design the system and better respond to people.
  3. It is unneccesarry to be restrict learning system and the user, because it will lead the interaction process more transparent and produce better results.

First of all, from the text, we know that models in interactive machine learning are updated faster and more concentrated. This is because the user checks interactively and adjusts subsequent inputs. Due to these fast interaction cycles, even users with little or no machine learning expertise can guide machine learning through low-cost trial and error or specialized experiments on input and output. This also shows that the foundation of interactive machine learning is fast, centralized and incremental interaction cycles. And these cycles will help users participate in the process of machine learning. These cycles also lead to tight coupling between users and the system, making it impossible to study the system in isolation. Therefore, we found that in the new system, the machine and the user interact with each other, and in my opinion, in the future, there will be more and more research on the user, and people will eventually pay more attention to the user, because the user experience can ultimately determine the quality of a product, and for this system, the user can influence the machine learning, and the feedback from the machine to the user can ultimately determine the quality of the learning process.

Secondly, the paper mentions that a common language across diverse fields should be developed, which coincides with last week’s paper “Affordance-based framework for human-computer collaboration”, although the domains mentioned are different, and this paper proposes is later, but I think this reflects a same idea, we should establish a common language, for example, in the process of interactive machine learning, there are many ways to analyze and describe the various interactions between humans and machine learners. Therefore, there is an important opportunity to bring together and adopt a common language in these areas to help accelerate research and development in this area, but also in other areas. In this way, in the process of cross-disciplinary integration, we will also have new discoveries and have new impacts.

Questions:

1.Do you think that frequent interactions must have a positive impact on machine learning?

2.For beginners in machine learning, do you think this interactive machine learning is beneficial?

3.In machine learning, which one have a significant impact on the learning result, human or the model’s efficiency.

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02-05-2020 Yuhang Liu -Principles of Mixed-Initiative User Interfaces

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This paper discusses the prospect of human-computer interaction. The paper proposes that the current prospect of human-computer interaction is controversial. Some people believe that people should focus on developing new metaphors and tools that enhance users ’abilities to directly manipulate objects. Others believe that directing effort toward developing interface agents that provide automation is much more important. So this paper later proposed a new system-outlook. This system is an email system, which creates a new interaction mode based on combining two ideas. The paper specifically describes the factors of the system, the realization ideas of each factor, and the realization principle and mathematical model of these factors are described in the second half of the paper. And these factors include: Value-added automation, Considering user’s goals, Considering user’s attention in the timing of services, Inferring ideal action, using dialog to resolve key uncertainties, Allowing invocation and termination, Minimizing the cost of poor guesses, Scoping precision of service to match uncertainty, variation in goals, Providing mechanisms for efficient agent−user collaboration to refine results, Employment socially appropriate behaviors for agent−user interaction, Maintaining working memory, Continuing to learn by observing.

Aiming at these factors and methods mentioned in this article, I would like to put forward my views on several of them.

  1. Continuing to learn by observing. I think this will be the inevitable development direction of human-computer interaction, and even the development direction of other computer science fields. Because people have different habits and education backgrounds, a universal interaction system, Not enough good for everyone. In a interaction, people are required to adapt to the system, but at the same time, the system should also learn human habits to serve people more conveniently.
  2. Maintaining working memory. I think this is came from the idea of computer caching. The introduction of the concept of caching in the system will undoubtedly serve people better. At the same time, I think there are two other benefits. 1. Recording the previous operations can become a training set for machine learning. Since we want the system to become more intelligent, and we hope the system can adapt to different people, so it is necessary to record the operations of each person. 2. This idea also inspired us to bring other successful functions that have been practiced and applied to other fields, it might bring some great impact.
  3. Most of the remaining factors are designed for human uncertainty. In my opinion, in the development of human-computer interaction, these factors can be merged into one factor, that is, human uncertainty. However, it can be seen from these factors that the difficulty of human-computer interaction is focused on human diversity and uncertainty, so overcoming this problem will directly affect the interaction experience.

questions:

1.For the two directions mentioned in the paper, which one do you think is more important, or the combination of the two?

2.Do you think it is urgent to add the concept of machine learning in human-computer interaction to better serve human beings.

3.Besides the factors of OutLook system mentioned in the paper, which factor do you think can be extended.

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01/29/20 – Yuhang Liu – Human computation: a survey and taxonomy of a growing field

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In this article, Alexander J. Quinn, Benjamin B. Bederson. First introduced the background, then discussed the definition of human computer, distinguished and compared with related technologies, and then put forward the classification system for human computer system, and explained how to find new research points based on the proposed classification system. The article first proposed the definition of human computation. The author believes that human computation should meet:

  1. The problems fit the general paradigm of computation, and as such might someday be solvable by computers.
  2. The human participation is directed by the computational system or process. Then the author compares human computation with other concepts, which mainly include: crowd sourcing, social computing, data mining, and collective intelligence.

The main differences from these concepts are the presence of computers and the application direction. Then the author proposed a new classification dimension. According to the proposed dimension, problems can be considered the following aspects:

  1. Combining different dimensions to discover new applications.
  2. Create new values for a given dimension.
  3. When encountering a new human computer system, classify it according to the current dimensions to discover new things

I think this article is similar to another article “An Affordance-Based Framework for Human Computation and Human-Computer Collaboration”. This article is all about the new direction of human computation. To help people find new methods through new classification systems, and find new applications based on the combination of different dimensions. The other article is about a new research method “Affordance”, which achieves better research results based on the relationship between humans and machines. And I think the arguments of the two articles coincide: The classification system mentioned in this article has six dimensions, motivation, quality control, aggregation, human skill, process order, task request cardinality. Among them, human skills can correspond to human advantages, that is the part of “affordance” that humans can take part in human computation. And motivation, quality control, aggregation as the description in another article, humans cannot be like computers, People cannot completely give up subjective thinking and realize unbiased analysis. The process order reflects different interaction methods and different interaction orders in human computation. Task request cardinality can correspond to other “affordance” methods. When the number of participants is large, there will be different methods. So I think in some ways the two articles are complementary. At the same time, in this article, the author also mentioned the difference between human computation and other concepts. I think this is very important in future research. In future research, there will be more and more interdisciplinary crossings, so it is important to distinguish these disciplines, determine the boundaries of the disciplines, and lay a solid foundation for different disciplines. The foundation, universal methods, and efficient solutions are not only good for the development of each discipline, but also have a very important impact in the interdisciplinary process.

What is the significance of distinguishing human computation from other definitions?

What are the characteristic of human computation corresponding with the six dimensions mentioned in article?

Is there a new dimension, and if it is combined with the dimensions mentioned in the article, what new applications will it have?

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01/29/20 – Yuhang Liu – Affordance-based framework for human-computer collaboration

yuhang Liu 1 Comment

In 1993, researchers from different backgrounds jointly discussed the challenges and benefits in the field of human-computer collaboration. They define collaboration as the process of two or more agents working together to achieve a common goal, while human-machine collaboration is defined as a collaboration involving at least one person and a computing agent. The field of visual analysis is deeply rooted in human-machine collaboration. That is, Visual Analytics attempts to leverage analyst intelligence and machine computing power in collaborations that analyze complex problems. The author has studied thousands of papers from many top conferences such as visual analysis, human-computer interaction and visualization. Provides a comprehensive overview of the latest technologies and provides a general framework based on the authors’ research. The author of this framework calls it “Affordance,” pointing out that there is a heuristic between humans and machines, which exists independently of each other instead of the other.

From reading the article, we learned that the word “Affordance” was first proposed by American psychologist J.J. Gibson. It means that the object and the environment provide the opportunity for action. When the word is used in human-computer collaboration, it means that both human and machine provide partners with opportunities for action. In this two-way relationship, it must be effectively perceived, By using it, we can achieve better human-machine collaboration. In this two-way relationship, people and computers have different abilities.

First of all, this kind of Affordance behaves differently for different human abilities. These different abilities mainly include visual perception, visuospatial thinking, audio linguistic ability, sociocultural awareness, creativity, and domain knowledge. Among them, the first three abilities belong to human strengths, especially visual abilities. Humans have powerful visual perception abilities and can easily distinguish color, shape, and even the texture and motion of the images, human have unparalleled advantages in this aspect of the machine, so it is very reasonable for people to work instead of computers in this respect. The latter three abilities require years of systematic learning and are difficult to fully embed in the computer, so using manual analysis and personnel experience as part of collaboration can greatly improve efficiency.

On the contrary, machines also have the abilities that humans do not have, large-scale data manipulation, collecting and storing large amounts of data, efficient data movement. These capabilities are not possessed by human beings, people cannot complete this series of tasks, and people cannot completely give up subjective thinking and realize unbiased analysis. There are other ways of revelation.

All in all, the author analyzed a large number of papers and finally got a general model, which can lay the foundation for future work, so it can well solve the problems encountered in previous research, and can judge whether it can be based on the “Affordance” idea. Collaborative technologies solve problems, when tasks are assigned to one party, and the ability to develop common languages. And I think that the middle frame is also very reasonable, and it can achieve the mutual cooperation between human and machine, and the inspiration effect. The integration of problems and the convergence of their solutions should also be the direction of development.

What are the disadvantages of the previous framework?

What characteristics of people and computers correspond to the “affordance” of human and computers?

How to make humans and machines play nicely for the extensions mentioned in the article?

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